Remote Sensing applications for effective fire disaster management plans: A review
Abstract
The current context of climate change and imminent global warming is leading to changes in temperature and rainfall patterns worldwide which affect soil moisture, vegetation and soil conditions, the incidence of dry and wet events, and consequently, the occurrence, intensity, and magnitude of fires. Fires harm people’s quality of life as they can disrupt economic activities and affect public health. Additionally, fires damage the environment, accelerating water and wind erosion processes, altering air quality, and contributing to ecosystem degradation. Pampas in Argentina was selected as an example to study fires at a regional scale using Remote Sensing techniques due to its status as one of the most fertile plains in the world and the country’s most densely populated area. The fires are carefully analyzed and described considering three stages: i) pre-fires, ii) fires, and iii) post-fires. Afterwards, fire disaster management plans are described to assess these events, reduce their impacts on society and biodiversity, and minimize the ecosystems’ recovery time. In this sense, this manuscript aims to review the relationships between climate change, global warming, and the occurrence of fires. Additionally, it proposes to analyze the potential of Remote Sensing in analyzing these events at a regional scale to provide the mechanisms and tools necessary for formulating fire disaster management plans.
References
[1] Pinsky E, Guerrero APS, Livingston R. Our house is on fire: Child and adolescent psychiatrists in the era of the climate crisis. Journal of the American Academy of Child and Adolescent Psychiatry 2020; 59(5): 580–582. doi: 10.1016/j.jaac.2020.01.016
[2] Alexander B. Universities on Fire: Higher Education in the Climate Crisis. Johns Hopkins University Press; 2023.
[3] Harris N, Minnemeyer S, Sizer N, et al. With Latest Fires Crisis, Indonesia Surpasses Russia as World’S Fourth-Largest Emitter. World Resources Institute; 2015.
[4] Klein N. On Fire: The (Burning) Case for A Green New Deal. Simon & Schuster; 2019.
[5] Masson-Delmotte V, Moufouma-Okia W. Climate risks: Why each half-degree matters. Financial Stability Review, Banque de France 2019; (23): 17–27.
[6] Zhou Z, Zhang L, Chen J, et al. Projecting global drought risk under various SSP-RCP scenarios. Earth’s Future 2023; 11(5): e2022EF003420. doi: 10.1029/2022EF003420
[7] Xu R, Yu P, Abramson MJ, et al. Wildfires, global climate change, and human health. The New England Journal of Medicine 2020; 383(22): 2173–2181. doi: 10.1056/NEJMsr2028985
[8] Ferrelli F, Brendel AS, Perillo GME, Piccolo MC. Warming signals emerging from the analysis of daily changes in extreme temperature events over Pampas (Argentina). Environmental Earth Sciences 2021; 80: 422. doi: 10.1007/s12665-021-09721-4
[9] Negi MS, Kumar A. Assessment of increasing threat of forest fires in Uttarakhand, using remote sensing and GIS techniques. Global Journal of Advanced Research 2016; 3(6): 457–468.
[10] Kanga S, Singh SK. Forest fire simulation modeling using remote sensing & GIS. International Journal of Advanced Research in Computer Science 2017; 8(5): 326–332.
[11] Matin MA, Chitale VS, Murthy MSR, et al. Understanding forest fire patterns and risk in Nepal using remote sensing, geographic information system and historical fire data. International Journal of Wildland Fire 2017; 26(4): 276–286. doi: 10.1071/WF16056
[12] Parajuli A, Gautam AP, Sharma SP, et al. Forest fire risk mapping using GIS and remote sensing in two major landscapes of Nepal. Geomatics, Natural Hazards and Risk 2020; 11(1): 2569–2586. doi: 10.1080/19475705.2020.1853251
[13] Jin R, Lee KS. Investigation of forest fire characteristics in North Korea using remote sensing data and GIS. Remote Sensing 2022; 14(22): 5836. doi: 10.3390/rs14225836
[14] Dutta S, Vaishali A, Khan S, Das S. Forest fire risk modeling using GIS and remote sensing in major landscapes of Himachal Pradesh. In: Chatterjee U, Akanwa AO, Kumar S, et al. (editors). Ecological Footprints of Climate Change. Springer, Cham; 2023. pp. 421–442.
