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《Forest Resources Management》 2015-02
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Spectral Unmixing of MODIS Data Based on Improved Endmember Purification Model for Forest Type Identification

CHEN Li;LIN Hui;TAO Ji;Hunan Provincial Forest Inventory and Planning Design Institute;Research Center of Forestry Remote Sensing& Information Engineering,Central South University of Forestry & Technology;  
Because of high spectral resolutions,large coverage and low cost MODIS( Moderate Resolution Imaging Spectroradiometer) data has been widely used to quickly extract information of forest types at regional,national and global scales. However,its coarse spatial resolution often leads to mixed pixels and low classification accuracy of forest types. Using spectral unmixing model can,to some extent,increase the accuracy of classification. But how to accurately extract pure endmembers for a study area is often a great challenge. The selection of linear or non-linear spectral unmixing algorithm is another challenge. In this study,a method to extract endmembers from MODIS images was developed. In this method,the time series of MODIS derived vegetation index was first derived and the phenological variation of forest types was analyzed. Decision tree classification was then conducted and the obtained results were used to extract endmembers. In addition,for comparison,the classification was also made using a widely used classifier—maximum likelihood. It showed that linear spectral unmixing was the best regardless of with and without constraints,the maximum likelihood classification and non-linear spectral unmixing were in the 2nd and3 rd places respectively.
【Fund】: “十二五”国家高技术研究发展计划(863计划)课题“数字化森林资源监测关键技术研究”(2012AA102001);; 国家自然科学基金(30871962);; 湖南省高校产业化培育项目(13CY011)
【CateGory Index】: S771.8
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