植物生态学报 ›› 2005, Vol. 29 ›› Issue (6): 934-944.doi: 10.17521/cjpe.2005.0115

• 论文 • 上一篇    下一篇

基于NDVI_Ts特征空间的中国土地覆盖分类研究

喻锋1,2, 李晓兵1, 王宏1, 余弘婧1, 陈云浩1   

  1. 1 北京师范大学环境演变与自然灾害教育部重点实验室,北京师范大学资源学院,北京 100875 2 国土资源部信息中心,北京 100812
  • 出版日期:2005-09-30 发布日期:2005-09-30
  • 通讯作者: 李晓兵

LAND COVER CLASSIFICATION IN CHINA BASED ON THE NDVI_TSFEATURE SPACE

YU Feng1,2, LI Xiao_Bing1, WANG Hong1, YU Hong_Jing1, and CHEN Yun _Hao1   

  1. 1 Key Laboratory of Environmental Change and Natural Disaster of the Ministry of Education, Beijing Normal University, Beijing 100875, China 2 Information Center of Ministry of Land and Resources, Beijing 100812, China
  • Online:2005-09-30 Published:2005-09-30
  • Contact: LI Xiao_Bing

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0.020 2;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。

Abstract:
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 parameters 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 vegeta tion 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 classificat ion based on Ts/NDVI was 72.0%, a 3.3% improvement in accuracy as comp ared 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 cl assification 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 research potential and practical value.