Peer reviewed publications

Contents

Peer reviewed publications by members of the MINA forest inventory research group (pr. June 27 2023):

2023

  • [DOI] Grima, N., Jutras-Perreault, M., Gobakken, T., Ole Ørka, H., & Vacik, H.. (2023). Systematic review for a set of indicators supporting the common international classification of ecosystem services. Ecological indicators, 147, 109978.
    [Bibtex] 
    @Article{Grima2023,
author = {Grima, Nelson and Jutras-Perreault, Marie-Claude and Gobakken, Terje and Ole Ørka, Hans and Vacik, Harald},
journal = {Ecological Indicators},
title = {Systematic review for a set of indicators supporting the Common International Classification of Ecosystem Services},
year = {2023},
issn = {1470-160X},
pages = {109978},
volume = {147},
abstract = {Ecosystem services (ES) contribute to human well-being and provide an important contribution to economies at all scales. However, ES are often difficult to measure and quantify, and thus, it is difficult to adequately account for the true value of their contributions. The use of indicators, understood as proxies for estimating the provision of ES, has been proposed as a solution to this obstacle. In this context, indicators are physical elements of the ecosystems that can be relatively easily quantified with available tools and knowledge, and that can usually be easily communicated to decision-makers and practitioners. In this study, we conducted a literature review of peer reviewed publications, aiming to provide a complete and up-to-date list of indicators to measure ES. In total, we generated a list of 85 individual indicators that have been previously used in practice to measure ES, and we linked them to each one of the ES described by the CICES (v5.1) classification system. Moreover, we identified which of those indicators could be derived from remotely sensed (RS) data following three categories: i) RS data in direct relation with the indicator, ii) RS data in indirect relation with the indicator that requires additional information or modelling, and iii) Indicators not derivable from RS data or currently without enough information available. Only a minority of these indicators (6) can be directly derived from RS data, while most of the indicators (46) can be derived indirectly, and some (33) are not derivable from RS data.},
doi = {https://doi.org/10.1016/j.ecolind.2023.109978},
keywords = {Data sources Remote sensing CICES classification},
type = {Journal Article},
url = {https://www.sciencedirect.com/science/article/pii/S1470160X23001206},
}
  • [DOI] Jutras-Perreault, M., Gobakken, T., Næsset, E., & Ørka, H. O.. (2023). Comparison of different remotely sensed data sources for detection of presence of standing dead trees using a tree-based approach. Remote sensing, 15(9), 2223.
    [Bibtex] 
    @Article{JutrasPerreault2023,
author = {Jutras-Perreault, Marie-Claude and Gobakken, Terje and Næsset, Erik and Ørka, Hans Ole},
journal = {Remote Sensing},
title = {Comparison of Different Remotely Sensed Data Sources for Detection of Presence of Standing Dead Trees Using a Tree-Based Approach},
year = {2023},
issn = {2072-4292},
number = {9},
pages = {2223},
volume = {15},
doi = {10.3390/rs15092223},
type = {Journal Article},
url = {https://www.mdpi.com/2072-4292/15/9/2223},
}
  • [DOI] Jutras-Perreault, M., Næsset, E., Gobakken, T., & Ørka, H. O.. (2023). Detecting the presence of standing dead trees using airborne laser scanning and optical data. Scandinavian journal of forest research, 38(4), 208-220.
    [Bibtex] 
    @Article{JutrasPerreault2023a,
author = {Jutras-Perreault, Marie-Claude and Næsset, Erik and Gobakken, Terje and Ørka, Hans Ole},
journal = {Scandinavian Journal of Forest Research},
title = {Detecting the presence of standing dead trees using airborne laser scanning and optical data},
year = {2023},
issn = {0282-7581},
note = {doi: 10.1080/02827581.2023.2211807},
number = {4},
pages = {208-220},
volume = {38},
abstract = {ABSTRACTDeadwood is an important indicator of biodiversity in forest ecosystems. Identifying areas with large density of standing dead trees through field inventory is challenging, and remotely sensed data can provide a more systematic approach. In this study, we used metrics derived from airborne laser scanning (ALS) data (7.1 points m?2) and vegetation indices from optical images (HySpex sensor VNIR-1800: 0.3?m, SWIR-384: 0.7?m) to predict the presence of standing dead trees over a 15.9 km2 managed forest in Southern Norway. The dead basal area (DBA) of 40 sample plots was computed and used to classify the plots into presence/absence of standing dead trees. An area-based approach (ABA) using logistic regression was initially tested, but due to limited ground reference information, no statistically significant models could be formulated. A tree-based approach (TBA) was used to overcome this limitation. It identified trees on the ALS point cloud with a local maxima function and used a vegetation index to determine if the trees were dead. Between 18% and 42% of the predicted area with standing dead trees intersected a field recorded validation dataset. The TBA provided a good alternative to area-based regression models in the context of few standing dead trees.},
doi = {10.1080/02827581.2023.2211807},
type = {Journal Article},
url = {https://doi.org/10.1080/02827581.2023.2211807},
}
  • [DOI] Strîmbu, V. F., Næsset, E., Ørka, H. O., Liski, J., Petersson, H., & Gobakken, T.. (2023). Estimating biomass and soil carbon change at the level of forest stands using repeated forest surveys assisted by airborne laser scanner data. Carbon balance and management, 18(1), 10.
    [Bibtex] 
    @Article{Strimbu2023,
author = {Strîmbu, Victor F. and Næsset, Erik and Ørka, Hans Ole and Liski, Jari and Petersson, Hans and Gobakken, Terje},
journal = {Carbon Balance and Management},
title = {Estimating biomass and soil carbon change at the level of forest stands using repeated forest surveys assisted by airborne laser scanner data},
year = {2023},
issn = {1750-0680},
number = {1},
pages = {10},
volume = {18},
abstract = {Under the growing pressure to implement mitigation actions, the focus of forest management is shifting from a traditional resource centric view to incorporate more forest ecosystem services objectives such as carbon sequestration. Estimating the above-ground biomass in forests using airborne laser scanning (ALS) is now an operational practice in Northern Europe and is being adopted in many parts of the world. In the boreal forests, however, most of the carbon (85%) is stored in the soil organic (SO) matter. While this very important carbon pool is “invisible” to ALS, it is closely connected and feeds from the growing forest stocks. We propose an integrated methodology to estimate the changes in forest carbon pools at the level of forest stands by combining field measurements and ALS data.},
doi = {10.1186/s13021-023-00222-4},
type = {Journal Article},
url = {https://doi.org/10.1186/s13021-023-00222-4},
}
  • [DOI] Ørka, H. O., Gailis, J., Vege, M., Gobakken, T., & Hauglund, K.. (2023). Analysis-ready satellite data mosaics from landsat and sentinel-2 imagery. Methodsx, 10, 101995.
    [Bibtex] 
    @Article{Oerka2023,
author = {Ørka, Hans Ole and Gailis, Jãnis and Vege, Mathias and Gobakken, Terje and Hauglund, Kenneth},
journal = {MethodsX},
title = {Analysis-ready satellite data mosaics from Landsat and Sentinel-2 imagery},
year = {2023},
issn = {2215-0161},
pages = {101995},
volume = {10},
abstract = {Today's enormous amounts of freely available high-resolution satellite imagery provide the demand for effective preprocessing methods. One such preprocessing method needed in many applications utilizing optical satellite imagery from the Landsat and Sentinel-2 archives is mosaicking. Merging hundreds of single scenes into a single satellite data mosaic before conducting analysis such as land cover classification, change detection, or modelling is often a prerequisite. Maintaining the original data structure and preserving metadata for further modelling or classification would be advantageous for many applications. Furthermore, in other applications, e.g., connected to land cover classification creating the mosaic for a specific period matching the phenological state of the phenomena in nature would be beneficial. In addition, supporting in-house and computing centers not directly connected to a specific cloud provider could be a requirement for some institutions or companies. In the current work, we present a method called Geomosaic that meets these criteria and produces analysis-ready satellite data mosaics from Landsat and Sentinel-2 imagery.•The method described produces analysis-ready satellite data mosaics.•The satellite data mosaics contain pixel metadata usable for further analysis.•The algorithm is available as an open-source tool coded in Python and can be used on multiple platforms.},
doi = {https://doi.org/10.1016/j.mex.2022.101995},
keywords = {Earth observation Remote sensing Optical satellite imagery Preprocessing Land cover classification},
type = {Journal Article},
url = {https://www.sciencedirect.com/science/article/pii/S2215016122003697},
}
  • [DOI] Devos, C., Ohlson, M., Næsset, E., Klanderud, K., & Bollandsås, O.. (2023). Tree biomass does not correlate with soil carbon stocks in forest-tundra ecotones along a 1100 km latitudinal gradient in norway. Ecography.
    [Bibtex] 
    @Article{Devos2023,
author = {Devos, Claire and Ohlson, Mikael and Næsset, Erik and Klanderud, Kari and Bollandsås, Ole},
journal = {Ecography},
title = {Tree biomass does not correlate with soil carbon stocks in forest-tundra ecotones along a 1100 km latitudinal gradient in Norway},
year = {2023},
doi = {10.22541/au.168233773.34514401/v1},
type = {Journal Article},
}
  • [DOI] Mienna, I. M., Klanderud, K., Næsset, E., Gobakken, T., & Bollandsås, O. M.. (2023). Quantifying the roles of climate, herbivory, topography, and vegetation on tree establishment in the treeline ecotone. Authorea, January 12.
    [Bibtex] 
    @Article{Mienna2023,
author = {Mienna, Ida M. and Klanderud, Kari and Næsset, Erik and Gobakken, T and Bollandsås, Ole M.},
journal = {Authorea},
title = {Quantifying the roles of climate, herbivory, topography, and vegetation on tree establishment in the treeline ecotone},
year = {2023},
volume = {January 12},
doi = {10.22541/au.167355889.98228807/v1},
type = {Journal Article},
}
  • [DOI] Moan, M. A., Noordermeer, L., White, J. C., Coops, N. C., & Bollandsas, O. M.. (2023). Detecting and excluding disturbed forest areas improves site index determination using bitemporal airborne laser scanner data. Forestry, cpad025.
    [Bibtex] 
    @Article{Moan2023,
author = {Moan, M. A. and Noordermeer, L. and White, J. C. and Coops, N. C. and Bollandsas, O. M.},
journal = {Forestry},
title = {Detecting and excluding disturbed forest areas improves site index determination using bitemporal airborne laser scanner data},
year = {2023},
issn = {0015-752x},
note = {F8sp1 Times Cited:0 Cited References Count:66},
pages = {cpad025},
abstract = {Bitemporal airborne laser scanning (ALS) data are increasingly being used in forest management inventories for the determination of site index (SI). SI determination using bitemporal ALS data requires undisturbed height growth of dominant trees. Therefore, areas with disturbed top height development are unsuitable for SI determination, and should be identified and omitted before modelling, predicting and estimating SI using bitemporal ALS data. The aim of this study was to explore methods for classifying the suitability of forest areas for SI determination based on bitemporal ALS data. The modelling approaches k-nearest neighbour, logistic regression and random forest were compared for classifying disturbed (at least one dominant tree has disappeared) and undisturbed plots. A forest inventory with plot re-measurements and corresponding bitemporal ALS data from the Petawawa Research Forest in Ontario, Canada, was used as a case study. Based on the field data, two definitions of a disturbed plot were developed: (1) at least one dominant tree had died, was harvested or had fallen during the observation period, or (2) at least one dominant tree was harvested or had fallen during the observation period. The first definition included standing dead trees, which we hypothesized would be more difficult to accurately classify from bitemporal ALS data. Models of disturbance definition 1 and 2 yielded Matthews correlation coefficients of 0.46-0.59 and 0.62-0.80, respectively. Fit statistics of SI prediction models fitted to undisturbed plots were significantly better (P < 0.05) than fit statistics of SI prediction models fitted to all plots. Our results show that bitemporal ALS data can be used to separate disturbed from undisturbed forest areas with moderate to high accuracy in complex temperate mixedwood forests and that excluding disturbed forest areas significantly improves fit statistics of SI prediction models.},
doi = {10.1093/forestry/cpad025},
keywords = {top height lidar data inventory trees classification productivity management dynamics criteria models},
type = {Journal Article},
url = {://WOS:000984993500001},
}
  • [DOI] de Lera Garrido, A., Gobakken, T., Hauglin, M., Næsset, E., & Bollandsås, O. M.. (2023). Accuracy assessment of the nationwide forest attribute map of norway constructed by using airborne laser scanning data and field data from the national forest inventory. Scandinavian journal of forest research, 38(1-2), 9-22.
    [Bibtex] 
    @Article{LeraGarrido2023,
author = {de Lera Garrido, Ana and Gobakken, Terje and Hauglin, Marius and Næsset, Erik and Bollandsås, Ole Martin},
journal = {Scandinavian Journal of Forest Research},
title = {Accuracy assessment of the nationwide forest attribute map of Norway constructed by using airborne laser scanning data and field data from the national forest inventory},
year = {2023},
issn = {0282-7581},
note = {doi: 10.1080/02827581.2023.2184488},
number = {1-2},
pages = {9-22},
volume = {38},
abstract = {ABSTRACTThe aim of this study was to analyze the accuracy of predictions of dominant height, mean height, basal area, and volume from the nationwide forest attribute map (SR16). The analysis took advantage of field observations from 33 different forest inventory projects across Norway used for validation. Forest attributes for more than 5000 plots were predicted using non-stratified and stratified models of SR16 and the predictions were compared against corresponding ground reference values. Finally, the effect of different factors that might have influenced the prediction errors were analyzed using partial least squared regression (PLSR) to determine under which conditions the SR16 is less apt. The overall results across all plots were adequate (RMSE of 10%, MD of 2% for dominant and mean height; RMSE of 28%, MD of 4% for basal area; RMSE of 31%, MD of 5% for volume). However, when the accuracy was assessed locally for each inventory project, large differences in accuracy were observed. The MD% values for some inventory projects were substantial (>30% for basal area and volume). The results showed that stratification did not necessarily improve the results and that factors related to the forest structure had the greatest impact on the PLSR analysis.},
doi = {10.1080/02827581.2023.2184488},
type = {Journal Article},
url = {https://doi.org/10.1080/02827581.2023.2184488},
}
  • [DOI] Noordermeer, L., Bielza, J. C., Saarela, S., Gobakken, T., Bollandsås, O. M., & Næsset, E.. (2023). Monitoring tree occupancy and height in the norwegian alpine treeline using a time series of airborne laser scanner data. International journal of applied earth observation and geoinformation, 117, 103201.
    [Bibtex] 
    @Article{Noordermeer2023a,
author = {Noordermeer, Lennart and Bielza, Jaime Candelas and Saarela, Svetlana and Gobakken, Terje and Bollandsås, Ole Martin and Næsset, Erik},
journal = {International Journal of Applied Earth Observation and Geoinformation},
title = {Monitoring tree occupancy and height in the Norwegian alpine treeline using a time series of airborne laser scanner data},
year = {2023},
issn = {1569-8432},
pages = {103201},
volume = {117},
abstract = {The main objective of this study was to demonstrate a method for monitoring tree occupancy and height in the alpine treeline ecotone using a time series of ALS data. We applied data collected in a longitudinal survey, comprising three spatially consistent campaigns from the years 2008, 2012 and 2018, on 25 sites along the Scandinavian Mountain Range (60–69°N). We compared ALS-based estimates of tree occupancy and height with corresponding field-based estimates and provided ALS-based estimates of uncertainty. Cross validation of a longitudinal model for predicting tree occurrence probability from ALS data revealed an overall accuracy of 83 %. ALS data were useful for predicting the height of pioneer trees, despite sparse laser points. Both models needed to account for the time of measurement. ALS-based estimates of tree occupancy were 4.6, 6.7 and 6.0 % for the three measurement occasions, respectively, and corresponding field-based estimates were 4.3, 4.6 and 5.0 %. ALS-based estimates of tree height were 2.2, 2.1 and 2.2 m, respectively, and corresponding field-based height estimates were 2.3, 2.2 and 2.2 m. Overlapping confidence intervals of ALS-based estimates for both variables and for all three measurement occasions indicated no statistically significant changes in either of the studied variables. The proposed method can be used to monitor alpine treeline ecotones and to provide accompanying uncertainty estimates to inform whether changes are significant.},
doi = {https://doi.org/10.1016/j.jag.2023.103201},
keywords = {Alpine treeline Forest monitoring Tree occupancy Tree height ALS time series},
type = {Journal Article},
url = {https://www.sciencedirect.com/science/article/pii/S1569843223000237},
}
  • [DOI] Ramtvedt, E. N., & Næsset, E.. (2023). A simple slope correction of horizontally measured albedo in sloping terrain. Agricultural and forest meteorology, 339, 109547.
    [Bibtex] 
    @Article{Ramtvedt2023,
author = {Eirik Næsset Ramtvedt and Erik Næsset},
journal = {Agricultural and Forest Meteorology},
title = {A simple slope correction of horizontally measured albedo in sloping terrain},
year = {2023},
issn = {0168-1923},
pages = {109547},
volume = {339},
abstract = {In sloping terrain, albedo measured in the horizontal plane is typically not representative for the underlying surface. Accordingly, albedo should be measured either parallel (termed slope-parallel albedo) or corrected from horizontal measurements (termed slope-corrected albedo) to represent the actual sloping surface. This study presents the theory and the effect when applying a simple slope correction with the aim to transform albedo measured in the horizontal plane over a slope (termed horizontally measured albedo) to the sloping surface. Simultaneous measurements of horizontal and slope-parallel albedo for three different classes of atmospheric clearness conditions (clear, partly overcast, cloudy) and for four different terrain aspects (north, east, west, south) were collected during the study period. The results show that applying the slope correction improved the linear correlation coefficient between the slope-parallel and horizontally measured albedo by 0.60, 0.51 and 0.24 for clear, partly overcast, and cloudy conditions, respectively. During clear atmospheric conditions, slope-parallel and slope-corrected albedo deviated by 5% in terms of mean absolute error, while the slope correction reduced the deviation between the horizontally measured and slope-parallel albedo by 70%. Diurnal trends revealed a large discrepancy with smaller/larger horizontally measured albedo than slope-parallel albedo for the western/eastern slope during morning (opposite during afternoon). For the southern/northern slope the horizontal orientation of the radiation sensors overestimated/underestimated the albedo for all atmospheric clearness conditions. The horizontally measured reflected radiation had on average a difference of 3 Wm-2 from the slope-parallel reflected radiation, corresponding to a deviation of 3%. The findings show that the simple slope correction considerably improve the reliability of the horizontally measured albedo in sloping terrain when slope-parallel measurements are not possible. However, during partly overcast conditions, the simple slope correction suffers under the complex radiation regime and should preferably not be applied.},
doi = {https://doi.org/10.1016/j.agrformet.2023.109547},
keywords = {Albedo, Sloping terrain, Horizontal measurements, Slope correction},
url = {https://www.sciencedirect.com/science/article/pii/S0168192323002381},
}
  • [DOI] McRoberts, R. E., Næsset, E., Hou, Z., Ståhl, G., Saarela, S., Esteban, J., Travaglini, D., Mohammadi, J., & Chirici, G.. (2023). How many bootstrap replications are necessary for estimating remote sensing-assisted, model-based standard errors?. Remote sensing of environment, 288, 113455.
    [Bibtex] 
    @Article{McRoberts2023,
author = {Ronald E. McRoberts and Erik Næsset and Zhengyang Hou and Göran Ståhl and Svetlana Saarela and Jessica Esteban and Davide Travaglini and Jahangir Mohammadi and Gherardo Chirici},
journal = {Remote Sensing of Environment},
title = {How many bootstrap replications are necessary for estimating remote sensing-assisted, model-based standard errors?},
year = {2023},
issn = {0034-4257},
pages = {113455},
volume = {288},
abstract = {When probability samples are not available, the model-based framework may be the only option for constructing inferences in the form of prediction intervals for population means. Further, for machine learning and some non-parametric and nonlinear regression prediction techniques, resampling methods such as the bootstrap may be the only option for obtaining the standard errors necessary for constructing those prediction intervals. All bootstrap approaches entail repeatedly sampling from the original sample, estimating the parameter of interest for each replication, and estimating the standard error of the estimate of the parameter as the standard deviation of the bootstrap estimates over replications. The objective of the study was to develop a procedure for terminating resampling such that the resulting number of replications assures, at least in probability, that the estimate of the standard error stabilizes to the standard error corresponding to one million replications. The analyses used a variety of datasets: five forest inventory datasets with either volume or aboveground biomass as the dependent variable and metrics from either airborne laser scanning or Landsat as independent variables, three from Europe, one from Southwest Asia, and one from Africa; and two forest/non-forest versus Landsat datasets, one from Minnesota and one from Wisconsin, both in the USA. The primary contribution of the study was development and demonstration of a procedure that specifies criteria for terminating resampling that assure in probability that the bootstrap estimate of the standard error stabilizes to the estimate obtained with one million replications.},
doi = {https://doi.org/10.1016/j.rse.2023.113455},
keywords = {Nonlinear model, Logistic regression model, Prediction interval, bias, Uncertainty},
url = {https://www.sciencedirect.com/science/article/pii/S0034425723000068},
}

