Breast height diameter estimation from high-density airborne LiDAR data
High-density airborne light detection and ranging (LiDAR) data with point densities over 50 points/m2 provide new opportunities, because previously inaccessible quantities of an individual tree can be derived directly from the data. We introduce a skeleton measurement methodology to extract the diam...
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| Main Authors: | , , , |
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| Format: | Article |
| Published: |
IEEE
2014
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/52036/ http://dx.doi.org/10.1109/LGRS.2013.2285471 |
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| Institution: | Universiti Teknologi Malaysia |
| Summary: | High-density airborne light detection and ranging (LiDAR) data with point densities over 50 points/m2 provide new opportunities, because previously inaccessible quantities of an individual tree can be derived directly from the data. We introduce a skeleton measurement methodology to extract the diameter at breast height (DBH) from airborne point clouds of trees. The estimates for the DBH are derived by analyzing the point distances to a suitable tree skeleton. The method is validated in three scenarios: 1) on a synthetic point cloud, simulating the point cloud acquisition over a forest; 2) on examples of free-standing and partly occluded trees; and 3) on automatically extracted trees from a sampled forest. The proposed diameter estimation performed well in all three scenarios, although influences of the tree extraction method and the field validation could not be fully excluded. |
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