Land Desertification Estimate Based On Merged Images And Topography Classification

Desertification,land degradation in arid,semi-arid and dry sub-humid regions resulting from abominable natural conditions and uncomfortable human activities,is a global ecological and environmental problem.Monitoring and assessment of desertification is a key content of desertification research.Only if spatial and temporal information of desertification changes is obtained in time,effective management can be planed and implemented,and hence prevention and control of desertification can be achieved and natural resources can be protected and developed sustainably.However,there is no consensus on the proper way to assess desertification.But the rapid development of computer hardware technology and remote sensing technology provide an opportunity to achieving this goal.Based on image merging of multiple source and topography classification,the relationships between field inventory data and remote sensing data were conducted and achieved quantitative estimation of land desertification in a typical semi-arid region Mu Us in inner Mongolia.The main research achievements are as following:The quality of merging high resolution SPOT panchromatic image and multispectral TM images using three methods was compared.Among three methods principle component transform,Brovey transform and multiplicative transform,the last one not only inherited more spectral quality of TM images but also merged more spatial context of SPOT panchromatic image just as the average differences between the pixel value of the merged image and corresponding original TM(the registered and the resampled one ) is 22.886 and the correlation coefficients of the high-pass filtered merged image with the high-pass filtered SPOT panchromatic image is 0.949.The image merged by the means of multiplicative transform was the best one among three merged images and original TM image to classify the topography and the overall classification precision was 82.42%and the Kappa coefficient was 0.616.In addition,according to further analyzing remote sensing data corresponding to field inventory data,the ratio of green index to humid index of tasseled cap transform can be used to recognized water and road from other classes and meanwhile the second component can be used to divide highland from sandy land. In terms of the hierarchical classification method the overall classification precision rise to 85.85%and the Kappa coefficient rise to 0.635.In view of selecting variables,multi-correlation coefficient rule based on traditional least squares estimate was compared with the Bootstrap method and the rule of Variable importance in Projection based on partial least squares estimate.Selected variables were different due to used methods and those variables selected by the latters induced to a higher precision of model and took very limited time.Furthormore,those variables selected by VIP rule were interpreted distinctly though the VIP rule is a method applied in quantitative assessment.The effect of Topography types on percent vegetation cover estimating was analyzed.The root mean square error(RMSE)of estimated fractional cover decreased 3%and relative RMSE even decreased 13%due to topography classification.This illuminated that topography classification contributed to improving the estimate precision of percent vegetation cover.Considering that collinearity existed generally among variables the methods ridge regression,partial least square regression(PLSR) and general regression neural network (GRNN) were introduced to estimated biomass due to their robustness of collinearity data.The PLSR among three methods had the best precision of estimating biomass in highland and the RMSE was 64.39 g.m-2,relative RMSE was 57.68%.However The GRNN methods had the best precision of estimating biomass in sandy land and the RMSE was only 53.59 g.m-2, relative RMSE was 21.75%.The precision of estimated biomass in sandy land is better than that in highland.The relationships of soil water content,percent vegetation cover and biomass with land desertification were examined and the correlation of percent vegetation cover with desertification is the biggest,the correlation of soil water content with desertification is the least.The model introducing percent vegetation cover and biomass properly predicted 81.2% of sample plots of land desertificati0n degree and the deviation is lower than one grade.But percent vegetation cover and biomass estimated according to remote sensing data included some error unavoidably,in terms of the error spread rule these errors would be introduced into the model and decrease the predicted precision of land desertification degree.Hence the model only included remote sensing data factors as independent variables was derived to estimate land desertification degree.The result showed the ratio of properly predicted sample plots got an average level of 83.9%and the deviation was less than one grade.In a word,based on merged image and topography classification,quantitative estimate and visualization of land desertification degree can certainly be achieved according to the relationship derived from field inventory and corresponding remote sensing data.