Phenology-based classification of Sentinel-2 data to detect coastal mangroves. Issue 26 (13th December 2022)

• Record Type: Journal Article
• Authors: Mahmud, Sultan; Redowan, Mohammad; Ahmed, Romel; Khan, Asif Alvee; Rahman, Md. Mokshedur
• Is Part Of: Geocarto international. Volume 37:Issue 26(2023)
• Page Start: 14335
• Page End: 14354
• Publication Date: 2022-12-13
• Subjects: Mangrove -- Sentinel-2 -- random forest -- image classification -- vegetation index; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2022.2087754
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: 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. Taking advantage of this principle, this study utilized some mangrove and non-mangrove vegetation indices (VIs) as predictor variables sourced from monthly Sentinel-2 data into the random forest algorithm to derive a phenology-based classification outcome. It also ascertained a suitable month for thresholding mangroves across different VIs. 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.