Review and new methodological approaches in human-caused wildfire modeling and ecological vulnerability: Risk modeling at mainland Spain

En las últimas décadas, las autoridades en materia de incendios han fomentado la investigación acerca de los factores desencadenantes del fuego, parámetro decisivo para lograr un entendimiento mayor de los patrones de la ocurrencia de incendios y mejorar las medidas preventivas. Existe por tanto una necesidad de mejorar y actualizar los enfoques metodológicos para el modelado de incendios forestales, teniendo en cuenta no sólo algoritmos innovadores, sino también la mejora y/o superación de los métodos clásicos de regresión. Por otra parte, es también imprescindible fomentar la evaluación de los posibles daños potenciales en los ecosistemas naturales, promoviendo así la conservación de los servicios de valor económico, ambiental, cultural y estético que éstos proporcionan a la sociedad. El objetivo principal de esta tesis doctoral es explorar nuevos métodos para el modelado de la causalidad humana en incendios forestales así como de los efectos adversos sobre las comunidades vegetales potencialmente afectadas. El modelado de la causalidad humana se ha realizado a partir de métodos de aprendizaje artificial y de técnicas de regresión geográficamente ponderada. Estas técnicas permiten por una parte el ajuste de modelos de probabilidad de ocurrencia espacialmente explícitos y, por otra, el estudio de la variabilidad espacial de los factores explicativos. La estimación de la vulnerabilidad de la vegetación frente al fuego, se ha llevado a cabo utilizando un enfoque cuantitativo, que permita superar los métodos existentes, que, si bien pueden ser útiles en algunas áreas de la gestión del territorio, son inadecuados para otros tipos de análisis, tales como la estimación de las pérdidas económicas inducidas por el fuego como consecuencia de la interrupción de los servicios ambientales (por ejemplo, la madera, la caza, y la recolección de setas). Para abordar el análisis de la vulnerabilidad se propone un método basado en la estimación del tiempo de recuperación de las comunidades vegetales tras el fuego, desarrollado mediante álgebra de mapas en entorno SIG. Los resultados indican que la utilización de métodos de aprendizaje artificial (concretamente el algoritmo Random Forest) supone una mejora sustancial respecto a los métodos clásicos de regresión, si bien parece que existe cierta incertidumbre en los modelos desarrollados, relacionada principalmente con la calidad de los datos de ocurrencia. Además, la aplicación de modelos GWR ha revelado la existencia de una elevada heterogeneidad espacial en la relación y capacidad explicativa de los factores relacionados con la ocurrencia de incendios con origen antrópico. Por otra parte, la aplicación del modelo propuesto para la estimación cuantitativa de la vulnerabilidad ecológica sugiere que la capacidad de respuesta de la vegetación se encuentra estrechamente relacionada con la estrategia reproductiva de las especies afectadas.Over the last decades, authorities responsible on forest fire have encouraged research on fire triggering factors, recognizing this as a critical point to achieve a greater understanding of fire occurrence patterns and improve preventive measures. There is therefore a need to improve and update the methodological approaches for modeling forest fires, taking into account not only innovative algorithms, but also improving and/or overcoming classical regression methods. On the other hand it is also essential to encourage the assessment of potential damage on natural ecosystems, promoting the conservation of its economic, environmental, cultural and aesthetic assets they provide to society. The main objective of this PhD thesis is to explore new methods for modeling human causality in forest fires and adverse effects on the plant communities potentially affected. Human causality modeling was carried out from machine learning methods and geographically weighted regression techniques. These procedures allow the adjustment spatially explicit probability models of occurrence and, secondly, the study of the spatial variability of wildfire explanatory factors. The estimation of the vulnerability of vegetation to fire was carried out using a quantitative approach to overcome current methods, which, while they may be useful in some areas of land management, are inadequate for other types of analysis, such as estimating economic losses induced by interrupting ecosystem services (e.g., wood, hunting, and gathering mushrooms). To address the vulnerability a method based on evaluating the recovery time of plant communities after the fire using a GIS map algebra approach is proposed. The results suggest that the use of machine learning methods (specifically the Random Forest algorithm) represents a substantial improvement over traditional methods of regression, although it appears that there is some uncertainty in the models, primarily related to the quality of ignition. Furthermore, the application of GWR models has revealed the existence of a high spatial heterogeneity in the relationship and explanatory power of the factors related to the occurrence of anthropogenic fires. Moreover, the application of the proposed model for the quantitative estimation of ecological vulnerability suggests that the responsiveness of vegetation is closely related to the reproductive strategy of the fire-affected species

