Application of normalized difference vegetation index in classifying land cover change over Bangli regency by using Landsat 8 imagery

  • Putu Aryastana International Ph.D. Program in Environmental Science and Technology (University System of Taiwan), National Central University, Taoyuan, 32001, Taiwan (R.O.C.)
  • Maria Imaculata Goran Mosa Master Program of Infrastructure and Environmental Engineering, Postgraduate Program, Warmadewa University, Denpasar, 80235, Indonesia
  • Wayan Widiana Master Program of Infrastructure and Environmental Engineering, Postgraduate Program, Warmadewa University, Denpasar, 80235, Indonesia
  • I Made Eryana Eka Putra Master Program of Infrastructure and Environmental Engineering, Postgraduate Program, Warmadewa University, Denpasar, 80235, Indonesia
  • Gede Rustiawan Master Program of Infrastructure and Environmental Engineering, Postgraduate Program, Warmadewa University, Denpasar, 80235, Indonesia
DOI: https://doi.org/10.22225/jipe.1.1.2022.8-14
Keywords: Bangli, classification, Landsat, NDVI

Abstract

The information on land cover changes is very important in regional spatial planning. Remote sensing technology can minimize the cost and time in analyzing land cover changes. Normalized Difference Vegetation Index (NDVI) is a vegetation index that combines red and near-infrared channels so that it can provide approximate information about land cover in an area. The objective of this study is to extract land cover change information from Landsat 8 images based on NDVI values in Bangli Regency in 2015 and 2021. The classification method used to estimate the type of land cover is supervised classification. The results reveal the decrease of the land cover in the category of water body, sand, dry land/soil, rice fields, and vegetation, which are 1.62%, 14.14%, 7.93%, 8.63%, and 2.45%, respectively, while an increase in the settlement category by 30.12%. The overall accuracy of land cover classification result based on NDVI value is 86.54%.

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Published
2022-04-13
Issue
Vol. 1 No. 1 (2022)
Section
Articles
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