Tree species classification in an extensive forest area using airborne hyperspectral data under varying light conditions

Abstract

Although airborne hyperspectral data with detailed spatial and spectral information has demonstrated significant potential for tree species classification, it has not been widely used over large areas. A comprehensive process based on multi-flightline airborne hyperspectral data is lacking over large, forested areas influenced by both the effects of bidirectional reflectance distribution function (BRDF) and cloud shadow contamination. In this study, hyperspectral data were collected over the Mengjiagang Forest Farm in Northeast China in the summer of 2017 using the Chinese Academy of Forestry’s LiDAR, CCD, and hyperspectral systems (CAF-LiCHy). After BRDF correction and cloud shadow detection processing, a tree species classification workflow was developed for sunlit and cloud-shaded forest areas with input features of minimum noise fraction reduced bands, spectral vegetation indices, and texture information. Results indicate that BRDF-corrected sunlit hyperspectral data can provide a stable and high classification accuracy based on representative training data. Cloud-shaded pixels also have good spectral separability for species classification. The red-edge spectral information and ratio-based spectral indices with high importance scores are recommended as input features for species classification under varying light conditions. According to the classification accuracies through field survey data at multiple spatial scales, it was found that species classification within an extensive forest area using airborne hyperspectral data under various illuminations can be successfully carried out using the effective radiometric consistency process and feature selection strategy.

Keywords

Tree species classification / BRDF effects / Cloud shadow / Airborne hyperspectral data / Random forest