中文摘要：

以洞庭湖流域为研究区,对大范围湿地信息遥感提取方法进行了研究。先基于时间序列MODIS EVI及物候特征参数,通过J-M（Jeffries-Matusita distance）距离分析,构建了MODIS（250 m）最佳时序组合分类数据;其次,通过Johnson指数确定了最佳分割尺度,采用面向对象的遥感分类方法（Random tree分类器）提取了洞庭湖流域的湿地信息,并验证该方法的适用性。研究结果表明,基于时序数据与面向对象的Random tree分类的总体精度和Kappa系数分别为78.84%和0.71,较之基于像元的相同算法的总体分类精度和Kappa系数分别提高了5.79%和0.04。同时,基于面向对象方法的湿地整体的用户精度与生产者精度较基于像元方法分别提高了4.56%和6.21%,可有效提高大区域湿地信息提取的精度。

英文摘要：

The mapping of large-scale wetlands involves time-series coarse spatial resolution remote sensing data and pixel-based methods, such as the decision tree and threshold techniques. However, few studies use low spatial-resolution images（such as moderate-resolution ima- ging spectroradiometer （MODIS）） and object-oriented methods to extract information from large-area wetlands. Although spatial resolution has some disadvantages, the coarse spatial resolution image has high time resolution, considerable spectral information, and low cost. There- fore, the high temporal characteristics of coarse image and object-oriented method can be used to extract wetland information over a large area, such as basins and continents. In this study, the object-oriented method and time-series MODIS Enhanced Vegetation Index （EVI） data are utilized to map the wet- land of the Dongting Lake Basin. The time-series MODIS EVI images are smoothed using the double logistic function fitting method of TIMESAT software package, which is based on MATLAB. Meanwhile, the phenology indices are calculated from the time-series MODIS EVI data. Subsequently, the best combination of images and optimal segmentation scale are determined with the JBh distance and Johnson index. Wetland mapping is then verified using a random tree classifier based on the segmented images. In addition, validation data are de- rived from the visual interpretation of Landsat 8 images, Google Earth, and land-use data. To verify the classification effect of the object-ori- ented classification method on coarse spatial resolution images, the pixel-based method is also utilized to classify the best combination of images and is then compared with the upper method. The phenology of various ground cover types is obviously different, which indicates that they can be used to distinguish different land types, especially vegetation types. Given the image combination of the critical periods （DOY 113, DOY 145, DOY 173, DOY 193, DOY241, and DOY289） of vegetation growth and