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<p>This study utilized GIS and remote sensing data to forecast areas of spatiotemporal drought risk affecting agriculture and meteorology in the southern Coimbatore region. Drought risk has been evaluated using Landsat 8 OLI/TIRS and 7 ETM+ temporal photographs, utilizing the Normalized Difference Vegetation Index, for the years 2000 and 2020. This assessment was complemented by the utilization of the meteorologically derived standardized precipitation indicator as a drought index. Finally, spatiotemporal drought risk maps were produced using a weighted overlay method using NDVI, seasonal rainfall, and SPI values. The study area has been split into five classes: none, slight drought, moderate drought, extreme drought, and very high drought. The entire area and proportion for each category are then given for both years. In addition to this study, changes in land use and land cover were examined. Comparing the drought changes based on the land use and cover patterns of both years 2000 and 2020. The comparative results demonstrate that when land is developing like built-up areas, it becomes drier, which causes drought in that region. In contrast, other regions have well-planned irrigation systems and have converted fallow land into active agricultural land, increasing the number of wet land conditions in such regions.</p>

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Drought assessment in Coimbatore South region, Tamil Nadu, India, using remote sensing and meteorological data

  • 期刊论文

  • 发表日期：

    2025年2月1日

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  • Ezhilarasi Murugesan,
  • Senthilkumar Shanmugamoorthy,
  • Senthilkumar Veerasamy,
  • Vivek Sivakumar

摘要

This study utilized GIS and remote sensing data to forecast areas of spatiotemporal drought risk affecting agriculture and meteorology in the southern Coimbatore region. Drought risk has been evaluated using Landsat 8 OLI/TIRS and 7 ETM+ temporal photographs, utilizing the Normalized Difference Vegetation Index, for the years 2000 and 2020. This assessment was complemented by the utilization of the meteorologically derived standardized precipitation indicator as a drought index. Finally, spatiotemporal drought risk maps were produced using a weighted overlay method using NDVI, seasonal rainfall, and SPI values. The study area has been split into five classes: none, slight drought, moderate drought, extreme drought, and very high drought. The entire area and proportion for each category are then given for both years. In addition to this study, changes in land use and land cover were examined. Comparing the drought changes based on the land use and cover patterns of both years 2000 and 2020. The comparative results demonstrate that when land is developing like built-up areas, it becomes drier, which causes drought in that region. In contrast, other regions have well-planned irrigation systems and have converted fallow land into active agricultural land, increasing the number of wet land conditions in such regions.