中文摘要：

长江三角洲地区是我国经济最发达的地区之一,人类活动对自然环境产生了很大影响.为了研究该地区人类活动与生态环境的相互作用,利用250 m分辨率MODIS数据进行土地覆盖制图研究,采用的主要数据为增强型植被指数EVI数据、反射率数据和DEM数据.通过基于时间序列的滤波方法消除EVI的噪声,通过PCA变换压缩数据量,并计算均质度来表征空间维的纹理信息,构造了一个综合性的分类数据矩阵,依据高分辨率影像选取了训练区,采用最大似然法进行分类,并采用缓冲区分析技术进行分类修正,得到长江三角洲地区的土地覆盖分类结果.利用高分辨率影像解译信息对分类结果进行了精度评价,并将分类结果与MODIS土地覆盖产品进行了对比,精度分析表明分类结果很好的反映了研究区的土地覆盖信息,显示了本研究分类方法与技术处理在实践中的可行性及250 m分辨率EVI时间序列数据在区域尺度土地覆盖分类方面的优势与潜力.

英文摘要：

The Yangtze River Delta is one of the most economically developed regions in China. During the last decade, this area experienced rapid urban expansion, and accordingly, masses of cropland have been converted into human buildings. To analyze the influence of landscape change, it is important to provide up-to-date land cover information of this area. This paper describes the development of land cover map of the Yangtze River Delta using 250 m MODIS data, and the main satellite data used in this study were MODIS EVI data, MODIS reflectance data and DEM. A filter method based on time series was applied to eliminate EVI noise, and a PCA analysis was performed to reduce the volume of data. Besides, homogeneity was calculated to present spatial texture information. Therefore, a compositive classification matrix was generated. Considering the natural and artificial conditions of the Yangtze River Delta, a classification scheme was defined. ROIs （Region of Interest） were selected from Landsat ETM＋ images by human interpretation consulting the 1： 1,000,000 Vegetation Atlas of China. Then the land cover map was generated using MLC method. After correction by buffering analysis, we got the final land cover classification of the Yangtze River Delta. The classification accuracy was assessed using fine-resolution Landsat images, with an overall accuracy of 95.98%. In addition, our classification result was compared with the MODIS-IGBP land cover production and showed better accuracy. The good result indicated the good behavior of the classification method and technical processing used in our research, and also suggested the advantage of 250 m MODIS data in regional land cover mapping.