Towards an Integrated Approach to Wildfire Risk Assessment: When, Where, What and How May the Landscapes Burn
Abstract
:1. Introduction: The Need of Wildfire Risk Assessment, Reduction and Adaptation
2. Frameworks for Wildfire Risk Assessment
2.1. Natural Hazards Terminology
2.2. Wildfire Risk Terminology and Components
2.2.1. Danger
2.2.2. Exposure
2.2.3. Vulnerability
2.3. Wildfire Risk Variables
2.3.1. Weather and Climate
2.3.2. Topography
2.3.3. Wildfire Fuels
2.3.4. Human Components
2.3.5. Ecosystem Services
2.3.6. Ecological Values
2.3.7. Resilience
2.4. Integration Approaches
2.4.1. Risk Integration Scales
2.4.2. Generation of Integrated Risk Indices
2.5. Risk Validation
2.6. Communication of Risk Conditions
- Risk awareness, which involves informing the public about wildfire risks and potential impacts. It is important to provide accurate and timely information that is easy to understand and to address any concerns or questions the public may have. This should be combined with community outreach. Participatory approaches, in which the local communities can express their own perception of risk, have also been proven very efficient [234];
- Public Messaging, which involves communicating important information to lay people, particularly during the fire season, through various channels, including social media, news releases and public meetings. Effective public messaging should be clear, concise and consistent. Formalized radio or SMS alerts shared through competent services, such as the telephone line 112 in Europe, may also be used to communicate risk conditions;
- Media Relations. Wildfire management agencies should work with the media to ensure that accurate and timely information is shared with the public. This involves providing regular updates to the media, arranging interviews with experts and officials, and providing access to fire scenes when appropriate [235];
- Social media can be an effective tool for wildfire communication, as it allows agencies to reach a large audience quickly and easily. Agencies can use social media to provide real-time updates, share photos and videos, and respond to questions and concerns from the public;
- Dedicated apps and web-based services. Depending on the recipient, the mean can differ from mobile apps [236] to web mapping services, of which many are already in place, such as those provided by the European Forest Fire Information System (EFFIS: https://effis.jrc.ec.europa.eu/, accessed on 30 March 2023) or the NASA Fire Information for Resource Management System (FIRMS: https://firms.modaps.eosdis.nasa.gov/, accessed on 30 March 2023).
2.7. Linking Wildfire Risk Assessment, Reduction and Adaptation
2.8. Non-Fire Hazards: Cascading Effects
3. Review of Existing Wildfire Risk Assessment Systems
3.1. USA
3.2. Canada
3.3. Australia
3.4. Europe
4. Description of the FirEUrisk Assessment System
4.1. Spatial and Temporal Resolution
4.2. FirEUrisk Integration Scheme
4.2.1. FirEUrisk Danger
4.2.2. FirEUrisk Exposure
4.2.3. FirEUrisk Vulnerability
4.3. Methods to Generate Risk Variables and Components
4.3.1. Natural Ignition
4.3.2. Human Ignition
4.3.3. Fuel
4.3.4. Propagation
4.3.5. Exposure to Population and Assets
4.3.6. Ecosystem Services
4.3.7. Ecological Values
4.3.8. Resilience
4.4. Integration Approaches
4.4.1. Conversion to Risk Values
4.4.2. Integrated Risk Indices
4.5. FirEUrisk Information System
4.6. Integration of Assessment, Reduction and Adaptation
5. Synthesis
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Van der Werf, G.R.; Randerson, J.T.; Giglio, L.; van Leeuwen, T.T.; Chen, Y.; Rogers, B.M.; Mu, M.; van Marle, M.J.E.; Morton, D.C.; Collatz, G.J.; et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 2017, 9, 697–720. [Google Scholar] [CrossRef]
- Gleason, K.E.; McConnell, J.R.; Arienzo, M.M.; Chellman, N.; Calvin, W.M. Four-fold increase in solar forcing on snow in western US burned forests since 1999. Nat. Commun. 2019, 10, 2026. [Google Scholar] [CrossRef] [PubMed]
- Bowman, D.M.; Kolden, C.A.; Abatzoglou, J.T.; Johnston, F.H.; van der Werf, G.R.; Flannigan, M. Vegetation fires in the Anthropocene. Nat. Rev. Earth Environ. 2020, 1, 500–515. [Google Scholar] [CrossRef]
- Lizundia-Loiola, J.; Otón, G.; Ramo, R.; Chuvieco, E. A spatio-temporal active-fire clustering approach for global burned area mapping at 250 m from MODIS data. Remote Sens. Environ. 2020, 236, 111493. [Google Scholar] [CrossRef]
- Giglio, L.; Boschetti, L.; Roy, D.P.; Humber, M.L.; Justice, C.O. The Collection 6 MODIS Burned Area Mapping Algorithm and Product. RSE Remote Sens. Environ. 2018, 217, 72–85. [Google Scholar] [CrossRef] [PubMed]
- Chuvieco, E.; Roteta, E.; Sali, M.; Stroppiana, D.; Boettcher, M.; Kirches, G.; Khairoun, A.; Pettinari, L.; Franquesa, M.; Albergel, C. Building a small fire database for Sub-Saharan Africa from Sentinel-2 high-resolution images. Sci. Total Environ. 2022, 845, 157139. [Google Scholar] [CrossRef]
- Kelly, L.; Brotons, L. Using fire to promote biodiversity. Science 2017, 355, 1264–1265. [Google Scholar] [CrossRef]
- Lewis, S.L.; Edwards, D.P.; Galbraith, D. Increasing human dominance of tropical forests. Science 2015, 349, 827–832. [Google Scholar] [CrossRef]
- Bowman, D.M.; Williamson, G.; Yebra, M.; Lizundia-Loiola, J.; Pettinari, M.L.; Shah, S.; Bradstock, R.; Chuvieco, E. Wildfires: Australia needs a national monitoring agency. Nature 2020, 584, 188–191. [Google Scholar] [CrossRef]
- Bowman, D.M.; Williamson, G.J.; Abatzoglou, J.T.; Kolden, C.A.; Cochrane, M.A.; Smith, A.M. Human exposure and sensitivity to globally extreme wildfire events. Nat. Ecol. Evol. 2017, 1, 0058. [Google Scholar] [CrossRef]
- Fitch, R.A.; Kim, Y.S.; Waltz, A.E.M.; Crouse, J.E. Changes in potential wildland fire suppression costs due to restoration treatments in Northern Arizona Ponderosa pine forests. For. Policy Econ. 2018, 87, 101–114. [Google Scholar] [CrossRef]
- Jain, P.; Castellanos-Acuna, D.; Coogan, S.C.P.; Abatzoglou, J.T.; Flannigan, M.D. Observed increases in extreme fire weather driven by atmospheric humidity and temperature. Nat. Clim. Chang. 2021, 12, 63–70. [Google Scholar] [CrossRef]
- Tedim, F.; Leone, V.; Amraoui, M.; Bouillon, C.; Coughlan, M.R.; Delogu, G.M.; Fernandes, P.M.; Ferreira, C.; McCaffrey, S.; McGee, T.K.; et al. Defining Extreme Wildfire Events: Difficulties, Challenges, and Impacts. Fire 2018, 1, 9. [Google Scholar] [CrossRef]
- Duane, A.; Castellnou, M.; Brotons, L. Towards a comprehensive look at global drivers of novel extreme wildfire events. Clim. Chang. 2021, 165, 43. [Google Scholar] [CrossRef]
- Calkin, D.E.; Thompson, M.P.; Finney, M.A.; Hyde, K.D. A real-time risk assessment tool supporting wildland fire decision making. J. For. 2011, 109, 274–280. [Google Scholar]
- Thompson, M.P.; MacGregor, D.G.; Calkin, D. Risk Management: Core Principles and Practices, and Their Relevance to Wildland Fire; U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station: Fort Collins, CO, USA, 2016. [Google Scholar]
- Moore, P.F. Global Wildland Fire Management Research Needs. Curr. For. Rep. 2019, 5, 210–225. [Google Scholar] [CrossRef]
- UNISDR. Technical Guidance for Monitoring and Reporting on Progress in Achieving the Global Targets of the Sendai Framework for Disaster Risk Reduction; United Nations: Geneva, Switzerland, 2017. Available online: https://www.undrr.org/publication/technical-guidance-monitoring-and-reporting-progress-achieving-global-targets-sendai (accessed on 5 May 2023).
- Tagarev, T.; Papadopoulos, G.A. Integrating the risk management cycle. In Science for Disaster Risk Management 2020: Acting Today, Protecting Tomorrow; Casajus Valles, A., Marin Ferrer, M., Poljanšek, K., Clark, I., Eds.; UR 30183 EN; Publications Office of the European Union: Luxembourg, 2020; pp. 49–106. [Google Scholar]
- McGlade, J.; Bankoff, G.; Abrahams, J.; Cooper-Knock, S.; Cotecchia, F.; Desanker, P.; Erian, W.; Gencer, E.; Gibson, L.; Girgin, S. Global Assessment Report on Disaster Risk Reduction 2019; UN Office for Disaster Risk Reduction: Geneva, Switzerland, 2019. [Google Scholar]
- Field, C.B.; Barros, V.R.; Dokken, D.J.; Mach, K.J.; Mastrandrea, M.D.; Bilir, T.E.; Chatterjee, M.; Ebi, K.L.; Estrada, Y.O.; Genova, R.C. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK, 2014. [Google Scholar]
- Thywissen, K. Components of Risk: A Comparative Glossary; UNU-EHS: Bonn, Germany, 2006. [Google Scholar]
- UNDRR. Bangladesh INFORM Sub-National Risk Index 2022; United Nations Office for Disaster Risk Reduction: Dhaka, Bangladesh, 2022. [Google Scholar]
- Poljanšek, K.; Marin Ferrer, M.; Clark, I.; De Groeve, T. Science for Disaster Risk Management 2017. Knowing Better and Losing Less; Publications Office of the European Union: Luxembourg, 2017. [Google Scholar]
- UNISDR. Terminology on Disaster Risk Reduction; United Nations International Strategy for Disaster Reduction (UNISDR): Geneva, Switzerland, 2009; Available online: http://www.unisdr.org/files/7817_UNISDRTerminologyEnglish.pdf (accessed on 30 April 2023).
- Bachmann, A.; Allgöwer, B. A consistent wildland fire risk terminology is needed! Fire Manag. Today 2001, 61, 28–33. [Google Scholar]
- FAO. Wildland Fire Management Terminology. Terminologie de la Lutte Contre les Incendies de Forêt. Terminología del Control de Incendios en Tierras Incultas; Report nº 70; Food and Agriculture Organization, Forestry Paper: Rome, Italy, 1986. [Google Scholar]
- San Miguel-Ayanz, J.; Chuvieco, E.; Handmer, J.; Moffat, A.; Montiel-Molina, C.; Sandahl, L.; Viegas, D. Climatological risk: Wildfires. In Science for Disaster Risk Management: Knowing Better and Losing Less; Poljanšek, K., Marín Ferrer, M., De Groeve, T., Clark, I., Eds.; Scientific and Technical Research; Publications Office of the European Union: Luxembourg, 2017; pp. 294–305. Available online: http://drmkc.jrc.ec.europa.eu/portals/0/Knowledge/ScienceforDRM/ch03_s03/ch03_s03_subch0310.pdf (accessed on 6 May 2023).
- Chuvieco, E.; Salas, F.J. Mapping the spatial distribution of forest fire danger using GIS. IJGIS Int. J. Geogr. Inf. Sci. 1996, 10, 333–345. [Google Scholar] [CrossRef]
- Costa, H.; de Rigo, D.; Libertà, G.; Houston Durrant, T.; San-Miguel-Ayanz, J. European Wildfire Danger and Vulnerability in a Changing Climate: Towards Integrating Risk Dimensions; Publications Office of the European Union: Luxembourg, 2020; p. 59.
- Flannigan, M.D.; Wotton, B.D. A study of interpolation methods for forest fire danger rating in Canada. Can. J. For. Res. 1989, 19, 1059–1066. [Google Scholar] [CrossRef]
- Vasilakos, C.; Kalabokidis, K.; Hatzopoulos, J.; Kallos, G.; Matsinos, Y. Integrating new methods and tools in fire danger rating. Int. J. Wildland Fire 2007, 16, 306–316. [Google Scholar] [CrossRef]
- Viegas, D.X.; Bovio, G.; Ferreira, A.; Nosenzo, A.; Sol, B. Comparative study of various methods of fire danger evaluation in southern Europe. Int. J. Wildland Fire 1999, 9, 235–246. [Google Scholar] [CrossRef]
- Wotton, B.M. Interpreting and using outputs from the Canadian Forest Fire Danger Rating System in research applications. Environ. Ecol. Stat. 2009, 16, 107–131. [Google Scholar] [CrossRef]
- Zacharakis, I.; Tsihrintzis, V.A. Environmental Forest Fire Danger Rating Systems and Indices around the Globe: A Review. Land 2023, 12, 194. [Google Scholar] [CrossRef]
- Chuvieco, E.; Aguado, I.; Jurdao, S.; Pettinari, M.L.; Yebra, M.; Salas, J.; Hantson, S.; de la Riva, J.; Ibarra, P.; Rodrigues, M.; et al. Integrating geospatial information into fire risk assessment. Int. J. Wildland Fire 2014, 23, 606–619. [Google Scholar] [CrossRef]
- Thompson, M.P.; Calkin, D.E.; Finney, M.A.; Ager, A.A.; Gilbertson-Day, J.W. Integrated national-scale assessment of wildfire risk to human and ecological values. Stoch. Environ. Res. Risk Assess. 2011, 25, 761–780. [Google Scholar] [CrossRef]
- Chuvieco, E.; Aguado, I.; Yebra, M.; Nieto, H.; Salas, J.; Martín, P.; Vilar, L.; Martínez, J.; Martín, S.; Ibarra, P.; et al. Development of a framework for fire risk assessment using remote sensing and geographic information system technologies. Ecol. Model. 2010, 221, 46–58. [Google Scholar] [CrossRef]
- Thompson, M.P.; Calkin, D.E.; Gilbertson-Day, J.W.; Ager, A.A. Advancing effects analysis for integrated, large-scale wildfire risk assessment. Environ. Monit. Assess. 2010, 179, 217–239. [Google Scholar] [CrossRef]
- Oliveira, S.; Rocha, J.; Sá, A. Wildfire risk modeling. Curr. Opin. Environ. Sci. Health 2021, 23, 100274. [Google Scholar] [CrossRef]
- Oliveira, S.; Gonçalves, A.; Benali, A.; Sá, A.; Zêzere, J.L.; Pereira, J.M. Assessing Risk and Prioritizing Safety Interventions in Human Settlements Affected by Large Wildfires. Forests 2020, 11, 859. [Google Scholar] [CrossRef]
- Prestemon, J.P.; Butry, D.T. Time to Burn: Modeling Wildland Arson as an Autoregressive Crime Function. Am. J. Agric. Econ. 2005, 87, 756–770. [Google Scholar] [CrossRef]
- Rothermel, R.C. A Mathematical Model for Predicting Fire Spread in Wildland Fuels; USDA, Forest Service. Research Paper INT-115; USDA: Ogden, UT, USA, 1972.
