Mapping of Land Use in Cijengkol Village, Subang Regency Using Sentinel-2 MSI (MultiSpectral Instrument)

Hafid Alwysihah; Salsa Fauziyyah Adni; Rahmat Asy’Ari

doi:10.36706/jlso.12.1.2023.627

Authors

Hafid Alwysihah Department of Silviculture, Faculty of Forestry and Environment, IPB University, Bogor 16680, West Java, Indonesia
Salsa Fauziyyah Adni Department of Silviculture, Faculty of Forestry and Environment, IPB University, Bogor 16680, West Java, Indonesia
Rahmat Asy’Ari Department of Forest Management, Faculty of Forestry and Environment, IPB University, Bogor 16680, West Java, Indonesia

DOI:

https://doi.org/10.36706/jlso.12.1.2023.627

Keywords:

index algorithm, land use, mapping

Abstract

Every year, land use in Indonesia has increased, both for settlements, agriculture, and other uses that are used to meet the needs of human life for certain purposes. Cijengkol Village is one of the agricultural development villages in Subang Regency and is affected by topography, resulting in different types of land use. This mapping aimed to provide information related to the classification of land use for settlements, agriculture, plantations, fields, and others in Cijengkol Village. Land use mapping was carried out in this village to reveal the distribution of land use so that it could be taken into consideration, as well as directions for determining spatial planning by the local government. Therefore, this mapping was carried out by involving the Sentinel-2 MultiSpectral Instrument (MSI) image data source and processed using a cloud computing-based Google Earth Engine (GEE) platform. Six spectral scoring index algorithms exist the Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Specific Leaf Area Vegetation Index (SLAVI), Index-Based Built-Up Index (IBI), Normalized Difference Built-up Index (NDBI), and the Normalized Difference Water Index (NDWI). The results of the random forest (RF) classification algorithm resulted in six types of land use with percentages, namely mixed gardens (39.69%), agriculture (34.08%), homogeneous gardens (13.57%), residential (10.58%), open land (2.09%), and water bodies (0.001%). Image classification in this mapping also produces an accuracy rate of 82.43% (Overall Accuracy) and 0.78 (Kappa Statistics). The results of this research are of a good level of accuracy, so it is hoped that this research will become a database for the local village government and become a reference for further research.

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