【摘要】 城市湿地是上海重要的生态基础并具有复杂多变的自然特性。研究采用决策树分类方法,以TM影像多光谱波段特征为主要分类变量,采用经K-T变换、IHS变换等光谱增强后的数据以及利用灰度共生矩阵分析影像第一主成分的纹理统计量作为辅助分类变量,结合城市湿地几何特征信息,构建上海城市湿地决策树分类模型,进行上海市湿地信息的遥感提取和分类。结果表明:(1)上海城市湿地总面积为1277.40 km2;其中水田面积最大,占总面积的65.30%;其次为河流、库塘、湖泊和芦苇。(2)决策树模型的分类方法在一定程度上提高了城市湿地提取和分类的精度,使其达到89.05%;与传统的最大似然法相比,总精度提高了约10%。更多还原

【Abstract】 Urban wetland is an important ecological basis of Shanghai and it is characterized with complex properties.In this study,a decision tree based classification method is used to extract and classify the urban wetland information in Shanghai area.The method uses multispectral bands of Landsat-5 TM image as the main variables,and a series of derivative data as the auxiliary inputs,derived from the Landsat-5 TM images by using respectively K-T transformation,IHS transformation,principal component analysis and textural analysis.With these variables in association with the spatial characteristics of the urban wetland in Shanghai,the method builds a decision tree model for urban wetland extraction and classification.The application of the model shows that the total area of the urban wetland in Shanghai is about 1 277.40 km2.The rice cultivated area occupies the highest portion up to 65.30% of the total wetland,and the next the area of rivers,ponds,lakes and reed fields.The decision tree model based method has a relative high precision in the urban wetland extraction and classification.The classification result indicates that the overall accuracy reaches 89.05%,more than 10% increase when compared with the maximum likelihood algorithm.更多还原