- Rothermel, R.C. How to Predict the Spread and Intensity of Forest and Range Fires; USDA, Forest Service, GTR INT-143; USDA: Ogden, UT, USA, 1983; p. 161.
- Deville, P.; Linard, C.; Martin, S.; Gilbert, M.; Stevens, F.R.; Gaughan, A.E.; Blondel, V.D.; Tatem, A.J. Dynamic population mapping using mobile phone data. Proc. Natl. Acad. Sci. USA 2014, 111, 15888–15893. [Google Scholar] [CrossRef] [PubMed]
- Bar Massada, A.; Radeloff, V.C.; Stewart, S.I.; Hawbaker, T.J. Wildfire risk in the wildland–urban interface: A simulation study in northwestern Wisconsin. For. Ecol Manag. 2009, 258, 1990–1999. [Google Scholar] [CrossRef]
- Finney, M.A. FARSITE: Fire Area Simulator–Model Development and Evaluation; USDA Forest Service. RMRS-RP-4; USDA: Missoula, MT, USA, 1998; p. 51.
- Alcasena, F.J.; Salis, M.; Vega-García, C. A fire modeling approach to assess wildfire exposure of valued resources in central Navarra, Spain. Eur. J. For. Res. 2016, 135, 87–107. [Google Scholar] [CrossRef]
- Finney, M.A. An Overview of FlamMap Fire Modeling Capabilities. In Fuels Management-How to Measure Success: Conference Proceedings RMRS-P-41, Portland, OR, USA 28–30 March 2006; Andrews, P.L., Butler, B.W., Eds.; U.S. Department of Agriculture, Forest Service, Rocky Mountain Research: Portland, OR, USA, 2006; pp. 213–220. [Google Scholar]
- Beverly, J.; Bothwell, P.; Conner, J.; Herd, E. Assessing the exposure of the built environment to potential ignition sources generated from vegetative fuel. Int. J. Wildland Fire 2010, 19, 299–313. [Google Scholar] [CrossRef]
- Radeloff, V.C.; Hammer, R.B.; Stewart, S.I.; Fried, J.S.; Holcomb, S.S.; McKeefry, J.F. The wildland-urban interface in the United States. Ecol. Appl. 2005, 15, 799–805. [Google Scholar] [CrossRef]
- Argañaraz, J.P.; Radeloff, V.C.; Bar-Massada, A.; Gavier-Pizarro, G.I.; Scavuzzo, C.M.; Bellis, L.M. Assessing wildfire exposure in the Wildland-Urban Interface area of the mountains of central Argentina. J. Environ. Manag. 2017, 196, 499–510. [Google Scholar] [CrossRef]
- Bar-Massada, A.; Stewart, S.I.; Hammer, R.B.; Mockrin, M.H.; Radeloff, V.C. Using structure locations as a basis for mapping the wildland urban interface. J. Environ. Manag. 2013, 128, 540–547. [Google Scholar] [CrossRef]
- Modugno, S.; Balzter, H.; Cole, B.; Borrelli, P. Mapping regional patterns of large forest fires in Wildland–Urban Interface areas in Europe. J. Environ. Manag. 2016, 172, 112–126. [Google Scholar] [CrossRef]
- Carlson, A.R.; Helmers, D.P.; Hawbaker, T.J.; Mockrin, M.H.; Radeloff, V.C. The wildland–urban interface in the United States based on 125 million building locations. Ecol. Appl. 2022, 32, e2597. [Google Scholar] [CrossRef]
- Oliveira, S.; Félix, F.; Nunes, A.; Lourenço, L.; Laneve, G.; Sebastián-López, A. Mapping wildfire vulnerability in Mediterranean Europe. Testing a stepwise approach for operational purposes. J. Environ. Manag. 2018, 206, 158–169. [Google Scholar] [CrossRef]
- Turner, B.L.; Kasperson, R.E.; Matson, P.A.; McCarthy, J.J.; Corell, R.W.; Christensen, L.; Eckley, N.; Kasperson, J.X.; Luers, A.; Martello, M.L. A framework for vulnerability analysis in sustainability science. Proc. Natl. Acad. Sci. USA 2003, 100, 8074–8079. [Google Scholar] [CrossRef] [PubMed]
- Kasperson, R.E.; Kasperson, J.X. Climate Change, Vulnerability and Social Justice; Stockholm Environment Institute: Stockholm, Sweden, 2001. [Google Scholar]
- Weisshuhn, P.; Müller, F.; Wiggering, H. Ecosystem vulnerability review: Proposal of an interdisciplinary ecosystem assessment approach. Environ. Manag. 2018, 61, 904–915. [Google Scholar] [CrossRef] [PubMed]
- Swanston, C.W.; Janowiak, M.K.; Brandt, L.A.; Butler, P.R.; Handler, S.D.; Shannon, P.D.; Derby Lewis, A.; Hall, K.; Fahey, R.T.; Scott, L.; et al. Forest Adaptation Resources: Climate Change Tools and Approaches for Land Managers, 2nd ed.; U.S. Department of Agriculture, Forest Service, Northern Research Station: Newtown Square, PA, USA, 2016. [Google Scholar]
- De Lange, H.; Sala, S.; Vighi, M.; Faber, J.H. Ecological vulnerability in risk assessment—A review and perspectives. Sci. Total Environ. 2010, 408, 3871–3879. [Google Scholar] [CrossRef] [PubMed]
- Beroya-Eitner, M.A. Ecological vulnerability indicators. Ecol. Indic. 2016, 60, 329–334. [Google Scholar] [CrossRef]
- Ingrisch, J.; Bahn, M. Towards a Comparable Quantification of Resilience. Trends Ecol. Evol. 2018, 33, 251–259. [Google Scholar] [CrossRef]
- Lei, Y.; Wang, J.a.; Yue, Y.; Zhou, H.; Yin, W. Rethinking the relationships of vulnerability, resilience, and adaptation from a disaster risk perspective. Nat. Hazards 2014, 70, 609–627. [Google Scholar] [CrossRef]
- Ciais, P.; Sabine, C.; Bala, G.; Bopp, L.; Brovkin, V.; Canadell, J.; Chhabra, A.; DeFries, R.; Galloway, J.; Heimann, M. Carbon and other biogeochemical cycles. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M., Eds.; Cambridge University Press: Cambridge, UK, 2014; pp. 465–570. [Google Scholar]
- Jones, M.W.; Abatzoglou, J.T.; Veraverbeke, S.; Andela, N.; Lasslop, G.; Forkel, M.; Smith, A.J.P.; Burton, C.; Betts, R.A.; van der Werf, G.R.; et al. Global and Regional Trends and Drivers of Fire Under Climate Change. Rev. Geophys. 2022, 60, e2020RG000726. [Google Scholar] [CrossRef]
- Hantson, S.; Kelley, D.I.; Arneth, A.; Harrison, S.P.; Archibald, S.; Bachelet, D.; Forrest, M.; Hickler, T.; Lasslop, G.; Li, F. Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project. Geosci. Model Dev. 2020, 13, 3299–3318. [Google Scholar] [CrossRef]
- Zheng, B.; Ciais, P.; Chevallier, F.; Yang, H.; Canadell, J.G.; Chen, Y.; van der Velde, I.R.; Aben, I.; Chuvieco, E.; Davis, S.J.; et al. Record-high CO2 emissions from boreal fires in 2021. Science 2023, 379, 912–917. [Google Scholar] [CrossRef]
- Abatzoglou, J.T.; Williams, A.P.; Boschetti, L.; Zubkova, M.; Kolden, C.A. Global patterns of interannual climate-fire relationships. Glob. Chang. Biol. 2018, 24, 5164–5175. [Google Scholar] [CrossRef]
- Ellis, T.M.; Bowman, D.M.; Jain, P.; Flannigan, M.D.; Williamson, G.J. Global increase in wildfire risk due to climate-driven declines in fuel moisture. Glob. Chang. Biol. 2022, 28, 1544–1559. [Google Scholar] [CrossRef] [PubMed]
- Flannigan, M.D.; Krawchuk, M.A.; de Groot, W.J.; Wotton, B.M.; Gowman, L.M. Implications of changing climate for global wildland fire. Int. J. Wildland Fire 2009, 18, 483–507. [Google Scholar] [CrossRef]
- Flannigan, M.D.; Wotton, B.M. Lightning-ignited forest fires in northwestern Ontario. Can. J. For. Res. 1991, 21, 277–287. [Google Scholar] [CrossRef]
- Veraverbeke, S.; Rogers, B.M.; Goulden, M.L.; Jandt, R.R.; Miller, C.E.; Wiggins, E.B.; Randerson, J.T. Lightning as a major driver of recent large fire years in North American boreal forests. Nat. Clim. Chang. 2017, 7, 529. [Google Scholar] [CrossRef]
- Nieto, H.; Aguado, I.; Garcia, M.; Chuvieco, E. Lightning-caused fires in Central Spain: Development of a probability model of occurrence for two Spanish regions. Agric. For. Meteorol. 2012, 162–163, 35–43. [Google Scholar] [CrossRef]
- Moris, J.V.; Conedera, M.; Nisi, L.; Bernardi, M.; Cesti, G.; Pezzatti, G.B. Lightning-caused fires in the Alps: Identifying the igniting strokes. Agric. For. Meteorol. 2020, 290, 107990. [Google Scholar] [CrossRef]
- Wotton, B.M.; Martell, D.L. A lightning fire occurrence model for Ontario. Can. J. For. Res. 2005, 35, 1389–1401. [Google Scholar] [CrossRef]
- Schulz, W.; Diendorfer, G.; Pedeboy, S.; Poelman, D.R. The European lightning location system EUCLID–Part 1: Performance analysis and validation. Nat. Hazards Earth Syst. Sci. 2016, 16, 595–605. [Google Scholar] [CrossRef]
- Coughlan, R.; Di Giuseppe, F.; Vitolo, C.; Barnard, C.; Lopez, P.; Drusch, M. Using machine learning to predict fire-ignition occurrences from lightning forecasts. Meteorol. Appl. 2021, 28, e1973. [Google Scholar] [CrossRef]
- Tymstra, C.; Bryce, R.W.; Wotton, B.M.; Taylor, S.W.; Armitage, O.B. Development and Structure of Prometheus: The Canadian Wildland Fire Growth Simulation Model; Information Report NOR-X-417; Northern Forestry Centre: Edmonton, AB, Canada, 2010; p. 102. [Google Scholar]
- Nyman, P.; Metzen, D.; Noske, P.J.; Lane, P.N.J.; Sheridan, G.J. Quantifying the effects of topographic aspect on water content and temperature in fine surface fuel. Int. J. Wildland Fire 2015, 24, 1129–1142. [Google Scholar] [CrossRef]
- Viegas, D.X.; Pita, L.P. Fire spread in canyons. Int. J. Wildland Fire 2004, 13, 253–274. [Google Scholar] [CrossRef]
- Pyne, S.J.; Andrews, P.L.; Laven, R.D. Introdution to Wildland Fire; Jonh Wiley & Sons, Inc.: Hoboken, NJ, USA, 1996. [Google Scholar]
- Riccardi, C.L.; Prichard, S.J.; Sandberg, D.V.; Ottmar, R.D. Quantifying physical characteristics of wildland fuels using the fuel characteristic classification system. Can. J. For. Res. 2007, 37, 2413–2420. [Google Scholar] [CrossRef]
- Barrows, J. Fire Behavior in the Northern Rocky Mountain Forests; 29; USDA, Forest Service, Northern Rocky Mountain Forest and Range Experiment Station: Ogden, UT, USA, 1951.
- Keane, R.E.; Burgan, R.; Van Wagtendonk, J. Mapping wildland fuels for fire management across multiple scales: Integrating remote sensing, GIS, and biophysical modeling. Int. J. Wildland Fire 2001, 10, 301–319. [Google Scholar] [CrossRef]
- Arroyo, L.A.; Pascual, C.; Manzanera, J.A. Fire models and methods to map fuel types: The role of remote sensing. For. Ecol. Manag. 2008, 256, 1239–1252. [Google Scholar] [CrossRef]
- Anderson, H.E. Aids to Determining Fuel Models for Estimating Fire Behavior; USDA, Forest Service, General Technical Report INT-122; USDA: Ogden, UT, USA, 1982.
