Skip to main content
SpringerLink
Log in

Improved Fire Safety in the Wildland-Urban Interface Through Smart Technologies

  • Chapter
  • First Online:
Intelligent Building Fire Safety and Smart Firefighting

  • 390 Accesses

Abstract

Wildfire activity across the globe has increased in frequency and intensity in recent years. Alongside this increased fire activity has been rapid population growth bringing human settlements closer to wildlands. This has resulted in the growth of the wildland urban interface (WUI) where an increased risk to people and their property from fires exists. To better plan for increased WUI fire activity, various initiatives have focused on building and planning practices that decrease the chance of ignition and increase building survivability in the case of a fire. Crucial to understanding how to best protect the built environment during a WUI fire is a thorough understanding of the causes of WUI fire ignition and spread and the factors that lead to building survivability. In this chapter, we describe key factors leading to building ignition, namely ember ignition and direct flame contact, building features shown to enhance building survivability, land management and fuel treatment practices. We use these basic principles of WUI fire behavior to propose the adoption of artificial intelligence and machine learning to tackle the WUI fire safety problem.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. J.T. Abatzoglou, A.P. Williams, Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl. Acad. Sci. 113(42), 11770–11775 (2016). https://doi.org/10.1073/pnas.1607171113

    Article  Google Scholar 

  2. L. Johnston, R. Blanchi, M. Jappiot, Wildland urban interface, in Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires, 2020th ed. (2019), pp. 1–13

    Google Scholar 

  3. K.M. Hoffman, A.C. Christianson, R.W. Gray, L. Daniels, Western Canada’s new wildfire reality needs a new approach to fire management. Environ. Res. Lett. 17(6) (2022). https://doi.org/10.1088/1748-9326/ac7345

  4. M.M. Godoy, S. Martinuzzi, H.A. Kramer, G.E. Defossé, J. Argañaraz, V.C. Radeloff, Rapid WUI growth in a natural amenity-rich region in central-western Patagonia, Argentina. Int. J. Wildl. Fire 28(7), 473–484 (2019). https://doi.org/10.1071/WF18097

    Article  Google Scholar 

  5. J. McAneney, K. Chen, A. Pitman, 100-years of Australian bushfire property losses: Is the risk significant and is it increasing? J. Environ. Manage. 90(8), 2819–2822 (2009). https://doi.org/10.1016/j.jenvman.2009.03.013

    Article  Google Scholar 

  6. P. Deb et al., Causes of the widespread 2019–2020 Australian bushfire season. Earth’s Futur. 8(11) (2020). https://doi.org/10.1029/2020EF001671

  7. S. Martinuzzi, S.I. Stewart, D.P. Helmers, M.H. Mockrin, R.B. Hammer, V.C. Radeloff, The 2010 wildland-urban interface of the conterminous United States . Research Map NRS-8. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station, 124 p. includes pull-out map

    Google Scholar 

  8. V.C. Radeloff, R.B. Hammer, S.I. Stewart, J.S. Fried, S.S. Holcomb, J.F. McKeefry, The Wildland – urban interface in the United States. Ecol. Appl. 15(3), 799–805 (2005)

    Article  Google Scholar 

  9. A.C. Fernandez-Pello et al., Spot fire ignition of natural fuel beds by hot metal particles, embers, and sparks. Combust. Sci. Technol. 187(1–2), 269–295 (2015). https://doi.org/10.1080/00102202.2014.973953

    Article  Google Scholar 

  10. V. Babrauskas, Firebrands and Embers. Encycl. Wildfires Wildland-Urban Interface Fires 431–444 (2020). https://doi.org/10.1007/978-3-319-52090-2_3

  11. S.L. Quarles, Y. Valachovic, G.M. Nakamura, G.A. Nader, M.J. de Lasaux, Home survival in wildfire-prone areas: building materials and design considerations (University of California, Agriculture and Natural Resources, 2010)

    Book  Google Scholar 

  12. S.E. Caton, R.S.P. Hakes, D.J. Gorham, A. Zhou, M.J. Gollner, Review of pathways for building fire spread in the Wildland urban interface part I: exposure conditions. Fire Technol. 53(2), 429–473 (2017). https://doi.org/10.1007/s10694-016-0589-z

