Mapping coastal green infrastructure along the Pondicherry coast using remote sensing data and machine learning algorithm

Coastal green infrastructure provides numerous ecosystem services, including flood protection, erosion control, carbon sequestration, and habitat for marine life. Mapping and monitoring these critical coastal habitats are essential for sustainable management and conservation efforts. This study employed the Google Earth Engine (GEE) cloud platform with high-resolution multispectral satellite imagery (Sentinel-2 data with 10 m spatial resolution), Airborne Laser Terrain Mapper (ALTM) elevation data with 5 m resolution, and advanced machine learning (ML) algorithms used to map the distribution and extent of coastal green infrastructure along the Pondicherry coastline in southern India. A random forest (RF) classifier was trained on a diverse set of reference data (70% for training and 30% for validation) collected through extensive field surveys and visual interpretation of very high-resolution aerial imagery. The model integrated spectral information from multiple satellite sensors along with derived biophysical indices to accurately delineate different coastal vegetation types. The resulting maps revealed detailed spatial patterns of mangroves, sand dunes, and coastal plantations with an overall accuracy exceeding 90% verified with the field data. Analyses quantified their spatial coverage and fragmentation along the study area. This high-resolution, accurate baseline data can inform coastal management strategies, including targeted conservation efforts, ecological restoration projects, climate change adaptation planning, and sustainable development practices that preserve vital green infrastructure. The workflow demonstrated the robust capabilities of ML methods coupled with multi-source remote sensing data for effectively mapping complex and dynamic coastal ecosystems at a regional scale. The techniques can be adapted for other coastal regions to understand green infrastructure dynamics better and support evidence-based policies promoting ecological and community resilience.