- Scott, J.; Burgan, R.E. Standard Fire Behavior Fuel Models: A Comprehensive Set for Use with Rothermel’s Surface Fire Spread Model; US Department of Agriculture, Forest Service, Rocky Mountain Research Station: Ogden, UT, USA, 2005. [Google Scholar]
- Fernandes, P.A.M. Fire spread prediction in shrub fuels in Portugal. For. Ecol. Manag. 2001, 144, 67–74. [Google Scholar] [CrossRef]
- Ottmar, R.D.; Sandberg, D.V.; Riccardi, C.L.; Prichard, S.J. An overview of the Fuel Characteristic Classification System–Quantifying, classifying, and creating fuelbeds for resource planning. Can. J. For. Res. 2007, 37, 2383–2393. [Google Scholar] [CrossRef]
- Pettinari, M.; Chuvieco, E. Fire Behavior Simulation from Global Fuel and Climatic Information. Forests 2017, 8, 179. [Google Scholar] [CrossRef]
- Prichard, S.J.; Sandberg, D.V.; Ottmar, R.D.; Eberhardt, E.; Andreu, A.; Eagle, P.; Swedin, K. Fuel Characteristic Classification System Version 3.0: Technical Documentation; United States Department of Agriculture, Forest Service, Pacific Northwest: Missoula, MT, USA, 2013. [Google Scholar]
- Dimitrakopoulos, A. Mediterranean fuel models and potential fire behaviour in Greece. Int. J. Wildland Fire 2002, 11, 127–130. [Google Scholar] [CrossRef]
- Salis, M.; Arca, B.; Alcasena, F.; Arianoutsou, M.; Bacciu, V.; Duce, P.; Duguy, B.; Koutsias, N.; Mallinis, G.; Mitsopoulos, I. Predicting wildfire spread and behaviour in Mediterranean landscapes. Int. J. Wildland Fire 2016, 25, 1015–1032. [Google Scholar] [CrossRef]
- Rodríguez y Silva, F.; Molina-Martínez, J.R. Modeling Mediterranean forest fuels by integrating field data and mapping tools. Eur. J. For. Res. 2012, 131, 571–582. [Google Scholar] [CrossRef]
- Ascoli, D.; Vacchiano, G.; Scarpa, C.; Arca, B.; Barbati, A.; Battipaglia, G.; Elia, M.; Esposito, A.; Garfì, V.; Lovreglio, R.; et al. Harmonized dataset of surface fuels under Alpine, temperate and Mediterranean conditions in Italy. A synthesis supporting fire management. Iforest-Biogeosci. For. 2020, 13, 513–522. [Google Scholar] [CrossRef]
- Cruz, M.G.; Gould, J.S.; Hollis, J.J.; McCaw, W.L. A Hierarchical Classification of Wildland Fire Fuels for Australian Vegetation Types. Fire 2018, 1, 13. [Google Scholar] [CrossRef]
- Simard, A.J. The Moisture Content of Forest Fuels–A Review of the Basic Concepts; FF-X-14; Forest Fire Research Institute: Ottawa, ON, USA, 1968; p. 47. [Google Scholar]
- Yebra, M.; Dennison, P.; Chuvieco, E.; Riaño, D.; Zylstra, P.; Hunt, E.R.; Danson, F.M.; Qi, Y.; Jurdao, S. A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving towards operational products. Remote Sens. Environ. 2013, 136, 455–468. [Google Scholar] [CrossRef]
- Aguado, I.; Chuvieco, E.; Boren, R.; Nieto, H. Estimation of dead fuel moisture content from meteorological data in Mediterranean areas. Applications in fire danger assessment. Int. J. Wildland Fire 2007, 16, 390–397. [Google Scholar] [CrossRef]
- Jolly, W.M.; Johnson, D.M. Pyro-Ecophysiology: Shifting the Paradigm of Live Wildland Fuel Research. Fire 2018, 1, 8. [Google Scholar] [CrossRef]
- Ruffault, J.; Martin-StPaul, N.; Pimont, F.; Dupuy, J.-L. How well do meteorological drought indices predict live fuel moisture content (LFMC)? An assessment for wildfire research and operations in Mediterranean ecosystems. Agric. For. Meteorol. 2018, 262, 391–401. [Google Scholar] [CrossRef]
- Viegas, D.X.; Viegas, T.P.; Ferreira, A.D. Moisture content of fine forest fuels and fire occurrence in central Portugal. Int. J. Wildland Fire 1992, 2, 69–85. [Google Scholar] [CrossRef]
- Viegas, D.X.; Viegas, T.P.; Ferreira, A.D. Characteristics of Some Forest Fuels and Their Relation to the Occurrence of Fires. In Proceedings of the International Conference of Forest Fire Research, Coimbra, Portugal, 19–22 November 1990; pp. 1–13. [Google Scholar]
- Chuvieco, E.; Aguado, I.; Dimitrakopoulos, A. Conversion of fuel moisture content values to ignition potential for integrated fire danger assessment. Can. J. For. Res. 2004, 34, 2284–2293. [Google Scholar] [CrossRef]
- Dimitrakopoulos, A.; Papaioannou, K.K. Flammability assessment of Mediterranean forest fuels. Fire Technol. 2001, 37, 143–152. [Google Scholar] [CrossRef]
- Rossa, C.G.; Veloso, R.; Fernandes, P.M. A laboratory-based quantification of the effect of live fuel moisture content on fire spread rate. Int. J. Wildland Fire 2016, 25, 569–573. [Google Scholar] [CrossRef]
- Alexander, M.E.; Cruz, M.G. Are the applications of wildland fire behaviour models getting ahead of their evaluation again? Environ. Model. Softw. 2013, 41, 65–71. [Google Scholar] [CrossRef]
- Rossa, C.G. The effect of fuel moisture content on the spread rate of forest fires in the absence of wind or slope. Int. J. Wildland Fire 2017, 26, 24–31. [Google Scholar] [CrossRef]
- Jurdao, S.; Chuvieco, E.; Arevalillo, J.M. Modelling fire ignition probability from satellite estimates of live fuel moisture content. Fire Ecol. 2012, 8, 77–97. [Google Scholar] [CrossRef]
- Pimont, F.; Ruffault, J.; Martin-StPaul, N.K.; Dupuy, J.-L. Why is the effect of live fuel moisture content on fire rate of spread underestimated in field experiments in shrublands? Int. J. Wildland Fire 2019, 28, 127–137. [Google Scholar] [CrossRef]
- Yebra, M.; Scortechini, G.; Badi, A.; Beget, M.E.; Boer, M.M.; Bradstock, R.; Chuvieco, E.; Danson, F.M.; Dennison, P.; Resco de Dios, V.; et al. Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications. Sci. Data 2019, 6, 155. [Google Scholar] [CrossRef]
- Chuvieco, E.; González, I.; Verdú, F.; Aguado, I.; Yebra, M. Prediction of fire occurrence from live fuel moisture content measurements in a Mediterranean ecosystem. Int. J. Wildland Fire 2009, 18, 430–441. [Google Scholar] [CrossRef]
- Dennison, P.E.; Roberts, D.A.; Thorgusen, S.R.; Regelbrugge, J.C.; Weise, D.; Lee, C. Modeling seasonal changes in live fuel moisture and equivalent water thickness using a cumulative water balance index. Remote Sens. Environ. 2003, 88, 442–452. [Google Scholar] [CrossRef]
- Jurdao, S.; Yebra, M.; Guerschman, J.P.; Chuvieco, E. Regional estimation of woodland moisture content by inverting Radiative Transfer Models. Remote Sens. Environ. 2013, 132, 59–70. [Google Scholar] [CrossRef]
- Stow, D.; Niphadkar, M.; Kaiser, J. MODIS-derived visible atmospherically resistant index for monitoring chaparral moisture content. Int. J. Remote Sens. 2005, 26, 3867–3873. [Google Scholar] [CrossRef]
- Luo, K.; Quan, X.; He, B.; Yebra, M. Effects of Live Fuel Moisture Content on Wildfire Occurrence in Fire-Prone Regions over Southwest China. Forests 2019, 10, 887. [Google Scholar] [CrossRef]
- Fox-Hughes, P.; Yebra, M.; Kumar, V.; Dowdy, A.; Hope, P.; Peace, M.; Narsey, S.; Shokirov, S.; Delage, F.; Zhang, H. Soil and Fuel Moisture Precursors of Fire Activity during the 2019–20 Fire Season; Bushfire and Natural Hazards CRC: Melbourne, Australia, 2021. [Google Scholar]
- Yebra, M.; Chuvieco, E. Linking ecological information and radiative transfer models to estimate fuel moisture content in the Mediterranean region of Spain: Solving the ill-posed inverse problem. Remote Sens. Environ. 2009, 113, 2403–2411. [Google Scholar] [CrossRef]
- Yebra, M.; Quan, X.; Riaño, D.; Larraondo, P.R.; van Dijk, A.I.; Cary, G.J. A fuel moisture content and flammability monitoring methodology for continental Australia based on optical remote sensing. Remote Sens. Environ. 2018, 212, 260–272. [Google Scholar] [CrossRef]
- Cunill Camprubí, À.; González-Moreno, P.; Resco de Dios, V. Live fuel moisture content mapping in the Mediterranean Basin using random forests and combining MODIS spectral and thermal data. Remote Sens. 2022, 14, 3162. [Google Scholar] [CrossRef]
- Garcia, M.; Chuvieco, E.; Nieto, H.; Aguado, I. Combining AVHRR and meteorological data for estimating live fuel moisture content in forest fire danger rating. Remote Sens. Environ. 2008, 112, 3618–3627. [Google Scholar] [CrossRef]
- Chuvieco, E.; Riaño, D.; Aguado, I.; Cocero, D. Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: Applications in fire danger assessment. Int. J. Remote Sens. 2002, 23, 2145–2162. [Google Scholar] [CrossRef]
- Chuvieco, E.; Aguado, I.; Salas, J.; García, M.; Yebra, M.; Oliva, P. Satellite Remote Sensing Contributions to Wildland Fire Science and Management. Curr. For. Rep. 2020, 6, 81–96. [Google Scholar] [CrossRef]
- Marino, E.; Yebra, M.; Guillén-Climent, M.; Algeet, N.; Tomé, J.L.; Madrigal, J.; Guijarro, M.; Hernando, C. Investigating live fuel moisture content estimation in fire-prone shrubland from remote sensing using empirical modelling and RTM simulations. Remote Sens. 2020, 12, 2251. [Google Scholar] [CrossRef]
- Yebra, M.; Chuvieco, E.; Riaño, D. Estimation of live Fuel Moisture Content from MODIS images for fire risk assessment. Agric. For. Meteorol. 2008, 148, 523–536. [Google Scholar] [CrossRef]
- Chuvieco, E.; Cocero, D.; Riaño, D.; Martín, M.P.; Martínez-Vega, J.; de la Riva, J.; Pérez, F. Combining NDVI and Surface Temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sens. Environ. 2004, 92, 322–331. [Google Scholar] [CrossRef]
- Forkel, M.; Schmidt, L.; Zotta, R.-M.; Dorigo, W.; Yebra, M. Estimating leaf moisture content at global scale from passive microwave satellite observations of vegetation optical depth. Hydrol. Earth Syst. Sci. 2023, 27, 39–68. [Google Scholar] [CrossRef]
- Rao, K.; Williams, A.P.; Flefil, J.F.; Konings, A.G. SAR-enhanced mapping of live fuel moisture content. Remote Sens. Environ. 2020, 245, 111797. [Google Scholar] [CrossRef]
- Moghaddam, M.; Saatchi, S.S. Monitoring tree moisture using an estimation algorithm applied to SAR data from BOREAS. IEEE Trans. Geosci. Remote. Sens. 1999, 37, 901–916. [Google Scholar] [CrossRef]
- Tanase, M.A.; Nova, J.P.G.; Marino, E.; Aponte, C.; Tomé, J.L.; Yáñez, L.; Madrigal, J.; Guijarro, M.; Hernando, C. Characterizing Live Fuel Moisture Content from Active and Passive Sensors in a Mediterranean Environment. Forests 2022, 13, 1846. [Google Scholar] [CrossRef]
- Younes, N.; Yebra, M.; Sharp, R.; Lee, J.-U.; Hughes, D.; Gilbert, J.; Grigoriev, A.; Mathew, J.; Griffin, D.; Barraclough, S. OzFuel Pre-Phase A Study. Space-Based Australian Forest Fuel Flammability Monitoring; The University of New South Wales: Canberra, Australia, 2022. [Google Scholar]
- Bilbao, B.; Mistry, J.; Millán, A.; Berardi, A. Sharing Multiple Perspectives on Burning: Towards a Participatory and Intercultural Fire Management Policy in Venezuela, Brazil, and Guyana. Fire 2019, 2, 39. [Google Scholar] [CrossRef]
- Rodríguez, I.; Sletto, B.; Bilbao, B.; Sánchez-Rose, I.; Leal, A. Speaking of fire: Reflexive governance in landscapes of social change and shifting local identities. J. Environ. Policy Plan. 2018, 20, 689–703. [Google Scholar] [CrossRef]
