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| Titel |
LiDAR improves fire behaviour predictions using a biophysical, mechanistic model |
| VerfasserIn |
Philip Zylstra, Bronwyn Horsey, Marta Yebra, Suzanne Marselis |
| Konferenz |
EGU General Assembly 2016
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 18 (2016) |
| Datensatznummer |
250129308
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| Publikation (Nr.) |
 EGU/EGU2016-9402.pdf |
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| Zusammenfassung |
| Numerous studies have attempted to address the utility of LiDAR as a tool for measuring fuel inputs to fire behaviour models, however the direct effect of this approach on fire behaviour prediction requires quantification.
We used a biophysical, mechanistic model validated for eucalypt forest in SE Australia to assess the improvement in prediction accuracy afforded using LiDAR-derived inputs. The accuracy of modelling with these inputs was compared to modelling using detailed site-specific field surveys of a dry sclerophyll forest to represent the highest standard of inputs, and values derived from desktop-available community-wide descriptors to represent baseline inputs.
Use of LiDAR significantly improved on baseline predictions and enabled site-specific decision making across the study area. When used with an appropriate model, LiDAR can facilitate improved decision-making in regard to forest fire behaviour. |
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