DocumentCode :
3023678
Title :
The automatic tree detection and delineation from Airborne LiDAR
Author :
Haibing Xiang ; Chunxiang Cao ; Jinsong Liu ; Wei Zhou
Author_Institution :
State Key Lab. of Remote Sensing Sci., Inst. of Remote Sensing & Digital Earth, Beijing, China
fYear :
2013
fDate :
21-26 July 2013
Firstpage :
536
Lastpage :
539
Abstract :
The individual tree information is the most important parameter of biomass inversion. Recently, LiDAR has been widely and successfully applied in forest research, and it shows promise to map individual trees in complex and heterogeneous forests. Based on Airborne LiDAR point cloud, this paper uses local maximum filtering technique to extract the height and crown of individual tree of Qinghai spruce forest in Qilian Mountains. The analysis shows that accuracy of the three methods is depended on the Thresholds. All of them only detect less than 50% trees in the study area. There are two reasons. The first is the density of the point clouds is only 6 dot/m2. The second is some trees are understory and the height is too small, even less than the error range of CHM.
Keywords :
remote sensing by laser beam; vegetation; CHM error range; Qinghai spruce forest; airborne LiDAR point cloud; automatic tree detection; biomass inversion parameter; forest research; individual tree information; local maximum filtering technique; Clouds; Estimation; Filtering; Laser radar; Probability distribution; Remote sensing; Vegetation; Airborne LiDAR; DEM; DSM; forest; mountainous region;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International
Conference_Location :
Melbourne, VIC
ISSN :
2153-6996
Print_ISBN :
978-1-4799-1114-1
Type :
conf
DOI :
10.1109/IGARSS.2013.6721211
Filename :
6721211
Link To Document :
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