NORKLIMA

Rapid changes are expected in the mountain forest and the forest-alpine transition zone due to global warming. Since steep temperature-productivity gradients characterize these marginal areas, two fundamental effects are expected: (1) an expansion of the forest by colonization of non-forested areas and migration of the alpine tree line; (2) increased growth of existing trees which will have a significant impact on carbon sequestration and future carbon pools. Continue reading

WW-IRIS

WW-IRIS: New Technologies to Optimize the Wood Information Basis – Developing an Integrated Resource Information System.

In cooperation with several academic and industry partners, INA is working to develop technologies to optimize the wood information basis by developing an integrated resource information system. The duration of the project is from January 2008 to December 2010.

Related documents

DOWNLOAD FINAL REPORT
WW-IRIS flyer
SILVILASER 2010 presentation

Objectives

The main objective is to develop and optimize ground-based and airborne laser scanner methods for assessment of wood qualities and quantities at high spatial resolution. These methods will be validated across datasets collected in participating countries. A further aim is to improve the flow of information regarding wood resources along the forest/wood-products chain by adapting forest information and planning systems to utilize improved information from laser scanner aided inventories.

Laser point cloud from an airborne laser scanner.

Work packages

The project is divided into 15 different work packages, relating to following scientific issues:

  • Data sources and data requirements; defining user needs
  • Methods for single-tree detection and tree parameter extraction; international comparison of methods across forest types
  • Methods for stand-based assessment of wood quality using airborne laser
  • Methods for combining estimates derived for individual trees and at an area base to produce resource information for large geographical areas
  • Product recovery by combining single-tree airborne laser with terrestrial laser or harvester data
  • Data base design
  • Development of inventory and planning systems, utilization modeling
  • Dissemination and training

(The article continues)

A terrestrial laser scan and derived data products.

Results so far

Sustainable forest management and wood industries profit from forest resource information with high spatial and temporal resolution. This information is especially important to make correct planning decisions under difficult market conditions. Forest inventories commonly provide this information but are highly restricted regarding spatial and temporal resolution. The WW-IRIS project aims on resolving these restrictions by combining forest inventories with airborne laser scanner (ALS) data. The major research objectives of the WW-IRIS project, which were developed on the basis of the identified needs of the forest industries, are:

  • Development and optimization of laser scanner methods for assessment of wood qualities and quantities  at high spatial resolution and validation of these methods across countries.
  • Further improvement of the information flow regarding wood resources along the forest/wood-products chain by adapting forest information and planning systems to utilize improved information from laser scanner aided inventories.

Since these two objectives have a somewhat consecutive character, the main emphasis so far has been on the first objective. To produce valuable results, the research objectives were transposed into solid tasks in close cooperation with forest stakeholders represented in the advisory board of the project.The 3D structural information of the forest derived from ALS data can be used to detect and measure trees. Several teams within the project worked on the development of algorithms for segmentation of individual trees. These algorithms are currently being evaluated on data sets from different participating countries. However, not all trees in the forest can be detected, even with the most advanced algorithms. Therefore, other teams have developed methods, e.g. statistical models, which predict the missing trees and thus allow for unbiased estimates.We have also developed methods for prediction of product recovery from pre-harvest inventories with airborne laser scanning data trained by ground-based laser scanning or harvester data. Stem-files are efficiently being produced from harvesters or ground based scanners. The stem-files are imputed based on airborne laser scanning of an inventory area to predict stem attributes for forest stands without ground measurements.Another important research topic within the project is the prediction of tree species and tree species specific attributes. Several peer reviewed scientific articles from researchers within WW-IRIS regarding this subject were published or are currently in press.Finally, research work on the prediction of wood quality-related parameters and the extraction of forest roads using ALS has been carried out. Some partners have already started with adapting information systems to optimize the usage of the newly available data.