- Cochrane, M.A.; Bowman, D.M.J.S. Manage fire regimes, not fires. Nat. Geosci. 2021, 14, 455–457. [Google Scholar] [CrossRef]
- FAO. Fire Management–Global Assessment 2006. A Thematic Study Prepared in the Framework of the Global Forest Resources Assessment 2005; FAO Forestry Paper 151; FAO: Rome, Italy, 2007. [Google Scholar]
- Brotons, L.; Aquilué, N.; De Cáceres, M.; Fortin, M.-J.; Fall, A. How fire history, fire suppression practices and climate change affect wildfire regimes in Mediterranean landscapes. PLoS ONE 2013, 8, e62392. [Google Scholar] [CrossRef] [PubMed]
- Coughlan, M.R.; Ellison, A.; Cavanaugh, A.H. Social Vulnerability and Wildfire in the Wildland-Urban Interface: Literature Synthesis; Northwest Fire Science Consortium, University of Oregon: Eugene, OR, USA, 2019. [Google Scholar]
- Scott, J.H.; Thompson, M.P.; Gilbertson-Day, J.W. Exploring how alternative mapping approaches influence fireshed assessment and human community exposure to wildfire. GeoJournal 2017, 82, 201–215. [Google Scholar] [CrossRef]
- Costafreda-Aumedes, S.; Comas, C.; Vega-Garcia, C. Human-caused fire occurrence modelling in perspective: A review. Int. J. Wildland Fire 2017, 26, 983–998. [Google Scholar] [CrossRef]
- Hesseln, H. Wildland Fire Prevention: A Review. Curr. For. Rep. 2018, 4, 178–190. [Google Scholar] [CrossRef]
- Martínez, J.; Vega-García, C.; Chuvieco, E. Human-caused wildfire risk rating for prevention planning in Spain. J. Environ. Manag. 2009, 90, 1241–1252. [Google Scholar] [CrossRef]
- Parisien, M.-A.; Miller, C.; Parks, S.A.; DeLancey, E.R.; Robinne, F.-N.; Flannigan, M.D. The spatially varying influence of humans on fire probability in North America. Environ. Res. Lett. 2016, 11, 075005. [Google Scholar] [CrossRef]
- Chuvieco, E.; Pettinari, M.L.; Koutsias, N.; Forkel, M.; Hantson, S.; Turco, M. Human and climate drivers of global biomass burning variability. Sci. Total Environ. 2021, 779, 146361. [Google Scholar] [CrossRef]
- Knorr, W.; Arneth, A.; Jiang, L. Demographic controls of future global fire risk. Nat. Clim. Chang. 2016, 6, 781–785. [Google Scholar] [CrossRef]
- Hantson, S.; Lasslop, G.; Kloster, S.; Chuvieco, E. Anthropogenic effects on global mean fire size. Int. J. Wildland Fire 2015, 24, 589–596. [Google Scholar] [CrossRef]
- Rodrigues, M.; de la Riva, J.; Fotheringham, S. Modeling the spatial variation of the explanatory factors of human-caused wildfires in Spain using geographically weighted logistic regression. Appl. Geogr. 2014, 48, 52–63. [Google Scholar] [CrossRef]
- Canadas, M.J.; Leal, M.; Soares, F.; Novais, A.; Ribeiro, P.F.; Schmidt, L.; Delicado, A.; Moreira, F.; Bergonse, R.; Oliveira, S.; et al. Wildfire mitigation and adaptation: Two locally independent actions supported by different policy domains. Land Use Policy 2023, 124, 106444. [Google Scholar] [CrossRef]
- Fuchs, R.; Alexander, P.; Brown, C.; Cossar, F.; Henry, R.C.; Rounsevell, M. Why the US–China trade war spells disaster for the Amazon. Nature 2019, 567, 451–454. [Google Scholar] [CrossRef]
- Górriz-Mifsud, E.; Burns, M.; Marini Govigli, V. Civil society engaged in wildfires: Mediterranean forest fire volunteer groupings. For. Policy Econ. 2019, 102, 119–129. [Google Scholar] [CrossRef]
- Andersen, L.M.; Sugg, M.M. Geographic multi-criteria evaluation and validation: A case study of wildfire vulnerability in Western North Carolina, USA following the 2016 wildfires. Int. J. Disaster Risk Reduct. 2019, 39, 101123. [Google Scholar] [CrossRef]
- Palaiologou, P.; Ager, A.A.; Nielsen-Pincus, M.; Evers, C.R.; Day, M.A. Social vulnerability to large wildfires in the western USA. Landsc. Urban Plan. 2019, 189, 99–116. [Google Scholar] [CrossRef]
- Vallejo-Villalta, I.; Rodríguez-Navas, E.; Márquez-Pérez, J. Mapping Forest Fire Risk at a Local Scale—A Case Study in Andalusia (Spain). Environments 2019, 6, 30. [Google Scholar] [CrossRef]
- Andela, N.; Morton, D.C.; Giglio, L.; Chen, Y.; van der Werf, G.R.; Kasibhatla, P.S.; DeFries, R.S.; Collatz, G.J.; Hantson, S.; Kloster, S.; et al. A human-driven decline in global burned area. Science 2017, 356, 1356–1362. [Google Scholar] [CrossRef] [PubMed]
- Forkel, M.; Andela, N.; Harrison, S.P.; Lasslop, G.; van Marle, M.; Chuvieco, E.; Dorigo, W.; Forrest, M.; Hantson, S.; Heil, A.; et al. Emergent relationships on burned area in global satellite observations and fire-enabled vegetation models. Biogeosciences 2019, 16, 47–76. [Google Scholar] [CrossRef]
- Bateman, I.; Mace, G. The natural capital framework for sustainable, efficient and equitable decision making. Nat. Sustain. 2020, 3, 776–783. [Google Scholar] [CrossRef]
- Costanza, R.; de Groot, R.; Sutton, P.; van der Ploeg, S.; Anderson, S.J.; Kubiszewski, I.; Farber, S.; Turner, R.K. Changes in the global value of ecosystem services. Glob. Environ. Chang. 2014, 26, 152–158. [Google Scholar] [CrossRef]
- La Notte, A.; Vallecillo, S.; Garcia Bendito, E.; Grammatikopoulou, I.; Czucz, B.; Ferrini, S.; Grizzetti, B.; Rega, C.; Herrando, S.; Villero, D. Ecosystem Services Accounting: Part III-Pilot Accounts for Habitat and Species Maintenance, On-Site Soil Retention and Water Purification; Publication Office of the European Commission: Luxembourg, 2021. [Google Scholar]
- Molina-Martínez, J.R.; Zamora, R.; Rodríguez y Silva, F. The role of flagship species in the economic valuation of wildfire impacts: An application to two Mediterranean protected areas. Sci. Total Environ. 2019, 675, 520–530. [Google Scholar] [CrossRef]
- Molina-Martínez, J.R.; Herrera, M.A.; Rodríguez y Silva, F. Wildfire-induced reduction in the carbon storage of Mediterranean ecosystems: An application to brush and forest fires impacts assessment. Environ. Impact Assess. Rev. 2019, 76, 88–97. [Google Scholar] [CrossRef]
- Faith, D.P. Valuation and appreciation of biodiversity: The “maintenance of options” provided by the variety of life. Front. Ecol. Evol. 2021, 9, 635670. [Google Scholar] [CrossRef]
- Sil, Ã.n.; Fernandes, P.M.; Rodrigues, A.P.; Alonso, J.M.; Honrado, J.o.P.; Perera, A.; Azevedo, J.o.C. Farmland abandonment decreases the fire regulation capacity and the fire protection ecosystem service in mountain landscapes. Ecosyst. Serv. 2019, 36, 1. [Google Scholar] [CrossRef]
- Martino, S.; Roberts, M.; Stevenson, T.; Ovando, P.; Mouillot, F.; Pernice, U.; Ortega, M.; Velea, R.; Laterza, R.; Moreira, B. Developing an Integrated Capitals Approach to Understanding Wildfire Vulnerability: Preliminary Considerations from a Literature Review. In Proceedings of the IX International Conference on Forest Fire Research, Coimbra, Portugal, 11–18 November 2022; Viegas, D.X., Ribeiro, L.M., Eds.; 2022; pp. 1073–1083. [Google Scholar]
- Pearce, D.W.; Turner, R.K. Economics of Natural Resources and the Environment; Johns Hopkins University Press: Baltimore, MD, USA, 1990; p. 378. [Google Scholar]
- Turner, R.; Badura, T.; Ferrini, S. Valuation, Natural Capital Accounting and Decision-Support Systems: Process, Tools and Methods; University of East Anglia Centre for Social and Economic Research on the Global Environment: Norwich, UK, 2019. [Google Scholar]
- Potschin, M.B.; Haines-Young, R.H. Ecosystem services: Exploring a geographical perspective. Prog. Phys. Geogr. Earth Environ. 2011, 35, 575–594. [Google Scholar] [CrossRef]
- Molina-Martínez, J.R.; González-Cabán, A.; Rodríguez y Silva, F. Wildfires impact on the economic susceptibility of recreation activities: Application in a Mediterranean protected area. J. Environ. Manag. 2019, 245, 454–463. [Google Scholar] [CrossRef] [PubMed]
- Valatin, G. Carbon Valuation in Forestry and Prospects for European Harmonisation. EFI Tech. Rep. 2014, 97. [Google Scholar]
- Boxall, P.C.; Englin, J.E. Fire and Recreational Values in Fire-Prone Forests: Exploring an Intertemporal Amenity Function Using Pooled RP-SP Data. J. Agric. Resour. Econ. 2008, 33, 1–15. [Google Scholar]
- Abiodun, A.A. The Economic Implications of Remote Sensing from Space for the Developing Countries; ESA SP-134; Earth Observation from Space and Management of Planetary Resources: Paris, France, 1978; pp. 575–584. [Google Scholar]
- Loomis, J.; Gonzalez-Caban, A.; Englin, J. Testing for differential effects of forest fires on hiking and mountain biking demand and benefits. J. Agric. Resour. Econ. 2001, 508–522. [Google Scholar]
- Sánchez, J.J.; Baerenklau, K.; González-Cabán, A. Valuing hypothetical wildfire impacts with a Kuhn–Tucker model of recreation demand. For. Policy Econ. 2016, 71, 63–70. [Google Scholar] [CrossRef]
- Molina-Martínez, J.R.; Moreno, R.; Castillo, M.; Rodríguez y Silva, F. Economic susceptibility of fire-prone landscapes in natural protected areas of the southern Andean Range. Sci. Total Environ. 2018, 619–620, 1557–1565. [Google Scholar] [CrossRef] [PubMed]
- Molina-Martínez, J.R.; Rodríguez-Silva, F.; Machuca, M. Economic vulnerability of fire-prone landscapes in protected natural areas: Application in a Mediterranean Natural Park. Eur. J. For. Res. 2017, 136, 609–624. [Google Scholar] [CrossRef]
- Varela, E.; Jacobsen, J.B.; Mavsar, R. Social demand for multiple benefits provided by Aleppo pine forest management in Catalonia, Spain. Reg. Environ. Chang. 2017, 17, 539–550. [Google Scholar] [CrossRef]
- Wu, T.; Kim, Y.-S. Pricing ecosystem resilience in frequent-fire ponderosa pine forests. For. Policy Econ. 2013, 27, 8–12. [Google Scholar] [CrossRef]
- Roman, M.V.; Azqueta, D.; Rodrígues, M. Methodological approach to assess the socio-economic vulnerability to wildfires in Spain. For. Ecol. Manag. 2012, 294, 158–165. [Google Scholar] [CrossRef]
- Treasury, H. The Green Book: Central Government Guidance on Appraisal and Evaluation; OGL Press: London, UK, 2018. Available online: www.gov.uk/government/publications (accessed on 30 March 2023).
- Ackerman, F.; Heinzerling, L. Pricing the priceless: Cost-benefit analysis of environmental protection. Univ. Pa. Law Rev. 2002, 150, 1553–1584. [Google Scholar] [CrossRef]
- Baveye, P.C.; Baveye, J.; Gowdy, J. Monetary valuation of ecosystem services: It matters to get the timeline right. Ecol. Econ. 2013, 95, 231–235. [Google Scholar] [CrossRef]
- Victor, P.A. Cents and nonsense: A critical appraisal of the monetary valuation of nature. Ecosyst. Serv. 2020, 42, 101076. [Google Scholar] [CrossRef]
- Masood, E. More than dollars: Mega-review finds 50 ways to value nature. Nature 2022. [Google Scholar] [CrossRef] [PubMed]
- IPBES. Methodological Assessment Report on the Diverse Values and Valuation of Nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services; IPBES Secretariat: Bonn, Germany, 2022.
