Mapping Mangroves Ecosystems Using Synthetic Aperture Radar (SAR)

Mangrove ecosystems provide essential ecological services, including shoreline protection, carbon storage, and habitat support. However, they are increasingly under threat from anthropogenic pressures such as agricultural encroachment, logging, and climate-induced sea-level rise. Monitoring mangrove extent and condition is therefore critical, yet challenging due to their location in remote intertidal zones and frequent cloud cover that limits field-based and optical observations. Remote sensing offers an effective solution by enabling consistent, large-scale monitoring of vegetation across time and space. This study assessed the effectiveness of Sentinel-1 Synthetic Aperture Radar (SAR) data for mangrove mapping in Ambanja Bay, Madagascar, and evaluated whether fusing it with Sentinel-2 optical imagery enhances classification performance. Using a Random Forest classifier, both SAR-only and multi-sensor datasets were analyzed to classify land cover and assess changes between 2019 and 2024. The SAR-only classification achieved 60% accuracy, with notable confusion between mangroves and water due to similar backscatter responses. In contrast, the fusion approach significantly improved classification, achieving an overall accuracy of 94%. Land cover change analysis revealed transitions from barren land to non-mangrove vegetation and localized expansion of mangrove cover. These findings demonstrate that integrating SAR and optical data substantially improves classification accuracy, reinforcing the value of multi-sensor remote sensing for environmental monitoring and conservation planning in dynamic coastal ecosystems. Google Earth Engine ArcGIS Pro Python