Please use this identifier to cite or link to this item: https://doi.org/10.5194/isprs-archives-XLII-4-W10-105-2018
Title: Integration of tree database derived from satellite imagery and LiDAR point cloud data
Authors: Liew, S.C. 
Huang, X. 
Lin, E.S.
Shi, C. 
Yee, A.T.K.
Tandon, A.
Keywords: Camera Model
Canopy Height Model
Lidar Point Cloud
Tree Crown Identification and Delineation
Issue Date: 2018
Publisher: International Society for Photogrammetry and Remote Sensing
Citation: Liew, S.C., Huang, X., Lin, E.S., Shi, C., Yee, A.T.K., Tandon, A. (2018). Integration of tree database derived from satellite imagery and LiDAR point cloud data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 42 (4/W10) : 105-111. ScholarBank@NUS Repository. https://doi.org/10.5194/isprs-archives-XLII-4-W10-105-2018
Rights: Attribution 4.0 International
Abstract: 3D tree database provides essential information of tree species abundance, spatial distribution and tree height for forest mapping, sustainable urban planning and 3D city modelling. Fusion of passive optical satellite imagery and active Lidar data can potentially be exploited for operational forest inventory. However, such fusion requires very high geometric accuracy for both data sets. This paper proposes an approach for 3D tree information extracted from passive and active data integrating into existing tree database by effectively using geometric information of satellite camera model and laser scanner scanning geometry. The paper also presents the individual methods for tree crown identification and delineation from satellite images and lidar point cloud data respectively, the geometric correction of tree position from tree top to tree base. The ground truth accuracy assessment for the tree extracted is also present. © Authors 2018. CC BY 4.0 License.
Source Title: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
URI: https://scholarbank.nus.edu.sg/handle/10635/209655
ISSN: 1682-1750
DOI: 10.5194/isprs-archives-XLII-4-W10-105-2018
Rights: Attribution 4.0 International
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