Review of state-of-the-art decision support systems (DSSs) for prevention and suppression of forest fires

Abstract

Forest ecosystems are our priceless natural resource and are a key component of the global carbon budget. Forest fires can be a hazard to the viability and sustainable management of forests with consequences for natural and cultural environments, economies, and the life quality of local and regional populations. Thus, the selection of strategies to manage forest fires, while considering both functional and economic efficiency, is of primary importance. The use of decision support systems (DSSs) by managers of forest fires has rapidly increased. This has strengthened capacity to prevent and suppress forest fires while protecting human lives and property. DSSs are a tool that can benefit incident management and decision making and policy, especially for emergencies such as natural disasters. In this study we reviewed state-of-the-art DSSs that use: database management systems and mathematical/economic algorithms for spatial optimization of firefighting forces; forest fire simulators and satellite technology for immediate detection and prediction of evolution of forest fires; GIS platforms that incorporate several tools to manipulate, process and analyze geographic data and develop strategic and operational plans.

Keywords

Decision support systems / Fire behavior simulation / Forest fires / Geographic information system / Mathematical algorithms / Risk management

References

[1] Ager AA, Preisler HK, Arca B, Spano D, Salis M. Wildfire risk estimation in the Mediterranean area. Environmetrics, 2014, 25(6): 384-396. Special Issue: Wildland Fires)

[2] Akay AE, Wing MG, Sivrikaya F, Sakar D. A GIS-based decision support system for determining the shortest and safest route to forest fires: a case study in Mediterranean Region of Turkey. Environ Monit Assess, 2011, 184: 1391-1407.

[3] Arca B, Duce P, Laconi M, Pellizzaro G, Salis M, Spano D. Evaluation of FARSITE simulator in Mediterranean maquis. Int J Wildland Fire, 2007, 16(5): 563-572.

[4] Barbosa MR, Sicoli Seoane JC, Guimaraes Buratto M, de Oliveira Santana, Dias L, Carvalho Raivel JP, Lobos Martins F. Forest fire alert system: a GeoWeb GIS prioritization model considering land susceptibility and hotspots—a case study in the Caraja´s National Forest, Brazilian Amazon. Int J Geogr Inf Sci, 2010, 24(6): 873-901.

[5] Berjak SJ, Hearne JW. An improved cellular automaton model for simulating fire in a spatially heterogeneous Savanna system. Ecol Model, 2002, 148(2): 133-151.

[6] Beverly JL, Herd EPK, Conner JCR. Modeling fire susceptibility in west central Alberta, Canada. For Ecol Manag, 2009, 258(7): 1465-1478.

[7] Bonazountas M, Kallidromitou D, Kassomenos P, Passas N. A decision support system for managing forest fire casualties. J Environ Manag, 2007, 84: 412-418.

[8] Bonnicksen TM (2008) Greenhouse gas emissions from four California wildfires: opportunities to prevent and reverse environmental and climate impacts. FCEM Report 2. The Forest Foundation, Auburn, California. p 19

[9] Brown WJ, Jones BL, Lee JSW, Woolford DG, Wotton BM. Forest fire risk assessment: An illustrative example from Ontario, 2010, Canada: J Probab Stat.

[10] Calkin DC, Finney MA, Ager AA, Thompson MP, Gebert KM. Progress towards and barriers to implementation of a risk framework for US federal wildland fire policy and decision making. For Policy Econ, 2011, 13: 378-389.

[11] Carmel Y, Paz S, Jahashan F, Shoshany M. Assessing fire risk using Monte Carlo simulations of fire spread. For Ecol Manag, 2009, 257: 370-377.

[12] Chen K, Blong R, Jacobson C. Towards an integrated approach to natural hazards risk assessment using GIS: with reference to bushfires. Environ Manag, 2003, 31(4): 546-560.

[13] Chen K, Jacobson C, Blong R. Artificial neural networks for risk decision support in natural hazards: a case study of assessing the probability of house survival from bushfires. Environ Model Assess, 2004, 9: 189-199.