- Cordell, H.K.; Murphy, D.; Riitters, K.H.; Harvard, J. The natural ecological value of wilderness. In The Natural Ecological Value of Wilderness; Cordell, H.K., Bergstrom, J.C., Bowker, J.M., Eds.; Venture Publishing Inc.: Calgary, AB, Canada, 2005; pp. 205–249. [Google Scholar]
- Ratcliffe, D. A Nature Conservation Review: Volume 1: The Selection of Biological Sites of National Importance to Nature Conservation in Britain; Cambridge University Press: Cambridge, UK, 1997; Volume 1. [Google Scholar]
- Tilman, D.; Isbell, F.; Cowles, J.M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 2014, 45, 471–493. [Google Scholar] [CrossRef]
- Tribot, A.-S.; Deter, J.; Mouquet, N. Integrating the aesthetic value of landscapes and biological diversity. Proc. R. Soc. B Biol. Sci. 2018, 285, 20180971. [Google Scholar] [CrossRef]
- Fry, G.; Tveit, M.S.; Ode, Å.; Velarde, M. The ecology of visual landscapes: Exploring the conceptual common ground of visual and ecological landscape indicators. Ecol. Indic. 2009, 9, 933–947. [Google Scholar] [CrossRef]
- Watson, J.E.; Evans, T.; Venter, O.; Williams, B.; Tulloch, A.; Stewart, C.; Thompson, I.; Ray, J.C.; Murray, K.; Salazar, A. The exceptional value of intact forest ecosystems. Nat. Ecol. Evol. 2018, 2, 599–610. [Google Scholar] [CrossRef] [PubMed]
- Kangas, K.M.; Tolvanen, A.; Tarvainen, O.; Nikula, A.; Nivala, V.; Huhta, E.; Jäkäläniemi, A. A method for assessing ecological values to reconcile multiple land use needs. Ecol. Soc. 2016, 21. [Google Scholar] [CrossRef]
- Cutter, S.L.; Ash, K.D.; Emrich, C.T. The geographies of community disaster resilience. Glob. Environ. Chang. 2014, 29, 65–77. [Google Scholar] [CrossRef]
- Scherzer, S.; Lujala, P.; Rød, J.K. A community resilience index for Norway: An adaptation of the Baseline Resilience Indicators for Communities (BRIC). Int. J. Disaster Risk Reduct. 2019, 36, 101–107. [Google Scholar] [CrossRef]
- Cutter, S.L. Resilience to What? Resilience for Whom? Geogr. J. 2016, 182, 110–113. [Google Scholar] [CrossRef]
- Fekete, A. Social Vulnerability (Re-)Assessment in Context to Natural Hazards: Review of the Usefulness of the Spatial Indicator Approach and Investigations of Validation Demands. Int. J. Disaster Risk Sci. 2019, 10, 220–232. [Google Scholar] [CrossRef]
- Cutter, S.L. Linkages between Vulnerability and Resilience. In Vulnerability and Resilience to Natural Hazards; Cambridge University Press: Cambridge, UK, 2018; pp. 257–270. [Google Scholar]
- Cutter, S.L.; Boruff, B.J.; Shirley, W.L. Social Vulnerability to Environmental Hazard. Soc. Sci. Q. 2003, 84, 242–261. [Google Scholar] [CrossRef]
- Guillard-Gonçalves, C.; Cutter, S.L.; Emrich, C.T.; Zêzere, J.L. Application of Social Vulnerability Index (SoVI) and delineation of natural risk zones in Greater Lisbon, Portugal. J. Risk Res. 2015, 18, 651–674. [Google Scholar] [CrossRef]
- Archibald, S.; Hempson, G.P.; Lehmann, C. A unified framework for plant life-history strategies shaped by fire and herbivory. New Phytol. 2019, 224, 1490–1503. [Google Scholar] [CrossRef]
- Amani, B.H.; N’Guessan, A.E.; Van der Meersch, V.; Derroire, G.; Piponiot, C.; Elogne, A.G.; Traoré, K.; N’Dja, J.K.; Hérault, B. Lessons from a regional analysis of forest recovery trajectories in West Africa. Environ. Res. Lett. 2022, 17, 115005. [Google Scholar] [CrossRef]
- Moghli, A.; Santana, V.M.; Baeza, M.J.; Pastor, E.; Soliveres, S. Fire recurrence and time since last fire interact to determine the supply of multiple ecosystem services by Mediterranean forests. Ecosystems 2021, 25, 1358–1370. [Google Scholar] [CrossRef]
- Hood, S.M.; Varner, J.M.; Jain, T.B.; Kane, J.M. A framework for quantifying forest wildfire hazard and fuel treatment effectiveness from stands to landscapes. Fire Ecol. 2022, 18, 1–12. [Google Scholar]
- Noble, I.R.; Slatyer, R. The use of vital attributes to predict successional changes in plant communities subject to recurrent disturbances. Vegetatio 1980, 43, 5–21. [Google Scholar] [CrossRef]
- Ibanez, T.; Platt, W.J.; Bellingham, P.J.; Vieilledent, G.; Franklin, J.; Martin, P.H.; Menkes, C.; Pérez-Salicrup, D.R.; Russell-Smith, J.; Keppel, G. Altered cyclone–fire interactions are changing ecosystems. Trends Plant Sci. 2022, 27, 1218–1230. [Google Scholar] [CrossRef] [PubMed]
- Nabuurs, G.-J.; Verweij, P.; Van Eupen, M.; Pérez-Soba, M.; Pülzl, H.; Hendriks, K. Next-generation information to support a sustainable course for European forests. Nat. Sustain. 2019, 2, 815–818. [Google Scholar] [CrossRef]
- Hong, H.; Tsangaratos, P.; Ilia, I.; Liu, J.; Zhu, A.X.; Xu, C. Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models. The case of Dayu County, China. Sci. Total Environ. 2018, 630, 1044–1056. [Google Scholar] [CrossRef] [PubMed]
- Yool, S.R.; Eckhardt, D.W.; Estes, J.E.; Cosentino, M.J. Describing the brushfire hazard in southern California. Ann. Assoc. Am. Geogr. 1985, 75, 417–430. [Google Scholar] [CrossRef]
- Bistinas, I.; Harrison, S.; Prentice, I.C.; Pereira, J. Causal relationships versus emergent patterns in the global controls of fire frequency. Biogeosciences 2014, 11, 5087–5101. [Google Scholar] [CrossRef]
- Syphard, A.D.; Radeloff, V.C.; Keeley, J.E.; Hawbaker, T.J.; Clayton, M.K.; Stewart, S.I.; Hammer, R.B. Human Influence on California Fire Regimes. Ecol. Appl. 2007, 17, 1388–1402. [Google Scholar] [CrossRef] [PubMed]
- Wendling, Z.A.; Emerson, J.W.; de Sherbinin, A.; Etsy, D.C. Environmental Performance Index 2020; Yale Center for Environmental Law and Policy: New Haven, CT, USA, 2020; p. 205. [Google Scholar]
- Alcasena, F.J.; Ager, A.A.; Bailey, J.D.; Pineda, N.; Vega-Garcia, C. Towards a comprehensive wildfire management strategy for Mediterranean areas: Framework development and implementation in Catalonia, Spain. J. Environ. Manag. 2019, 231, 303–320. [Google Scholar] [CrossRef]
- Chuvieco, E.; Martinez, S.; Roman, M.V.; Hantson, S.; Pettinari, L. Integration of ecological and socio-economic factors to assess global wildfire vulnerability Glob. Ecol. Biogeogr. 2014, 23, 245–258. [Google Scholar] [CrossRef]
- Gómez-Delgado, M.; Tarantola, S. GLOBAL sensitivity analysis, GIS and multicriteria evaluation for a sustainable planning of a hazardous waste disposal site in Spain. Int. J. Geogr. Inf. Sci. 2006, 20, 449–466. [Google Scholar] [CrossRef]
- Alcázar, J.; Vega-García, C.; Grauet, M.; Pemán, J.; Fernández, A. Human risk and fire danger estimation through multicriteria evaluation methods for forest fire prevention in Barcelona, Spain. In III International Conference on Forest Fire Research–14th Conference on Fire and Forest Meteorology, Coimbra; Viegas, D.X., Ed.; ADAI: Coimbra, Portugal, 1998; pp. 2379–2387. [Google Scholar]
- Bachmann, A.; Allgöwer, B. Framework for Wildfire Risk Analysis. In III International Conference on Forest Fire Research–14th Conference on Fire and Forest Meteorology, Coimbra; Viegas, D.X., Ed.; ADAI: Coimbra, Portugal, 1998; pp. 2177–2190. [Google Scholar]
- Parente, J.; Pereira, M.G. Structural fire risk: The case of Portugal. Sci. Total Environ. 2016, 573, 883–893. [Google Scholar] [CrossRef] [PubMed]
- Tarantola, A. Inverse Problem Theory and Methods for Model Parameter Estimation; Society for Industrial and Applied Mathematics: Philadelphia, PA, USA, 2005; p. 342. [Google Scholar]
- Oliveira, S.; Oehler, F.; San-Miguel-Ayanz, J.; Camia, A.; Pereira, J.M. Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest. For. Ecol Manag. 2012, 275, 117–129. [Google Scholar] [CrossRef]
- Vasconcelos, M.J.P.; Silva, S.; Tomé, M.; Alvim, M.; Pereira, J.M.C. Spatial prediction of fire ignition probabilities: Comparing logistic regression and neural networks. PERS 2001, 67, 73–83. [Google Scholar]
- Koutsias, N.; Martínez-Fernández, J.; Allgöwer, B. Do Factors Causing Wildfires Vary in Space? Evidence from Geographically Weighted Regression. GISci. Remote Sens. 2010, 47, 221–240. [Google Scholar] [CrossRef]
- Fernandez-Anez, N.; Krasovskiy, A.; Müller, M.; Vacik, H.; Baetens, J.; Hukić, E.; Kapovic Solomun, M.; Atanassova, I.; Glushkova, M.; Bogunović, I.; et al. Current Wildland Fire Patterns and Challenges in Europe: A Synthesis of National Perspectives. Air Soil Water Res. 2021, 14, 11786221211028185. [Google Scholar] [CrossRef]
- Leuenberger, M.; Parente, J.; Tonini, M.; Pereira, M.G.; Kanevski, M. Wildfire susceptibility mapping: Deterministic vs. stochastic approaches. Environ. Model. Softw. 2018, 101, 194–203. [Google Scholar] [CrossRef]
- Vega-García, C.; Lee, B.; Wooddard, T. Applying neural network technology to human-caused wildfire occurence prediction. AI Appl. 1996, 10, 9–18. [Google Scholar]
- Yang, X.; Jin, X.; Zhou, Y. Wildfire risk assessment and zoning by integrating Maxent and GIS in Hunan province, China. Forests 2021, 12, 1299. [Google Scholar] [CrossRef]
- Ben-Tal, A. Characterization of Pareto and Lexicographic Optimal Solutions; Fandel, G., Gal, T., Eds.; Springer: Berlin/Heidelberg, Germany, 1980; pp. 1–11. [Google Scholar]
- Oom, D.; DeRigo, D.; Pfeiffer, H.; Branco, A.; Ferrari, D.; Grecchi, R.; Artes, T.; Durrant, T.; Boca, R.; Maianti, P.; et al. Pan-European Wildfire Risk Assessment; Publications Office of the European Union: Luxembourg, 2022. [Google Scholar]
- Moreno, V.; Chuvieco, E. Characterising fire regimes in Spain from fire statistics. Int. J. Wildland Fire 2013, 22, 296–305. [Google Scholar] [CrossRef]
- Chen, D.M.; Pereira, J.M.C.; Masiero, A.; Pirotti, F. Mapping fire regimes in China using MODIS active fire and burned area data. Appl. Geogr. 2017, 85, 14–26. [Google Scholar] [CrossRef]
- Hawbaker, T.J.; Vanderhoof, M.K.; Schmidt, G.L.; Beal, Y.-J.; Picotte, J.J.; Takacs, J.D.; Falgout, J.T.; Dwyer, J.L. The Landsat Burned Area algorithm and products for the conterminous United States. Remote Sens. Environ. 2020, 244, 111801. [Google Scholar] [CrossRef]
- Andela, N.; Morton, D.C.; Giglio, L.; Paugam, R.; Chen, Y.; Hantson, S.; van der Werf, G.R.; Randerson, J.T. The Global Fire Atlas of individual fire size, duration, speed, and direction. Earth Syst. Sci. Data 2019, 11, 529–552. [Google Scholar] [CrossRef]
- Artés, T.; Oom, D.; De Rigo, D.; Durrant, T.H.; Maianti, P.; Libertà, G.; San-Miguel-Ayanz, J. A global wildfire dataset for the analysis of fire regimes and fire behaviour. Sci. Data 2019, 6, 1–11. [Google Scholar] [CrossRef] [PubMed]
- Stehman, S.V.; Foody, G.M. Key issues in rigorous accuracy assessment of land cover products. Remote Sens. Environ. 2019, 231, 111199. [Google Scholar] [CrossRef]
- Shah, S.U.; Yebra, M.; Van Dijk, A.I.J.M.; Cary, G.J. Relating McArthur fire danger indices to remote sensing derived burned area across Australia. Int. J. Wildland Fire 2023, 32, 133–148. [Google Scholar] [CrossRef]
- Rohrmann, B. The role of risk communication processes in disaster preparedness. In Sharing the Future: Risk Communicaiton in Practice; Gough, J., Ed.; Centre for Advanced Engineering: Christchurch, New Zealand, 2003; pp. 85–104. [Google Scholar]
- Arvai, J.L. Rethinking of risk communication: Lessons from the decision sciences. Tree Genet. Genomes 2007, 3, 173–185. [Google Scholar] [CrossRef]
- Walker, H.M.; Reed, M.G.; Fletcher, A.J. Wildfire in the news media: An intersectional critical frame analysis. Geoforum 2020, 114, 128–137. [Google Scholar] [CrossRef]