[15] Teodoro AC, Duarte L. Chapter 10—The role of satellite remote sensing in natural disaster management. In: Nanotechnology-Based Smart Remote Sensing Networks for Disaster Prevention. Elsevier; 2022. pp. 189–216.
[16] Yu B, She J, Liu G, et al. Coal fire identification and state assessment by integrating multitemporal thermal infrared and InSAR remote sensing data: A case study of Midong District, Urumqi, China. ISPRS Journal of Photogrammetry and Remote Sensing 2022; 190: 144–164. doi: 10.1016/j.isprsjprs.2022.06.007
[17] Higa L, Marcato J, Rodrigues T, et al. Active fire mapping on Brazilian Pantanal based on deep learning and CBERS 04A imagery. Remote Sensing 2022; 14(3): 688. doi: 10.3390/rs14030688
[18] Kureel N, Sarup J, Shafique M, et al. Modelling vegetation health and stress using hypersepctral remote sensing data. Modeling Earth Systems and Environment 2022; 8: 733–748. doi: 10.1007/s40808-021-01113-8
[19] Camprubí ÀC, González-Moreno P, de Dios VR. Live fuel moisture content mapping in the Mediterranean basin using random forests and combining MODIS spectral and thermal data. Remote Sensing 2022; 14(13): 3162. doi: 10.3390/rs14133162
[20] Hussain S, Qin S, Nasim W, et al. Monitoring the dynamic changes in vegetation cover using spatio-temporal remote sensing data from 1984 to 2020. Atmosphere 2021; 13(10): 1609. doi: 10.3390/atmos13101609
[21] Attiya AA, Jones BG. Impact of smoke plumes transport on air quality in Sydney during extensive bushfires (2019) in New South Wales, Australia using remote sensing and ground data. Remote Sensing 2022; 14(21): 5552. doi: 10.3390/rs14215552
[22] Kurbanov E, Vorobev O, Lezhnin S, et al. Remote sensing of forest burnt area, burn severity, and post-fire recovery: A review. Remote Sensing 2022; 14(19): 4714. doi: 10.3390/rs14194714
[23] Avetisyan D, Velizarova E, Filchev L. Post-fire forest vegetation state monitoring through satellite remote sensing and in situ data. Remote Sensing 2022; 14(24): 6266. doi: 10.3390/rs14246266
[24] Jin T, Hu X, Liu B, et al. Susceptibility prediction of post-fire debris flows in Xichang, China, using a logistic regression model from a spatiotemporal perspective. Remote Sensing 2022; 14(6): 1306. doi: 10.3390/rs14061306
[25] Kasyap VL, Sumathi D, Alluri K, et al. Early detection of forest fire using mixed learning techniques and UAV. Computational Intelligence and Neuroscience 2022; 2022: 3170244. doi: 10.1155/2022/3170244
[26] Farooq M, Gazali S, Dada M, et al. Forest fire alert system of India with a special reference to fire vulnerability assessment of the UT of Jammu and Kashmir. In: Kanga S, Meraj G, Farooq M, et al. (editors). Disaster Management in the Complex Himalayan Terrains. Springer, Cham; 2022. pp. 155–167.