[14] Christopoulou OG (2011) Deforestation/reforestation in Mediterranean Europe: the case of Greece. Soil Erosion Studies. Dr. Danilo Godone (Ed.), ISBN: 978-953-307-710-9, InTech. doi: 10.5772/23466. http://www.intechopen.com/books/soil-erosion-studies/deforestation-reforestation-in-mediterranean-europe-the-case-of-greece; last accessed Apr 15 2013

[15] Condorelli A, Mussumeci G Konecny M GIS procedure to forecast and manage woodland fires. Geographic information and cartography for risk and crisis management, lecture notes in geoinformation and cartography, 2010, Berlin: Springer.

[16] Calkin DC, Thompson MP, Finney MA, Hyde KD. A real-time risk assessment tool supporting wildland fire decisionmaking. J For, 2011, 109(5): 274-280.

[17] De Longueville B, Annoni A, Schade S, Ostlaender N, Whitmore C. Digital earth’s nervous system for crisis events: real-time sensor web enablement of volunteered geographic information. Int J Digit Earth, 2010, 3(3): 242-259.

[18] Demir M, Kucukosmanoglu A, Hasdemir M, Ozturk T, Acar H. Assessment of forest roads and firebreaks in Turkey. Afr J Biotechnol, 2009, 8(18): 4553-4561.

[19] Dijkstra EW. A note on two problems on connection with graphs. Numer Math, 1959, 1: 269-271.

[20] Dimitrakopoulos AP, Bemmerzouk AM, Mitsopoulos ID. Evaluation of the Canadian fire weather index system in an eastern Mediterranean environment. Meteorol Appl, 2011, 18: 83-93.

[21] Dimopoulou M, Giannikos I. Towards an integrated framework for forest fire control. Eur J Oper Res, 2004, 152: 476-486.

[22] Dong XU, Shao G, Dai L, Hao Z, Tang L, Wang H. Mapping forest fire risk zones with spatial data and principal component analysis. Sci China Ser E, 2006, 49: 140-149.

[23] European Commission (2012a) European on-line Decision Support System for Forest Fire. http://ec.europa.eu/information_society/activities/eten/cf/opdb/cf/project/index.cfm?mode=desc&project_ref=ETEN-26789#pastnavigation; last accessed 14 Dec 2012

[24] European Commission (2012b). Forest Fires in Europe, Middle East and North Africa 2011. http://www.fire.uni-freiburg.de/inventory/database/EU-Forest-Fires-in-Europe-2011.pdf; last accessed 7 April 2013

[25] European Commission (2014) Forest fires in the Mediterranean: a burning issue—WWF. http://ec.europa.eu/environment/forests/pdf/meeting140504_wwffirstdocument.pdf; last accessed 28 Dec 2014

[26] Fan D, Shi P (2010) Improvement of Dijkstra’s algorithm and its application in route planning. In: Proceedings of 2010 seventh international conference on fuzzy systems and knowledge discovery (FSKD), 10–12 August, 2010. Yantai, Shandong. Volume: 4. pp. 1901–1904. doi: 10.1109/FSKD.2010.5569452

[27] Finney MA (1994) Modeling the spread and behavior of prescribed natural fires. In: Proceedings of the 12th conference on fire and forest meteorology, 26–28 October, 1993; Jekyll Island, GA. Bethesda, MD: Society of American Foresters: 138–143

[28] Finney MA (1998) FARSITE: Fire Area Simulator—Model development and evaluation. Res. Pap. RMRS-RP-4. Ogden, UT: USDA Forest Service, Rocky Mountain Research Station. p 47

[29] Finney MA (2004) FARSITE: Fire Area Simulator—model development and evaluation (Revised). Res. Pap. RMRS-RP-4, Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p 47

[30] Flannigan M, Stocks B, Turetsky M, Wotton M. Impacts of climate change on fire activity and fire management in the circumboreal forest. Glob Change Biol, 2009, 15: 549-560.

