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《Remote Sensing Technology and Application》 2008-02
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Detection of Robinia Pseudoacacia Planted Forest Canopy Health Using Landsat ETM+ Image Data

LIU Qing-sheng1,LIU Gao-huan1,YAO Ling2(1.Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Scienses,Beijing 100101,China;2.Wuhan University College of Resources and Environmental Science,Wuhan 430072,China)  
The principal goal of this experiment study was to develop an objective,reliable and simple methodology for detection of Robinia Pseudoacacia Planted forest canopy health using Landsat ETM+ image data which would provide a cost-effective first-level indication of forest canopy health for forest managers.Digital procedures to optimize the information content of Landsat ETM+ image data for detection of Robinia Pseudoacacia planted forest canopy health were described.On the basis of phonological calendar of Robinia Pseudoacacia in the local region,imagery acquired on May 2,2000 was calibrated to exoatmospheric reflectance to minimize sensor calibration offsets and standardize data acquisition aspects,and Band 6 was converted into effective at satellite temperature in Kelvin.Then,a nearly pure artificial Robinia Pseudoacacia forest land was selected as the experimental area.Robinia Pseudoacacia forests were classified into healthy or slight dieback,moderate dieback,dead or severe dieback or shrub and grass lands,non-vegetation land using ISODATA classifier with three types of different band composition such as Band 1~5 and 7(Group 1),normalized Green and Moisture component of Tasseled Cap transform(Group 2),normalized Green and Moisture component of Tasseled Cap transform and normalized effective at satellite temperature converted from Band 6(Group 3).The results show that ISODATA classification of Group 3 was more effective method for detection of Robinia Pseudoacacia Planted Forest Canopy Health.
【Fund】: 中国科学院院方向项目(kzcx2-yw-308);; 国家自然科学基金项目(40771172)资助
【CateGory Index】: TP75
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