植物生态学报 ›› 2005, Vol. 29 ›› Issue (3): 436-443.DOI: 10.17521/cjpe.2005.0058

• 论文 • 上一篇    下一篇

基于NOAA-AVHRR数据的中国东部地区植被遥感分类研究

李俊祥(), 达良俊, 王玉洁, 宋永昌   

  1. 华东师范大学环境科学系,上海200062
  • 收稿日期:2004-02-26 接受日期:2004-10-19 出版日期:2004-02-26 发布日期:2005-05-30
  • 作者简介:E-mail: jxli@des.ecnu.edu.cn
  • 基金资助:
    本研究得到国家自然科学基金重点项目(30130060);国家重点基础研究发展规划项目(2000046801);上海市生态学重点学科;国家“211”生态学重点学科的资助

VEGETATION CLASSIFICATION OF EAST CHINA USING MULTI-TEMPORAL NOAA-AVHRR DATA

LI Jun-Xiang(), DA Liang-Jun, WANG Yu-Jie, SONG Yong-Chang   

  1. Department of Environmental Science, East China Normal University, Shanghai 200062, China
  • Received:2004-02-26 Accepted:2004-10-19 Online:2004-02-26 Published:2005-05-30

摘要:

该文采用 19幅 (时间跨 8个月 ) 时间序列的NOAAAVHRR的归一化植被指数 (NDVI) 最大值合成影像遥感数据, 经过主分量分析 (Principlecomponentanalysis, PCA) 处理后, 用非监督分类方法的ISODATA算法, 对中国东部地区的 (五省一市 ) 植被进行分类, 结果可以分出 2 8种土地覆盖类型, 除了两种类型为水体和城市或裸地外, 其余 2 6种类型均为植被类型, 根据中国植被分类系统, 这 2 6类可以归并为 6大植被类型 :1) 常绿阔叶林 ;2 ) 针叶林 ;3) 竹林 ;4 ) 灌草丛 ;5 ) 水生植被 ;6 ) 农业植被。用 1∶10 0 0 0 0 0数字化《中国植被图集》的植被类型检验遥感分类结果表明, 针叶林、灌草丛、常绿阔叶林和农业植被的分类具有较高的位置精度和面积精度, 位置精度分别为 79.2 %、91.3%、6 8.2 %和 95.9%, 面积精度分别达到 92.1%、95.9%、6 3.8%和 90.5 %。这 6大植被类型在地理空间上的分布规律与中国东部常绿阔叶林区植被的地带性分布基本一致。

关键词: 植被分类, 遥感, NDVI, 主分量分析, 中国东部

Abstract:

East China lies in the subtropical monsoon climatic zone and is dominated by subtropical evergreen broad-leaved forests, a unique vegetation type on earth mainly distributed in East Asia with the largest and most concentrated distribution in China. It is important to be able to monitor and estimate forest biomass and production, regional carbon storage, and global climate change impacts of these important vegetation types. In this paper, we used coarse resolution remote sensing data to identify the vegetation types in East China and develop a map of the spatial distribution of vegetation types in this region. Nineteen maximum normalized difference vegetation index (NDVI) composite images (Acquisition time span 7 months from February through August), which were derived from 10-days of National Oceanographic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) channel 1 and channel 2 observations, and an unsupervised classification method, the ISODATA algorithm, was employed to identify the vegetation types. The image was processed using principal component analysis (PCA) to reduce the dimensions of the dataset resulting in a total of 28 spectral clusters of land cover of which 2 clusters were urban/bare soil and water. The 26 remaining spectral clusters were merged into 6 vegetation types: evergreen broad-leaved forest, coniferous forest, bamboo forest, shrub-grass, aquatic vegetation and agricultural vegetation using the Chinese vegetation taxonomy system. The spatial distribution and areal extent calculated for the coniferous forests, shrub-grass, evergreen broad-leaved forests and agricultural vegetation compared well with the Vegetation Atlas of China at a 1∶1 000 000 scale. The spatial accuracy for coniferous forests, shrub-grass, evergreen broad-leaved forests and agricultural vegetation was 79.2%, 91.3%, 68.2% and 95.9%, respectively, and the area accuracy was 92.1%, 95.9%, 63.8% and 90.5%, respectively. The spatial and area accuracy of the bamboo forest was 28.7% and 96.5%, the spatial accuracy of aquatic vegetation was 69.6%, but there is great error in its area accuracy because image acquisition did not cover the full year. Our research demonstrated the feasibility of using NOAA-AVHRR to identify the different vegetation types in the subtropical evergreen broad-leaved forest zone in East China. The spatial location of the 6 identified vegetation types coincided with the actual geographical distribution of the actual vegetation types in East China.

Key words: Vegetation classification, Remote sensing, NDVI, Principal component analysis, East China