Actions planned

Next Project Board meeting is scheduled for 13 September 2010, in combination with the international SilviLaser conference 2010 to be held in Freiburg, Germany. For the SilviLaser conference, we plan to fill a full session with results from the IRIS project. The SilviLaser conference is organized by the two German research partners FeLis and FVA. In Norway, several researchers will continue to work on methods for tree segmentation, species discrimination and wood quality assessment. Publications on methods for operational single tree inventories will be refined and published. Several manuscripts are in review. They will be followed up.In Finland, work in all three work packages will continue. In the case of forest inventory several research papers using different methodologies will be written. For forest information systems pilot implementations for integrating XML- and GML-based forest data formats will be developed with laser dataset in the coming period and the digital road network, digital elevation models, and tree data are planned to be utilized in planning of harvesting operation. Correspondingly, forest planning calculations will be continued.In Sweden, methods for estimation of complete tree lists from airborne laser scanner data will be further developed and validated using the new dataset from northern Sweden. The predicted (imputed) stem-files from harvester measurements will be used for bucking simulations. Also, the stem-files produced from terrestrial laser scanning will be imputed with airborne laser scanner data and then used for bucking simulations. A raster database produced at a test site in northern Sweden will be used as input for the spatial optimization methodology developed in WP FI-3.In Germany, the comparison between single tree and stand based approaches will be carried out. A concept will be presented to utilize the obtained information within a forest information system (FVA).Algorithms for single tree detection and estimation will be further enhanced. It is also intended to use the data from the new flight campaign (November 2009) to develop a method which allows the explicit detection of tree stems. The algorithm for forest road extraction will be enhanced and expanded mainly by using a more intensive data fusion (FeLis).FoBaWi will work towards a realistic 3D representation of single tree crowns and analyze the correlation between the length and base diameter of branches. Since branch length can be measured with airborne laser data, branch diameter as one of the most credible quality parameters for the timber industry, can be estimated.

Project information

Funding

The project is funded by the EU under the WoodWisdom framework.

Project partners

Academic and Research Organizations (Role: executing)

Norwegian University of Life Sciences, Norway (coordinator, national coordinator)
Albert-Ludwigs University of Freiburg, Germany (national coordinator)
University of Joensuu, Finland (national coordinator)
Swedish University of Agricultural Sciences, Sweden (national coordinator)
Norwegian forest and landscape institute, Norway
Forest Research Institute Baden-Württemberg, Germany
Skogforsk, The Forestry Research Institute of Sweden, Sweden

Industry Partners:

Viken Skog BA, Norway (user requirements, data support)
Klenk Holz AG, Germany (user requirements, validation of results)
Stora Enso OYJ, Finland (user requirements, data support, validation of results)
Arbonaut, Finland (validation of results, assessment of potential software/data products)
Savcor, Brasil/Finland (user requirements, data support, validation of results, assessment of potential software/data products)
Sveaskog, Sweden (user requirements)
Swedish Forest Agency, Sweden (data support, validation of results)
TreeMetrics Ltd, Ireland (data support, validation of results)