Fuel Type Classification Using Airborne Laser Scanning and Sentinel 2 Data in Mediterranean Forest Affected by Wildfires

Mediterranean forests are recurrently affected by fire. The recurrence of fire in such environments and the number and severity of previous fire events are directly related to fire risk. Fuel type classification is crucial for estimating ignition and fire propagation for sustainable forest management of these wildfire prone environments. The aim of this study is to classify fuel types according to Prometheus classification using low-density Airborne Laser Scanner (ALS) data, Sentinel 2 data, and 136 field plots used as ground-truth. The study encompassed three different Mediterranean forests dominated by pines (Pinus halepensis, P. pinaster y P. nigra), oaks (Quercus ilex) and quercus (Q. faginea) in areas affected by wildfires in 1994 and their surroundings. Two metric selection approaches and two non-parametric classification methods with variants were compared to classify fuel types. The best-fitted classification model was obtained using Support Vector Machine method with radial kernel. The model includes three ALS and one Sentinel-2 metrics: the 25th percentile of returns height, the percentage of all returns above mean, rumple structural diversity index and NDVI. The overall accuracy of the model after validation was 59%. The combination of data from active and passive remote sensing sensors as well as the use of adapted structural diversity indices derived from ALS data improved accuracy classification. This approach demonstrates its value for mapping fuel type spatial patterns at a regional scale under different heterogeneous and topographically complex Mediterranean forests

Improving our understanding of individual wildfires by combining satellite data with fire spread modelling

Doutoramento em Engenharia Florestal e dos Recursos Naturais - Instituto Superior de AgronomiaWildfires pose real threats to life and property. In Portugal, the recent year of 2017 had the largest burnt area extent and number of casualties. A knowledge gap still exists in wildfire research related with better understanding individual wildfires, which has important implications for fire suppression, management, and policies. Wildfire spread models have been used to study individual wildfires, however, associated uncertainties and the lack of systematic evaluation methods hamper their capability for accurately predicting their spread. Understanding how fire spread predictions can be improved is a critical research task, as they will only be deemed useful if they can provide accurate and reliable information to fire managers. The present Thesis proposes to contribute to improve fire spread predictions by: i) Developing a methodology to systematically evaluate fire spread predictions ii) Thoroughly characterizing input data uncertainty and its impact on predictions; iii) Improving predictions using data-driven model calibration. The spread of large historical wildfires were studied by combining satellite data and models. The major findings of the present Thesis were: i) Satellite data accurately contributed to provide accurate fire dates and ignition information for large wildfires. ii) The evaluation metrics were very useful in identifying areas and periods of low/high spatio-temporal agreement, highlighting the strong underprediction bias and poor accuracy of the predictions. iii) Uncertainties in wind speed and direction, fuel model assignment and typology, location and timing of ignitions, had a major impact on prediction accuracy. iv) Predictions iii) Uncertainties in wind speed and direction, fuel model assignment and typology, location and timing of ignitions, had a major impact on prediction accuracy. iv) Predictions were improved by ‘learning’ from past wildfires, significantly reducing the impact of data uncertainty on the accuracy of fire spread predictions Overall, the work contributed to advance the body of knowledge regarding individual wildfires and identified future research steps towards a reliable operational fire system capable of supporting more effective and safer fire management decisions with the aim of reducing the dramatic impacts of wildfiresN/

Artificial neural networks to detect forest fire prone areas in the southeast fire district of Mississippi

