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REMOTE SENSING FOR LAND & RESOURCES    2016, Vol. 28 Issue (1) : 172-177     DOI: 10.6046/gtzyyg.2016.01.25
Technology Application |
Establishment of remote sensing monitoring classification system and interpretation criteria for the reclamation area based on the high-resolution remote sensing image
WEN Li1, WU Haiping1, JIANG Fangfang2, SU Wei2, ZHU Dehai2, ZHANG Chao2
1. China Land Surveying and Planning Institute, Ministry of Land Resources, Beijing 100035, China;
2. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
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Reclamation is a double-edged sword:on the one hand, it can ease the shortage land resources, but on the other hand, it also brings serious negative effect to the coastal ecological environment. The first step is to make the classification system of reclamation in order that the government can monitor and manage reclamation reasonably for controlling and making decision. Reclamation is an intermediate process from sea to land, and this paper tries to connect sea use classification system and status of land use classification so as to make the classification system used in reclamation annual monitoring by remote sensing based on the level of multi-source high-resolution remote sensing image which can be interpreted, the definition of reclamation, the use properties and formation mechanism of reclamation and other factors. In addition, corresponding to the reclamation classification system of remote sensing monitoring, this paper has established remote sensing monitoring interpretation criteria of reclamation based on multi-source high-resolution remote sensing image.

Keywords unmanned aerial vehicle(UAV)      vegetation extraction      color index      RGB color space     
:  TP79  
Issue Date: 27 November 2015
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DING Leilong
LI Qiangzi
DU Xin
TIAN Yichen
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DING Leilong,LI Qiangzi,DU Xin, et al. Establishment of remote sensing monitoring classification system and interpretation criteria for the reclamation area based on the high-resolution remote sensing image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(1): 172-177.
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