    Article  Google Scholar 

  13. H.E. Anderson, Aids to determining fuel models for estimating fire behavior, in USDA Forest Service General Technical Report INT-122, pp. 1–22 (1982). http://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=1147&context=barkbeetles

  14. J.H. Scott, R.E. Burgan, Standard fire behavior fuel models: a comprehensive set for use with rothermel’s surface fire spread model. General Technical Report RMRS-GTR-153, pp. 1–80 (2005); U.S Forest Service General Technical Report RMRS-GTR-153

    Google Scholar 

  15. T. Beer, The interaction of wind and fire. Boundary-Layer Meteorol. 54(3), 287–308 (1991). https://doi.org/10.1007/BF00183958

    Article  Google Scholar 

  16. W.L. Fons, Analysis of fire spread in light forest fuels. J. Agric. Res. 3 (1946)

    Google Scholar 

  17. R.C. Rothermel, A mathematical model for predicting fire spread in wildland fuels, in USDA Forest Service Res. Pap. INT-115, p. 40 (1972)

    Google Scholar 

  18. F.A. Albini, Estimating windspeeds for predicting wildland fire behavior, vol. 221. Intermountain Forest and Range Experiment Station, Forest Service, US (1979)

    Google Scholar 

  19. F.A. Albini, A model for fire spread in wildland fuels by radiation. Combust. Sci. Technol. 42(5–6), 229–258 (1985). https://doi.org/10.1080/00102208508960381

    Article  Google Scholar 

  20. R.J. Krupar, Wind, in Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires, pp. 1–4 (2019)

    Google Scholar 

  21. H.E. Anderson, R.C. Rothermel, Influence of moisture and wind upon the characteristics of free-burning fires. Symp. Combust. 10(1), 1009–1019 (1965). https://doi.org/10.1016/S0082-0784(65)80243-0

    Article  Google Scholar 

  22. N.P. Cheneyl, J.S. Gouldl, W.R. Catchpole, The influence of fuel, weather and fire shape variables on fire-spread in grasslands, vol. 3, pp. 31–44 (1993)

    Google Scholar 

  23. F.A. Albini, A model for fire spread in wildland fuels by- radiation † a model for fire spread in wildland fuels by radiation. Combust. Flame 42 (1985). https://doi.org/10.1080/00102208508960381

  24. D.R. Weise, X. Zhou, L. Sun, S. Mahalingam, Fire spread in chaparral—’go or no-go?’. Int. J. Wildl. Fire 14, 99–106 (2005). https://doi.org/10.1071/WF04049

    Article  Google Scholar 

  25. D.X. Viegas, Slope and wind effects on fire propagation. Int. J. Wildl. Fire 13(2), 143–156 (2004)

    Article  Google Scholar 

  26. V. Tihay, F. Morandini, P.-A. Santoni, Y. Perez-Ramirez, T. Barboni, Study of the influence of fuel load and slope on a fire spreading across a bed of pine needles by using oxygen consumption calorimetry. J. Phys: Conf. Ser. 395(1), 12075 (2012)

    Google Scholar 

  27. D.D. Drysdale, A.J.R. Macmillan, Flame spread on inclined surfaces. Fire Saf. J. 18(3), 245–254 (1992)

    Article  Google Scholar 

  28. M.J. Gollner, X. Huang, J. Cobian, A.S. Rangwala, F.A. Williams, Experimental study of upward flame spread of an inclined fuel surface. Proc. Combust. Inst. 34(2), 2531–2538 (2013). https://doi.org/10.1016/j.proci.2012.06.063

    Article  Google Scholar 

  29. J.-L. Dupuy, J. Maréchal, D. Portier, J.-C. Valette, The effects of slope and fuel bed width on laboratory fire behaviour. Int. J. Wildl. Fire 20(2), 272–288 (2011)

    Article  Google Scholar 

  30. A. Maranghides, W. Mell, A case study of a community affected by the Witch and Guejito wildland fires. Fire Technol. 47(2), 379–420 (2011). https://doi.org/10.1007/s10694-010-0164-y

