Individual tree crown approach for predicting site index in boreal forests using airborne laser scanning and hyperspectral data |
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| Institution: | 1. Finnish Geodetic Institute, Geodeetinrinne 2, FI-02431 Masala, Finland;2. Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 Helsinki, Finland;1. Virginia Polytechnic Institute and State University, Department of Forest Resources and Environmental Conservation, Blacksburg, VA 24061, USA;2. Department of Life and Environmental Sciences, Bournemouth University, Poole, Dorset, UK;3. Centre for Ecology and Hydrology, Wallingford, Oxfordshire, UK;1. Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway;2. USDA Forest Service, Pacific Northwest Research Station, Seattle, WA 98195, USA;3. Code 618/Biospheric Science Laboratory, NASA-Goddard Space Flight Center, Greenbelt, MD 20771, USA;4. School of Environmental and Forest Sciences, University of Washington, Seattle, WA 98195, USA;5. Code 618/Biospheric Science Laboratory–retired, NASA-Goddard Space Flight Center, Greenbelt, MD 20771, USA;1. Centre de recherche sur les matériaux renouvelables, Département des sciences du bois et de la forêt, Pavillon Gene-H.-Kruger, 2425 rue de la Terrasse, Université Laval, Québec QC G1 V 0A6, Canada;2. Faculty of Forestry, University of Toronto, Toronto, Canada;3. Department of Geomatics Sciences, Pavillon Louis-Jacques-Casault, 1055, avenue du Séminaire, Université Laval, Québec G1 V 0A6, Canada |
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| Abstract: | Site productivity is essential information for sustainable forest management and site index (SI) is the most common quantitative measure of it. The SI is usually determined for individual tree species based on tree height and the age of the 100 largest trees per hectare according to stem diameter. The present study aimed to demonstrate and validate a methodology for the determination of SI using remotely sensed data, in particular fused airborne laser scanning (ALS) and airborne hyperspectral data in a forest site in Norway. The applied approach was based on individual tree crown (ITC) delineation: tree species, tree height, diameter at breast height (DBH), and age were modelled and predicted at ITC level using 10-fold cross validation. Four dominant ITCs per 400 m2 plot were selected as input to predict SI at plot level for Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.). We applied an experimental setup with different subsets of dominant ITCs with different combinations of attributes (predicted or field-derived) for SI predictions. The results revealed that the selection of the dominant ITCs based on the largest DBH independent of tree species, predicted the SI with similar accuracy as ITCs matched with field-derived dominant trees (RMSE: 27.6% vs 23.3%). The SI accuracies were at the same level when dominant species were determined from the remotely sensed or field data (RMSE: 27.6% vs 27.8%). However, when the predicted tree age was used the SI accuracy decreased compared to field-derived age (RMSE: 27.6% vs 7.6%). In general, SI was overpredicted for both tree species in the mature forest, while there was an underprediction in the young forest. In conclusion, the proposed approach for SI determination based on ITC delineation and a combination of ALS and hyperspectral data is an efficient and stable procedure, which has the potential to predict SI in forest areas at various spatial scales and additionally to improve existing SI maps in Norway. |
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| Keywords: | ALS Hyperspectral data Individual tree crowns Biophysical attribute prediction Site index Forestry Fusion Remote sensing |
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