Integration of deep learning algorithms with a Bayesian method for improved characterization of tropical deforestation frontiers using Sentinel-1 SAR imagery

Tropical deforestation frontiers continue to expand at alarming rates, but their fine-scale temporal patterns (e.g., start timing, patch forming speed, temporal clustering within a year) remain unresolved. Previous deforestation monitoring focus on the annual dynamics or the timely identification of deforestation activities; however, improved methods are needed for accurate mapping of deforestation patches at higher temporal resolution (i.e., sub-monthly) to better reveal their fine-scale temporal dynamics. We propose an optimization method integrating the spatial and temporal context information to improve the sub-monthly deforestation mapping from Sentinel-1 (S1) SAR data: (1) a deep learning-based spatial optimization to suppress speckle noises; (2) a Bayesian-based temporal optimization to meaningfully combine deforestations detected in the S1 data streams. The proposed method was comprehensively assessed in three deforestation hotspots in Brazil - Acre, Rondônia and Pará, for the whole year of 2019. Results showed: (1) the spatial optimization alone can improve the accuracies of deforestation mapping from single-date S1 images for up to 7.3%; (2) the Bayesian-based temporal optimization improved the deforestation mapping accuracies for about 5.9% after three post-deforestation S1 observations (about 18 ± 3 days after deforestation); (3) combining the spatial and temporal optimizations achieved the highest classification accuracies (overall accuracy of 91.0%, IoU of 89.1%), surpassing the baseline monthly composite method (overall accuracy of 89.3%, IoU of 87.3%) within fewer observation days. Further frontier analysis based on these sub-monthly results showed varying distributions of patch size and forming speed in these three study sites during the wet and dry seasons. The temporal clustering of deforestation also differed among sites during 2019: deforestations in Rondônia were most concentrated during the dry season (CV = 1.1), followed by Pará (CV = 0.75), while Acre showed more even temporal distribution in deforestation year-round (CV = 0.57). The proposed method thus can be used for revealing unprecedented temporal details regarding tropical deforestation frontiers, which is critical for evaluating the ecological consequences and formulating scientific conservation strategies. •Combine multi-level spatio-temporal optimization method to improve S1-based deforestation mapping•Characterize fine-scale temporal patterns (start timing, patch forming