Integrating multi-resolution Earth Observation and ground-based data for enhanced coastal and mangrove monitoring in Kenya

Sedimentation and coastal erosion pose significant threats to Kenya's shoreline, impacting both ecosystems and livelihoods. A major challenge lies in the spatial and temporal resolution of open-source datasets, limiting their effectiveness for routine coastal monitoring in localized areas. While commercial Earth Observation (EO) satellites offer higher precision, their cost restricts accessibility for administrative use in developing countries. This study proposes a methodology that integrates multi-resolution EO data with ground-based measurements to enhance coastal monitoring and the accessibility of satellite data. It utilizes Landsat, Sentinel-2 multispectral and Sentinel-1 SAR imagery to improve change detection and sedimentation analysis, focusing on land-sea interactions, shoreline changes, and sediment transport. Sedimentation affects local vegetation, including mangroves, which are influenced by shoreline evolution. Geodetic GPS measurements are incorporated for ground truthing. An algorithm developed on Google Earth Engine processes data, with post-processing in ERDAS, QGIS, and ESA SNAP. Preliminary findings indicate improved detection of erosion hotspots and sediment redistribution. This multiscale approach supports sustainable coastal planning in Kenya.