Spatial and Temporal Dynamic Changes of Vegetation Cover and Management Factor in Nanjing City

The vegetation cover and management factor (C)in the revised universal soil loss equation (RUSLE)model is used to indicate the effect of vegetation cover on soil erosion, and is one of the key parameters for evaluating soil erosion. The precise estimation and the spatial distribution characteristics of the long-term sequence of C factor at the regional scale are important for the prediction of dynamics of regional soil erosion. Therefore, timely and accurate grasping of the long-term sequence of C factor at the regional scale is very important for studying the dynamic relationship between soil erosion and vegetation. The remote sensing image in Nanjing City from 1988 to 2013 was selected and used. The quantification coupling model between leaf area index (LAI)and C factor was constructed based on BP neural network and the vegetation structure factor LAI inversed by remote sensing. The field measurement of C factor was obtained by ¹³⁷Cs isotope tracer technique, and the accuracies of the inversion models were verified. The results showed that: (1)The traditional vegetation coverage based on normalized difference vegetation index (NDVI)was generally larger than that based on LAI. The C value was generally smaller than the measured value, and the RMSE was 87.829%. While the C value calculated by LAI was close to the measured value, and the RMSE was 30.017%. Therefore, the C value calculated by LAI can reflect the actual vegetation structure information better than by NDVI. (2)The area with C value greater than 0.3 was mainly distributed in the urban areas with dense buildings, sparse vegetation and simple vegetation structure or even no vegetation cover. The area with C value less than 0.05 was mainly distributed in the hills and mountainous regions with dense vegetation structure. The distribution of C value in Nanjing City was closely related to vegetation cover and land use types. (3)Over the whole city where the C values were less than 0.05, during the period from 1988 to 2013, the area with strong resistance to soil erosion firstly decreased from 15.66% (1988)to 9.43% (before 2006)and then gradually increased to 12.07%. For the region where C values were greater than 0.3, the area with weaker resistance to soil erosion firstly slowly increased from 7.29% to 9.22% (2002), then it quickly increased to 12.31% (2002-2006)and then slowly decreased to 11.77%. Therefore, it is feasible to retrieve the long-term sequence C factor based on BP neural network and LAI proposed in this paper, which provides a new way for quantitative remote sensing monitoring of soil erosion on regional scale.