<![CDATA[This study aims to identify mangrove species and map the density of mangrove vegetation in the Tanjung Pemancingan area, Kotabaru, using an object-based classification method (OBIA) applied to Sentinel‑2 imagery and Unmanned Aerial Vehicle (UAV) data. Mangroves play a crucial role in protecting coastlines from erosion and serving as habitats for various species, making an in‑depth understanding of mangrove distribution and types essential for coastal conservation and environmental management. The OBIA method allows for more accurate mapping by considering texture, shape, and more complex spatial patterns compared to traditional pixel‑based methods. In this study, OBIA is combined with the Support Vector Machine (SVM) algorithm and an optimized multiscale segmentation scheme to improve mangrove species discrimination, and the resulting products are linked with NDVI‑based canopy density classes for management‑oriented analysis. The analysis utilizes Sentinel‑2 Level‑2A satellite imagery with a spatial resolution of 10 × 10 meters (bands 8, 4, 3, and 2) and very high‑resolution UAV data (≈ 4.0 cm/pixel) generated from flights at 150 m altitude. The NDVI values for mangroves in the study area range from −0.30 to 0.686 and are classified into three canopy density classes using the equal interval method: sparse (−0.30 to 0.026), moderate (0.027 to 0.356), and dense (0.357 to 0.686). The OBIA method combined with the SVM algorithm successfully discriminates seven mangrove species with an overall accuracy (OA) of 72.46%, which exceeds the minimum 70% accuracy standard for mangrove land cover and canopy density interpretation set by the Geospatial Information Agency (BIG). The identified mangrove species include Avicennia alba, Avicennia marina, Avicennia officinalis, Avicennia rumphiana, Bruguiera gymnorhiza, Rhizophora apiculata, and Sonneratia alba, with A. rumphiana being the most dominant species, covering an area of 13.87 hectares, while A. officinalis occupies only 0.6 hectares. The mangrove vegetation density and species composition are successfully mapped and integrated, providing valuable information that can be used in conservation planning, coastal resource management, disaster mitigation, and ecotourism development in the area. Furthermore, these results highlight the potential of combining Sentinel‑2, UAV, OBIA, and SVM as an operational framework for mangrove ecosystem monitoring in coastal industrial settings]]>
