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Integration of GIS and Remote Sensing with RUSLE Model for Estimation of Soil Erosion

Author

Listed:
  • Amlan Ghosh

    (Symbiosis Institute of Geo-Informatics, Symbiosis International (Deemed University), Pune 411016, Maharashtra, India)

  • Sayandeep Rakshit

    (Symbiosis Institute of Geo-Informatics, Symbiosis International (Deemed University), Pune 411016, Maharashtra, India)

  • Suvarna Tikle

    (Environmental Modeling Division, Max Planck Institute for Meteorology, Bundesstrasse 53, 20146 Hamburg, Germany)

  • Sandipan Das

    (Symbiosis Institute of Geo-Informatics, Symbiosis International (Deemed University), Pune 411016, Maharashtra, India)

  • Uday Chatterjee

    (Department of Geography, Bhatter College, Dantan (Vidyasagar University), Paschim Medinipur 721426, West Bengal, India)

  • Chaitanya B. Pande

    (Indian Institute of Tropical Meteorology, Pune 411008, Maharashtra, India)

  • Abed Alataway

    (Prince Sultan Bin Abdulaziz International Prize for Water Chair, Prince Sultan Institute for Environmental, Water and Desert Research, King Saud University, Riyadh 11451, Saudi Arabia)

  • Ahmed A. Al-Othman

    (Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia)

  • Ahmed Z. Dewidar

    (Prince Sultan Bin Abdulaziz International Prize for Water Chair, Prince Sultan Institute for Environmental, Water and Desert Research, King Saud University, Riyadh 11451, Saudi Arabia
    Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia)

  • Mohamed A. Mattar

    (Prince Sultan Bin Abdulaziz International Prize for Water Chair, Prince Sultan Institute for Environmental, Water and Desert Research, King Saud University, Riyadh 11451, Saudi Arabia
    Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia
    Centre for Carbon, Water and Food, The University of Sydney, Camperdown, NSW 2570, Australia
    Agricultural Engineering Research Institute (AEnRI), Agricultural Research Centre, Giza 12618, Egypt)

Abstract

Globally, soil erosion is a significant problem contributing to nutrient loss, water quality degradation, and sand accumulation in water bodies. Currently, various climate factors are affecting the natural resources entire worldwide. Agricultural intensification, soil degradation, and some other human impacts all contribute to soil erosion, which is a significant issue. Management and conservation efforts in a watershed can benefit from a soil erosion study. Modeling can establish a scientific and accurate method to calculate sediment output and soil erosion below a variety of circumstances. The measured soil loss tolerance was compared to the risk of soil erosion (T value).In this study, GIS and remote sensing techniques have been integrated with the Revised Universal Soil Loss Equation (RUSLE) model to estimate soil loss in the Mayurakshi river basin of eastern India. To determine soil erosion-prone areas, rainfall, land use, and land cover maps, as well as a digital elevation model (DEM), were used as input. The annual soil loss in the basin area is estimated to be 4,629,714.8 tons. Accordingly, the study basin was categorized into five soil loss severity classes: very low (40.92%), low (49%), moderate (6.5%), high (2.4%) and very high (1.18%) risk classes. Soil erosion rates ranged from very slight to slight throughout the majority of the region. The section of the basin’s lower plain has been discovered to be least affected by soil loss. The results of study area can be helpful to conservation of soil management practices and watershed development program in the basin area.

Suggested Citation

  • Amlan Ghosh & Sayandeep Rakshit & Suvarna Tikle & Sandipan Das & Uday Chatterjee & Chaitanya B. Pande & Abed Alataway & Ahmed A. Al-Othman & Ahmed Z. Dewidar & Mohamed A. Mattar, 2022. "Integration of GIS and Remote Sensing with RUSLE Model for Estimation of Soil Erosion," Land, MDPI, vol. 12(1), pages 1-15, December.
    • Handle: RePEc:gam:jlands:v:12:y:2022:i:1:p:116-:d:1020278
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    References listed on IDEAS

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    1. Rabin Chakrabortty & Subodh Chandra Pal & Mehebub Sahana & Ayan Mondal & Jie Dou & Binh Thai Pham & Ali P. Yunus, 2020. "Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(2), pages 1259-1294, November.
    2. V. Chowdary & D. Ramakrishnan & Y. Srivastava & Vinu Chandran & A. Jeyaram, 2009. "Integrated Water Resource Development Plan for Sustainable Management of Mayurakshi Watershed, India using Remote Sensing and GIS," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(8), pages 1581-1602, June.
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