Assessment of soil erosion using RUSLE, GIS and remote sensing in NW Ethiopia

Endalew, Mengesha; Mohammedyasin, Mohammed Seid; Sewnet, Demeke; Adem, Anwar A.; Lakew, Mindesilew

doi:10.1016/j.geodrs.2018.01.002

Cited by 148 publications

(91 citation statements)

References 29 publications

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“…e details are as follows. e RUSLE model, which was used in this study to predict the soil loss and to identify the erosion-prone areas, can be expressed as [20,29,30]…”

Section: Methodsmentioning

confidence: 99%

“…Panagos et al [19] evaluated the mean soil loss rate was 2.46 t•ha − 1 •yr − 1 in erosion-prone areas of the European Union. Zerihun et al [20] classified NW Ethiopia into five soil erosion severity classes and estimated the total soil loss of 526.996 t•ha − 1 •yr − 1 . Review of these studies reveals most have focused on quantitative assessments of soil loss, while few have considered the ground truth checking of the RUSLE and underlying key influencing factors of soil erosion.…”

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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“…e details are as follows. e RUSLE model, which was used in this study to predict the soil loss and to identify the erosion-prone areas, can be expressed as [20,29,30]…”

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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“…The rainfall erosivity (R) factor expresses the erosivity occurring from rainfall and runoff at a particular location. An increase in the intensity and amount of rainfall results in an increase in the erosivity of rainfall and an increase in the value of R which is interpolated using excel based on the values Modified in (Mekuria Argaw, 2005;Hurni, 1993;Hurni, 1988) for Ethiopian condition and also formula developed by Hurni (1985) in Ethiopia context and adopted by different researchers (Mengesha et al, 2018;Asnake & Amare, 2019;Bewket and Teferi, 2009) and presented as shown in Table 3 and R values derived between 478.3 to 700 for Meki river watershed.…”

Section: Determination Of Rainfall-runoff Erosivity Factor (R)mentioning

confidence: 99%

“…for Meki river watershed and mapped as shown in Figure 6. A similar approach was adopted to compute the R factor in Ethiopia (Bewket and Teferi, 2009;Abate, 2011;Mengesha et al, 2018; Asnake & Amare, 2019). The soil erodibility factor K can be approximated from a monograph if this information is known.…”

Section: Determination Of Rainfall-runoff Erosivity Factor (R)mentioning

confidence: 99%

Enset-Based Land Use Land Cover Change Detection and Its Impact on Soil Erosion in Meki River Watershed for Sustainability of Lake Ziway, Western Lake Ziway Sub-Basin, Central Rift Valley of Ethiopia

Woldesenbet

Wudmatas

Argaw

et al. 2020

Preprint

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Background Water erosion, upland degradation and deforestation are the key environmental problems in Meki river watershed where this study was conducted. This study assessed the land use land cover change (LULCC) over the last 30 years, examined the contribution of Enset-Based land use system (EBLUS) to manage soil erosion problem for sustainability of Lake Ziway and suggested appropriate management interventions for the watershed ERDAS imagine 2014, Geo-statistical interpolation and RUSLE model was devised for LULCC detection and analysis, for different spatial inputs and soil loss modeling respectively. Result Meki river watershed covers 2110.39056 km² of area which is dominantly covered by cultivated land use system (41.5%), Enset-Based land use system (EBLUS)(10.65%), Bush and Chat land use system (25.6%), Forest and plantations land use system (14.14%), built up (7.4%) and water bodies (0.75%). Severity class of High to severe range (18-125tha-1yr-1) recorded in the sub-watersheds irrespective of the land use systems and facing sever degradation problem that increase in soil loss in all land use systems from 1987 to 2017. The average soil loss of 30.5tha-1yr-1 and 31.905tha-1yr-1 recorded from Enset growing zones and non-Enset growing zones of the watershed respectively.Conclusion Enset-Based land use system (EBLUS) saves significant amount of soil despite the steepness of the slopes of the Enset growing zones of the watershed. Hence, expansion of EBLUS can contribute in sustaining Lake Ziway by reducing soil loss rate and sedimentation problem for ecological sustainability of the watershed. Therefore, separate land use policy and awareness creation are mandatory for such EBLUS expansion, integrated watershed management and conservation of the natural environment.

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“…(2016), [6] Temesgen Gashaw et al (2017), [4] Mengesha Zerihun et al (2018)). Geographical information system (GIS) has provided a platform in the application of RUSLE to bring about effective and accurate results of the model.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Water Erosion in Southern Districts of Tamil Nadu using Geo-spatial Technologies

C.Nachiyar¹

2019

IJRASET

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Land degradation can be considered as deterioration of the original quality of land and deterioration or total loss of the production capacity of the soil. Land degradation is caused due to changes in climate and human activities. This study was carried out to map land degradation caused by erosion due to water in Tirunelveli, Thoothukudi and Kanyakumari district using Sentinel-2 optical data. Water erosion is mapped by analyzing the data across Kharif, Rabi and Zaid season between 2017 and 2018. Sentinel-2 optical data has 13 bands with spectral resolution ranging from 0.443 µm to 2.190 µm. The water eroded areas are visually interpreted from the FCC image composed of NIR (8), Red (4) and Green (3) bands with spatial resolution of 10 m. It is estimated that 1563.165 sq.km is degraded due to water erosion. Water erosion areas are more clearly visible in Zaid season when the land conditions are dry. Besides, rainfall, soil, land use land cover and slope data has been used as an input to derive the parameters required for spatial modeling of soil erosion using USLE. Depending on the amount of soil loss they are six categorized into six classes viz. very low erosion (0 -5 t ha -1 yr -1 ), low erosion ( 5 -10 t ha -1 yr -1 ) , moderate erosion (10 -20 t ha -1 yr -1 ), moderately severe erosion (20 -40 t ha -1 yr -1 ) and severe erosion (> 40 t ha -1 yr -1 ). In the study area soil loss classes ranges from low (0-5 t ha -1 yr -1 ) to severe (40-873.145 t ha -1 yr -1 ). The rate of annual soil loss ranges from 0 t ha -1 to 873.145 t ha -1 . The visually interpreted water erosion layer is integrated with soil loss map and the accuracy of the erosion layer is found to be 87.3%. High rate of erosion occurred at bare soil and down slope.

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Assessment of soil erosion using RUSLE, GIS and remote sensing in NW Ethiopia

Cited by 148 publications

References 29 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

Enset-Based Land Use Land Cover Change Detection and Its Impact on Soil Erosion in Meki River Watershed for Sustainability of Lake Ziway, Western Lake Ziway Sub-Basin, Central Rift Valley of Ethiopia

Estimation of Water Erosion in Southern Districts of Tamil Nadu using Geo-spatial Technologies

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