[27] Li XY, Jin HJ, Wang HW, et al. Influences of forest fires on the permafrost environment: A review. Advances in Climate Change Research 2021; 12(1): 48–65. doi: 10.1016/j.accre.2021.01.001
[28] Gamze ÖNCÜ, Çorumluoğlu Ö. Assessment of forest fire damage severity by remote sensing techniques. International Journal of Environment and Geoinformatics 2023; 10(2): 151–158. doi: 10.30897/ijegeo.1089014
[29] Yilmaz OS, Acar U, Sanli FB, et al. Mapping burn severity and monitoring CO content in Türkiye’s 2021 Wildfires, using Sentinel-2 and Sentinel-5P satellite data on the GEE platform. Earth Science Informatics 2023; 16(1): 221–240. doi: 10.1007/s12145-023-00933-9
[30] DaSilva MD, Bruce D, Hesp PA, et al. Post-wildfire coastal dunefield response using photogrammetry and satellite indices. Earth Surface Processes and Landforms 2023; 48(9): 1845–1868. doi: 10.1002/esp.5591
[31] Senande-Rivera M, Insua-Costa D, Miguez-Macho G. Spatial and temporal expansion of global wildland fire activity in response to climate change. Nature Communications 2022; 13(1): 1208. doi: 10.1038/s41467-022-28835-2
[32] Rao KH, Rao PSS. Disaster Management. Serials Publications; 2008.
[33] Kathleen Geale S. The ethics of disaster management. Disaster Prevention and Management 2012; 21(4): 445–462. doi: 10.1108/09653561211256152
[34] Oktari RS, Munadi K, Idroes R, Sofyan H. Knowledge management practices in disaster management: Systematic review. International Journal of Disaster Risk Reduction 2020; 51: 101881. doi: 10.1016/j.ijdrr.2020.101881
[35] Wahyuningtyas N, Tanjung A, Idris I, Dewi K. Disaster mitigation on cultural tourism in Lombok, Indonesia. GeoJournal of Tourism and Geosites 2019; 27(4): 1227–1235. doi: 10.30892/gtg.27409-428
[36] Sun Q, Miao C, Hanel M, et al. Global heat stress on health, wildfires, and agricultural crops under different levels of climate warming. Environment International 2019; 128: 125–136. doi: 10.1016/j.envint.2019.04.025
[37] Al Kurdi OF. A critical comparative review of emergency and disaster management in the Arab world. Journal of Business and Socioeconomic Development 2021; 1(1): 24–46. doi: 10.1108/JBSED-02-2021-0021
[38] Sarker MNI, Peng Y, Yiran C, Shouse RC. Disaster resilience through big data: Way to environmental sustainability. International Journal of Disaster Risk Reduction 2020; 51: 101769. doi: 10.1016/j.ijdrr.2020.101769
[39] Rehman J, Sohaib O, Asif M, Pradhan B. Applying systems thinking to flood disaster management for a sustainable development. International Journal of Disaster Risk Reduction 2019; 36: 101101. doi: 10.1016/j.ijdrr.2019.101101
[40] Damaševičius R, Bacanin N, Misra S. From sensors to safety: Internet of emergency services (IoES) for emergency response and disaster management. Journal of Sensor and Actuator Networks 2023; 12(3): 41. doi: 10.3390/jsan12030041
[41] Schumann RL III, Mockrin M, Syphard AD, et al. Wildfire recovery as a “hot moment” for creating fire-adapted communities. International Journal of Disaster Risk Reduction 2020; 42: 101354. doi: 10.1016/j.ijdrr.2019.101354
[42] Khan A, Gupta S, Gupta SK. Multi-hazard disaster studies: Monitoring, detection, recovery, and management, based on emerging technologies and optimal techniques. International Journal of Disaster Risk Reduction 2020; 47: 101642. doi: 10.1016/j.ijdrr.2020.101642
[43] Matsuura S, Razak KA. Exploring transdisciplinary approaches to facilitate disaster risk reduction. Disaster Prevention and Management 2019; 28(6): 817–830. doi: 10.1108/DPM-09-2019-0289
[44] Abid SK, Sulaiman N, Chan S, et al. Toward an integrated disaster management approach: How artificial intelligence can boost disaster management. Sustainability 2021; 13(22): 12560. doi: 10.3390/su132212560
[45] Siders AR. Adaptive capacity to climate change: A synthesis of concepts, methods, and findings in a fragmented field. WIREs Climate Change 2019; 10(3): e573. doi: 10.1002/wcc.573
[46] Simpson NP, Mach KJ, Constable AC, et al. A framework for complex climate change risk assessment. One Earth 2021; 4(4): 489–501. doi: 10.1016/j.oneear.2021.03.005
[47] van Ginkel KCH, Botzen WJW, Haasnoot M, et al. Climate change induced socioeconomic tipping points: Review and stakeholder consultation for policy relevant research. Environmental Research Letters 2020; 15(2): 023001. doi: 10.1088/1748-9326/ab6395
[48] Dino IG, Akgül CM. Impact of climate change on the existing residential building stock in Turkey: An analysis on energy use, greenhouse gas emissions and occupant comfort. Renewable Energy 2019; 141: 828–846. doi: 10.1016/j.renene.2019.03.150
[49] Allen M, Antwi-Agyei P, Aragon-Durand F, et al. Technical Summary: Global Warming of 1.5 °C. Intergovernmental Panel on Climate Change; 2019.
