MAPPING SPATIAL-TEMPORAL FOREST HETEROGENEITY IN THE TROPICAL BELT BY ALOS-2/PALSAR-2 BIG DATA ANALYSIS

Insufficient knowledge about spatial-temporal forest heterogeneities in the tropics is a major impediment to better estimation of carbon storage and prevention of deforestation by remote sensing. While the differences between the dense evergreen rainforest and the open (semi-) deciduous dry forest is obviously large, variations caused by seasonal changes can easily introduce fluctuations in the same order of magnitude within the same forest class. In this study, we present a comprehensive analysis of the variability of tropical forests based on homogeneous big data analysis on multitemporal ALOS-2 dual-polarized ScanSAR data. The first, easy to understand global forest variability maps provide unseen insights into forest structures for the entire tropical belt. Based on these results, we discuss the development of a new global classification scheme for tropical forests. Preliminary results demonstrate how the use of various statistical parameters obtained from the long-term systematic L-band SAR forest monitoring data, including the average gamma(0), its temporal standard deviation and the gamma(0) range, can improve the classical global-scale forest classifications. In addition to the basic separation into the three main forest types i) rainforest, ii) moist forest and iii) dry forest, the results provide a detailed mapping of the seasonally flooded forest areas.