基于NDVI-T_s特征空间的中国土地覆盖分类研究  

LAND COVER CLASSIFICATION IN CHINA BASED ON THE NDVI-T_S FEATURE SPACE

作　　者：喻锋 李晓兵[1] 王宏[1] 余弘婧[1] 陈云浩[1] 

机构地区：[1]北京师范大学环境演变与自然灾害教育部重点实验室

出　　处：《植物生态学报》2005年第6期934-944,共11页Chinese Journal of Plant Ecology

基　　金：国家自然科学基金(30370265);霍英东教育基金会高等院校青年教师基金(91019)项目

摘　　要：归一化植被指数(NDVI)与地表温度(Ts)是描述地表覆盖特征的两个重要参数,其构成的NDVI_Ts特征空间具有丰富的地学和生态学内涵。该文在NOAA/AVHRR连续时间序列数据反演Ts的基础上,通过主成分分析、非监督分类和基于DEM的分类后处理等方法,以Ts/NDVI为指标对中国土地覆盖进行分类。结果表明,Ts/NDVI对中国较大尺度上不同土地覆盖类型的差异具有较强的敏感性,其对中国土地覆盖分类结果的野外抽样检验精度比传统的单独利用NDVI时间序列进行非监督分类提高了3.3%,Kappa系数提高了0.0202;在综合其它反映植被特征及其环境的指标(如气候、地形等)的基础上,利用Ts/NDVI将有可能较为准确地提取中国植被或土地覆盖的信息,有利于对其进行分类和变化监测,具有深远的研究潜力和应用价值。Mapping and quantifying land use and land cover changes are important for evaluating recent changes in the regional and global environment and for simulating future changes under different climate change and human land use scenarios. The normalized difference vegetation index （NDVI） and surface temperature （ Ts） are two important parmaeters used to describe the characteristics of land cover. The NDVI- Ts feature space combines these two parameters into one variable. By and large, Ts/NDVI is synchronized to the growing season of vegetation so it can approximate the different phases and status of vegetation growth. Compared to NDVI and Ts, NDVI-Ts contains more land cover information and should be more suitable for characterizing the distribution of vegetation or land cover. The aim of this paper was to discuss the feasibility of using the NDVI- Ts feature space to better characterize the current distribution and changes in vegetation and land cover in China. We used several classification methods, including Principal Component Analysis （PCA）, unsupervised classification and post-classification sustained based on digital elevation models （DEM）. The results indicated that Ts/NDVI was highly sensitive and could discriminate different vegetation cover categories in China at large-scales. The accuracy of vegetation classification based on Ts/NDVo was 72.0%, a 3.3% improvement in accuracy as compared to NDVI images, using unsupervised classification, and the Kappa value increased 0. 020 2. Moreover, because of the simplexity of remote sensing information, the classification based on seasonal Ts/NDVI data could not avoid completely the mixed classification phenomena. It was necessary to add other information, reflecting vegetation characteristic and its environment to implement post-classification, such as DEM. When the inversing accuracy of Ts improved, Ts/NDVI data can precisely describe the status of vegetation or land cover in China and improve monitoring of land use changes. This technique has great

关 键 词：TS NDVI Ts/NDVI土地覆盖分类 

分 类 号：P237[天文地球—摄影测量与遥感]