LIDAR image–based fuel construction in a computational fluid dynamics simulation domain
LiDAR image-based vegetation fuel construction in a computational fluid dynamic (CFD) simulation domain was investigated. Using LiDAR images to convey fuel information to CFD would improve the accuracy of wildfire spread prediction. The obtained vegetation information using LiDAR appears as point signals in LiDAR images, and the point signals were dispatched to nodes using the K-D tree algorithm. Then, each node is transferred to the meshing algorithm along with the number of signals and location information. In a CFD domain, 3-dimension vegetation fuel information is reconstructed, and fuel mass is estimated by using the number of signals within each mesh. It appears that utilizing LiDAR images to obtain fuel information improves the accuracy in fuel shapes and mass distribution compared to the conventional way that assigns pre-determined shape and mass distribution for each vegetation. It is expected that the outcomes of this research would improve the liability of CFD-based wildfire prediction. Keywords: Sustainable forest management, Research, Climate change ID: 3617419
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| Format: | Document biblioteca |
| Language: | English |
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FAO ;
2022
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| Online Access: | https://openknowledge.fao.org/handle/20.500.14283/cc4416en http://www.fao.org/3/cc4416en/cc4416en.pdf |
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