Open Collections

ANTWI BOASIAKO, BENEDICTA

2025-04-08; 2025-04-22

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.

Diana; Ambanja Bay; Ambanja

English

University of British Columbia Library

10.14288/1.0448468

https://doi.org/10.5683/SP3/LRL6TF

Madagascar