Study On Grassland Desertification Remote Sensing Monitoring In Inner Mongolia

The indexes of grassland desertification remotesensing monitoring include productivity,soil,surface overlay and so on.The thesis work mainly studied how to find an efficiently autoclassification method of remote-sensing image for classifying surface overlay ,which can improve efficiency of image classification at the same time keeping the classify veracity during grassland desertification monitoring.In this study ,several different treatment rooted in different consideration were used to search after the space subarea and autoclassification method the of grassland desertification remotesensing monitoring in Inner Mongolia,while MODIS remote sensing image with middle resolving power as data resouce and other graphics of special factors as reference ,Inner Mongolia Municipality was fixed as research object.At last,the classification results were verified by groud observation data and the result of visual interpretation,the conclusion of which is desirable.Mainly conclusion:· It can availably improve the precision of surface overlay autoclassification that the whole area was segmented into several subareas according to soil texture and aridity.· Unsupervised classification was explored in each subarea ,and the feasible number of the first classification and the appropriate way of recoding were fixed while the precision can reach 60-70%.· The feasible number of the first classification and the appropriate pattern of recoding can be used in the process of grassland desertification remotesensing monitoring in Inner Mongolia.· (PCA)-Renverse principal component analysis is a kind of technique to decrease correlation and stand out the useful information. The study result shows that this thechnique is unfit for grassland desertification remotesensing monitoring in Inner Mongolia Municipality.