Assessment of soil erosion susceptibility using empirical modeling

Soil erosion is one of the most serious land degradation problems all over the world, causing irreversible land quality reduction. In this paper, we modify the Revised Universal Soil Loss Equation (RUSLE) model by replacing the factors of slope length and gradient with Sediment Transport Index (STI). The Digital Elevation Model, terrain parameters, Normalized Difference Vegetation Index (NDVI), and rainfall data are used as inputs to the model. Along with the application of remote sensing techniques and ground survey measurements, erosion susceptibility maps are produced. The revised models are then used to obtain the optimal estimate of soil erosion susceptibility at Alianello of southern Italy, which is prone to soil erosion.The soil loss estimated from the modified RUSLE model shows a large spatial variance, ranging from 10 to as much as 7000 ton ha−1 yr−1. The high erosion susceptible area constitutes about 46.8% of the total erosion area, and when classified by land cover type, 33% is “mixed bare with shrubs and grass”, followed by 5.29% of “mixture of shrubs and trees”, with “shrubs” having the lowest percentage of 0.06%. In terms of slope types, very steep slope accounts for a total of 40.90% and belongs to high susceptibility, whereas flat slope accounts for only 0.12%, indicating that flat topography has little effect on the erosion hazard. As far as the geomorphologic types are concerned, the type of “moderate steep-steep slopes with moderate to severe erosion” is most favorable to high soil erosion, which comprises about 9.34%.Finally, we validate the soil erosion map from the adapted RUSLE model against the visual interpretation map, and find a similarity degree of 71.9%, reflecting the efficiency of the adapted RUSLE model in mapping the soil erosion in this study area.

[1]  R. Kheir,et al.  Regional soil erosion risk mapping in Lebanon , 2006 .

[2]  Luca Montanarella,et al.  Soil erosion risk assessment in Italy , 1999 .

[3]  C. J. Gantzer,et al.  Rainfall erosivity in the Republic of Korea , 2000 .

[4]  C. Prat,et al.  Erosion extension of indurated volcanic soils of Mexico by aerial photographs and remote sensing analysis , 2003 .

[5]  G. Delpont,et al.  Spatial assessment of erosion: Contribution of remote sensing, a review , 1993 .

[6]  Jean Poesen,et al.  Soil patterns as a key controlling factor of soil erosion by water , 2002 .

[7]  R. Pelletier.  Evaluating nonpoint pollution using remotely sensed data in soil erosion models , 1985 .

[8]  Gary A. Peterson,et al.  Soil Attribute Prediction Using Terrain Analysis , 1993 .

[9]  W. H. Wischmeier,et al.  Predicting rainfall erosion losses : a guide to conservation planning , 1978 .

[10]  Mark A. Nearing,et al.  Soil Erosion and Conservation , 2013 .

[11]  Sindhu George,et al.  Principles of Geographic Information Systems , 2002 .

[12]  J. B. Thornes,et al.  Atlas of Mediterranean environments in Europe : the desertification context , 1998 .

[13]  S. J. Smith,et al.  Prediction of sediment yield from southern plains grasslands with the modified universal soil loss equation. , 1984 .

[14]  G. R. Foster,et al.  Predicting soil erosion by water : a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE) , 1997 .

[15]  A. Vrieling.  Satellite remote sensing for water erosion assessment: A review , 2006 .

[16]  Geert Sterk,et al.  Spatial evaluation of soil erosion risk in the West Usambara Mountains, Tanzania , 2006 .

[17]  Anthony J. Jakeman,et al.  A review of erosion and sediment transport models , 2003, Environ. Model. Softw..

[18]  Stuart Oliver,et al.  20th-century urban landslides in the Basilicata region of Italy , 1993 .

[19]  R. Morgan.  Soil Erosion and Conservation , 1988 .

[20]  Kenneth G. Renard,et al.  Using monthly precipitation data to estimate the R-factor in the revised USLE , 1994 .