中文摘要：

土地利用／覆盖信息是区域气候与环境研究的基础，是土地资源规划与管理、合理开发与保护的信息保障。为此，该文选取长株潭城市群核心区为试验区，以时间序列HJ卫星影像为数据源，首先构建了时间序列归一化植被指数（normalizeddifferencevegetationindex，NDVI）、时间序列光谱第一主成分（firstprincipalcomponent，PCI）数据集，通过J-M（Jeffries．Matusita）距离变量可分离性分析结合地表覆盖的物候特征，确定最佳时序HJ组合数据；其次，采用面向对象的随机森林算法对研究区土地利用／覆盖信息进行分类，并对分类结果进行精度评价与比较分析。研究结果表明：采用时间序列HJ组合数据与面向对象的分类方法，提取城市土地利用／覆盖信息的总体精度和Kappa系数分别达到91．55％和0．90，其中水田、水浇地、旱地、林地、建设用地的生产者精度均达到90％及以上；相对于时间序列基于像元分类、单时相面向对象的分类方法，该文提出的土地利用／覆盖信息提取方法的总体分类精度和Kappa系数分别提高了2．26％、0．02和6．82％、0．08，有效提高了区域土地利用／覆盖信息提取的精度，为大范围土地利用／覆盖精细化分类提供了有效的途径。

英文摘要：

Land use/cover information is the basis for the study of regional climate and environment, and the information security of land resources planning and management. However, the accuracy of urban areas land use/cover information extraction is significantly affected by the high heterogeneity of land surface. Remote sensing technology, providing large-scale, timely, continuous and comprehensive measurements, has become an important means of land use/cover information extraction. HJ satellites, with high temporal and spatial resolution and wide coverage, provide a new way to fast and accurately extract large-scale land use/cover information. They have been widely used in land use/cover classification, crop information extraction, wetlands information extraction, and so on. The object-oriented classification method, which makes full use of the spectral information of remote sensing images and takes into account the spatial distribution characteristics and correlations of geographical objects, can compensate for the deficiency of traditional pixel-based classification methods. This study developed a supervised classification method for regional land use based on the object-oriented random trees algorithm to quickly extract land surface information with low cost and high precision. We selected the Changsha-Zhuzhou-Xiangtan core area as the study area and used the multi-temporal and multi-spectral information of HJ satellite CCD （charge-coupled device） data. Firstly, high quality HJ-CCD data （10 phases in total） were selected, and preprocessed by radiometric calibration, atmospheric correction, accurate geometric correction and image registration. The time series of normalized difference vegetation index （NDVI） and of the first principal component （PC1） were calculated, and their results overlapped each other. The best time series of HJ classification data were determined by the J-M （Jeffries-Matusita） distance variable separability analysis, combined with land cover of study area, and phenotypic chara