2019
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, 2145. https://doi.org/10.3390/rs11182145
2018
Banja, M., Sikkema, R., Jégard, M., Motola, V., Dallemand, J.-F., 2019. Biomass for energy in the EU – The support framework. Energy Policy 131, 215–228. https://doi.org/10.1016/j.enpol.2019.04.038
Blujdea, V., Marin, G., 2018. Obligații
Dutcă, I., 2018. Biomass data for young, planted Norway spruce (Picea abies (L.) Karst.) trees in Eastern Carpathians of Romania. Data Brief 19, 2384–2392. https://doi.org/10.1016/j.dib.2018.07.033
Dutca, I., Mather, R., Viorel, B., Ioras, F., Abrudan, I.V., 2018. Site-effects on biomass allometric models for early growth plantations of Norway spruce (Picea abies (L.) Karst.). Biomass and Bioenergy 116, 8–17. https://doi.org/10.1016/j.biombioe.2018.05.013
Dutcă, I., Stăncioiu, P.T., Abrudan, I.V., Ioraș, F., 2018. Using clustered data to develop biomass allometric models: The consequences of ignoring the clustered data structure. PLoS One 13, e0200123. https://doi.org/10.1371/journal.pone.0200123
Flinkman, M., Sikkema, R., Spelter, H., Jonsson, R.K.H., 2018. Exploring the drivers of demand for non-industrial wood pellets for heating (European bioenergy markets). Baltic Forestry 24, 86–98.
Jonsson, R., Blujdea, V.N., Fiorese, G., Pilli, R., Rinaldi, F., Baranzelli, C., Camia, A., 2018. Outlook of the European forest-based sector: forest growth, harvest demand, wood-product markets, and forest carbon dynamics implications. iForest 11, 315–328. https://doi.org/10.3832/ifor2636-011
Nabuurs, G.-J.,
Nguyen, H.H., Erfanifard, Y., Petritan, I.C., 2018a. Nearest Neighborhood Characteristics of a Tropical Mixed Broadleaved Forest Stand. Forest 9, 33. https://doi.org/10.3390/f9010033
Nguyen, H.H., Erfanifard, Y., Pham, V.D., Le, X.T., Bui, T. D.,
Nguyen, H.H., Petritan, I.C., Burslem, D.F.R.P., 2018c. High frequency of positive interspecific interactions revealed by individual species–area relationships for tree species in a tropical evergreen forest. Plant Ecol. Divers. 11, 441–450. https://doi.org/10.1080/17550874.2018.1541486
Pilli, R., Kull, S.J., Blujdea, V.N.B., Grassi, G., 2018. The Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3): customization of the Archive Index Database for European Union countries. Ann. For. Sci. 75, 71. https://doi.org/10.1007/s13595-018-0743-5
Saarela, S., Holm, S., Healey, S.P., Andersen, H.-E., 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, 1832.
2017
Dutcă, I., Mather, R., Ioraş, F., 2017. Tree biomass allometry during the early growth of Norway spruce (Picea abies) varies between pure stands and mixtures with European beech (Fagus sylvatica). Can. J. For. Res. 48, 77–84.
Saarela, S., Andersen, H.-E., Grafström, A., Schnell, S., Gobakken, T., Næsset, E., Nelson, R.F., McRoberts, R.E., Gregoire, T.G., Ståhl, G., 2017a. A new prediction-based variance estimator for two-stage model-assisted surveys of forest resources. Remote Sens. Environ. 192, 1–11. https://doi.org/10.1016/j.rse.2017.02.001
Saarela, S., Breidenbach, J., Raumonen, P., Grafström, A., Ståhl, G., Ducey, M.J., Astrup, R., 2017b. Kriging prediction of stand-level forest information using mobile laser scanning data adjusted for nondetection. Can. J. For. Res. 47, 1257–1265. https://doi.org/10.1139/cjfr-2017-0019