Spatial modelling of soil erosion potential in a mountainous watershed of South-eastern Serbia

Perović, Velјko; Životić, Ljubomir; Kadović, Ratko; Đorđević, Aleksandar; Jaramaz, Darko; Mrvić, Vesna; Todorović, Mladen

doi:10.1007/s12665-012-1720-1

Cited by 53 publications

(19 citation statements)

References 36 publications

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“…More comparable results on soil erosion potential with central and northern Jordan, are obtained for Foupana river watershed in Algarave (Mediterranean Portugal) [64], and Nisava river watershed (Southerneast Serbia), not far from the Mediterranean [65]. Potential erosion across Foupana watershed was estimated between 76 to 99 ton·ha…”

Section: Resultssupporting

confidence: 52%

Spatial Estimation of Soil Erosion Risk Using RUSLE Approach, RS, and GIS Techniques: A Case Study of Kufranja Watershed, Northern Jordan

Farhan¹,

Zregat²,

Farhan³

2013

JWARP

105

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Wadi Kufranja catchment (126.3 km 2 ), northern Jordan, was selected to estimate annual soil loss using the Revised Universal Soil Loss Equation (RUSLE), remote sensing (RS), and geographic information system (GIS). RUSLE factors (R, K, LS, C and P) were computed and presented by raster layers in a GIS environment, then multiplied together to predict soil erosion rates, and to generate soil erosion risk categories and soil erosion severity maps. The estimated potential average annual soil loss is 10 ton·ha . Apart from the gentle slopes of the alluvial fan (Krayma town and surroundings), the lower and the middle reaches of the watershed suffer from severe to extreme erosion risk. High terrain, slope steepness, removal of vegetation, and poor conservation practices are the most prominent causes of soil erosion. This investigation demonstrates that remote sensing (RS) and GIS technologies are effective tools in modeling erosion, thus enabling extraction of significant information for implementing soil conservation plans in the north Jordan highlands.

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Section: Resultssupporting

confidence: 52%

Spatial Estimation of Soil Erosion Risk Using RUSLE Approach, RS, and GIS Techniques: A Case Study of Kufranja Watershed, Northern Jordan

Farhan¹,

Zregat²,

Farhan³

2013

JWARP

105

View full text Add to dashboard Cite

show abstract

“…Some studies determine C factor value using land cover classification map assuming that the same land covers have the same C factor values (Cebecauer and Hofierka 2008;Chatterjee et al 2014;Lee 2004;Xu et al 2011;Yang et al 2012;Zhou et al 2011), while some others use spectral mixture analysis (De Asis and Omasa 2007;Lu et al 2004) or a regression analysis between C factor and spectral indices (e.g., NDVI, PVI) (Ma et al 2003;Perovic et al 2013;Tweddale et al 2000;Xu et al 2013;Zivotic et al 2012). The regression analysis method was tested using NDVI data, but the result was not agreed well with other studies (Cai et al 2000;Liu et al 2009;Peng et al 2007).…”

Section: Cover Management Factor (C)mentioning

confidence: 99%

Estimation of soil erosion in some sections of Lower Jinsha River based on RUSLE

et al. 2015

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Soil erosion increasingly poses a great threat to human food security and environmental quality. It is necessary to implement the assessment of soil erosion so as to provide precautionary measures and relevant suggestions for soil conservation. In this paper, the soil erosion model, revised universal soil loss equation (RUSLE), was used to quantify the soil loss in a large mountainous area of Lower Jinsha River Basin. The analysis of the datasets by means of geographic information systems (GIS) together with RUSLE led to the estimation of soil erosion. Results show that the average annual soil erosion was estimated at 52.1 t ha -1 year -1 and the total annual soil loss was 4.5 9 10 8 t. The highest erosion was found along the main course of Jinsha River. Soil erosion was serious in the elevation zone between 1,675 and 2,153 m and slope zone with slopes between 15°and 35°. As for land use types, cropland and grassland contributed 84.1 % to total soil loss due to the large areas and higher erosion rates. The results can be used to advice the local government in prioritizing the areas of immediate conservation practices.

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“…Integrated into GIS and remote sensing, several models for predictive evaluation on soil erosion by water have been reported in some literature (e.g., Wischmeier and Smith 1978;Lal 2001;Fullen 2003;Merritt et al 2003). Universal Soil Loss Equation (USLE) is considered as the best model and is being used worldwide for the estimation of surface erosion (e.g., Zhang et al 2008;Alexakis et al 2013;Perović et al 2013;Chatterjee et al 2014;Kourgialas et al 2016). Its revised version, RUSLE (Renard et al 1997), became mostly used to provide estimates of soil loss (Demirci and Karaburun 2012;Kumar et al 2014;Ganasri and Ramesh 2015;Napoli et al 2016;Rawat et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Soil erosion modeled with USLE, GIS, and remote sensing: a case study of Ikkour watershed in Middle Atlas (Morocco)

et al. 2017

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The Ikkour watershed located in the Middle Atlas Mountain (Morocco) has been a subject of serious soil erosion problems. This study aimed to assess the soil erosion susceptibility in this mountainous watershed using Universal Soil Loss Equation (USLE) and spectral indices integrated with Geographic Information System (GIS) environment. The USLE model required the integration of thematic factors' maps which are rainfall aggressiveness, length and steepness of the slope, vegetation cover, soil erodibility, and erosion control practices. These factors were calculated using remote sensing data and GIS. The USLE-based assessment showed that the estimated total annual potential soil loss was about 70.66 ton ha. This soil loss is favored by the steep slopes and degraded vegetation cover. The spectral index method, offering a qualitative evaluation of water erosion, showed different degrees of soil degradation in the study watershed according to FI, BI, CI, and NDVI. The results of this study displayed an agreement between the USLE model and spectral index approach, and indicated that the predicted soil erosion rate can be due to the most rugged land topography and an increase in agricultural areas. Indeed, these results can further assist the decision makers in implementation of suitable conservation program to reduce soil erosion.

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Spatial modelling of soil erosion potential in a mountainous watershed of South-eastern Serbia

Cited by 53 publications

References 36 publications

Spatial Estimation of Soil Erosion Risk Using RUSLE Approach, RS, and GIS Techniques: A Case Study of Kufranja Watershed, Northern Jordan

Spatial Estimation of Soil Erosion Risk Using RUSLE Approach, RS, and GIS Techniques: A Case Study of Kufranja Watershed, Northern Jordan

Estimation of soil erosion in some sections of Lower Jinsha River based on RUSLE

Soil erosion modeled with USLE, GIS, and remote sensing: a case study of Ikkour watershed in Middle Atlas (Morocco)

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