    Article  Google Scholar 

  31. J.D. Cohen, Preventing disaster: home ignitabilitiy in the wildland-urban interface. J. For. 15–21 (2000)

    Google Scholar 

  32. J.D. Cohen, Relating flame radiation to home ignition using modeling and experimental crown fires. Can. J. For. Res. 34(8), 1616–1626 (2004). https://doi.org/10.1139/X04-049

    Article  Google Scholar 

  33. A. Maranghides, E.L. Johnsson, Residential structure separation fire experiments. NIST Technol. Note 1600, 42p (2008)

    Google Scholar 

  34. S.L. Manzello, S. Suzuki, Y. Hayashi, Exposing siding treatments, walls fitted with eaves, and glazing assemblies to firebrand showers. Fire Saf. J. 50, 25–34 (2012). https://doi.org/10.1016/j.firesaf.2012.01.006

    Article  Google Scholar 

  35. National Fire Protection Agency, Standard for reducing structure ignition hazards from wildland fire (2018)

    Google Scholar 

  36. International Code Council, International wildland-urban interface code (2021)

    Google Scholar 

  37. A. Maranghides, E.D. Link, S.L. Quarles, D.J. Gorham, NIST Technical Note 2205 WUI Structure/Parcel/Community Fire Hazard Mitigation Methodology (2022)

    Google Scholar 

  38. E.L. Johnsson, A. Maranghides, Effects of wind speed and angle on fire spread along privacy fences, Gaithersburg, MD, 2016. https://doi.org/10.6028/NIST.TN.1894

  39. R.S.P. Hakes, S.E. Caton, D.J. Gorham, M.J. Gollner, A review of pathways for building fire spread in the wildland urban interface part II: response of components and systems and mitigation strategies in the United States. Fire Technol. 53(2), 475–515 (2017). https://doi.org/10.1007/s10694-016-0601-7

    Article  Google Scholar 

  40. S.S. Wessies, M.K. Chang, K.C. Marr, O.A. Ezekoye, Experimental and analytical characterization of firebrand ignition of home insulation materials. Fire Technol. 55(3), 1027–1056 (2019). https://doi.org/10.1007/s10694-019-00818-8

    Article  Google Scholar 

  41. C. Schevin, ASTM E108 standard test methods for fire test of roof coverings. Retrieved from palmex international (2013. http://www.palmex.fr/wp-content

  42. A.D. Syphard, T.J. Brennan, J.E. Keeley, The importance of building construction materials relative to other factors affecting structure survival during wildfire. Int. J. Disaster Risk Reduct. 21, 140–147 (2017). https://doi.org/10.1016/j.ijdrr.2016.11.011

  43. State of California, California building code: materials and construction methods for exterior wildfire exposure (2012)

    Google Scholar 

  44. K. Butler et al., Wind-driven fire spread to a structure from Fences and Mulch (2022). https://doi.org/10.6028/NIST.TN.2228-upd1

  45. S.L. Manzello, S. Suzuki, Y. Hayashi, Enabling the study of structure vulnerabilities to ignition from wind driven firebrand showers: a summary of experimental results. Fire Saf. J. 54, 181–196 (2012). https://doi.org/10.1016/j.firesaf.2012.06.012

    Article  Google Scholar 

  46. W.E. Mell, S.L. Manzello, A. Maranghides, D. Butry, R.G. Rehm, The wildland-urban interface fire problem - current approaches and research needs. Int. J. Wildl. Fire 19(2), 238–251 (2010). https://doi.org/10.1071/WF07131

    Article  Google Scholar 

  47. Texas Forest Service Mitigation and Prevention Department Managing Vegetation in the WUI, “Vegetation Managementin the Wildland Urban Interface,” Texas A&M (2022)

    Google Scholar 

  48. T.J. Duff, J.G. Cawson, T.D. Penman, “Prescribed Burning”, in encyclopedia of wildfires and wildland-urban interface (WUI) fires (Springer International Publishing, Cham, 2018), pp. 1–11

    Book  Google Scholar 

  49. D. Mitchell, M. Smidt, Costs of mechanical fuel reduction treatments, in Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires (Springer International Publishing, 2019), pp. 1–7

