Publications

Ståhl, G., Gobakken, T., Saarela, S., Persson, H.J., Ekström, M.,Healey, S.P., Yang, Z., Holmgren, J., Lindberg, E., Nyström, K.,Papucci, E., Ulvdal, P., Ørka, H.O., Næsset, E., Hou, Z., Olsson, H., & McRoberts, R.E., 2024. Why ecosystem characteristics predicted from remotely sensed data are unbiased and biased at the same time – and how this affects applications. Forest Ecosystems 11, 100164.

Grima, N., Jutras-Perreault, M.-C., Gobakken, T., **Ørka, H.O.**, & Vacik, H., 2023. Systematic review for a set of indicators supporting the common international classification of ecosystem services. Ecol. Indic. 147, 109978.

Hansen, E., Wold, J., Dalponte, M., Gobakken, T., Noordermeer, L., & Ørka, H.O., 2023. Estimation of the occurrence, severity, and volume of heartwood rot using airborne laser scanning and optical satellite data. European Journal of Remote Sensing 56, 2229501.

Jutras-Perreault, M.-C., Gobakken, T., Næsset, E., & Ørka, H.O., 2023a. Detecting the presence of natural forests using airborne laser scanning data. Forest Ecosystems 10, 100146.

Jutras-Perreault, M.-C., Gobakken, T., Næsset, E., & Ørka, H.O., 2023b. Comparison of different remotely sensed data sources for detection of presence of standing dead trees using a Tree-Based approach. Remote Sensing 15, 2223.

Jutras-Perreault, M.-C., Næsset, E., Gobakken, T., & Ørka, H.O., 2023c. Detecting the presence of standing dead trees using airborne laser scanning and optical data. Scand. J. For. Res. 1–13.

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.

Strı̂mbu, 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 Manag. 18, 10.

Allen, B., Dalponte, M., Hietala, A., Ørka, H., Næsset, E., & Gobakken, T., 2022a. Detection of root, butt, and stem rot presence in norway spruce with hyperspectral imagery. Silva Fenn. 56.

Allen, B., Dalponte, M., Ørka, H.O., Næsset, E., Puliti, S., Astrup, R., & Gobakken, T., 2022b. UAV-Based hyperspectral imagery for detection of root, butt, and stem rot in norway spruce. Remote Sensing 14, 3830.

Dalponte, M., Kallio, A.J.I., Ørka, H.O., Næsset, E., & Gobakken, T., 2022a. Wood decay detection in norway spruce forests based on airborne hyperspectral and ALS data. Remote Sensing 14, 1892.

Dalponte, M., Solano-Correa, Y.T., Ørka, H.O., Gobakken, T., & Næsset, E., 2022b. Detection of heartwood rot in norway spruce trees with lidar and multi-temporal satellite data. Int. J. Appl. Earth Obs. Geoinf. 109, 102790.

Mienna, I.M., Klanderud, K., Ørka, H.O., Bryn, A., & Bollandsås, O.M.,

1. Land cover classification of treeline ecotones along a 1100 km latitudinal transect using spectral‐ and three‐dimensional information from UAV ‐based aerial imagery. Remote Sens. Ecol. Conserv. 8, 536–550.

Ørka, H.O., Jutras-Perreault, M.-C., Candelas-Bielza, J., & Gobakken, T., 2022a. Delineation of geomorphological woodland key habitats using airborne laser scanning. Remote Sensing 14, 1184.

Ørka, H.O., Jutras-Perreault, M.-C., Næsset, E., & Gobakken, T., 2022b. A framework for a forest ecological base map – an example from norway. Ecol. Indic. 136, 108636.

Jutras-Perreault, M.-C., Gobakken, T., & Ørka, H.O., 2021. Comparison of two algorithms for estimating stand-level changes and change indicators in a boreal forest in norway. Int. J. Appl. Earth Obs. Geoinf. 98, 102316.

Ørka, H.O., 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, 1–23.

Strı̂mbu, V.F., Ørka, H.O., & Næsset, E., 2021. Consistent forest biomass stock and change estimation across stand, property, and landscape levels. Can. J. For. Res. 51, 848–858.

Lera Garrido, A. de, Gobakken, T., Ørka, H.O., Næsset, E., & Bollandsås, O.M., 2020. Reuse of field data in ALS-assisted forest inventory. Silva Fenn. 54, 1–18.

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, 3335.

Bollandsås, O.M., Ørka, H.O., Dalponte, M., Gobakken, T., & Næsset, E.,

1. Modelling site index in forest stands using airborne hyperspectral imagery and Bi-Temporal laser scanner data. Remote Sensing 11, 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, 948.

Noordermeer, L., Bollandsås, O.M., Ørka, H.O., Næsset, E., & Gobakken, T., 2019a. Comparing the accuracies of forest attributes predicted from airborne laser scanning and digital aerial photogrammetry in operational forest inventories. Remote Sens. Environ. 226, 26–37.

Noordermeer, L., Økseter, R., Ørka, H.O., Gobakken, T., Næsset, E., & Bollandsås, O.M., 2019b. Classifications of forest change by using bitemporal airborne laser scanner data. Remote Sensing 11, 2145.

Asrat, Z., Taddese, H., Ørka, H.O., 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, 92.

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. Ecol. Indic. 85, 367–376.

