Application of Digital Image Classification in the Identification of Culturable Wastelands Using Contrast in Vegetation Activity in Vadodara District, India

Increasing population worldwide has put tremendous pressure on the land. Recent studies reported that many areas covered by wastelands are decreasing because parts of wastelands are being converted into arable land. It is important to identify and monitor these changes in spatial planning and management. This paper adopts a remote sensing-based identification of culturable wastelands based on seasonal vegetation changes in Vadodara district, India. Supervised classification was applied on three MODIS images of 2016-17 of 3 different seasons. Separability analysis was applied to get the best data combination for image classification. Validation was done by ground referencing and Google earth images. The composite of winter season image with NDVI and EVI performed best with an overall accuracy of 78.2% with the kappa co-efficient of 0.7580. This method opens a possibility of using digital classification for identification of culturable wastelands in the study area which are so far mapped with visual interpretations only.