GU Feng,DING Jian-li,WANG Jing-zhe,GE Xiang-yu
China Rural Water and Hydropower. 2019, (6):
44-50.
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In this study, the Wetlands in Werigan-Kuqia River Delta Oasis (WKRDO), as a typical wetland on the banks of the Tarim River, is considered as study area. The method of Random Forests, which has obvious advantages in feature selection and classification, was chosen in order to extract wetland information from the study area. First of all, four different characteristic variables, including spectral features, vegetation indices, water indices, salinity indices and texture features, were generated based on Landsat8 OLI data with rich multi-temporal and spectral information, and then six different classification schemes were constructed based on the above characteristic information. At last, the random forest classifier was used to extract the wetland information of the WKRDO, and to verify the extraction accuracy of different results. The purpose is to select the best plan to improve the effect of wetland information extraction. The results show that :(1) The effective use of multiple feature variables is the key to improving information wetland of extraction. For the contribution of different characteristics to the wetland information extraction, spectral features > vegetation indices and water indices > texture feature > salinity indices;(2) the preferred features based on the Random Forest algorithm are significant to extraction accuracy, with the overall accuracy is up to 90.09 %, and the Kappa coefficient is 0.8825. The extracted types reflect the differences in soil properties. It shows that the random forest algorithm can effectively process the feature selection. While the feature variable data mining, the accuracy of the wetland information extraction can be guaranteed and the operation efficiency can be improved. The result has important practical significance for formulating scientific water and fertilizer management measures and evaluating drought situation in the local oasis.