MAPPING FOREST DISTURBANCE USING PURE FOREST INDEX TIME SERIES AND CCDC ALGORITHM

Abstract

Forest dynamics are closely related to climate change, natural disasters, and ecological diversity. The accumulated Landsat archive provides an unprecedented opportunity for long-term forest dynamics monitoring globally. However, using Landsat time series to detect small-scale and low-intensity disturbance events is still challenging since the moderate spatial resolution of Landsat images and the mixed pixel problem. Towards improving the ability of vegetation index (VI) in characterizing sub-pixel forest dynamics, this paper introduced the spectral mixture analysis (SMA) to develop a novel Pure Forest Index (PFI). The Continuous Change Detection and Classification (CCDC) algorithm was used to detect forest disturbance based on the PFI time series. Cross-comparison shows that PFI is far superior to other conventional VI in indicating forest conditions since it can enhance the spectral signal of the forest and suppress noises from the background. Time series analysis further demonstrates the superiority of PFI in accurately characterizing forest dynamics. The high overall accuracy of 0.96 for the forest disturbance map generated by the proposed approach was achieved. This study highlights a novel VI for accurately tracking subtle forest changes in a heterogeneous landscape.