Mapping dominant mangrove genera using SPOT-6 imagery and Maximum Likelihood Classification for conservation planning in Langsa City, Aceh, Indonesia
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Abstract. Rahmadi MT, Damayani WN, Harefa MS. 2026. Mapping dominant mangrove genera using SPOT-6 imagery and Maximum Likelihood Classification for conservation planning in Langsa City, Aceh, Indonesia. Asian J For 10 (1): r100120. https://doi.org/10.13057/asianjfor/r100120. Mangrove forests are among the world's most productive and ecologically significant coastal ecosystems, providing essential services such as shoreline stabilization, carbon sequestration, and habitat provision. Indonesia accounts for approximately 19.5% of the global mangrove area, underscoring its critical role in coastal resilience and in supporting coastal communities. Despite their importance, these ecosystems face increasing pressures from anthropogenic activities, land-use change, and climate change, necessitating accurate and up-to-date information for effective monitoring and restoration planning. While remote sensing is indispensable, mangrove mapping at the genus level remains limited. Such detailed data are essential for robust zoning-based conservation planning and targeted strategies. This study addresses this gap by characterizing the distribution of dominant mangrove genera in Langsa City, Aceh, an area with mangrove representation. Utilizing high-resolution SPOT-6 imagery and the Maximum Likelihood Classification method, supported by comprehensive field validation with 30 control points, we produced a genus-level classification map. The total mangrove area identified in Langsa City is 5,837.46 ha, distributed across five sub-districts. Based on the classification and field data, three dominant mangrove genera were reliably identified: Rhizophora (2,216.01 ha), Avicennia (2,086.34 ha), and Ceriops (1,535.11 ha), which collectively account for 95% of the mangrove area, with Rhizophora being the most dominant. The overall accuracy and kappa coefficient achieved were 83% and 76%, respectively, indicating robust and reliable mapping results. This up-to-date, genus-level spatial information is invaluable for effective, zoning-based mangrove management, targeted conservation planning, and sustainable resource utilization in Langsa City and similar coastal regions. The findings underscore the potential of high-resolution SPOT-6 imagery for precise mapping of mangrove genera and support evidence-based conservation efforts.
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