Identification of Wastelands Using Contrast in Vegetation Activity in Vadodara District, India – A Remote Sensing Approach
High population pressure, rapid urbanisation, rapid industrialization, and extensive agriculture have put major pressures on land resources, resulting in a significant reduction in agricultural land and natural resources. Increased global population has resulted in deforestation and resource destruction, threatening the balance of terrestrial ecosystems. According to recent reports, the area covered by wastelands is declining as parts of the wastelands are transformed to arable land. It’s crucial to consider and monitor these shifts in spatial planning and management. In the Vadodara district of India, this paper uses remote sensing to classify culturable wastelands based on seasonal vegetation changes. Three MODIS photos from 2016-17, from three separate seasons, were subjected to supervised classification. To find the best data combination for image classification, separability analysis was used. Ground referencing and Google Earth photos were used to verify the findings. With a kappa co-efficient of 0.7580, the composite of winter season image with NDVI and EVI performed best, with an overall accuracy of 78.2 percent. This method allows for the identification of culturable wastelands in the study field, which have previously been mapped solely through visual interpretations.
Author (s) Details
Dr. Mudit D. Mankad
Department of Geography, Faculty of Science, The Maharaja Sayajirao University of Baroda, Fatehgunj, Vadodara, Gujarat, India.
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