Forest biodiversity mapping using airborne LiDAR and hyperspectral data

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 (R
²
>0.58, P<;0.01) to indicate the forest biodiversity, and the max species number detecting by the 7 biochemical combination is 14. Then, 7 vegetation indices are derived to represent the corresponding biochemical components, and scaled from the canopy to leaf scale by divided leaf area index. In addition, we use the morphological crown control method based on watershed algorithm to isolate individual tree crown by LiDAR (>4 points/m
²
). 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 (R
²
=0.65,P<;0.01) and the predicted Shannon-Wiener index provide higher estimated accuracy (R
²
=0.83, P<;0.01) than the species richness.