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Shandong University of Technology (山东理工大学) 2021

Research on Desertification Grade Evaluation Based on Multi-source Data


Titre : Research on Desertification Grade Evaluation Based on Multi-source Data

Auteur : 王树祥

Grade : Master 2021

Université : Shandong University of Technology (山东理工大学)

Résumé partiel
The classification of land desertification grades is an important part of desertification monitoring,and it is also the basis for comprehensive management and scientific protection of land desertification.Aiming at the problem that the single vegetation index method in arid or sandy areas occurs abnormally when extracting desertification information.This study uses series of Landsat satellite data,land cover products and statistical yearbook data in the period of 1990-2015 in Horqin area as the data source,through sample statistical analysis method,a desertification extraction model is proposed,which integrates three indicators:vegetation coverage(FVC),modified soil adjustment vegetation index(MSAVI),and enhanced vegetation index(EVI).The algorithm is applied to the dynamic monitoring of land desertification in the Horqin Sandy Land in the past 25years,and the driving force of the land desertification in the Horqin Sandy Land is further explored from the political,economic,demographic and environmental factors using the grey correlation method.The main content and conclusions are as follows :(1)Due to the influence of soil background and vegetation index oversaturation,the classification accuracy of land types with low and high vegetation coverage based on the traditional FVC method is low,while the three index methods of Albedo,BSI,and MSAVI have poor results for the classification of slight and moderate desertification land.Based on the traditional FVC desertification information extraction algorithm,this paper introduces EVI and MSAVI indexes,and proposes a desertification information extraction model that integrates three indexes of FVC,EVI,MSAVI,and compares the classification results with the FVC method and three-index method of Albedo,BSI,MSAVI to do accuracy evaluation.The research results show that:compared with the traditional FVC vegetation information extraction method,the algorithm proposed in this paper has higher classification accuracy,especially for arid/semi-arid regions.The fusion vegetation index method has better applicability and robustness ;compared with the three index methods of Albedo,BSI,and MSAVI,and the multi-index fusion algorithm are more ideal for distinguishing lightly and moderately desertified land with higher accuracy.

Mots clés : Desertification ;multi-index fusion algorithm ;decision tree classification ;correlation analysis ;driving force analysis ;

Présentation (CNKI)

Page publiée le 18 mars 2022