Published works

  • H. O. {O}rka, E. Næsset, and O. M. Bollandsås, “Classifying species of individual trees by intensity and structure features derived from airborne laser scanner data,” Remote sensing of environment, vol. 113, iss. 6, pp. 1163-1174, 2009.
    [Bibtex]
    @ARTICLE{Orka2009,
    author = {{\O}rka, H.O. and N{\ae}sset, E. and Bollands{\aa}s, O.M.},
    title = {Classifying species of individual trees by intensity and structure
    features derived from airborne laser scanner data},
    journal = {Remote Sensing of Environment},
    year = {2009},
    volume = {113},
    pages = {1163--1174},
    number = {6},
    publisher = {Elsevier}
    }
  • J. Breidenbach, A. Nothdurft, and G. Kändler, “Comparison of nearest neighbour approaches for small area estimation of tree species-specific forest inventory attributes in central europe using airborne laser scanner data,” European journal of forest research, vol. 129, iss. 5, pp. 833-846, 2010.
    [Bibtex]
    @ARTICLE{Breidenbach2010,
    author = {Breidenbach, J. and Nothdurft, A. and Kändler, G.},
    title = {Comparison of nearest neighbour approaches for small area estimation
    of tree species-specific forest inventory attributes in central Europe
    using airborne laser scanner data},
    journal = {European Journal of Forest Research},
    year = {2010},
    volume = {129},
    pages = {833--846},
    number = {5},
    publisher = {Springer}
    }
  • J. Breidenbach, E. N{k{e}}sset, V. Lien, T. Gobakken, and S. Solberg, “Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data,” Remote sensing of environment, vol. 114, iss. 4, pp. 911-924, 2010.
    [Bibtex] [Download PDF]
    @ARTICLE{Breidenbach2010a,
    author = {Breidenbach, J. and N{\k{e}}sset, E. and Lien, V. and Gobakken, T.
    and Solberg, S.},
    title = {Prediction of species specific forest inventory attributes using
    a nonparametric semi-individual tree crown approach based on fused
    airborne laser scanning and multispectral data},
    journal = {Remote Sensing of Environment},
    year = {2010},
    volume = {114},
    pages = {911--924},
    number = {4},
    publisher = {Elsevier},
    url = {http://dx.doi.org/DOI:10.1016/j.rse.2009.12.004}
    }
  • J. N. Heinzel, H. Weinacker, and B. Koch, “Prior-knowledge-based single-tree extraction,” International journal of remote sensing, vol. 32, iss. 17, pp. 4999-5020, 2011.
    [Bibtex]
    @ARTICLE{Heinzel2011,
    author = {Heinzel, J.N. and Weinacker, H. and Koch, B.},
    title = {Prior-knowledge-based single-tree extraction},
    journal = {International Journal of Remote Sensing},
    year = {2011},
    volume = {32},
    pages = {4999--5020},
    number = {17},
    publisher = {Taylor \& Francis}
    }
  • I. Korpela, H. O. Ørka, J. Hyyppä, V. Heikkinen, and T. Tokola, “Range and agc normalization in airborne discrete-return lidar intensity data for forest canopies,” Isprs journal of photogrammetry and remote sensing, vol. 65, iss. 4, pp. 369-379, 2010.
    [Bibtex]
    @ARTICLE{Korpela2010,
    author = {Korpela, I. and Ørka, H.O. and Hyyppä, J. and Heikkinen, V. and Tokola,
    T.},
    title = {Range and AGC normalization in airborne discrete-return LiDAR intensity
    data for forest canopies},
    journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
    year = {2010},
    volume = {65},
    pages = {369--379},
    number = {4},
    publisher = {Elsevier}
    }