2022

  • [DOI] Dutcă, I., McRoberts, R. E., Næsset, E., & Blujdea, V. N. B.. (2022). Accommodating heteroscedasticity in allometric biomass models. Forest ecology and management, 505, 119865.
    [Bibtex] 
    @article{RN5332,
author = {Dutcă, Ioan and McRoberts, Ronald E. and Næsset, Erik and Blujdea, Viorel N. B.},
title = {Accommodating heteroscedasticity in allometric biomass models},
journal = {Forest Ecology and Management},
volume = {505},
pages = {119865},
abstract = {Allometric models are commonly used to predict forest biomass. These models typically take nonlinear power-law forms that predict individual tree aboveground biomass (AGB) as functions of diameter at breast height (D) and/or tree height (H). Because the residual variance is in most cases heteroscedastic, accommodating the heteroscedasticity (i.e., heterogeneity of variance) becomes necessary when estimating model parameters. We tested several weighting procedures and a logarithmic transformation for nonlinear allometric biomass models. We further evaluated the effectiveness of these procedures with emphasis on how they affected estimates of mean AGB per hectare and their standard errors for large forest areas. Our results revealed that some weighting procedures were more effective for accommodating heteroscedasticity than others and that effectiveness was greater for single predictor models but less for models based on both D and H. Failing to effectively accommodate heteroscedasticity produced small to moderate differences in the estimates of mean AGB per hectare and their standard errors. However, these differences were greater between model forms (models based on D and H versus models based on D only), regardless of the weighting approach. Similar consequences were observed with respect to whether model prediction uncertainty was or was not included when estimating mean AGB per hectare and standard errors. When including model prediction uncertainty, the standard errors of the estimated means increased substantially, by 44–59%. Therefore, to avoid possible negative consequences on large-area biomass estimation, we recommend: (i) testing the effectiveness of a weighting procedure when accommodating heteroscedasticity in allometric biomass models, (ii) incorporating model prediction uncertainty in the total uncertainty estimate and (iii) including H as an additional predictor variable in allometric biomass models.},
keywords = {Aboveground biomass
Allometric model
Weighted regression
Error propagation
Homoscedasticity},
ISSN = {0378-1127},
DOI = {https://doi.org/10.1016/j.foreco.2021.119865},
url = {https://www.sciencedirect.com/science/article/pii/S0378112721009567},
year = {2022},
type = {Journal Article}
}
  • [DOI] Stehman, S. V., Mousoupetros, J., McRoberts, R. E., Næsset, E., Pengra, B. W., Xing, D., & Horton, J. A.. (2022). Incorporating interpreter variability into estimation of the total variance of land cover area estimates under simple random sampling. Remote sensing of environment, 269, 112806.
    [Bibtex] 
    @article{RN5331,
author = {Stehman, Stephen V. and Mousoupetros, John and McRoberts, Ronald E. and Næsset, Erik and Pengra, Bruce W. and Xing, Dingfan and Horton, Josephine A.},
title = {Incorporating interpreter variability into estimation of the total variance of land cover area estimates under simple random sampling},
journal = {Remote Sensing of Environment},
volume = {269},
pages = {112806},
abstract = {Area estimates of land cover and land cover change are often based on reference class labels determined by analysts interpreting satellite imagery and aerial photography. Different interpreters may assign different reference class labels to the same sample unit. This interpreter variability is typically not accounted for in variance estimators applied to area estimates of land cover. A simple measurement model provides the basis for an estimator of the total variance (VTotal) that takes into account both sampling variance and interpreter variance. This method requires two or more reference class interpretations (i.e., repeated measurements) obtained by analysts, working independently of each other, for the full sample or a random subsample of the full sample. Estimators of the total variance (V̂Total) and the variance component attributable to interpreters (V̂1) were obtained for the case of two reference class interpretations per repeated sample unit. To evaluate the effect of interpreter variability on variance estimation, we used land cover reference data interpreted by seven analysts who each interpreted the same 300 sample pixels from a region of the Pacific Northwest of the United States. From these data, we estimated the contribution of interpreter variance to the total variance (i.e., V̂1/V̂Total) and the relative bias of the standard simple random sampling variance estimator (V̂stand) as an estimator of VTotal, defined as 100%*(V̂stand−V̂Total)/V̂Total. For each of five land cover classes, we computed V̂1, V̂Total, and V̂stand using the sample data from each of the 21 possible pairwise combinations of the seven interpreters, and then calculated the mean of V̂1/V̂Total and the mean of the estimated relative bias of V̂stand over these 21 pairs. Based on the mean of V̂1/V̂Total per class, interpreter variance contributed from 25% (cropland) to 76% (grass/shrub) of the total variance, indicating that interpreter variance was a non-negligible component of the total variance. Typically, the standard variance estimator, V̂stand, underestimated the total variance with the mean estimated relative bias ranging from −3% (cropland) to −33% (grass/shrub). Classes with greater inconsistency between pairs of interpreters had larger contributions of interpreter variance to the total variance (V̂1/V̂Total) and larger negative estimated relative bias of V̂stand. Given that interpreter variance can contribute substantially to the total variance, the repeated measurements approach offers a practical way to incorporate this variability into an estimator of the total variance.},
keywords = {Variance estimation
Repeated measurements
Simple measurement model
LCMAP
Area estimation
Remote sensing},
ISSN = {0034-4257},
DOI = {https://doi.org/10.1016/j.rse.2021.112806},
url = {https://www.sciencedirect.com/science/article/pii/S0034425721005265},
year = {2022},
type = {Journal Article}
}
  • [DOI] Allen, B., Dalponte, M., Hietala, A., Ørka, H., Næsset, E., & Gobakken, T.. (2022). Detection of root, butt, and stem rot presence in norway spruce with hyperspectral imagery. Silva fennica, 56(2).
    [Bibtex] 
    @Article{Allen2022,
author = {Allen, Benjamin and Dalponte, Michele and Hietala, Ari and Ørka, Hans and Næsset, Erik and Gobakken, Terje},
journal = {SILVA FENNICA},
title = {Detection of Root, Butt, and Stem Rot presence in Norway spruce with hyperspectral imagery},
year = {2022},
number = {2},
volume = {56},
abstract = {
    Pathogenic wood decay fungi such as species of Heterobasidion are some of the most serious forest pathogens in Europe, causing rot of tree boles and loss of growth, with estimated economic losses of eight hundred million euros per year. In conifers with low resinous heartwood such as species of Picea and Abies, these fungi are commonly confined to heartwood and thus external infection signs on the bark or foliage of trees are normally absent. Consequently, determining the extent of disease presence in a forest stand with field surveys is not practical for guiding forest management decisions such as optimal rotation time. Remote sensing technologies such as airborne laser scanning and aerial imagery are already used to reduce the reliance on fieldwork in forest inventories. This study aimed to use remote sensing to detect rot in spruce (Picea abies L. Karst.) forests in Norway. An airborne hyperspectral imager provided information for classifying the presence or absence of rot in a single-tree-based framework. Ground reference data showing the presence of rot were collected by harvest machine operators during the harvest of forest stands. Random forest and support vector machine algorithms were used to classify the presence and absence of rot. Results indicate a 64% overall classification accuracy for presence-absence classification of rot, although additional work remains to make the classifications usable for practical forest management.
    },
doi = {doi:10.14214/sf.10606},
type = {Journal Article},
url = {https://www.silvafennica.fi/article/10606},
}
  • Allen, B., Dalponte, M., Ørka, H. O., Næsset, E., Puliti, S., Astrup, R., & Gobakken, T.. (2022). Uav-based hyperspectral imagery for detection of root, butt, and stem rot in norway spruce. Remote sensing, 14(15), 3830.
    [Bibtex] 
    @Article{Allen2022a,
author = {Allen, Benjamin and Dalponte, Michele and Ørka, Hans Ole and Næsset, Erik and Puliti, Stefano and Astrup, Rasmus and Gobakken, Terje},
journal = {Remote Sensing},
title = {UAV-Based Hyperspectral Imagery for Detection of Root, Butt, and Stem Rot in Norway Spruce},
year = {2022},
issn = {2072-4292},
number = {15},
pages = {3830},
volume = {14},
type = {Journal Article},
url = {https://www.mdpi.com/2072-4292/14/15/3830},
}
  • [DOI] Aza, A., Kallio, M. A. I., Pukkala, T., Hietala, A., Gobakken, T., & Astrup, R.. (2022). Species selection in areas subjected to risk of root and butt rot: applying precision forestry in norway. Silva fennica, 56(3).
    [Bibtex] 
    @Article{Aza2022,
author = {Aza, Ana and Kallio, A. Maarit I. and Pukkala, Timo and Hietala, Ari and Gobakken, Terje and Astrup, Rasmus},
journal = {SILVA FENNICA},
title = {Species selection in areas subjected to risk of root and butt rot: applying Precision forestry in Norway},
year = {2022},
number = {3},
volume = {56},
abstract = {
    Norway’s most common tree species, Picea abies (L.) Karst. (Norway spruce), is often infected with Heterobasidion parviporum Niemelä & Korhonen and Heterobasidion annosum (Fr.) Bref.. Because Pinus sylvestris L. (Scots pine) is less susceptible to rot, it is worth considering if converting rot-infested spruce stands to pine improves economic performance. We examined the economically optimal choice between planting Norway spruce and Scots pine for previously spruce-dominated clear-cut sites of different site indexes with initial rot levels varying from 0% to 100% of stumps on the site. While it is optimal to continue to plant Norway spruce in regions with low rot levels, shifting to Scots pine pays off when rot levels get higher. The threshold rot level for changing from Norway spruce to Scots pine increases with the site index. We present a case study demonstrating a practical method (“Precision forestry”) for determining the tree species in a stand at the pixel level when the stand is heterogeneous both in site indexes and rot levels. This method is consistent with the concept of Precision forestry, which aims to plan and execute site-specific forest management activities to improve the quality of wood products while minimising waste, increasing profits, and maintaining environmental quality. The material for the study includes data on rot levels and site indexes in 71 clear-cut stands. Compared to planting the entire stand with a single species, pixel-level optimised species selection increases the net present value in almost every stand, with average increase of approximately 6%.
    },
doi = {doi:10.14214/sf.10732},
type = {Journal Article},
url = {https://silvafennica.fi/article/10732},
}
  • [DOI] Björk, S., Anfinsen, S. N., E, N., Gobakken, T., & Zahabu, E.. (2022). On the potential of sequential and nonsequential regression models for sentinel-1-based biomass prediction in tanzanian miombo forests. Ieee journal of selected topics in applied earth observations and remote sensing, 15, 4612-4639.
    [Bibtex] 
    @Article{Bjoerk2022,
author = {Björk, S. and Anfinsen, S. N. and E, Næsset and Gobakken, T. and Zahabu, E.},
journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
title = {On the Potential of Sequential and Nonsequential Regression Models for Sentinel-1-Based Biomass Prediction in Tanzanian Miombo Forests},
year = {2022},
issn = {2151-1535},
pages = {4612-4639},
volume = {15},
abstract = {This study derives regression models for aboveground biomass (AGB) estimation in miombo woodlands of Tanzania that utilize the high availability and low cost of Sentinel-1 data. The limited forest canopy penetration of C-band SAR sensors along with the sparseness of available ground truth restricts their usefulness in traditional AGB regression models. Therefore, we propose to use AGB predictions based on airborne laser scanning (ALS) data as a surrogate response variable for SAR data. This dramatically increases the available training data and opens for flexible regression models that capture fine-scale AGB dynamics. This becomes a sequential modeling approach, where the first regression stage has linked in situ data to ALS data and produced the AGB prediction map; we perform the subsequent stage, where this map is related to Sentinel-1 data. We develop a traditional, parametric regression model and alternative nonparametric models for this stage. The latter uses a conditional generative adversarial network (cGAN) to translate Sentinel-1 images into ALS-based AGB prediction maps. The convolution filters in the neural networks make them contextual. We compare the sequential models to traditional, nonsequential regression models, all trained on limited AGB ground reference data. Results show that our newly proposed nonsequential Sentinel-1-based regression model performs better quantitatively than the sequential models, but achieves less sensitivity to fine-scale AGB dynamics. The contextual cGAN-based sequential models best reproduce the distribution of ALS-based AGB predictions. They also reach a lower RMSE against in situ AGB data than the parametric sequential model, indicating a potential for further development.},
doi = {10.1109/JSTARS.2022.3179819},
type = {Journal Article},
}
  • [DOI] Breidenbach, J., Ellison, D., Petersson, H., Korhonen, K. T., Henttonen, H. M., Wallerman, J., Fridman, J., Gobakken, T., Astrup, R., & Næsset, E.. (2022). Harvested area did not increase abruptly—how advancements in satellite-based mapping led to erroneous conclusions. Annals of forest science, 79(1), 2.
    [Bibtex] 
    @Article{Breidenbach2022,
author = {Breidenbach, Johannes and Ellison, David and Petersson, Hans and Korhonen, Kari T. and Henttonen, Helena M. and Wallerman, Jörgen and Fridman, Jonas and Gobakken, Terje and Astrup, Rasmus and Næsset, Erik},
journal = {Annals of Forest Science},
title = {Harvested area did not increase abruptly—how advancements in satellite-based mapping led to erroneous conclusions},
year = {2022},
issn = {1297-966X},
number = {1},
pages = {2},
volume = {79},
abstract = {Using satellite-based maps, Ceccherini et al. (Nature 583:72-77, 2020) report abruptly increasing harvested area estimates in several EU countries beginning in 2015. Using more than 120,000 National Forest Inventory observations to analyze the satellite-based map, we show that it is not harvested area but the map’s ability to detect harvested areas that abruptly increases after 2015 in Finland and Sweden.},
doi = {10.1186/s13595-022-01120-4},
type = {Journal Article},
url = {https://doi.org/10.1186/s13595-022-01120-4},
}
  • Dalponte, M., Kallio, A. J. I., Ørka, H. O., Næsset, E., & Gobakken, T.. (2022). Wood decay detection in norway spruce forests based on airborne hyperspectral and als data. Remote sensing, 14(8), 1892.
    [Bibtex] 
    @Article{Dalponte2022,
author = {Dalponte, Michele and Kallio, Alvar J. I. and Ørka, Hans Ole and Næsset, Erik and Gobakken, Terje},
journal = {Remote Sensing},
title = {Wood Decay Detection in Norway Spruce Forests Based on Airborne Hyperspectral and ALS Data},
year = {2022},
issn = {2072-4292},
number = {8},
pages = {1892},
volume = {14},
type = {Journal Article},
url = {https://www.mdpi.com/2072-4292/14/8/1892},
}
  • [DOI] Dalponte, M., Solano-Correa, Y. T., Ørka, H. O., Gobakken, T., & Næsset, E.. (2022). Detection of heartwood rot in norway spruce trees with lidar and multi-temporal satellite data. International journal of applied earth observation and geoinformation, 109, 102790.
    [Bibtex] 
    @Article{Dalponte2022a,
author = {Dalponte, Michele and Solano-Correa, Yady Tatiana and Ørka, Hans Ole and Gobakken, Terje and Næsset, Erik},
journal = {International Journal of Applied Earth Observation and Geoinformation},
title = {Detection of heartwood rot in Norway spruce trees with lidar and multi-temporal satellite data},
year = {2022},
issn = {1569-8432},
pages = {102790},
volume = {109},
abstract = {Norway spruce pathogenic fungi causing root, butt and stem rot represent a substantial problem for the forest sector in many countries. Early detection of rot presence is important for efficient management of the forest resources but due to its nature, which does not generate evident exterior signs, it is very difficult to detect without invasive measurements. Remote sensing has been widely used to monitor forest health status in relation to many pathogens and infestations. In particular, multi-temporal remotely sensed data have shown to be useful in detecting degenerative diseases. In this study, we explored the possibility of using multi-temporal and multi-spectral satellite data to detect rot presence in Norway spruce trees in Norway. Images with four bands were acquired by the Dove satellite constellation with a spatial resolution of 3 m, ranging over three years from June 2017 to September 2019. Field data were collected in 2019–2020 by a harvester during the logging: 16163 trees were recorded, classified in terms of species and presence of rot at the stump and automatically geo-located. The analysis was carried out at individual tree crown (ITC) level, and ITCs were delineated using lidar data. ITCs were classified as healthy, infested and other species using a weighted Support Vector Machine. The results showed an underestimation of the rot presence (balanced accuracy of 56.3%, producer’s accuracies of 64.3 and 48.4% and user’s accuracies of 81.0% and 32.7% respectively for healthy and rot ITCs). The method can be used to provide a tentative map of the rot presence to guide more detailed assessments in field and harvesting activities.},
doi = {https://doi.org/10.1016/j.jag.2022.102790},
keywords = {Heartwood rot Multi-temporal Dove Norway spruce Individual tree crowns Vegetation indices Classification},
type = {Journal Article},
url = {https://www.sciencedirect.com/science/article/pii/S0303243422001167},
}
  • [DOI] Duncanson, L., Kellner, J. R., Armston, J., Dubayah, R., Minor, D. M., Hancock, S., Healey, S. P., Patterson, P. L., Saarela, S., Marselis, S., Silva, C. E., Bruening, J., Goetz, S. J., Tang, H., Hofton, M., Blair, B., Luthcke, S., Fatoyinbo, L., Abernethy, K., Alonso, A., Andersen, H., Aplin, P., Baker, T. R., Barbier, N., Bastin, J. F., Biber, P., Boeckx, P., Bogaert, J., Boschetti, L., Boucher, P. B., Boyd, D. S., Burslem, D. F. R. P., Calvo-Rodriguez, S., Chave, J., Chazdon, R. L., Clark, D. B., Clark, D. A., Cohen, W. B., Coomes, D. A., Corona, P., Cushman, K. C., Cutler, M. E. J., Dalling, J. W., Dalponte, M., Dash, J., de-Miguel , S., Deng, S., Ellis, P. W., Erasmus, B., Fekety, P. A., Fernandez-Landa, A., Ferraz, A., Fischer, R., Fisher, A. G., García-Abril, A., Gobakken, T., Hacker, J. M., Heurich, M., Hill, R. A., Hopkinson, C., Huang, H., Hubbell, S. P., Hudak, A. T., Huth, A., Imbach, B., Jeffery, K. J., Katoh, M., Kearsley, E., Kenfack, D., Kljun, N., Knapp, N., Král, K., Krůček, M., Labrière, N., Lewis, S. L., Longo, M., Lucas, R. M., Main, R., Manzanera, J. A., Martínez, R. V., Mathieu, R., Memiaghe, H., Meyer, V., Mendoza, A. M., Monerris, A., Montesano, P., Morsdorf, F., Næsset, E., Naidoo, L., Nilus, R., O’Brien, M., Orwig, D. A., Papathanassiou, K., Parker, G., Philipson, C., Phillips, O. L., Pisek, J., Poulsen, J. R., Pretzsch, H., Rüdiger, C., & others. (2022). Aboveground biomass density models for nasa’s global ecosystem dynamics investigation (gedi) lidar mission. Remote sensing of environment, 270, 112845.
    [Bibtex] 
    @Article{Duncanson2022,
author = {Duncanson, Laura and Kellner, James R. and Armston, John and Dubayah, Ralph and Minor, David M. and Hancock, Steven and Healey, Sean P. and Patterson, Paul L. and Saarela, Svetlana and Marselis, Suzanne and Silva, Carlos E. and Bruening, Jamis and Goetz, Scott J. and Tang, Hao and Hofton, Michelle and Blair, Bryan and Luthcke, Scott and Fatoyinbo, Lola and Abernethy, Katharine and Alonso, Alfonso and Andersen, Hans-Erik and Aplin, Paul and Baker, Timothy R. and Barbier, Nicolas and Bastin, Jean Francois and Biber, Peter and Boeckx, Pascal and Bogaert, Jan and Boschetti, Luigi and Boucher, Peter Brehm and Boyd, Doreen S. and Burslem, David F. R. P. and Calvo-Rodriguez, Sofia and Chave, Jérôme and Chazdon, Robin L. and Clark, David B. and Clark, Deborah A. and Cohen, Warren B. and Coomes, David A. and Corona, Piermaria and Cushman, K. C. and Cutler, Mark E. J. and Dalling, James W. and Dalponte, Michele and Dash, Jonathan and de-Miguel, Sergio and Deng, Songqiu and Ellis, Peter Woods and Erasmus, Barend and Fekety, Patrick A. and Fernandez-Landa, Alfredo and Ferraz, Antonio and Fischer, Rico and Fisher, Adrian G. and García-Abril, Antonio and Gobakken, Terje and Hacker, Jorg M. and Heurich, Marco and Hill, Ross A. and Hopkinson, Chris and Huang, Huabing and Hubbell, Stephen P. and Hudak, Andrew T. and Huth, Andreas and Imbach, Benedikt and Jeffery, Kathryn J. and Katoh, Masato and Kearsley, Elizabeth and Kenfack, David and Kljun, Natascha and Knapp, Nikolai and Král, Kamil and Krůček, Martin and Labrière, Nicolas and Lewis, Simon L. and Longo, Marcos and Lucas, Richard M. and Main, Russell and Manzanera, Jose A. and Martínez, Rodolfo Vásquez and Mathieu, Renaud and Memiaghe, Herve and Meyer, Victoria and Mendoza, Abel Monteagudo and Monerris, Alessandra and Montesano, Paul and Morsdorf, Felix and Næsset, Erik and Naidoo, Laven and Nilus, Reuben and O’Brien, Michael and Orwig, David A. and Papathanassiou, Konstantinos and Parker, Geoffrey and Philipson, Christopher and Phillips, Oliver L. and Pisek, Jan and Poulsen, John R. and Pretzsch, Hans and Rüdiger, Christoph and others},
journal = {Remote Sensing of Environment},
title = {Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission},
year = {2022},
issn = {0034-4257},
pages = {112845},
volume = {270},
abstract = {NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI’s footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI’s waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.},
doi = {https://doi.org/10.1016/j.rse.2021.112845},
keywords = {LiDAR GEDI Waveform Forest Aboveground biomass Modeling},
type = {Journal Article},
url = {https://www.sciencedirect.com/science/article/pii/S0034425721005654},
}
  • [DOI] Noordermeer, L., Næsset, E., & Gobakken, T.. (2022). Effects of harvester positioning errors on merchantable timber volume predicted and estimated from airborne laser scanner data in mature norway spruce forests. Silva fennica, 56(1).
    [Bibtex] 
    @Article{Noordermeer2022,
author = {Noordermeer, Lennart and Næsset, Erik and Gobakken, Terje},
journal = {SILVA FENNICA},
title = {Effects of harvester positioning errors on merchantable timber volume predicted and estimated from airborne laser scanner data in mature Norway spruce forests},
year = {2022},
number = {1},
volume = {56},
abstract = {
    Newly developed positioning systems in cut-to-length harvesters enable georeferencing of individual trees with submeter accuracy. Together with detailed tree measurements recorded during processing of the tree, georeferenced harvester data are emerging as a valuable tool for forest inventory. Previous studies have shown that harvester data can be linked to airborne laser scanner (ALS) data to estimate a range of forest attributes. However, there is little empirical evidence of the benefits of improved positioning accuracy of harvester data. The two objectives of this study were to (1) assess the accuracy of timber volume estimation using harvester data and ALS data acquired with different scanners over multiple years and (2) assess how harvester positioning errors affect merchantable timber volume predicted and estimated from ALS data. We used harvester data from 33 commercial logging operations, comprising 93 731 harvested stems georeferenced with sub-meter accuracy, as plot-level training data in an enhanced area-based inventory approach. By randomly altering the tree positions in Monte Carlo simulations, we assessed how prediction and estimation errors were influenced by different combinations of simulated positioning errors and grid cell sizes. We simulated positioning errors of 1, 2, …, 15 m and used grid cells of 100, 200, 300 and 400 m2. Values of root mean square errors obtained for cell-level predictions of timber volume differed significantly for the different grid cell sizes. The use of larger grid cells resulted in a greater accuracy of timber volume predictions, which were also less affected by positioning errors. Accuracies of timber volume estimates at logging operation level decreased significantly with increasing levels of positioning error. The results highlight the benefit of accurate positioning of harvester data in forest inventory applications. Further, the results indicate that when estimating timber volume from ALS data and inaccurately positioned harvester data, larger grid cells are beneficial.
    },
doi = {doi:10.14214/sf.10608},
type = {Journal Article},
url = {https://www.silvafennica.fi/article/10608},
}
  • Ramtvedt, E. N., Gobakken, T., & Næsset, E.. (2022). Fine-spatial boreal–alpine single-tree albedo measured by uav: experiences and challenges. Remote sensing, 14(6), 1482.
    [Bibtex] 
    @Article{Ramtvedt2022,
author = {Ramtvedt, Eirik Næsset and Gobakken, Terje and Næsset, Erik},
journal = {Remote Sensing},
title = {Fine-Spatial Boreal–Alpine Single-Tree Albedo Measured by UAV: Experiences and Challenges},
year = {2022},
issn = {2072-4292},
number = {6},
pages = {1482},
volume = {14},
type = {Journal Article},
url = {https://www.mdpi.com/2072-4292/14/6/1482},
}
  • [DOI] Saarela, S., Holm, S., Healey, S. P., Patterson, P. L., Yang, Z., Andersen, H., Dubayah, R. O., Qi, W., Duncanson, L. I., Armston, J. D., Gobakken, T., Næsset, E., Ekström, M., & Ståhl, G.. (2022). Comparing frameworks for biomass prediction for the global ecosystem dynamics investigation. Remote sensing of environment, 278, 113074.
    [Bibtex] 
    @Article{Saarela2022,
author = {Saarela, Svetlana and Holm, Sören and Healey, Sean P. and Patterson, Paul L. and Yang, Zhiqiang and Andersen, Hans-Erik and Dubayah, Ralph O. and Qi, Wenlu and Duncanson, Laura I. and Armston, John D. and Gobakken, Terje and Næsset, Erik and Ekström, Magnus and Ståhl, Göran},
journal = {Remote Sensing of Environment},
title = {Comparing frameworks for biomass prediction for the Global Ecosystem Dynamics Investigation},
year = {2022},
issn = {0034-4257},
pages = {113074},
volume = {278},
abstract = {NASA's Global Ecosystem Dynamics Investigation (GEDI) mission offers data for temperate and pan-tropical estimates of aboveground forest biomass (AGB). The spaceborne, full-waveform LiDAR from GEDI provides sample footprints of canopy structure, expected to cover about 4% of the land area following two years of operation. Several options are available for estimating AGB at different geographical scales. Using GEDI sample data alone, gridded biomass predictions are based on hybrid inference which correctly propagates errors due to the modeling and accounts for sampling variability, but this method requires at least two GEDI tracks in the area of interest. However, there are significant gaps in GEDI coverage and in some areas of interest GEDI data may need to be combined with other wall-to-wall remotely sensed (RS) data, such as those from multispectral or SAR sensors. In these cases, we may employ hierarchical model-based (HMB) inference that correctly considers the additional model errors that result from relating GEDI data to the wall-to-wall data. Where predictions are possible from both hybrid and HMB inference the question arises which framework to choose, and under what circumstances? In this paper, we make progress towards answering these questions by comparing the performance of the two prediction frameworks under conditions relevant for the GEDI mission. Conventional model-based (MB) inference with wall-to-wall TanDEM-X data was applied as a baseline prediction framework, which does not involve GEDI data at all. An important feature of the study was the comparison of AGB predictors in terms of both standard deviation (SD: the square root of variance) and root mean square error (RMSE: the square root of mean square error – MSE). Since, in model-based inference, the true AGB in an area of interest is a random variable, comparisons of the performance of prediction frameworks should preferably be made in terms of their RMSEs. However, in practice only the SD can be estimated based on empirical survey data, and thus it is important also to study whether or not the difference between the two uncertainty measures is small or large under conditions relevant for the GEDI mission. Our main findings were that: (i) hybrid and HMB prediction typically resulted in smaller RMSEs than conventional MB prediction although the difference between the three frameworks in terms of SD often was small; (ii) in most cases the difference between hybrid and HMB inference was small in terms of both RMSE and SD; (iii) the RMSEs for all frameworks was substantially larger than the SDs in small study areas whereas the two uncertainty measures were similar in large study areas, and; (iv) spatial autocorrelation of model residual errors had a large effect on the RMSEs of AGB predictors, especially in small study areas. We conclude that hybrid inference is suitable in most GEDI applications for AGB assessment, due to its simplicity compared to HMB inference. However, where GEDI data are sparse HMB inference should be preferred.},
doi = {https://doi.org/10.1016/j.rse.2022.113074},
keywords = {Carbon monitoring GEDI Hierarchical model-based inference Hybrid inference Mean square error Model-based inference Remote sensing TanDEM-X},
type = {Journal Article},
url = {https://www.sciencedirect.com/science/article/pii/S0034425722001882},
}
  • [DOI] Ørka, H. O., Jutras-Perreault, M., Candelas-Bielza, J., & Gobakken, T.. (2022). Delineation of geomorphological woodland key habitats using airborne laser scanning. Remote sensing, 14(5), 1184.
    [Bibtex] 
    @Article{Oerka2022,
author = {Ørka, Hans Ole and Jutras-Perreault, Marie-Claude and Candelas-Bielza, Jaime and Gobakken, Terje},
journal = {Remote Sensing},
title = {Delineation of Geomorphological Woodland Key Habitats Using Airborne Laser Scanning},
year = {2022},
issn = {2072-4292},
number = {5},
pages = {1184},
volume = {14},
doi = {10.3390/rs14051184},
type = {Journal Article},
url = {https://www.mdpi.com/2072-4292/14/5/1184},
}
  • [DOI] Ørka, H. O., Jutras-Perreault, M., Næsset, E., & Gobakken, T.. (2022). A framework for a forest ecological base map – an example from norway. Ecological indicators, 136, 108636.
    [Bibtex] 
    @Article{Oerka2022a,
author = {Ørka, Hans Ole and Jutras-Perreault, Marie-Claude and Næsset, Erik and Gobakken, Terje},
journal = {Ecological Indicators},
title = {A framework for a forest ecological base map – An example from Norway},
year = {2022},
issn = {1470-160X},
pages = {108636},
volume = {136},
abstract = {In the management of forest ecosystems, spatial information about the extent, condition and pressures are essential. In the current study, we present a framework for a remote sensing-based forest ecological base map covering Norway. Combining remotely sensed imagery from optical satellite systems such as Sentinel-2 and Landsat provides information about forest ecosystem extent and change over time. Utilizing a national dataset of airborne laser scanning (ALS) data allowed predicting a range of attributes describing forest condition, including naturalness. In total, seven definitions of naturalness were evaluated. Pressures on the forest ecosystems were mapped using a change detection algorithm and satellite data from 1986 to 2020. Change detection is the cornerstone in monitoring and for understanding the pressures on the ecosystems. The predicted forest extent had an overall accuracy of 85 to 89% using Sentinel-2 imagery from 2020 and 71 to 81% using Landsat imagery from 1986. For the forest condition attributes, the explained portion of the variances were >70% for biomass, height and volume and from 21% to 64% for number of stems, crown coverage and a diversity index. Naturalness was classified with accuracies of 77 to 98%, except for age-based definitions. Nevertheless, a large number of false positives were present. Change detection was evaluated in terms of final harvest and was identified with an overall accuracy of 84–92%. The land cover change classification had an overall accuracy of 70–92%. The detailed maps of forest condition and forest pressures were aggregated to a local level using model-based inference, providing estimates of mean values and uncertainty at a scale suitable for ecosystem indicator development. The collection of map layers describing forest extent, condition and pressures form a forest ecological base map important for environmental management.},
doi = {https://doi.org/10.1016/j.ecolind.2022.108636},
keywords = {Ecosystem services Environmental management Naturalness Remote sensing Model-based inference},
type = {Journal Article},
url = {https://www.sciencedirect.com/science/article/pii/S1470160X22001078},
}
  • [DOI] Devos, C. C., Ohlson, M., Næsset, E., & Bollandsås, O. M.. (2022). Soil carbon stocks in forest-tundra ecotones along a 500 km latitudinal gradient in northern norway. Scientific reports, 12(1), 13358.
    [Bibtex] 
    @Article{Devos2022,
author = {Devos, Claire Céline and Ohlson, Mikael and Næsset, Erik and Bollandsås, Ole Martin},
journal = {Scientific Reports},
title = {Soil carbon stocks in forest-tundra ecotones along a 500 km latitudinal gradient in northern Norway},
year = {2022},
issn = {2045-2322},
number = {1},
pages = {13358},
volume = {12},
abstract = {As shrubs and trees are advancing into tundra ecosystems due to climate warming, litter input and microclimatic conditions affecting litter decomposition are likely to change. To assess how the upward shift of high-latitude treeline ecotones might affect soil organic carbon stocks (SOC), we sampled SOC stocks in the surface layers of 14 mountain birch forest-tundra ecotones along a 500 km latitudinal transect in northern Norway. Our objectives were to examine: (1) how SOC stocks differ between forest and tundra soils, and (2) the relative role of topography, vegetation and climate in explaining variability in SOC stock sizes. Overall, forest soils had higher SOC stocks (median: 2.01 kg m−2) than tundra soils (median: 1.33 kg m−2). However, SOC storage varied greatly within and between study sites. Two study sites had higher SOC stocks in the tundra than in the nearby forest, five sites had higher SOC stocks in the forest, and seven sites did not show differences in SOC stocks between forest and tundra soils. Thus, our results suggest that an upwards forest expansion does not necessarily lead to a change in SOC storage at all sites. Further, a partial least-squares regression (PLSR) model indicated that elevation, temperature, and slope may be promising indicators for SOC stock size at high-latitude treelines. Precipitation and vegetation were in comparison only of minor importance.},
doi = {10.1038/s41598-022-17409-3},
type = {Journal Article},
url = {https://doi.org/10.1038/s41598-022-17409-3},
}
  • [DOI] Kolstad, A. L., Snøan, I. B., Austrheim, G., Bollandsås, O. M., Solberg, E. J., & Speed, J. D. M.. (2022). Airborne laser scanning reveals increased growth and complexity of boreal forest canopies across a network of ungulate exclosures in norway. Remote sensing in ecology and conservation, 8(1), 5-17.
    [Bibtex] 
    @Article{Kolstad2022,
author = {Kolstad, Anders L. and Snøan, Ingrid Bekken and Austrheim, Gunnar and Bollandsås, Ole Martin and Solberg, Erling J. and Speed, James D. M.},
journal = {Remote Sensing in Ecology and Conservation},
title = {Airborne laser scanning reveals increased growth and complexity of boreal forest canopies across a network of ungulate exclosures in Norway},
year = {2022},
issn = {2056-3485
2056-3485},
note = {https://doi.org/10.1002/rse2.224},
number = {1},
pages = {5-17},
volume = {8},
abstract = {Abstract Large herbivores are often classed as ecosystem engineers, and when they become scarce or overabundant, this can alter ecosystem states and influence climate forcing potentials. This realization has spurred a call to integrate large herbivores in earth system models. However, we lack a good understanding of their net effects on climate forcing, including carbon and energy exchange. A possible solution to this lies in harmonizing data across the myriad of large herbivore exclosure experiments around the world. This is challenging due to differences in experimental designs and field protocols. We used airborne laser scanning (ALS) to describe the effect of herbivore removal across 43 young boreal forest stands in Norway and found that exclusion caused the canopy height to increase from 1.7 ± 0.2 to 2.5 ± 0.2 m (means ± se), and also causing a marked increase in vertical complexity and above-ground biomass. We then go on to discuss some of the issues with using ALS; we propose ALS as an approach for studying the effects of multiple large herbivore exclosure experiments simultaneously, and producing area-based estimates on canopy structure and forest biomass in a cheap, efficient, standardized and reproducible way. We suggest that this is a vital next step towards generating biome-wide predictions for the effects of large herbivores on forest ecosystem structure which can both inform both local management goals and earth system models.},
doi = {10.1002/rse2.224},
keywords = {biomass herbivory large herbivores LiDAR moose remote sensing},
type = {Journal Article},
url = {https://doi.org/10.1002/rse2.224},
}
  • [DOI] Mienna, I. M., Austrheim, G., Klanderud, K., Bollandsås, O. M., & Speed, J. D. M.. (2022). Legacy effects of herbivory on treeline dynamics along an elevational gradient. Oecologia, 198(3), 801-814.
    [Bibtex] 
    @Article{Mienna2022,
author = {Mienna, Ida M. and Austrheim, Gunnar and Klanderud, Kari and Bollandsås, Ole Martin and Speed, James D. M.},
journal = {Oecologia},
title = {Legacy effects of herbivory on treeline dynamics along an elevational gradient},
year = {2022},
issn = {1432-1939},
number = {3},
pages = {801-814},
volume = {198},
abstract = {Treelines are expected to expand into alpine ecosystems with global warming, but herbivory may delay this expansion. This study quantifies long-term effects of temporally varying sheep densities on birch recruitment and growth in the treeline ecotone. We examined treeline ecotone successional trajectories and legacy effects in a replicated experimental setup, where enclosures were present for 14 years with three different sheep densities (0, 25, 80 sheep km−2). Before and after the enclosures were present, the site had an ambient sheep density of 20–25 km−2. We sampled field data 4 years after enclosure removal and compared these to data sampled 8 and 9 years after enclosure erection. We sampled data on birch browsing pressure, birch distribution across life-stages (recruits, saplings, and mature trees), and birch annual radial growth. Fourteen years of increased or decreased sheep density had observable legacy effects depending on birch life-stage. Birch recruit prevalence decreased in areas, where sheep were reintroduced after being absent for 14 years. For the same areas, sapling and mature tree prevalence increased, indicating that these areas have entered alternative successional trajectories compared to areas, where sheep were present the whole time. Birch annual radial growth showed a lag effect of 2 years after enclosure removal, with growth decreasing in areas where sheep had been absent for 14 years and increasing where sheep densities were high. Thus, decadal-scale absences of herbivores can leave legacy effects due to increased numbers of trees that have high resistance to later-introduced herbivore browsing.},
doi = {10.1007/s00442-022-05125-8},
type = {Journal Article},
url = {https://doi.org/10.1007/s00442-022-05125-8},
}
  • [DOI] Mienna, I. M., Klanderud, K., Ørka, H. O., Bryn, A., & Bollandsås, O. M.. (2022). Land cover classification of treeline ecotones along a 1100 km latitudinal transect using spectral- and three-dimensional information from uav-based aerial imagery. Remote sensing in ecology and conservation, 8(4), 536-550.
    [Bibtex] 
    @Article{Mienna2022a,
author = {Mienna, Ida Marielle and Klanderud, Kari and Ørka, Hans Ole and Bryn, Anders and Bollandsås, Ole Martin},
journal = {Remote Sensing in Ecology and Conservation},
title = {Land cover classification of treeline ecotones along a 1100 km latitudinal transect using spectral- and three-dimensional information from UAV-based aerial imagery},
year = {2022},
issn = {2056-3485},
note = {https://doi.org/10.1002/rse2.260},
number = {4},
pages = {536-550},
volume = {8},
abstract = {Abstract The alpine treeline ecotone is expected to move upwards in elevation with global warming. Thus, mapping treeline ecotones is crucial in monitoring potential changes. Previous remote sensing studies have focused on the usage of satellites and aircrafts for mapping the treeline ecotone. However, treeline ecotones can be highly heterogenous, and thus the use of imagery with higher spatial resolution should be investigated. We evaluate the potential of using unmanned aerial vehicles (UAVs) for the collection of ultra-high spatial resolution imagery for mapping treeline ecotone land covers. We acquired imagery and field reference data from 32 treeline ecotone sites along a 1100?km latitudinal gradient in Norway (60?69°N). Before classification, we performed a superpixel segmentation of the UAV-derived orthomosaics and assigned land cover classes to segments: rock, water, snow, shadow, wetland, tree-covered area and five classes within the ridge-snowbed gradient. We calculated features providing spectral, textural, three-dimensional vegetation structure, topographical and shape information for the classification. To evaluate the influence of acquisition time during the growing season and geographical variations, we performed four sets of classifications: global, seasonal-based, geographical regional-based and seasonal-regional-based. We found no differences in overall accuracy (OA) between the different classifications, and the global model with observations irrespective of data acquisition timing and geographical region had an OA of 73%. When accounting for similarities between closely related classes along the ridge-snowbed gradient, the accuracy increased to 92.6%. We found spectral features related to visible, red-edge and near-infrared bands to be the most important to predict treeline ecotone land cover classes. Our results show that the use of UAVs is efficient in mapping treeline ecotones, and that data can be acquired irrespective of timing within a growing season and geographical region to get accurate land cover maps. This can overcome constraints of a short field-season or low-resolution remote sensing data.},
doi = {https://doi.org/10.1002/rse2.260},
keywords = {Drone EcoSyst multi-site spatiotemporal variation UAV vegetation},
type = {Journal Article},
url = {https://doi.org/10.1002/rse2.260},
}
  • [DOI] de Lera Garrido, A., Gobakken, T., Ørka, H. O., Næsset, E., & Bollandsås, O. M.. (2022). Estimating forest attributes in airborne laser scanning based inventory using calibrated predictions from external models. Silva fennica, 56(2).
    [Bibtex] 
    @Article{LeraGarrido2022,
author = {de Lera Garrido, Ana and Gobakken, Terje and Ørka, Hans Ole and Næsset, Erik and Bollandsås, Ole M.},
journal = {SILVA FENNICA},
title = {Estimating forest attributes in airborne laser scanning based inventory using calibrated predictions from external models},
year = {2022},
number = {2},
volume = {56},
abstract = {
    Forest management inventories assisted by airborne laser scanner data rely on predictive models traditionally constructed and applied based on data from the same area of interest. However, forest attributes can also be predicted using models constructed with data external to where the model is applied, both temporal and geographically. When external models are used, many factors influence the predictions’ accuracy and may cause systematic errors. In this study, volume, stem number, and dominant height were estimated using external model predictions calibrated using a reduced number of up-to-date local field plots or using predictions from reparametrized models. We assessed and compared the performance of three different calibration approaches for both temporally and spatially external models. Each of the three approaches was applied with different numbers of calibration plots in a simulation, and the accuracy was assessed using independent validation data. The primary findings were that local calibration reduced the relative mean difference in 89% of the cases, and the relative root mean squared error in 56% of the cases. Differences between application of temporally or spatially external models were minor, and when the number of local plots was small, calibration approaches based on the observed prediction errors on the up-to-date local field plots were better than using the reparametrized models. The results showed that the estimates resulting from calibrating external models with 20 plots were at the same level of accuracy as those resulting from a new inventory.
    },
doi = {doi:10.14214/sf.10695},
type = {Journal Article},
url = {https://silvafennica.fi/article/10695},
}
  • [DOI] McRoberts, R. E., Næsset, E., Heikkinen, J., Chen, Q., Strimbu, V., Esteban, J., Hou, Z., Giannetti, F., Mohammadi, J., & Chirici, G.. (2022). On the model-assisted regression estimators using remotely sensed auxiliary data. Remote sensing of environment, 281, 113168.
    [Bibtex] 
    @Article{McRoberts2022,
author = {Ronald E. McRoberts and Erik Næsset and Juha Heikkinen and Qi Chen and Victor Strimbu and Jessica Esteban and Zhengyang Hou and Francesca Giannetti and Jahangir Mohammadi and Gherardo Chirici},
journal = {Remote Sensing of Environment},
title = {On the model-assisted regression estimators using remotely sensed auxiliary data},
year = {2022},
issn = {0034-4257},
pages = {113168},
volume = {281},
abstract = {The model-assisted difference and regression estimators are increasingly used with forest inventory and remotely sensed data to increase the precision of estimates of inventory parameters. Although these estimators date back at least 50 years and appear in multiple current sampling textbooks, the associated terminology is inconsistently defined, even among the prominent authorities. Further, two of the most prominent statistical sampling textbooks, Cochran (1977) and Särndal et al. (1992), use considerably different notation. The study focused on three objectives: (1) to formulate consistent and operationally useful definitions via a synthesis of the literature, (2) to construct a bridge between the more complex Särndal et al. (1992) notation and the more commonly used Cochran (1977) notation, and (3) to assess sample size, model form and g-weight effects on the unbiasedness of the regression estimators of both the population mean and the variance of its estimate. The data analyses entailed Monte Carlo simulations using an artificial population constructed using inventory and airborne laser scanning data and both across- and within-dataset analyses for 11 inventory datasets representing six countries on four continents. The analyses focused on assessing the unbiasedness of the regression estimators of both the mean and variance, the role of the g-weights on the unbiasedness of the variance estimator, and differences for linear versus nonlinear models. Key terminological distinctions were that the generalized estimators accommodate unequal probability sampling and that the difference estimator of the mean is unbiased whereas the regression estimator of the mean is only asymptotically unbiased, meaning it only approaches unbiasedness as the sample size increases. The key analytical conclusions were threefold: (1) the regression variance estimator was confirmed as asymptotically unbiased, (2) the form of the regression variance estimator that incorporated the g-weights was more accurate, and (3) the regression variance estimator was more accurate for linear models than for nonlinear models.},
doi = {https://doi.org/10.1016/j.rse.2022.113168},
keywords = {Generalized estimators, Difference estimator, Regression estimator, g-weights, Nonlinear model},
url = {https://www.sciencedirect.com/science/article/pii/S0034425722002826},
}
  • [DOI] McRoberts, R. E., Næsset, E., Saatchi, S., & Quegan, S.. (2022). Statistically rigorous, model-based inferences from maps. Remote sensing of environment, 279, 113028.
    [Bibtex] 
    @Article{McRoberts2022a,
author = {Ronald E. McRoberts and Erik Næsset and Sassan Saatchi and Shaun Quegan},
journal = {Remote Sensing of Environment},
title = {Statistically rigorous, model-based inferences from maps},
year = {2022},
issn = {0034-4257},
pages = {113028},
volume = {279},
abstract = {Statistically rigorous inferences in the form of confidence intervals for map-based estimates require model-based inferential methods. Model-based mean square errors (MSE) incorporate estimates of both residual variability and sampling variability, of which the latter includes population unit variance estimates and pairwise population unit covariance estimates. Bootstrapping, which can be used with any prediction technique, provides a means of estimating the required variances and covariances. The objectives of the study were to to demonstrate a method for estimating the sampling variability, Var̂samμ̂, that can be used with all prediction techniques, to develop an efficient method that map makers can use to disseminate metadata that facilitates calculation of Var̂samμ̂ for arbitrary subregions of maps, and to estimate the individual contributions of sampling variability and residual variability to the overall standard error of the prediction of the population mean. The primary results were fourfold: (i) map makers must provide metadata that facilitate estimation of population unit variances and covariances for arbitrary map subregions, (ii) bootstrapping was demonstrated as an effective means of estimating the variances and covariances, (iii) the very large matrix of pairwise population unit covariances can be aggregated into a much smaller matrix that can be readily communicated by map makers to map users, and (iv) MSEs that include only estimates of residual variability and/or estimates of population unit variances, but not estimates of the pairwise population unit covariances, grossly under-estimate the actual MSEs.},
doi = {https://doi.org/10.1016/j.rse.2022.113028},
keywords = {Bootstrapping, Sampling variability, Residual variability, Pairwise population unit covariances},
url = {https://www.sciencedirect.com/science/article/pii/S0034425722001420},
}

