Introducción al comportamiento del fuego

En este documento describen, y conectan entre sí, aspectos básicos del comportamiento del fuego en los incendios forestales. También se hace referencia a diversas herramientas de predicción de utilidad para el manejo del fuego

Novel approach to assessing residual biomass from pruning: A case study in Atlantic Pinus pinaster Ait. timber forests

Forestry residual biomass from pruning operations is an important, though little studied, potential resource. Residues normally remain in the stand, since tools for their accurate quantification do not exist and it has no particular end use. Traditional tree biomass estimation models consider the whole-tree, but estimating pruned biomass requires the development of more specific equations. This work provides a modelling approach for assessing biomass along the stem and the corresponding residual biomass from forest pruning, and quantitative results from different pruning intensities in Pinus pinaster Ait. are presented. Two types of models were considered: allometric biomass equations (whole-tree) and biomass ratio equations (tree by height along the stem), and the 2-parameter Weibull distribution function resulted in the best characterization. Diameter at breast height was the best explanatory variable in all equations, and model accuracy increased when models were combined with total tree height for the tree stem and thicker branches, or with crown ratio for the remaining tree crown components. This study provides a powerful tool to estimate residual pruned biomass, enabling its better management as a valuable source of bioenergy, as well as the importance in nutrient balance and fire risk which it plays in a sustainable forestry productionWe thank the Forest Services of the Government of the Principality of Asturias for access to the forests used in this study and for financial support. Thank you to people from CETEMAS (L. González, M. García, P. Vallejo) and SERIDA (J.C. Hernández) for their participation in the fieldwork. Thank you to Ronnie Lendrum for reviewing the English and FORRISK project (Interreg IV B SUDOE 2007–2013) for its support during data analysis. Andrea Hevia was financially supported during fieldwork and data analysis by the Spanish Ministry of Education and Science through the FPU scholarship program (Reference AP2006-03890)S

Assessing the effect of pruning and thinning on crown fire hazard in young Atlantic maritime pine forests

Management of fuel to minimize crown fire hazard is a key challenge in Atlantic forests, particularly for pine species. However, a better understanding of effectiveness of silvicultural treatments, especially forest pruning, for hazard reduction is required. Here we evaluate pruning and thinning as two essential silvicultural treatments for timber pine forests. Data came from a network of permanent plots of young maritime pine stands in northwestern Spain. Vertical profiles of canopy bulk density were estimated for field data and simulated scenarios of pruning and thinning using individual tree biomass equations. Analyses of variance were conducted to establish the influence of each silvicultural treatment on canopy fuel variables. Results confirm the important role of both pruning and thinning in the mitigation of crown fire hazard, and that the effectiveness of the treatments is related to their intensity. Finally, models to directly estimate the vertical profile of canopy bulk density (CBD) were fitted using the Weibull probability density function and usual stand variables as regressors. The models developed include variables sensitive to pruning and thinning interventions and provide useful information to prevent extreme fire behavior through effective silvicultureWe thank the Forest Services of the Government of the Principality of Asturias for financial support and access to the forests used in this study. Funding during data analysis was provided by projects SCALyFOR (AGL2013-46028-R), GEPRIF (RTA2014-00011-C06-04), PLURIFOR (SOE1/P4/F0112 Interreg SUDOE) and FORRISK (SOE3/P2/F523 Interreg IV B SUDOE). Andrea Hevia was financially supported during fieldwork and data analysis by the Spanish Ministry of Education and Science through the FPU scholarship program (Reference AP2006-03890)S

Estimating Fuel Loads and Structural Characteristics of Shrub Communities by Using Terrestrial Laser Scanning

Forest fuel loads and structural characteristics strongly affect fire behavior, regulating the rate of spread, fireline intensity, and flame length. Accurate fuel characterization, including disaggregation of the fuel load by size classes, is therefore essential to obtain reliable predictions from fire behavior simulators and to support decision-making in fuel management and fire hazard prediction. A total of 55 sample plots of four of the main non-tree covered shrub communities in NW Spain were non-destructively sampled to estimate litter depth and shrub cover and height for species. Fuel loads were estimated from species-specific equations. Moreover, a single terrestrial laser scanning (TLS) scan was collected in each sample plot and features related to the vertical and horizontal distribution of the cloud points were calculated. Two alternative approaches for estimating size-disaggregated fuel loads and live/dead fractions from TLS data were compared: (i) a two-steps indirect estimation approach (IE) based on fitting three equations to estimate shrub height and cover and litter depth from TLS data and then use those estimates as inputs of the existing species-specific fuel load equations by size fractions based on these three variables; and (ii) a direct estimation approach (DE), consisting of fitting seven equations, one for each fuel fraction, to relate the fuel load estimates to TLS data. Overall, the direct approach produced more balanced goodness-of-fit statistics for the seven fractions considered jointly, suggesting that it performed better than the indirect approach, with equations explaining more than 80% of the observed variability for all species and fractions, except the litter loadsThis research was funded by the projects GEPRIF (RTA2014-00011-c06-04) and VIS4FIRE (RTA 2017-0042-C05-05) of the Spanish Ministry of Economy, Industry, and CompetitivenessS

