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Research Article Open access CC BY 4.0

Assessment of Eco-Environmental Vulnerability Using Remote Sensing and GIS Tools in Maharashtra Region, India

Vikash Singh, Anupam Kumar Nema, Arpit Chouksey, Ashwini Suryawanshi

International Journal of Environment and Climate Change · pp. 119–129 · Published 6 Apr 2024

10.9734/ijecc/2024/v14i44103

Abstract

Maharashtra region is prone to various disasters such as drought, floods, cyclones and earthquake and has been exposed to extreme weather events like dry spells. Communities within these dry lands are poor and face extreme conditions of water stress. This study has been carried out to analyze and quantify climatic and anthropogenic effect on eco-environmental vulnerability dynamic change. To achieve that a numerical model is set up, consisting of eight factors that are elevation, land use, drought, slope, NDVI, soil-type, soil erosion (water), and population density index & has been evaluated using the method of spatial principle component analysis (SPCA) on Remote Sensing and GIS platform. The integrated eco-environmental vulnerability index (EVI) of study area is estimated to analyse spatial-temporal dynamic vulnerability changes in the 11 years gap from 2000 and 2011. The results show that the study area has become eco-environmental vulnerable slightly (about 80% of the region) with an increased eco-environmental vulnerability integrated index (EVSI) value by more than 50% (i.e., about 74%) and the driving force of dynamic change is mainly caused by socio-economic activities. In addition the estimation has been regionalized into thirty-four districts to serve as a base for decision-making for eco-environmental recovering and rebuilding. It is found that the most vulnerable district in 2011 is Ratnagiri and the least one is Sangli. There are nine districts which shows more than 100% increase in EVSI value, with the highest increase in Hingoli(100.65%), indicating that the districts have become most environmental vulnerable in the study-period. The research concludes that the method, supported by G.I.S using SPCA can’t only represent distinctly the input spatial distribution of plain-mountain-belt feature, but also respect the whole river-valley as a single unit.

Eco-environmental Vulnerability Index (EVI) Spatial Principal Component Analysis (SPCA) Standardized Precipitation Index (SPI) Revised Universal Soil Loss Equation (RUSLE)

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