2021

  • [DOI] Aza, A., Kangas, A., Gobakken, T., & Kallio, M. A. I.. (2021). Effect of root and butt rot uncertainty on optimal harvest schedules and expected incomes at the stand level. Annals of forest science, 78(3), 70.
    [Bibtex] 
    @article{RN5307,
author = {Aza, Ana and Kangas, Annika and Gobakken, Terje and Kallio, A. Maarit I.},
title = {Effect of root and butt rot uncertainty on optimal harvest schedules and expected incomes at the stand level},
journal = {Annals of Forest Science},
volume = {78},
number = {3},
pages = {70},
abstract = {Root and rot (RBR) caused byHeterobasidion parviporumNiemelä & Korhonen andHeterobasidion annosum(Fr.) Bref. damages Fennoscandian spruce stands. In case the rot infection and its severity are unknown, the mere risk of infection should seldom affect the harvest timing. When it does, the gains by harvesting earlier are minimal.},
ISSN = {1297-966X},
DOI = {10.1007/s13595-021-01072-1},
url = {https://doi.org/10.1007/s13595-021-01072-1},
year = {2021},
type = {Journal Article}
}
  • [DOI] Jutras-Perreault, M., Gobakken, T., & Ørka, H. O.. (2021). Comparison of two algorithms for estimating stand-level changes and change indicators in a boreal forest in norway. International journal of applied earth observation and geoinformation, 98, 102316.
    [Bibtex] 
    @article{RN5262,
author = {Jutras-Perreault, Marie-Claude and Gobakken, Terje and Ørka, Hans Ole},
title = {Comparison of two algorithms for estimating stand-level changes and change indicators in a boreal forest in Norway},
journal = {International Journal of Applied Earth Observation and Geoinformation},
volume = {98},
pages = {102316},
abstract = {In Europe, the change in forest cover caused by felling activities, especially clear-cutting, is the most significant driver of forest ecosystem change. Monitoring the magnitude of harvest activities and their spatial and temporal distribution can provide essential change indicators of the pressure on forest ecosystems.Satellite remote sensing offers the means to assess and map indicators of forest change dynamics related to a high spatial- and temporal resolution over large areas cost-effectively and objectively. Large-scale maps of forest cover change over time produced by LandTrendr (LT), a temporal segmentation algorithm, and Global Forest Watch (GFW) are evaluated in a Norwegian boreal environment. Their accuracy to detect change at stand-level and their potential to produce landscape-level spatial and temporal change indicators was assessed against a 20-year historical record of harvest activities in Southern Norway. Data from LT were found to be spatially and temporally more coherent with the reference data than GFW. LT detected 85.4% of the clear-cuts and a decrease in harvest activities between 2001 and 2017, a trend confirmed by the reference data, while GFW detected 63.1% of the clear-cuts and an increase in harvest activities for the same period. It was concluded that LT map of changes (LT-map) provides efficient spatial and temporal indicators of forest change dynamics and performed better than GFW tree cover loss map (GFW-map) to identify and monitor clear-cuts in a Norwegian boreal forest.},
keywords = {Clear-cut detection
LandTrendr
Large-scale mapping
Landsat
Global Forest Watch
Change indicators},
ISSN = {0303-2434},
DOI = {https://doi.org/10.1016/j.jag.2021.102316},
url = {https://www.sciencedirect.com/science/article/pii/S0303243421000234},
year = {2021},
type = {Journal Article}
}
  • [DOI] Knoke, T., Kindu, M., Schneider, T., & Gobakken, T.. (2021). Inventory of forest attributes to support the integration of non-provisioning ecosystem services and biodiversity into forest planning—from collecting data to providing information. Current forestry reports, 7(1), 38-58.
    [Bibtex] 
    @article{RN5308,
author = {Knoke, Thomas and Kindu, Mengistie and Schneider, Thomas and Gobakken, Terje},
title = {Inventory of Forest Attributes to Support the Integration of Non-provisioning Ecosystem Services and Biodiversity into Forest Planning—from Collecting Data to Providing Information},
journal = {Current Forestry Reports},
volume = {7},
number = {1},
pages = {38-58},
abstract = {Our review provides an overview of forest attributes measurable by forest inventory that may support the integration of non-provisioning ecosystem services (ES) and biodiversity into forest planning. The review identifies appropriate forest attributes to quantify the opportunity for recreation, biodiversity promotion and carbon storage, and describes new criteria that future forest inventories may include. As a source of information, we analyse recent papers on forest inventory and ES to show if and how they address these criteria. We further discuss how mapping ES could benefit from such new criteria and conclude with three case studies illustrating the importance of selected criteria delivered by forest inventory.},
ISSN = {2198-6436},
DOI = {10.1007/s40725-021-00138-7},
url = {https://doi.org/10.1007/s40725-021-00138-7},
year = {2021},
type = {Journal Article}
}
  • [DOI] Næsset, E., Gobakken, T., Jutras-Perreault, M., & Ramtvedt, E. N.. (2021). Comparing 3d point cloud data from laser scanning and digital aerial photogrammetry for height estimation of small trees and other vegetation in a boreal–alpine ecotone. Remote sensing, 13(13), 2469.
    [Bibtex] 
    @Article{RN5315,
author = {Næsset, Erik and Gobakken, Terje and Jutras-Perreault, Marie-Claude and Ramtvedt, Eirik Næsset},
journal = {Remote Sensing},
title = {Comparing 3D Point Cloud Data from Laser Scanning and Digital Aerial Photogrammetry for Height Estimation of Small Trees and Other Vegetation in a Boreal–Alpine Ecotone},
year = {2021},
issn = {2072-4292},
number = {13},
pages = {2469},
volume = {13},
doi = {10.3390/rs13132469},
type = {Journal Article},
url = {https://www.mdpi.com/2072-4292/13/13/2469},
}
  • [DOI] Ramtvedt, E. N., Bollandsås, O. M., Næsset, E., & Gobakken, T.. (2021). Relationships between single-tree mountain birch summertime albedo and vegetation properties. Agricultural and forest meteorology, 307, 108470.
    [Bibtex] 
    @article{RN5301,
author = {Ramtvedt, Eirik Næsset and Bollandsås, Ole Martin and Næsset, Erik and Gobakken, Terje},
title = {Relationships between single-tree mountain birch summertime albedo and vegetation properties},
journal = {Agricultural and Forest Meteorology},
volume = {307},
pages = {108470},
abstract = {Understanding and quantifying the influence of tree structure and ground vegetation on albedo in the boreal-alpine treeline is urgently needed to assess and predict the biophysical climatic feedback effect of forest- and treeline expansion. Fine-spatial resolution in-situ radiation measurements sensitive to small-scale variability enable precise albedo estimation for complex heterogeneous landscapes. In this study, horizontally measured single-tree albedo of mountain birch and their spatially consistent tree- and ground vegetation properties were collected in the boreal-alpine treeline over a period of 14 days in summertime. The aim was to identify properties of tree structure and ground vegetation driving single-tree mountain birch albedo. In addition, it was of interest to analyze the relationship between the vegetation properties and a slope-estimated albedo when a simplified correction of slope and aspect of the terrain was applied to the horizontally measured incoming shortwave radiation. Both properties of tree structure and ground vegetation were strongly related to albedo. The results imply that expansion of mountain birch forests at the expense of the prevalence of bright-colored lichens, bare rock, graminoids and mosses will reduce summertime boreal-alpine treeline albedo. Taller trees with wider tree crowns will absorb more solar radiation than smaller trees and hence also reduce albedo. Overall average difference of albedo of sample plots with and without presence of mountain birch was 0.06, corresponding to 27% of the albedo for plots without birch. Horizontally measured albedo was more strongly correlated with the vegetation properties than when corrected for terrain slope and aspect. The findings show that the appropriateness of horizontally measured albedo of single trees and tree clusters in open sloping terrain, depends on the spatial size of the footprint of the downward-looking radiation sensor relative to the size of the tree subject to observation.},
keywords = {Albedo
Mountain Birch
Vegetation Properties
Fine-scale},
ISSN = {0168-1923},
DOI = {https://doi.org/10.1016/j.agrformet.2021.108470},
url = {https://www.sciencedirect.com/science/article/pii/S0168192321001532},
year = {2021},
type = {Journal Article}
}
  • [DOI] {O}rka, H., Hansen, E., Dalponte, M., Gobakken, T., & Næsset, E.. (2021). Large-area inventory of species composition using airborne laser scanning and hyperspectral data. Silva fenn., 55(4), 1–23.
    [Bibtex] 
    @Article{Orka2021-vq,
author = {{\O}rka, Hans and Hansen, Endre and Dalponte, Michele and Gobakken, Terje and N{\ae}sset, Erik},
journal = {Silva Fenn.},
title = {Large-area inventory of species composition using airborne laser scanning and hyperspectral data},
year = {2021},
number = {4},
pages = {1--23},
volume = {55},
abstract = {Tree species composition is an essential attribute in
stand-level forest management inventories and remotely sensed
data might be useful for its estimation. Previous studies on
this topic have had several operational drawbacks, e.g.,
performance studied at a small scale and at a single tree-level
with large fieldwork costs. The current study presents the
results from a large-area inventory providing species
composition following an operational area-based approach. The
study utilizes a combination of airborne laser scanning and
hyperspectral data and 97 field sample plots of 250 m collected
over 350 km of productive forest in Norway. The results show
that, with the availability of hyperspectral data,
species-specific volume proportions can be provided in
operational forest management inventories with acceptable
results in 90\% of the cases at the plot level. Dominant species
were classified with an overall accuracy of 91\% and a
kappa-value of 0.73. Species-specific volumes were estimated
with relative root mean square differences of 34\%, 87\%, and
102\% for Norway spruce ( (L.) Karst.), Scots pine ( L.), and
deciduous species, respectively. A novel tree-based approach for
selecting pixels improved the results compared to a traditional
approach based on the normalized difference vegetation
index.22Picea abiesPinus sylvestris},
doi = {doi:10.14214/sf.10244},
publisher = {Finnish Society of Forest Science},
url = {https://www.silvafennica.fi/article/10244},
}
  • [DOI] Karjalainen, T., Mehtätalo, L., Packalen, P., Malinen, J., Næsset, E., Gobakken, T., & Maltamo, M.. (2021). In-situ calibration of stand level merchantable and sawlog volumes using cut-to-length harvester measurements and airborne laser scanning data. Forestry: an international journal of forest research.
    [Bibtex] 
    @Article{RN5304,
author = {Karjalainen, Tomi and Mehtätalo, Lauri and Packalen, Petteri and Malinen, Jukka and Næsset, Erik and Gobakken, Terje and Maltamo, Matti},
journal = {Forestry: An International Journal of Forest Research},
title = {In-situ calibration of stand level merchantable and sawlog volumes using cut-to-length harvester measurements and airborne laser scanning data},
year = {2021},
issn = {0015-752X},
abstract = {Forest management inventories assisted by airborne laser scanning (ALS) can be used to predict different forest attributes. These predictions are utilized in practical forestry, but in the case of timber assortment-specific volumes, the ALS-based predictions can be inaccurate. This causes uncertainty in harvest planning. However, ALS-based predictions can be calibrated to achieve greater accuracy with local measurements. In this study, we used ALS data and accurately positioned cut-to-length harvester measurements from Norway spruce (Picea abies (L.) Karst.) dominated clear-cuts. We fitted linear mixed-effects (LME) models with exponential correlation structure for merchantable volume and sawlog volume for 225 m2 cells. Our aim was to study the effect of local harvester measurements on the accuracy of stand level merchantable and sawlog volumes. LME-based predictions were calibrated repeatedly up to 40 times as the cutting progressed. ALS data and harvester measurements were used to predict both the random effects and residual errors for each validation unit. At best, relative root mean square error (RMSE%) of initial predictions of 15.4 per cent for merchantable volume and 22.1 per cent for sawlog volume were reduced to 4.1 and 5.3 per cent, respectively, when measurements from 40 harvested cells of size 225 m2 were used. These results suggest that spatially accurate harvester data could be utilized during harvesting to increase the accuracy of volume and timber assortment predictions.},
doi = {10.1093/forestry/cpab031},
type = {Journal Article},
url = {https://doi.org/10.1093/forestry/cpab031},
}
  • [DOI] Noordermeer, L., Sørngård, E., Astrup, R., Næsset, E., & Gobakken, T.. (2021). Coupling a differential global navigation satellite system to a cut-to-length harvester operating system enables precise positioning of harvested trees. International journal of forest engineering, 32(2), 119-127.
    [Bibtex] 
    @Article{RN5314,
author = {Noordermeer, Lennart and Sørngård, Erik and Astrup, Rasmus and Næsset, Erik and Gobakken, Terje},
journal = {International Journal of Forest Engineering},
title = {Coupling a differential global navigation satellite system to a cut-to-length harvester operating system enables precise positioning of harvested trees},
year = {2021},
issn = {1494-2119},
note = {doi: 10.1080/14942119.2021.1899686},
number = {2},
pages = {119-127},
volume = {32},
abstract = {ABSTRACTCut-to-length harvesters collect detailed information on the dimensions and characteristics of individual harvested trees. When equipped with global navigation satellite system (GNSS) receivers and motion sensors, the obtained measurements can be linked to locations of single harvested trees, benefitting a range of forest inventory applications. We propose a way of georeferencing harvested trees using a Komatsu 931XC harvester, which measures and records the machine's bearing, crane angle and crane length for each harvested tree. We replaced the harvester?s standard GNSS receiver with a dual-antenna differential GNSS receiver. From the coordinates obtained, rotations calculated from the GNSS receiver and data on crane length, we determined the location of 285 trees harvested in eight final fellings in Norway. We compared the obtained locations to control measurements taken on the corresponding stumps directly after harvest using a differential GNSS receiver. The mean distance between planimetric coordinates of trees measured by the harvester and corresponding control measurements was 0.88 m with a standard deviation of 0.38 m. By correcting the crane lengths for systematic deviations between harvester and control locations, the mean distance was reduced to 0.79 m. This study shows that measurements of single harvested trees can be georeferenced with sub-meter accuracy, by mounting a differential GNSS receiver on a harvester and without installing additional sensors. The results also suggest that the positional accuracy can be further improved by measuring and recording the length of the telescopic boom, and that with minor adjustments, the system could be fully automated.},
doi = {10.1080/14942119.2021.1899686},
type = {Journal Article},
url = {https://doi.org/10.1080/14942119.2021.1899686},
}

2020

  • [DOI] Asrat, Z., Eid, T., Gobakken, T., & Negash, M.. (2020). Aboveground tree biomass prediction options for the dry afromontane forests in south-central ethiopia. Forest ecology and management, 473, 118335.
    [Bibtex] 
    @article{RN5238,
author = {Asrat, Zerihun and Eid, Tron and Gobakken, Terje and Negash, Mesele},
title = {Aboveground tree biomass prediction options for the Dry Afromontane forests in south-central Ethiopia},
journal = {Forest Ecology and Management},
volume = {473},
pages = {118335},
abstract = {Biomass of trees may be predicted either directly applying allometric models or indirectly from volume and biomass expansion factors (BEFs). For the Dry Afromontane forests, the second largest biomass pool in Ethiopia, such methods are not devised and properly documented. The main objective of this study was to explore different aboveground tree biomass prediction options based on destructively sampled tree biomass data. We explored the direct method by means of 1) new mixed-species general biomass models developed in the present study, and 2) some previously developed models including the pan-tropical models, and the indirect method by means of 3) volume and BEFs. From two sites in south-central Ethiopia, based on information from systematic sample plot inventories, 63 trees from 30 different species that contributed about 87% to the total forest basal area, were destructively sampled. Weighted nonlinear regression was applied to fit new models and their performance was assessed using root mean squared error (RMSE, %), mean prediction error (MPE, %) and pseudo-R2 based on leave-one-out-cross-validation. Previously developed models and the indirect method were also evaluated by means of RMSE and MPE. The new general total biomass models performed well with pseudo-R2 ranging between 0.87 and 0.96 and are presented along with covariance matrices for the parameter estimates enabling error propagation in biomass estimation. Most previously developed models resulted in significant MPEs up to 78%, while the best pan-tropical model performed much better with an MPE of about 7%. The indirect method also showed poor performance with MPEs ranging between 5% and 30%. Generally, the new models are accurate and flexible, thus, preferred over all previously developed models and the indirect method for application. However, their application to Dry Afromontane forests outside the study sites should be made only after thoroughly evaluating growing conditions and species composition. The results are step forward to enhance decisions made towards sustainable forest management including the REDD+ implementation for Dry Afromontane forests in Ethiopia.},
keywords = {Aboveground biomass
Allometric models
Biomass expansion factors
Dry Afromontane forest},
ISSN = {0378-1127},
DOI = {https://doi.org/10.1016/j.foreco.2020.118335},
url = {http://www.sciencedirect.com/science/article/pii/S037811272031104X},
year = {2020},
type = {Journal Article}
}
  • [DOI] Asrat, Z., Eid, T., Gobakken, T., & Negash, M.. (2020). Modelling and quantifying tree biometric properties of dry afromontane forests of south-central ethiopia. Trees, 34(6), 1411-1426.
    [Bibtex] 
    @article{RN5252,
author = {Asrat, Zerihun and Eid, Tron and Gobakken, Terje and Negash, Mesele},
title = {Modelling and quantifying tree biometric properties of dry Afromontane forests of south-central Ethiopia},
journal = {Trees},
volume = {34},
number = {6},
pages = {1411-1426},
abstract = {Models for quantifying tree biometric properties, imperative for forest management decision-making, including height, diameter, bark thickness and volume were developed, and wood basic density was documented for dry Afromontane forests of south-central Ethiopia.},
ISSN = {1432-2285},
DOI = {10.1007/s00468-020-02012-8},
url = {https://doi.org/10.1007/s00468-020-02012-8},
year = {2020},
type = {Journal Article}
}
  • [DOI] Cosenza, D. N., Korhonen, L., Maltamo, M., Packalen, P., Strunk, J. L., Næsset, E., Gobakken, T., Soares, P., & Tomé, M.. (2020). Comparison of linear regression, k-nearest neighbour and random forest methods in airborne laser-scanning-based prediction of growing stock. Forestry: an international journal of forest research.
    [Bibtex] 
    @article{RN5241,
author = {Cosenza, Diogo N and Korhonen, Lauri and Maltamo, Matti and Packalen, Petteri and Strunk, Jacob L and Næsset, Erik and Gobakken, Terje and Soares, Paula and Tomé, Margarida},
title = {Comparison of linear regression, k-nearest neighbour and random forest methods in airborne laser-scanning-based prediction of growing stock},
journal = {Forestry: An International Journal of Forest Research},
abstract = {In this study, for five sites around the world, we look at the effects of different model types and variable selection approaches on forest yield modelling performances in an area-based approach (ABA). We compared ordinary least squares regression (OLS), k-nearest neighbours (kNN) and random forest (RF). Our objective was to test if there are systematic differences in accuracy between OLS, kNN and RF in ABA predictions of growing stock volume. The analyses are based on a 5-fold cross-validation at five study sites: an eucalyptus plantation, a temperate forest and three different boreal forests. Two completely independent validation datasets were also available for two of the boreal sites. For the kNN, we evaluated multiple measures of distance including Euclidean, Mahalanobis, most similar neighbour (MSN) and an RF-based distance metric. The variable selection approaches we examined included a heuristic approach (for OLS, kNN and RF), exhaustive search among all combinations (OLS only) and all variables together (RF only). Performances varied by model type and variable selection approaches among sites. OLS and RF had similar accuracies and were more efficient than any of the kNN variants. Variable selection did not affect RF performance. Heuristic and exhaustive variable selection performed similarly for OLS. kNN fared the poorest amongst model types, and kNN with RF distance was prone to overfitting when compared with a validation dataset. Additional caution is therefore required when building kNN models for volume prediction though ABA, being preferable instead to opt for models based on OLS with some variable selection, or RF with all variables together.},
ISSN = {0015-752X},
DOI = {10.1093/forestry/cpaa034},
url = {https://doi.org/10.1093/forestry/cpaa034},
year = {2020},
type = {Journal Article}
}
  • [DOI] Kangas, A., Gobakken, T., & Næsset, E.. (2020). Benefits of past inventory data as prior information for the current inventory. Forest ecosystems, 7(1), 20.
    [Bibtex] 
    @article{RN5250,
author = {Kangas, Annika and Gobakken, Terje and Næsset, Erik},
title = {Benefits of past inventory data as prior information for the current inventory},
journal = {Forest Ecosystems},
volume = {7},
number = {1},
pages = {20},
abstract = {When auxiliary information in the form of airborne laser scanning (ALS) is used to assist in estimating the population parameters of interest, the benefits of prior information from previous inventories are not self-evident. In a simulation study, we compared three different approaches: 1) using only current data, 2) using non-updated old data and current data in a composite estimator and 3) using updated old data and current data with a Kalman filter. We also tested three different estimators, namely i) Horwitz-Thompson for a case of no auxiliary information, ii) model-assisted estimation and iii) model-based estimation. We compared these methods in terms of bias, precision and accuracy, as estimators utilizing prior information are not guaranteed to be unbiased.},
ISSN = {2197-5620},
DOI = {10.1186/s40663-020-00231-6},
url = {https://doi.org/10.1186/s40663-020-00231-6},
year = {2020},
type = {Journal Article}
}
  • [DOI] Karjalainen, T., Mehtätalo, L., Packalen, P., Gobakken, T., Næsset, E., & Maltamo, M.. (2020). Field calibration of merchantable and sawlog volumes in forest inventories based on airborne laser scanning. Canadian journal of forest research, 50(12), 1352-1364.
    [Bibtex] 
    @Article{RN5244,
author = {Karjalainen, Tomi and Mehtätalo, Lauri and Packalen, Petteri and Gobakken, Terje and Næsset, Erik and Maltamo, Matti},
journal = {Canadian Journal of Forest Research},
title = {Field calibration of merchantable and sawlog volumes in forest inventories based on airborne laser scanning},
year = {2020},
number = {12},
pages = {1352-1364},
volume = {50},
doi = {10.1139/cjfr-2020-0033},
keywords = {LiDAR,area-based approach,quality,estimated best linear unbiased predictor,harvester data},
type = {Journal Article},
url = {https://cdnsciencepub.com/doi/abs/10.1139/cjfr-2020-0033 %X In many countries, airborne laser scanning (ALS) inventories are implemented to produce predictions for stand-level forest attributes. Nevertheless, mature stands are usually field-visited prior to clear-cutting, so some measurements can be conducted on these stands to calibrate the ALS-based predictions. In this paper, we developed a seemingly unrelated multivariate mixed-effects model system that includes component models for basal area, merchantable volume, and sawlog volume for 225 m2 cells. We used ALS data and accurately positioned cut-to-length harvester observations from clear-cut stands dominated by Norway spruce (Picea abies (L.) Karst.). Our aim was to study the effect of 1–10 local angle-gauge basal area measurements on the accuracy of predicted merchantable and sawlog volumes. A seemingly unrelated mixed-effect model system was fitted to estimate cross-model correlations in residuals and random effects, which were then utilized to predict all the random effects of the system for calibrated stand-level predictions. The 10 angle-gauge plots decreased the relative root mean square error (RMSE%) of the basal area and merchantable volume predictions from 16.8% to 10.5% and from 15.8% to 11.9%, respectively. Cross-model correlations of the stand effects of sawlog volume with the other responses were low; therefore, the initial RMSE% of ∼22% was decreased only marginally by the calibration.},
}
  • [DOI] Noordermeer, L., Gobakken, T., Næsset, E., & Bollandsås, O. M.. (2020). Economic utility of 3d remote sensing data for estimation of site index in nordic commercial forest inventories: a comparison of airborne laser scanning, digital aerial photogrammetry and conventional practices. Scandinavian journal of forest research, 1-13.
    [Bibtex] 
    @article{RN5247,
author = {Noordermeer, Lennart and Gobakken, Terje and Næsset, Erik and Bollandsås, Ole Martin},
title = {Economic utility of 3D remote sensing data for estimation of site index in Nordic commercial forest inventories: a comparison of airborne laser scanning, digital aerial photogrammetry and conventional practices},
journal = {Scandinavian Journal of Forest Research},
pages = {1-13},
abstract = {ABSTRACT Forest productivity is a crucial variable in forest planning, usually expressed as site index (SI). In Nordic commercial forest inventories, SI is commonly estimated by a combination of aerial image interpretation, field assessment and information obtained from previous inventories. Airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) data can alternatively be used for SI estimation, however the economic utilities of the inventory methods have not been compared. We compared seven methods of SI estimation in a cost-plus-loss analysis, by which we added the expected economic losses due to sub-optimal treatment decisions to the inventory costs. The methods comprised direct and indirect estimation from combinations of ALS, DAP and stand register data, and manual interpretation from aerial imagery supported by field assessment and information from previous inventories (conventional practices). The choice of method had great impact on both the accuracy and the economic value of the produced estimates. Direct methods using bitemporal ALS and DAP data gave the best accuracy and the smallest total cost. DAP was a suitable and low-cost data source for SI estimation. Estimation from single-date ALS and DAP data and age obtained from the stand register provided practical alternatives when applied to even-aged stands.},
ISSN = {0282-7581},
DOI = {10.1080/02827581.2020.1854340},
url = {https://www.tandfonline.com/doi/abs/10.1080/02827581.2020.1854340},
year = {2020},
type = {Journal Article}
}
  • [DOI] Næsset, E., McRoberts, R. E., Pekkarinen, A., Saatchi, S., Santoro, M., Trier, Ø. D., Zahabu, E., & Gobakken, T.. (2020). Use of local and global maps of forest canopy height and aboveground biomass to enhance local estimates of biomass in miombo woodlands in tanzania. International journal of applied earth observation and geoinformation, 89, 102109.
    [Bibtex] 
    @article{RN5233,
author = {Næsset, Erik and McRoberts, Ronald E. and Pekkarinen, Anssi and Saatchi, Sassan and Santoro, Maurizio and Trier, Øivind D. and Zahabu, Eliakimu and Gobakken, Terje},
title = {Use of local and global maps of forest canopy height and aboveground biomass to enhance local estimates of biomass in miombo woodlands in Tanzania},
journal = {International Journal of Applied Earth Observation and Geoinformation},
volume = {89},
pages = {102109},
abstract = {Field surveys are often a primary source of aboveground biomass (AGB) data, but plot-based estimates of parameters related to AGB are often not sufficiently precise, particularly not in tropical countries. Remotely sensed data may complement field data and thus help to increase the precision of estimates and circumvent some of the problems with missing sample observations in inaccessible areas. Here, we report the results of a study conducted in a 15,867 km² area in the dry miombo woodlands of Tanzania, to quantify the contribution of existing canopy height and biomass maps to improving the precision of canopy height and AGB estimates locally. A local and a global height map and three global biomass maps, and a probability sample of 513 inventory plots were subject to analysis. Model-assisted sampling estimators were used to estimate mean height and AGB across the study area using the original maps and then with the maps calibrated with local inventory plots. Large systematic map errors – positive or negative – were found for all the maps, with systematic errors as great as 60–70 %. The maps contributed nothing or even negatively to the precision of mean height and mean AGB estimates. However, after being calibrated locally, the maps contributed substantially to increasing the precision of both mean height and mean AGB estimates, with relative efficiencies (variance of the field-based estimates relative to the variance of the map-assisted estimates) of 1.3–2.7 for the overall estimates. The study, although focused on a relatively small area of dry tropical forests, illustrates the potential strengths and weaknesses of existing global forest height and biomass maps based on remotely sensed data and universal prediction models. Our results suggest that the use of regional or local inventory data for calibration can substantially increase the precision of map-based estimates and their applications in assessing forest carbon stocks for emission reduction programs and policy and financial decisions.},
keywords = {Biomass maps
Model-assisted estimation
Systematic map errors
Dry tropical forests},
ISSN = {0303-2434},
DOI = {https://doi.org/10.1016/j.jag.2020.102109},
url = {http://www.sciencedirect.com/science/article/pii/S0303243419312103},
year = {2020},
type = {Journal Article}
}
  • Taddese, H., Asrat, Z., Burud, I., Gobakken, T., Ørka, H. O., Dick, Ø. B., & Næsset, E.. (2020). Use of remotely sensed data to enhance estimation of aboveground biomass for the dry afromontane forest in south-central ethiopia. Remote sensing, 12(20), 3335.
    [Bibtex] 
    @article{RN5240,
author = {Taddese, Habitamu and Asrat, Zerihun and Burud, Ingunn and Gobakken, Terje and Ørka, Hans Ole and Dick, Øystein B. and Næsset, Erik},
title = {Use of Remotely Sensed Data to Enhance Estimation of Aboveground Biomass for the Dry Afromontane Forest in South-Central Ethiopia},
journal = {Remote Sensing},
volume = {12},
number = {20},
pages = {3335},
ISSN = {2072-4292},
url = {https://www.mdpi.com/2072-4292/12/20/3335},
year = {2020},
type = {Journal Article}
}
  • [DOI] de Lera Garrido, A., Gobakken, T., {O}rka, H. O., Næsset, E., & Bollandsås, O. M.. (2020). Reuse of field data in ALS-assisted forest inventory. Silva fenn., 54, 1–18.
    [Bibtex] 
    @Article{De_Lera_Garrido2020-xl,
author = {de Lera Garrido, Ana and Gobakken, Terje and {\O}rka, Hans O and N{\ae}sset, Erik and Bollands{\aa}s, Ole M},
journal = {Silva Fenn.},
title = {Reuse of field data in {ALS-assisted} forest inventory},
year = {2020},
pages = {1--18},
volume = {54},
abstract = {Forest inventories assisted by wall-to-wall airborne laser
scanning (ALS), have become common practice in many countries.
One major cost component in these inventories is the measurement
of field sample plots used for constructing models relating
biophysical forest …},
doi = {doi:10.14214/sf.10272},
publisher = {researchgate.net},
url = {https://www.silvafennica.fi/article/10272},
}
  • [DOI] Mohammadi, J., Shataee, S., & Næsset, E.. (2020). Modeling tree species diversity by combining als data and digital aerial photogrammetry. Science of remote sensing, 2, 100011.
    [Bibtex] 
    @Article{Mohammadi2020,
author = {Jahangir Mohammadi and Shaban Shataee and Erik Næsset},
journal = {Science of Remote Sensing},
title = {Modeling tree species diversity by combining ALS data and digital aerial photogrammetry},
year = {2020},
issn = {2666-0172},
pages = {100011},
volume = {2},
abstract = {Monitoring and assessment of tree species diversity in forests are essential tasks for forest managers. In the present study, we investigated the effectiveness of airborne laser scanner (ALS) and digital aerial photogrammetry (DAP) data for modeling tree diversity indices using machine learning algorithms in the uneven-aged Hyrcanian forests of Iran. Systematic sampling was adopted for the collection of field data on 300 circular plots (0.1 ​ha) in the study area. Menhenick, Margalef, Simpson’s heterogeneity, reciprocal of Simpson’s heterogeneity, Shanon-Winer heterogeneity and Simpson’s evenness indices were computed as measures of tree species diversity for each plot. Several commonly used variables were extracted from the ALS and DAP data. The results showed that the random forests (RF) algorithm produced the greatest accuracy among all machine learning algorithms. Additionally, combining ALS and DAP (ALS ​+ ​DAP) data increased prediction accuracy compared to separate modeling (0.2–6.6% reduction in relative root mean square error). The smallest RMSE% values based on independent validation data for Menhenick, Margalef, Simpson’s, reciprocal of Simpson’s, Shanon-Winer heterogeneity and Simpson’s evenness indices were 34.6%, 32.2%, 27.6%, 27.4%, 30.7% and 24.4%, respectively. These results demonstrate that the combination of ALS and DAP could be useful for monitoring tree species diversity in multi-story stands in the north of Iran and likely also in forests with complex canopy structures and multiple tree species.},
doi = {https://doi.org/10.1016/j.srs.2020.100011},
keywords = {Biodiversity, Airborne laser scanning, Digital aerial photogrammetry, Machine learning algorithms},
url = {https://www.sciencedirect.com/science/article/pii/S2666017220300109},
}
  • [DOI] McRoberts, R. E., Næsset, E., Sannier, C., Stehman, S. V., & Tomppo, E. O.. (2020). Remote sensing support for the gain-loss approach for greenhouse gas inventories. Remote sensing, 12(11).
    [Bibtex] 
    @Article{McRoberts2020,
author = {McRoberts, Ronald E. and Næsset, Erik and Sannier, Christophe and Stehman, Stephen V. and Tomppo, Erkki O.},
journal = {Remote Sensing},
title = {Remote Sensing Support for the Gain-Loss Approach for Greenhouse Gas Inventories},
year = {2020},
issn = {2072-4292},
number = {11},
volume = {12},
abstract = {For tropical countries that do not have extensive ground sampling programs such as national forest inventories, the gain-loss approach for greenhouse gas inventories is often used. With the gain-loss approach, emissions and removals are estimated as the product of activity data defined as the areas of human-caused emissions and removals and emissions factors defined as the per unit area responses of carbon stocks for those activities. Remotely sensed imagery and remote sensing-based land use and land use change maps have emerged as crucial information sources for facilitating the statistically rigorous estimation of activity data. Similarly, remote sensing-based biomass maps have been used as sources of auxiliary data for enhancing estimates of emissions and removals factors and as sources of biomass data for remote and inaccessible regions. The current status of statistically rigorous methods for combining ground and remotely sensed data that comply with the good practice guidelines for greenhouse gas inventories of the Intergovernmental Panel on Climate Change is reviewed.},
article-number = {1891},
doi = {10.3390/rs12111891},
url = {https://www.mdpi.com/2072-4292/12/11/1891},
}

