Abstract
AbstractThe arid part in the Indian subcontinent displays a significant variance in vegetation and climate. The varying climate, lack of perennial rivers, rainfall, and harsh weather conditions only allow sparse vegetation to grow, since proper mapping of such areas is essential for the livelihood of people. In this work, we classify normalized difference vegetation index(NDVI) values to get vegetation health, for this, the study area is taken as Jodhpur district in Rajasthan. With the use of Google Earth API and Python script is designed for extraction of the study area, with the specification of the time frame and download the .tiff images in a more convenient way. USGS Earth Explorer and Landsat 8 imagery raster band 4,5 composite dataset is used to compute pre-monsoon and post-monsoon months to extract the NDVI values in .tiff image format. Analysis of the results concluded that a significant spike in the less dense vegetation category was due to rainfall in the post-monsoon months according to NDVI extraction values.KeywordsNDVIBandLandsatGoogle EarthVegetationRainfall
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