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Desertification is a worldwide issue receiving broad attention due to deforestation, climate change, and land abuse. In India, nearly 81.4 million ha are undergoing the desertification process. A long-term assessment of the key drivers of desertification and land degradation (DLD) was done over the state of Jharkhand in the central highlands of India. The region is highly vulnerable to desertification and land degradation due to its unique geographical and climatic features, with 68.77% (5.48 Mha) of the total geographical area of 7.97 Mha undergoing DLD. This study aims to quantify desertification in Jharkhand using various satellite imageries and supervised classification using machine learning (ML) techniques. The results showed five distinct classes of DLD cases, i.e., severe, intense, moderate, light, and no desertification. The severe and intense class areas make up about 5.11 Mha (64.43%) of the total geographic area (TGA). The moderate and light classes of DLD make up 0.93 Mha (11.79%) and 1.40 Mha (17.73%) of TGA, respectively. Remarkably, the districts of Giridih, Gumla, Ranchi, Dumka, Jamtara, Deoghar, Garhwa, and Palamu are considered to be the most prone regions to DLD. This study will help to demonstrate the application of remote sensing techniques to quantify DLD-prone regions and severity over the regions, which can help policymakers manage the local administrative bodies and state government departments to demarcate the region to continuously monitor and lay policies to tackle desertification. Keywords: Desertification, Central Highlands, GEE, Random Forest, Vulnerability, Machine Learning
Mahato et al. (Mon,) studied this question.
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