Classification of planted forest species in southern China with airborne hyperspectral and LiDAR data

Abstract

ABSTRACT Plantations in southern China are important components of forest ecosystems. However, the distribution of the native tree species is fragmented. Accurate tree species classification can provide scientific support for forest management and measurement. In this study, Gaofeng forest farm in Guangxi Province was selected as the research area, and hyperspectral image combining with LiDAR was used for tree species classification. Derivative reflectance features, vegetation indices, and spatial texture features were extracted from hyperspectral imagery and screened by random forest method. Then, these features were combined with height information provided by LiDAR used for tree species classification, using an object-based classifier. We found that the derivative reflectance feature of hyperspectral imagery was most effective for the tree species with hard leaves, the vegetation index was best suited for the tree species with dark leaves, and the texture feature was ideal for tree species with prominent veins and bright leaves. Tree height data most significantly identified taller tree species. Fusion of hyperspectral imagery with LiDAR data resulted in a maximum classification accuracy of 91.59% and a kappa coefficient of 0.897. The classification accuracy can meet the requirements of forest resource monitoring.