[50] Mikhaylov A, Moiseev N, Aleshin K, Burkhardt T. Global climate change and greenhouse effect. Entrepreneurship and Sustainability Issues 2020; 7(4): 2897–2913. doi: 10.9770/jesi.2020.7.4(21)
[51] Toimil A, Losada IJ, Nicholls RJ, et al. Addressing the challenges of climate change risks and adaptation in coastal areas: A review. Coastal Engineering 2020; 156: 103611. doi: 10.1016/j.coastaleng.2019.103611
[52] Glasser R. The climate change imperative to transform disaster risk management. International Journal of Disaster Risk Science 2020; 11(2): 152–154. doi: 10.1007/s13753-020-00248-z
[53] Bustamante MMC, Silva JS, Scariot A, et al. Ecological restoration as a strategy for mitigating and adapting to climate change: Lessons and challenges from Brazil. Mitigation and Adaptation Strategies for Global Change 2019; 24: 1249–1270. doi: 10.1007/s11027-018-9837-5
[54] Seddon N. Harnessing the potential of nature-based solutions for mitigating and adapting to climate change. Science 2022; 376(6600): 1410–1416. doi: 10.1126/science.abn9668
[55] Wang B, Zhang M, Wei J, et al. Changes in extreme events of temperature and precipitation over Xinjiang, northwest China, during 1960–2009. Quaternary International 2013; 298: 141–151. doi: 10.1016/j.quaint.2012.09.010
[56] Worku G, Teferi E, Bantider A, Dile Y. Observed changes in extremes of daily rainfall and temperature in Jemma Sub-Basin, Upper Blue Nile Basin, Ethiopia. Theoretical and Applied Climatology 2019; 135: 839–854. doi: 10.1007/s00704-018-2412-x
[57] Sun W, Bocchini P, Davison BD. Applications of artificial intelligence for disaster management. Natural Hazards 2020; 103(3): 2631–2689. doi: 10.1007/s11069-020-04124-3
[58] Abram NJ, Henley BJ, Gupta AS, et al. Connections of climate change and variability to large and extreme forest fires in southeast Australia. Communications Earth & Environment 2021; 2(1): 8. doi: 10.1038/s43247-020-00065-8
[59] Greve P, Roderick ML, Ukkola AM, Wada Y. The aridity index under global warming. Environmental Research Letters 2019; 14(12): 124006. doi: 10.1088/1748-9326/ab5046
[60] Ostad-Ali-Askari K, Kharazi HG, Shayannejad M, Zareian MJ. Effect of climate change on precipitation patterns in an arid region using GCM models: Case study of Isfahan-Borkhar plain. Natural Hazards Review 2020; 21(2): 04020006. doi: 10.1061/(ASCE)NH.1527-6996.0000367
[61] Almazroui M, Saeed F, Saeed S, et al. Projected change in temperature and precipitation over Africa from CMIP6. Earth Systems and Environment 2020; 4: 455–475. doi: 10.1007/s41748-020-00161-x
[62] Kim SK, Shin J, An SI, et al. Widespread irreversible changes in surface temperature and precipitation in response to CO2 forcing. Nature Climate Change 2022; 12(9): 834–840. doi: 10.1038/s41558-022-01452-z
[63] Dantas LG, dos Santos CAC, Santos CAG, et al. Future changes in temperature and precipitation over northeastern Brazil by CMIP6 model. Water 2022; 14(24): 4118. doi: 10.3390/w14244118
[64] Viloria JA, Olivares BO, García P, et al. Mapping projected variations of temperature and precipitation due to climate change in Venezuela. Hydrology 2023; 10(4): 96. doi: 10.3390/hydrology10040096
