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Remote Sensing Technology and Application  2019, Vol. 34 Issue (3): 445-454    DOI: 10.11873/j.issn.1004-0323.2019.3.0445
    
Research Progress of Remote Sensing Classification and Change Monitoring on Forest Types
Yan Wei1,2,Zhou Wen2,Yi Lilong2,Tian Xin3
(1.Forest Resources Management Station of Guiyang City,Guiyang 550003,China;
2.Institute of Forestry Greening Inventory and Planning of Guiyang City,Guiyang 550003,China;
3.Institute of Forest Resource Information Techniques,Chinese Academy of Forestry,Beijing 100091,China)
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Abstract  Forest is one of the main vegetation type in the terrestrial ecosystem,and using remote sensing technology on discriminating and change monitoring forest types are of great significance importance for the global carbon cycle study and sustainable development of forest resources.This article reviewed the classical remotely sensed classification methods forest remote sensing classification methods,including pixel-based,object-oriented,red-edge spectral information based and deep learning methods,separately.We also introduced the details and individual advantages of these methods in the some specific applications.Finally,the limitations of the current study on forest remote sensing classification and change monitoring on forest types were indicated in order to provide reference for the dynamic supervision of forest resources under the new situation.
Key words:  Multi-source remote sensing data      Forest classification      Deep learning      Change monitoring     
Received:  28 March 2018      Published:  01 July 2019
ZTFLH:  P351.3  
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Yan Wei, Zhou Wen, Yi Lilong, Tian Xin. Research Progress of Remote Sensing Classification and Change Monitoring on Forest Types. Remote Sensing Technology and Application, 2019, 34(3): 445-454.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2019.3.0445     OR     http://www.rsta.ac.cn/EN/Y2019/V34/I3/445

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