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

Amlan Ghosh, Sayandeep Rakshit, Suvarna Tikle, Sandipan Das, Uday Chatterjee, Chaitanya B. Pande (), Abed Alataway, Ahmed A. Al-Othman, Ahmed Z. Dewidar and Mohamed A. Mattar ()
Additional contact information
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
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

Land, 2022, vol. 12, issue 1, 1-15

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.

Keywords: GIS; remote sensing; RUSLE; soil erosion; Mayurakshi River Basin (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/12/1/116/pdf (application/pdf)
https://www.mdpi.com/2073-445X/12/1/116/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:12:y:2022:i:1:p:116-:d:1020278

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().