[31] Forestry Canada Fire Danger Group (1992) Development and structure of the Canadian forest fire behavior prediction system. Ottawa (ON): Forestry Canada, Fire Danger Group and Science and Sustainable Development Directorate. p 64

[32] Forestry Commission (2014) Forest fires and climate change. http://www.forestry.gov.uk/fr/infd-7wlahk; last accessed 22 Dec 2014

[33] Giovando C, Whitmore C, Camia A, San Miguel J, Boca R, Kucera J (2013) Geospatial support to forest fire crisis management at the European level. http://www.gdmc.nl/zlatanova/Gi4DM2010/gi4dm/Pdf/p77.pdf; last accessed 16 Jan 2013

[34] Glasa J. Computer simulation and predicting dangerous forest fire behavior. Int J Math Comput Simul, 2009, 3(2): 65-72.

[35] Gumusay MU, Sahin K. Visualization of forest fires interactively on the internet. Sci Res Essays, 2009, 4(11): 1163-1174.

[36] Hatfield DC, Wiitala MR, Wilson AE, Levy EJ (2008) A fast method for calculating emergency response times using travel resistance surfaces. In: Proceedings of the second international symposium on fire economics, planning, and policy: a global view. González-Cabán, Armando, tech. coord. Gen. Tech. Rep. PSW-GTR-208, Albany, CA: Pacific Southwest Research Station, Forest Service, U.S. Department of Agriculture. 720 p

[37] Hernandez Encinas A, Hernandez Encinas L, Hoya White S, Martin del Rey A, Rodriguez Sanchez G. Simulation of forest fire fronts using cellular automata. Adv Eng Softw, 2007, 38: 372-378.

[38] Hirsch KG (1996) Canadian Forest Fire Behavior Prediction (FBP) System: user’s guide. Special Report 7. Natural Resources Canada, Canadian Forest Service

[39] Hurteau MD, Koch GW, Hungate BA. Carbon protection and fire risk reduction: toward a full accounting of forest carbon offsets. Front Ecol Environ, 2008, 6: 493-498.

[40] Ichoua S, Gendreau M, Potvin JY. Diversion issues in real-time vehicle dispatching. Transp Sci, 2000, 34(4): 426-438.

[41] Iliadis LS, Papastavrou AK, Lefakis PD. A computer-system that classifies the prefectures of Greece in forest fire risk zones using fuzzy sets. For Policy Econ, 2002, 4: 43-54.

[42] Intergovernmental Panel on Climate Change (IPCC) (2007) Climate change 2007: Synthesis report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, (eds.) R. K. Pachauri and A. Reisinger. Geneva: IPCC. http://www.ipcc.ch/pdf/assessment-report/ar4/syr/ar4_syr.pdf; last accessed 04 May 2016

[43] Jaiswal RK, Mukherjee S, Raju KD, Saxena R. Forest fire risk zone mapping from satellite imagery and GIS. Int J Appl Earth Obs Geoinf, 2002, 4: 1-10.

[44] Kai N, Zhang YT, Ma YP. Shortest path analysis based on Dijkstra’s algorithm in emergency response system. IJEECS, 2014, 12(5): 3476-3482.

[45] Kalabokidis K, Xanthopoulos G, Moore P, Caballero D, Kallos G, Llorens J, Roussou O, Vasilakos C. Decision support system for forest fire protection in the Euro-Mediterranean region. Eur J For Res, 2012, 131(3): 597-608.

[46] Kaloudis S, Tocatlidou A, Lorentzos NA, Sideridis AB, Karteris M. Assessing wildfire destruction danger: a decision support system incorporating uncertainty. Ecol Model, 2005, 181: 25-38.

[47] Karafyllidis I, Thanailakis A. A model for predicting forest fire spreading using cellular automata. Ecol Model, 1997, 99: 87-97.