[65] Dittrich R, McCallum S. How to measure the economic health cost of wildfires—A systematic review of the literature for northern America. International Journal of Wildland Fire 2020; 29(11): 961–973. doi: 10.1071/WF19091
[66] Hunter ME, Robles MD. Tamm review: The effects of prescribed fire on wildfire regimes and impacts: A framework for comparison. Forest Ecology and Management 2020; 475: 118435. doi: 10.1016/j.foreco.2020.118435
[67] Nolan RH, Anderson LO, Poulter B, Varner JM. Increasing threat of wildfires: The year 2020 in perspective: A global ecology and biogeography special issue. Global Ecology and Biogeography 2022; 31(10): 1898–1905. doi: 10.1111/geb.13588
[68] Prosperi P, Bloise M, Tubiello FN, et al. New estimates of greenhouse gas emissions from biomass burning and peat fires using MODIS Collection 6 burned areas. Climatic Change 2020; 161: 415–432. doi: 10.1007/s10584-020-02654-0
[69] Aliaga VS, Ferrelli F, Piccolo MC. Regionalization of climate over the Argentine Pampas. International Journal of Climatology 2017; 37(S1): 1237–1247. doi: 10.1002/joc.5079
[70] Scian B, Labraga JC, Reimers W, Frumento O. Characteristics of large-scale atmospheric circulation related to extreme monthly rainfall anomalies in the Pampa Region, Argentina, under non-ENSO conditions. Theoretical and Applied Climatology 2006; 85(1–2): 89–106. doi: 10.1007/s00704-005-0182-8
[71] Scian B, Pierini J. Variability and trends of extreme dry and wet seasonal precipitation in Argentina. A retrospective analysis. Atmósfera 2013; 26(1): 3–26. doi: 10.1016/S0187-6236
[72] Ferrelli F, Brendel AS, Piccolo MC, Perillo GME. Assessment of precipitation trends in pampas region (Argentina) during the period 1960–2018 (Spanish). RA’EGA 2021; 51: 41–56. doi: 10.5380/raega.v51i0.69962
[73] Barros VR, Boninsegna JA, Camilloni IA, et al. Climate change in Argentina: Trends, projections, impacts and adaptation. WIREs Climate Change 2015; 6(2): 151–169. doi: 10.1002/wcc.316
[74] Garay DD. Rural and Forest Fires: The Importance of Remote Sensing and Geographic Information Systems (Spanish). Estación Experimental Agropecuaria La Rioja, INTA; 2020.
[75] Delegido J, Pezzola A, Casella A, et al. Fire severity estimation in southern of the Buenos Aires province, Argentina, using Sentinel-2 and its comparison with Landsat-8 (Spanish). Revista de Teledetección 2018; 51: 47–60. doi: 10.4995/raet.2018.8934
[76] Ferrelli F, Casado A. Relationship between climatic variability and fires in the southern Pampas region (Spanish). In: Proceedings of the XIV Jornadas Nacionales de Geografía Física; 23–27 May 2022; Corrientes, Argentina.
[77] Ferrelli F, Brendel AS, Aliaga VS, et al. Climate regionalization and trends of climate based on daily temperature and precipitation extremes in the south of the Pampas (Argentina). Geographical Research Letters 2019; 45(1): 393–416. doi: 10.18172/cig.3707
[78] Fick SE, Hijmans RJ. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology 2017; 37(12): 4302–4315. doi: 10.1002/joc.5086
[79] Angeles G. Evaluation of Potential Fire Risk in A Semi-Natural Area (Spanish). Villa Ventana y sectores adyacentes Universidad Nacional del Sur press; 1995.