- Psaroudakis, C.; Xanthopoulos, G.; Stavrakoudis, D.; Barnias, A.; Varela, V.; Gkotsis, I.; Karvouniari, A.; Agorgianitis, S.; Chasiotis, I.; Vlachogiannis, D.; et al. Development of an Early Warning and Incident Response System for the Protection of Visitors from Natural Hazards in Important Outdoor Sites in Greece. Sustainability 2021, 13, 5143. [Google Scholar] [CrossRef]
- Jones, G.; Hessburg, P.F.; Keane II, R.E.; Spies, T.; North, M.P.; Collins, B.M.; Finney, M.A.; Lydersen, J. Counteracting wildfire misinformation. Front. Ecol. Environ. 2022, 20, 392–393. [Google Scholar] [CrossRef]
- Aliperti, G.; Cruz, A.M. Risk communication to tourists: Towards the definition of a research agenda for a more effective disaster preparedness in Japan. Almatourism-J. Tour. Cult. Territ. Dev. 2018, 9, 1–12. [Google Scholar]
- Thompson, M.P.; Ager, A.A.; Finney, M.A.; Calkin, D.E.; Vaillant, N.M. The science and opportunity of wildfire risk assessment. In Novel Approaches and Their Applications in Risk Assessment; Luo, Y., Ed.; InTech: London, UK, 2012; pp. 99–120. [Google Scholar]
- Fairbrother, A.; Turnley, J.G. Predicting risks of uncharacteristic wildfires: Application of the risk assessment process. For. Ecol. Manag 2005, 211, 28–35. [Google Scholar] [CrossRef]
- Finney, M.A. The challenge of quantitative risk analysis for wildland fire. For. Ecol Manag. 2005, 211, 97–108. [Google Scholar] [CrossRef]
- Thompson, M.P.; Calkin, D.E. Uncertainty and risk in wildland fire management: A review. J. Environ. Manag. 2011, 92, 1895–1909. [Google Scholar] [CrossRef] [PubMed]
- Abatzoglou, J.T.; Williams, A.P.; Barbero, R. Global emergence of anthropogenic climate change in fire weather indices. Geophys. Res. Lett. 2019, 46, 326–336. [Google Scholar] [CrossRef]
- Turco, M.; Jerez, S.; Augusto, S.; Tarín-Carrasco, P.; Ratola, N.; Jiménez-Guerrero, P.; Trigo, R.M. Climate drivers of the 2017 devastating fires in Portugal. Sci. Rep. 2019, 9, 13886. [Google Scholar] [CrossRef] [PubMed]
- Fernandes, P.M.; Davies, G.M.; Ascoli, D.; Fernández, C.; Moreira, F.; Rigolot, E.; Stoof, C.R.; Vega, J.A.; Molina, D. Prescribed burning in southern Europe: Developing fire management in a dynamic landscape. Front. Ecol. Environ. 2013, 11, e4–e14. [Google Scholar] [CrossRef]
- Moreira, F.; Viedma, O.; Arianoutsou, M.; Curt, T.; Koutsias, N.; Rigolot, E.; Barbati, A.; Corona, P.; Vaz, P.; Xanthopoulos, G.; et al. Landscape–wildfire interactions in southern Europe: Implications for landscape management. J. Environ. Manag. 2011, 92, 2389–2402. [Google Scholar] [CrossRef]
- Calkin, D.; Price, O.; Salis, M. WUI Risk Assessment at the Landscape Level. In Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires; Manzello, S.L., Ed.; Springer International Publishing: Cham, Switzerland, 2020; pp. 1184–1195. [Google Scholar]
- Calkin, D.E.; Cohen, J.D.; Finney, M.A.; Thompson, M.P. How risk management can prevent future wildfire disasters in the wildland-urban interface. Proc. Natl. Acad. Sci. USA 2014, 111, 746–751. [Google Scholar] [CrossRef]
- Radeloff, V.C.; Helmers, D.P.; Kramer, H.A.; Mockrin, M.H.; Alexandre, P.M.; Bar-Massada, A.; Butsic, V.; Hawbaker, T.J.; Martinuzzi, S.; Syphard, A.D. Rapid growth of the US wildland-urban interface raises wildfire risk. Proc. Natl. Acad. Sci. USA 2018, 115, 3314–3319. [Google Scholar] [CrossRef]
- Wunder, S.; Calkin, D.E.; Charlton, V.; Feder, S.; Arano, I.; Moore, P.; Rodríguez; Silva, F.; Tacconi, L.; Vega-García, C. Resilient landscapes to prevent catastrophic forest fires: Socioeconomic insights towards a new paradigm. For. Policy Econ. 2021, 128, 102458. [Google Scholar] [CrossRef]
- Moritz, M.A.; Batllori, E.; Bradstock, R.A.; Gill, A.M.; Handmer, J.; Hessburg, P.F.; Leonard, J.; McCaffrey, S.; Odion, D.C.; Schoennagel, T. Learning to coexist with wildfire. Nature 2014, 515, 58–66. [Google Scholar] [CrossRef] [PubMed]
- North, M.P.; Stephens, S.L.; Collins, B.; Agee, J.; Aplet, G.; Franklin, J.; Fule, P. Reform forest fire management. Science 2015, 349, 1280–1281. [Google Scholar] [CrossRef] [PubMed]
- Ager, A.A.; Vaillant, N.M.; Finney, M.A. A comparison of landscape fuel treatment strategies to mitigate wildland fire risk in the urban interface and preserve old forest structure. For. Ecol. Manag. 2010, 259, 1556–1570. [Google Scholar] [CrossRef]
- Palaiologou, P.; Kalabokidis, K.; Ager, A.A.; Galatsidas, S.; Papalampros, L.; Day, M.A. Spatial Optimization and Tradeoffs of Alternative Forest Management Scenarios in Macedonia, Greece. Forests 2021, 12, 697. [Google Scholar] [CrossRef]
- Salis, M.; Laconi, M.; Ager, A.A.; Alcasena, F.J.; Arca, B.; Lozano, O.; Oliveira, A.; Spano, D. Evaluating alternative fuel treatment strategies to reduce wildfire losses in a Mediterranean area. For. Ecol. Manag. 2016, 368, 207–221. [Google Scholar] [CrossRef]
- Calkin, D.E.; Ager, A.A.; Gilbertson-Day, J.; Scott, J.; Finney, M.; Schrader-Patton, C.; Quigley, T.; Strittholt, J.; Kaiden, J. Wildfire Risk and Hazard: Procedures for the First Approximation; Gen Tech. Rep. RMRS-GTR-235; US Department of Agriculture, Forest Service, Rocky Mountain Research Station: Fort Collins, CO, USA, 2010; 62p. [Google Scholar]
- Hand, M.S.; Gebert, K.M.; Liang, J.; Calkin, D.E.; Thompson, M.P.; Zhou, M. Development and Application of Wildland Fire Expenditures Models. In Economics of Wildfire Management: The Development and Application of Suppression Expenditure Models, SpringerBriefs in Fire; Hand, M.S., Gebert, K.M., Liang, J., Calkin, D.E., Thompson, M.P., Zhou, M., Eds.; Springer: New York, NY, USA, 2014; pp. 5–18. [Google Scholar]
- Reinhardt, E.D. Objectives and considerations for wildland fuel treatment in forested ecosystems of the interior western United States. For. Ecol. Manag. 2008, 256, 1997–2006. [Google Scholar] [CrossRef]
- Parisien, M.A.; Dawe, D.A.; Miller, C.; Stockdale, C.A.; Armitage, O.B. Applications of simulation-based burn probability modelling: A review. Int. J. Wildland Fire 2020, 28, 913–926. [Google Scholar] [CrossRef]
- Sample, M.; Thode, A.E.; Peterson, C.; Gallagher, M.R.; Flatley, W.; Friggens, M.; Evans, A.; Loehman, R.; Hedwall, S.; Brandt, L.; et al. Adaptation Strategies and Approaches for Managing Fire in a Changing Climate. Climate 2022, 10, 58. [Google Scholar] [CrossRef]
- Ager, A.A.; Vaillant, N.M.; Finney, M.A.; Preisler, H.K. Analyzing wildfire exposure and source–sink relationships on a fire prone forest landscape. For. Ecol. Manag. 2012, 267, 271–283. [Google Scholar] [CrossRef]
- Salis, M.; Arca, B.; Del Giudice, L.; Palaiologou, P.; Alcasena-Urdiroz, F.; Ager, A.; Fiori, M.; Pellizzaro, G.; Scarpa, C.; Schirru, M.; et al. Application of simulation modeling for wildfire exposure and transmission assessment in Sardinia, Italy. Int. J. Disaster Risk Reduct. 2021, 58, 102189. [Google Scholar] [CrossRef]
- Campos, J.C.; Bernhardt, J.; Aquilué, N.; Brotons, L.; Domínguez, J.; Lomba, Â.; Marcos, B.; Martínez-Freiría, F.; Moreira, F.; Pais, S. Using fire to enhance rewilding when agricultural policies fail. Sci. Total Environ. 2021, 755, 142897. [Google Scholar] [CrossRef] [PubMed]
- Pais, S.; Aquilué, N.; Campos, J.; Sil, Â.; Marcos, B.; Martínez-Freiría, F.; Domínguez, J.; Brotons, L.; Honrado, J.P.; Regos, A. Mountain farmland protection and fire-smart management jointly reduce fire hazard and enhance biodiversity and carbon sequestration. Ecosyst. Serv. 2020, 44, 101143. [Google Scholar] [CrossRef]
- Neidermeier, A.N.; Zagaria, C.; Pampanoni, V.; West, T.A.P.; Verburg, P.H. Mapping Opportunities for Wildfire Hazard Reduction in Europe through Targeted Land Management Strategies. 2023. Available online: https://ssrn.com/abstract=4410640 (accessed on 30 April 2023).
- Baijnath-Rodino, J.A.; Foufoula-Georgiou, E.; Banerjee, T. Reviewing the “Hottest” Fire Indices Worldwide. Authorea Prepr. 2022. [Google Scholar] [CrossRef]
- Kupfer, J.A.; Terando, A.J.; Gao, P.; Teske, C.; Hiers, J.K. Climate change projected to reduce prescribed burning opportunities in the south-eastern United States. Int. J. Wildland Fire 2020, 29, 764–778. [Google Scholar] [CrossRef]
- Hurteau, M.D.; Bradford, J.B.; Fulé, P.Z.; Taylor, A.H.; Martin, K.L. Climate change, fire management, and ecological services in the southwestern US. For. Ecol Manag. 2014, 327, 280–289. [Google Scholar] [CrossRef]
- Cobb, R.C. The Intertwined Problems of Wildfire, Forest Disease, and Climate Change Interactions. Curr. For. Rep. 2022, 8, 214–228. [Google Scholar] [CrossRef]
- Biedermann, P.H.W.; Müller, J.; Grégoire, J.-C.; Gruppe, A.; Hagge, J.; Hammerbacher, A.; Hofstetter, R.W.; Kandasamy, D.; Kolarik, M.; Kostovcik, M.; et al. Bark Beetle Population Dynamics in the Anthropocene: Challenges and Solutions. Trends Ecol. Evol. 2019, 34, 914–924. [Google Scholar] [CrossRef]
- Paine, R.T.; Tegner, M.J.; Johnson, E.A. Compounded perturbations yield ecological surprises. Ecosystems 1998, 1, 535–545. [Google Scholar] [CrossRef]
- Seidl, R.; Schelhaas, M.-J.; Rammer, W.; Verkerk, P.J. Increasing forest disturbances in Europe and their impact on carbon storage. Nat. Clim. Chang. 2014, 4, 806–810. [Google Scholar] [CrossRef]
- Lausch, A.; Heurich, M.; Fahse, L. Spatio-temporal infestation patterns of Ips typographus (L.) in the Bavarian Forest National Park, Germany. Ecol. Indic. 2013, 31, 73–81. [Google Scholar] [CrossRef]
- Löwe, R.; Sedlecký, M.; Sikora, A.; Prokůpková, A.; Modlinger, R.; Novotný, K.; Turčáni, M. How Bark Beetle Attack Changes the Tensile and Compressive Strength of Spruce Wood (Picea abies (L.) H. Karst.). Forests 2022, 13, 87. [Google Scholar] [CrossRef]
- Morris, J.L.; Cottrell, S.; Fettig, C.J.; Hansen, W.D.; Sherriff, R.L.; Carter, V.A.; Clear, J.L.; Clement, J.; DeRose, R.J.; Hicke, J.A.; et al. Managing bark beetle impacts on ecosystems and society: Priority questions to motivate future research. J. Appl. Ecol. 2017, 54, 750–760. [Google Scholar] [CrossRef]
- Senf, C.; Seidl, R. Storm and fire disturbances in Europe: Distribution and trends. Glob. Chang. Biol. 2021, 27, 3605–3619. [Google Scholar] [CrossRef] [PubMed]
- Heisig, J.; Olson, E.; Pebesma, E. Predicting Wildfire Fuels and Hazard in a Central European Temperate Forest Using Active and Passive Remote Sensing. Fire 2022, 5, 29. [Google Scholar] [CrossRef]
- Fernandez-Carrillo, A.; Patočka, Z.; Dobrovolný, L.; Franco-Nieto, A.; Revilla-Romero, B. Monitoring Bark Beetle Forest Damage in Central Europe. A Remote Sensing Approach Validated with Field Data. Remote Sens. 2020, 12, 3634. [Google Scholar] [CrossRef]
- Raffa, K.F.; Aukema, B.H.; Bentz, B.J.; Carroll, A.L.; Hicke, J.A.; Turner, M.G.; Romme, W.H. Cross-scale Drivers of Natural Disturbances Prone to Anthropogenic Amplification: The Dynamics of Bark Beetle Eruptions. BioScience 2008, 58, 501. [Google Scholar] [CrossRef]
- Sommerfeld, A.; Rammer, W.; Heurich, M.; Hilmers, T.; Müller, J.; Seidl, R. Do bark beetle outbreaks amplify or dampen future bark beetle disturbances in Central Europe? J. Ecol. 2021, 109, 737–749. [Google Scholar] [CrossRef]
- Colavito, M. The human dimensions of spatial, pre-wildfire planning decision support systems: A review of barriers, facilitators, and recommendations. Forests 2021, 12, 483. [Google Scholar] [CrossRef]
- Deeming, J.E. The National Fire Danger Rating System; Rocky Mountain Forest and Range Experiment Station, Forest Service, US Department of Agriculture: Ogden, UT, USA, 1977; p. 63. [Google Scholar]
