Abstract
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.
Highlights
Soil erosion is a vital environmental issue around the world leading to the destruction of soil resources, the decline of soil fertility, the deterioration of the ecological environment, and the elevation of riverbeds to exacerbate floods [1,2,3,4]
Calculated Result of the Revised Universal Soil Loss Equation (RUSLE) Model. e spatial distribution maps of the RULSE result and its input variables are shown in Figure 5. e detailed results are described as follows: e average monthly rainfall data of 11 meteorological stations in National Nature Reserve (NNNR) (Figure 1) used to compute the rainfall erosivity factor (R-factor) by equation (2) are listed in Table 1 and Figure 5(b). e R-factor value was between 8181.52 and 14621.56 MJ mm·ha− 1·h− 1·yr− 1. e average of R-factor was 12131.28 MJ mm·ha− 1·h− 1·yr− 1. e maximum and minimum values of the R-factor appeared in 2009 and 2010, respectively
E soil characteristics and soil erodibility K-factor in NNNR are given in Table 2 and Figure 5(c), from which we could observe that the value of K-factor was ranging from 0.1456 to 0.2384 t ha h·ha− 1·MJ− 1·mm− 1, with an average value of 0.1714 t ha h·ha− 1·MJ− 1·mm− 1
Summary
Test of the RUSLE and Key Influencing Factors Using GIS and Probability Methods: A Case Study in Nanling National Nature Reserve, South China. E 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. We 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. 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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