Application of NDVI Index and Supervised Decoration Methods on Sentinel-2A and Landsat 5 TM Satellite Images in ArcGIS Software

Abstract:

Monitoring of Land Use and Land Cover changes is very important in the planning and management programs required for development activities at the regional levels of any country. The main goal of this study is to observe the dynamics of the vegetation cover of Siyazan region for 7 years from 2010 to 2017 using Remote Sensing and Geographic Information System. Sentinel-2A MSI (Multi Spectral Imager), Landsat-5 TM (Thematic Mapper) satellite images are used to create vegetation maps. Vegetation change in the study area is calculated by the Normalized Vegetation Cover Index (NDVI), and the results show that the vegetation cover increased from 0.8% in 2010 to 22.5% in 2017. Supervised classification is performed using the Maximum Likelihood Classification (supervised classification). The 5 main classes considered for classification are: Watersheds, cropland/vegetation, gray land, settlements and productive land. The ArcGIS software package is used to carry out the proposed study and the accuracy assessment is carried out by taking the base values for appropriate classification through the Google Earth Pro software. The results show that the overall accuracy of the proposed system is 78.12%.

• Periodical: Engineering Headway (Volume 7)
• Pages: 181-186
• DOI: https://doi.org/10.4028/p-OsfCt0
• Online since: April 2024
• Authors: Nasimi Valehov*, Fidan Valehova
• Keywords: Maximum Likelihood Classification, Normalized Vegetation Index, Remote Sensing, Supervised Decoding

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