Mapping individual trees from airborne multi-sensor imagery

Individual tree species mapping is important to understand forest dynamics and species distribution patterns. Airborne LiDAR with hyperspectral imaging has been extensively used to extract biophysical traits of vegetation and detect species. However, its application for individual tree mapping is limited due to technical problems. To address the problems, this paper presents effective and efficient algorithms in terms of tackling co-alingment of LiDAR and hyperspectral datasets, classifying individual trees, thus detecting tree species and leaf chemistry from the tree mapping.