    Google Scholar 

  50. S.L. Stephens et al., The effects of forest fuel-reduction treatments in the United States. Bioscience 62(6), 549–560 (2012). https://doi.org/10.1525/bio.2012.62.6.6

    Article  Google Scholar 

  51. G. Xanthopoulos, M. Athanasiou, V. Varela, K. Kaoukis, Deliverable 3.3.–fuel management scenarios (2021)

    Google Scholar 

  52. L.R. Green, Fuelbreaks and other fuel modification for wildland fire control, vol. 499. US Department of Agriculture, Forest Service (1977)

    Google Scholar 

  53. CALFIRE, Fuels Reduction Guide (2021)

    Google Scholar 

  54. A.M. Van Der Bles et al., Communicating uncertainty about facts, numbers and science. R. Soc. Open Sci. 6(5), 181870 (2019)

    Article  Google Scholar 

  55. J.K. Agee, C.N. Skinner, Basic principles of forest fuel reduction treatments. For. Ecol. Manage. 211, 83–96 (2005). https://doi.org/10.1016/j.foreco.2005.01.034

    Article  Google Scholar 

  56. M.A. Moritz et al., Beyond a focus on fuel reduction in the WUI: the need for regional wildfire mitigation to address multiple risks. Front. For. Glob. Chang. 5, 71 (2022)

    Article  Google Scholar 

  57. I. Wildland Resource Management and Baseline Environmental Consulting, “Wildfire Hazard Reduction and Resource Management Plan (Plan), FUEL TREATMENT METHODS, Part 5,” East Bay Hills (2009)

    Google Scholar 

  58. F. Ratcliff et al., Cattle grazing reduces fuel and leads to more manageable fire behavior. Calif. Agric. 76(2), 60–69 (2022)

    Article  Google Scholar 

  59. P.G. Sikkink, Masticated fuels, in Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires (Springer International Publishing, 2018), pp. 1–7

    Google Scholar 

  60. D.J. Shinneman et al., Future direction of fuels management in sagebrush rangelands. Rangel. Ecol. Manag. 86, 50–63 (2023)

    Article  Google Scholar 

  61. S.L. Stephens et al., The effects of forest fuel-reduction treatments in the United States. Bioscience 62(6), 549–560 (2012)

    Article  Google Scholar 

  62. A. Ganteaume, M. Jappiot, C. Lampin, M. Guijarro, C. Hernando, Flammability of some ornamental species in wildland-urban interfaces in southeastern France: laboratory assessment at particle level. Environ. Manage. 52(2), 467–480 (2013). https://doi.org/10.1007/s00267-013-0067-z

    Article  Google Scholar 

  63. D.R. Weise, R.H. White, F.C. Beall, M. Etlinger, Use of the cone calorimeter to detect seasonal differences in selected combustion characteristics of ornamental vegetation. Int. J. Wildl. Fire 14(3), 321–338 (2005). https://doi.org/10.1071/WF04035

    Article  Google Scholar 

  64. T.B. Paveglio, C.M. Edgeley, Fire adapted community. Encycl. Wildfires Wildland-Urban Interface Fires 1–9 (2020). https://doi.org/10.1007/978-3-319-51727-8_114-1

  65. NFPA, Firewise toolkit, in Wildfire Prep. (2016)

    Google Scholar 

  66. H.A. Kramer, M.H. Mockrin, P.M. Alexandre, S.I. Stewart, V.C. Radeloff, Where wildfires destroy buildings in the US relative to the wildland-urban interface and national fire outreach programs. Int. J. Wildl. Fire 27(5), 329–341 (2018). https://doi.org/10.1071/WF17135

    Article  Google Scholar 

  67. T.I. Zohdi, A machine-learning framework for rapid adaptive digital-twin based fire-propagation simulation in complex environments. Comput. Methods Appl. Mech. Eng. 363, 112907 (2020). https://doi.org/10.1016/j.cma.2020.112907

    Article  MathSciNet  Google Scholar 

  68. A. Tohidi, M. Gollner, C. Standohar- S. Quarles, Computer vision techniques for firebrand detection and characterization (2017)

    Google Scholar 

  69. F. Hedayati, B. Bahrani, A. Zhou, S.L. Quarles, D.J. Gorham, A framework to facilitate firebrand characterization. Front. Mech. Eng. 5, 1–14 (2019). https://doi.org/10.3389/fmech.2019.00043

