Phenology-based classification of Sentinel-2 data to detect coastal mangroves

Precise categorization of mangrove forests with medium spatial resolution satellite data is challenging and occasionally yields mixed outcomes. The available methods to estimate mangrove vegetation cover using moderately high-resolution images lack differentiation between mangrove and homestead vegetation. Mangrove vegetation displays a range of responses across the phenological cycle at different wavelengths of an optical sensor. By taking advantage of this principle, the study applied some mangrove and non-mangrove VIs as predictor variables sourced from monthly Sentinel-2 data. These variables were grouped by individual VIs and fed into the random forest algorithm to derive phenology-based classification. A suitable month for thresholding mangroves across different VIs was also ascertained. Results indicated that phenology-based classification with three classes was more accurate (95% overall accuracy) than threshold-based or WorldCover v100 classifications. MI and MVI layers from December image performed better in discerning mangroves. Findings have important implications in separating mangroves from other coastal vegetations.