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Dahlan
Syiah Kuala University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Education Materials Science & Nanotechnology Physics

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Application of Remote Sensing for Mapping Vegetation Density Using Normalized Difference Vegetation Index (NDVI) in Langsa City Mangrove Forest Fuji Attariq Unsha; Saida Rasnovi; Dahlan
Jurnal Penelitian Pendidikan IPA Vol 11 No 7 (2025): July
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i7.11530

Abstract

Mangroves are vital coastal ecosystems that thrive in tidal environments and play a crucial role in biodiversity and shoreline protection. This study aims to assess the vegetation density of the Langsa City Mangrove Forest using the Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2A satellite imagery and analyzed through Geographic Information System (GIS) tools, particularly ArcGIS. NDVI values were categorized into four classes: very low (-0.15–0.34), low (0.35–0.48), medium (0.49–0.61), and high (0.62–0.83). The spatial analysis revealed that 57.2% of the area (approx. 128.5 ha) exhibited high vegetation density, while 24.6% (55.2 ha) showed medium density, and 13.3% (29.9 ha) had low vegetation. Approximately 4.9% (11.0 ha) of the area was classified as very low density, indicating regions with potential for ecological rehabilitation. These findings demonstrate that NDVI is an effective and reliable indicator for monitoring mangrove vegetation health. Routine application of NDVI analysis is essential for supporting sustainable management strategies and long-term conservation planning in coastal forest ecosystems..
Co-Authors Fuji Attariq Unsha SAIDA RASNOVI