Mapping and Identification of Mangroves in the Coastal Area of Langkat Regency Using Sentinel-2 Imagery and Google Earth Engine

Authors

  • Tengku Abdillah Azis Department of Geography Education, Faculty of Social Sciences, Universitas Negeri Medan, Indonesia Author
  • Risbue Siregar Department of Geography Education, Faculty of Social Sciences, Universitas Negeri Medan, Indonesia Author
  • Aisyah Dwi Ramadhani Department of Geography Education, Faculty of Social Sciences, Universitas Negeri Medan, Indonesia Author
  • Amanda Anggraini Department of Geography Education, Faculty of Social Sciences, Universitas Negeri Medan, Indonesia Author
  • Nazwa Amelia Putri Department of Geography Education, Faculty of Social Sciences, Universitas Negeri Medan, Indonesia Author
  • Ruth Sahana Manalu Department of Geography Education, Faculty of Social Sciences, Universitas Negeri Medan, Indonesia Author
  • Vinny Natasya Hura Department of Geography Education, Faculty of Social Sciences, Universitas Negeri Medan, Indonesia Author
  • Yogi Marselinus Turnip Department of Geography Education, Faculty of Social Sciences, Universitas Negeri Medan, Indonesia Author
  • Ayu Surya Saputri Department of Geography Education, Faculty of Social Sciences, Universitas Negeri Medan, Indonesia Author
  • Widya Khairunisa Department of Geography Education, Faculty of Social Sciences, Universitas Negeri Medan, Indonesia Author

DOI:

https://doi.org/10.63639/yxa4r642

Keywords:

Mangrove, Sentinel-2, Google Earth Engine, Langkat Coast

Abstract

The mangrove ecosystem is a crucial component of tropical coastal biomes, providing various ecosystem services, including erosion protection, fish spawning habitats, and blue carbon storage. In Langkat Regency, North Sumatra Province, mangroves face significant pressure due to land conversion into aquaculture ponds and oil palm plantations. This study aims to map the spatial distribution of mangroves and identify their extent across nine coastal subdistricts influenced by marine dynamics and estuarine ecosystems. The primary data used were 2024 Sentinel-2 Level-2A imagery processed using the Google Earth Engine (GEE) platform. Pre-processing included cloud masking, median compositing, and extraction of vegetation indices (NDVI, NDWI, NDBI) to enhance classification accuracy. Land cover classification was performed using the Random Forest algorithm with seven classes, including mangrove, water bodies, aquaculture/swamp, oil palm, terrestrial vegetation, open land, and built-up areas. Validation was conducted using 210 sample points, resulting in an overall accuracy of 90% and a mangrove classification accuracy of 100%. Comparative analysis with the National Mangrove Map (BIG) revealed a discrepancy in area, with 18,856.69 ha in this study compared to 15,093.13 ha in the BIG data, influenced by differences in delineation methods and data resolution. The results indicate that although mangroves are still widely distributed, some coastal areas are under pressure from land-use change and require more targeted protection. The Sentinel-2 and GEE-based classification maps provide accurate and up-to-date spatial information that can support management, conservation, and sustainable spatial planning in the coastal areas of Langkat Regency

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Published

2026-01-02

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Articles

How to Cite

Mapping and Identification of Mangroves in the Coastal Area of Langkat Regency Using Sentinel-2 Imagery and Google Earth Engine. (2026). YKP JOURNAL, 1(3), 114-121. https://doi.org/10.63639/yxa4r642