    Article  Google Scholar 

  70. X. Huang, X. Wu, A. Usmani, Perspectives of using artificial intelligence in building fire safety, in Handbook of Cognitive and Autonomous Systems for Fire Resilient Infrastructures (2022)

    Google Scholar 

  71. O. Rios, M.M. Valero, E. Pastor, E. Planas, A data-driven fire spread simulator: validation in vall-llobrega’s fire, vol. 5 (2019), pp. 1–11. https://doi.org/10.3389/fmech.2019.00008

  72. J. Mandel et al., A dynamic data driven wildland fire model. Comput. Sci. 4487(1), 1042–1049 (2007). https://doi.org/10.1007/978-3-540-72584-8_137

    Article  Google Scholar 

  73. M.C. Rochoux, B. Delmotte, B. Cuenot, S. Ricci, A. Trouvé, Regional-scale simulations of wildland fire spread informed by real-time flame front observations. Proc. Combust. Inst. 34(2), 2641–2647 (2013)

    Article  Google Scholar 

  74. M. Gollner et al., Towards data-driven operational wildfire spread modeling: a report of the NSF-funded WIFIRE workshop (2015)

    Google Scholar 

  75. F. Mattern, C. Floerkemeier, From the internet of computers to the internet of things, in Active Data Management to Event-Based Systems and More. Lecture Notes in Computer Science, ed. by K. Sachs, I. Petrov, P. Guerrero. (Springer, Berlin, 2010), pp. 242–259

    Google Scholar 

  76. L. Yang, S.H. Yang, L. Plotnick, Technological forecasting & social change how the internet of things technology enhances emergency response operations. Technol. Forecast. Soc. Chang. 80(9), 1854–1867 (2013). https://doi.org/10.1016/j.techfore.2012.07.011

  77. M. Antunes, L.M. Ferreira, C. Viegas, A.P. Coimbra, A.T. De Almeida, Low-cost system for early detection and deployment of countermeasures against wild fires, in IEEE 5th World Forum Internet Things, WF-IoT 2019 - Conference Proceedings (2019), pp. 418–423. https://doi.org/10.1109/WF-IoT.2019.8767331

  78. J.C. Rosas, J.A. Aguilar, C. Trip-Barba, R. Espinosa, P. Aguilar, A Mobile-Sensor Fire Prevention System Based on the Internet of Things. Comput. Sci. Its Appl.-ICCSA 2017, 274–283 (2017)

    Google Scholar 

  79. M. Hefeeda, M. Bagheri, Wireless sensor networks for early detection of forest fires, in IEEE International Conference on Mobile Adhoc and Sensor Systems (2007), pp. 1–6

    Google Scholar 

  80. X. Wu, Y. Park, A. Li, X. Huang, F. Xiao, A. Usmani, Smart detection of fire source in tunnel based on the numerical database and artificial intelligence. Fire Technol. 57(2), 657–682 (2021)

    Article  Google Scholar 

  81. J.-M. Cabrera, O.A. Ezekoye, R.D. Moser, Bayesian inference of fire evolution within a compartment using heat flux measurements. Fire Technol. 57(6), 2887–2903 (2021)

    Article  Google Scholar 

  82. T. Buffington, J.-M. Cabrera, A. Kurzawski, O.A. Ezekoye, Deep-learning emulators of transient compartment fire simulations for inverse problems and room-scale calorimetry. Fire Technol. 57(6), 2859–2885 (2021)

    Article  Google Scholar 

  83. X. Zhong, M. Duckham, D. Chong, K. Tolhurst, Real-time estimation of wildfire perimeters from curated crowdsourcing. Sci. Rep. 6(1), 1–10 (2016)

    Google Scholar 

  84. W.C. Tam et al., Generating synthetic sensor data to facilitate machine learning paradigm for prediction of building fire hazard. Fire Technol. (2020). https://doi.org/10.1007/s10694-020-01022-9

  85. A. Troy et al., An analysis of factors influencing structure loss resulting from the 2018 Camp Fire. Int. J. Wildl. Fire 31(6), 586–598 (2022). https://doi.org/10.1071/WF21176

