Geoinformatics-Based Mapping of Environmental Sensitive Areas for Desertification over Satara and Sangli Districts of Maharashtra, India

Chandra Shekhar Dwivedi, Dishant, Bikash Ranjan Parida, A. Pandey, Ravi Kumar, Navneet Kumar
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Abstract

Desertification processes in arid, semi-arid, and dry sub-humid conditions have been enhanced in recent decades. The geospatial database and associated satellite data can be effectively employed for regional planning to address desertification and land degradation. In this study, the Mediterranean Desertification and Land Use (MEDALUS) model has been used to map environmentally sensitive areas due to desertification in the Satara and Sangli districts of Maharashtra, India. This was achieved by combining Landsat-8 multispectral data, Census data, soil data, and climatic variables like temperature, rainfall, and evapotranspiration. The algorithm of MEDALUS is the geometric mean of four indicators, namely soil quality index (SQI), climate quality index (CQI), vegetation quality index (VQI), and socio-economic quality index (SEQI). The findings indicated that the majority of the study area comes under the potential category of desertification (60.32%) followed by fragile (27.87%) and critical (11.81%). Areas with a high propensity for desertification were found over the low to very low climatic quality and moderate to high soil quality including lower socio-economic quality. The lower socio-economic quality is mainly due to high to very high population density (>100 people/km2), low to moderate illiteracy rate (<16%), and low to moderate work participation rate (<50%) that incentivize unsustainable land use practices. The study provides a valuable tool for understanding and managing natural resources. It offers a detailed analysis of the environmental sensitivity of the study area, taking into account various factors like land use, vegetation cover, slope, and soil erosion potential. The developed comprehensive map of the area helps in identifying the most sensitive regions and developing appropriate conservation strategies. The information obtained from the study can be utilized to develop and implement successful measures to prevent or alleviate desertification, which is crucial for sustaining the health of ecosystems and the welfare of local residents.
基于地理信息学的印度马哈拉施特拉邦萨塔拉和桑利地区荒漠化环境敏感区绘图
近几十年来,干旱、半干旱和亚湿润干旱地区的荒漠化进程有所加剧。地理空间数据库和相关卫星数据可有效用于区域规划,以解决荒漠化和土地退化问题。在这项研究中,地中海荒漠化和土地利用(MEDALUS)模型被用于绘制印度马哈拉施特拉邦萨塔拉和桑利地区因荒漠化造成的环境敏感区域地图。该模型结合了 Landsat-8 多光谱数据、人口普查数据、土壤数据以及温度、降雨量和蒸散量等气候变量。MEDALUS 的算法是四个指标的几何平均数,即土壤质量指数 (SQI)、气候质量指数 (CQI)、植被质量指数 (VQI) 和社会经济质量指数 (SEQI)。研究结果表明,大部分研究区域属于潜在荒漠化类别(60.32%),其次是脆弱类别(27.87%)和严重类别(11.81%)。在气候质量低至极低和土壤质量中至高的地区,包括社会经济质量较低的地区,都有较高的荒漠化倾向。社会经济质量较低的主要原因是人口密度较高至非常高(>100 人/平方公里)、文盲率较低至中等(<16%)、工作参与率较低至中等(<50%),这些因素助长了不可持续的土地利用方式。这项研究为了解和管理自然资源提供了宝贵的工具。考虑到土地利用、植被覆盖、坡度和土壤侵蚀潜力等各种因素,它对研究区域的环境敏感性进行了详细分析。绘制的区域综合地图有助于确定最敏感的区域,并制定适当的保护战略。研究获得的信息可用于制定和实施预防或缓解荒漠化的成功措施,这对维持生态系统的健康和当地居民的福祉至关重要。
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