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All Journal Jurnal Sains Teknologi dan Lingkungan (JSTL)
Muhammad Anwar Hadi
Program Studi Kehutanan, Fakultas Pertanian, Universitas Mataram
Agriculture, Biological Sciences & Forestry Computer Science & IT Public Health

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Identifikasi Sebaran Spasial dan Kerapatan Mangrove Gili Lawang menggunakan Citra Landsat 9 OLI-2/TIRS-2: Identification Gili Lawang Mangrove Spatial Distribution and Density with Landsat 9 OLI-2/TIRS-2 Imagery Andrie Ridzki Prasetyo; Niechi Valentino; Muhammad Anwar Hadi
JURNAL SAINS TEKNOLOGI & LINGKUNGAN Vol. 9 No. 2 (2023): JURNAL SAINS TEKNOLOGI & LINGKUNGAN
Publisher : LPPM Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jstl.v9i2.450

Abstract

Mangrove ecosystems have a great influence on the sustainability of human life and the environment. The high level of vulnerability of mangrove ecosystems has implications for the importance of quality planning. This study aims to identify the spatial distribution and density of mangrove forests in Gili Lawang using Landsat 9 OLI-2/TIRS-2 satellite imagery. Data processing is done with the help of the QGIS 3.30 application. Data processing consists of band combinations, image classification with the SVM algorithm, classification results accuracy test, NDVI value extract, and reclass NDVI. The results showed that the use of band 564 in Landsat 9 imagery visually resulted in an increase in sharpness in identifying mangrove ecosystems. Classification of objects with the SVM algorithm has overall accuracy and kappa accuracy > 80%. The identified area of Gili Lawang is 432.72 ha, consisting of 37.89 ha of mangroves, 58.11 ha of non-mangrove and 3.75 ha of water bodies. NDVI values at the study sites ranged from 0.068 to 0.87. The maximum NDVI value is found in mangrove objects, while the minimum NDVI value is found in water body objects. Mangrove density in Gili Lawang is dominated by high and very high density. The use of Landsat 9 OLI-2/TIRS-2 imagery in the future is expected to provide positive benefits in providing data and information related to natural resources.  
Co-Authors Andrie Ridzki Prasetyo Niechi Valentino