2019

  • [DOI] Magnussen, S., Næsset, E., & Gobakken, T.. (2019). An application niche for finite mixture models in forest resource surveys. Canadian journal of forest research, 49(11), 1453-1462.
    [Bibtex] 
    @Article{Magnussen2019,
author = {Magnussen, Steen and Næsset, Erik and Gobakken, Terje},
journal = {Canadian Journal of Forest Research},
title = {An application niche for finite mixture models in forest resource surveys},
year = {2019},
issn = {0045-5067},
number = {11},
pages = {1453-1462},
volume = {49},
doi = {10.1139/cjfr-2019-0170},
groups = {hanso:6},
type = {Journal Article},
url = {https://doi.org/10.1139/cjfr-2019-0170},
}
  • [DOI] Næsset, E., Gobakken, T., & McRoberts, R. E.. (2019). A model-dependent method for monitoring subtle changes in vegetation height in the boreal–alpine ecotone using bi-temporal, three dimensional point data from airborne laser scanning. Remote sensing, 11(15).
    [Bibtex] 
    @Article{Naesset2019,
author = {Næsset, Erik and Gobakken, Terje and McRoberts, E. Ronald},
journal = {Remote Sensing},
title = {A Model-Dependent Method for Monitoring Subtle Changes in Vegetation Height in the Boreal–Alpine Ecotone Using Bi-Temporal, Three Dimensional Point Data from Airborne Laser Scanning},
year = {2019},
issn = {2072-4292},
number = {15},
volume = {11},
doi = {10.3390/rs11151804},
groups = {hanso:6},
type = {Journal Article},
}
  • [DOI] Noordermeer, L., Økseter, R., Ørka, H. O., Gobakken, T., Næsset, E., & Bollandsås, O. M.. (2019). Classifications of forest change by using bitemporal airborne laser scanner data. Remote sensing, 11(18).
    [Bibtex] 
    @Article{Noordermeer2019,
author = {Noordermeer, Lennart and Økseter, Roar and Ørka, O. Hans and Gobakken, Terje and Næsset, Erik and Bollandsås, M. Ole},
journal = {Remote Sensing},
title = {Classifications of Forest Change by Using Bitemporal Airborne Laser Scanner Data},
year = {2019},
issn = {2072-4292},
number = {18},
volume = {11},
doi = {10.3390/rs11182145},
groups = {hanso:6},
type = {Journal Article},
}
  • [DOI] Malek, S., Miglietta, F., Gobakken, T., Naesset, E., Gianelle, D., & Dalponte, M.. (2019). Prediction of stem diameter and biomass at individual tree crown level with advanced machine learning techniques. Iforest, 12(3), 323-329.
    [Bibtex] 
    @Article{Malek2019,
author = {Malek, S and Miglietta, F and Gobakken, T and Naesset, E and Gianelle, D and Dalponte, M},
journal = {iForest},
title = {Prediction of stem diameter and biomass at individual tree crown level with advanced machine learning techniques},
year = {2019},
issn = {1824-0119},
number = {3},
pages = {323-329},
volume = {12},
doi = {10.3832/ifor2980-012},
groups = {hanso:6},
type = {Journal Article},
url = {https://iforest.sisef.org/contents/?id=ifor2980-012},
}
  • Bollandsås, O. M., Ørka, H. O., Dalponte, M., Gobakken, T., & Næsset, E.. (2019). Modelling site index in forest stands using airborne hyperspectral imagery and bi-temporal laser scanner data. Remote sensing, 11(9), 1020.
    [Bibtex] 
    @Article{Bollandsaas2019,
author = {Bollandsås, Ole Martin and Ørka, Hans Ole and Dalponte, Michele and Gobakken, Terje and Næsset, Erik},
journal = {Remote sensing},
title = {Modelling Site Index in Forest Stands Using Airborne Hyperspectral Imagery and Bi-Temporal Laser Scanner Data},
year = {2019},
issn = {2072-4292},
number = {9},
pages = {1020},
volume = {11},
groups = {hanso:6},
type = {Journal Article},
url = {https://www.mdpi.com/2072-4292/11/9/1020},
}
  • Domingo, D., Ørka, H. O., Næsset, E., Kachamba, D., & Gobakken, T.. (2019). Effects of uav image resolution, camera type, and image overlap on accuracy of biomass predictions in a tropical woodland. Remote sensing, 11(8), 948.
    [Bibtex] 
    @Article{Domingo2019,
author = {Domingo, Darío and Ørka, Hans Ole and Næsset, Erik and Kachamba, Daud and Gobakken, Terje},
journal = {Remote sensing},
title = {Effects of UAV Image Resolution, Camera Type, and Image Overlap on Accuracy of Biomass Predictions in a Tropical Woodland},
year = {2019},
issn = {2072-4292},
number = {8},
pages = {948},
volume = {11},
groups = {hanso:6},
type = {Journal Article},
url = {https://www.mdpi.com/2072-4292/11/8/948},
}
  • Malek, S., Miglietta, F., Gobakken, T., Næsset, E., Gianelle, D., & Dalponte, M.. (2019). Optimizing field data collection for individual tree attribute predictions using active learning methods. Remote sensing, 11(8), 949.
    [Bibtex] 
    @Article{Malek2019a,
author = {Malek, Salim and Miglietta, Franco and Gobakken, Terje and Næsset, Erik and Gianelle, Damiano and Dalponte, Michele},
journal = {Remote sensing},
title = {Optimizing Field Data Collection for Individual Tree Attribute Predictions Using Active Learning Methods},
year = {2019},
issn = {2072-4292},
number = {8},
pages = {949},
volume = {11},
groups = {hanso:6},
type = {Journal Article},
url = {https://www.mdpi.com/2072-4292/11/8/949},
}
  • [DOI] Noordermeer, L., Bollandsås, O. M., Ørka, H. O., Næsset, E., & Gobakken, T.. (2019). Comparing the accuracies of forest attributes predicted from airborne laser scanning and digital aerial photogrammetry in operational forest inventories. Remote sensing of environment, 226, 26-37.
    [Bibtex] 
    @Article{Noordermeer2019a,
author = {Noordermeer, Lennart and Bollandsås, Ole Martin and Ørka, Hans Ole and Næsset, Erik and Gobakken, Terje},
journal = {Remote Sensing of Environment},
title = {Comparing the accuracies of forest attributes predicted from airborne laser scanning and digital aerial photogrammetry in operational forest inventories},
year = {2019},
issn = {0034-4257},
pages = {26-37},
volume = {226},
doi = {https://doi.org/10.1016/j.rse.2019.03.027},
groups = {hanso:6},
type = {Journal Article},
url = {http://www.sciencedirect.com/science/article/pii/S0034425719301178},
}
  • [DOI] Maltamo, M., Hauglin, M., Næsset, E., & Gobakken, T.. (2019). Estimating stand level stem diameter distribution utilizing harvester data and airborne laser scanning. Silva fennica, 53.
    [Bibtex] 
    @Article{Maltamo2019,
author = {Maltamo, Matti and Hauglin, Marius and Næsset, Erik and Gobakken, Terje},
journal = {Silva Fennica},
title = {Estimating stand level stem diameter distribution utilizing harvester data and airborne laser scanning},
year = {2019},
volume = {53},
doi = {10.14214/sf.10075},
groups = {hanso:6},
type = {Journal Article},
}
  • [DOI] Dutcă, I., McRoberts, R. E., Næsset, E., & Blujdea, V. N. B.. (2019). A practical measure for determining if diameter (d) and height (h) should be combined into d2h in allometric biomass models. Forestry: an international journal of forest research, 92(5), 627-634.
    [Bibtex] 
    @Article{Dutca2019,
author = {Dutcă, I and McRoberts, R E and Næsset, E and Blujdea, V N B},
journal = {Forestry: An International Journal of Forest Research},
title = {A practical measure for determining if diameter (D) and height (H) should be combined into D2H in allometric biomass models},
year = {2019},
issn = {0015-752X},
number = {5},
pages = {627-634},
volume = {92},
doi = {10.1093/forestry/cpz041 %J Forestry: An International Journal of Forest Research},
groups = {hanso:6},
type = {Journal Article},
url = {https://doi.org/10.1093/forestry/cpz041},
}
  • [DOI] McRoberts, R. E., Næsset, E., Saatchi, S., Liknes, G. C., Walters, B. F., & Chen, Q.. (2019). Local validation of global biomass maps. International journal of applied earth observation and geoinformation, 83, 101931.
    [Bibtex] 
    @Article{McRoberts2019,
author = {McRoberts, Ronald E. and Næsset, Erik and Saatchi, Sassan and Liknes, Greg C. and Walters, Brian F. and Chen, Qi},
journal = {International Journal of Applied Earth Observation and Geoinformation},
title = {Local validation of global biomass maps},
year = {2019},
issn = {0303-2434},
pages = {101931},
volume = {83},
doi = {https://doi.org/10.1016/j.jag.2019.101931},
groups = {hanso:6},
type = {Journal Article},
url = {http://www.sciencedirect.com/science/article/pii/S0303243419305033},
}
  • [DOI] Duncanson, L., Armston, J., Disney, M., Avitabile, V., Barbier, N., Calders, K., Carter, S., Chave, J., Herold, M., Crowther, T. W., Falkowski, M., Kellner, J. R., Labrière, N., Lucas, R., MacBean, N., McRoberts, R. E., Meyer, V., Næsset, E., Nickeson, J. E., Paul, K. I., Phillips, O. L., Réjou-Méchain, M., Román, M., Roxburgh, S., Saatchi, S., Schepaschenko, D., Scipal, K., Siqueira, P. R., Whitehurst, A., & in Williams, S. G. M. %J.. (2019). The importance of consistent global forest aboveground biomass product validation. Surveys in geophysics, 40(4), 979-999.
    [Bibtex] 
    @Article{Duncanson2019,
author = {Duncanson, L. and Armston, J. and Disney, M. and Avitabile, V. and Barbier, N. and Calders, K. and Carter, S. and Chave, J. and Herold, M. and Crowther, T. W. and Falkowski, M. and Kellner, J. R. and Labrière, N. and Lucas, R. and MacBean, N. and McRoberts, R. E. and Meyer, V. and Næsset, E. and Nickeson, J. E. and Paul, K. I. and Phillips, O. L. and Réjou-Méchain, M. and Román, M. and Roxburgh, S. and Saatchi, S. and Schepaschenko, D. and Scipal, K. and Siqueira, P. R. and Whitehurst, A. and Williams, M. %J Surveys in Geophysics},
journal = {Surveys in Geophysics},
title = {The Importance of Consistent Global Forest Aboveground Biomass Product Validation},
year = {2019},
issn = {1573-0956},
number = {4},
pages = {979-999},
volume = {40},
doi = {10.1007/s10712-019-09538-8},
groups = {hanso:6},
type = {Journal Article},
url = {https://doi.org/10.1007/s10712-019-09538-8},
}
  • Esteban, J., McRoberts, R. E., Fernández-Landa, A., Tomé, J. L., & Nӕsset, E.. (2019). Estimating forest volume and biomass and their changes using random forests and remotely sensed data. Remote sensing, 11(16), 1944.
    [Bibtex] 
    @Article{Esteban2019,
author = {Esteban, Jessica and McRoberts, Ronald E. and Fernández-Landa, Alfredo and Tomé, José Luis and Nӕsset, Erik},
journal = {Remote sensing},
title = {Estimating Forest Volume and Biomass and Their Changes Using Random Forests and Remotely Sensed Data},
year = {2019},
issn = {2072-4292},
number = {16},
pages = {1944},
volume = {11},
groups = {hanso:6},
type = {Journal Article},
url = {https://www.mdpi.com/2072-4292/11/16/1944},
}
  • [DOI] Herold, M., Carter, S., Avitabile, V., Espejo, A. B., Jonckheere, I., Lucas, R., McRoberts, R. E., Næsset, E., Nightingale, J., Petersen, R., Reiche, J., Romijn, E., Rosenqvist, A., Rozendaal, D. M. A., Seifert, F. M., Sanz, M. J., & De Sy, V.. (2019). The role and need for space-based forest biomass-related measurements in environmental management and policy. Surveys in geophysics, 40(4), 757-778.
    [Bibtex] 
    @Article{Herold2019,
author = {Herold, Martin and Carter, Sarah and Avitabile, Valerio and Espejo, Andrés B. and Jonckheere, Inge and Lucas, Richard and McRoberts, Ronald E. and Næsset, Erik and Nightingale, Joanne and Petersen, Rachael and Reiche, Johannes and Romijn, Erika and Rosenqvist, Ake and Rozendaal, Danaë M. A. and Seifert, Frank Martin and Sanz, María J. and De Sy, Veronique},
journal = {Surveys in Geophysics},
title = {The Role and Need for Space-Based Forest Biomass-Related Measurements in Environmental Management and Policy},
year = {2019},
issn = {1573-0956},
number = {4},
pages = {757-778},
volume = {40},
doi = {10.1007/s10712-019-09510-6},
groups = {hanso:6},
type = {Journal Article},
url = {https://doi.org/10.1007/s10712-019-09510-6},
}
  • [DOI] McRoberts, R. E., Næsset, E., Liknes, G. C., Chen, Q., Walters, B. F., Saatchi, S., & Herold, M.. (2019). Using a finer resolution biomass map to assess the accuracy of a regional, map-based estimate of forest biomass. Surveys in geophysics, 40(4), 1001-1015.
    [Bibtex] 
    @Article{McRoberts2019a,
author = {McRoberts, Ronald E. and Næsset, Erik and Liknes, Greg C. and Chen, Qi and Walters, Brian F. and Saatchi, Sassan and Herold, Martin},
journal = {Surveys in Geophysics},
title = {Using a Finer Resolution Biomass Map to Assess the Accuracy of a Regional, Map-Based Estimate of Forest Biomass},
year = {2019},
issn = {1573-0956},
number = {4},
pages = {1001-1015},
volume = {40},
abstract = {National greenhouse gas inventories often use variations of the gain–loss approach whereby emissions are estimated as the products of estimates of areas of land-use change characterized as activity data and estimates of emissions per unit area characterized as emission factors. Although the term emissions is often intuitively understood to mean release of greenhouse gases from terrestrial sources to the atmosphere, in fact, emission factors can also be negative, meaning removal of the gases from the atmosphere to terrestrial sinks. For remote and inaccessible forests for which ground sampling is difficult if not impossible, emission factors may be based on map-based estimates of biomass or biomass change obtained from regional maps. For the special case of complete deforestation, the emission factor for the aboveground biomass pool is simply mean aboveground, live-tree, biomass per unit area prior to the deforestation. If biomass maps are used for these purposes, estimates must still comply with the first IPCC good practice guideline regarding accuracy relative to the true value and the second guideline regarding uncertainty. Accuracy assessment for a map-based estimate entails comparison of the estimate to a second estimate obtained using independent reference data. Assuming ground sampling is not feasible, a map of greater quality than the regional map may be considered as a source of reference data where greater quality connotes attributes such as finer resolution and/or greater accuracy. For a local, sub-regional study area in Minnesota in the USA, the accuracy of an estimate of mean aboveground, live-tree biomass per unit area (AGB, Mg/ha) obtained from a coarser resolution, regional, MODIS-based biomass map was assessed using reference data sampled from a finer resolution, local, airborne laser scanning (ALS)-based biomass map. The rationale for a local assessment of a regional map is that, although assessment of a regional map would be difficult for the entire extent of the map, it can likely be assessed for multiple local sub-regions in which case expected local regional accuracy for the entire map can perhaps be inferred. For this study, the local assessment was in the form of a test of the hypothesis that the local sub-regional estimate from the regional map did not deviate from the local true value. A hybrid approach to inference was used whereby design-based inferential techniques were used to estimate uncertainty due to sampling from the finer resolution map, and model-based inferential techniques were used to estimate uncertainty resulting from using the finer resolution map unit values which were subject to prediction error as reference data. The test revealed no statistically significant difference between the MODIS-based and ALS-based map estimates, thereby indicating that for the local sub-region, the regional, MODIS-based estimate complied with the first IPCC good practice guideline for accuracy.},
doi = {10.1007/s10712-019-09507-1},
type = {Journal Article},
url = {https://doi.org/10.1007/s10712-019-09507-1},
}

2018

  • Asrat, Z., Taddese, H., Ørka, H., Gobakken, T., Burud, I., & Næsset, E.. (2018). Estimation of forest area and canopy cover based on visual interpretation of satellite images in ethiopia. Land, 7(3), 92.
    [Bibtex] 
    @Article{Asrat2018,
author = {Asrat, Zerihun and Taddese, Habitamu and Ørka, Hans and Gobakken, Terje and Burud, Ingunn and Næsset, Erik},
title = {Estimation of Forest Area and Canopy Cover Based on Visual Interpretation of Satellite Images in Ethiopia},
journal = {Land},
year = {2018},
volume = {7},
number = {3},
pages = {92},
issn = {2073-445X},
type = {Journal Article},
url = {http://www.mdpi.com/2073-445X/7/3/92},
}
  • [DOI] Babcock, C., Finley, A. O., Andersen, H., Pattison, R., Cook, B. D., Morton, D. C., Alonzo, M., Nelson, R., Gregoire, T., Ene, L., Gobakken, T., & Næsset, E.. (2018). Geostatistical estimation of forest biomass in interior alaska combining landsat-derived tree cover, sampled airborne lidar and field observations. Remote sensing of environment, 212, 212-230.
    [Bibtex] 
    @Article{Babcock2018,
author = {Babcock, Chad and Finley, Andrew O. and Andersen, Hans-Erik and Pattison, Robert and Cook, Bruce D. and Morton, Douglas C. and Alonzo, Michael and Nelson, Ross and Gregoire, Timothy and Ene, Liviu and Gobakken, Terje and Næsset, Erik},
title = {Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations},
journal = {Remote Sensing of Environment},
year = {2018},
volume = {212},
pages = {212-230},
issn = {0034-4257},
doi = {https://doi.org/10.1016/j.rse.2018.04.044},
type = {Journal Article},
url = {http://www.sciencedirect.com/science/article/pii/S0034425718302013},
}
  • [DOI] Bollandsås, O. M., Ene, L. T., Gobakken, T., & Næsset, E.. (2018). Estimation of biomass change in montane forests in norway along a 1200 km latitudinal gradient using airborne laser scanning: a comparison of direct and indirect prediction of change under a model-based inferential approach. Scandinavian journal of forest research, 33(2), 155-165.
    [Bibtex] 
    @Article{Bollandsaas2018,
author = {Bollandsås, Ole Martin and Ene, Liviu Theodor and Gobakken, Terje and Næsset, Erik},
title = {Estimation of biomass change in montane forests in Norway along a 1200 km latitudinal gradient using airborne laser scanning: a comparison of direct and indirect prediction of change under a model-based inferential approach},
journal = {Scandinavian Journal of Forest Research},
year = {2018},
volume = {33},
number = {2},
pages = {155-165},
issn = {0282-7581},
doi = {10.1080/02827581.2017.1338354},
type = {Journal Article},
url = {https://doi.org/10.1080/02827581.2017.1338354},
}
  • Dalponte, M., Ene, L., Gobakken, T., Næsset, E., & Gianelle, D.. (2018). Predicting selected forest stand characteristics with multispectral als data. Remote sensing, 10(4), 586.
    [Bibtex] 
    @Article{Dalponte2018,
author = {Dalponte, Michele and Ene, Liviu and Gobakken, Terje and Næsset, Erik and Gianelle, Damiano},
title = {Predicting Selected Forest Stand Characteristics with Multispectral ALS Data},
journal = {Remote Sensing},
year = {2018},
volume = {10},
number = {4},
pages = {586},
issn = {2072-4292},
type = {Journal Article},
url = {http://www.mdpi.com/2072-4292/10/4/586},
}
  • [DOI] Dalponte, M., Frizzera, L., Ørka, H. O., Gobakken, T., Næsset, E., & Gianelle, D.. (2018). Predicting stem diameters and aboveground biomass of individual trees using remote sensing data. Ecological indicators, 85, 367-376.
    [Bibtex] 
    @Article{Dalponte2018a,
author = {Dalponte, Michele and Frizzera, Lorenzo and Ørka, Hans Ole and Gobakken, Terje and Næsset, Erik and Gianelle, Damiano},
title = {Predicting stem diameters and aboveground biomass of individual trees using remote sensing data},
journal = {Ecological Indicators},
year = {2018},
volume = {85},
pages = {367-376},
issn = {1470-160X},
doi = {https://doi.org/10.1016/j.ecolind.2017.10.066},
type = {Journal Article},
url = {https://www.sciencedirect.com/science/article/pii/S1470160X17307033},
}
  • [DOI] Ene, L. T., Gobakken, T., Andersen, H., Næsset, E., Cook, B. D., Morton, D. C., Babcock, C., & Nelson, R.. (2018). Large-area hybrid estimation of aboveground biomass in interior alaska using airborne laser scanning data. Remote sensing of environment, 204(Supplement C), 741-755.
    [Bibtex] 
    @Article{Ene2018,
author = {Ene, Liviu T. and Gobakken, Terje and Andersen, Hans-Erik and Næsset, Erik and Cook, Bruce D. and Morton, Douglas C. and Babcock, Chad and Nelson, Ross},
title = {Large-area hybrid estimation of aboveground biomass in interior Alaska using airborne laser scanning data},
journal = {Remote Sensing of Environment},
year = {2018},
volume = {204},
number = {Supplement C},
pages = {741-755},
issn = {0034-4257},
doi = {https://doi.org/10.1016/j.rse.2017.09.027},
type = {Journal Article},
url = {http://www.sciencedirect.com/science/article/pii/S003442571730439X},
}
  • [DOI] Fischer, C., Høibø, O. A., Vestøl, G. I., Hauglin, M., Hansen, E. H., & Gobakken, T.. (2018). Predicting dynamic modulus of elasticity of norway spruce structural timber by forest inventory, airborne laser scanning and harvester-derived data. Scandinavian journal of forest research, 1-10.
    [Bibtex] 
    @Article{Fischer2018,
author = {Fischer, Carolin and Høibø, Olav A. and Vestøl, Geir I. and Hauglin, Marius and Hansen, Endre H. and Gobakken, Terje},
title = {Predicting dynamic modulus of elasticity of Norway spruce structural timber by forest inventory, airborne laser scanning and harvester-derived data},
journal = {Scandinavian Journal of Forest Research},
year = {2018},
pages = {1-10},
issn = {0282-7581},
doi = {10.1080/02827581.2018.1427790},
type = {Journal Article},
url = {https://doi.org/10.1080/02827581.2018.1427790},
}
  • [DOI] Giannetti, F., Chirici, G., Gobakken, T., Næsset, E., Travaglini, D., & Puliti, S.. (2018). A new approach with dtm-independent metrics for forest growing stock prediction using uav photogrammetric data. Remote sensing of environment, 213, 195-205.
    [Bibtex] 
    @Article{Giannetti2018,
author = {Giannetti, Francesca and Chirici, Gherardo and Gobakken, Terje and Næsset, Erik and Travaglini, Davide and Puliti, Stefano},
title = {A new approach with DTM-independent metrics for forest growing stock prediction using UAV photogrammetric data},
journal = {Remote Sensing of Environment},
year = {2018},
volume = {213},
pages = {195-205},
issn = {0034-4257},
doi = {https://doi.org/10.1016/j.rse.2018.05.016},
type = {Journal Article},
url = {http://www.sciencedirect.com/science/article/pii/S0034425718302372},
}
  • [DOI] Hauglin, M., Hansen, E., Sørngård, E., Næsset, E., & Gobakken, T.. (2018). Utilizing accurately positioned harvester data: modelling forest volume with airborne laser scanning. Canadian journal of forest research, 1-10.
    [Bibtex] 
    @Article{Hauglin2018,
author = {Hauglin, Marius and Hansen, Endre and Sørngård, Erik and Næsset, Erik and Gobakken, Terje},
title = {Utilizing accurately positioned harvester data: modelling forest volume with airborne laser scanning},
journal = {Canadian Journal of Forest Research},
year = {2018},
pages = {1-10},
issn = {0045-5067},
doi = {10.1139/cjfr-2017-0467},
type = {Journal Article},
url = {https://doi.org/10.1139/cjfr-2017-0467},
}
  • [DOI] Kangas, A., Astrup, R., Breidenbach, J., Fridman, J., Gobakken, T., Korhonen, K. T., Maltamo, M., Nilsson, M., Nord-Larsen, T., Næsset, E., & Olsson, H.. (2018). Remote sensing and forest inventories in nordic countries – roadmap for the future. Scandinavian journal of forest research, 33(4), 397-412.
    [Bibtex] 
    @Article{Kangas2018,
author = {Kangas, Annika and Astrup, Rasmus and Breidenbach, Johannes and Fridman, Jonas and Gobakken, Terje and Korhonen, Kari T. and Maltamo, Matti and Nilsson, Mats and Nord-Larsen, Thomas and Næsset, Erik and Olsson, Håkan},
title = {Remote sensing and forest inventories in Nordic countries – roadmap for the future},
journal = {Scandinavian Journal of Forest Research},
year = {2018},
volume = {33},
number = {4},
pages = {397-412},
issn = {0282-7581},
doi = {10.1080/02827581.2017.1416666},
type = {Journal Article},
url = {https://doi.org/10.1080/02827581.2017.1416666},
}
  • [DOI] Kangas, A., Gobakken, T., Puliti, S., Hauglin, M., & Naesset, E.. (2018). Value of airborne laser scanning and digital aerial photogrammetry data in forest decision making. Silva fennica, 52(1), article id 9923.
    [Bibtex] 
    @Article{Kangas2018a,
author = {Kangas, Annika and Gobakken, Terje and Puliti, Stefano and Hauglin, Marius and Naesset, Erik},
title = {Value of airborne laser scanning and digital aerial photogrammetry data in forest decision making},
journal = {SILVA FENNICA},
year = {2018},
volume = {52},
number = {1},
pages = {article id 9923},
doi = {https://doi.org/10.14214/sf.9923},
type = {Journal Article},
url = {https://www.silvafennica.fi/article/9923},
}
  • [DOI] McRoberts, R. E., Næsset, E., & Gobakken, T.. (2018). Comparing the stock-change and gain–loss approaches for estimating forest carbon emissions for the aboveground biomass pool. Canadian journal of forest research, 48(12), 1535-1542.
    [Bibtex] 
    @Article{McRoberts2018,
author = {McRoberts, Ronald E. and Næsset, Erik and Gobakken, Terje},
title = {Comparing the stock-change and gain–loss approaches for estimating forest carbon emissions for the aboveground biomass pool},
journal = {Canadian Journal of Forest Research},
year = {2018},
volume = {48},
number = {12},
pages = {1535-1542},
issn = {0045-5067},
doi = {10.1139/cjfr-2018-0295},
type = {Journal Article},
url = {https://doi.org/10.1139/cjfr-2018-0295},
}
  • [DOI] McRoberts, R. E., Næsset, E., Gobakken, T., Chirici, G., Condés, S., Hou, Z., Saarela, S., Chen, Q., Ståhl, G., & Walters, B. F.. (2018). Assessing components of the model-based mean square error estimator for remote sensing assisted forest applications. Canadian journal of forest research, 48(6), 642-649.
    [Bibtex] 
    @Article{McRoberts2018a,
author = {McRoberts, Ronald E. and Næsset, Erik and Gobakken, Terje and Chirici, Gherardo and Condés, Sonia and Hou, Zhengyang and Saarela, Svetlana and Chen, Qi and Ståhl, Göran and Walters, Brian F.},
title = {Assessing components of the model-based mean square error estimator for remote sensing assisted forest applications},
journal = {Canadian Journal of Forest Research},
year = {2018},
volume = {48},
number = {6},
pages = {642-649},
issn = {0045-5067},
doi = {10.1139/cjfr-2017-0396},
type = {Journal Article},
url = {https://doi.org/10.1139/cjfr-2017-0396},
}
  • [DOI] McRoberts, R. E., Stehman, S. V., Liknes, G. C., Næsset, E., Sannier, C., & Walters, B. F.. (2018). The effects of imperfect reference data on remote sensing-assisted estimators of land cover class proportions. Isprs journal of photogrammetry and remote sensing, 142, 292-300.
    [Bibtex] 
    @Article{McRoberts2018b,
author = {McRoberts, Ronald E. and Stehman, Stephen V. and Liknes, Greg C. and Næsset, Erik and Sannier, Christophe and Walters, Brian F.},
title = {The effects of imperfect reference data on remote sensing-assisted estimators of land cover class proportions},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
year = {2018},
volume = {142},
pages = {292-300},
issn = {0924-2716},
doi = {https://doi.org/10.1016/j.isprsjprs.2018.06.002},
type = {Journal Article},
url = {http://www.sciencedirect.com/science/article/pii/S0924271618301655},
}
  • [DOI] Noordermeer, L., Bollandsås, O. M., Gobakken, T., & Næsset, E.. (2018). Direct and indirect site index determination for norway spruce and scots pine using bitemporal airborne laser scanner data. Forest ecology and management, 428, 104-114.
    [Bibtex] 
    @Article{Noordermeer2018,
author = {Noordermeer, Lennart and Bollandsås, Ole Martin and Gobakken, Terje and Næsset, Erik},
title = {Direct and indirect site index determination for Norway spruce and Scots pine using bitemporal airborne laser scanner data},
journal = {Forest Ecology and Management},
year = {2018},
volume = {428},
pages = {104-114},
issn = {0378-1127},
doi = {https://doi.org/10.1016/j.foreco.2018.06.041},
type = {Journal Article},
url = {https://doi.org/10.1016/j.foreco.2018.06.041},
}
  • Oveland, I., Hauglin, M., Giannetti, F., Schipper Kjørsvik, N., & Gobakken, T.. (2018). Comparing three different ground based laser scanning methods for tree stem detection. Remote sensing, 10(4), 538.
    [Bibtex] 
    @Article{Oveland2018,
author = {Oveland, Ivar and Hauglin, Marius and Giannetti, Francesca and Schipper Kjørsvik, Narve and Gobakken, Terje},
title = {Comparing Three Different Ground Based Laser Scanning Methods for Tree Stem Detection},
journal = {Remote Sensing},
year = {2018},
volume = {10},
number = {4},
pages = {538},
issn = {2072-4292},
type = {Journal Article},
url = {http://www.mdpi.com/2072-4292/10/4/538},
}
  • [DOI] Puliti, S., Saarela, S., Gobakken, T., Ståhl, G., & Næsset, E.. (2018). Combining uav and sentinel-2 auxiliary data for forest growing stock volume estimation through hierarchical model-based inference. Remote sensing of environment, 204, 485-497.
    [Bibtex] 
    @Article{Puliti2018,
author = {Puliti, Stefano and Saarela, Svetlana and Gobakken, Terje and Ståhl, Göran and Næsset, Erik},
title = {Combining UAV and Sentinel-2 auxiliary data for forest growing stock volume estimation through hierarchical model-based inference},
journal = {Remote Sensing of Environment},
year = {2018},
volume = {204},
pages = {485-497},
issn = {0034-4257},
doi = {https://doi.org/10.1016/j.rse.2017.10.007},
type = {Journal Article},
url = {http://www.sciencedirect.com/science/article/pii/S0034425717304704},
}
  • Saarela, S., Holm, S., Healey, S. P., Andersen, H., Petersson, H., Prentius, W., Patterson, P. L., Næsset, E., Gregoire, T. G., & Ståhl, G.. (2018). Generalized hierarchical model-based estimation for aboveground biomass assessment using gedi and landsat data. Remote sensing, 10(11), 1832.
    [Bibtex] 
    @Article{Saarela2018,
author = {Saarela, Svetlana and Holm, Sören and Healey, Sean P. and Andersen, Hans-Erik and Petersson, Hans and Prentius, Wilmer and Patterson, Paul L. and Næsset, Erik and Gregoire, Timothy G. and Ståhl, Göran},
title = {Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data},
journal = {Remote Sensing},
year = {2018},
volume = {10},
number = {11},
pages = {1832},
issn = {2072-4292},
type = {Journal Article},
url = {http://www.mdpi.com/2072-4292/10/11/1832},
}
  • [DOI] Trier, Ø. D., Salberg, A., Haarpaintner, J., Aarsten, D., Gobakken, T., & Næsset, E.. (2018). Multi-sensor forest vegetation height mapping methods for tanzania. European journal of remote sensing, 51(1), 587-606.
    [Bibtex] 
    @Article{Trier2018,
author = {Trier, Øivind Due and Salberg, Arnt-Børre and Haarpaintner, Jörg and Aarsten, Dagrun and Gobakken, Terje and Næsset, Erik},
title = {Multi-sensor forest vegetation height mapping methods for Tanzania},
journal = {European Journal of Remote Sensing},
year = {2018},
volume = {51},
number = {1},
pages = {587-606},
issn = {null},
doi = {10.1080/22797254.2018.1461533},
type = {Journal Article},
url = {https://doi.org/10.1080/22797254.2018.1461533},
}
  • [DOI] Trier, Ø. D., Salberg, A., Kermit, M., Rudjord, Ø., Gobakken, T., Næsset, E., & Aarsten, D.. (2018). Tree species classification in norway from airborne hyperspectral and airborne laser scanning data. European journal of remote sensing, 51(1), 336-351.
    [Bibtex] 
    @Article{Trier2018a,
author = {Trier, Øivind Due and Salberg, Arnt-Børre and Kermit, Martin and Rudjord, Øystein and Gobakken, Terje and Næsset, Erik and Aarsten, Dagrun},
title = {Tree species classification in Norway from airborne hyperspectral and airborne laser scanning data},
journal = {European Journal of Remote Sensing},
year = {2018},
volume = {51},
number = {1},
pages = {336-351},
issn = {null},
doi = {10.1080/22797254.2018.1434424},
type = {Journal Article},
url = {https://doi.org/10.1080/22797254.2018.1434424},
}
  • [DOI] Ørka, H. O., Bollandsås, O. M., Hansen, E. H., Næsset, E., & Gobakken, T.. (2018). Effects of terrain slope and aspect on the error of als-based predictions of forest attributes. Forestry: an international journal of forest research, 91(2), 225-237.
    [Bibtex] 
    @Article{Oerka2018,
author = {Ørka, Hans Ole and Bollandsås, Ole Martin and Hansen, Endre Hofstad and Næsset, Erik and Gobakken, Terje},
title = {Effects of terrain slope and aspect on the error of ALS-based predictions of forest attributes},
journal = {Forestry: An International Journal of Forest Research},
year = {2018},
volume = {91},
number = {2},
pages = {225-237},
issn = {0015-752X},
doi = {10.1093/forestry/cpx058},
type = {Journal Article},
url = {http://dx.doi.org/10.1093/forestry/cpx058},
}