Modelling the vertical distribution of canopy fuel load using national forest inventory and low-density airbone laser scanning data

[EN] 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 lowdensity 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 hazardSIFunding was provided by projects DIABOLO (H2020 GA 633464) and GEPRIF (RTA 2014-00011-c06-04). The funders did not participate in designing the study, data collection and analysis, decision to publish or preparation of the manuscript. We are grateful to the Galician Government and European Social Fund (Official Journal of Galicia – DOG n° 52, 17/03/2014, p. 11343, exp: POS-A/2013/049) for financing the postdoctoral research stays of Dr Eduardo González-Ferreiro at different institutions. Copyright of LiDAR data, Instituto Geográfico Nacional-Xunta de Galici

Estimación de la distribución vertical de combustibles finos del dosel de copas en masas de Pinus sylvestris empleando datos LiDAR de baja densidad

[ES] La altura de la base de la copa, la carga de combustible disponible y la densidad aparente son características estructurales del dosel de copas utilizadas para predecir la actividad de fuego de copas. La medición directa en campo de estas variables es impráctica y por tanto sus valores se estiman habitualmente mediante el empleo de modelos predictivos. Avances en la modelización del comportamiento del fuego hacen que sea de gran interés explorar la posibilidad de estimar de forma precisa y a escala de paisaje la distribución vertical de los combustibles en el dosel de copas. En este sentido, este estudio pretende analizar el potencial de los datos obtenidos de sensores LiDAR (Light Detection and Ranging) aerotransportados para modelizar dicha distribución vertical en masas de pino silvestre en Galicia. Para ello se usaron datos del vuelo LiDAR del PNOA (Plan Nacional de Ortofotografía Aérea) con una densidad de 0,5 primeros retornos m–2 y datos de campo procedentes del Cuarto Inventario Forestal Nacional (IFN4). En un primer paso, la distribución vertical fue caracterizada empleando la función de densidad de probabilidad de Weibull para, en un segundo paso, ajustar un sistema de ecuaciones que relacionan las variables del dosel con métricas derivadas de los datos LiDAR. Las ecuaciones se ajustaron simultáneamente para corregir los posibles problemas de correlación entre errores. Las distribuciones verticales finalmente estimadas explicaron el 41% de la variabilidad observada en las parcelas de estudio. El sistema de ecuaciones propuesto puede ser usado también para evaluar la efectividad de diferentes alternativas de gestión del combustible para reducir el riesgo de fuego de copa en rodales de pino silvestre[EN] Canopy fuel load, canopy bulk density and canopy base height are structural variables used to predict crown fire initiation and spread. Direct measurement of these variables is not functional, and they are usually estimated indirectly by modelling. Advances in fire behaviour modelling require accurate and landscape scale estimates of the complete vertical distribution of canopy fuels. The goal of the present study is to model the vertical profile of available canopy fuels in Scots pine stands by using data from the Spanish national forest inventory and low-density LiDAR data (0.5 first returns m–2) provided by Spanish PNOA project (Plan Nacional de Ortofotografía Aérea). In a first step, the vertical distribution of the canopy fuel load was modelled using the Weibull probability density function. In a second step, a system of models was fitted to relate the canopy variables to Lidar-derived metrics. Models were fitted simultaneously to compensate the effects of the inherent cross-model correlation between errors. Heteroscedasticity was also analyzed, but correction in the fitting process was not necessary. The estimated canopy fuel load profiles from LiDAR-derived metrics explained 41% of the variation in canopy fuel load in the analysed plots. The proposed models can be used to assess the effectiveness of different forest management alternatives for reducing crown fire hazardS

Modelling the vertical distribution of canopy fuel load using national forest inventory and low-density airbone laser scanning data

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 lowdensity 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 hazardWe are grateful to the Galician Government and European Social Fund (Official Journal of Galicia—DOG n° 52, 17/03/2014, p. 11343, exp: POS-A/2013/049) for financing the postdoctoral research stays of Dr Eduardo González-Ferreiro at different institutions. Copyright of LiDAR data, Instituto Geográfico Nacional-Xunta de GaliciaS