An analysis of the fire occurrences parameters is essential to save human lives, property, timber resources and conservation of biodiversity. Data conversion formats such as raster to ASCII facilitate the integration of various GIS software’s in the context of RS and GIS modeling. This research explores fire occurrences in relation to human interaction, fuel density interaction, euclidean distance from the perennial streams and slope using artificial neural networks. The human interaction (ignition source) and density of fuels is assessed by Newton’s Gravitational theory. Euclidean distance to perennial streams and slope that do posses a significant role were derived using GIS tools. All the four non linear predictor variables were modeled using the inductive nature of neural networks. The Self organizing feature map (SOM) utilized for fire size risk classification produced an overall classification accuracy of 62% and an overall kappa coefficient of 0.52 that is moderate (fair) for annual fires

A new wildland fire danger index for a Mediterranean region and some validation aspects

Wildland fires are the main cause of tree mortality in Mediterranean Europe and a major threat to Spanish forests. This paper focuses on the design and validation of a new wildland fire index especially adapted to a Mediterranean Spanish region. The index considers ignition and spread danger components. Indicators of natural and human ignition agents, historical occurrence, fuel conditions and fire spread make up the hierarchical structure of the index. Multi-criteria methods were used to incorporate experts¿ opinion in the process of weighting the indicators and to carry out the aggregation of components into the final index, which is used to map the probability of daily fire occurrence on a 0.5-km grid. Generalised estimating equation models, which account for possible correlated responses, were used to validate the index, accommodating its values onto a larger scale because historical records of daily fire occurrence, which constitute the dependent variable, are referred to cells on a 10-km grid. Validation results showed good index performance, good fit of the logistic model and acceptable discrimination power. Therefore, the index will improve the ability of fire prevention services in daily allocation of resources.The authors acknowledge the support received from the Ministry of Science and Innovation through the research project Modelling and Optimisation Techniques for a Sustainable Development, Ref. EC02008-05895-C02-01/ECON.Vicente López, FJD.; Crespo Abril, F. (2012). A new wildland fire danger index for a Mediterranean region and some validation aspects. International Journal of Wildland Fire. 21(8):1030-1041. https://doi.org/10.1071/WF11046S10301041218Aguado, I., Chuvieco, E., Borén, R., & Nieto, H. (2007). Estimation of dead fuel moisture content from meteorological data in Mediterranean areas. Applications in fire danger assessment. International Journal of Wildland Fire, 16(4), 390. doi:10.1071/wf06136Andrews, P. L., Loftsgaarden, D. O., & Bradshaw, L. S. (2003). Evaluation of fire danger rating indexes using logistic regression and percentile analysis. International Journal of Wildland Fire, 12(2), 213. doi:10.1071/wf02059Bradstock, R. A., Cohn, J. S., Gill, A. M., Bedward, M., & Lucas, C. (2009). Prediction of the probability of large fires in the Sydney region of south-eastern Australia using fire weather. International Journal of Wildland Fire, 18(8), 932. doi:10.1071/wf08133Buizza, R., & Hollingsworth, A. (2002). Storm prediction over Europe using the ECMWF Ensemble Prediction System. Meteorological Applications, 9(3), 289-305. doi:10.1017/s1350482702003031Carmel, Y., Paz, S., Jahashan, F., & Shoshany, M. (2009). Assessing fire risk using Monte Carlo simulations of fire spread. Forest Ecology and Management, 257(1), 370-377. doi:10.1016/j.foreco.2008.09.039Castedo-Dorado, F., Rodríguez-Pérez, J. R., Marcos-Menéndez, J. L., & Álvarez-Taboada, M. F. (2011). Modelling the