    Article  Google Scholar 

  86. A.D. Syphard, J.E. Keeley, M. Gough, M. Lazarz, J. Rogan, What makes wildfires destructive in California? Fire 5, 133 (2022)

    Google Scholar 

  87. E. Knapp, Y. Valachovic, S.L. Quarles, N.G. Johnson, Factors associated with single-family home survival in the 2018 Camp Fire , California. Fire Ecol. 1–27 (2021)

    Google Scholar 

  88. P. Gupta, V. Gaidhane, A new approach for flame image edges detection, in International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014) (2014), pp. 1–6. https://doi.org/10.1109/ICRAIE.2014.6909178

  89. T. Qiu, Y. Yan, G. Lu, An autoadaptive edge-detection algorithm for flame and fire image processing. IEEE Trans. Instrum. Meas. 61(5), 1486–1493 (2012). https://doi.org/10.1109/TIM.2011.2175833

    Article  Google Scholar 

  90. J. Cobian-Iñiguez, A. Aminfar, D.R. Weise, M. Princevac, On the use of semi-empirical flame models for spreading chaparral crown fire. Front. Mech. Eng. 5(August), 1–13 (2019). https://doi.org/10.3389/fmech.2019.00050

    Article  Google Scholar 

  91. J. Cobian-Iñiguez, A. Aminfar, S. Saha, K. Awayan, D.R. Weise, M. Princevac, The transition and spread of a chaparral crown fire: insights from laboratory scale wind tunnel experiments. J. Combust. (2022)

    Google Scholar 

  92. Y. Zhang, S. Lim, J.J. Sharples, Modelling spatial patterns of wildfire occurrence in South-Eastern Australia, Geomatics. Nat. Hazards Risk 7(6), 1800–1815 (2016). https://doi.org/10.1080/19475705.2016.1155501

    Article  Google Scholar 

  93. J. Sharma, O.C. Granmo, M. Goodwin, J.T. Fidje, Deep convolutional neural networks for fire detection in images. Commun. Comput. Inf. Sci. 744, 183–193 (2017). https://doi.org/10.1007/978-3-319-65172-9_16

    Article  Google Scholar 

  94. K. Muhammad, J. Ahmad, S.W. Baik, Early fire detection using convolutional neural networks during surveillance for effective disaster management. Neurocomputing 288, 30–42 (2018). https://doi.org/10.1016/j.neucom.2017.04.083

    Article  Google Scholar 

  95. Y. Wang, L. Dang, J. Ren, Forest fire image recognition based on convolutional neural network. J. Algorithms Comput. Technol. 13 (2019). https://doi.org/10.1177/1748302619887689

  96. S. Frizzi, R. Kaabi, M. Bouchouicha, J.M. Ginoux, E. Moreau, F. Fnaiech, Convolutional neural network for video fire and smoke detection, in IECON Proceedings (Industrial Electronics Conference (2016), pp. 877–882. https://doi.org/10.1109/IECON.2016.7793196

  97. J. Ryu, D. Kwak, A study on a complex flame and smoke detection method using computer vision detection and convolutional neural network. Fire 5(4) (2022). https://doi.org/10.3390/fire5040108

  98. P. Li, W. Zhao, Image fire detection algorithms based on convolutional neural networks, in Case Studies in Thermal Engineering, vol. 19 (2020). https://doi.org/10.1016/j.csite.2020.100625

  99. B.U. Töreyin, Y. Dedeoǧlu, U. Güdükbay, A.E. Çetin, Computer vision based method for real-time fire and flame detection. Pattern Recognit. Lett. 27(1), 49–58 (2006). https://doi.org/10.1016/j.patrec.2005.06.015

    Article  Google Scholar 

  100. T. Celik, H. Demirel, H. Ozkaramanli, M. Uyguroglu, Fire detection using statistical color model in video sequences. J. Vis. Commun. Image Represent. 18(2), 176–185 (2007). https://doi.org/10.1016/j.jvcir.2006.12.003

    Article  Google Scholar 

  101. F. Bu, M.S. Gharajeh, Intelligent and vision-based fire detection systems: a survey. Image Vis. Comput. 91, 103803 (2019). https://doi.org/10.1016/j.imavis.2019.08.007