  • I. Korpela, H. O. Ørka, M. Maltamo, T. Tokola, and J. Hyyppä, “Tree species classification using airborne lidar—effects of stand and tree parameters, downsizing of training set, intensity normalization, and sensor type,” Silva fennica, vol. 44, iss. 2, pp. 319-339, 2010.
    [Bibtex]
    @ARTICLE{Korpela2010a,
    author = {Korpela, I. and Ørka, H.O. and Maltamo, M. and Tokola, T. and Hyyppä,
    J.},
    title = {Tree species classification using airborne LiDAR—Effects of stand
    and tree parameters, downsizing of training set, intensity normalization,
    and sensor type},
    journal = {Silva Fennica},
    year = {2010},
    volume = {44},
    pages = {319--339},
    number = {2}
    }
  • E. Lindberg, J. Holmgren, K. Olofsson, J. Wallerman, and H. Olsson, “Estimation of tree lists from airborne laser scanning by combining single-tree and area-based methods,” International journal of remote sensing, vol. 31, iss. 5, pp. 1175-1192, 2010.
    [Bibtex]
    @ARTICLE{Lindberg2010,
    author = {Lindberg, E. and Holmgren, J. and Olofsson, K. and Wallerman, J.
    and Olsson, H.},
    title = {Estimation of tree lists from airborne laser scanning by combining
    single-tree and area-based methods},
    journal = {International Journal of Remote Sensing},
    year = {2010},
    volume = {31},
    pages = {1175--1192},
    number = {5},
    publisher = {Taylor and Francis Ltd}
    }
  • M. Maltamo, O. Bollandsås, J. Vauhkonen, J. Breidenbach, T. Gobakken, and E. Næsset, “Comparing different methods for prediction of mean crown height in norway spruce stands using airborne laser scanner data,” Forestry, vol. 83, iss. 3, p. 257, 2010.
    [Bibtex]
    @ARTICLE{Maltamo2010,
    author = {Maltamo, M. and Bollandsås, OM and Vauhkonen, J. and Breidenbach,
    J. and Gobakken, T. and Næsset, E.},
    title = {Comparing different methods for prediction of mean crown height in
    Norway spruce stands using airborne laser scanner data},
    journal = {Forestry},
    year = {2010},
    volume = {83},
    pages = {257},
    number = {3},
    publisher = {Inst Chartered Foresters}
    }
  • C. Straub, M. Dees, H. Weinacker, and B. Koch, “Using airborne laser scanner data and cir orthophotos to estimate the stem volume of forest stands,” Photogrammetrie-fernerkundung-geoinformation, vol. 2009, iss. 3, pp. 277-287, 2009.
    [Bibtex]
    @ARTICLE{Straub2009,
    author = {Straub, C. and Dees, M. and Weinacker, H. and Koch, B.},
    title = {Using airborne laser scanner data and CIR orthophotos to estimate
    the stem volume of forest stands},
    journal = {Photogrammetrie-Fernerkundung-Geoinformation},
    year = {2009},
    volume = {2009},
    pages = {277--287},
    number = {3},
    publisher = {E. Schweizerbart'sche Verlagsbuchhandlung}
    }
  • H. O. Ørka, E. Næsset, and O. M. Bollandsås, “Effects of different sensors and leaf-on and leaf-off canopy conditions on echo distributions and individual tree properties derived from airborne laser scanning,” Remote sensing of environment, vol. 114, iss. 7, pp. 1445-1461, 2010.
    [Bibtex]
    @ARTICLE{Oerka2010,
    author = {Ørka, H.O. and N{\ae}sset, E. and Bollands{\aa}s, O.M.},
    title = {Effects of different sensors and leaf-on and leaf-off canopy conditions
    on echo distributions and individual tree properties derived from
    airborne laser scanning},
    journal = {Remote Sensing of Environment},
    year = {2010},
    volume = {114},
    pages = {1445--1461},
    number = {7},
    publisher = {Elsevier}
    }

