This study incorporates big data analysis and can be indispensable in exploring the interactions between ecosystem services and anthropogenic activities in river basin systems.
Authors
Rajarshi Bhattacharjee, Department of Civil Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India.
Shishir Gaur, Department of Civil Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India.
Anurag Ohri, Department of Civil Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India.
Nilendu Das, Department of Civil Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India.
Sadashiv Chaturvedi, Department of Civil Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India.
Rupesh Kumar, Associate Professor, Jindal Global Business School (JGBS), O.P. Jindal Global University, Sonipat, Haryana, India.
Summary
Assessment of ecological environment is an indispensable part of the eco-environment protection and restoration. This study utilizes a remote sensing-based ecological index (RSEI) to better understand the environmental scenario in the Ganga basin. RSEI has been computed using five parameters: Wetness, Dryness, Greenness, Heat, and a newly incorporated parameter Albedo representative of land degradation. Median-based RSEI maps have been constructed using LANDSAT archives in Google Earth Engine (GEE) platform, covering three decades (1990–2021). The Ganga basin has been divided into five agro-climatic zones.
For each zone and time frame (1990–99, 2000–09, 2010–19, and 2020–21), a median-based RSEI map has been generated. The analysis reveals that RSEI becomes poorer for sub-basin 1 (SB1), sub-basin 2 (SB2), and sub-basin 5 (SB5) in the 2010–19 period compared to the 1990–99 and 2000–09 periods. On the other hand, RSEI for sub-basin 3 (SB3) improved in the 2010–19 period compared to the previously mentioned periods.
Sub-basin 4 (SB4) remained the least fluctuated region compared to the other sub-basins. The Global Moran’s I value is highest for SB3 for the 1990–99 and 2000–09 periods, while for the 2010–19 and 2020–21 periods, SB2 has the highest Global Moran’s I. This study incorporates big data analysis and can be indispensable in exploring the interactions between ecosystem services and anthropogenic activities in river basin systems.
Published in: Physics and Chemistry of the Earth, Parts A/B/C
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