[80] Brendel AS, Ferrelli F, Piccolo MC, Perillo GME. Assessment of the effectiveness of supervised and unsupervised methods: Maximizing land-cover classification accuracy with spectral indices data. Journal of Applied Remote Sensing 2019; 13(1): 014503. doi: 10.1117/1.JRS.13.014503
[81] Herrera LP, Hermida VG, Martínez GA, et al. Remote sensing assessment of Paspalum quadrifarium grasslands in the flooding Pampa, Argentina. Rangeland Ecology & Management 2005; 58(4): 406–412. doi: 10.2111/1551-5028(2005)058[0406:RSAOPQ]2.0.CO;2
[82] Lara B, Gandini M. Quantifying the land cover changes and fragmentation patterns in the Argentina Pampas, in the last 37 years (1974–2011). GeoFocus. International Review of Geographical Information Science and Technology 2014; (14): 163–180.
[83] Sánchez M, Baldassini P, Fischer MdlÁ, et al. Where, when and how large fires occur in the province of La Pampa, Argentina: A characterization based on remote sensing (Spanish). Ecología Austral 2023; 33(1): 211–228. doi: 10.25260/EA.23.33.1.0.1972
[84] Khakim MYN, Bama AA, Yustian I, et al. Peatland subsidence and vegetation cover degradation as impacts of the 2015 El niño event revealed by Sentinel-1A SAR data. International Journal of Applied Earth Observation and Geoinformation 2020; 84: 101953. doi: 10.1016/j.jag.2019.101953
[85] Junaidi SN, Khalid N, Othman AN, et al. Analysis of the relationship between forest fire and land surface temperature using Landsat 8 OLI/TIRS imagery. In: Proceedings of the IOP Conference Series: Earth and Environmental Science; 23–24 March 2021; Shah Alam, Malaysia. pp. 012005.
[86] Abatzoglou JT, Williams AP, Barbero R. Global emergence of anthropogenic climate change in fire weather indices. Geophysical Research Letters 2019; 46(1): 326–336. doi: 10.1029/2018GL080959
[87] Shen X, Liu B, Jiang M, et al. Spatiotemporal change of marsh vegetation and its response to climate change in China from 2000 to 2019. Journal of Geophysical Research: Biogeosciences 2021; 126(2): e2020JG006154. doi: 10.1029/2020JG006154
[88] Cardil A, Rodrigues M, Ramirez J, et al. Coupled effects of climate teleconnections on drought, Santa Ana winds and wildfires in southern California. Science of the Total Environment 2021; 765: 142788. doi: 10.1016/j.scitotenv.2020.142788
[89] Brown EK, Wang J, Feng Y. US wildfire potential: A historical view and future projection using high-resolution climate data. Environmental Research Letters 2021; 16(3): 034060. doi: 10.1088/1748-9326/aba868
[90] Richardson D, Black AS, Irving D, et al. Global increase in wildfire potential from compound fire weather and drought. npj Climate and Atmospheric Science 2022; 5(1): 23. doi: 10.1038/s41612-022-00248-4
[91] Brendel A, Bohn VY, Piccolo MC. Climatic variability effects on the vegetation state and water coverage in a watershed of temperate climate (Argentina). Anuãrio do Instituto de Geociencias 2017; 40: 5–16. doi: 10.11137/2017_2_05_16
[92] Vicente-Serrano SM, Beguería S, López-Moreno JI. A multi-scalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. Journal of Climate 2010; 23(7): 1696–1718. doi: 10.1175/2009JCLI2909.1
[93] Lasslop G, Coppola AI, Voulgarakis A, et al. Influence of fire on the carbon cycle and climate. Current Climate Change Reports 2019; 5: 112–123. doi: 10.1007/s40641-019-00128-9
[94] Sawalha IH. A contemporary perspective on the disaster management cycle. Foresight 2020; 2(4): 469–482. doi: 10.1108/FS-11-2019-0097
[95] Carmen E, Fazey I, Ross H, et al. Building community resilience in a context of climate change: The role of social capital. Ambio 2022; 51(6): 1371–1387. doi: 10.1007/s13280-021-01678-9