[48] Keenan P. Modelling vehicle routing in GIS. Oper Res, 2008, 8: 201-218.

[49] Keramitsoglou I, Kiranoudis C, Sarimveis H, Sifakis N. A multidisciplinary decision support system for forest fire crisis management. Environ Manag, 2004, 33(2): 212-225.

[50] Kourtz P, Nozaki S, O’ Regan WG (1977) Forest fires in the computer—a model to predict the perimeter location of a forest fire. Information Report FF-X-65. Fisheries and Environment Canada

[51] Lapucci A, Lombardo S, Petri M, Santucci A (2005) A KDD based multicriteria decision making model for fire risk evaluation. In: Conference proceedings on 8th AGILE conference on GIScience. Association Geographic Information Laboratories Europe. 26–28 May 2005. Estoril, Portugal

[52] Lee BS, Alexander ME, Hawkes BC, Lynham TJ, Stocks BJ, Englefield P. Information systems in support of wildland fire management decision making in Canada. Comput Electron Agric, 2002, 37: 185-198.

[53] Lee H, Lim S, Paik H. An assessment of fire-damaged forest using spatial analysis techniques. J Spat Sci, 2010, 55(2): 289-301.

[54] Lopes AMG, Sousa ACM, Viegas DX. Numerical simulation of turbulent flow and fire propagation in complex topography. Numer Heat Transf A Appl, 1995, 7(5): 229-253.

[55] Lu A, Tian H, Liu M, Liu J, Melillo JM. Spatial and temporal patterns of carbon emissions from forest fires in China from 1950 to 2000. J Geophys Res, 2006, 111: D05313.

[56] Mavsar R, Caban AG, Varela E. The state of development of fire management decision support systems in America and Europe. For Policy Econ, 2012, 29: 45-55.

[57] Mitsakis E, Stamos I, Papanikolaou A, Aifadopoulou G, Kontoes H. Assessment of extreme weather events on transport networks: case study of the 2007 wildfires in Peloponnesus. Nat Hazards, 2014, 72: 87-107.

[58] Moreno JM (2014) Forest Fires under climate, social and economic changes in Europe, the Mediterranean and other fire-affected areas of the world. FUME. Lessons learned and outlook. http://fumeproject.uclm.es/; last accessed 25 Oct 2014

[59] Morton DC, Roessing ME, Camp AE, Tyrrell ML (2003) Assessing the Environmental, Social, and Economic Impacts of Wildfire. GISF Research Paper 001. Forest Health Initiative. Yale University. School of Forestry and Environmental Studies. Global Institute of Sustainable Forestry. 360 Prospect Street, New Haven, Connecticut 06511 USA

[60] Mutlu M, Popescu SC, Zhao K. Sensitivity analysis of fire behavior modeling with LIDAR-derived surface fuel maps. For Ecol Manag, 2008, 256(3): 289-294.

[61] National Interagency Fire Center (2013). Interagency Standards for Fire and Fire Aviation Operations. http://www.nifc.gov/PUBLICATIONS/redbook/2013/2013RedBook.pdf; last accessed 30 Jan 2013

[62] Noonan-Wright EK, Opperman TS, Finney MA, Zimmerman TG, Seli RC, Elenz LM, Calkin DE, Fiedler JR (2011) Developing the US wildland fire decision support system. J Combust 2011: Article ID 168473. doi:10.1155/2011/168473

[63] Parisien MA, Kafka VG, Hirsch KG, Todd JB, Lavoie SG, Maczek PD (2005) Mapping wildfire susceptibility with the BURN-P3 simulation model. Natural Resources Canada, Canadian Forest Service, Northern Forestry Center, Edmonton, Alberta. Information Report NOR-X-405

[64] Parisien MA, Parks SA, Miller C, Krawchuk MA, Heathcott M, Moritz MA. Contributions of ignitions, fuels, and weather to the spatial patterns of burn probability of a boreal landscape. Ecosystems, 2011, 14: 1141-1155.