2017

  • [DOI] Egberth, M., Nyberg, G., Næsset, E., Gobakken, T., Mauya, E., Malimbwi, R., Katani, J., Chamuya, N., Bulenga, G., & Olsson, H.. (2017). Combining airborne laser scanning and landsat data for statistical modeling of soil carbon and tree biomass in tanzanian miombo woodlands. Carbon balance and management, 12(1), 8.
    [Bibtex] 
    @Article{Egberth2017,
author = {Egberth, Mikael and Nyberg, Gert and Næsset, Erik and Gobakken, Terje and Mauya, Ernest and Malimbwi, Rogers and Katani, Josiah and Chamuya, Nurudin and Bulenga, George and Olsson, Håkan},
title = {Combining airborne laser scanning and Landsat data for statistical modeling of soil carbon and tree biomass in Tanzanian Miombo woodlands},
journal = {Carbon Balance and Management},
year = {2017},
volume = {12},
number = {1},
pages = {8},
issn = {1750-0680},
doi = {10.1186/s13021-017-0076-y},
owner = {hanso},
timestamp = {2018.01.29},
type = {Journal Article},
url = {https://doi.org/10.1186/s13021-017-0076-y},
}
  • [DOI] Ene, L. T., Næsset, E., Gobakken, T., Bollandsås, O. M., Mauya, E. W., & Zahabu, E.. (2017). Large-scale estimation of change in aboveground biomass in miombo woodlands using airborne laser scanning and national forest inventory data. Remote sensing of environment, 188, 106-117.
    [Bibtex] 
    @Article{Ene2017,
author = {Ene, Liviu Theodor and Næsset, Erik and Gobakken, Terje and Bollandsås, Ole Martin and Mauya, Ernest William and Zahabu, Eliakimu},
title = {Large-scale estimation of change in aboveground biomass in miombo woodlands using airborne laser scanning and national forest inventory data},
journal = {Remote Sensing of Environment},
year = {2017},
volume = {188},
pages = {106-117},
issn = {0034-4257},
doi = {https://doi.org/10.1016/j.rse.2016.10.046},
owner = {hanso},
timestamp = {2018.01.29},
type = {Journal Article},
url = {http://www.sciencedirect.com/science/article/pii/S0034425716304254},
}
  • Hansen, E., Ene, L., Gobakken, T., Ørka, H., Bollandsås, O., & Næsset, E.. (2017). Countering negative effects of terrain slope on airborne laser scanner data using procrustean transformation and histogram matching. Forests, 8(10), 401.
    [Bibtex] 
    @Article{Hansen2017a,
author = {Hansen, Endre and Ene, Liviu and Gobakken, Terje and Ørka, Hans and Bollandsås, Ole and Næsset, Erik},
title = {Countering Negative Effects of Terrain Slope on Airborne Laser Scanner Data Using Procrustean Transformation and Histogram Matching},
journal = {Forests},
year = {2017},
volume = {8},
number = {10},
pages = {401},
issn = {1999-4907},
owner = {hanso},
timestamp = {2018.01.29},
type = {Journal Article},
url = {http://www.mdpi.com/1999-4907/8/10/401},
}
  • Hansen, E., Ene, L., Mauya, E., Patocka, Z., Mikita, T., Gobakken, T., & Næsset, E.. (2017). Comparing empirical and semi-empirical approaches to forest biomass modelling in different biomes using airborne laser scanner data. Forests, 8(5), 170.
    [Bibtex] 
    @Article{Hansen2017,
author = {Hansen, Endre and Ene, Liviu and Mauya, Ernest and Patocka, Zdenek and Mikita, Tomæ and Gobakken, Terje and Næsset, Erik},
title = {Comparing Empirical and Semi-Empirical Approaches to Forest Biomass Modelling in Different Biomes Using Airborne Laser Scanner Data},
journal = {Forests},
year = {2017},
volume = {8},
number = {5},
pages = {170},
issn = {1999-4907},
owner = {hanso},
timestamp = {2018.01.29},
type = {Journal Article},
url = {http://www.mdpi.com/1999-4907/8/5/170},
}
  • [DOI] Hauglin, M., Hansen, E. H., Næsset, E., Busterud, B. E., Gjevestad, J. G. O., & Gobakken, T.. (2017). Accurate single-tree positions from a harvester: a test of two global satellite-based positioning systems. Scandinavian journal of forest research, 32(8), 774-781.
    [Bibtex] 
    @Article{Hauglin2017,
author = {Hauglin, Marius and Hansen, Endre Hofstad and Næsset, Erik and Busterud, Bjørn Even and Gjevestad, Jon Glenn Omholt and Gobakken, Terje},
title = {Accurate single-tree positions from a harvester: a test of two global satellite-based positioning systems},
journal = {Scandinavian Journal of Forest Research},
year = {2017},
volume = {32},
number = {8},
pages = {774-781},
issn = {0282-7581},
doi = {10.1080/02827581.2017.1296967},
owner = {hanso},
timestamp = {2018.01.29},
type = {Journal Article},
url = {https://doi.org/10.1080/02827581.2017.1296967},
}
  • Kachamba, D., Ørka, H., Næsset, E., Eid, T., & Gobakken, T.. (2017). Influence of plot size on efficiency of biomass estimates in inventories of dry tropical forests assisted by photogrammetric data from an unmanned aircraft system. Remote sensing, 9(6), 610.
    [Bibtex] 
    @Article{Kachamba2017,
author = {Kachamba, Daud and Ørka, Hans and Næsset, Erik and Eid, Tron and Gobakken, Terje},
title = {Influence of Plot Size on Efficiency of Biomass Estimates in Inventories of Dry Tropical Forests Assisted by Photogrammetric Data from an Unmanned Aircraft System},
journal = {Remote Sensing},
year = {2017},
volume = {9},
number = {6},
pages = {610},
issn = {2072-4292},
owner = {hanso},
timestamp = {2018.01.29},
type = {Journal Article},
url = {http://www.mdpi.com/2072-4292/9/6/610},
}
  • [DOI] Kandare, K., Ørka, H. O., Dalponte, M., Næsset, E., & Gobakken, T.. (2017). Individual tree crown approach for predicting site index in boreal forests using airborne laser scanning and hyperspectral data. International journal of applied earth observation and geoinformation, 60, 72-82.
    [Bibtex] 
    @Article{Kandare2017a,
author = {Kandare, Kaja and Ørka, Hans Ole and Dalponte, Michele and Næsset, Erik and Gobakken, Terje},
title = {Individual tree crown approach for predicting site index in boreal forests using airborne laser scanning and hyperspectral data},
journal = {International Journal of Applied Earth Observation and Geoinformation},
year = {2017},
volume = {60},
pages = {72-82},
issn = {0303-2434},
doi = {https://doi.org/10.1016/j.jag.2017.04.008},
owner = {hanso},
timestamp = {2018.01.29},
type = {Journal Article},
url = {http://www.sciencedirect.com/science/article/pii/S0303243417300922},
}
  • [DOI] Lone, K., Mysterud, A., Gobakken, T., Odden, J., Linnell, J., & Loe, L. E.. (2017). Temporal variation in habitat selection breaks the catch-22 of spatially contrasting predation risk from multiple predators. Oikos, 126(5), 624-632.
    [Bibtex] 
    @Article{Lone2017,
author = {Lone, Karen and Mysterud, Atle and Gobakken, Terje and Odden, John and Linnell, John and Loe, Leif Egil},
title = {Temporal variation in habitat selection breaks the catch-22 of spatially contrasting predation risk from multiple predators},
journal = {Oikos},
year = {2017},
volume = {126},
number = {5},
pages = {624-632},
issn = {1600-0706},
doi = {10.1111/oik.03486},
owner = {hanso},
timestamp = {2018.01.29},
type = {Journal Article},
url = {http://dx.doi.org/10.1111/oik.03486},
}
  • [DOI] McRoberts, R. E., Chen, Q., Domke, G. M., Næsset, E., Gobakken, T., Chirici, G., & Mura, M.. (2017). Optimizing nearest neighbour configurations for airborne laser scanning-assisted estimation of forest volume and biomass. Forestry: an international journal of forest research, 90(1), 99-111.
    [Bibtex] 
    @Article{McRoberts2017,
author = {McRoberts, Ronald E. and Chen, Qi and Domke, Grant M. and Næsset, Erik and Gobakken, Terje and Chirici, Gherardo and Mura, Matteo},
title = {Optimizing nearest neighbour configurations for airborne laser scanning-assisted estimation of forest volume and biomass},
journal = {Forestry: An International Journal of Forest Research},
year = {2017},
volume = {90},
number = {1},
pages = {99-111},
issn = {0015-752X},
doi = {10.1093/forestry/cpw035},
owner = {hanso},
timestamp = {2018.01.29},
type = {Journal Article},
url = {http://dx.doi.org/10.1093/forestry/cpw035},
}
  • [DOI] Moser, P., Vibrans, A. C., McRoberts, R. E., Næsset, E., Gobakken, T., Chirici, G., Mura, M., & Marchetti, M.. (2017). Methods for variable selection in lidar-assisted forest inventories. Forestry: an international journal of forest research, 90(1), 112-124.
    [Bibtex] 
    @Article{Moser2017,
author = {Moser, Paolo and Vibrans, Alexander C. and McRoberts, Ronald E. and Næsset, Erik and Gobakken, Terje and Chirici, Gherardo and Mura, Matteo and Marchetti, Marco},
title = {Methods for variable selection in LiDAR-assisted forest inventories},
journal = {Forestry: An International Journal of Forest Research},
year = {2017},
volume = {90},
number = {1},
pages = {112-124},
issn = {0015-752X},
doi = {10.1093/forestry/cpw041},
owner = {hanso},
timestamp = {2018.01.29},
type = {Journal Article},
url = {http://dx.doi.org/10.1093/forestry/cpw041},
}
  • [DOI] Mugasha, W. A., Bollandsås, O. M., Gobakken, T., Zahabu, E., Katani, J. Z., & Eid, T.. (2017). Decision-support tool for management of miombo woodlands: a matrix model approach. Southern forests: a journal of forest science, 79(1), 65-77.
    [Bibtex] 
    @Article{Mugasha2017,
author = {Mugasha, Wilson A. and Bollandsås, Ole M. and Gobakken, Terje and Zahabu, Eliakimu and Katani, Josiah Z. and Eid, Tron},
title = {Decision-support tool for management of miombo woodlands: a matrix model approach},
journal = {Southern Forests: a Journal of Forest Science},
year = {2017},
volume = {79},
number = {1},
pages = {65-77},
issn = {2070-2620},
doi = {10.2989/20702620.2016.1233776},
owner = {hanso},
timestamp = {2018.01.29},
type = {Journal Article},
url = {https://doi.org/10.2989/20702620.2016.1233776},
}
  • [DOI] Mugasha, W. A., Eid, T., Bollandsås, O. M., & Mbwambo, L.. (2017). Modelling diameter growth, mortality and recruitment of trees in miombo woodlands of tanzania. Southern forests: a journal of forest science, 1-14.
    [Bibtex] 
    @Article{doi:10.2989/20702620.2016.1233755,
author = {Wilson A Mugasha and Tron Eid and Ole M Bollandsås and Lawrence Mbwambo},
journal = {Southern Forests: a Journal of Forest Science},
title = {Modelling diameter growth, mortality and recruitment of trees in miombo woodlands of Tanzania},
year = {2017},
number = {0},
pages = {1-14},
volume = {0},
doi = {10.2989/20702620.2016.1233755},
eprint = {http://dx.doi.org/10.2989/20702620.2016.1233755},
owner = {hanso},
timestamp = {2017.01.13},
url = {http://dx.doi.org/10.2989/20702620.2016.1233755},
}
  • Oveland, I., Hauglin, M., Gobakken, T., Næsset, E., & Maalen-Johansen, I.. (2017). Automatic estimation of tree position and stem diameter using a moving terrestrial laser scanner. Remote sensing, 9(4), 350.
    [Bibtex] 
    @Article{Oveland2017,
author = {Oveland, Ivar and Hauglin, Marius and Gobakken, Terje and Næsset, Erik and Maalen-Johansen, Ivar},
title = {Automatic Estimation of Tree Position and Stem Diameter Using a Moving Terrestrial Laser Scanner},
journal = {Remote Sensing},
year = {2017},
volume = {9},
number = {4},
pages = {350},
issn = {2072-4292},
owner = {hanso},
timestamp = {2018.01.29},
type = {Journal Article},
url = {http://www.mdpi.com/2072-4292/9/4/350},
}
  • [DOI] Puliti, S., Ene, L. T., Gobakken, T., & Næsset, E.. (2017). Use of partial-coverage uav data in sampling for large scale forest inventories. Remote sensing of environment, 194, 115-126.
    [Bibtex] 
    @Article{Puliti2017a,
author = {Puliti, Stefano and Ene, Liviu Theodor and Gobakken, Terje and Næsset, Erik},
title = {Use of partial-coverage UAV data in sampling for large scale forest inventories},
journal = {Remote Sensing of Environment},
year = {2017},
volume = {194},
pages = {115-126},
issn = {0034-4257},
doi = {https://doi.org/10.1016/j.rse.2017.03.019},
owner = {hanso},
timestamp = {2018.01.29},
type = {Journal Article},
url = {http://www.sciencedirect.com/science/article/pii/S0034425717301220},
}
  • Puliti, S., Solberg, S., Næsset, E., Gobakken, T., Zahabu, E., Mauya, E., & Malimbwi, R.. (2017). Modelling above ground biomass in tanzanian miombo woodlands using tandem-x worlddem and field data. Remote sensing, 9(10), 984.
    [Bibtex] 
    @Article{Puliti2017b,
author = {Puliti, Stefano and Solberg, Svein and Næsset, Erik and Gobakken, Terje and Zahabu, Eliakimu and Mauya, Ernest and Malimbwi, Rogers},
title = {Modelling above Ground Biomass in Tanzanian Miombo Woodlands Using TanDEM-X WorldDEM and Field Data},
journal = {Remote Sensing},
year = {2017},
volume = {9},
number = {10},
pages = {984},
issn = {2072-4292},
owner = {hanso},
timestamp = {2018.01.29},
type = {Journal Article},
url = {http://www.mdpi.com/2072-4292/9/10/984},
}
  • [DOI] Saarela, S., Andersen, H., Grafström, A., Schnell, S., Gobakken, T., Næsset, E., Nelson, R. F., McRoberts, R. E., Gregoire, T. G., & Ståhl, G.. (2017). A new prediction-based variance estimator for two-stage model-assisted surveys of forest resources. Remote sensing of environment, 192, 1-11.
    [Bibtex] 
    @Article{Saarela2017,
author = {Saarela, Svetlana and Andersen, Hans-Erik and Grafström, Anton and Schnell, Sebastian and Gobakken, Terje and Næsset, Erik and Nelson, Ross F. and McRoberts, Ronald E. and Gregoire, Timothy G. and Ståhl, Göran},
title = {A new prediction-based variance estimator for two-stage model-assisted surveys of forest resources},
journal = {Remote Sensing of Environment},
year = {2017},
volume = {192},
pages = {1-11},
issn = {0034-4257},
doi = {https://doi.org/10.1016/j.rse.2017.02.001},
owner = {hanso},
timestamp = {2018.01.29},
type = {Journal Article},
url = {http://www.sciencedirect.com/science/article/pii/S0034425717300524},
}
  • [DOI] Solberg, S., Hansen, E. H., Gobakken, T., Næssset, E., & Zahabu, E.. (2017). Biomass and insar height relationship in a dense tropical forest. Remote sensing of environment, 192, 166-175.
    [Bibtex] 
    @Article{Solberg2017,
author = {Solberg, Svein and Hansen, Endre Hofstad and Gobakken, Terje and Næssset, Erik and Zahabu, Eliakimu},
title = {Biomass and InSAR height relationship in a dense tropical forest},
journal = {Remote Sensing of Environment},
year = {2017},
volume = {192},
pages = {166-175},
issn = {0034-4257},
doi = {https://doi.org/10.1016/j.rse.2017.02.010},
owner = {hanso},
timestamp = {2018.01.29},
type = {Journal Article},
url = {http://www.sciencedirect.com/science/article/pii/S0034425717300603},
}
  • [DOI] Strïmbu, V. F., Ene, L. T., Gobakken, T., Gregoire, T. G., Astrup, R., & Næsset, E.. (2017). Post-stratified change estimation for large-area forest biomass using repeated als strip sampling. Canadian journal of forest research, 47(6), 839-847.
    [Bibtex] 
    @Article{Strimbu2017a,
author = {Strïmbu, Victor Felix and Ene, Liviu Theodor and Gobakken, Terje and Gregoire, Timothy G. and Astrup, Rasmus and Næsset, Erik},
title = {Post-stratified change estimation for large-area forest biomass using repeated ALS strip sampling},
journal = {Canadian Journal of Forest Research},
year = {2017},
volume = {47},
number = {6},
pages = {839-847},
issn = {0045-5067},
doi = {10.1139/cjfr-2017-0031},
owner = {hanso},
timestamp = {2018.01.29},
type = {Journal Article},
url = {https://doi.org/10.1139/cjfr-2017-0031},
}

