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

doi:10.30595/jrst.v9i2.23470

Authors

Arrafi Malika Ardy Universitas Pendidikan Indonesia
Amelia Rosmayanti Universitas Pendidikan Indonesia
Riki Ridwana Universitas Pendidikan Indonesia
Lili Somantri Universitas Pendidikan Indonesia

DOI:

https://doi.org/10.30595/jrst.v9i2.23470

Keywords:

Vegetation Density, MSARVI, Sentinel-2, Remote Sensing, Kappa Accuracy

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.

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