[65] Parisien MA, Walker GR, Little JM, Simpson BN, Wang X, Perrakis DDB. Considerations for modeling burn probability across landscapes with steep environmental gradients: an example from the Columbia Mountains, Canada. Nat Hazards, 2013, 66: 439-462.

[66] Power CJ (2006) A spatial decision support system for mapping bushfire hazard potential using remotely sensed data. In: Proceedings of Bushfire conference 2006—Life in a Fire-Prone Environment: Translating Science Into Practice. Brisbane, 6–9 June 2006

[67] Rabade JM, Aragoneses C (2008) Social impact of large-scale forest fires. In: Proceedings of the second international symposium on fire economics, planning, and policy: a global view. González-Cabán, Armando, tech. coord. Gen. Tech. Rep. PSW-GTR-208, Albany, CA: Pacific Southwest Research Station, Forest Service, U.S. Department of Agriculture. 720 p

[68] Rees DW. Least-cost paths in mountainous terrain. Comput Geosci, 2004, 30(3): 203-209.

[69] Richards GD. An elliptical growth model of forest fire fronts and its numerical solution. Int J Numer Methods Eng, 1990, 30: 1163-1179.

[70] Rodriguez-Puente R, Lazo-Cortes MS. Algorithm for shortest path search in Geographic Information Systems by using reduced graphs. SpringerPlus, 2013, 2: 291.

[71] Rothermel RC (1972) A mathematical model for predicting fire spread in wildland fuels. USDA For Serv Res Pap, INT-115

[72] Rothermel RC (1991) Predicting behavior and size of crown fires in the northern Rocky Mountains. USDA For Serv Res Pap, INT-438

[73] Ryu SR, Chen J, Zheng D, Lacroix JJ. Relating surface fire spread to landscape structure: an application of FARSITE in a managed forest landscape. Landsc Urban Plan, 2007, 83(4): 275-283.

[74] Sakellariou S, Samara F, Tampekis S, Sfougaris A, Christopoulou O (2015) Targeting to an efficient prevention strategy of forest fires, estimating the fire hazard on islands. The case study of Thasos island, Greece. IJAENT 2(11):27–32

[75] Schmidt DA, Taylor AH, Skinner CN. The influence of fuels treatment and landscape arrangement on simulated fire behavior, Southern Cascade range, California. For Ecol Manag, 2008, 255(8–9): 3170-3184.

[76] Sharma LK, Kanga S, Nathawat MC, Sinha S, Pandey PC. Fuzzy AHP for forest fire risk modeling. DPM, 2012, 21(2): 160-171.

[77] Stephens SL. Evaluation of the effects of silvicultural and fuels treatments on potential fire behaviour in Sierra Nevada mixed-conifer forests. For Ecol Manag, 1998, 105(1–3): 21-35.

[78] Stratton RD. Assessing the effectiveness of landscape fuel treatments on fire growth and behavior. J For, 2004, 102(7): 32-40.

[79] Sui M, Wang WF. Application of GIS best path algorithm in Harbin roads. World Rural Obs, 2012, 4(1): 86-90.

[80] Temiz N, Tecim V (2009) Spatial multi criteria decision making in forest fire fighting planning. Turkish Chamber of Mechanical Engineers. http://www.mmo.org.tr/resimler/dosya_ekler/340ab740e0e799b_ek.pdf?dergi=753; last accessed 12 Feb 2014

[81] Thompson M, Calkin D. Uncertainty and risk in wildland fire management: a review. J Environ Manag, 2011, 92: 1895-1909.

[82] Thompson MP, Calkin DE, Gilbertson-Day JW, Ager AA. Advancing effects analysis for integrated, large-scale wildfire risk assessment. Environ Monit Assess, 2011, 179: 217-239.

