Temporal variation of threshold segmentation-based mangrove mapping indices in karimunjawa-kemujan islands with sentinel images

Mangroves are essential vegetation that makes coastal communities resilient to water-related disasters while also helping in climate change mitigation as effective carbon sinks. However, they are subject to numerous stressors and threats. This study analyzed and compared the temporal variation of threshold segmentation-based mangrove mapping indices on quarterly-composited Sentinel imagery of Karimunjawa-Kemujan Islands as an alternative to using machine learning classification algorithms. Results show that Mangrove Vegetation Index (MVI), Automatic Mangrove Map and Index (AMMI), and SWIR Bands (SWIRB) had good separability potential and accuracies exceeded 80% in all quarters. However, SWIRB yielded the best results in simplicity, accuracy, minimal variation, and agreement with the proponent's set threshold.