Spatial distribution of soil erosion risk in red eroded area of South China

2011 ◽  
Author(s):  
Xiaofei Li ◽  
Zhibiao Chen ◽  
Zhiqiang Chen ◽  
Lihui Chen
Keyword(s):  
Spatial Distribution ◽  
Soil Erosion ◽  
South China ◽  
Erosion Risk ◽  
Soil Erosion Risk

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

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Jun Wang ◽  
Qian He ◽  
Ping Zhou ◽  
Qinghua Gong
Keyword(s):  
Spatial Distribution ◽  
Soil Erosion ◽  
Influencing Factors ◽  
South China ◽  
Nature Reserve ◽  
Ground Truth ◽  
Landsat 8 ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
National Nature Reserve

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.

Soil erosion risk and its spatial pattern in upstream area of Guanting reservoir

2011 ◽  
Vol 65 (1) ◽  
pp. 221-229 ◽  
Author(s):  
Xi Wang Zhang ◽  
Bing Fang Wu ◽  
Xiao Song Li ◽  
Shan Long Lu
Keyword(s):  
Soil Erosion ◽  
Spatial Pattern ◽  
Erosion Risk ◽  
Guanting Reservoir ◽  
Soil Erosion Risk

scholarly journals Soil Erosion Estimation Using Remote Sensing Techniques in Wadi Yalamlam Basin, Saudi Arabia

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Jarbou A. Bahrawi ◽  
Mohamed Elhag ◽  
Amal Y. Aldhebiani ◽  
Hanaa K. Galal ◽  
Ahmad K. Hegazy ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Soil Erosion ◽  
Decision Support Tool ◽  
Data Availability ◽  
Productive Capacity ◽  
Support Tool ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
Key Aspects

Soil erosion is one of the major environmental problems in terms of soil degradation in Saudi Arabia. Soil erosion leads to significant on- and off-site impacts such as significant decrease in the productive capacity of the land and sedimentation. The key aspects influencing the quantity of soil erosion mainly rely on the vegetation cover, topography, soil type, and climate. This research studies the quantification of soil erosion under different levels of data availability in Wadi Yalamlam. Remote Sensing (RS) and Geographic Information Systems (GIS) techniques have been implemented for the assessment of the data, applying the Revised Universal Soil Loss Equation (RUSLE) for the calculation of the risk of erosion. Thirty-four soil samples were randomly selected for the calculation of the erodibility factor, based on calculating theK-factor values derived from soil property surfaces after interpolating soil sampling points. Soil erosion risk map was reclassified into five erosion risk classes and 19.3% of the Wadi Yalamlam is under very severe risk (37,740 ha). GIS and RS proved to be powerful instruments for mapping soil erosion risk, providing sufficient tools for the analytical part of this research. The mapping results certified the role of RUSLE as a decision support tool.

scholarly journals Soil erosion risk, Sicilian Region (1:250,000 scale)

2014 ◽  
Vol 11 (2) ◽  
pp. 323-341 ◽  
Author(s):  
M. Fantappiè ◽  
S. Priori ◽  
E.A.C. Costantini
Keyword(s):  
Soil Erosion ◽  
Erosion Risk ◽  
Soil Erosion Risk
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