Study On Remote Sensing Information Model Of Desertification Current Situation Quantitation Evaluation

Desertification belongs to the deteriorative phenomenon of global environment. Its dynamic changes should be monitored timely and accurately by the county in order to control the desertification scientifically. The remote sensing technology has a lot of problems in terms of desertification evaluation, such as improper evaluation indexes selection, subjective weight, the low index inversion precision, and so on. A quantitative evaluation index system that is widely recognized and practical is still lacked. Perhaps the factors in a leading role are the same in different regions and different land use types, but the influence degree of the factors to the desertification must have differences. At present, there is still no remote sensing quantitative evaluation research report of desertification based on the land use interiorly. How to evaluate desertification quantitatively using remote sensing from the point of view of land use? To solve this problem will help to know the content of the mechanism and genesis in the process of land degradation and help to establish desertification evaluation index system. In this paper, taking Beijing-Tianjin sandstorm source control project district as an example, the specific desertification evaluation methods of multispectral and hyperspectral remote sensing based on land use in arid and semiarid regions were discussed respectively. The mainly achieved results were summarized as follows:1. The stratified remote sensing information extraction model of land use combining linear spectral mixture model, vegetation index and expert knowledge was put forward. The model achieves stratified extraction of land use information with high precision.2. The multispectral remote sensing indexes distinguishing tree species were sifted, and the improved SVM algorithm was also quoted to extract tree species information in returning farmland to forest region. The results have shown that the average accuracy was increased by 9.2%than that of the traditional method. The method in this paper has the important meaning to the fast evaluation of the project quality of returning farmland to forest.3. Texture features and spatial information were blended in land use information extraction of hyperspectral images. The land use type information of hyperspectral images was extracted by spectral analysis of the reflectance and texture features. And then the vegetation types were further classified using the method of spatial information. The results show that the average classification accuracy is increased by 17.8%than that of maximum likelihood method. The feasibility of tree species classification of hyperspectral remote sensing was analyzed, and the bands and spectral characteristic parameters that have great differences were selected. At last, the improved BP neural network model was quoted to complete the tree species information extraction of forest land.4. The remote sensing index system of desertification evaluation based on land use was set up firstly and the determination method of the "standards" was determined. A new index weight calculation method was put forward on the basis of analyzing large quantities of measured data. The evaluation accuracy of the model in this paper was higher than that of the traditional model and the evaluation results were closer to the real condition of desertification.5. A model that was fit for the extraction of vegetation coverage in arid zones was presented. In this model, TM image was decomposed by linear spectral mixture mode and then the vegetation component of TM image was amended by the high-resolution satellite image. The result shows that the model can not only provide more pure vegetation spectrum information but also reduce the sensitivity to the soil background, so it is more suitable for quantifying vegetation coverage of arid and semi-arid regions by medium-resolution satellite images.6. In this study, nineteen characteristic parameters that had significant level correlation coefficients with forest volume were selected to forecast forest volume. A variety of hyperspectral vegetation coverage extraction methods that are currently popular were compared. The conclusion is that the partial least-squares regression model based on first order differential is the best.7. The specific method that eliminated interferences of vegetation spectrum by decomposing hyperspectral imaging to predict soil water content more reasonably was put forward. The prediction ability of soil sediment concentration of the minimum noise fraction regression model and the principal component regression model were analyzed.