地理科学 ›› 2010, Vol. 30 ›› Issue (2): 248-253.doi: 10.13249/j.cnki.sgs.2010.02.248

• 论文 • 上一篇    下一篇

基于MODIS的土地覆盖遥感分类特征的评价与比较

张景1, 姚凤梅1, 徐永明2, 张佳华3   

  1. 1. 中国科学院研究生院地球动力学实验室, 北京 100049;
    2. 南京信息工程大学中美合作 遥感中心, 江苏 南京 210044;
    3. 中国气象科学研究院, 北京 100081
  • 收稿日期:2009-06-26 修回日期:2009-11-08 出版日期:2010-03-20 发布日期:2010-03-20
  • 通讯作者: 姚凤梅,副教授,主要研究方向为全球变化。E-mail:yaofm@gucas.ac.cn E-mail:yaofm@gucas.ac.cn
  • 作者简介:张景(1985- ),女,宁夏银川人,硕士研究生,研究方向为生态环境遥感与气候变化。E-mail:zjztxfx@yahoo.com.cn
  • 基金资助:
    国家自然科学基金(40771147)、科技部863项目(2006AA10Z213)资助。

Comparison and Evaluation of Classification Features in Land Cover Based on Remote Sensing

ZHANG Jing1, YAO Feng-mei1, XU Yong-ming2, ZHANG Jia-hua3   

  1. 1. College of Earth Sciences, Graduate University of the Chinese Academy of Sciences, Beijing 100049;
    2. Sino-America Cooperative Remote Sensing Center, School of Remote Sensing, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044;
    3. Chinese Academy of Meteorological Sciences, Beijing 100081
  • Received:2009-06-26 Revised:2009-11-08 Online:2010-03-20 Published:2010-03-20

摘要: 选取华北地区为研究区,利用MODIS遥感数据多光谱、多时相优势进行分类特征提取,依据土地覆盖分类特征如地表反射率、植被指数、纹理特征等,并对这些分类特征分别从光谱维、时间维、空间维三个角度进行阐述,结合DEM数据,使用最大似然法进行土地覆盖遥感分类特征的评价与比较。结果表明,不同分类特征对分类精度影响不同,将多种分类特征结合能够有效提高区域尺度土地覆盖分类精度,但分类特征的加入不一定能提高某些类别的分类精度。

Abstract: This paper briefly introduced the application of some classification features, based on the current research status of the regional land cover classification. Classification features play a chief and basic role in the research of land cover, and it is significant for the classification accuracy to select the features. Due to the advantages of integration of multi-temporal and multi-spectral MODIS data in regional land cover, this paper presented the research on land cover classification in area of North China, selected the features such as surface reflectance (MODIS 7-band reflectance), vegetation index (MODIS-NDVI, MODIS-EVI), two characters of texture (homogeneity, entropy), and DEM to classify by the method of MLC, finally compared and evaluated the classification accuracy by using different features. The result indicates that it has higher overall classification accuracy using MODIS 7-band reflectance data than using vegetation index. And combining the two features above, the classification accuracy of cropland and grassland can be improved obviously. The result also shows the classification accuracy varies with the different features. It can increase the overall classification accuracy to integrate MODIS 7-band reflectance data with some other features, which contain vegetation index, texture and DEM. However, classification accuracy of some types can not be always enhanced by the combination of the above classification characters.

中图分类号: 

  • S127