2016

  • [DOI] Ene, L. T., Næsset, E., & Gobakken, T.. (2016). Simulation-based assessment of sampling strategies for large-area biomass estimation using wall-to-wall and partial coverage airborne laser scanning surveys. Remote sensing of environment, 176, 328-340.
    [Bibtex] 
    @Article{Ene2016,
Title = {Simulation-based assessment of sampling strategies for large-area biomass estimation using wall-to-wall and partial coverage airborne laser scanning surveys},
Author = {Ene, Liviu Theodor and Næsset, Erik and Gobakken, Terje},
Journal = {Remote Sensing of Environment},
Year = {2016},
Pages = {328-340},
Volume = {176},
Abstract = {Airborne laser scanning (ALS) has been demonstrated to be an excellent source of auxiliary information for increasing the precision of estimating stand-level attributes in forest inventories. It has also been proposed to use ALS for estimating biomass and carbon stocks under the United Nations Collaborative Program on Reduced Emissions from Deforestation and Forest Degradation in Developing Countries (UN-REDD). The benefits of REDD depend among other facts on the cost-efficiency of the carbon accounting systems, which should be economically feasible and highly accurate. Acquiring full-coverage ALS data would provide highly accurate estimates but might be too expensive for limited inventory budgets. As an alternative, the ALS data might be collected as a sample by acquiring data from a portion of the area of interest. However, in surveys involving complex multi-phase and multi-stage systematic sampling designs, the efficiency of ALS-based estimates is hampered by the ability of estimating the sampling variability correctly. It has been demonstrated recently that the precision of such complex analytical estimators may be largely underestimated. In order to make an informed decision, simulated sampling from artificial populations generated from empirical data may provide a means for assessing the cost-efficiency of various sampling strategies when analytical approaches fail. This study presents a simulation-based assessment of sampling strategies employing ALS with focus on large-area (27,400 km2) biomass estimation. Simulated sampling mimicking the two contrasting cases “wall-to-wall” and two-phase ALS-aided surveys is exemplified using Norwegian National Forest Inventory data for creating an artificial population. The main results indicated that (1) the gain in precision (10%) when using “wall-to-wall” ALS data may not be worth the very high inventory costs, (2) using variance estimators based on higher-order successive differences produced correct confidence intervals for two-phase systematic sampling, and (3) two-phase ALS-aided systematic surveys are cost-efficient solutions for large-area biomass estimation.},
Doi = {http://dx.doi.org/10.1016/j.rse.2016.01.025},
ISSN = {0034-4257},
Keywords = {Airborne laser scanning Forest inventory Variance estimation Simulated sampling},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.sciencedirect.com/science/article/pii/S003442571630027X}
}
  • [DOI] Ene, L. T., Næsset, E., Gobakken, T., Mauya, E. W., Bollandsås, O. M., Gregoire, T. G., Ståhl, G., & Zahabu, E.. (2016). Large-scale estimation of aboveground biomass in miombo woodlands using airborne laser scanning and national forest inventory data. Remote sensing of environment, 186, 626-636.
    [Bibtex] 
    @Article{Ene2016a,
Title = {Large-scale estimation of aboveground biomass in miombo woodlands using airborne laser scanning and national forest inventory data},
Author = {Ene, Liviu Theodor and Næsset, Erik and Gobakken, Terje and Mauya, Ernest William and Bollandsås, Ole Martin and Gregoire, Timothy G. and Ståhl, Göran and Zahabu, Eliakimu},
Journal = {Remote Sensing of Environment},
Year = {2016},
Pages = {626-636},
Volume = {186},
Doi = {http://dx.doi.org/10.1016/j.rse.2016.09.006},
ISSN = {0034-4257},
Owner = {hanso},
Timestamp = {2017.01.13},
Type = {Journal Article},
Url = {http://www.sciencedirect.com/science/article/pii/S0034425716303455}
}
  • [DOI] Gizachew, B., Solberg, S., Næsset, E., Gobakken, T., Bollandsås, O. M., Breidenbach, J., Zahabu, E., & Mauya, E. W.. (2016). Mapping and estimating the total living biomass and carbon in low-biomass woodlands using landsat 8 cdr data. Carbon balance and management, 11(1), 13.
    [Bibtex] 
    @Article{Gizachew2016,
Title = {Mapping and estimating the total living biomass and carbon in low-biomass woodlands using Landsat 8 CDR data},
Author = {Gizachew, Belachew and Solberg, Svein and Næsset, Erik and Gobakken, Terje and Bollandsås, Ole Martin and Breidenbach, Johannes and Zahabu, Eliakimu and Mauya, Ernest William},
Journal = {Carbon Balance and Management},
Year = {2016},
Number = {1},
Pages = {13},
Volume = {11},
Doi = {10.1186/s13021-016-0055-8},
ISSN = {1750-0680},
Owner = {hanso},
Timestamp = {2017.01.12},
Type = {Journal Article},
Url = {http://dx.doi.org/10.1186/s13021-016-0055-8}
}
  • [DOI] Gregoire, T. G., Næsset, E., McRoberts, R. E., Ståhl, G., Andersen, H., Gobakken, T., Ene, L., & Nelson, R.. (2016). Statistical rigor in lidar-assisted estimation of aboveground forest biomass. Remote sensing of environment, 173, 98-108.
    [Bibtex] 
    @Article{Gregoire2016,
Title = {Statistical rigor in LiDAR-assisted estimation of aboveground forest biomass},
Author = {Gregoire, Timothy G. and Næsset, Erik and McRoberts, Ronald E. and Ståhl, Göran and Andersen, Hans-Erik and Gobakken, Terje and Ene, Liviu and Nelson, Ross},
Journal = {Remote Sensing of Environment},
Year = {2016},
Pages = {98-108},
Volume = {173},
Abstract = {For many decades remotely sensed data have been used as a source of auxiliary information when conducting regional or national surveys of forest resources. In the past decade, airborne scanning LiDAR (Light Detection and Ranging) has emerged as a promising tool for sample surveys aimed at improving estimation of above-ground forest biomass. This technology is now employed routinely in forest management inventories of some Nordic countries, and there is eager anticipation for its application to assess changes in standing biomass in vast tropical regions of the globe in concert with the UN REDD program to limit C emissions. In the rapidly expanding literature on LiDAR-assisted biomass estimation the assessment of the uncertainty of estimation varies widely, ranging from statistically rigorous to ad hoc. In many instances, too, there appears to be no recognition of different bases of statistical inference which bear importantly on uncertainty estimation. Statistically rigorous assessment of uncertainty for four large LiDAR-assisted surveys is expounded.},
Doi = {http://dx.doi.org/10.1016/j.rse.2015.11.012},
ISSN = {0034-4257},
Keywords = {Sampling Statistical inference Variance estimation},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.sciencedirect.com/science/article/pii/S0034425715302017}
}
  • [DOI] Halvorsen, R., Mazzoni, S., Dirksen, J. W., Næsset, E., Gobakken, T., & Ohlson, M.. (2016). How important are choice of model selection method and spatial autocorrelation of presence data for distribution modelling by maxent?. Ecological modelling, 328, 108-118.
    [Bibtex] 
    @Article{Halvorsen2016,
Title = {How important are choice of model selection method and spatial autocorrelation of presence data for distribution modelling by MaxEnt?},
Author = {Halvorsen, Rune and Mazzoni, Sabrina and Dirksen, John Wirkola and Næsset, Erik and Gobakken, Terje and Ohlson, Mikael},
Journal = {Ecological Modelling},
Year = {2016},
Pages = {108-118},
Volume = {328},
Doi = {http://dx.doi.org/10.1016/j.ecolmodel.2016.02.021},
ISSN = {0304-3800},
Owner = {hanso},
Timestamp = {2017.01.12},
Type = {Journal Article},
Url = {http://www.sciencedirect.com/science/article/pii/S0304380016300503}
}
  • Hauglin, M., & Næsset, E.. (2016). Detection and segmentation of small trees in the forest-tundra ecotone using airborne laser scanning. Remote sensing, 8(5), 407.
    [Bibtex] 
    @Article{Hauglin2016a,
Title = {Detection and Segmentation of Small Trees in the Forest-Tundra Ecotone Using Airborne Laser Scanning},
Author = {Hauglin, Marius and Næsset, Erik},
Journal = {Remote Sensing},
Year = {2016},
Number = {5},
Pages = {407},
Volume = {8},
ISSN = {2072-4292},
Owner = {hanso},
Timestamp = {2017.01.13},
Type = {Journal Article},
Url = {http://www.mdpi.com/2072-4292/8/5/407}
}
  • [DOI] Hauglin, M., & Ørka, H. O.. (2016). Discriminating between native norway spruce and invasive sitka spruce—a comparison of multitemporal landsat 8 imagery, aerial images and airborne laser scanner data. Remote sensing, 8(5), 363.
    [Bibtex] 
    @Article{Hauglin2016,
Title = {Discriminating between Native Norway Spruce and Invasive Sitka Spruce—A Comparison of Multitemporal Landsat 8 Imagery, Aerial Images and Airborne Laser Scanner Data},
Author = {Hauglin, Marius and Ørka, Hans Ole},
Journal = {Remote Sensing},
Year = {2016},
Number = {5},
Pages = {363},
Volume = {8},
Abstract = {Invasive species can be considered a threat to biodiversity, and remote sensing has been proposed as a tool for detection and monitoring of invasive species. In this study, we test the ability to discriminate between two tree species of the same genera, using data from Landsat 8 satellite imagery, aerial images, and airborne laser scanning. Ground observations from forest stands dominated by either Norway spruce (Picea abies) or Sitka spruce (Picea sitchensis) were coupled with variables derived from each of the three sets of remote sensing data. Random forest, support vector machine, and logistic regression classification models were fit to the data, and the classification accuracy tested by performing a cross-validation. Classification accuracies were compared for different combinations of remote sensing data and classification methods. The overall classification accuracy varied from 0.53 to 0.79, with the highest accuracy obtained using logistic regression with a combination of data derived from Landsat imagery and aerial images. The corresponding kappa value was 0.58. The contribution to the classification accuracy from using airborne data in addition to Landsat imagery was not substantial in this study. The classification accuracy varied between models using data from individual Landsat images.},
Doi = {10.3390/rs8050363},
ISSN = {2072-4292},
Owner = {hanso},
Timestamp = {2016.09.28},
Url = {http://www.mdpi.com/2072-4292/8/5/363}
}
  • Kachamba, D., Eid, T., & Gobakken, T.. (2016). Above- and belowground biomass models for trees in the miombo woodlands of malawi. Forests, 7(2), 38.
    [Bibtex] 
    @Article{Kachamba2016,
Title = {Above- and Belowground Biomass Models for Trees in the Miombo Woodlands of Malawi},
Author = {Kachamba, Daud and Eid, Tron and Gobakken, Terje},
Journal = {Forests},
Year = {2016},
Number = {2},
Pages = {38},
Volume = {7},
ISSN = {1999-4907},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.mdpi.com/1999-4907/7/2/38}
}
  • Kachamba, D., Ørka, H. O., Gobakken, T., Eid, T., & Mwase, W.. (2016). Biomass estimation using 3d data from unmanned aerial vehicle imagery in a tropical woodland. Remote sensing, 8(11), 968.
    [Bibtex] 
    @Article{Kachamba2016a,
Title = {Biomass Estimation Using 3D Data from Unmanned Aerial Vehicle Imagery in a Tropical Woodland},
Author = {Kachamba, Daud and Ørka, Hans Ole and Gobakken, Terje and Eid, Tron and Mwase, Weston},
Journal = {Remote Sensing},
Year = {2016},
Number = {11},
Pages = {968},
Volume = {8},
ISSN = {2072-4292},
Owner = {hanso},
Timestamp = {2017.01.12},
Type = {Journal Article},
Url = {http://www.mdpi.com/2072-4292/8/11/968}
}
  • [DOI] Kangas, A., Myllymäki, M., Gobakken, T., & Næsset, E.. (2016). Model-assisted forest inventory with parametric, semiparametric, and nonparametric models. Canadian journal of forest research, 46(6), 855-868.
    [Bibtex] 
    @Article{Kangas2016,
Title = {Model-assisted forest inventory with parametric, semiparametric, and nonparametric models},
Author = {Kangas, Annika and Myllymäki, Mari and Gobakken, Terje and Næsset, Erik},
Journal = {Canadian Journal of Forest Research},
Year = {2016},
Number = {6},
Pages = {855-868},
Volume = {46},
Doi = {10.1139/cjfr-2015-0504},
ISSN = {0045-5067},
Owner = {hanso},
Timestamp = {2017.01.12},
Type = {Journal Article},
Url = {http://dx.doi.org/10.1139/cjfr-2015-0504}
}
  • [DOI] Korhonen, L., Salas, C., Østgård, T., Lien, V., Gobakken, T., & Næsset, E.. (2016). Predicting the occurrence of large-diameter trees using airborne laser scanning. Canadian journal of forest research, 461-469.
    [Bibtex] 
    @Article{Korhonen2016,
Title = {Predicting the occurrence of large-diameter trees using airborne laser scanning},
Author = {Korhonen, Lauri and Salas, Christian and Østgård, Torgrim and Lien, Vegard and Gobakken, Terje and Næsset, Erik},
Journal = {Canadian Journal of Forest Research},
Year = {2016},
Pages = {461-469},
Doi = {10.1139/cjfr-2015-0384},
ISSN = {0045-5067},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://dx.doi.org/10.1139/cjfr-2015-0384}
}
  • [DOI] Magnussen, S., Mandallaz, D., Lanz, A., Ginzler, C., Næsset, E., & Gobakken, T.. (2016). Scale effects in survey estimates of proportions and quantiles of per unit area attributes. Forest ecology and management, 364, 122-129.
    [Bibtex] 
    @Article{Magnussen2016,
Title = {Scale effects in survey estimates of proportions and quantiles of per unit area attributes},
Author = {Magnussen, Steen and Mandallaz, Daniel and Lanz, Adrian and Ginzler, Christian and Næsset, Erik and Gobakken, Terje},
Journal = {Forest Ecology and Management},
Year = {2016},
Pages = {122-129},
Volume = {364},
Abstract = {Quantiles and proportions in a sampling distribution of a per unit area attribute (Y) depend on the spatial support (area) of employed survey plots. This is a nuisance for managers, and policy developers; in particular when the underlying data have been collected with different spatial supports. Users of these statistics may wish to calibrate their estimates to a common scale of spatial support. The easiest way to do this is through scaling to a common plot size. We demonstrate a statistical method for upscaling. The method is illustrated in the context of a design-based forest inventory of a target attribute Y with a census of a co-located vector of auxiliary variables (X) correlated with Y. Two case studies from Norway and Switzerland confirmed significant and practically important scale effects in quantiles and proportions of above ground live tree biomass (Mg ha−1) and stem volume (m3 ha−1). Upscaling requires an estimate of the spatial autocorrelation of Y given X at the scale of the original spatial support. We present an expedient method to this end. Our method affords estimation of scaled quantiles and proportions and assures consistency of sampling distribution across scales.},
Doi = {http://dx.doi.org/10.1016/j.foreco.2016.01.013},
ISSN = {0378-1127},
Keywords = {Quantiles Area proportions Spatial support Spatial autocorrelation Scaled quantiles Scaled area proportions},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.sciencedirect.com/science/article/pii/S0378112716000141}
}
  • [DOI] Magnussen, S., Næsset, E., Kändler, G., Adler, P., Renaud, J. P., & Gobakken, T.. (2016). A functional regression model for inventories supported by aerial laser scanner data or photogrammetric point clouds. Remote sensing of environment, 184, 496-505.
    [Bibtex] 
    @Article{Magnussen2016a,
Title = {A functional regression model for inventories supported by aerial laser scanner data or photogrammetric point clouds},
Author = {Magnussen, S. and Næsset, E. and Kändler, G. and Adler, P. and Renaud, J. P. and Gobakken, T.},
Journal = {Remote Sensing of Environment},
Year = {2016},
Pages = {496-505},
Volume = {184},
Doi = {http://dx.doi.org/10.1016/j.rse.2016.07.035},
ISSN = {0034-4257},
Owner = {hanso},
Timestamp = {2017.01.12},
Type = {Journal Article},
Url = {http://www.sciencedirect.com/science/article/pii/S0034425716302929}
}
  • [DOI] Maltamo, M., Bollandsås, O. M., Gobakken, T., & Næsset, E.. (2016). Large-scale prediction of aboveground biomass in heterogeneous mountain forests by means of airborne laser scanning. Canadian journal of forest research, 46(9), 1138-1144.
    [Bibtex] 
    @Article{Maltamo2016,
Title = {Large-scale prediction of aboveground biomass in heterogeneous mountain forests by means of airborne laser scanning},
Author = {Maltamo, M. and Bollandsås, O. M. and Gobakken, T. and Næsset, E.},
Journal = {Canadian Journal of Forest Research},
Year = {2016},
Number = {9},
Pages = {1138-1144},
Volume = {46},
Doi = {10.1139/cjfr-2016-0086},
ISSN = {0045-5067},
Owner = {hanso},
Timestamp = {2017.01.12},
Type = {Journal Article},
Url = {http://dx.doi.org/10.1139/cjfr-2016-0086}
}
  • [DOI] McRoberts, R. E., Domke, G. M., Chen, Q., Næsset, E., & Gobakken, T.. (2016). Using genetic algorithms to optimize k-nearest neighbors configurations for use with airborne laser scanning data. Remote sensing of environment, 184, 387-395.
    [Bibtex] 
    @Article{McRoberts2016c,
Title = {Using genetic algorithms to optimize k-Nearest Neighbors configurations for use with airborne laser scanning data},
Author = {McRoberts, Ronald E. and Domke, Grant M. and Chen, Qi and Næsset, Erik and Gobakken, Terje},
Journal = {Remote Sensing of Environment},
Year = {2016},
Pages = {387-395},
Volume = {184},
Doi = {http://dx.doi.org/10.1016/j.rse.2016.07.007},
ISSN = {0034-4257},
Owner = {hanso},
Timestamp = {2017.01.12},
Type = {Journal Article},
Url = {http://www.sciencedirect.com/science/article/pii/S0034425716302644}
}
  • [DOI] McRoberts, R. E., Næsset, E., & Gobakken, T.. (2016). The effects of temporal differences between map and ground data on map-assisted estimates of forest area and biomass. Annals of forest science, 73(4), 839-847.
    [Bibtex] 
    @Article{McRoberts2016a,
Title = {The effects of temporal differences between map and ground data on map-assisted estimates of forest area and biomass},
Author = {McRoberts, Ronald E. and Næsset, Erik and Gobakken, Terje},
Journal = {Annals of Forest Science},
Year = {2016},
Number = {4},
Pages = {839-847},
Volume = {73},
Doi = {10.1007/s13595-015-0485-6},
ISSN = {1297-966X},
Owner = {hanso},
Timestamp = {2017.01.12},
Type = {Journal Article},
Url = {http://dx.doi.org/10.1007/s13595-015-0485-6}
}
  • [DOI] McRoberts, R. E., Vibrans, A. C., Sannier, C., Næsset, E., Hansen, M. C., Walters, B. F., & Lingner, D. V.. (2016). Methods for evaluating the utilities of local and global maps for increasing the precision of estimates of subtropical forest area. Canadian journal of forest research, 46(7), 924-932.
    [Bibtex] 
    @Article{McRoberts2016,
Title = {Methods for evaluating the utilities of local and global maps for increasing the precision of estimates of subtropical forest area},
Author = {McRoberts, Ronald E. and Vibrans, Alexander C. and Sannier, Christophe and Næsset, Erik and Hansen, Matthew C. and Walters, Brian F. and Lingner, Débora V.},
Journal = {Canadian Journal of Forest Research},
Year = {2016},
Number = {7},
Pages = {924-932},
Volume = {46},
Doi = {10.1139/cjfr-2016-0064},
ISSN = {0045-5067},
Owner = {hanso},
Timestamp = {2017.01.12},
Type = {Journal Article},
Url = {http://dx.doi.org/10.1139/cjfr-2016-0064}
}
  • Næsset, E.. (2016). Discrimination between ground vegetation and small pioneer trees in the boreal-alpine ecotone using intensity metrics derived from airborne laser scanner data. Remote sensing, 8(7), 548.
    [Bibtex] 
    @Article{Naesset2016a,
Title = {Discrimination between Ground Vegetation and Small Pioneer Trees in the Boreal-Alpine Ecotone Using Intensity Metrics Derived from Airborne Laser Scanner Data},
Author = {Næsset, Erik},
Journal = {Remote Sensing},
Year = {2016},
Number = {7},
Pages = {548},
Volume = {8},
ISSN = {2072-4292},
Owner = {hanso},
Timestamp = {2017.01.12},
Type = {Journal Article},
Url = {http://www.mdpi.com/2072-4292/8/7/548}
}
  • [DOI] Næsset, E., Ørka, H. O., Solberg, S., Bollandsås, O. M., Hansen, E. H., Mauya, E., Zahabu, E., Malimbwi, R., Chamuya, N., Olsson, H., & Gobakken, T.. (2016). Mapping and estimating forest area and aboveground biomass in miombo woodlands in tanzania using data from airborne laser scanning, tandem-x, rapideye, and global forest maps: a comparison of estimated precision. Remote sensing of environment, 175, 282-300.
    [Bibtex] 
    @Article{Naesset2016,
Title = {Mapping and estimating forest area and aboveground biomass in miombo woodlands in Tanzania using data from airborne laser scanning, TanDEM-X, RapidEye, and global forest maps: A comparison of estimated precision},
Author = {Næsset, Erik and Ørka, Hans Ole and Solberg, Svein and Bollandsås, Ole Martin and Hansen, Endre Hofstad and Mauya, Ernest and Zahabu, Eliakimu and Malimbwi, Rogers and Chamuya, Nurdin and Olsson, Håkan and Gobakken, Terje},
Journal = {Remote Sensing of Environment},
Year = {2016},
Pages = {282-300},
Volume = {175},
Abstract = {Field surveys are often a primary source of data for aboveground biomass (AGB) and forest area estimates — two fundamental parameters in forest resource assessments and for measurement, reporting, and verification (MRV) under the United Nations Collaborative Program on Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD +). However, plot-based estimates of such parameters are often not sufficiently precise for their intended purposes, and especially so in developing and tropical countries in which implementation of extensive sample surveys can be cost-prohibitive or infeasible due to inaccessibility. Remotely sensed data can improve the precision of estimates and thereby reduce the need for field samples. To guide investment decision in MRV systems, comparative analyses of the contribution of different types of remotely sensed data to improve precision of estimates are required. The aim of the current study was to quantify the contribution of data from (1) airborne laser scanning (ALS), (2) interferometric synthetic aperture radar (InSAR) derived from TanDEM-X, (3) RapidEye optical imagery, and global forest map products derived from (4) Landsat and (5) ALOS PALSAR L-band radar imagery to improve precision of AGB and forest area estimates beyond the precision that could be obtained by a pure field-based survey in miombo woodlands of Tanzania. Miombo woodlands is one among the most wide-spread vegetation types in eastern, central, and southern Africa, occupying about 9% of the entire African land area. A 365.6 km2 region in Liwale district in Tanzania served as area of interest for this study. Eighty-eight ground plots distributed on 11 clusters of eight plots each according to a probability-based single-stage cluster sampling design served as field data for regression model calibration used for mapping and estimation of AGB and forest area. Model-assisted estimators were used in the estimation. The relative efficiency (RE) of the ALS-assisted estimates of mean AGB per hectare (variance of the field-based estimate relative to the variance of the ALS-assisted estimate) was 3.6. Relative efficiency translates directly to the factor by which the sample size used for the ALS-assisted estimate would have to be multiplied to arrive at the same precision for a pure field-based estimate. RE values for InSAR and RapidEye were 2.8 and 3.3, while the global Landsat and PALSAR map products contributed only marginally to improve precision (RE = 1.3–1.4). For forest area estimation, ALS-assisted estimates showed an RE of 3.7–4.6, while InSAR, RapidEye, and global Landsat and PALSAR maps resulted in RE values of 1.0–1.3, 2.0–2.1, 1.4–1.8, and 1.7, respectively.},
Doi = {http://dx.doi.org/10.1016/j.rse.2016.01.006},
ISSN = {0034-4257},
Keywords = {Miombo woodlands Tropical forests REDD + Model-assisted estimation Aboveground biomass Forest area Airborne laser TanDEM-X InSAR RapidEye Global Landsat maps Global ALOS PALSAR maps},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.sciencedirect.com/science/article/pii/S0034425716300062}
}
  • [DOI] Puliti, S., Gobakken, T., Ørka, H. O., & Næsset, E.. (2016). Assessing 3d point clouds from aerial photographs for species-specific forest inventories. Scandinavian journal of forest research, 1-12.
    [Bibtex] 
    @Article{Puliti2016,
Title = {Assessing 3D point clouds from aerial photographs for species-specific forest inventories},
Author = {Puliti, Stefano and Gobakken, Terje and Ørka, Hans Ole and Næsset, Erik},
Journal = {Scandinavian Journal of Forest Research},
Year = {2016},
Pages = {1-12},
Doi = {10.1080/02827581.2016.1186727},
ISSN = {0282-7581},
Owner = {hanso},
Timestamp = {2017.01.12},
Type = {Journal Article},
Url = {http://dx.doi.org/10.1080/02827581.2016.1186727}
}
  • [DOI] Ringvall, A. H., Ståhl, G., Ene, L. T., Næsset, E., Gobakken, T., & Gregoire, T. G.. (2016). A poststratified ratio estimator for model-assisted biomass estimation in sample-based airborne laser scanning surveys. Canadian journal of forest research, 46(11), 1386-1395.
    [Bibtex] 
    @Article{Ringvall2016,
Title = {A poststratified ratio estimator for model-assisted biomass estimation in sample-based airborne laser scanning surveys},
Author = {Ringvall, Anna H. and Ståhl, Göran and Ene, Liviu T. and Næsset, Erik and Gobakken, Terje and Gregoire, Timothy G.},
Journal = {Canadian Journal of Forest Research},
Year = {2016},
Number = {11},
Pages = {1386-1395},
Volume = {46},
Doi = {10.1139/cjfr-2016-0158},
ISSN = {0045-5067},
Owner = {hanso},
Timestamp = {2017.01.12},
Type = {Journal Article},
Url = {http://dx.doi.org/10.1139/cjfr-2016-0158}
}
  • [DOI] Saarela, S., Holm, S., Grafström, A., Schnell, S., Næsset, E., Gregoire, T. G., Nelson, R. F., & Ståhl, G.. (2016). Hierarchical model-based inference for forest inventory utilizing three sources of information. Annals of forest science, 1-16.
    [Bibtex] 
    @Article{Saarela2016,
Title = {Hierarchical model-based inference for forest inventory utilizing three sources of information},
Author = {Saarela, Svetlana and Holm, Sören and Grafström, Anton and Schnell, Sebastian and Næsset, Erik and Gregoire, Timothy G. and Nelson, Ross F. and Ståhl, Göran},
Journal = {Annals of Forest Science},
Year = {2016},
Pages = {1-16},
Doi = {10.1007/s13595-016-0590-1},
ISSN = {1297-966X},
Owner = {hanso},
Timestamp = {2017.01.12},
Type = {Journal Article},
Url = {http://dx.doi.org/10.1007/s13595-016-0590-1}
}
  • [DOI] Ståhl, G., Saarela, S., Schnell, S., Holm, S., Breidenbach, J., Healey, S. P., Patterson, P. L., Magnussen, S., Næsset, E., McRoberts, R. E., & Gregoire, T. G.. (2016). Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation. Forest ecosystems, 3(1), 1-11.
    [Bibtex] 
    @Article{Staahl2016,
Title = {Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation},
Author = {Ståhl, Göran and Saarela, Svetlana and Schnell, Sebastian and Holm, Sören and Breidenbach, Johannes and Healey, Sean P. and Patterson, Paul L. and Magnussen, Steen and Næsset, Erik and McRoberts, Ronald E. and Gregoire, Timothy G.},
Journal = {Forest Ecosystems},
Year = {2016},
Number = {1},
Pages = {1-11},
Volume = {3},
Doi = {10.1186/s40663-016-0064-9},
ISSN = {2197-5620},
Owner = {hanso},
Timestamp = {2017.01.12},
Type = {Journal Article},
Url = {http://dx.doi.org/10.1186/s40663-016-0064-9
http://download.springer.com/static/pdf/83/art%253A10.1186%252Fs40663-016-0064-9.pdf?originUrl=http%3A%2F%2Fforestecosyst.springeropen.com%2Farticle%2F10.1186%2Fs40663-016-0064-9&token2=exp=1481638696~acl=%2Fstatic%2Fpdf%2F83%2Fart%25253A10.1186%25252Fs40663-016-0064-9.pdf*~hmac=520135514ba7bbd3fb265be1ec170f6b2ac82120c74d3222ebd5216751f8ea1a}
}
  • [DOI] Strîmbu, V. F., Ene, L. T., & Næsset, E.. (2016). Spatially consistent imputations of forest data under a semivariogram model. Canadian journal of forest research, 46(9), 1145-1156.
    [Bibtex] 
    @Article{Strimbu2016,
Title = {Spatially consistent imputations of forest data under a semivariogram model},
Author = {Strîmbu, Victor Felix and Ene, Liviu Teodor and Næsset, Erik},
Journal = {Canadian Journal of Forest Research},
Year = {2016},
Number = {9},
Pages = {1145-1156},
Volume = {46},
Doi = {10.1139/cjfr-2016-0068},
ISSN = {0045-5067},
Owner = {hanso},
Timestamp = {2017.01.12},
Type = {Journal Article},
Url = {http://dx.doi.org/10.1139/cjfr-2016-0068}
}
  • [DOI] Sverdrup-Thygeson, A., Ørka, H. O., Gobakken, T., & Næsset, E.. (2016). Can airborne laser scanning assist in mapping and monitoring natural forests?. Forest ecology and management, 369, 116-125.
    [Bibtex] 
    @Article{Sverdrup-Thygeson2016,
Title = {Can airborne laser scanning assist in mapping and monitoring natural forests?},
Author = {Sverdrup-Thygeson, Anne and Ørka, Hans Ole and Gobakken, Terje and Næsset, Erik},
Journal = {Forest Ecology and Management},
Year = {2016},
Pages = {116-125},
Volume = {369},
Doi = {http://dx.doi.org/10.1016/j.foreco.2016.03.035},
ISSN = {0378-1127},
Owner = {hanso},
Timestamp = {2017.01.12},
Type = {Journal Article},
Url = {http://www.sciencedirect.com/science/article/pii/S0378112716301128}
}
  • [DOI] Ørka, H. O., Gobakken, T., & Næsset, E.. (2016). Predicting attributes of regeneration forests using airborne laser scanning. Canadian journal of remote sensing, 42(5), 541-553.
    [Bibtex] 
    @Article{Oerka2016a,
Title = {Predicting Attributes of Regeneration Forests Using Airborne Laser Scanning},
Author = {Ørka, Hans Ole and Gobakken, Terje and Næsset, Erik},
Journal = {Canadian Journal of Remote Sensing},
Year = {2016},
Number = {5},
Pages = {541-553},
Volume = {42},
Doi = {10.1080/07038992.2016.1199269},
ISSN = {0703-8992},
Owner = {hanso},
Timestamp = {2017.01.12},
Type = {Journal Article},
Url = {http://dx.doi.org/10.1080/07038992.2016.1199269}
}
  • [DOI] Myllymäki, M., Gobakken, T., Næsset, E., & Kangas, A.. (2016). The efficiency of post-stratification compared to model-assisted estimation. Canadian journal of forest research, 47(4), 515-526.
    [Bibtex] 
    @Article{Myllymaeki2016,
author = {Myllymäki, Mari and Gobakken, Terje and Næsset, Erik and Kangas, Annika},
title = {The efficiency of post-stratification compared to model-assisted estimation},
journal = {Canadian Journal of Forest Research},
year = {2016},
volume = {47},
number = {4},
pages = {515-526},
issn = {0045-5067},
doi = {10.1139/cjfr-2016-0383},
owner = {hanso},
timestamp = {2017.01.12},
type = {Journal Article},
url = {http://dx.doi.org/10.1139/cjfr-2016-0383},
}

