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All Journal JRST (Jurnal Riset Sains dan Teknologi)
Amelia Rosmayanti
Universitas Pendidikan Indonesia
Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering

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Utilization of Sentinel-2A Imagery to Analyze Vegetation Density Using the MSARVI Method in Cigugurgirang Village, Parongpong Sub-district, West Bandung Regency Arrafi Malika Ardy; Amelia Rosmayanti; Riki Ridwana; Lili Somantri
JRST (Jurnal Riset Sains dan Teknologi) Volume 9 No. 2 September 2025: JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v9i2.23470

Abstract

Vegetation monitoring using vegetation indices is widely applied in satellite image analysis to understand vegetation distribution and density. However, its accuracy is often affected by atmospheric disturbances and soil background noise. MSARVI (Modified Soil and Atmospheric Resistance Vegetation Index) was developed to address these issues, yet its application remains limited. This study aims to analyze vegetation density and evaluate the accuracy of the MSARVI index using Sentinel-2A imagery in Cigugurgirang Village. The methodology includes processing Sentinel-2A imagery to calculate MSARVI values, classifying vegetation density levels, and validating results using field reference data. MSARVI is computed by integrating atmospheric and soil background corrections to enhance vegetation detection accuracy. The results indicate that Cigugurgirang Village is predominantly covered by high-density vegetation, spanning an area of 147.71 ha. The accuracy assessment yielded an overall accuracy of 87.50% and a kappa accuracy of 83.34%, demonstrating high reliability. These findings confirm that MSARVI is an effective method for vegetation density mapping with high accuracy.
Co-Authors Arrafi Malika Ardy Lili Somantri Ridwana, Riki