Potential of Sentinel-2A Data to Model Surface and Canopy Fuel Characteristics in Relation to Crown Fire Hazard

[EN] 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.SIWe are grateful to the Galician Government and European Social Fund (Official Journal of Galicia DOG n° 52, 17 March 2014, p. 11343, exp: POS-A/2013/049) for financing the postdoctoral research stays of Eduardo González-Ferreiro at different institutions

Estimating Stand and Fire-Related Surface and Canopy Fuel Variables in Pine Stands Using Low-Density Airborne and Single-Scan Terrestrial Laser Scanning Data

[EN] In this study, we used data from a thinning trial conducted on 34 different sites and 102 sample plots established in pure and even-aged Pinus radiata and Pinus pinaster stands, to test the potential use of low-density airborne laser scanning (ALS) metrics and terrestrial laser scanning (TLS) metrics to provide accurate estimates of variables related to surface and canopy fires. An exhaustive field inventory was carried out in each plot to estimate the main stand variables and the main variables related to fire hazard: surface fuel loads by layers, fuel strata gap, surface fuel height, stand mean height, canopy base height, canopy fuel load and canopy bulk density. In addition, the point clouds from low-density ALS and single-scan TLS of each sample plot were used to calculate metrics related to the vertical and horizontal distribution of forest fuels. The comparative performance of the following three non-parametric machine learning techniques used to estimate the main stand-and fire-related variables from those metrics was evaluated: (i) multivariate adaptive regression splines (MARS), (ii) support vector machine (SVM), and (iii) random forest (RF). The selection of the best modeling approach was based on a comparison of the root mean square error (RMSE), obtained by optimizing the parameters of each technique and performing crossvalidation. Overall, the best results were obtained with the MARS techniques for data from both sensors. The TLS data provided the best results for variables associated with the internal characteristics of canopy structure and understory fuel but were less reliable for estimating variables associated with the upper canopy, due to occlusion by mid-canopy foliage. The combination of ALS and TLS metrics improved the accuracy of estimates for all variables analyzed, except the height and the biomass of the understory shrubs. The variability demonstrated by the combined use of both types of metrics ranged from 43.11% for the biomass of duff litter layers to 94.25% for dominant height. The results suggest that the combination of machine learning techniques and metrics derived from low-density ALS data, drawn from a single-scan TLS or a combination of both metrics, may represent a promising alternative to traditional field inventories for obtaining valuable information about surface and canopy fuel variables at large scalesSIThis research was funded by the projects GEPRIF (RTA2014-00011-C06-04) and VIS4FIRE (RTA2017-00042-C05-05) of the Spanish Ministry of Economy, Industry, and Competitiveness and a pre-doctoral grant of the first author funded by the “Consejería de Educación, Universidad y Formación Profesional” and the “Consejería de Economía, Empleo e Industria” of the Galician Government and the EU operational program “FSE Galicia 2014–2020”

Modelling aboveground biomass and fuel load components at stand level in shrub communities in NW Spain

Shrub-dominated ecosystems cover large areas globally and play essential roles in ecological processes. Aboveground biomass expressed on an area basis (AGB) is central to many of the ecological processes and services provided by shrublands and is important as the main fuel source for wildfires. Hence, its accurate estimation in shrublands is crucial for ecologists and land managers. This is especially relevant in fire-prone regions such as NW Spain, where shrublands are an important part of the landscape, providing multiple services, but are severely impacted by wildfires. Although biomass models are available for numerous shrub species at the individual plant level, operational models based directly on easily measured shrub stand attributes are scarce. In this study, equations for estimating AGB and loads of different fuel components by size and condition (live and dead) from stand biometric variables were developed for the nine most prevalent shrub communities in NW Spain. Non-linear iterative seemingly unrelated regression was used to fit compatible systems of equations for estimating fuel loads, with shrub stand height and cover and litter depth as predictors for individual shrub communities and all data combined. In general, the goodness-of-fit statistics indicated that the estimates were reasonably accurate for all communities (grouped and ungrouped). The best results were obtained for AGB and total fuel load, including litter, whereas the poorest results were obtained for standing live and dead fine fuel load. Model performance was reduced when height was the only independent variable, although the reduction was small for most fuel categories, except litter load for which the variability was adequately explained by the litter depth. These results illustrate the feasibility of the stand level approach for constructing operational models of shrub fuel load that are accurate for most of fuel components, while also highlighting the ongoing challenges in live and dead fine fuel modelling. The equations developed represent an appreciable advance in shrubland biomass assessment in the region and areas with similar characteristics and may be instrumental in generating fuel maps, fire management improvement and better C storage assessment by vegetation, among other many usesS