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林业科学 ›› 2012, Vol. 48 ›› Issue (2): 54-62.doi: 10.11707/j.1001-7488.20120208

• 论文 • 上一篇    下一篇

基于时间序列MODIS EVI数据的森林生长异常监测

刘丽娟1,2,3, 庞勇1, Zhang Xiaoyang4, Svein Solberg5, 范文义3, 李增元1, 李明泽3   

  1. 1. 中国林业科学研究院资源信息研究所 北京 100091;2. 杭州师范大学遥感与地球科学研究院 杭州 310036;3. 东北林业大学林学院 哈尔滨 150040;4. NOAA/DESDIS/STAR,5200 AUth RD, Camp Springs,MD 20746 United States;5. Norwegian Forest and Landscape Institute, Postboks 115, 1431ÅS, Norway
  • 收稿日期:2010-05-24 修回日期:2010-07-26 出版日期:2012-02-25 发布日期:2012-02-25
  • 通讯作者: 庞勇

Monitoring Forest Growth Disturbance Using Time Series MODIS EVI Data

Liu Lijuan1,2,3, Pang Yong1, Zhang Xiaoyang4, Svein Solberg5, Fan Wenyi3, Li Zengyuan1, Li Mingze3   

  1. 1. Institute of Forest Resources Information Techniques,CAF Beijing 100091;2. Institute of Remote Sensing and Earth Sciences, Hangzhou Normal University Hangzhou 310036;3. College of Forestry, Northeast Forestry University Harbin 150040;4. NOAA/DESDIS/STAR,5200 AUth RD, Camp Springs,MD 20746 United States;5. Norwegian Forest and Landscape Institute, Postboks 115, 1431ÅS, Norway
  • Received:2010-05-24 Revised:2010-07-26 Online:2012-02-25 Published:2012-02-25

摘要:

利用遥感技术,基于时间序列的MODIS数据对2004—2006年东北三省的林区进行森林生长异常监测。首先利用MODIS数据时间分辨率高的特点,采用Savitzky-Golay滤波函数平滑8天合成的EVI,计算生长季面积和年EVI曲线熵值,两指标联合得到3年间变化量大的像素点,定义为森林生长异常点;然后抽取异常点的时间序列曲线进行分析,并结合森林灾害事件进行比较验证。结果表明:异常点曲线的熵值明显大于正常年,生长季峰值低,并且在生长旺季会出现峰值突然持续下降或双峰等异常现象,这与该区域森林生长异常发生时的植被反射率表征一致,说明用该法对森林生长异常进行监测是基本可行的。

关键词: 时间序列, MODIS EVI, 生长季面积, 熵, 异常监测

Abstract:

Forest growth is mainly currently monitored using in-situ measurements in northeast of China. To effectively monitor forest growth disturbance at large scale, we attempted to use remote sensing technique, particularly, time series MODIS data from 2004 to 2006. The annual time series of 8-day enhanced vegetation index (EVI) dataset was generated and smoothed using a Savitzky-Golay filter. The EVI trajectory during growth season was simulated using a logistic model. From the simulated trajectory, the EVI area of growth season and annual EVI entropy were calculated. These two factors were combined to map the disturbance regions of forest growth. Finally, the disturbance regions were verified using a set of random samples. The result indicates that the disturbance points have distinctively higher entropy and lower peak. Some of these points also show abrupt EVI decline during the midseason of the peak phases or double peaks. This approach is demonstrated to be feasible for disturbance monitoring of forest growth.

Key words: time series, MODIS EVI, growth season area, entropy, disturbance monitoring

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