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Integrated Remote Sensing and Geographic Information System Based RUSLE Modelling for Estimation of Soil Loss in Western Himalaya, India

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  • Amit Kumar
  • Mamta Devi
  • Benidhar Deshmukh

Abstract

Soil loss due to water erosion was estimated in Kangra region of western Himalaya using revised universal soil loss equation modelling (RUSLE) in conjunction with Remote Sensing (RS) and Geographic Information Systems (GIS). The various parameters such as rainfall erosivity (R), soil erodibility (K), topographic factor (LS), crop management factor (C) and support practice factor (P) were derived using standard techniques. The study revealed that forest cover, crop land and scrub/grass land constitute 87.4 % of soil erosion susceptible area. The rate of depletion of soil was estimated at 25.63 t/ha/yr. It was highest in stony/barren land (60.3 t/ha/yr) and lowest in case of tea garden (16.09 t/ha/yr). It was felt that there is a need of implementation of soil and water conservation measures in the region to curb the soil loss. The undulating nature of terrain was observed as the main contributing factor for soil erosion. It was concluded that RS and GIS based RUSLE model can be efficiently used in mountainous regions to determine the status and extent of soil erosion. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Amit Kumar & Mamta Devi & Benidhar Deshmukh, 2014. "Integrated Remote Sensing and Geographic Information System Based RUSLE Modelling for Estimation of Soil Loss in Western Himalaya, India," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(10), pages 3307-3317, August.
    • Handle: RePEc:spr:waterr:v:28:y:2014:i:10:p:3307-3317
    DOI: 10.1007/s11269-014-0680-5
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    References listed on IDEAS

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    2. Liu, Chao & Xu, Yueqing & Lu, Xinhai & Han, Jing, 2021. "Trade-offs and driving forces of land use functions in ecologically fragile areas of northern Hebei Province: Spatiotemporal analysis," Land Use Policy, Elsevier, vol. 104(C).
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    4. Sumedh R. Kashiwar & Manik Chandra Kundu & Usha R. Dongarwar, 2022. "Soil erosion estimation of Bhandara region of Maharashtra, India, by integrated use of RUSLE, remote sensing, and GIS," 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. 110(2), pages 937-959, January.
    5. Arun Mondal & Deepak Khare & Sananda Kundu, 2016. "Impact assessment of climate change on future soil erosion and SOC loss," 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. 82(3), pages 1515-1539, July.
    6. I. Gaubi & A. Chaabani & A. Ben Mammou & M. H. Hamza, 2017. "A GIS-based soil erosion prediction using the Revised Universal Soil Loss Equation (RUSLE) (Lebna watershed, Cap Bon, Tunisia)," 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. 86(1), pages 219-239, March.
    7. Giorgos Mallinis & Ioannis Z. Gitas & Georgios Tasionas & Fotis Maris, 2016. "Multitemporal Monitoring of Land Degradation Risk Due to Soil Loss in a Fire-Prone Mediterranean Landscape Using Multi-decadal Landsat Imagery," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(3), pages 1255-1269, February.
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