The Extraction of Freshwater Marsh Wetland Information Based on Decision Tree Algorithm——A Case Study in the Northeast of the Sanjiang Plain
基于决策树方法的淡水沼泽湿地信息提取——以三江平原东北部为例

Wetlands are considered an integral part of the global ecosystem.Enhancement of their scientific management informed by quantitative,accurate and repeatable observations of wetlands' landscape would obviously be significant.Taking the northeast of the Sanjiang Plain as a case study,we use classification and regression tree (CART) algorithm for purposes of mining classification rules from training samples.Classification tree model of wetland information extraction was built from these samples through CART algorithm,which integrates spectral,texture and the assistant geographical characteristics.The classification results based on CART algorithm were checked by statistical confusion matrix accuracy assessment using field GPS samples.Validation shows that total classification accuracy is 82.65%,Kappa coefficient is 0.7935.The results had suggested that the accuracy of classification based on the CART algorithm was higher than the MLC supervised classification method.The developed method is portable,relatively easy to implement,and should be applicable in other settings and larger extents.