 

 

Overview of other former research project

Overview of former research project with links to relevant project sites.

33. Enhancing the measuring, reporting and verification (MRV) of forest in Tanzania through the application of advanced remote sensing techniques (2011 – 2015). Funded by Norwegian Ministry of Foreign Affairs. Research partners Sokoine University of Agriculture – Tanzania, Kongsberg Satellite Services, Northern Research Institution, Norwegian Computing Centre, Norwegian Forest and Landscape Institute, University of Tromsø
32. Use of 3D-data from image matching in forest standwise inventories (2012 – 2014). Funded by the “Utviklingsfondet for skogbruket” and “Skogtiltaksfondet”.
31. Estimating tree species composition in using airborne laser scanning inoperational forest inventories (2013 – 2014). Funded by the Norwegian forest trust found.
30. Inventory of young forest using airborne laser scanning (2011 – 2013). Funded by the Research Council of Norway and “Skogtiltaksfondet”.
29. Detection of cultural heritage in forests utilizing interpretation of terrain models generated from laserdata (2009 – 2011). The project is a collaboration with the Norwegian Institute for Cultural Heritage Research (NIKU)  and is funded by the Directorate for Cultural Heritage.
28. Monitoring habitat type (NatTOv) – preparation of a scientific basis for intensive habitat monitoring. Sub-project within a larger research project funded by the Inter-ministerial Committee for mapping and monitoring biodiversity. Collaboration with the Natural History Museum, University of Oslo, Norwegian Institute for Nature Research, NINA.
27. Improving the efficiency of field plot measurements in area based laser forest inventory (2009-2010). Funded by Innovation Norway and “Skogtiltaksfondet”
26. FlexWood – Flexible Wood Supply Chain (2009 – 2012). Funded by EUs Seventh Framework Programme.
25. BALABU – Bakkemontert laser som verktøy for bedre utnyttelse av skogressursene (2009 – 2012). Funded by the Research Council of Norway and SkatteFUNN.
24. Integrating airborne laser scanning (ALS) and optical data for forest inventory (2009). Funded by the Research Council of Norway.
23. Effects of changing climate on the alpine tree line and mountain forest carbon pools along 1500 km N-S and elevation gradient (2008-2015). Funded by the Research Council of Norway and TerraTec AS.
22. WW-IRIS: New Technologies to Optimize the Wood Information Basis for Forest Industries – Developing an Integrated Resource Information System – IRIS (2008-2010) Funded by the Research Council of Norway, the Nordic Forest Research Cooperation Committee, and “Skogtiltaksfondet”.
21. Estimating biomass of tree tops and branches for large areas using airborne LiDAR (2008-2011). PhD scholarship funded by UMB.
20. LIDAR-based sampling procedures for regional forest biomass and carbon estimation (2004-2009). Samarbeid med University of Minnesota.
19. Image data collection for testing of image sensors in Aurskog (2008). Funded by the Research Council of Norway.
18. Field data collection for testing of image sensors in Aurskog (2008). Funded by the Research Council of Norway.
17. Assessing wood properties of forest resources by airborne LIDAR – PhD scholarship and travel grants (2007-2010). Funded by the Research Council of Norway.
16. Detection of tree growth of small trees in the alpine tree line (2008). Funded by the Research Council of Norway.
15. Assessing wood properties of forest resources by airborne LIDAR (2006-2009). Funded by the Research Council of Norway, Viken Skog BA, and “Skogtiltaksfondet”.
14. Developing and testing a national system for inventory of timber resources and biomass/carbon stocks combining airborne laser and field data (2005-2009). Funded by the Research Council of Norway, UMB, NASA, and Blom Geomatics AS.
13. Linking soil organic carbon pools and LIDAR remote sensing of above-ground biomass in boreal forest ecosystems (2007-2010). PhD scholarship funded by UMB.
12. Assessment of error sources in forest carbon estimates (2006-2010). PhD scholarship funded by UMB.
11. Improved forest planning using airborne laser scanning (2005-2007). Funded by the Research Council of Norway, Prevista AS, and Blom Geomatics AS.
10. Inventories of forest biomass, timber resources, and bio-energy feedstocks using airborne LiDAR (2005-2006). Funded by the Research Council of Norway.
 9. Forest inventory of mixed stands using airborne laser scanning (2004-2005). Funded by Borregaard Research Fund.
 8. Determination of diameter distribution of forest stands by a practical method using airborne laser scanning (2004-2005). Funded by the Research Council of Norway.
 7. Effects of flying altitude on the accuracy of forest inventories using airborne laser (2003-2004). Funded by Borregaard Research Fund.
 6. Testing airborne laser scanning as a method for forest stand inventory over large areas (2001-2002). Funded by the Research Council of Norway, Prevista AS, Fotonor AS, and “Skogtiltaksfondet”.
 5. Estimating timber volume, wood quality, and vegetation using airborne laser (2000-2003). Funded by the Research Council of Norway and Fotonor AS.
 4. Estimation of tree height and stem number in young forest using data from airborne laser (2000-2001). Funded by Borregaard Research Fund.
 3. Determination of height and standing volume of forest stand using digital photogrammetry (1998-1999). Funded by the Research Council of Norway and Fotokart AS.
 2. Estimating timber volume and tree height by means of airborne laser (1998-1999). Funded by Borregaard Research Fund.
 1. Determination of tree height and stand volume using airborne laser scanning (1995-1996). Funded by Fotonor AS, the Forest Trust Fund, and UMB.