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Cabo, Carlos 3D forest fuel mapping for wildfire behaviour modelling Poster
Carlos Cabo1,2, Cristina Santín1, Stefan Doerr1 and Celestino Ordóñez2
(1) College of Science, Swansea University(2) Mining Exploitation Dep. University of Oviedo

We are developing a fully automatic algorithm to classify forest fuels (vegetation available to burn) from ground-based point clouds of large forest plots, and to incorporate (from this classification) real 3D fuel data into physics-based wildfire behaviour models. The new algorithm will transform and adapt the point clouds into the 3D fuel model standards FUEL3D and STANDFIRE that inform the widely-used fire behaviour models FIRETEC and WFDS.

The algorithm inputs are ground-based point clouds (TLS/WLS) that are optionally complemented with aerial point clouds (ALS/SfM). Processes are fully automatic and aim at characterizing the 3D structure of the fuels in large and complex forest plots. The first step consists in the initial classification of the raw point cloud into ground, stems and branches/leaves, based on local multiscale classification at each point. From this, the 3D structure of the stems is modelled, the main branches are identified and characterized, and the tree crowns are individualized. Finally, the classified point clouds and fuel structure parameters are adapted to the standards of existent 3D fuel models.

The algorithms will be tested in forest plots of 0.5-2ha in UK and US using different combinations of ground-based an aerial point clouds. The 3D characterization and classification of the fuels at plot level will allow simulating different fuel treatments (e.g. thinning, pruning, clearing). This novel approach for designing and testing of 'virtual fuel treatments' is aimed at decreasing fuel hazard and, thus, fire risk, under current and predicted future climatic and land use scenarios.