probability of lightning-induced forest fire occurrence in the province of León (NW Spain). Forest Systems, 20(1), 95. doi:10.5424/fs/2011201-9409Catry, F. X., Rego, F. C., Bação, F. L., & Moreira, F. (2009). Modeling and mapping wildfire ignition risk in Portugal. International Journal of Wildland Fire, 18(8), 921. doi:10.1071/wf07123CHUVIECO, E., & SALAS, J. (1996). Mapping the spatial distribution of forest fire danger using GIS. International journal of geographical information systems, 10(3), 333-345. doi:10.1080/02693799608902082Chuvieco, E., Cocero, D., Riaño, D., Martin, P., Martı́nez-Vega, J., de la Riva, J., & Pérez, F. (2004). Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sensing of Environment, 92(3), 322-331. doi:10.1016/j.rse.2004.01.019Chuvieco, E., Aguado, I., Yebra, M., Nieto, H., Salas, J., Martín, M. P., … Zamora, R. (2010). Development of a framework for fire risk assessment using remote sensing and geographic information system technologies. Ecological Modelling, 221(1), 46-58. doi:10.1016/j.ecolmodel.2008.11.017Danson, F. M., & Bowyer, P. (2004). Estimating live fuel moisture content from remotely sensed reflectance. Remote Sensing of Environment, 92(3), 309-321. doi:10.1016/j.rse.2004.03.017Dasgupta, S., Qu, J. J., & Hao, X. (2006). Design of a Susceptibility Index for Fire Risk Monitoring. IEEE Geoscience and Remote Sensing Letters, 3(1), 140-144. doi:10.1109/lgrs.2005.858484Fairbrother, A., & Turnley, J. G. (2005). Predicting risks of uncharacteristic wildfires: Application of the risk assessment process. Forest Ecology and Management, 211(1-2), 28-35. doi:10.1016/j.foreco.2005.01.026Finney, M. A. (2005). The challenge of quantitative risk analysis for wildland fire. Forest Ecology and Management, 211(1-2), 97-108. doi:10.1016/j.foreco.2005.02.010Gouma, V., & Chronopoulou-Sereli, A. (1998). Wildland Fire Danger Zoning - a Methodology. International Journal of Wildland Fire, 8(1), 37. doi:10.1071/wf9980037Hernandez-Leal, P. A., Arbelo, M., & Gonzalez-Calvo, A. (2006). Fire risk assessment using satellite data. Advances in Space Research, 37(4), 741-746. doi:10.1016/j.asr.2004.12.053Li, L.-M., Song, W.-G., Ma, J., & Satoh, K. (2009). Artificial neural network approach for modeling the impact of population density and weather parameters on forest fire risk. International Journal of Wildland Fire, 18(6), 640. doi:10.1071/wf07136Maingi, J. K., & Henry, M. C. (2007). Factors influencing wildfire occurrence and distribution in eastern Kentucky, USA. International Journal of Wildland Fire, 16(1), 23. doi:10.1071/wf06007Martell, D. L., Otukol, S., & Stocks, B. J. (1987). A logistic model for predicting daily people-caused forest fire occurrence in Ontario. Canadian Journal of Forest Research, 17(5), 394-401. doi:10.1139/x87-068Martínez, J., Vega-Garcia, C., & Chuvieco, E. (2009). Human-caused wildfire risk rating for prevention planning in Spain. Journal of Environmental Management, 90(2), 1241-1252. doi:10.1016/j.jenvman.2008.07.005Moffett, A., Garson, J., & Sarkar, S. (2005). MultCSync: a software package for incorporating multiple criteria in conservation planning. Environmental Modelling & Software, 20(10), 1315-1322. doi:10.1016/j.envsoft.2004.10.001Nieto, H., Aguado, I., Chuvieco, E., & Sandholt, I. (2010). Dead fuel moisture estimation with MSG–SEVIRI data. Retrieval of meteorological data for the calculation of the equilibrium moisture content. Agricultural and Forest Meteorology, 150(7-8), 861-870. doi:10.1016/j.agrformet.2010.02.007Noble, B. F., & Christmas, L. M. (2007). Strategic Environmental Assessment of Greenhouse Gas Mitigation Options in the Canadian Agricultural Sector. Environmental Management, 41(1), 64-78. doi:10.1007/s00267-007-9017-yNúñez-Regueira, L. (1997). Calorific values and flammability of forest species in galicia. Continental high mountainous and humid Atlantic zones. Bioresource Technology, 61(2), 111-119. doi:10.1016/s0960-8524(97)00053-9Padilla, M., & Vega-García, C. (2011). On the comparative importance of fire