收稿日期: 2003-01-28
修回日期: 2003-04-29
网络出版日期: 2004-02-01
基金资助
国家自然科学基金项目“基于‘3S’检测中国北方典型草原植被盖度及其尺度转换研究”（编号：30370265）；国家重点基础研究发展规划项目“草地与农牧交错带生态系统重建机理及优化生态—生产范式”（编号：G2000018604）；北京师范大学青年科学基金项目（理科）联合资助
GLOBAL AND REGIONAL COVER MAPPING FROM REMOTE SENSING DATA： STATUS QUO, STRATEGIES AND TRENDS
Received date: 2003-01-28
Revised date: 2003-04-29
Online published: 2004-02-01
土地利用／土地覆盖变化研究是近年来全球变化研究的焦点之一。全球和区域尺度的土地覆盖特征对全球环境状况的评估、模拟未来全球环境的情景有重要的作用。2000年在Internat ionalJournalofRemoteSensing杂志上出版了题为"GlobalandRegionalLandCoverCharacterizat ion from Remotely Sensed Data"的专辑。在此基础上，介绍、总结了国际上利用遥感影像进行全球和区域等大尺度土地覆盖研究的新进展。分别从数据源与制图的时空尺度、制图方法（数据预处理、分类、精度评估）等方面进行了介绍，并对现今的两个全球土地覆盖数据库进行了比较分析。
喻锋 , 李晓兵 , 陈云浩 . 基于遥感数据的全球及区域土地覆盖制图———现状、战略和趋势[J]. 地球科学进展, 2004 , 19(1) : 71 -080 . DOI: 10.11867/j.issn.1001-8166.2004.01.0071
Land use and land cover change is one of the focuses in the study of global change in recent years. Land cover characteristics, at the global and regional scale, are very important for evaluating global environment status and simulating global environment scenario in the future. A special issue named "Global and regional land cover characterization from remotely sensed data" was published in 2000 in International Journal of Remote Sensing. Based on this, international advance of land cover characterization from remotely sensed data at global and regional scale was summarized and introduced in this paper, including data sources, dimension and land cover mapping methods (pre-processing, classification and accuracy assessment), etc. Two global land cover databases-IGBP DISover and University of Maryland 1 km products were introduced as examples.
The domain of land cover mapping spans the range between two extremes: "coarse" resolution at frequent time intervals, and "fine" resolution at long intervals. Major steps in extracting land cover information by using satellite data at fine and coarse resolutions include: geometric corrections, compositing, radiometric corrections, classification and accuracy assessment. Land cover information that can be gleaned from satellite images is the spectral and spatial attributes of individual cover types. Two types of numerical classification approaches for satellite image classification have been evolved for more than 30 years, which are unsupervised algorithm and supervised algorithm. In recent years, numerous variants of these two basic classification methods have been developed, including decision trees, neural networks, fuzzy classification and mixture modeling for supervised classification, and classification by progressive generalization, classification through enhancement and post-processing adjustment for unsupervised techniques. "No land cover classification project would be complete without an accuracy assessment". In addition to purely methodological considerations, accuracy assessment tends to be strongly constrained by the resources available. Thus, in practice, accuracy assessment is "A balance between what is statistically sound and what is practically attainable must be found".
On board of EOS-AM-1 satellite, MODIS sensor acquires data with 250 m，500 m，1000 m spatial resolution and 36 bands spanning 0.4~14 μm. MODIS data, free for users all over the world, is up to date and is becoming the most important data source for land cover classification at the global and regional scale.
Key words： Remote sensing data; Large scale; Land cover; Mapping.
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