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林业科学 ›› 2011, Vol. 47 ›› Issue (2): 25-29.doi: 10.11707/j.1001-7488.20110204

• 论文 • 上一篇    下一篇

基于MERSI数据的山西森林覆盖监测

武永利, 田国珍   

  1. 山西省气候中心 太原 030006
  • 收稿日期:2010-04-16 修回日期:2010-12-22 出版日期:2011-02-25 发布日期:2011-02-25

Forest Remote Monitoring Based on MERSI Data in Shanxi

Wu Yongli, Tian Guozhen   

  1. Shanxi Climate Center Taiyuan 030006
  • Received:2010-04-16 Revised:2010-12-22 Online:2011-02-25 Published:2011-02-25

摘要:

利用FY3-3A/MERSI可见光和近红外波段数据合成得到假彩色卫星数字图像,结合区域土地利用、植被类型及森林资源分布等数字化图件,在对图像进行目视解译及多波段数据非监督分类基础上,将地物覆盖划分为林地、冬小麦、水体和裸地4种类型;采用4种监督分类方法(最大似然分类法、平行算法、最小距离法和马氏距离法)进行地物分类;在分析分类结果可行基础上,依据区域森林覆盖数字化图件对林地分类结果进行精度评估,确定研究区域林地空间分布结果。结果表明:4种监督分类方法中,最大似然分类法分类结果的精度最高,达到85%,其次是最小距离和马氏距离法,平行算法的结果较差。可见,利用MERSI数据采用监督分类和非监督分类相结合的方法提取的山西省森林资源分布结果精度较高,符合实际情况,可作为区域宏观森林资源分布监测的有效手段。

关键词: MERSI数据, 监督分类, 森林

Abstract:

In this study, the false color images of visible and near-infrared spectra of FY3/MERSI data were used, combined with other digital maps of regional land use, vegetation type and distribution of forests. The multi-channel data were classified into 4 categories: forest, winter wheat, water and bare land, by visual interpretation and unsupervised classification. These features were then analyzed their spectral statistical characteristics of visible and near-infrared spectra. Based on all of these analyses, the multi-channel data were classified by using four supervised classification, namely parallelpiped, minimum distance, mahalanobis distance, Maximum Likelihood. After the feasibility of the forest classification was analyzed, the classification accuracy was assessed with digital forest map, and then the distribution of forests in the study area was determined. The results showed that the classification obtained by the Maximum Likelihood method had the highest accuracy, which was 85%, followed by the Minimum Distance and the Mahalanobis Distance method, and the results obtained by the Parallelepiped method were the worst. On the whole, with combining supervised classification and unsupervised classification method. It was found that extracting the macro distribution of forest resources with MERSI data was feasible, and the extracted distribution of forest resources in Shanxi Province of this study was accurate, therefore it could be a effective means for macro monitoring of regional forest resources.

Key words: MERSI data, supervised classification, forest

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