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林业资源管理 ›› 2010, Vol. 0 ›› Issue (6): 97-101.

• 科学技术 • 上一篇    下一篇

基于植被丰度分析的城市植被胁迫遥感监测

潘灼坤1,2, 王芳1, 夏丽华1, 王晓轩1, 刘汉湖2   

  1. 1.广州大学 地理科学学院,广州 510006;
    2.成都理工大学 地球科学学院,成都 610009
  • 收稿日期:2010-09-21 修回日期:2010-11-01 发布日期:2020-12-14
  • 通讯作者: 王芳,女,副教授,博士。Email:wfdili@163.com
  • 作者简介:潘灼坤(1986-),男,广东东莞人,硕士研究生,主要研究方向为遥感与GIS应用。Email:xyslz114@sina.com
  • 基金资助:
    国家自然科学基金项目(40801034);广州市高校科技计划项目(08C025)

Remote Sensing Monitoring of Urban Vegetation Stress Based on Vegetation Abundance Analysis

PAN Zhuokun1,2, WANG Fang1, XIA Lihua1, WANG Xiaoxuan1, LIU Hanhu2   

  1. 1. Guangzhou University, School of Geographical Sciences, Guangzhou 510006, China;
    2. Chengdu University of Technology, College of Earth Sciences, Chengdu 610009, China
  • Received:2010-09-21 Revised:2010-11-01 Published:2020-12-14

摘要: 以混合像元分解提取植被丰度作为主要手段,研究基于广州市区东边建成区的Hyperion高光谱遥感影像,以遥感影像预处理—特征选择—SMACC混合像元分解的步骤提取出植被丰度图,再进行PPI迭代运算纯化,提取出7种表征植被健康状况差异端元的PPI影像。经实地考察植被胁迫位置的周边人类活动的情况,结合植物生理学和光谱学分析反射率波谱曲线的变化,解释植物受到胁迫的原因,以期为城市绿地调查管理提供参考。

关键词: 植被胁迫, 特征提取, 混合像元分解, 植被丰度

Abstract: This study is mainly based on mixed pixel decomposing to extract vegetation abundance. The Hyperion hyperspectral remotely sensed image was used for eastern part of Guangzhou City. The steps such as image preprocessing, feature extraction and SMACC mixed pixel decomposing were taken to obtain vegetation abundance images. Furthermore the PPI iteration was used to purify these images, and then 7 kinds of vegetation endmembers which present defferent health state extracted. On-site survey was conducted to check out the human activities around the vegetation stress area, combined with other discipline just as plant physiology and spectroscopy to analyze the plant reflectance spectral curve changes and explain the causes. The research provides accurate information for urban green space survey and management.

Key words: vegetation stress, feature extraction, mixed pixel decomposing, vegetation abundance

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