Advanced Bayesian Method for Timely Small-Scale Forest Loss Detection in the Brazilian Amazon and Cerrado with Sentinel-1 Time-Series

Abstract. The world’s forests are undergoing significant changes due to loss and degradation, emphasizing the need for Near Real-Time (NRT) monitoring to prevent further damage. Traditional monitoring methods using optical imagery are hindered by cloud coverage, while newer Synthetic Aperture Radar (SAR) systems, although operational in all weather conditions, face challenges such as sensitivity to soil moisture and the need for spatial filtering to reduce speckle effects. These limitations affect the detection of small-scale forest loss, especially in seasonally variable regions like dry forests and savannas. This paper presents a SAR-based forest disturbance detection method using Bayesian inference. Unlike traditional methods, this approach maintains the native resolution of the data by avoiding spatial filtering. Forest disturbance is modelled as a change-point detection problem within a non-filtered Sentinel-1 time series, where each new observation updates the probability of forest loss by leveraging prior information and a data model. This sequential adaptation ensures robustness against variations and trends, making it effective in monitoring disturbances across diverse forest types, including areas affected by seasonality. The proposed method was tested against other NRT monitoring systems for the year 2020, using small validation polygons (under 1 hectare) in the Brazilian Amazon and Cerrado savanna. Results demonstrate significant improvements in detecting small-scale disturbances and drastically reduced false alarm rates in both biomes. Notably, in the seasonality-sensitive Cerrado, our solution completely outperforms the leading and only existing optical technology.