Remote Sensing Quantitative Inversion Of Soil Erosion Factors Based On Vegetation Structural Characteristics

With the extensive application of remote sensing (RS)technology, the quantitative estimation of vegetation coverage in the soil erosion model using RS method was carried out gradually.But in the earlier studies,the attention was mainly paid on the relationship between the vegetation index and vegetation coverage, the vegetation coverage was extracted from the vegetation index in the RS images, then the value of vegetation management factor C was estimated by the vegetation coverage. This method not only caused the loss of vegetation structural information implicitting in the vegetation index, and made the prediction accuracy low, but also the extraction difficulty of vegetation coverage was increased as it was afluenced by the characteristics of vegetation structure. Now, The references using leaf area index (LAI) index to caculate vegetation factors by RS retrieval are still rare.The LAI index and annual soil erosion modulus in the different landuse were monitoring in the hilly area of the southern Jiangsu Province. The vegetation structure information LAI containing in the vegetation index was extracted on the the basis of RS spectral model using multi-scale (runoff plot, catchment) experimental and observation data, then the quantitive coupled model of LAI and vegetation management factor C was constructed, meanwhile, the model accuracy was verified.(1) The revised vegetation management factor C in the soil erosion prediction modelThe vegetation factor is one of the key parameters of the soil erosion prediction model, and it was replaced by the vegetation structural factor LAI to quantitative evaluate the vegetation coverage factor in the soil and water conservation. Which not only could reflect the vegetation communities cover status and vertical stucture, but also would reflect the litter and the biomass amount underground, it improved the forecasting accuracy of soli erosion prediction model effectively. Y＝2.1956e-10.7696(x-0.7779)2(2) The construction of vegetation structure factor LAI based on RS technologyThe regression equation of vegetation index and LAI was established through the simulation of unary regression equations, multiple regression equaiton and unary non-linear regression equation by selecting the Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), Perpendicular Vegetation Index (PVI), Atmospheric Resistance Vegetation Index (ARVI), Soil Adjusted Vegetation Index (SAVI), Modified Soil adjusted Vegetation Index (MSAVI) and Enhanced Vegetation Index (EVI), y=2.1956e-10.7696(x-0.7779)2 Further chi-square test (χ0.252(26)=29.339> 23) indicated that the simulation data calculated by the model was effective, and the LAI inversion by this modle was feasible. C=1 LAI<0.02 C=0.4138028-0.17888LAI 0.2≤LAI<1.8 C=0 LAI≥1.8(3) The quantitative coupled model construction of LAI and vegetation management factor CThe LAI index synthesized vegetation cover and vertical structure characteristics highly, and the quantitative coupled model of LAI index and vegetation management factor was brought forward by taking a comprehensive analysis of relevant research results through many tests. The results as following: C=1 LAI<0.02 C=0.4138028-0.17888LAI 0.2<LAI<1.8 C=0 LAI>1.8(4) Coupled model validationThe rationality of vegetation management factor C-value was qualitatively validated through the C-value comparion between different landuse type and the value of universal soil loss equation(USLE). the validity of LAI inversion C-value was quantitatively verified through the soil erosion comparion calculated by USLE model in the catchment and measured soil loss in the runoff plot.The simulation results of soil erosion amount caculated by different algorithms show that 1) the precision was the worst using the method of linear pixel decomposition(RMSE=26.0986),2) the soil erosion calculation precision was middle which was calculated from vegetation coverage factor C (RSEM=2.7429),3) but the precision was the best using LAI inversion of vegetation management factor C to calculate the soil loss through USLE model (RSEM=0.0856). and then, the reliability and accuracy of LAI inversion of vegetation management factor C was verified for further.