danger rating indices and their integration with spatial and temporal variables for predicting daily human-caused fire occurrences in Spain. International Journal of Wildland Fire, 20(1), 46. doi:10.1071/wf09139Pendergast, J. F., Gange, S. J., Newton, M. A., Lindstrom, M. J., Palta, M., & Fisher, M. R. (1996). A Survey of Methods for Analyzing Clustered Binary Response Data. International Statistical Review / Revue Internationale de Statistique, 64(1), 89. doi:10.2307/1403425Pew, K. ., & Larsen, C. P. . (2001). GIS analysis of spatial and temporal patterns of human-caused wildfires in the temperate rain forest of Vancouver Island, Canada. Forest Ecology and Management, 140(1), 1-18. doi:10.1016/s0378-1127(00)00271-1Podur, J., Martell, D. L., & Csillag, F. (2003). Spatial patterns of lightning-caused forest fires in Ontario, 1976–1998. Ecological Modelling, 164(1), 1-20. doi:10.1016/s0304-3800(02)00386-1Preisler, H. K., Brillinger, D. R., Burgan, R. E., & Benoit, J. W. (2004). Probability based models for estimation of wildfire risk. International Journal of Wildland Fire, 13(2), 133. doi:10.1071/wf02061Preisler, H. K., Chen, S.-C., Fujioka, F., Benoit, J. W., & Westerling, A. L. (2008). Wildland fire probabilities estimated from weather model-deduced monthly mean fire danger indices. International Journal of Wildland Fire, 17(3), 305. doi:10.1071/wf06162Romero-Calcerrada, R., Novillo, C. J., Millington, J. D. A., & Gomez-Jimenez, I. (2008). GIS analysis of spatial patterns of human-caused wildfire ignition risk in the SW of Madrid (Central Spain). Landscape Ecology, 23(3), 341-354. doi:10.1007/s10980-008-9190-2Saaty, T. L. (1987). RANK GENERATION, PRESERVATION, AND REVERSAL IN THE ANALYTIC HIERARCHY DECISION PROCESS. Decision Sciences, 18(2), 157-177. doi:10.1111/j.1540-5915.1987.tb01514.xSahin, Y. G., & Ince, T. (2009). Early Forest Fire Detection Using Radio-Acoustic Sounding System. Sensors, 9(3), 1485-1498. doi:10.3390/s90301485López, A. S., San-Miguel-Ayanz, J., & Burgan, R. E. (2002). Integration of satellite sensor data, fuel type maps and meteorological observations for evaluation of forest fire risk at the pan-European scale. International Journal of Remote Sensing, 23(13), 2713-2719. doi:10.1080/01431160110107761Sharples, J. J., McRae, R. H. D., Weber, R. O., & Gill, A. M. (2009). A simple index for assessing fire danger rating. Environmental Modelling & Software, 24(6), 764-774. doi:10.1016/j.envsoft.2008.11.004Stocks, B. J., Lynham, T. J., Lawson, B. D., Alexander, M. E., Wagner, C. E. V., McAlpine, R. S., & Dubé, D. E. (1989). The Canadian Forest Fire Danger Rating System: An Overview. The Forestry Chronicle, 65(6), 450-457. doi:10.5558/tfc65450-6Sturtevant, B. R., & Cleland, D. T. (2007). Human and biophysical factors influencing modern fire disturbance in northern Wisconsin. International Journal of Wildland Fire, 16(4), 398. doi:10.1071/wf06023Swets, J. (1988). Measuring the accuracy of diagnostic systems. Science, 240(4857), 1285-1293. doi:10.1126/science.3287615Vadrevu, K. P., Eaturu, A., & Badarinath, K. V. S. (2009). Fire risk evaluation using multicriteria analysis—a case study. Environmental Monitoring and Assessment, 166(1-4), 223-239. doi:10.1007/s10661-009-0997-3Vasilakos, C., Kalabokidis, K., Hatzopoulos, J., Kallos, G., & Matsinos, Y. (2007). Integrating new methods and tools in fire danger rating. International Journal of Wildland Fire, 16(3), 306. doi:10.1071/wf05091Verde, J. C., & Zêzere, J. L. (2010). Assessment and validation of wildfire susceptibility and hazard in Portugal. Natural Hazards and Earth System Science, 10(3), 485-497. doi:10.5194/nhess-10-485-2010Wotton, B. M., & Martell, D. L. (2005). A lightning fire occurrence model for Ontario. Canadian Journal of Forest Research, 35(6), 1389-1401. doi:10.1139/x05-071Yebra, M., Chuvieco, E., & Riaño, D. (2008). Estimation of live fuel moisture content from MODIS images for fire risk assessment. Agricultural and Forest Meteorology, 148(4), 523-536. doi:10.1016/j.agrformet.2007.12.00

