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Characterizing forest species composition using multiple remote sensing data sources and inventory approaches

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The purpose of the study was to evaluate tree species composition estimated using combinations of different remotely sensed data with different inventory approaches for a forested area in Norway. Basal area species composition was estimated as both species proportions and main species by using data from airborne laser scanning (ALS) and airborne (multispectral and hyperspectral) imagery as auxiliary information in combination with three different inventory approaches: individual tree crown (ITC) approach; semi-individual tree crown (SITC) approach; and area-based approach (ABA). The main tree species classification obtained an overall accuracy higher than 86% for all ABA alternatives and for the two other inventory approaches (ITC and SITC) when combining ALS and hyperspectral imagery. The correlation between estimated species proportions and species proportions measured in the field was higher for coniferous species than for deciduous species and increased with the spectral resolution used. Especially, the ITC approach provided more accurate information regarding the proportion of deciduous species that occurred only in small proportions in the study area. Furthermore, the species proportion estimates of 83% of the plots deviated from field measured species proportions by two-tenths or less. Thus, species composition could be accurately estimated using the different approaches and the highest levels of accuracy were attained when ALS was used in combination with hyperspectral imagery. The accuracies obtained using the ABA in combination with only ALS data were encouraging for implementation in operational forest inventories.
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Keywords: Species composition; airborne laser scanning; forest inventory; hyperspectral imagery; multispectral imagery; tree species

Document Type: Research Article

Affiliations: 1: Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway 2: Department of Sustainable Agro-ecosystems and Bioresources, Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige, (TN,), Italy

Publication date: October 1, 2013

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