COMPARISON OF SOIL ADJUSTED VEGETATION INDEX (SAVI) AND MODIFIED SOIL ADJUSTED VEGETATION INDEX (MSAVI) METHODS TO VIEW VEGETATION DENSITY IN PADANG CITY USING LANDSAT 8 IMAGE

  • Gilang Novando Student of the D3 Remote Sensing Technology Study Program, Universitas Negeri Padang
  • Dian Adhetya Arif Lecturer Study Program D3 Remote Sensing Technology, Universitas Negeri Padang
DOI: https://doi.org/10.24036/irsaj.v2i1.23
Keywords: Vegetation Density, Vegetation Index, Landsat Imagery, Remote Sensing

Abstract

This study aims to see how the shape of the vegetation density map uses the SAVI (Soil Adjusted Vegetation Index) and MSAVI (Modified Soil Adjusted Vegetation Index) methods in Padang City using remote sensing data in the form of Landsat 8 imagery. This type of research is quantitative using numerical data. and analysis, as well as presenting data in the form of a numerical table to see a comparison of the accuracy of the SAVI (Soil Adjusted Vegetation Index) and MSAVI (Modified Soil Adjusted Vegetation Index) methods in Padang City. In this study, it was found that the results of the accuracy test of the SAVI (Soil Adjusted Vegetation Index) method were 86.95% while the MSAVI (Modified Soil Adjusted Vegetation Index) method was 91.30%. This research uses the SAVI (Soil Adjusted Vegetation Index) and MSAVI (Modified Soil Adjusted Vegetation Index) vegetation index methods by entering the formula that has been determined for each index to find out how the vegetation density forms in the city of Padang. The results of this study are maps of vegetation density using the SAVI (Soil Adjusted Vegetation Index) and MSAVI (Modified Soil Adjusted Vegetation Index) methods and tables of SAVI (Soil Adjusted Vegetation Index) and MSAVI (Modified Soil Adjusted Vegetation Index) accuracy test results.

Published
2023-02-17
How to Cite
Novando, G., & Arif, D. (2023). COMPARISON OF SOIL ADJUSTED VEGETATION INDEX (SAVI) AND MODIFIED SOIL ADJUSTED VEGETATION INDEX (MSAVI) METHODS TO VIEW VEGETATION DENSITY IN PADANG CITY USING LANDSAT 8 IMAGE. International Remote Sensing Applied Journal, 2(1), 31-36. https://doi.org/10.24036/irsaj.v2i1.23
Issue
Vol 2 No 1 (2021): international remote sensing application journal (June Edition 2021)
Section
Articles

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