Ørka, H.O., Bollandsås, O.M., Hansen, E.H., Næsset, E., & Gobakken, T.,

1. Effects of terrain slope and aspect on the error of ALS-based predictions of forest attributes. Forestry 91, 225–237.

Hansen, E.H., Ene, L.T., Gobakken, T., Ørka, H.O., Bollandsås, O.M., & Næsset, E., 2017. Countering negative effects of terrain slope on airborne laser scanner data using procrustean transformation and histogram matching. Forests 8, 401.

Kachamba, D.J., Ørka, H.O., 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, 610.

Kandare, K., Dalponte, M., Ørka, H.O., Frizzera, L., & Næsset, E., 2017a. Prediction of Species-Specific volume using different inventory approaches by fusing airborne laser scanning and hyperspectral data. Remote Sensing 9, 400.

Kandare, K., Ørka, H.O., Dalponte, M., Næsset, E., & Gobakken, T., 2017b. Individual tree crown approach for predicting site index in boreal forests using airborne laser scanning and hyperspectral data. Int. J. Appl. Earth Obs. Geoinf. 60, 72–82.

Puliti, S., Gobakken, T., Ørka, H.O., & Næsset, E., 2017. Assessing 3D point clouds from aerial photographs for species-specific forest inventories. Scand. J. For. Res. 32, 68–79.

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, 363.

Kachamba, D.J., 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, 968.

Kandare, K., Ørka, H.O., Chan, J.C.-W., & Dalponte, M., 2016. Effects of forest structure and airborne laser scanning point cloud density on 3D delineation of individual tree crowns. European Journal of Remote Sensing 49, 337–359.

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 Sens. Environ. 175, 282–300.

Ørka, H.O., Gobakken, T., & Næsset, E., 2016. Predicting attributes of regeneration forests using airborne laser scanning. Can. J. Remote Sens. 42, 541–553.

Sverdrup-Thygeson, A., Ørka, H.O., Gobakken, T., & Næsset, E., 2016. Can airborne laser scanning assist in mapping and monitoring natural forests? For. Ecol. Manage. 369, 116–125.

Maltamo, M., Ørka, H.O., Bollandsås, O.M., Gobakken, T., & Næsset, E.,

1. Using pre-classification to improve the accuracy of species-specific forest attribute estimates from airborne laser scanner data and aerial images. Scand. J. For. Res. 30, 336–345.

Puliti, S., Ørka, H.O., Gobakken, T., & Næsset, E., 2015. Inventory of small forest areas using an unmanned aerial system. Remote Sensing 7, 9632–9654.

Bergseng, E., Ørka, H.O., Næsset, E., & Gobakken, T., 2014. Assessing forest inventory information obtained from different inventory approaches and remote sensing data sources. Ann. For. Sci. 72, 33–45.

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, 3560–3569.

Dalponte, Michele, Ørka, H.O., Ene, L.T., Gobakken, T., & Næsset, E.,

1. Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data. Remote Sens. Environ. 140, 306–317.

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, 204–219.

Ørka, H.O., Dalponte, M., Gobakken, T., Næsset, E., & Ene, L.T., 2013. Characterizing forest species composition using multiple remote sensing data sources and inventory approaches. Scand. J. For. Res. 28, 677–688.

Risbol, O., Bollandsas, O.M., Nesbakken, A., Orka, H.O., Naesset, E., & Gobakken, T., 2013. Interpreting cultural remains in airborne laser scanning generated digital terrain models: Effects of size and shape on detection success rates. J. Archaeol. Sci. 40, 4688–4700.

Gobakken, T., Naesset, E., Nelson, R., Bollandsas, O.M., Gregoire, T.G., Stahl, G., Holm, S., Orka, H.O., & Astrup, R., 2012. Estimating biomass in hedmark county, norway using national forest inventory field plots and airborne laser scanning. Remote Sens. Environ. 123, 443–456.

Orka, H.O., Wulder, M.A., Gobakken, T., & Naesset, E., 2012. Subalpine zone delineation using LiDAR and landsat imagery. Remote Sens. Environ. 119, 11–20.

Ørka, H.O., Gobakken, T., Næsset, E., Ene, L., & Lien, V., 2012. Simultaneously acquired airborne laser scanning and multispectral imagery for individual tree species identification. Can. J. Remote Sens. 38, 125–138.

Stumberg, N., Ørka, H.O., Bollandsås, O.M., Gobakken, T., & Næsset, E.,

1. Classifying tree and nontree echoes from airborne laser scanning in the forest–tundra ecotone. Can. J. Remote Sens. 38, 655–666.

Wulder, M.A., White, J.C., Nelson, R.F., Naesset, E., Ørka, H.O., Coops, N.C., Hilker, T., Bater, C.W., & Gobakken, T., 2012. Lidar sampling for large-area forest characterization: A review. Remote Sens. Environ. 121, 196–209.

Korpela, I., Ørka, H.O., Hyyppä, J., Heikkinen, V., & Tokola, T., 2010a. Range and AGC normalization in airborne discrete-return LiDAR intensity data for forest canopies. ISPRS J. Photogramm. Remote Sens. 65, 369–379.

Korpela, I., Ørka, H.O., Maltamo, M., Tokola, T., Hyyppä, J., & Others, 2010b. Tree species classification using airborne LiDAR - effects of stand and tree parameters, downsizing of training set, intensity normalization, and sensor type. Silva Fenn. 44, 319–339.

Ørka, H.O., Næsset, E., & Bollandsås, O.M., 2010. 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 Sens. Environ. 114, 1445–1461.

Ørka, H.O., Næsset, E., & Bollandsås, O.M., 2009. Classifying species of individual trees by intensity and structure features derived from airborne laser scanner data. Remote Sens. Environ. 113, 1163–1174.