Fire behavior modeling for operational decision-making

Simulation frameworks are necessary to facilitate decision-making to many fire agencies. An accurate estimation of fire behavior is required to analyze potential impact and risk. Applied research and technology together have improved the implementation of fire modeling, and decision-making in operational environments.Dr Cardil acknowledges the support of Technosylva USA and Wageningen University in his research stays in the USA and the Netherlands to develop this work. The authors of this paper acknowledges the support of the EUfunded PYROLIFE project (Reference: 860787; https://pyrolife.lessonsonfire.eu/), a project in which a new generation of experts will be trained in integrated wildfire management

Using Pre-Fire High Point Cloud Density LiDAR Data to Predict Fire Severity in Central Portugal

[EN], The wall-to-wall prediction of fuel structural characteristics conducive to high fire severity is essential to provide integrated insights for implementing pre-fire management strategies designed to mitigate the most harmful ecological effects of fire in fire-prone plant communities. Here, we evaluate the potential of high point cloud density LiDAR data from the Portuguese áGiLTerFoRus project to characterize pre-fire surface and canopy fuel structure and predict wildfire severity. The study area corresponds to a pilot LiDAR flight area of around 21,000 ha in central Portugal intersected by a mixed-severity wildfire that occurred one month after the LiDAR survey. Fire severity was assessed through the differenced Normalized Burn Ratio (dNBR) index computed from pre- and post-fire Sentinel-2A Level 2A scenes. In addition to continuous data, fire severity was also categorized (low or high) using appropriate dNBR thresholds for the plant communities in the study area. We computed several metrics related to the pre-fire distribution of surface and canopy fuels strata with a point cloud mean density of 10.9 m−2. The Random Forest (RF) algorithm was used to evaluate the capacity of the set of pre-fire LiDAR metrics to predict continuous and categorized fire severity. The accuracy of RF regression and classification model for continuous and categorized fire severity data, respectively, was remarkably high (pseudo-R2 = 0.57 and overall accuracy = 81%) considering that we only focused on variables related to fuel structure and loading. The pre-fire fuel metrics with the highest contribution to RF models were proxies for horizontal fuel continuity (fractional cover metric) and the distribution of fuel loads and canopy openness up to a 10 m height (density metrics), indicating increased fire severity with higher surface fuel load and higher horizontal and vertical fuel continuity. Results evidence that the technical specifications of LiDAR acquisitions framed within the áGiLTerFoRus project enable accurate fire severity predictions through point cloud data with high density.SIPortuguese Foundation for Science and Technolog

Wildfire Hazard and Risk Assessment

Wildfire risk can be perceived as the combination of wildfire hazards (often described by likelihood and intensity) with the susceptibility of people, property, or other valued resources to that hazard. Reflecting the seriousness of wildfire risk to communities around the world, substantial resources are devoted to assessing wildfire hazards and risks. Wildfire hazard and risk assessments are conducted at a wide range of scales, from localized to nationwide, and are often intended to communicate and support decision making about risks, including the prioritization of scarce resources. Improvements in the underlying science of wildfire hazard and risk assessment and in the development, communication, and application of these assessments support effective decisions made on all aspects of societal adaptations to wildfire, including decisions about the prevention, mitigation, and suppression of wildfire risks. To support such efforts, this Special Issue of the journal Fire compiles articles on the understanding, modeling, and addressing of wildfire risks to homes, water resources, firefighters, and landscapes