Fuel and fire behavior modeling is essential for wildfire prevention and control. Currently, advanced 3D physics-based fire behavior models, such as Wildland Fire Dynamics Simulator (WFDS), are able to represent heterogeneous fuels and simulate fire behavior processes with much greater detail than conventional semi-empirical models. However, they require accurate information about the locations and dimensions of individual trees, species compositions, spatial distributions of understory fuels, as well as3D distributions of fuel mass and bulk density at the voxel level and finer spatial scales. Point clouds derived from airborne, terrestrial, and mobile laser scanners are uniquely suited for quantifying the three-dimensional structure of canopy and understory vegetation, but UAV-based digital aerial photogrammetric (DAP) point clouds have the advantage of allowing for a higher frequency of data acquisition and the integration of structural and spectral data.
Working in a Mediterranean ecosystem study area with four dominant shrub species and Pinus halepensis trees, we developed a methodology based on the use of geometric and spectral features from UAV-DAP point clouds for (i) species segmentation and classification using machine learning algorithms, (ii) the generation of biomass prediction models and estimation of bulk density at the individual plant level, and (iii) the creation of 3D fuel scenarios andwildfire behavior modeling with WFDS. Field measurements and allometric equations were used for the evaluation of classification and prediction models. Fire behavior variables, such as rate of spread, heat release rate, and mass loss rate, were monitored and assessed as outputs from WFDS. The overall species classification accuracy was 80.3%, and the biomass regression R2 values obtained by cross-validation were 0.77 for Pinus halepensis, 0.83 for Anthyllis cytisoides, 0.69 for Quercus coccifera, 0.60 for Genista scorpius, and 0.54 for Salvia rosmarinus. These results are encouraging for further improvement based on the integration of multi- and hyper-spectral sensors onboard UAVs, and the characterization of fuels for fire behavior modeling.