2015

  • [DOI] Borges, P., Bergseng, E., Eid, T., & Gobakken, T.. (2015). Impact of maximum opening area constraints on profitability and biomass availability in forestry – a large, real world case. Silva fennica, 49(5).
    [Bibtex] 
    @Article{Borges2015a,
Title = {Impact of maximum opening area constraints on profitability and biomass availability in forestry – a large, real world case},
Author = {Borges, Paulo and Bergseng, Even and Eid, Tron and Gobakken, Terje},
Journal = {SILVA FENNICA},
Year = {2015},
Number = {5},
Volume = {49},
Abstract = {
    The nature areas surrounding the capital of Norway (Oslomarka), comprising 1 700 km2 of forest land, are the recreational home turf for a population of 1.2 mill. people. These areas are highly valuable, not only for recreational purposes and biodiversity, but also for commercial activities. To assess the impacts of the challenges that Oslo municipality forest face in their management, we developed four optimization problems with different levels of management constraints. The constraints consider control of harvest level, guarantee of minimum old-growth forest area and maximum open area after final harvest. For the latter, to date, no appropriate analyses quantifying the impact of such a constraint on economy and biomass production have been carried out in Norway. The problem solved is large due to both the number of stands and number of treatment schedules. However, the model applied demonstrated its relevance for solving large problems involving maximum opening areas. The inclusion of maximum open area constraints caused 7.0% loss in NPV compared to the business as usual case with controlled harvest volume and minimum old-growth area. The estimated supply of 20-30 GWh annual energy from harvest residues could provide a small, but stable supply of energy to the municipality.
    },
Doi = {doi:10.14214/sf.1347},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.silvafennica.fi/article/1347}
}
  • [DOI] Borges, P., Martins, I., Bergseng, E., Eid, T., & Gobakken, T.. (2015). Effects of site productivity on forest harvest scheduling subject to green-up and maximum area restrictions. Scandinavian journal of forest research, 1-10.
    [Bibtex] 
    @Article{Borges2015,
Title = {Effects of site productivity on forest harvest scheduling subject to green-up and maximum area restrictions},
Author = {Borges, Paulo and Martins, Isabel and Bergseng, Even and Eid, Tron and Gobakken, Terje},
Journal = {Scandinavian Journal of Forest Research},
Year = {2015},
Pages = {1-10},
Abstract = {ABSTRACTGreen-up requirements are of great interest for forests near cities since these forests are commonly used for recreational activities by the local population as well as for commercial forestry activities. We present three formulations to establish green-up requirements, based on a dynamic green-up approach and constructed by means of: (i) a predefined fixed length for the green-up time, (ii) a predefined variable length for the green-up time and (iii) height information produced by the growth simulator. Additionally, restrictions on harvested volume and maximum open areas were applied. All the green-up formulations were applied to five datasets comprising different initial forest conditions regarding age and site index distribution. Results show that higher net present values are obtained by the formulation that allow a predefined variable length for the green-up time and by using the height information from the growth simulator compared to the formulations using a predefined fixed length for the green-up time. The increase in NPV was most pronounced for the old forest datasets and varied between 4.23% and 8.15%. The optimal solution was always found when modeling the green-up requirement using the height information. This formulation also tended to find optimal solutions faster than other formulations.},
Doi = {10.1080/02827581.2015.1089931},
ISSN = {0282-7581},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://dx.doi.org/10.1080/02827581.2015.1089931}
}
  • [DOI] Dalponte, M., Ene, L. T., Marconcini, M., Gobakken, T., & Næsset, E.. (2015). Semi-supervised svm for individual tree crown species classification. Isprs journal of photogrammetry and remote sensing, 110, 77-87.
    [Bibtex] 
    @Article{Dalponte2015,
Title = {Semi-supervised SVM for individual tree crown species classification},
Author = {Dalponte, Michele and Ene, Liviu Theodor and Marconcini, Mattia and Gobakken, Terje and Næsset, Erik},
Journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
Year = {2015},
Pages = {77-87},
Volume = {110},
Abstract = {In this paper a novel semi-supervised SVM classifier is presented, specifically developed for tree species classification at individual tree crown (ITC) level. In ITC tree species classification, all the pixels belonging to an ITC should have the same label. This assumption is used in the learning of the proposed semi-supervised SVM classifier (ITC-S3VM). This method exploits the information contained in the unlabeled ITC samples in order to improve the classification accuracy of a standard SVM. The ITC-S3VM method can be easily implemented using freely available software libraries. The datasets used in this study include hyperspectral imagery and laser scanning data acquired over two boreal forest areas characterized by the presence of three information classes (Pine, Spruce, and Broadleaves). The experimental results quantify the effectiveness of the proposed approach, which provides classification accuracies significantly higher (from 2% to above 27%) than those obtained by the standard supervised SVM and by a state-of-the-art semi-supervised SVM (S3VM). Particularly, by reducing the number of training samples (i.e. from 100% to 25%, and from 100% to 5% for the two datasets, respectively) the proposed method still exhibits results comparable to the ones of a supervised SVM trained with the full available training set. This property of the method makes it particularly suitable for practical forest inventory applications in which collection of in situ information can be very expensive both in terms of cost and time.},
Doi = {http://dx.doi.org/10.1016/j.isprsjprs.2015.10.010},
ISSN = {0924-2716},
Keywords = {Tree species classification Semi-supervised classification Hyperspectral data SVM Individual tree crowns},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.sciencedirect.com/science/article/pii/S0924271615002403}
}
  • [DOI] Gregoire, T. G., Ringvall, A. H., Ståhl, G., & Næsset, E.. (2015). Conditioning post-stratified inference following two-stage, equal-probability sampling. Environmental and ecological statistics, 23(1), 141-154.
    [Bibtex] 
    @Article{Gregoire2015,
Title = {Conditioning post-stratified inference following two-stage, equal-probability sampling},
Author = {Gregoire, Timothy G. and Ringvall, Anna H. and Ståhl, Göran and Næsset, Erik},
Journal = {Environmental and Ecological Statistics},
Year = {2015},
Number = {1},
Pages = {141-154},
Volume = {23},
Abstract = {This paper considers conditioning on the size of the samples observed in post-strata following a two-stage sampling design. We argue that it is reasonable to do so despite the complexity of the design. We derive an expression for the covariances among post-strata estimates resulting from secondary sampling units on the same primary sampling unit which reside in different post-strata. To motivate both issues we describe a two-stage LiDAR-assisted sample for aboveground biomass that was reported in Gregoire et al. (Can J For Res 41:83–95, 2011).},
Doi = {10.1007/s10651-015-0332-9},
ISSN = {1573-3009},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://dx.doi.org/10.1007/s10651-015-0332-9}
}
  • Hansen, E., Gobakken, T., Bollandsås, O., Zahabu, E., & Næsset, E.. (2015). Modeling aboveground biomass in dense tropical submontane rainforest using airborne laser scanner data. Remote sensing, 7(1), 788-807.
    [Bibtex] 
    @Article{Hansen2015b,
Title = {Modeling Aboveground Biomass in Dense Tropical Submontane Rainforest Using Airborne Laser Scanner Data},
Author = {Hansen, Endre and Gobakken, Terje and Bollandsås, Ole and Zahabu, Eliakimu and Næsset, Erik},
Journal = {Remote Sensing},
Year = {2015},
Number = {1},
Pages = {788-807},
Volume = {7},
ISSN = {2072-4292},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.mdpi.com/2072-4292/7/1/788}
}
  • Hansen, E., Gobakken, T., & Næsset, E.. (2015). Effects of pulse density on digital terrain models and canopy metrics using airborne laser scanning in a tropical rainforest. Remote sensing, 7(7), 8453-8468.
    [Bibtex] 
    @Article{Hansen2015a,
Title = {Effects of Pulse Density on Digital Terrain Models and Canopy Metrics Using Airborne Laser Scanning in a Tropical Rainforest},
Author = {Hansen, Endre and Gobakken, Terje and Næsset, Erik},
Journal = {Remote Sensing},
Year = {2015},
Number = {7},
Pages = {8453-8468},
Volume = {7},
ISSN = {2072-4292},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.mdpi.com/2072-4292/7/7/8453}
}
  • Hansen, E., Gobakken, T., Solberg, S., Kangas, A., Ene, L., Mauya, E., & Næsset, E.. (2015). Relative efficiency of als and insar for biomass estimation in a tanzanian rainforest. Remote sensing, 7(8), 9865-9885.
    [Bibtex] 
    @Article{Hansen2015c,
Title = {Relative Efficiency of ALS and InSAR for Biomass Estimation in a Tanzanian Rainforest},
Author = {Hansen, Endre and Gobakken, Terje and Solberg, Svein and Kangas, Annika and Ene, Liviu and Mauya, Ernest and Næsset, Erik},
Journal = {Remote Sensing},
Year = {2015},
Number = {8},
Pages = {9865-9885},
Volume = {7},
ISSN = {2072-4292},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.mdpi.com/2072-4292/7/8/9865}
}
  • [DOI] Kristensen, T., Næsset, E., Ohlson, M., Bolstad, P. V., & Kolka, R.. (2015). Mapping above- and below-ground carbon pools in boreal forests: the case for airborne lidar. Plos one, 10(10), e0138450.
    [Bibtex] 
    @Article{Kristensen2015,
Title = {Mapping Above- and Below-Ground Carbon Pools in Boreal Forests: The Case for Airborne Lidar},
Author = {Kristensen, Terje and Næsset, Erik and Ohlson, Mikael and Bolstad, Paul V. and Kolka, Randall},
Journal = {PLoS ONE},
Year = {2015},
Number = {10},
Pages = {e0138450},
Volume = {10},
Abstract = {
    A large and growing body of evidence has demonstrated that airborne scanning light detection and ranging (lidar) systems can be an effective tool in measuring and monitoring above-ground forest tree biomass. However, the potential of lidar as an all-round tool for assisting in assessment of carbon (C) stocks in soil and non-tree vegetation components of the forest ecosystem has been given much less attention. Here we combine the use airborne small footprint scanning lidar with fine-scale spatial C data relating to vegetation and the soil surface to describe and contrast the size and spatial distribution of C pools within and among multilayered Norway spruce (Picea abies) stands. Predictor variables from lidar derived metrics delivered precise models of above- and below-ground tree C, which comprised the largest C pool in our study stands. We also found evidence that lidar canopy data correlated well with the variation in field layer C stock, consisting mainly of ericaceous dwarf shrubs and herbaceous plants. However, lidar metrics derived directly from understory echoes did not yield significant models. Furthermore, our results indicate that the variation in both the mosses and soil organic layer C stock plots appears less influenced by differences in stand structure properties than topographical gradients. By using topographical models from lidar ground returns we were able to establish a strong correlation between lidar data and the organic layer C stock at a stand level. Increasing the topographical resolution from plot averages (~2000 m2) towards individual grid cells (1 m2) did not yield consistent models. Our study demonstrates a connection between the size and distribution of different forest C pools and models derived from airborne lidar data, providing a foundation for future research concerning the use of lidar for assessing and monitoring boreal forest C.
    },
Doi = {10.1371/journal.pone.0138450},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://dx.doi.org/10.1371%2Fjournal.pone.0138450}
}
  • [DOI] Magnussen, S., Næsset, E., & Gobakken, T.. (2015). Lidar-supported estimation of change in forest biomass with time-invariant regression models. Canadian journal of forest research, 45(11), 1514-1523.
    [Bibtex] 
    @Article{Magnussen2015,
Title = {LiDAR-supported estimation of change in forest biomass with time-invariant regression models},
Author = {Magnussen, S. and Næsset, E. and Gobakken, T.},
Journal = {Canadian Journal of Forest Research},
Year = {2015},
Number = {11},
Pages = {1514-1523},
Volume = {45},
Doi = {10.1139/cjfr-2015-0084},
ISSN = {0045-5067},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://dx.doi.org/10.1139/cjfr-2015-0084}
}
  • [DOI] Maltamo, Ørka, Bollandsås, Gobakken, & Næsset. (2015). Using pre-classification to improve the accuracy of species-specific forest attribute estimates from airborne laser scanner data and aerial images. Scandinavian journal of forest research, 30(4), 336-345.
    [Bibtex] 
    @Article{Maltamo2015,
Title = {Using pre-classification to improve the accuracy of species-specific forest attribute estimates from airborne laser scanner data and aerial images},
Author = {Maltamo and Ørka and Bollandsås and Gobakken and Næsset},
Journal = {Scandinavian Journal of Forest Research},
Year = {2015},
Number = {4},
Pages = {336-345},
Volume = {30},
Abstract = {The aim of this study was to examine whether pre-classification (stratification) of training data according to main tree species and stand development stage could improve the accuracy of species-specific forest attribute estimates compared to estimates without stratification using k-nearest neighbors (k-NN) imputations. The study included training data of 509 training plots and 80 validation plots from a conifer forest area in southeastern Norway. The results showed that stratification carried out by interpretation of aerial images did not improve the accuracy of the species-specific estimates due to stratification errors. The training data can of course be correctly stratified using field observations, but in the application phase the stratification entirely relies on auxiliary information with complete coverage over the entire area of interest which cannot be corrected. We therefore tried to improve the stratification using canopy height information from airborne laser scanning to discriminate between young and mature stands. The results showed that this approach slightly improved the accuracy of the k-NN predictions, especially for the main tree species (2.6% for spruce volume). Furthermore, if metrics from aerial images were used to discriminate between pine and spruce dominance in the mature plots, the accuracy of volume of pine was improved by 73.2% in pine-dominated stands while for spruce an adverse effect of 12.6% was observed.},
Doi = {10.1080/02827581.2014.986520},
Keywords = {LiDAR discrimination forest inventory k-NN stratified data},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.ingentaconnect.com/content/tandf/sfor/2015/00000030/00000004/art00009 http://dx.doi.org/10.1080/02827581.2014.986520}
}
  • Mauya, E., Ene, L., Bollandsas, O., Gobakken, T., Naesset, E., Malimbwi, R., & Zahabu, E.. (2015). Modelling aboveground forest biomass using airborne laser scanner data in the miombo woodlands of tanzania. Carbon balance and management, 10(1), 28.
    [Bibtex] 
    @Article{Mauya2015a,
Title = {Modelling aboveground forest biomass using airborne laser scanner data in the miombo woodlands of Tanzania},
Author = {Mauya, Ernest and Ene, Liviu and Bollandsas, Ole and Gobakken, Terje and Naesset, Erik and Malimbwi, Rogers and Zahabu, Eliakimu},
Journal = {Carbon Balance and Management},
Year = {2015},
Number = {1},
Pages = {28},
Volume = {10},
Abstract = {BACKGROUND:Airborne laser scanning (ALS) has emerged as one of the most promising remote sensing technologies for estimating aboveground biomass (AGB) in forests. Use of ALS data in area-based forest inventories relies on the development of statistical models that relate AGB and metrics derived from ALS. Such models are firstly calibrated on a sample of corresponding field- and ALS observations, and then used to predict AGB over the entire area covered by ALS data. Several statistical methods, both parametric and non-parametric, have been applied in ALS-based forest inventories, but studies that compare different methods in tropical forests in particular are few in number and less frequent than studies reported in temperate and boreal forests. We compared parametric and non-parametric methods, specifically linear mixed effects model (LMM) and k-nearest neighbor (k-NN).RESULTS:The results showed that the prediction accuracy obtained when using LMM was slightly better than when using the k-NN approach. Relative root mean square errors from the cross validation was 46.8% for the LMM and 58.1% for the k-NN. Post-stratification according to vegetation types improved the prediction accuracy of LMM more as compared to post-stratification by using land use types.CONCLUSION:Although there were differences in prediction accuracy between the two methods, their accuracies indicated that both of methods have potentials to be used for estimation of AGB using ALS data in the miombo woodlands. Future studies on effects of field plot size and the errors due to allometric models on the prediction accuracy are recommended.},
ISSN = {1750-0680},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.cbmjournal.com/content/10/1/28}
}
  • Mauya, E., Hansen, E., Gobakken, T., Bollandsas, O., Malimbwi, R., & Naesset, E.. (2015). Effects of field plot size on prediction accuracy of aboveground biomass in airborne laser scanning-assisted inventories in tropical rain forests of tanzania. Carbon balance and management, 10(1), 10.
    [Bibtex] 
    @Article{Mauya2015b,
Title = {Effects of field plot size on prediction accuracy of aboveground biomass in airborne laser scanning-assisted inventories in tropical rain forests of Tanzania},
Author = {Mauya, Ernest and Hansen, Endre and Gobakken, Terje and Bollandsas, Ole and Malimbwi, Rogers and Naesset, Erik},
Journal = {Carbon Balance and Management},
Year = {2015},
Number = {1},
Pages = {10},
Volume = {10},
Abstract = {BACKGROUND:Airborne laser scanning (ALS) has recently emerged as a promising tool to acquire auxiliary information for improving aboveground biomass (AGB) estimation in sample-based forest inventories. Under design-based and model-assisted inferential frameworks, the estimation relies on a model that relates the auxiliary ALS metrics to AGB estimated on ground plots. The size of the field plots has been identified as one source of model uncertainty because of the so-called boundary effects which increases with decreasing plot size. Recent research in tropical forests has aimed to quantify the boundary effects on model prediction accuracy, but evidence of the consequences for the final AGB estimates is lacking. In this study we analyzed the effect of field plot size on model prediction accuracy and its implication when used in a model-assisted inferential framework.RESULTS:The results showed that the prediction accuracy of the model improved as the plot size increased. The adjusted R2 increased from 0.35 to 0.74 while the relative root mean square error decreased from 63.6 to 29.2%. Indicators of boundary effects were identified and confirmed to have significant effects on the model residuals. Variance estimates of model-assisted mean AGB relative to corresponding variance estimates of pure field-based AGB, decreased with increasing plot size in the range from 200 to 3000m2. The variance ratio of field-based estimates relative to model-assisted variance ranged from 1.7 to 7.7.CONCLUSIONS:This study showed that the relative improvement in precision of AGB estimation when increasing field-plot size, was greater for an ALS-assisted inventory compared to that of a pure field-based inventory.},
ISSN = {1750-0680},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.cbmjournal.com/content/10/1/10}
}
  • [DOI] McRoberts, R. E., Næsset, E., & Gobakken, T.. (2015). Optimizing the k-nearest neighbors technique for estimating forest aboveground biomass using airborne laser scanning data. Remote sensing of environment, 163, 13-22.
    [Bibtex] 
    @Article{McRoberts2015b,
Title = {Optimizing the k-Nearest Neighbors technique for estimating forest aboveground biomass using airborne laser scanning data},
Author = {McRoberts, Ronald E. and Næsset, Erik and Gobakken, Terje},
Journal = {Remote Sensing of Environment},
Year = {2015},
Number = {0},
Pages = {13-22},
Volume = {163},
Abstract = {Nearest neighbors techniques calculate predictions as linear combinations of observations for a selected number of population units in a sample that are most similar, or nearest, in a space of auxiliary variables to the population unit requiring the prediction. Nearest neighbors techniques have been shown to be particularly effective when used with forest inventory and remotely sensed data. Recent attention has focused on developing an underlying foundation consisting of diagnostic tools, inferential extensions, and techniques for optimization. For a study area in Norway, forest inventory and airborne laser scanning data were used with the k-Nearest Neighbors technique to estimate mean aboveground biomass per unit area. Optimization entailed reduction of the dimension of feature space, deletion of influential outliers, and selection of optimal weights for the weighted Euclidean distance metric. These optimization steps increased the proportion of variability explained in the reference set by as much as 20%, reduced confidence interval widths by as much as 35%, and produced standard errors that were as small as 3% of the estimate of the mean.},
Doi = {http://dx.doi.org/10.1016/j.rse.2015.02.026},
ISSN = {0034-4257},
Keywords = {Distance metric Precision},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.sciencedirect.com/science/article/pii/S0034425715000851}
}
  • [DOI] McRoberts, R. E., Næsset, E., Gobakken, T., & Bollandsås, O. M.. (2015). Indirect and direct estimation of forest biomass change using forest inventory and airborne laser scanning data. Remote sensing of environment, 164, 36-42.
    [Bibtex] 
    @Article{McRoberts2015a,
Title = {Indirect and direct estimation of forest biomass change using forest inventory and airborne laser scanning data},
Author = {McRoberts, Ronald E. and Næsset, Erik and Gobakken, Terje and Bollandsås, Ole Martin},
Journal = {Remote Sensing of Environment},
Year = {2015},
Number = {0},
Pages = {36-42},
Volume = {164},
Abstract = {Remote sensing-based change estimation typically takes two forms. Indirect estimation entails constructing models of the relationship between the response variable of interest and remotely sensed auxiliary variables at two times and then estimating change as the differences in the model predictions for the two times. Direct estimation entails constructing models of change directly using observations of change in the response and the remotely sensed auxiliary variables for two dates. The direct method is generally preferred, although few statistically rigorous comparisons have been reported. This study focused on statistically rigorous, indirect and direct estimation of biomass change using forest inventory and airborne laser scanning (ALS) data for a Norwegian study area. Three sets of statistical estimators were used: simple random sampling estimators, indirect model-assisted regression estimators, and direct model-assisted regression estimators. In addition, three modeling approaches were used to support the direct model-assisted estimators. The study produced four relevant findings. First, use of the ALS auxiliary information greatly increased the precision of change estimates, regardless of whether indirect or direct methods were used. Second, contrary to previously reported results, the indirect method produced greater precision for the study area mean than the traditional direct method. Third, the direct method that used models whose predictor variables were selected in pairs but with separate coefficient estimates and models whose predictor variables were selected without regard to pairing produced the greatest precision. Finally, greater emphasis should be placed on the effects of model extrapolations for values of independent variables in the population that are beyond the range of the variables in the sample.},
Doi = {http://dx.doi.org/10.1016/j.rse.2015.02.018},
ISSN = {0034-4257},
Keywords = {Simple random sampling estimator Stratified estimator Model-assisted regression estimator},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.sciencedirect.com/science/article/pii/S0034425715000772}
}
  • Næsset, E.. (2015). Vertical height errors in digital terrain models derived from airborne laser scanner data in a boreal-alpine ecotone in norway. Remote sensing, 7(4), 4702.
    [Bibtex] 
    @Article{Naesset2015a,
Title = {Vertical Height Errors in Digital Terrain Models Derived from Airborne Laser Scanner Data in a Boreal-Alpine Ecotone in Norway},
Author = {Næsset, Erik},
Journal = {Remote Sensing},
Year = {2015},
Number = {4},
Pages = {4702},
Volume = {7},
ISSN = {2072-4292},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.mdpi.com/2072-4292/7/4/4702}
}
  • [DOI] Næsset, E., Bollandsås, O. M., Gobakken, T., Solberg, S., & McRoberts, R. E.. (2015). The effects of field plot size on model-assisted estimation of aboveground biomass change using multitemporal interferometric sar and airborne laser scanning data. Remote sensing of environment, 168, 252-264.
    [Bibtex] 
    @Article{Naesset2015,
Title = {The effects of field plot size on model-assisted estimation of aboveground biomass change using multitemporal interferometric SAR and airborne laser scanning data},
Author = {Næsset, Erik and Bollandsås, Ole Martin and Gobakken, Terje and Solberg, Svein and McRoberts, Ronald E.},
Journal = {Remote Sensing of Environment},
Year = {2015},
Pages = {252-264},
Volume = {168},
Abstract = {Remotely sensed data from airborne laser scanning (ALS) and interferometric synthetic aperture radar (InSAR) can greatly improve the precision of estimates of forest resource parameters such as mean biomass and biomass change per unit area. Field plots are typically used to construct models that relate the variable of interest to explanatory variables derived from the remotely sensed data. The models may then be used in combination with the field plots to provide estimates for a geographical area of interest with corresponding estimates of precision using model-assisted estimators. Previous studies have shown that field plot sizes found suitable for pure field surveys may be sub-optimal for use in combination with remotely sensed data. Plot boundary effects, co-registration problems, and misalignment problems favor larger plots because the relative impact of these effects on the models of relationships may decline by increasing plot size. In a case study in a small boreal forest area in southeastern Norway (852.6 ha) a probability sample of 145 field plots was measured twice over an 11 year period (1998/1999 and 2010). For each plot, field measurements were recorded for two plot sizes (200 m2 and 300/400 m2). Corresponding multitemporal ALS (1999 and 2010) and InSAR data (2000 and 2011) were also available. Biomass for each of the two measurement dates as well as biomass change were modeled for all plot sizes separately using explanatory variables from the ALS and InSAR data, respectively. Biomass change was estimated using model-assisted estimators. Separate estimates were obtained for different methods for estimation of change, like the indirect method (difference between predictions of biomass for each of the two measurement dates) and the direct method (direct prediction of change). Relative efficiency (RE) was calculated by dividing the variance obtained for a pure field-based change estimate by the variance of a corresponding estimate using the model-assisted approach. For ALS, the RE values ranged between 7.5 and 15.0, indicating that approximately 7.5–15.0 as many field plots would be required for a pure field-based estimate to provide the same precision as an ALS-assisted estimate. For InSAR, RE ranged between 1.8 and 2.5. The direct estimation method showed greater REs than the indirect method for both remote sensing technologies. There was clearly a trend of improved RE of the model-assisted estimates by increasing plot size. For ALS and the direct estimation method RE increased from 9.8 for 200 m2 plots to 15.0 for 400 m2 plots. Similar trends of increasing RE with plot size were observed for InSAR. ALS showed on average 3.2–6.0 times greater RE values than InSAR. Because remote sensing can contribute to improved precision of estimates, sample plot size is a prominent design issue in future sample surveys which should be considered with due attention to the great benefits that can be achieved when using remote sensing if the plot size reflects the specific challenges arising from use of remote sensing in the estimation. That is especially the case in the tropics where field resources may be scarce and inaccessibility and poor infrastructure hamper field work.},
Doi = {http://dx.doi.org/10.1016/j.rse.2015.07.002},
ISSN = {0034-4257},
Keywords = {Biomass estimation Biomass change estimation ALS Tandem-X InSAR Model-assisted estimation Precision Relative efficiency Plot size Plot boundary effect},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.sciencedirect.com/science/article/pii/S0034425715300596}
}
  • [DOI] Njana, M. A., Bollandsås, O. M., Eid, T., Zahabu, E., & Malimbwi, R. E.. (2015). Above- and belowground tree biomass models for three mangrove species in tanzania: a nonlinear mixed effects modelling approach. Annals of forest science.
    [Bibtex] 
    @Article{Njana2015,
Title = {Above- and belowground tree biomass models for three mangrove species in Tanzania: a nonlinear mixed effects modelling approach},
Author = {Njana, Marco Andrew and Bollandsås, Ole Martin and Eid, Tron and Zahabu, Eliakimu and Malimbwi, Rogers Ernest},
Journal = {Annals of forest science},
Year = {2015},
Doi = {10.1007/s13595-015-0524-3},
Owner = {hanso},
Timestamp = {2016.03.07},
Url = {http://link.springer.com/article/10.1007/s13595-015-0524-3}
}
  • [DOI] Puliti, S., Ørka, H., Gobakken, T., & Næsset, E.. (2015). Inventory of small forest areas using an unmanned aerial system. Remote sensing, 7(8), 9632-9654.
    [Bibtex] 
    @Article{Puliti2015,
Title = {Inventory of Small Forest Areas Using an Unmanned Aerial System},
Author = {Puliti, Stefano and Ørka, Hans and Gobakken, Terje and Næsset, Erik},
Journal = {Remote Sensing},
Year = {2015},
Number = {8},
Pages = {9632-9654},
Volume = {7},
Doi = {http://dx.doi.org/10.3390/rs70809632},
ISSN = {2072-4292},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.mdpi.com/2072-4292/7/8/9632}
}
  • Solberg, S., Gizachew, B., Naesset, E., Gobakken, T., Bollandsas, O., Mauya, E., Olsson, H., Malimbwi, R., & Zahabu, E.. (2015). Monitoring forest carbon in a tanzanian woodland using interferometric sar: a novel methodology for redd+. Carbon balance and management, 10(1), 14.
    [Bibtex] 
    @Article{Solberg2015,
Title = {Monitoring forest carbon in a Tanzanian woodland using interferometric SAR: a novel methodology for REDD+},
Author = {Solberg, Svein and Gizachew, Belachew and Naesset, Erik and Gobakken, Terje and Bollandsas, Ole and Mauya, Ernest and Olsson, Hakan and Malimbwi, Rogers and Zahabu, Eliakimu},
Journal = {Carbon Balance and Management},
Year = {2015},
Number = {1},
Pages = {14},
Volume = {10},
Abstract = {BACKGROUND:REDD+ implementation requires establishment of a system for measuring, reporting and verification (MRV) of forest carbon changes. A challenge for MRV is the lack of satellite based methods that can track not only deforestation, but also degradation and forest growth, as well as a lack of historical data that can serve as a basis for a reference emission level. Working in a miombo woodland in Tanzania, we here aim at demonstrating a novel 3D satellite approach based on interferometric processing of radar imagery (InSAR).RESULTS:Forest carbon changes are derived from changes in the forest canopy height obtained from InSAR, i.e. decreases represent carbon loss from logging and increases represent carbon sequestration through forest growth. We fitted a model of above-ground biomass (AGB) against InSAR height, and used this to convert height changes to biomass and carbon changes. The relationship between AGB and InSAR height was weak, as the individual plots were widely scattered around the model fit. However, we consider the approach to be unique and feasible for large-scale MRV efforts in REDD+ because the low accuracy was attributable partly to small plots and other limitations in the data set, and partly to a random pixel-to-pixel variation in trunk forms. Further processing of the InSAR data provides data on the categories of forest change.The combination of InSAR data from the Shuttle RADAR Topography Mission (SRTM) and the TanDEM-X satellite mission provided both historic baseline of change for the period 2000-2011, as well as annual change 2011-2012.CONCLUSIONS:A 3D data set from InSAR is a promising tool for MRV in REDD+. The temporal changes seen by InSAR data corresponded well with, but largely supplemented, the changes derived from Landsat data.},
ISSN = {1750-0680},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.cbmjournal.com/content/10/1/14}
}
  • Tarimo, B., Dick, O., Gobakken, T., & Totland, O.. (2015). Spatial distribution of temporal dynamics in anthropogenic fires in miombo savanna woodlands of tanzania. Carbon balance and management, 10(1), 18.
    [Bibtex] 
    @Article{Tarimo2015,
Title = {Spatial distribution of temporal dynamics in anthropogenic fires in miombo savanna woodlands of Tanzania},
Author = {Tarimo, Beatrice and Dick, Oystein and Gobakken, Terje and Totland, Orjan},
Journal = {Carbon Balance and Management},
Year = {2015},
Number = {1},
Pages = {18},
Volume = {10},
Abstract = {BACKGROUND:Anthropogenic uses of fire play a key role in regulating fire regimes in African savannas. These fires contribute the highest proportion of the globally burned area, substantial biomass burning emissions and threaten maintenance and enhancement of carbon stocks. An understanding of fire regimes at local scales is required for the estimation and prediction of the contributionof these fires to the global carbon cycle and for fire management. We assessed the spatio-temporal distribution of fires in miombo woodlands of Tanzania, utilizing the MODIS active fire product and Landsat satellite images for the past ~40years.RESULTS:Our results show that up to 50.6% of the woodland area is affected by fire each year. An early and a late dry season peak in wetter and drier miombo, respectively, characterize the annual fire season. Wetter miombo areas have higher fire activity within a shorter annual fire season and have shorter return intervals. The fire regime is characterized by small-sized fires, with a higher ratio of small than large burned areas in the frequency-size distribution (beta=2.16+/-0.04). Large-sized fires are rare, and occur more frequently in drier than in wetter miombo. Both fire prevalence and burned extents have decreased in the past decade. At a large scale, more than half of the woodland area has less than 2years of fire return intervals, which prevent the occurrence of large intense fires.CONCLUSION:The sizes of fires, season of burning and spatial extent of occurrence are generally consistent across time, at the scale of the current analysis. Where traditional use of fire is restricted, a reassessment of fire management strategies may be required, if sustainability of tree cover is a priority. In such cases, there is a need to combine traditional and contemporary fire management practices.},
ISSN = {1750-0680},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.cbmjournal.com/content/10/1/18}
}
  • [DOI] Økseter, Bollandsås, Gobakken, & Næsset. (2015). Modeling and predicting aboveground biomass change in young forest using multi-temporal airborne laser scanner data. Scandinavian journal of forest research, 30(5), 458-469.
    [Bibtex] 
    @Article{Oekseter2015a,
Title = {Modeling and predicting aboveground biomass change in young forest using multi-temporal airborne laser scanner data},
Author = {Økseter and Bollandsås and Gobakken and Næsset},
Journal = {Scandinavian Journal of Forest Research},
Year = {2015},
Number = {5},
Pages = {458-469},
Volume = {30},
Abstract = {The aim of this study was to explore the ability of estimating change in total aboveground biomass (AGB) in young forests using multi-temporal airborne laser scanner data. A field data-set covering 11 growth seasons of 39 circular plots of size 200 m2 from young forest in south-eastern Norway was used in the analyses. Different approaches for prediction of the AGB change were tested. One approach was based on modeling AGB for each point in time and predicting change as the difference between separate AGB predictions. We also tested two approaches based on modeling and predicting change directly, and two approaches where growth/reduction rates were modeled and used in prediction. The approach where change was predicted as a difference between biomass predictions seemed to yield the best results (root mean square error [RMSE] 14.8%). The other approaches yielded results that were similar in terms of RMSE, except for the approach where AGB change was predicted using a growth rate. The results indicate that prediction of change as a difference between AGB predictions works satisfactory for a wide range of forest conditions, but that the direct approaches can perform better in some cases.},
Doi = {10.1080/02827581.2015.1024733},
Keywords = {Norway airborne laser scanning biomass change carbon stock forest growth multi-temporal data},
Owner = {hanso},
Timestamp = {2016.03.01},
Type = {Journal Article},
Url = {http://www.ingentaconnect.com/content/tandf/sfor/2015/00000030/00000005/art00009 http://dx.doi.org/10.1080/02827581.2015.1024733}
}

2014

  • [DOI] Bergseng, E., Ørka, H. O., Næsset, E., & Gobakken, T.. (2014). Assessing forest inventory information obtained from different inventory approaches and remote sensing data sources. Annals of forest science, 1-13.
    [Bibtex] 
    @Article{Bergseng2014,
Title = {Assessing forest inventory information obtained from different inventory approaches and remote sensing data sources},
Author = {Bergseng, Even and Ørka, Hans Ole and Næsset, Erik and Gobakken, Terje},
Journal = {Annals of Forest Science},
Year = {2014},
Pages = {1-13},
Doi = {10.1007/s13595-014-0389-x},
ISSN = {1286-4560},
Keywords = {Aerial imagery Airborne laser scanning Forest management inventory Hyperspectral Multispectral Value of information},
Owner = {hanso},
Timestamp = {2014.10.15},
Type = {Journal Article},
Url = {http://dx.doi.org/10.1007/s13595-014-0389-x}
}
  • Dalponte, M., Ene, L. T., Ørka, H. O., Gobakken, T., & Næsset, E.. (2014). Unsupervised selection of training samples for tree species classification using hyperspectral data. Selected topics in applied earth observations and remote sensing, ieee journal of, 7(8), 3560-3569.
    [Bibtex] 
    @Article{Dalponte2014,
Title = {Unsupervised Selection of Training Samples for Tree Species Classification Using Hyperspectral Data},
Author = {Dalponte, M. and Ene, L. T. and Ørka, H. O. and Gobakken, T. and Næsset, E.},
Journal = {Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of},
Year = {2014},
Number = {8},
Pages = {3560-3569},
Volume = {7},
Keywords = {Accuracy Hyperspectral imaging Measurement Support vector machines Training Vegetation Classification forestry hyperspectral data training samples unsupervised selection},
Owner = {hanso},
Timestamp = {2014.10.14}
}
  • Dalponte, M., Ørka, H. O., Ene, L. T., Gobakken, T., & Næsset, E.. (2014). Tree crown delineation and tree species classification in boreal forests using hyperspectral and als data. Remote sensing of environment, 140, 306-317.
    [Bibtex] 
    @Article{Dalponte2014a,
Title = {Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data},
Author = {Dalponte, Michele and Ørka, Hans Ole and Ene, Liviu Theodor and Gobakken, Terje and Næsset, Erik},
Journal = {Remote Sensing of Environment},
Year = {2014},
Number = {0},
Pages = {306-317},
Volume = {140},
Abstract = {Tree species classification accuracy at the individual tree crown (ITC) level depends on many factors, among which in this paper we analyzed: i) the remote sensing data used for the ITC delineation process carried out prior to the classification, and ii) the pixels considered inside each ITC during the classification process. These two factors were analyzed on the ITC level classification accuracy of boreal tree species (Pine, Spruce and Broadleaves), considering two remote sensing data types: hyperspectral and airborne laser scanning (ALS). ITCs were delineated automatically on ALS and on hyperspectral data. A manual ITC delineation was used as reference in the analysis. The pixel level classification was performed on the hyperspectral bands using a non-linear support vector machine. The classification at ITC level was obtained by applying a majority voting rule to the classified pixels confined by each ITC. The results showed that ITCs automatically delineated from hyperspectral data were usually smaller than those from ALS, and the tree detection rate for hyperspectral data was much lower compared to ALS data (28.4 versus 48.5%). Regarding the classification results, using only manually delineated ITCs a kappa accuracy of 0.89 was obtained, while using only automatically delineated ITCs from hyperspectral or ALS data reduced the kappa values to 0.79 and 0.76, respectively. Slightly different results were achieved using semi-automatic approaches based on both manual and automatically delineated ITC (0.81 and 0.74, respectively). A selection of only certain pixels inside each ITC improved the classification accuracy from 1 to 7 percentage points. A selection based on the spectral values of the pixels was found more influential than the one based on the ALS-derived canopy height model. The best results were obtained after a selection based on the spectral values in the bands in the blue region of the spectrum using either the Otsu method or an ad-hoc percentile-based thresholding method.},
Keywords = {ALS Hyperspectral Tree species classification Individual tree crowns Delineation Forest inventory Post classification},
Owner = {hanso},
Timestamp = {2014.10.14}
}
  • Eldegard, K., Dirksen, J. W., Ørka, H. O., Halvorsen, R., Næsset, E., Gobakken, T., & Ohlson, M.. (2014). Modelling bird richness and bird species presence in a boreal forest reserve using airborne laser-scanning and aerial images. Bird study, 61(2), 204-219.
    [Bibtex] 
    @Article{Eldegard2014,
Title = {Modelling bird richness and bird species presence in a boreal forest reserve using airborne laser-scanning and aerial images},
Author = {Eldegard, Katrine and Dirksen, John Wirkola and Ørka, Hans Ole and Halvorsen, Rune and Næsset, Erik and Gobakken, Terje and Ohlson, Mikael},
Journal = {Bird Study},
Year = {2014},
Number = {2},
Pages = {204-219},
Volume = {61},
Abstract = {Capsule Variables obtained from airborne laser-scanning (ALS) enabled slight or fair predictions of bird presence, and including multispectral data further improved predictions slightly.Aims To assess the usefulness of ALS as a tool for predicting species richness and single-species presence, and to investigate if including information from multispectral aerial images further improved predictability of bird presence.Methods Bird presence data were sampled in a Norwegian boreal forest reserve. Prediction models were developed for species richness and presence of the eight most abundant species by the use of two different modelling approaches: generalized linear models and the machine learning method random forest. Predictor variables were descriptors of three-dimensional forest structure obtained by ALS, and descriptors of tree species composition obtained from multispectral aerial images.Results Cross-validation of the prediction models indicated overall slight or fair predictive capability. Best predictions were obtained for Goldcrest, Wren, and Willow Warbler. Inclusion of spectral variables derived from the aerial imagery slightly improved the predictive performance of several models, most notably for Willow Warbler.Conclusion We suggest that predictability of species richness and presence of single bird species can be improved by better matching of the scale of recording for birds and the predictor variables obtained by remote sensing.},
Owner = {hanso},
Timestamp = {2014.10.14}
}
  • Gobakken, T., Bollandsås, O. M., & Næsset, E.. (2014). Comparing biophysical forest characteristics estimated from photogrammetric matching of aerial images and airborne laser scanning data. Scandinavian journal of forest research, 1-14.
    [Bibtex] 
    @Article{Gobakken2014,
Title = {Comparing biophysical forest characteristics estimated from photogrammetric matching of aerial images and airborne laser scanning data},
Author = {Gobakken, Terje and Bollandsås, Ole Martin and Næsset, Erik},
Journal = {Scandinavian Journal of Forest Research},
Year = {2014},
Pages = {1-14},
Abstract = {Recent development in aerial digital cameras and software facilitate the photogrammetric point cloud as a new data source in forest management planning. A total of 151 field training plots were distributed systematically within three predefined strata in a 852.6 ha study area located in the boreal forest in southeastern Norway. Stratum-specific regression models were fitted for six studied biophysical forest characteristics. The explanatory variables were various canopy height and canopy density metrics derived by means of photogrammetric matching of aerial images and small-footprint laser scanning. The ground sampling distance was 17 cm for the images and the airborne laser scanning (ALS) pulse density was 7.4 points m?2. Resampled images were assessed to mimic acquisitions at higher flying altitudes. The digital terrain model derived from the ALS data was used to represent the ground surface. The results were evaluated using 63 independent test stands. When estimating height in young forest and mature forest on poor sites, the root mean square error (RMSE) values were slightly better using data from image matching compared to ALS. However, for all other combinations of biophysical forest characteristics and strata, better results were obtained using ALS data. In general, the best results were found using the highest image resolution.},
Owner = {hanso},
Timestamp = {2014.10.14}
}
  • Hauglin, M., Gobakken, T., Astrup, R., Ene, L., & Næsset, E.. (2014). Estimating single-tree crown biomass of norway spruce by airborne laser scanning: a comparison of methods with and without the use of terrestrial laser scanning to obtain the ground reference data. Forests, 5(3), 384-403.
    [Bibtex] 
    @Article{Hauglin2014,
Title = {Estimating Single-Tree Crown Biomass of Norway Spruce by Airborne Laser Scanning: A Comparison of Methods with and without the Use of Terrestrial Laser Scanning to Obtain the Ground Reference Data},
Author = {Hauglin, Marius and Gobakken, Terje and Astrup, Rasmus and Ene, Liviu and Næsset, Erik},
Journal = {Forests},
Year = {2014},
Number = {3},
Pages = {384-403},
Volume = {5},
Owner = {hanso},
Timestamp = {2014.10.14}
}
  • Hauglin, M., Lien, V., Næsset, E., & Gobakken, T.. (2014). Geo-referencing forest field plots by co-registration of terrestrial and airborne laser scanning data. International journal of remote sensing, 35(9), 3135-3149.
    [Bibtex] 
    @Article{Hauglin2014a,
Title = {Geo-referencing forest field plots by co-registration of terrestrial and airborne laser scanning data},
Author = {Hauglin, Marius and Lien, Vegard and Næsset, Erik and Gobakken, Terje},
Journal = {International Journal of Remote Sensing},
Year = {2014},
Number = {9},
Pages = {3135-3149},
Volume = {35},
Abstract = {Remote sensing plays an important role within the field of forest inventory. Airborne laser scanning (ALS) has become an effective tool for acquiring forest inventory data. In most ALS-based forest inventories, accurately positioned field plots are used in the process of relating ALS data to field-observed biophysical properties. The geo-referencing of these field plots is typically carried out by means of differential global navigation satellite systems (dGNSS), and often relies on logging times of 15?20 min to ensure adequate accuracy under different forest conditions. Terrestrial laser scanning (TLS) has been proposed as a possible tool for collection of field data in forest inventories and can facilitate rapid acquisition of these data. In the present study, a novel method for co-registration of TLS and ALS data by posterior analysis of remote-sensing data ? rather than using dGNSS ? was proposed and then tested on 71 plots in a boreal forest. The method relies on an initial position obtained with a recreational-grade GPS receiver, in addition to analysis of the ALS and TLS data. First, individual tree positions were derived from the remote-sensing data. A search algorithm was then used to find the best match for the TLS-derived trees among the ALS-derived trees within a search area, defined relative to the initial position. The accuracy of co-registration was assessed by comparison with an accurately measured reference position. With a search radius of 25 m and using low-density ALS data (0.7 points m?2), 82% and 51% of the TLS scans were co-registered with positional errors within 1 m and 0.5 m, respectively. By using ALS data of medium density (7.5 points m?2), 87% and 78% of the scans were co-registered with errors within 1 m and 0.5 m of the reference position, respectively. These results are promising and the method can facilitate rapid acquisition and geo-referencing of field data. Robust methods to identify and handle erroneous matches are, however, required before it is suitable for operational use.},
Owner = {hanso},
Timestamp = {2014.10.14}
}
  • Lone, K., van Beest, F. M., Mysterud, A., Gobakken, T., Milner, J. M., Ruud, H. P., & Loe, L. E.. (2014). Improving broad scale forage mapping and habitat selection analyses with airborne laser scanning; the case of moose.. Ecosphere, in press.
    [Bibtex] 
    @Article{Lone2014,
Title = {Improving broad scale forage mapping and habitat selection analyses with airborne laser scanning; the case of moose.},
Author = {Lone, Karen and van Beest, F.M. and Mysterud, A. and Gobakken, T. and Milner, J.M. and Ruud, H.P. and Loe, L.E.},
Journal = {Ecosphere},
Year = {2014},
Volume = {in press},
Owner = {hanso},
Timestamp = {2014.10.14}
}
  • [DOI] Lone, K., Loe, L. E., Gobakken, T., Linnell, J. D. C., Odden, J., Remmen, J., & Mysterud, A.. (2014). Living and dying in a multi-predator landscape of fear: roe deer are squeezed by contrasting pattern of predation risk imposed by lynx and humans. Oikos, 123(6), 641–651.
    [Bibtex] 
    @Article{Lone2014a,
Title = {Living and dying in a multi-predator landscape of fear: roe deer are squeezed by contrasting pattern of predation risk imposed by lynx and humans},
Author = {Lone, Karen and Loe, Leif Egil and Gobakken, Terje and Linnell, John D. C. and Odden, John and Remmen, Jørgen and Mysterud, Atle},
Journal = {Oikos},
Year = {2014},
Number = {6},
Pages = {641--651},
Volume = {123},
Abstract = {The theory of predation risk effects predicts behavioral responses in prey when risk of predation is not homogenous in space and time. Prey species are often faced with a tradeoff between food and safety in situations where food availability and predation risk peak in the same habitat type. Determining the optimal strategy becomes more complex if predators with different hunting mode create contrasting landscapes of risk, but this has rarely been documented in vertebrates. Roe deer in southeastern Norway face predation risk from lynx, as well as hunting by humans. These two predators differ greatly in their hunting methods. The predation risk from lynx, an efficient stalk-and-ambush predator is expected to be higher in areas with dense understory vegetation, while predation risk from human hunters is expected to be higher where visual sight lines are longer. Based on field observations and airborne LiDAR data from 71 lynx predation sites, 53 human hunting sites, 132 locations from 15 GPS-marked roe deer, and 36 roe deer pellet locations from a regional survey, we investigated how predation risk was related to terrain attributes and vegetation classes/structure. As predicted, we found that increasing cover resulted in a contrasting lower predation risk from humans and higher predation risk from lynx. Greater terrain ruggedness increased the predation risk from both predators. Hence, multiple predators may create areas of contrasting risk as well as double risk in the same landscape. Our study highlights the complexity of predator���prey relationship in a multiple predator setting.SynthesisIn this study of risk effects in a multi-predator context, LiDAR data were used to quantify cover in the habitat and relate it to vulnerability to predation in a boreal forest. We found that lynx and human hunters superimpose generally contrasting landscapes of fear on a common prey species, but also identified double-risk zones. Since the benefit of anti-predator responses depends on the combined risk from all predators, it is necessary to consider complete predator assemblages to understand the potential for and occurrence of risk effects across study systems.},
Doi = {10.1111/j.1600-0706.2013.00938.x},
ISSN = {1600-0706},
Owner = {hanso},
Publisher = {Blackwell Publishing Ltd},
Timestamp = {2014.06.30},
Url = {http://dx.doi.org/10.1111/j.1600-0706.2013.00938.x}
}
  • Magnussen, S., Næsset, E., & Gobakken, T.. (2014). An estimator of variance for two-stage ratio regression estimators. Forest science, 60(4), 663-676.
    [Bibtex] 
    @Article{Magnussen2014,
Title = {An Estimator of Variance for Two-Stage Ratio Regression Estimators},
Author = {Magnussen, Steen and Næsset, Erik and Gobakken, Terje},
Journal = {Forest Science},
Year = {2014},
Number = {4},
Pages = {663-676},
Volume = {60},
Abstract = {Design-based estimators for two-stage simple random sampling with regression can have a lack of precision (efficiency) when the primary sampling units (PSUs) vary in size, and PSU totals are approximately proportional to the size of a PSU. Precision and efficiency may deteriorate further for domain-specific estimators when PSUs contain elements from different domains. Design model-unbiased ratio-to-size estimators have been proposed as more efficient. This study introduces a variance estimator for a design model-unbiased ratio estimator. The estimator of variance is derived from a single-stage estimator of a variance of a ratio under two assumptions: the target variable (y) is equal to the sum of a model prediction and two error terms capturing residual errors and model estimation errors; and the existence of an unbiased estimator of model parameters. Extensive simulations confirmed the negative effects of unequal PSU sizes on the precision of the model-assisted estimators of variance and a superior performance of the proposed estimator of variance. These results were confirmed in the analysis of a regional two-stage survey of forest biomass. The proposed variance estimator was generally more efficient than an existing alternative and more stable across a large suite of design settings.},
Keywords = {auxiliary variables forest inventory large-scale survey ratio-to-size estimator sampling design},
Owner = {hanso},
Timestamp = {2014.10.14}
}
  • McRoberts, R. E., Næsset, E., & Gobakken, T.. (2014). Estimation for inaccessible and non-sampled forest areas using model-based inference and remotely sensed auxiliary information. Remote sensing of environment, 154, 226-233.
    [Bibtex] 
    @Article{McRoberts2014,
Title = {Estimation for inaccessible and non-sampled forest areas using model-based inference and remotely sensed auxiliary information},
Author = {McRoberts, Ronald E. and Næsset, Erik and Gobakken, Terje},
Journal = {Remote Sensing of Environment},
Year = {2014},
Number = {0},
Pages = {226-233},
Volume = {154},
Abstract = {For remote and inaccessible forest regions, lack of sufficient or possibly any sample data inhibits estimation and construction of confidence intervals for population parameters using familiar probability- or design-based inferential methods. Although maps based on remotely sensed data may provide information on the distribution of resources, map-based estimates are subject to classification and prediction error, and map accuracy measures do not directly inform the uncertainty of the estimates. Model-based inference does not require probability samples and when used with synthetic estimation can circumvent small or no-sample difficulties associated with probability-based inference. The study focused on estimating proportion forest area using Landsat data for a study area in Minnesota, USA, and aboveground biomass using airborne laser scanning data for a study area in Hedmark County, Norway. For both study areas, model-based inference was used to estimate the components necessary for constructing confidence intervals for population means for non-sampled areas. The estimates were compared to simple random sampling, model-assisted, and model-based estimates that would have been obtained if the areas had been sampled. All estimates were within two simple random sampling standard errors of each other, thereby illustrating the utility of model-based inference for non-sampled areas.},
Keywords = {Landsat Lidar Precision},
Owner = {hanso},
Timestamp = {2014.10.14}
}
  • Persello, C., Boularias, A., Dalponte, M., Gobakken, T., Næsset, E., & Scholkopf, B.. (2014). Cost-sensitive active learning with lookahead: optimizing field surveys for remote sensing data classification. Geoscience and remote sensing, ieee transactions on, PP(99), 1-13.
    [Bibtex] 
    @Article{Persello2014,
Title = {Cost-Sensitive Active Learning With Lookahead: Optimizing Field Surveys for Remote Sensing Data Classification},
Author = {Persello, C. and Boularias, A. and Dalponte, M. and Gobakken, T. and Næsset, E. and Scholkopf, B.},
Journal = {Geoscience and Remote Sensing, IEEE Transactions on},
Year = {2014},
Number = {99},
Pages = {1-13},
Volume = {PP},
Abstract = {Active learning typically aims at minimizing the number of labeled samples to be included in the training set to reach a certain level of classification accuracy. Standard methods do not usually take into account the real annotation procedures and implicitly assume that all samples require the same effort to be labeled. Here, we consider the case where the cost associated with the annotation of a given sample depends on the previously labeled samples. In general, this is the case when annotating a queried sample is an action that changes the state of a dynamic system, and the cost is a function of the state of the system. In order to minimize the total annotation cost, the active sample selection problem is addressed in the framework of a Markov decision process, which allows one to plan the next labeling action on the basis of an expected long-term cumulative reward. This framework allows us to address the problem of optimizing the collection of labeled samples by field surveys for the classification of remote sensing data. The proposed method is applied to the ground sample collection for tree species classification using airborne hyperspectral images. Experiments carried out in the context of a real case study on forest inventory show the effectiveness of the proposed method.},
Keywords = {Accuracy Hyperspectral imaging Labeling Support vector machines Training Uncertainty Active learning (AL) Markov decision process (MDP) field surveys forest inventories hyperspectral data image classification support vector machine (SVM)},
Owner = {hanso},
Timestamp = {2014.10.14}
}
  • Solberg, S., Næsset, E., Gobakken, T., & Bollandsås, O.. (2014). Forest biomass change estimated from height change in interferometric sar height models. Carbon balance and management, 9(1), 5.
    [Bibtex] 
    @Article{Solberg2014,
Title = {Forest biomass change estimated from height change in interferometric SAR height models},
Author = {Solberg, Svein and Næsset, Erik and Gobakken, Terje and Bollandsås, Ole-Martin},
Journal = {Carbon Balance and Management},
Year = {2014},
Number = {1},
Pages = {5},
Volume = {9},
Abstract = {BACKGROUND:There is a need for new satellite remote sensing methods for monitoring tropical forest carbon stocks. Advanced RADAR instruments on board satellites can contribute with novel methods. RADARs can see through clouds, and furthermore, by applying stereo RADAR imaging we can measure forest height and its changes. Such height changes are related to carbon stock changes in the biomass. We here apply data from the current Tandem-X satellite mission, where two RADAR equipped satellites go in close formation providing stereo imaging. We combine that with similar data acquired with one of the space shuttles in the year 2000, i.e. the so-called SRTM mission. We derive height information from a RADAR image pair using a method called interferometry.RESULTS:We demonstrate an approach for REDD based on interferometry data from a boreal forest in Norway. We fitted a model to the data where above-ground biomass in the forest increases with 15t/ha for every m increase of the height of the RADAR echo. When the RADAR echo is at the ground the estimated biomass is zero, and when it is 20m above the ground the estimated above-ground biomass is 300t/ha. Using this model we obtained fairly accurate estimates of biomass changes from 2000 to 2011. For 200m2 plots we obtained an accuracy of 65t/ha, which corresponds to 50% of the mean above-ground biomass value. We also demonstrate that this method can be applied without having accurate terrain heights and without having former in-situ biomass data, both of which are generally lacking in tropical countries. The gain in accuracy was marginal when we included such data in the estimation. Finally, we demonstrate that logging and other biomass changes can be accurately mapped. A biomass change map based on interferometry corresponded well to a very accurate map derived from repeated scanning with airborne laser.CONCLUSIONS:Satellite based, stereo imaging with advanced RADAR instruments appears to be a promising method for REDD. Interferometric processing of the RADAR data provides maps of forest height changes from which we can estimate temporal changes in biomass and carbon.},
Owner = {hanso},
Timestamp = {2014.10.14}
}
  • Stumberg, N., Bollandsås, O. M., Gobakken, T., & Næsset, E.. (2014). Automatic detection of small single trees in the forest-tundra ecotone using airborne laser scanning. Remote sensing, In press.
    [Bibtex] 
    @Article{Stumberg2014,
Title = {Automatic Detection of Small Single Trees in the Forest-Tundra Ecotone Using Airborne Laser Scanning},
Author = {Stumberg, Nadja and Bollandsås, Ole Martin and Gobakken, Terje and Næsset, Erik},
Journal = {Remote Sensing},
Year = {2014},
Volume = {In press},
Owner = {hanso},
Timestamp = {2014.10.14}
}
  • Stumberg, N., Hauglin, M., Bollandsås, O. M., Gobakken, T., & Erik, N.. (2014). Improving classification of airborne laser scanning echoes inã‚â the forest-tundra ecotone using geostatistical andã‚â statistical measures. Remote sensing, 6(5), 4582-4599.
    [Bibtex] 
    @Article{Stumberg2014a,
Title = {Improving Classification of Airborne Laser Scanning Echoes in the Forest-Tundra Ecotone Using Geostatistical and Statistical Measures},
Author = {Stumberg, Nadja and Hauglin, Marius and Bollandsås, Ole Martin and Gobakken, Terje and Erik, Næsset},
Journal = {Remote Sensing},
Year = {2014},
Number = {5},
Pages = {4582-4599},
Volume = {6},
Owner = {hanso},
Timestamp = {2014.10.14}
}
  • Vauhkonen, J., Næsset, E., & Gobakken, T.. (2014). Deriving airborne laser scanning based computational canopy volume for forest biomass and allometry studies. Isprs journal of photogrammetry and remote sensing, 96, 57-66.
    [Bibtex] 
    @Article{Vauhkonen2014,
Title = {Deriving airborne laser scanning based computational canopy volume for forest biomass and allometry studies},
Author = {Vauhkonen, Jari and Næsset, Erik and Gobakken, Terje},
Journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
Year = {2014},
Number = {0},
Pages = {57-66},
Volume = {96},
Abstract = {A computational canopy volume (CCV) based on airborne laser scanning (ALS) data is proposed to improve predictions of forest biomass and other related attributes like stem volume and basal area. An approach to derive the CCV based on computational geometry, topological connectivity and numerical optimization was tested with sparse-density, plot-level ALS data acquired from 40 field sample plots of 500–1000 m2 located in a boreal forest in Norway. The CCV had a high correspondence with the biomass attributes considered when derived from optimized filtrations, i.e. ordered sets of simplices belonging to the triangulations based on the point data. Coefficients of determination (R2) between the CCV and total above-ground biomass, canopy biomass, stem volume, and basal area were 0.88–0.89, 0.89, 0.83–0.97, and 0.88–0.92, respectively, depending on the applied filtration. The magnitude of the required filtration was found to increase according to an increasing basal area, which indicated a possibility to predict this magnitude by means of ALS-based height and density metrics. A simple prediction model provided CCVs which had R2 of 0.77–0.90 with the aforementioned forest attributes. The derived CCVs always produced complementary information and were mainly able to improve the predictions of forest biomass relative to models based on the height and density metrics, yet only by 0–1.9 percentage points in terms of relative root mean squared error. Possibilities to improve the CCVs by a further analysis of topological persistence are discussed.},
Keywords = {Light Detection and Ranging (LiDAR) Forest inventory Tree allometry Delaunay triangulation Alpha shape Simplicial homomorphism Persistent homology},
Owner = {hanso},
Timestamp = {2014.10.14}
}

