Forest biodiversity mapping using airborne LiDAR and hyperspectral data

摘要

Monitoring forest biodiversity is essential to the conservation and management of forest resource. A new method called “spectranomics” that map forest species richness based on leaf biochemical and spectroscopic traits using imaging spectroscopy has been developed. In this study, we use this method combined with the airborne imaging spectroscopy (PHI-3 with 1m spatial resolution) data to detect the relationship among the spectral, biochemical and taxonomic diversity of tree species based on 20 dominant canopy species collected in the Longmenhe Forest Nature Reserve of China. Seven optimal biochemical components (chlorophyll, carotenoid, water, specific leaf area, nitrogen, cellulose, and lignin) are selected (R2>0.58, P4 points/m2). Finally, a self-adaptive Fuzzy C-Means (FCM) clustering algorithm is applied to determine the optimal clustering numbers (i.e. species richness) and Shannon-Wiener for each 30×30m window based on the isolated individual tree height and 7 biochemical indices. According to total 22 sample plots, the mapping results show that the predicted species richness is close to the field measurements (R2=0.65,P<;0.01) and the predicted Shannon-Wiener index provide higher estimated accuracy (R2=0.83, P<;0.01) than the species richness.