Soil loss estimation using RUSLE model, GIS and remote sensing techniques: A case study from the Dembecha Watershed, Northwestern Ethiopia

Ostovari, Yaser; Ghorbani-Dashtaki, Shoja; Bahrami, H A; Naderi, Mehdi; Demattê, José A. M.

doi:10.1016/j.geodrs.2017.06.003

Cited by 91 publications

(31 citation statements)

References 28 publications

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“…e related scholars used different spectral vegetation indices and the fraction images from spectral mixture analysis of remotely sensed images to compute the C-factor [36,41,42]. In our study, the mean of NDVI generated from Landsat 8 images was used to calculate the Cfactor by using the equation proposed by Ostovari et al [16]:…”

Section: Methodsmentioning

confidence: 99%

“…e Revised Universal Soil Loss Equation (RUSLE) is the most widely used empirical model because of its simple, transparent, robust model structure and compatibility with geospatial platforms [10,11]. Recently, many studies have reported to predict soil erosion by combining the RUSLE and geographic information system (GIS) [12][13][14][15][16][17]. For example, Fayas et al [18] evaluated the maximum average annual soil erosion of 103.7 t•ha − 1 •yr − 1 in the Kelani River watershed in Sri Lanka.…”

Section: Introductionmentioning

confidence: 99%

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Test of the RUSLE and Key Influencing Factors Using GIS and Probability Methods: A Case Study in Nanling National Nature Reserve, South China

Wang¹,

Ping³

et al. 2019

Advances in Civil Engineering

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The main purposes of the study were to test the performance of the Revised Universal Soil Loss Equation (RUSLE) and to understand the key factors responsible for generating soil erosion in the Nanling National Nature Reserve (NNNR), South China, where soil erosion has become a very serious ecological and environmental problem. By combining the RUSLE and geographic information system (GIS) data, we first produced a map of soil erosion risk at 30 m-resolution pixel level with predicted factors. We then used consecutive Landsat 8 satellite images to obtain the spatial distribution of four types of soil erosion and carried out ground truth checking of the RUSLE. On this basis, we innovatively developed a probability model to explore the relationship between four types of soil erosion and the key influencing factors, identify high erosion area, and analyze the reason for the differences derived from the RUSLE. The results showed that the overall accuracy of image interpretation was acceptable, which could be used to represent the currently actual spatial distribution of soil erosion. Ground truth checking indicated some differences between the spatial distribution and class of soil erosion derived from the RUSLE and the actual situation. The performance of the RUSLE was unsatisfactory, producing differences and even some errors when used to estimate the ecological risks posed by soil erosion within the NNNR. We finally produced a probability table revealing the degree of influence of each factor on different types of soil erosion and quantitatively elucidated the reason for generating these differences. We suggested that soil erosion type and the key influencing factors should be identified prior to soil erosion risk assessment in a region.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Test of the RUSLE and Key Influencing Factors Using GIS and Probability Methods: A Case Study in Nanling National Nature Reserve, South China

Wang¹,

Ping³

et al. 2019

Advances in Civil Engineering

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“…Reliable local-scale measurements of runoff and erosion under natural rainfall for different soil cover conditions are still limited, especially in semiarid environments, with sparse vegetation cover [5,6].…”

Section: Introductionmentioning

confidence: 99%

Performance of Conservation Techniques for Semiarid Environments: Field Observations with Caatinga, Mulch, and Cactus Forage Palma

Lopes

Montenegro

Lima

2019

Water

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Understanding small-scale hydrologic processes and the impact of soil conservation techniques are crucial in reducing runoff and sediment losses in semi-arid regions. This study was conducted in the Alto Ipanema River Basin, in Pernambuco State (Brazil). Soil and water dynamics were intensely monitored in twelve experimental plots with different coverage conditions (plot with bare soil—Bare; plot with natural vegetation—Natur; plot with mulch—Mulch; plot with Cactus Palma—Palma). By far, bare soil conditions produced higher runoff and soil losses. Mulch cover was close to natural vegetation cover, but still presented higher runoff and sediment losses. Palma, which is a very popular spineless cactus for animal feed in the Brazilian semi-arid region, presented an intermediate hydrologic impact in controlling runoff, enhancing soil moisture, and also reducing soil losses. Experiments were conducted in one hydrologic year (2016/2017) at three different sites. They were intensely monitored and had the same number of plots. This enabled us to carry out a robust performance assessment of the two soil conservation practices adopted (Mulch and Palma), compared to natural vegetation cover and bare soil conditions. Such low-cost alternatives could be easily adopted by local farms in the region, and, hence, improve soil reclamation and regional resiliency in a water-scarce environment.

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“…Moreover, assessing the suitable scenario is an essential procedure to select the best measure to reduce Cd contamination and protect the Mae Tao watershed. Therefore, RUSLE model was applied to estimate soil loss (Atoma et al, 2020;Batista et al, 2017;Chuenchum et al, 2020;Farhan et al, 2013;Ganasri and Ramesh, 2016;Kassawmar et al, 2018;Kim, 2006;Lazzari et al, 2015;Lee and Lee, 2006;Millward and Mersey, 1999;Ostovari et al, 2017;Rammahi and Khassaf, 2018;Somprasong and Chaiwiwatworakul, 2015;Xu et al, 2012) and was integrated with IDW technique in GIS to estimate the spatial concentration of Cd (Mesnard, 2013;Somprasong and Chaiwiwatworakul, 2015). Soil loss with RUSLE model and Cd concentration with IDW were used as the parameters to estimate Cd contamination in soil loss in the last step (Somprasong and Chaiwiwatworakul, 2015).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Cadmium Contamination in Different Restoration Scenarios by RUSLE Model

Charoenpanyanet

Huttagosol

2020

Environ. Nat. Resour. J.

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The Mae Tao watershed of Thailand faced cadmium (Cd) contamination problems from zinc mining for a long time until the mining area was closed to decrease the level of Cd concentration. This study reproduced the possible scenarios of Cd contamination due to soil loss. Four scenarios of forest restoration were implemented in this study, all of which were calculated with the Revised Universal Soil Loss Equation (RUSLE) integrated with satellite imagery and Geographic Information Systems (GIS). Landsat 8-OLI was acquired and land use/land cover (LULC) was classified in each scenario. Soil loss maps were created. An inverse distance weighting (IDW) technique was used to estimate the concentration of Cd based on the field data consisting of 101 points of measured Cd concentration. Results from RUSLE model and IDW technique were combined to calculate Cd contamination due to soil loss for all four scenarios. Results showed that the restoration of Scenario 3, forest restoration in old and new mining areas in cooperation with reservoir construction, helped decrease Cd contamination the most. The lowest level of Cd contamination from soil loss was found in this scenario by about 156 ha (total of Cd contamination by 165,924.32 ton/year).

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Soil loss estimation using RUSLE model, GIS and remote sensing techniques: A case study from the Dembecha Watershed, Northwestern Ethiopia

Cited by 91 publications

References 28 publications

Test of the RUSLE and Key Influencing Factors Using GIS and Probability Methods: A Case Study in Nanling National Nature Reserve, South China

Test of the RUSLE and Key Influencing Factors Using GIS and Probability Methods: A Case Study in Nanling National Nature Reserve, South China

Performance of Conservation Techniques for Semiarid Environments: Field Observations with Caatinga, Mulch, and Cactus Forage Palma

Estimation of Cadmium Contamination in Different Restoration Scenarios by RUSLE Model

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