- Noonan-Wright, E.; Seielstad, C.A. Patterns of wildfire risk in the United States from systematic operational risk assessments: How risk is characterised by land managers. Int. J. Wildland Fire 2021, 30, 569–584. [Google Scholar] [CrossRef]
- Scott, J.H.; Thompson, M.P.; Calkin, D.E. A Wildfire Risk Assessment Framework for Land and Resource Management; CreateSpace Independent Publishing Platform: Scotts Valley, CA, USA, 2013. [Google Scholar] [CrossRef]
- Drury, S.A.; Rauscher, H.M.; Banwell, E.M.; Huang, S.; Lavezzo, T.L. The interagency fuels treatment decision support system: Functionality for fuels treatment planning. Fire Ecol. 2016, 12, 103–123. [Google Scholar] [CrossRef]
- Thompson, M.P.; Haas, J.R.; Gilbertson-Day, J.W.; Scott, J.H.; Langowski, P.; Bowne, E.; Calkin, D.E. Development and application of a geospatial wildfire exposure and risk calculation tool. Environ. Model. Softw. 2015, 63, 61–72. [Google Scholar] [CrossRef]
- McEvoy, A.; Kerns, B.K.; Kim, J.B. Hazards of risk: Identifying plausible community wildfire disasters in low-frequency fire regimes. Forests 2021, 12, 934. [Google Scholar] [CrossRef]
- Iglesias, V.; Stavros, N.; Balch, J.K.; Barrett, K.; Cobian-Iñiguez, J.; Hester, C.; Kolden, C.A.; Leyk, S.; Nagy, R.C.; Reid, C.E. Fires that matter: Reconceptualizing fire risk to include interactions between humans and the natural environment. Environ. Res. Lett. 2022, 17, 045014. [Google Scholar] [CrossRef]
- Metlen, K.L.; Fairbanks, T.; Bennett, M.; Volpe, J.; Kuhn, B.; Thompson, M.P.; Thrailkill, J.; Schindel, M.; Helmbrecht, D.; Scott, J. Integrating forest restoration, adaptation, and proactive fire management: Rogue River Basin case study. Can. J. For. Res. 2021, 51, 1292–1306. [Google Scholar] [CrossRef]
- Balch, J.K.; Iglesias, V.; Braswell, A.E.; Rossi, M.W.; Joseph, M.B.; Mahood, A.L.; Shrum, T.R.; White, C.T.; Scholl, V.M.; McGuire, B. Social-environmental extremes: Rethinking extraordinary events as outcomes of interacting biophysical and social systems. Earth’s Future 2020, 8, e2019EF001319. [Google Scholar] [CrossRef]
- Greiner, S.M.; Schultz, C.A.; Kooistra, C. Pre-season fire management planning: The use of Potential Operational Delineations to prepare for wildland fire events. Int. J. Wildland Fire 2020, 30, 170–178. [Google Scholar] [CrossRef]
- Thompson, M.P.; O’Connor, C.D.; Gannon, B.M.; Caggiano, M.D.; Dunn, C.J.; Schultz, C.A.; Calkin, D.E.; Pietruszka, B.; Greiner, S.M.; Stratton, R. Potential operational delineations: New horizons for proactive, risk-informed strategic land and fire management. Fire Ecol. 2022, 18, 1–20. [Google Scholar] [CrossRef]
- Jolly, W.M.; Freeborn, P.H.; Page, W.G.; Butler, B.W. Severe fire danger index: A forecastable metric to inform firefighter and community wildfire risk management. Fire 2019, 2, 47. [Google Scholar] [CrossRef]
- Stocks, B.J.; Lawson, B.D.; Alexander, M.E.; Van Wagner, C.E.; McAlpine, R.S.; Lynham, T.J.; Dubé, D.E. Canadian Forest Fire Danger Rating System: An Overview. For. Chron. 1989, 65, 258–265. [Google Scholar] [CrossRef]
- Van Wagner, C.E. Development and structure of the Canadian Forest Fire Weather Index System; 35; Canadian Forest Service: Otawa, ON, Canada, 1987; p. 48. [Google Scholar]
- Hirsch, K.G. Canadian Forest Fire Behavior Prediction (FBP) System: User’s Guide; Northern Forestry Centre: Edmonton, AB, Canada, 1996; p. 122. [Google Scholar]
- Woolford, D.G.; Martell, D.L.; McFayden, C.B.; Evens, J.; Stacey, A.; Wotton, B.M.; Boychuk, D. The development and implementation of a human-caused wildland fire occurrence prediction system for the province of Ontario, Canada. Can. J. For. Res. 2020, 51, 303–325. [Google Scholar] [CrossRef]
- Parisien, M.A.; Kafka, V.G.; Hirsch, K.G.; Todd, J.B.; Lavoie, S.G.; Maczek, P.D. Mapping Wildfire Susceptibility with the Burn-P3 Simulation Model; NOR-X-405; Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre: Edmonton, AB, Canada, 2005; p. 45. [Google Scholar]
- Reed, W.J. A note on fire frequency concepts and definitions. Can. J. For. Res. 2006, 36, 1884–1888. [Google Scholar] [CrossRef]
- Taylor, S.W.; Woolford, D.G.; Dean, C.B.; Martell, D.L. Wildfire Prediction to Inform Fire Management: Statistical Science Challenges. Stat. Sci. 2013, 28, 586–615. [Google Scholar] [CrossRef]
- IGBP. Report of the 4th IGBP-DIS Fire Working Group Meeting; IGBP-DIS Working Paper #21; IGBP-DIS: Toulousse, France, 1999. [Google Scholar]
- Beverly, J.L.; McLoughlin, N. Burn probability simulation and subsequent wildland fire activity in Alberta, Canada–implications for risk assessment and strategic planning. For. Ecol Manag. 2019, 451, 117490. [Google Scholar] [CrossRef]
- Parisien, M.-A.; Ager, A.A.; Barros, A.M.; Dawe, D.; Erni, S.; Finney, M.A.; McHugh, C.W.; Miller, C.; Parks, S.A.; Riley, K.L. Commentary on the article “Burn probability simulation and subsequent wildland fire activity in Alberta, Canada–Implications for risk assessment and strategic planning” by JL Beverly and N. McLoughlin. For. Ecol Manag. 2020, 460, 117698. [Google Scholar] [CrossRef]
- Johnston, L.M.; Wang, X.; Erni, S.; Taylor, S.W.; McFayden, C.B.; Oliver, J.A.; Stockdale, C.; Christianson, A.; Boulanger, Y.; Gauthier, S.; et al. Wildland fire risk research in Canada. Environ. Rev. 2020, 28, 164–186. [Google Scholar] [CrossRef]
- Fuglem, P.L.; Lawson, B.D.; Hawkes, B.C. Fire Protection Guidelines For Juvenile Spacing Projects; British Columbia Ministry of Forests, Canadian Forestry Service: Victoria, BC, Canada, 1983; p. 25. [Google Scholar]
- Muraro, S.J. Prescribed Fire–Evaluation Of Hazard Abatement. Departmental Publication Number 1231; Canadian Department of Forestry And Rural Development/Congress of the International Union of Forest Research Organizations: Ottawa, ON, Canada; Munich, Germany, 1968; p. 28. [Google Scholar]
- Simard, A.J. Wildland Fire Management–A Systems Approach. Forestry Technical Report 17. Catalogue No. F064-17/1977; Department Of Fisheries And Environment, Canadian Forestry Service: Gatineau, QC, Canada, 1977; p. 33. [Google Scholar]
- Boychuk, D.; Martell, D.L. A Multistage Stochastic Programming Model for Sustainable Forest-Level Timber Supply Under Risk of Fire. For. Sci. 1996, 42, 10–26. [Google Scholar]
- Savage, D.W.; Martell, D.L.; Wotton, B.M. Evaluation of two risk mitigation strategies for dealing with fire-related uncertainty in timber supply modelling. Can. J. For. Res. 2010, 40, 1136–1154. [Google Scholar] [CrossRef]
- McFayden, C.B.; Boychuk, D.; Woolford, D.G.; Wheatley, M.J.; Johnston, L. Impacts of wildland fire effects on resources and assets through expert elicitation to support fire response decisions. Int. J. Wildland Fire 2019, 28, 885–900. [Google Scholar] [CrossRef]
- Abo El Ezz, A.; Boucher, J.; Cotton-Gagnon, A.; Godbout, A. Framework for spatial incident-level wildfire risk modelling to residential structures at the wildland urban interface. Fire Saf. J. 2022, 131, 103625. [Google Scholar] [CrossRef]
- MNP. A Review of the 2016 Horse River Wildfire: Alberta Agriculture and Forestry Preparedness and Response; MNP LLP: Edmonton, AB, Canada, 2017; p. 92. [Google Scholar]
- Xi, D.D.Z.; Taylor, S.W.; Woolford, D.G.; Dean, C.B. Statistical Models of Key Components of Wildfire Risk. Annu. Rev. Stat. Its Appl. 2019, 6, 197–222. [Google Scholar] [CrossRef]
- McArthur, A.G. Fire Danger Rating Tables for Annual Grassland; Forestry and Timber Bureau: Canberra, Australia, 1960. [Google Scholar]
- Luke, R.H.; McArthur, A.G.; Luke, R. Bushfires in Australia; Luke, R., McArthur, A., Eds.; Australian Government Publishing Service: Canberra, Australia, 1978. [Google Scholar]
- Keetch, J.J.; Byram, G.M. A Drought Index for Forest Fire Control; US Department of Agriculture, Forest Service, Southeastern Forest Experiment Station: Asheville, NC, USA, 1968. [Google Scholar]
- Matthews, S.; Fox-Hughes, P.; Grootemaat, S.; Hollis, J.J.; Kenny, B.J.; Sauvage, S. Australian Fire Danger Rating System: Research Prototype; NSW Rural Fire Service: Lidcombe, Australia, 2019; p. 384. [Google Scholar]
- Alexander, M.E. Proposed Revision of Fire Danger Class Criteria for Forest and Rural Areas in New Zealand, 2nd ed.; National Rural Fire Authority, Wellington, in association with the Scion Rural Fire Research Group: Christchurch, New Zealand, 2008. [Google Scholar]
- Vinodkumar, V.; Dharssi, I.; Yebra, M.; Fox-Hughes, P. Continental-scale prediction of live fuel moisture content using soil moisture information. Agric. For. Meteorol. 2021, 307, 108503. [Google Scholar] [CrossRef]
- Dharssi, I. Vinodkumar, 2017: A Prototype high Resolution Soil Moisture Analysis System for Australia; Bureau of Meteorology Research Report: Canberra, Australia, 2017. [Google Scholar]
- eJenkins, M.J.; Holmes, A.; Monks, J.; Runcie, J.W.; Sauvage, S.; Matthews, S. Ignition, Suppression and Impact Research Prototype: Australian Fire Danger Rating System; NSW Rural Fire Service: Olympic Park, Australia, 2022. [Google Scholar]
- European Court of Auditors. Is EU Support for Preventing and Restoring Damage to Forests Caused by Fire and Natural Disasters Well Managed; ECA: Luxembourg, 2014.
- San-Miguel-Ayanz, J.; Costa, H.; de Rigo, D.; Libertà, G.; Artés Vivancos, T.; Houston Durrant, T.; Nuijten, D.; Löffler, P.; Moore, P.; Baetens, J.; et al. Basic Criteria to Assess Wildfire Risk at the Pan-European Level; Publications Office of the European Union: Luxembourg, 2019.
- Martín-López, B.; Gómez-Baggethun, E.; García-Llorente, M.; Montes, C. Trade-offs across value-domains in ecosystem services assessment. Ecol. Indic. 2014, 37, 220–228. [Google Scholar] [CrossRef]
- Oom, D.; de Rigo, D.; Pfeiffer, H.; San-Miguel-Ayanz, J.; Grecchi, R.; Durrant, T.H.; Libertà, G.; Artes-Vivancos, T.; Boca, R.; Maianti, P.; et al. Developing the European wildfire risk assessment (WRA). In Atlas of the Human Planet 2020; Publications Office of the European Union: Luxembourg, 2020; pp. 37–38. [Google Scholar]
- Corbane, C.; Florczyk, A.J.; Pesaresi, M.; Politis, P.; Syrris, V. GHS-BUILT R2018A–GHS Built-Up Grid, Derived from Landsat, Multitemporal (1975-1990-2000-2014). European Commission, Joint Research Centre (JRC) [Dataset]. 2018. Available online: http://data.europa.eu/89h/jrc-ghsl-10007 (accessed on 1 May 2023).
- Pekel, J.-F.; Cottam, A.; Gorelick, N.; Belward, A.S. High-resolution mapping of global surface water and its long-term changes. Nature 2016, 540, 418–422. [Google Scholar] [CrossRef] [PubMed]
- Le Saout, S.L.; Hoffmann, M.; Shi, Y.; Hughes, A.; Bernard, C.; Brooks, T.M.; Bertzky, B.; Butchart, S.H.M.; Stuart, S.N.; Badman, T.; et al. Protected areas and effective biodiversity conservation. Science 2013, 342, 803–805. [Google Scholar] [CrossRef] [PubMed]
- Mavsar, R.; Pettenella, D.; San-Miguel-Ayanz, J.; Camia, A. Development of a methodology for the analysis of socio-economic impact of forest fires in Europe. In Proceedings of the 5th International Wildland Fire Conference, Sun City, South Africa, 9–13 May 2011. [Google Scholar]
- Camia, A.; Libertà, G.; San-Miguel-Ayanz, J. Modeling the Impacts of Climate Change on Forest Fire Danger in Europe: Sectorial Results of the PESETA II Project; Publications Office of the European Union: Luxembourg, 2017; p. 24.
