ANALISIS PERUBAHAN LUAS HUTAN MANGROVE MENGGUNAKAN METODE NDVI (NORMALIZED DIFFERENCE INDEX) DI KECAMATAN BALIKPAPAN BARAT KOTA BALIKPAPAN

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Authors: Iitra Achbar Sahdian, Khairi Ahza Hail Keliwar, Muhammad Nur Ibnu Luthfi Saud, Muhammad Abdul Ghofur Al Hakim, Bayu Ashari Nugroho, Puspa Indah Nur Kholifah, Rosita Dewi Safitri
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Abstract

Mangrove ecosystems are a crucial component of coastal areas, serving both strategic ecological and economic functions; however, their existence is vulnerable to human activities and the dynamics of coastal environments. The Balikpapan Barat subdistrict is one of the areas in Balikpapan City that features various types of activities, including settlements, industrial activities, and port activities, which have the potential to impact the condition of the mangrove forests. This study aims to analyse changes in the area and density of mangrove forests in 2019 and 2025 using the Normalised Difference Vegetation Index (NDVI) method based on Sentinel-2A satellite imagery. The research method comprises three stages: image pre-processing, NDVI value calculation, and classification of mangrove density into three classes: low, medium, and high. The analysis results indicate a decline in the distribution area of mangroves in West Balikpapan District, primarily due to anthropogenic activities such as infrastructure development, settlement expansion, and coastal industrial activities. However, some areas still exhibit moderate to high vegetation density, indicating the presence of natural regeneration processes and the role of conservation areas in maintaining mangrove ecosystems. This study confirms that the use of remote sensing imagery and the NDVI method is effective in supporting spatial and sustainable mangrove monitoring, and can serve as a basis for planning the management and conservation of mangrove ecosystems in coastal areas.Keywords: mangrove, NDVI, Sentinel-2, Area, Density, West Balikpapan.

Fields of Study & Subjects

Coastal wetland ecosystem dynamics Wetland Management and Conservation Remote-Sensing Image Classification

Publication details

Journal / source
EDUSAINTEK JURNAL PENDIDIKAN SAINS DAN TEKNOLOGI
Publication date
1 February 2026
Language
EN