[96] Finney MA. The wildland fire system and challenges for engineering. Fire Safety Journal 2021; 120: 103085. doi: 10.1016/j.firesaf.2020.103085
[97] Ishiwatari M. Institutional coordination of disaster management: Engaging national and local governments in Japan. Natural Hazards Review 2021; 22(1): 04020059. doi: 10.1061/(ASCE)NH.1527-6996.0000423
[98] Ebi KL, Vanos J, Baldwin JW, et al. Extreme weather and climate change: Population health and health system implications. Annual Review of Public Health 2021; 42(1): 293–315. doi: 10.1146/annurev-publhealth-012420-105026
[99] Janizadeh S, Avand M, Jaafari A, et al. Prediction success of machine learning methods for flash flood susceptibility mapping in the Tafresh Watershed, Iran. Sustainability 2019; 11(19): 5426. doi: 10.3390/su11195426
[100] Van Hoang T, Chou TY, Fang YM, et al. Mapping forest fire risk and development of early warning system for NW Vietnam using AHP and MCA/GIS methods. Applied Sciences 2020; 10(12): 4348. doi: 10.3390/app10124348
[101] Sufri S, Dwirahmadi F, Phung D, et al. A systematic review of community engagement (CE) in disaster early warning systems (EWSs). Progress in Disaster Science 2020; 5: 100058. doi: 10.1016/j.pdisas.2019.100058
[102] Himoto K. Conceptual framework for quantifying fire resilience—A new perspective on fire safety performance of buildings. Fire Safety Journal 2021; 120: 103052. doi: 10.1016/j.firesaf.2020.103052
[103] Oliveira MJSP, Pinheiro P. Factors and barriers to tacit knowledge sharing in non-profit organizations—A case study of volunteer firefighters in Portugal. Journal of the Knowledge Economy 2021; 12: 1294–1313. doi: 10.1007/s13132-020-00665-x
[104] Lohmander P. Optimization of forestry, infrastructure and fire management. Caspian Journal of Environmental Sciences 2021; 19(2): 287–316. doi: 10.22124/CJES.2021.4746
[105] Stephens SL, Battaglia MA, Churchill DJ, et al. Forest restoration and fuels reduction: Convergent or divergent? Bioscience 2021; 71(1): 85–101. doi: 10.1093/biosci/biaa134
[106] Vaverková MD, Winkler J, Uldrijan D, et al. Fire hazard associated with different types of photovoltaic power plants: Effect of vegetation management. Renewable and Sustainable Energy Reviews 2022; 162: 112491. doi: 10.1016/j.rser.2022.112491
[107] McWethy DB, Schoennagel T, Higuera PE, et al. Rethinking resilience to wildfire. Nature Sustainability 2019; 2(9): 797–804. doi: 10.1038/s41893-019-0353-8
[108] Steel ZL, Foster D, Coppoletta M, et al. Ecological resilience and vegetation transition in the face of two successive large wildfires. Journal of Ecology 2021; 109(9): 3340–3355. doi: 10.1111/1365-2745.13764
[109] Cartier EA, Taylor LL. Living in a wildfire: The relationship between crisis management and community resilience in a tourism-based destination. Tourism Management Perspectives 2020; 34: 100635. doi: 10.1016/j.tmp.2020.100635
[110] Gil-Romera G, Adolf C, Benito BM, et al. Long-term fire resilience of the Ericaceous Belt, Bale Mountains, Ethiopia. Biology Letters 2019; 15(7): 20190357. doi: 10.1098/rsbl.2019.0357
[111] Robertson T, Docherty P, Millar F, et al. Theory and practice of building community resilience to extreme events. International Journal of Disaster Risk Reduction 2021; 59: 102253. doi: 10.1016/j.ijdrr.2021.102253
Copyright (c) 2023 Federico Ferrelli
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it for any purpose, even commercially, under the condition that the authors are given credit. With this license, authors hold the copyright.
