Annals of Forest Science, is a source of information about current developments and trends in forest research and forestry

The fuel complex variables canopy bulk density and canopy base height are often used to predict crown fire initiation and spread. Direct measurement of these variables is impractical, and they are usually estimated indirectly by modelling. Recent advances in predicting crown fire behaviour require accurate estimates of the complete vertical distribution of canopy fuels. The objectives of the present study were to model the vertical profile of available canopy fuel in pine stands by using data from the Spanish national forest inventory plus low-density airborne laser scanning (ALS) metrics. In a first step, the vertical distribution of the canopy fuel load was modelled using the Weibull probability density function. In a second step, two different systems of models were fitted to estimate the canopy variables defining the vertical distributions; the first system related these variables to stand variables obtained in a field inventory, and the second system related the canopy variables to airborne laser scanning metrics. The models of each system were fitted simultaneously to compensate the effects of the inherent cross-model correlation between the canopy variables. Heteroscedasticity was also analyzed, but no correction in the fitting process was necessary. The estimated canopy fuel load profiles from field variables explained 84% and 86% of the variation in canopy fuel load for maritime pine and radiata pine respectively; whereas the estimated canopy fuel load profiles from ALS metrics explained 52% and 49% of the variation for the same species. The proposed models can be used to assess the effectiveness of different forest management alternatives for reducing crown fire hazard.

Background: Crown fires are often intense and fast spreading and hence can have serious impacts on soil, vegetation, and wildlife habitats. Fire managers try to prevent the initiation and spread of crown fires in forested landscapes through fuel management. The minimum fuel conditions necessary to initiate and propagate crown fires are known to be strongly influenced by four stand structural variables: surface fuel load (SFL), fuel strata gap (FSG), canopy base height (CBH), and canopy bulk density (CBD). However, there is often a lack of quantitative data about these variables, especially at the landscape scale. Methods: In this study, data from 123 sample plots established in pure, even-aged, Pinus radiata and Pinus pinaster stands in northwest Spain were analyzed. In each plot, an intensive field inventory was used to characterize surface and canopy fuels load and structure, and to estimate SFL, FSG, CBH, and CBD. Equations relating these variables to Sentinel-2A (S-2A) bands and vegetation indices were obtained using two non-parametric techniques: Random Forest (RF) and Multivariate Adaptive Regression Splines (MARS). Results: According to the goodness-of-fit statistics, RF models provided the most accurate estimates, explaining more than 12%, 37%, 47%, and 31% of the observed variability in SFL, FSG, CBH, and CBD, respectively. To evaluate the performance of the four equations considered, the observed and estimated values of the four fuel variables were used separately to predict the potential type of wildfire (surface fire, passive crown fire, or active crown fire) for each plot, considering three different burning conditions (low, moderate, and extreme). The results of the confusion matrix indicated that 79.8% of the surface fires and 93.1% of the active crown fires were correctly classified; meanwhile, the highest rate of misclassification was observed for passive crown fire, with 75.6% of the samples correctly classified. Conclusions: The results highlight that the combination of medium resolution imagery and machine learning techniques may add valuable information about surface and canopy fuel variables at large scales, whereby crown fire potential and the potential type of wildfire can be classified.

Annals of Forest Science, is a source of information about current developments and trends in forest research and forestry

Crown fires combine high rates of spread, flame lengths, and intensities, making it virtually impossible to control them by direct action and having significant impact on soils, vegetation, and wildlife habitat. For these reasons, fire managers have great interest in preventive silviculture of forested landscapes to avoid the initiation and propagation of crown fires. The minimum conditions necessary to initiate and propagate crown fires are assumed to be strongly influenced by the stand structural variables canopy bulk density (CBD) and canopy base height (CBH). However, there is a lack of quantitative information on these variables and how to estimate them. To characterize the aerial fuel layers of Pinus radiata D. Don, the vertical profiles of canopy fuel in 180 sample plots of pure and even-aged P. radiata plantations were analysed. Effective CBD and CBH were obtained from the vertical profiles, and equations relating these variables to common stand variables were fitted simultaneously. Inclusion of the fitted equations in existing dynamic growth models, together with the use of current fire behaviour and hazard prediction tools, will provide a decision support system for assessing the crown fire potential of different silvicultural alternatives for this species.