Integrating TM and Ancillary Geographical Data with Classification Trees for Land Cover Classification of Marsh Area

作     者：NA Xiaodong ZHANG Shuqing ZHANG Huaiqing LI Xiaofeng YU Huan LIU Chunyue 

作者机构：Northeast Institute of Geograpky and Agroecology Chinese Academy of Sciences Ckangchun 130012 China Graduate University of the Chinese Academy of Sciences Beijing 100049 China Institute of Resources Information Chinese Academy of Forestry Beijing 100091 China 

出 版 物：《Chinese Geographical Science》 (中国地理科学（英文版）)

年 卷 期：2009年第19卷第2期

页      面：177-185页

核心收录：

学科分类：0810[工学-信息与通信工程] 07[理学] 08[工学] 081002[工学-信号与信息处理] 0713[理学-生态学] 

基　　金：Under the auspices of National Natural Science Foundation of China (No. 40871188) National Key Technologies R&D Program of China (No. 2006BAD23B03) 

主　　题：land cover classification classification trees Landsat TM ancillary geographical data marsh 

摘      要：The main objective of this research is to determine the capacity of land cover classification combining spec- tral and textural features of Landsat TM imagery with ancillary geographical data in wetlands of the Sanjiang Plain, Heilongjiang Province, China. Semi-variograms and Z-test value were calculated to assess the separability of grey-level co-occurrence texture measures to maximize the difference between land cover types. The degree of spatial autocorrelation showed that window sizes of 3×3 pixels and 11×11 pixels were most appropriate for Landsat TM im- age texture calculations. The texture analysis showed that co-occurrence entropy, dissimilarity, and variance texture measures, derived from the Landsat TM spectrum bands and vegetation indices provided the most significant statistical differentiation between land cover types. Subsequently, a Classification and Regression Tree (CART) algorithm was applied to three different combinations of predictors: 1) TM imagery alone (TM-only); 2) TM imagery plus image texture (TM+TXT model); and 3) all predictors including TM imagery, image texture and additional ancillary GIS in- formation (TM+TXT+GIS model). Compared with traditional Maximum Likelihood Classification (MLC) supervised classification, three classification trees predictive models reduced the overall error rate significantly. Image texture measures and ancillary geographical variables depressed the speckle noise effectively and reduced classification error rate of marsh obviously. For classification trees model making use of all available predictors, omission error rate was 12.90% and commission error rate was 10.99% for marsh. The developed method is portable, relatively easy to im- plement and should be applicable in other settings and over larger extents.