- Plummer, S.; Lecomte, P.; Doherty, M. The ESA Climate Change Initiative (CCI): A European contribution to the generation of the Global Climate Observing System. Remote Sens. Environ. 2017, 203, 2–8. [Google Scholar] [CrossRef]
- Laurent, P.; Mouillot, F.; Yue, C.; Ciais, P.; Moreno, M.V.; Nogueira, J.M.P. FRY, a global database of fire patch functional traits derived from space-borne burned area products. Sci. Data 2018, 5, 180132. [Google Scholar] [CrossRef] [PubMed]
- Tesfa, T.K.; Leung, L.R.; Ghan, S.J. Exploring topography-based methods for downscaling subgrid precipitation for use in Earth System Models. J. Geophys. Res. Atmos. 2020, 125, e2019JD031456. [Google Scholar] [CrossRef]
- Murtaugh, P.A. Methods of variable selection in regression modeling. Commun. Stat.-Simul. Comput. 1998, 27, 711–734. [Google Scholar] [CrossRef]
- Akaike, H. Likelihood and the Bayes procedure. In Bayesian Statistics; Bernardo, J.M., DeGroot, D., Lindley, V., Smith, A.F.M., Eds.; University Press: Valencia, Spain, 1980; pp. 143–166. [Google Scholar]
- Zhang, H.; Guo, P.; Chen, H.; Liu, N.; Qiao, Y.; Xu, M.; Zhang, L. Lightning-induced smoldering ignition of peat: Simulation experiments by an electric arc with long continuing current. Proc. Combust. Inst. 2023. [Google Scholar] [CrossRef]
- Latham, D.; Williams, E. Lightning and forest fires. In Forest Fires. Behavior and Ecological Aspects, Johnson, E.A., Miyanishi, K., Eds.; Academic Press: San Diego, CA, USA, 2001; pp. 375–418. [Google Scholar]
- Bitzer, P.M. Global distribution and properties of continuing current in lightning. J. Geophys. Res. Atmos. 2017, 122, 1033–1041. [Google Scholar] [CrossRef]
- Pérez-Invernón, F.J.; Gordillo-Vázquez, F.J.; Huntrieser, H.; Jöckel, P. Variation of lightning-ignited wildfire patterns under climate change. Nat. Commun. 2023, 14, 739. [Google Scholar] [CrossRef] [PubMed]
- Lopez, P. A lightning parameterization for the ECMWF integrated forecasting system. Mon. Weather Rev. 2016, 144, 3057–3075. [Google Scholar] [CrossRef]
- Breiman, L. Random forests. Mach. Learn. 2001, 45, 5–32. [Google Scholar] [CrossRef]
- Sueur-Ochoa, C.; Chuvieco, E. Mapping the likelihood of human-caused fire ignitions in the European territory. Appl. Geogr. 2023. in review. [Google Scholar]
- Aragoneses, E.; García, M.; Salis, M.; Ribeiro, L.M.; Chuvieco, E. Classification and mapping of European fuels using a hierarchical-multipurpose fuel classification system. Earth Syst. Sci. Data 2023, 15, 1287–1315. [Google Scholar] [CrossRef]
- Mohammadpour, P.; Viegas, D.X.; Viegas, C. Vegetation Mapping with Random Forest Using Sentinel 2 and GLCM Texture Feature—A Case Study for Lousã Region, Portugal. Remote Sens. 2022, 14, 4585. [Google Scholar]
- Nelson, R.M. Water Relations of Forest Fuels. In Forest Fires: Behavior and Ecological Effects; Johnson, E.A., Miyanishi, K., Eds.; Academic Press: San Diego, CA, USA, 2001; pp. 79–149. [Google Scholar]
- Quan, X.; Yebra, M.; Riaño, D.; He, B.; Lai, G.; Liu, X. Global fuel moisture content mapping from MODIS. Int. J. Appl. Earth Obs. Geoinf. 2021, 101, 102354. [Google Scholar] [CrossRef]
- Féret, J.-B.; Gitelson, A.; Noble, S.; Jacquemoud, S. PROSPECT-D: Towards modeling leaf optical properties through a complete lifecycle. Remote Sens. Environ. 2017, 193, 204–215. [Google Scholar] [CrossRef]
- Verhoef, W.; Jia, L.; Xiao, Q.; Su, Z. Unified optical-thermal four-stream radiative transfer theory for homogeneous vegetation canopies. IEEE Trans. Geosci. Remote Sens. 2007, 45, 1808–1822. [Google Scholar] [CrossRef]
- Jasinski, M.F.; Eagleson, P.S. Estimation of subpixel vegetation cover using red-infrared scattergrams. IEEE Trans. Geosci. Remote Sens. 1990, 28, 253–267. [Google Scholar] [CrossRef]
- Huemmrich, K.F. The GeoSail model: A simple addition to the SAIL model to describe discontinuous canopy reflectance. Remote Sens. Environ. 2001, 75, 423–431. [Google Scholar] [CrossRef]
- Pampanoni, V.; Laneve, G.; Santilli, G. Evaluating Sentinel-3 Viability for Vegetation Canopy Monitoring and Fuel Moisture Content Estimation. In Proceedings of the IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 17–22 July 2022; pp. 5634–5637. [Google Scholar]
- Yebra, M.; Chuvieco, E. Generation of a species-specific look-up table for fuel moisture content assessment. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2009, 2, 21–26. [Google Scholar] [CrossRef]
- Simard, M.; Pinto, N.; Fisher, J.B.; Baccini, A. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. 2011, 116, 12. [Google Scholar] [CrossRef]
- Fernandes, A.P.; Lopes, D.; Sorte, S.; Monteiro, A.; Gama, C.; Reis, J.; Menezes, I.; Osswald, T.; Borrego, C.; Almeida, M.; et al. Smoke emissions from the extreme wildfire events in central Portugal in October 2017. Int. J. Wildland Fire 2022, 31, 989–1001. [Google Scholar] [CrossRef]
- Albini, F.; Reinhardt, E. Improved Calibration of a Large Fuel Burnout Model. Int. J. Wildland Fire 1997, 7, 21–28. [Google Scholar] [CrossRef]
- Prichard, S.J.; Kennedy, M.C.; Wright, C.S.; Cronan, J.B.; Ottmar, R.D. Predicting forest floor and woody fuel consumption from prescribed burns in southern and western pine ecosystems of the United States. For. Ecol Manag. 2017, 405, 328–338. [Google Scholar] [CrossRef]
- Vicente, A.; Calvo, A.; Gonçalves, C.; Nunes, T.; Fernandes, A.P.; Monteiro, C.; Mirante, F.; Evtyugina, M.; Alves, C. Emission factors of trace gases and aerosols from wildfire events in central Portugal. Zenodo 2019. [Google Scholar] [CrossRef]
- Prichard, S.J.; O’Neill, S.M.; Eagle, P.; Andreu, A.G.; Drye, B.; Dubowy, J.; Urbanski, S.; Strand, T.M. Wildland fire emission factors in North America: Synthesis of existing data, measurement needs and management applications. Int. J. Wildland Fire 2020, 29, 132–147. [Google Scholar] [CrossRef]
- Fernandes, A.P.; Rafael, S.; Lopes, D.; Coelho, S.; Borrego, C.; Lopes, M. The air pollution modelling system URBAIR: How to use a Gaussian model to accomplish high spatial and temporal resolutions. Air Qual. Atmos. Health 2021, 14, 1969–1988. [Google Scholar] [CrossRef]
- Rodríguez y Silva, F.; Molina-Martínez, J.R.M.; Herrera Machuca, M.Á.; Rodríguez Leal, J.M. VISUAL-SEVEIF, a Tool for Integrating Fire Behavior Simulation and Economic Evaluation of the Impact of Wildfires. In Proceedings of the Fourth International Symposium on Fire Economics, Planning, and Policy; Climate Change and Wildfires, Department of Agriculture, Forest Service, Pacific Southwest Research Station: Albany, CA, USA, 2013; pp. 163–178. [Google Scholar]
- Rodríguez y Silva, F.; Ramón Molina, J.; Rodriguez Leal, J. The efficiency analysis of the fire control operations using the VISUAL-SEVEIF tool. In Advances in Forest fire Research. Social and Economic Issues; Viegas, D.X., Ed.; ADAI: Coimbra, Portugal, 2014. [Google Scholar] [CrossRef]
- Santoro, M.; Cartus, O.; Carvalhais, N.; Rozendaal, D.; Avitabile, V.; Araza, A.; De Bruin, S.; Herold, M.; Quegan, S.; Rodríguez-Veiga, P. The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations. Earth Syst. Sci. Data 2021, 13, 3927–3950. [Google Scholar] [CrossRef]
- Chen, X.; Niu, J. Relationships between tree height and tree species richness at small scales. Acta Oecologica 2020, 109, 103668. [Google Scholar] [CrossRef]
- Potapov, P.; Li, X.; Hernandez-Serna, A.; Tyukavina, A.; Hansen, M.C.; Kommareddy, A.; Pickens, A.; Turubanova, S.; Tang, H.; Silva, C.E. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sens. Environ. 2021, 253, 112165. [Google Scholar] [CrossRef]
- Hansen, M.C.; Potapov, P.V.; Moore, R.; Hancher, M.; Turubanova, S.; Tyukavina, A.; Thau, D.; Stehman, S.; Goetz, S.; Loveland, T. High-resolution global maps of 21st-century forest cover change. Science 2013, 342, 850–853. [Google Scholar] [CrossRef]
- Fusco, J.; Walker, E.; Papaïx, J.; Debolini, M.; Bondeau, A.; Barnagaud, J.-Y. Land Use Changes Threaten Bird Taxonomic and Functional Diversity Across the Mediterranean Basin: A Spatial Analysis to Prioritize Monitoring for Conservation. Front. Ecol. Evol. 2021, 9, 612356. [Google Scholar] [CrossRef]
- Sillero, N.; Campos, J.; Bonardi, A.; Corti, C.; Creemers, R.; Crochet, P.-A.; Isailović, J.C.; Denoël, M.; Ficetola, G.F.; Gonçalves, J. Updated distribution and biogeography of amphibians and reptiles of Europe. Amphib.-Reptil. 2014, 35, 1–31. [Google Scholar] [CrossRef]
- Potapov, P.; Hansen, M.C.; Laestadius, L.; Turubanova, S.; Yaroshenko, A.; Thies, C.; Smith, W.; Zhuravleva, I.; Komarova, A.; Minnemeyer, S.; et al. The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. 2017, 3, e1600821. [Google Scholar] [CrossRef]
- Bro, R.; Smilde, A.K. Principal component analysis. Anal. Methods 2014, 6, 2812–2831. [Google Scholar] [CrossRef]
- Salata, S.; Grillenzoni, C. A spatial evaluation of multifunctional Ecosystem Service networks using Principal Component Analysis: A case of study in Turin, Italy. Ecol. Indic. 2021, 127, 107758. [Google Scholar] [CrossRef]
- Mahnken, M.; Cailleret, M.; Collalti, A.; Trotta, C.; Biondo, C.; D’Andrea, E.; Dalmonech, D.; Marano, G.; Mäkelä, A.; Minunno, F.; et al. Accuracy, realism and general applicability of European forest models. Glob. Chang. Biol. 2022, 28, 6921–6943. [Google Scholar] [CrossRef]
- Schelhaas, M.-J.; Hengeveld, G.M.; Heidema, N.; Thürig, E.; Rohner, B.; Vacchiano, G.; Vayreda, J.; Redmond, J.; Socha, J.; Fridman, J.; et al. Species-specific, pan-European diameter increment models based on data of 2.3 million trees. For. Ecosyst. 2018, 5, 21. [Google Scholar] [CrossRef]
- Moreno-Martínez, Á.; Camps-Valls, G.; Kattge, J.; Robinson, N.; Reichstein, M.; van Bodegom, P.; Kramer, K.; Cornelissen, J.H.C.; Reich, P.; Bahn, M. A methodology to derive global maps of leaf traits using remote sensing and climate data. Remote Sens. Environ. 2018, 218, 69–88. [Google Scholar] [CrossRef]
- Borrelli, P.; Panagos, P.; Ballabio, C.; Lugato, E.; Weynants, M.; Montanarella, L. Towards a pan-European assessment of land susceptibility to wind erosion. Land Degrad. Dev. 2016, 27, 1093–1105. [Google Scholar] [CrossRef]
- Hengeveld, G.M.; Nabuurs, G.-J.; Didion, M.; van den Wyngaert, I.; Clerkx, A.; Schelhaas, M.-J. A forest management map of European forests. Ecol. Soc. 2012, 17. [Google Scholar] [CrossRef]
- Masson-Delmotte, V.; Zhai, P.; Pirani, A.; Connors, S.L.; Péan, C.; Berger, S.; Caud, N.; Chen, Y.; Goldfarb, L.; Gomis, M. Climate Change 2021: The Physical Science Basis; Cambridge University Press: Cambridge, UK, 2021. [Google Scholar]
- van Asselen, S.; Verburg, P.H. Land cover change or land-use intensification: Simulating land system change with a global-scale land change model. Glob. Chang. Biol. 2013, 19, 3648–3667. [Google Scholar] [CrossRef] [PubMed]
- Winkler, K.; Fuchs, R.; Rounsevell, M.; Herold, M. Global land use changes are four times greater than previously estimated. Nat. Commun. 2021, 12, 2501. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Chuvieco, E.; Yebra, M.; Martino, S.; Thonicke, K.; Gómez-Giménez, M.; San-Miguel, J.; Oom, D.; Velea, R.; Mouillot, F.; Molina, J.R.; et al. Towards an Integrated Approach to Wildfire Risk Assessment: When, Where, What and How May the Landscapes Burn. Fire 2023, 6, 215. https://doi.org/10.3390/fire6050215
Chuvieco E, Yebra M, Martino S, Thonicke K, Gómez-Giménez M, San-Miguel J, Oom D, Velea R, Mouillot F, Molina JR, et al. Towards an Integrated Approach to Wildfire Risk Assessment: When, Where, What and How May the Landscapes Burn. Fire. 2023; 6(5):215. https://doi.org/10.3390/fire6050215
Chicago/Turabian StyleChuvieco, Emilio, Marta Yebra, Simone Martino, Kirsten Thonicke, Marta Gómez-Giménez, Jesus San-Miguel, Duarte Oom, Ramona Velea, Florent Mouillot, Juan R. Molina, and et al. 2023. "Towards an Integrated Approach to Wildfire Risk Assessment: When, Where, What and How May the Landscapes Burn" Fire 6, no. 5: 215. https://doi.org/10.3390/fire6050215