Forest Change Mapping using Multi-Source Satellite SAR, Optical, and LiDAR Remote Sensing Data

Abstract. This study highlights the efficacy of leveraging multi-source satellite remote sensing for precise and dependable forest change mapping. Forests play a crucial role as carbon reservoirs and are indispensable components of the global carbon and water cycle, providing essential ecosystem services. Despite their significance, forests face deforestation, diseases, and climate change threats. Recent satellite remote sensing technology advancements have facilitated accurate, persistent, and large-scale forest dynamics monitoring. New generation satellite LiDAR systems, such as GEDI and ICESat-2, offer frequent and global height information at high spatial resolutions. This research presents a processing framework for mapping forest changes by integrating SAR and optical features from Sentinel-1 and Sentinel-2 imagery with canopy heights derived from GEDI and ICESat-2 datasets. Multiple experiments and analyses were conducted in two study areas. The findings underscore the significant impact of incorporating canopy height information in enhancing the accuracy of forest change mapping, resulting in a 15% improvement in precision and a 13% enhancement in F1-score in the experimental setups. Furthermore, the developed model exhibits increased reliability and confidence in identifying correctly changed and unchanged areas while being less confident in incorrect predictions.