2013

  • [DOI] Bollandsås, O. M., Gregoire, T. G., Næsset, E., & Øyen, B.. (2013). Detection of biomass change in a norwegian mountain forest area using small footprint airborne laser scanner data. Statistical methods & applications, 113-129.
    [Bibtex] 
    @Article{BollandsA¥s2013,
Title = {Detection of biomass change in a Norwegian mountain forest area using small footprint airborne laser scanner data},
Author = {Bollandsås, Ole Martin and Gregoire, Timothy G. and Næsset, Erik and Øyen, Bernt-Håvard},
Journal = {Statistical Methods \& Applications},
Year = {2013},
Pages = {113-129},
Doi = {10.1007/s10260-012-0220-5},
ISSN = {1618-2510},
Keywords = {Biomass change; Airborne laser scanning; Multitemporal data},
Language = {English},
Owner = {hanso},
Publisher = {Springer-Verlag},
Timestamp = {2012.11.30},
Url = {http://dx.doi.org/10.1007/s10260-012-0220-5}
}
  • [DOI] Bollandsås, O. M., Maltamo, M., Gobakken, T., & Næsset, E.. (2013). Comparing parametric and non-parametric modelling of diameter distributions on independent data using airborne laser scanning in a boreal conifer forest. Forestry, 86(4), 493-501.
    [Bibtex] 
    @Article{BollandsA¥s2013a,
Title = {Comparing parametric and non-parametric modelling of diameter distributions on independent data using airborne laser scanning in a boreal conifer forest},
Author = {Bollandsås, Ole Martin and Maltamo, Matti and Gobakken, Terje and Næsset, Erik},
Journal = {Forestry},
Year = {2013},
Number = {4},
Pages = {493-501},
Volume = {86},
Abstract = {This study used two different approaches to model diameter distributions on data from 201 field plots in a boreal conifer forest in south eastern Norway using airborne laser scanning. These two methods were a non-parametric most similar neighbour (MSN) approach and a parametric seemingly unrelated regression (SUR) approach to predict diameter percentiles, and their accuracies were compared by validation with an independent dataset. Based on calculated differences between predicted and observed number of stems on the entire validation dataset, we found that SUR gave unbiased results and that MSN slightly underestimated total number of stems. However, both methods overpredicted the number of stems per hectare in the range of 15.6–61.5 stems in the smallest diameter classes (between 4 and 12 cm). If the predicted diameter distributions were converted into basal area per hectare (G), both methods gave unbiased results. The average difference for G was 1.9 per cent of the observed value for the MSN approach. The corresponding number for the SUR model was 12.4 per cent. Neither of these differences were statistically significant (P > 0.05). We concluded that the even though both methods overall yielded accurate results, the MSN approach was more reliable in terms of predicting the number of large trees.},
Doi = {10.1093/forestry/cpt020},
Eprint = {http://forestry.oxfordjournals.org/content/86/4/493.full.pdf+html},
Owner = {hanso},
Timestamp = {2014.10.15},
Url = {http://forestry.oxfordjournals.org/content/86/4/493.abstract}
}
  • [DOI] Dalponte, M., Ørka, H. O., Gobakken, T., Gianelle, D., & Næsset, E.. (2013). Tree species classification in boreal forests with hyperspectral data. Ieee transactions on geoscience and remote sensing, 5, 2632-2645.
    [Bibtex] 
    @Article{Dalponte2013a,
Title = {Tree species classification in boreal forests with hyperspectral data},
Author = {Dalponte, Michele and Ørka, Hans Ole and Gobakken, Terje and Gianelle, D. and Næsset, Erik},
Journal = {IEEE Transactions on Geoscience and Remote Sensing},
Year = {2013},
Pages = {2632-2645},
Volume = {5},
Doi = {10.1109/TGRS.2012.2216272},
Owner = {hanso},
Timestamp = {2014.10.15},
Url = {http://dx.doi.org/10.1109/TGRS.2012.2216272}
}
  • [DOI] Ene, L. T., Næsset, E., & Gobakken, T.. (2013). Model-based inference for k-nearest neighbours predictions using a canonical vine copula. Scandinavian journal of forest research, 28(3), 266-281.
    [Bibtex] 
    @Article{Ene2013,
Title = {Model-based inference for k-nearest neighbours predictions using a canonical vine copula},
Author = {Ene, Liviu Theodor and Næsset, Erik and Gobakken, Terje},
Journal = {Scandinavian Journal of Forest Research},
Year = {2013},
Number = {3},
Pages = {266-281},
Volume = {28},
Abstract = {The k-near neighbours (k-NN) technique combines field data from forest inventories and auxiliary information for forest resource estimation at various geographical scales. In this study, auxiliary data consisting of Landsat 5 TM satellite imagery and terrain elevations were used to perform k-NN imputations of plot-level above ground biomass. Following the model-based inference, a superpopulation model consisting of a canonical vine copula was constructed from the empirical data, and new samples were generated from the model and used for k-NN predictions. The method used herein allows constructing the sampling distribution for the imputation errors and for assessing the statistical properties of the k-NN estimator. Using a data-splitting procedure, the copula-based approach was assessed against pair-bootstrap resampling. The imputations were performed using k (the number of neighbours) = 1 and by using optimal k values selected according to a bias-minimizing criterion. The empirical coverage probabilities of the confidence intervals constructed using the copula-based approach were closer to the nominal coverages. The improvements were due to significant bias reduction, while the standard errors were higher compared to the bootstrap. Still, the root mean squared error was significantly reduced. The best results were obtained using the copula approach and k-NN imputations with k=1.},
Doi = {10.1080/02827581.2012.723743},
Keywords = {copulas k-NN imputations variance estimation},
Owner = {hanso},
Timestamp = {2014.10.15},
Url = {http://dx.doi.org/10.1080/02827581.2012.723743}
}
  • Ene, L. T., Næsset, E., Gobakken, T., Gregoire, T. G., Ståhl, G., & Holm, S.. (2013). A simulation approach for accuracy assessment of two-phase post-stratified estimation in large-area lidar biomass surveys. Remote sensing of environment, 133, 210–224.
    [Bibtex] 
    @Article{Ene2013a,
Title = {A simulation approach for accuracy assessment of two-phase post-stratified estimation in large-area LiDAR biomass surveys},
Author = {Ene, Liviu Theodor and Næsset, Erik and Gobakken, Terje and Gregoire, Timothy G and Ståhl, Göan and Holm, Söen},
Journal = {Remote Sensing of Environment},
Year = {2013},
Pages = {210--224},
Volume = {133},
Owner = {hanso},
Publisher = {Elsevier},
Timestamp = {2014.10.15},
Url = {http://dx.doi.org/10.1016/j.rse.2013.02.002}
}
  • Gobakken, T., Korhonen, L., & Næsset, E.. (2013). Laser-assisted selection of field plots for an area-based forest inventory. Silva fennica, 47(5), 1-20.
    [Bibtex] 
    @Article{Gobakken2013,
author = {Gobakken, Terje and Korhonen, Lauri and Næsset, Erik},
journal = {SILVA FENNICA},
title = {Laser-assisted selection of field plots for an area-based forest inventory},
year = {2013},
number = {5},
pages = {1-20},
volume = {47},
abstract = {Field measurements conducted on sample plots are a major cost component in airborne laser scanning (ALS) based forest inventories, as field data is needed to obtain reference variables for the statistical models. The ALS data also provides an excellent source of prior information that may be used in the design phase of the field survey to reduce the size of the field data set. In the current study, we acquired two independent modeling data sets: one with ALS-assisted and another with random plot selection. A third data set was used for validation. One canopy height and one canopy density variable were used as a basis for the ALS-assisted selection. Ordinary and partial least squares regressions for stem volume were fitted for four different strata using the two data sets separately. The results show that the ALS-assisted plot selection helped to decrease the root mean square error (RMSE) of the predicted volume. Although the differences in RMSE were relatively small, models based on random plot selection showed larger mean differences from the reference in the independent validation data. Furthermore, a sub-sampling experiment showed that 40 well placed plots should be enough for fairly reliable predictions.},
keywords = {airborne laser scanning; lidar; area-based approach; forest inventory; stratified sampling},
owner = {hanso},
timestamp = {2014.10.15},
}
  • [DOI] Hauglin, M., Astrup, R., Gobakken, T., & Næsset, E.. (2013). Estimating single-tree branch biomass of norway spruce with terrestrial laser scanning using voxel-based and crown dimension features. Scandinavian journal of forest research, 28(5), 456-469.
    [Bibtex] 
    @Article{Hauglin2013,
Title = {Estimating single-tree branch biomass of Norway spruce with terrestrial laser scanning using voxel-based and crown dimension features},
Author = {Hauglin, Marius and Astrup, Rasmus and Gobakken, Terje and Næsset, Erik},
Journal = {Scandinavian Journal of Forest Research},
Year = {2013},
Number = {5},
Pages = {456-469},
Volume = {28},
Abstract = {Many remote sensing-based methods estimating forest biomass rely on allometric biomass models for field reference data. Terrestrial laser scanning (TLS) has emerged as a tool for detailed data collection in forestry applications, and the methods have been proposed to derive, e.g. tree position, diameter-at-breast-height, and stem volume from TLS data. In this study, TLS-derived features were related to destructively sampled branch biomass of Norway spruce at the single-tree level, and the results were compared to conventional allometric models with field measured diameter and height. TLS features were derived following two approaches: one voxel-based approach with a detailed analysis of the interaction between individual voxels and each laser beam. The features were derived using voxels of size 0.1, 0.2, and 0.4 m, and the effect of the voxel size was assessed. The voxel-derived features were compared to features derived from crown dimension measurements in the unified TLS point cloud data. TLS-derived variables were used in regression models, and prediction accuracies were assessed through a Monte Carlo cross-validation procedure. The model based on 0.4 m voxel data yielded the best prediction accuracy, with a root mean square error (RMSE) of 32%. The accuracy was found to decrease with an increase in voxel size, i.e. the model based on the 0.1 m voxel yielded the lowest accuracy. The model based on crown measurements had an RMSE of 34%. The accuracies of the predictions from the TLS-based models were found to be higher than from conventional allometric models, but the improvement was relatively small.},
Doi = {10.1080/02827581.2013.777772},
Keywords = {terrestrial laser scanning biomass forest inventory lidar},
Owner = {hanso},
Timestamp = {2014.10.15},
Url = {http://dx.doi.org/10.1080/02827581.2013.777772}
}
  • [DOI] Hauglin, M., Dibdiakova, J., Gobakken, T., & Næsset, E.. (2013). Estimating single-tree branch biomass of norway spruce by airborne laser scanning. Isprs journal of photogrammetry and remote sensing, 79, 147-156.
    [Bibtex] 
    @Article{Hauglin2013a,
Title = {Estimating single-tree branch biomass of Norway spruce by airborne laser scanning},
Author = {Hauglin, Marius and Dibdiakova, Janka and Gobakken, Terje and Næsset, Erik},
Journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
Year = {2013},
Number = {0},
Pages = {147-156},
Volume = {79},
Abstract = {The use of forest biomass for bioenergy purposes, directly or through refinement processes, has increased in the last decade. One example of such use is the utilization of logging residues. Branch biomass constitutes typically a considerable part of the logging residues, and should be quantified and included in future forest inventories. Airborne laser scanning (ALS) is widely used when collecting data for forest inventories, and even methods to derive information at the single-tree level has been described. Procedures for estimation of single-tree branch biomass of Norway spruce using features derived from ALS data are proposed in the present study. As field reference data the dry weight branch biomass of 50 trees were obtained through destructive sampling. Variables were further derived from the ALS echoes from each tree, including crown volume calculated from an interpolated crown surface constructed with a radial basis function. Spatial information derived from the pulse vectors were also incorporated when calculating the crown volume. Regression models with branch biomass as response variable were fit to the data, and the prediction accuracy assessed through a cross-validation procedure. Random forest regression models were compared to stepwise and simple linear least squares models. In the present study branch biomass was estimated with a higher accuracy by the best ALS-based models than by existing allometric biomass equations based on field measurements. An improved prediction accuracy was observed when incorporating information from the laser pulse vectors into the calculation of the crown volume variable, and a linear model with the crown volume as a single predictor gave the best overall results with a root mean square error of 35% in the validation.},
Doi = {10.1016/j.isprsjprs.2013.02.013},
Keywords = {Forestry LIDAR Inventory Estimation Accuracy},
Owner = {hanso},
Timestamp = {2014.10.15},
Url = {http://dx.doi.org/10.1016/j.isprsjprs.2013.02.013}
}
  • Magnussen, S., Næsset, E., & Gobakken, T.. (2013). Prediction of tree-size distributions and inventory variables from cumulants of canopy height distributions. Forestry, 86(5), 583-595.
    [Bibtex] 
    @Article{Magnussen2013,
Title = {Prediction of tree-size distributions and inventory variables from cumulants of canopy height distributions},
Author = {Magnussen, Steen and Næsset, Erik and Gobakken, Terje},
Journal = {Forestry},
Year = {2013},
Number = {5},
Pages = {583-595},
Volume = {86},
Abstract = {The method of predicting an unknown target probability distribution via a Gram–Charlier A-series expansion (GCAE) of a user-defined base probability function and cumulants of a known distribution of an auxiliary variable is demonstrated in two applications. Both applications concern predictions of the distribution of tree stem diameters with cumulants of airborne laser scanning (ALS) canopy heights and an index of canopy density as predictors. All predictions were generated in a leave-one-out cross-validation scheme, and statistical inference was based on 100 stochastic predictions of the tree sizes in 308 plots of 400 m2. The mean and variance of GCAE-predicted distributions were rarely significantly different from actual values, yet between 19 and 32% of the predicted GCAE distributions were significantly different from the actual distribution. The rejection rate with predictions generated from a simpler DECILE method was, on average, 2.5% lower. GCAE is still recommended due to its potential usefulness. Cumulants of ALS canopy heights are independent of plot area and effective for area-based least-squares predictions of forest inventory variables.},
Owner = {hanso},
Timestamp = {2014.10.15}
}
  • [DOI] McRoberts, R. E., Næsset, E., & Gobakken, T.. (2013). Inference for lidar-assisted estimation of forest growing stock volume. Remote sensing of environment, 128, 268-275.
    [Bibtex] 
    @Article{McRoberts2013,
Title = {Inference for lidar-assisted estimation of forest growing stock volume},
Author = {McRoberts, R.E. and Næsset, E. and Gobakken, T.},
Journal = {Remote Sensing of Environment},
Year = {2013},
Pages = {268-275},
Volume = {128},
Doi = {10.1016/j.rse.2012.10.007},
Owner = {hanso},
Timestamp = {2014.10.15},
Url = {http://www.sciencedirect.com/science/article/pii/S0034425712003896}
}
  • McRoberts, R. E., Naesset, E., & Gobakken, T.. (2013). Accuracy and precision for remote sensing applications of nonlinear model-based inference. Selected topics in applied earth observations and remote sensing, ieee journal of selected topics in applied earth observations and remote sensing, 6(1), 27-34.
    [Bibtex] 
    @Article{McRoberts2013a,
Title = {Accuracy and Precision for Remote Sensing Applications of Nonlinear Model-Based Inference},
Author = {McRoberts, R. E. and Naesset, E. and Gobakken, T.},
Journal = {Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
Year = {2013},
Number = {1},
Pages = {27-34},
Volume = {6},
Abstract = {In a forest inventory context, estimation for small areas and for remote and inaccessible regions may be problematic using traditional probability- or design-based inference because acquisition of sufficiently large samples to satisfy precision requirements is financially and/or logistically difficult. These problems can often be partially alleviated for inventory applications by enhancing inferences using models and remotely sensed independent variables. However, estimates obtained using probability-based, model-assisted estimators may still suffer detrimental effects as the result of small sample sizes. Model-based inference has the potential to alleviate these problems because precision is affected by other factors such as model specification. Nevertheless, model specification in the form of selection of independent variables often focuses exclusively on quality of fit with little consideration given to the precision of estimates of areal population parameters. Model-based inference is illustrated for two forest inventory applications, estimation of mean proportion forest area using Landsat-based independent variables for a study area in the USA and estimation of mean growing stock volume per unit area using lidar-based independent variables for a study area in Norway. Variations of a nonlinear logistic regression model are used for both applications. The results indicate selection of subsets of remotely sensed independent variables to maximize precision had negligible effects on the quality of fit of the models to the data and on estimates of means but substantial proportional beneficial effects on precision.},
Keywords = {Biological system modeling Data models Logistics Mathematical model Remote sensing Sociology Statistics Landsat lidar variable selection},
Owner = {hanso},
Timestamp = {2014.10.15}
}
  • [DOI] McRoberts, R. E., Næsset, E., & Gobakken, T.. (2013). Accuracy and precision for remote sensing applications of nonlinear model-based inference. Selected topics in applied earth observations and remote sensing, ieee journal of selected topics in applied earth observations and remote sensing, 6(1), 27-34.
    [Bibtex] 
    @Article{McRoberts2013b,
Title = {Accuracy and Precision for Remote Sensing Applications of Nonlinear Model-Based Inference},
Author = {McRoberts, R. E. and Næsset, E. and Gobakken, T.},
Journal = {Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
Year = {2013},
Number = {1},
Pages = {27-34},
Volume = {6},
Abstract = {In a forest inventory context, estimation for small areas and for remote and inaccessible regions may be problematic using traditional probability- or design-based inference because acquisition of sufficiently large samples to satisfy precision requirements is financially and/or logistically difficult. These problems can often be partially alleviated for inventory applications by enhancing inferences using models and remotely sensed independent variables. However, estimates obtained using probability-based, model-assisted estimators may still suffer detrimental effects as the result of small sample sizes. Model-based inference has the potential to alleviate these problems because precision is affected by other factors such as model specification. Nevertheless, model specification in the form of selection of independent variables often focuses exclusively on quality of fit with little consideration given to the precision of estimates of areal population parameters. Model-based inference is illustrated for two forest inventory applications, estimation of mean proportion forest area using Landsat-based independent variables for a study area in the USA and estimation of mean growing stock volume per unit area using lidar-based independent variables for a study area in Norway. Variations of a nonlinear logistic regression model are used for both applications. The results indicate selection of subsets of remotely sensed independent variables to maximize precision had negligible effects on the quality of fit of the models to the data and on estimates of means but substantial proportional beneficial effects on precision.},
Doi = {10.1109/JSTARS.2012.2227299},
Keywords = {Biological system modeling Data models Logistics Mathematical model Remote sensing Sociology Statistics Landsat lidar variable selection},
Owner = {hanso},
Timestamp = {2014.10.15},
Url = {http://dx.doi.org/10.1109/JSTARS.2012.2227299}
}
  • [DOI] Mugasha, W. A., Eid, T., Bollandsås, O. M., Malimbwi, R. E., Chamshama, S. A. O., Zahabu, E., & Katani, J. Z.. (2013). Allometric models for prediction of above- and belowground biomass of trees in the miombo woodlands of tanzania. Forest ecology and management, 310, 87-101.
    [Bibtex] 
    @Article{Mugasha2013,
author = {Wilson Ancelm Mugasha and Tron Eid and Ole Martin Bollandsås and Rogers Ernest Malimbwi and Shabani Athumani Omari Chamshama and Eliakimu Zahabu and Josiah Zephania Katani},
journal = {Forest Ecology and Management},
title = {Allometric models for prediction of above- and belowground biomass of trees in the miombo woodlands of Tanzania},
year = {2013},
issn = {0378-1127},
number = {0},
pages = {87 - 101},
volume = {310},
abstract = {Abstract Miombo woodland is a significant forest type occupying about 9% of the African land area and forms a dominant vegetation type in many southeastern African countries including Tanzania. Quantification of the amount of carbon stored in forests presently is an important component in the implementation of the emerging carbon credit market mechanisms. This calls for appropriate allometric models predicting biomass which currently are scarce. The aim of this study was to develop above- and belowground allometric general and site-specific models for trees in miombo woodland. The data were collected from four sites in Tanzania and covers a wide range of conditions and tree sizes (diameters at breast height from 1.1 to 110 cm). Above- and belowground biomass models were developed from 167 and 80 sample trees, respectively. The model fitting showed that large parts of the variation (up to 97%) in biomass were explained by diameter at breast height and tree height. Since including tree height only marginally increased the explanation of the biomass variation (from 95% to 96–97% for aboveground biomass), the general recommendation is to apply the models with diameter at breast height only as an independent variable. The results also showed that the general models can be applied over a wide range of conditions in Tanzania. The comparison with previously developed models revealed that these models can probably also be applied for miombo woodland elsewhere in southeastern Africa if not used beyond the tree size range of the model data.},
doi = {http://dx.doi.org/10.1016/j.foreco.2013.08.003},
keywords = {