[83] Thompson MP, Ager AA, Finney MA, Calkin DE, Vaillant NM (2012) The science and opportunity of wildfire risk assessment. Chapter 6: pp. 99–120 in the book « Novel Approaches and Their Applications in Risk Assessment » , Edited by Yuzhou Luo, ISBN 978-953-51-0519-0, Publisher: InTech, Chapters published April 20, 2012 under CC BY 3.0 license. doi: 10.5772/2548. http://www.intechopen.com/books/novel-approaches-and-their-applications-in-risk-assessment/advancements-in-integrated-wildfire-risk-assessment; DOI: 10.5772/38210; last accessed 20 Sept 2013

[84] Tsagari K, Karetsos G, Proutsos N (2011) Forest Fires in Greece, 1983–2008. WWF Hellas and NAGREF-IMFE and FPT (In Greek)

[85] Tymstra C, Flannigan MD, Armitage OB, Logan K. Impact of climate change on area burned in Alberta’s boreal forest. Int J Wildland Fire, 2007, 16(2): 153-160.

[86] Tymstra C, Bryce RW, Wotton BM, Taylor SW, Armitage OB (2010) Development and structure of Prometheus: the Canadian Wildland Fire Growth Simulation Model. Nat Resour Can, Can For Serv, North For Cent, Edmonton, AB. Inf. Rep. NOR-X-417

[87] University of Alberta (2006) Forest fires a huge cost to health. www.sciencedaily.com/releases/2006/08/060810211036.htm; last accessed 10 Nov 2015

[88] Vadrevu KP, Eaturu A, Badarinath KVS. Fire risk evaluation using multicriteria analysis—a case study. Environ Monit Assess, 2010, 166: 223-239.

[89] Vakalis D, Sarimveis H, Kiranoudis C, Alexandridis A, Bafas G. A GIS based operational system for wildland fire crisis management I. Mathematical modelling and simulation. Appl Math Model, 2004, 28: 389-410.

[90] Vakalis D, Sarimveis H, Kiranoudis C, Alexandridis A, Bafas G. A GIS based operational system for wildland fire crisis management II. System architecture and case studies. Mathematical modelling and simulation. Appl Math Model, 2004, 28: 411-425.

[91] Van der Werf GR, Randerson JT, Giglio L, Collatz GJ, Mu M, Kasibhatla PS, Morton DC, DeFries RS, Jin Y, Van Leeuwen TT. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmos Chem Phys, 2010, 10: 11707-11735.

[92] Van Wagner CE (1987) Development and Structure of the Canadian Forest Fire Weather Index System. Canadian Forestry Service. Forestry Technical Report 35. Ottawa

[93] Vilen T, Fernandes PM. Forest fires in Mediterranean countries: CO₂ emissions and mitigation possibilities through prescribed burning. Environ Manag, 2011, 48: 558-567.

[94] Western Partnership for Wildland Fire Science (2014) Burn-P3. Burn Probability Model (Burn-P3, Version 4.3, User’s Manual, 2014)

[95] WFDSS (2016) Wildland Fire Decision Support System. http://wfdss.usgs.gov/wfdss/WFDSS_Home.shtml; last accessed 10 June 2016

[96] Williams CA, Hanan NP, Neff JC, Scholes RJ, Berry JA, Denning AS, Baker DF. Africa and the global carbon cycle. Carbon Balance Manag, 2007

[97] Wing MG, Eklund A, Sessions J. Applying LiDAR technology for tree measurements in burned landscapes. Int J Wildland Fire, 2010, 19: 104-114.

[98] Wybo JL. FMIS: a decision support system for forest fire prevention and fighting. IEEE Trans Eng Manag, 1998, 45(2): 127-131.

[99] Yassemi S, Dragicevic S, Schmidt M. Design and implementation of an integrated GIS-based cellular automata model to characterize forest fire behavior. Ecol Model, 2008, 210(1–2): 71-84.

[100] Zhan BF. Three fastest shortest path algorithms on real road networks: data structures and procedures. J Geogr Inf Decis Anal, 1997, 1(1): 70-82.