    Article  Google Scholar 

  102. J.R. Martinez-de Dios, B.C. Arrue, A. Ollero, L. Merino, F. Gómez-Rodríguez, Computer vision techniques for forest fire perception. Image Vis. Comput. 26(4), 550–562 (2008). https://doi.org/10.1016/j.imavis.2007.07.002

  103. B.U. Töreyin, R.G. Cinbis, Y. Dedeoglu, A. enis Cetin, Fire detection in infrared video using wavelet analysis. Opt. Eng. 46(6), 067204 (2007). https://doi.org/10.1117/1.2748752

  104. K.L.B. Xavier, V.K. Nanayakkara, Development of an early fire detection and deep neural networks. Fire Technol. 58(6), 3529–3552 (2022). https://doi.org/10.1007/s10694-022-01319-x

    Article  Google Scholar 

  105. L. Giglio, L. Boschetti, D. Roy, A.A. Hoffmann, M. Humber, Collection 6 MODIS burned area product user’s guide, version 1.0 (2016), p. 26. https://modis-land.gsfc.nasa.gov/pdf/MODIS_C6_BA_User_Guide_1.0.pdf

  106. W. Schroeder, L. Giglio, NASA VIIRS land science investigator processing system (SIPS) visible infrared imaging radiometer suite (VIIRS) 375 m & 750 m active fire products: product user’s guide version 1.4 (2018)

    Google Scholar 

  107. N.S. Skowronski, S. Haag, J. Trimble, K.L. Clark, M.R. Gallagher, R.G. Lathrop, Structure-level fuel load assessment in the wildland-urban interface: a fusion of airborne laser scanning and spectral remote-sensing methodologies. Int. J. Wildl. Fire 25(5), 547–557 (2016). https://doi.org/10.1071/WF14078

    Article  Google Scholar 

  108. D. McNamara, W. Mell, Towards the use of remote sensing for identification of building damage, destruction, and defensive actions at wildland-urban interface fires. Fire Technol. 58(1), 641–672 (2022). https://doi.org/10.1007/s10694-021-01170-6

    Article  Google Scholar 

  109. A. Badia, P. Serra, S. Modugno, Identifying dynamics of fire ignition probabilities in two representative Mediterranean wildland-urban interface areas. Appl. Geogr. 31(3), 930–940 (2011). https://doi.org/10.1016/j.apgeog.2011.01.016

    Article  Google Scholar 

  110. S.L. Manzello, A. Maranghides, W.E. Mell, Firebrand generation from burning vegetation. Int. J. Wildl. Fire 16(4), 458–462 (2007). https://doi.org/10.1071/WF06079

    Article  Google Scholar 

  111. S.L. Manzello, A. Maranghides, J.R. Shields, W.E. Mell, Y. Hayashi, D. Nii, Mass and size distribution of firebrands generated from burning Korean pine (Pinus koraiensis) trees. Fire Mater 21–31 (2009). https://doi.org/10.1002/fam.977

  112. S. Suzuki, S.L. Manzello, M. Lage, G. Laing, Firebrand generation data obtained from a full-scale structure burn. Int. J. Wildl. Fire 21(8), 961–968 (2012). https://doi.org/10.1071/WF11133

    Article  Google Scholar 

  113. S.L. Manzello, E.I.D. Foote, Characterizing firebrand exposure from wildland-urban interface (WUI) fires: results from the 2007 Angora Fire. Fire Technol. 50(1), 105–124 (2014). https://doi.org/10.1007/s10694-012-0295-4

    Article  Google Scholar 

  114. A. Filkov et al., Investigation of firebrand production during prescribed fires conducted in a pine forest. Proc. Combust. Inst. 36(2), 3263–3270 (2017). https://doi.org/10.1016/j.proci.2016.06.125

    Article  Google Scholar 

  115. A. Jha, A. Zhou, Applying machine learning for firebrand production prediction. Fire Technol. 58(5), 3261–3290 (2022). https://doi.org/10.1007/s10694-022-01309-z

    Article  Google Scholar 

  116. A. Zhou, S. Quarles, D.R. Weise, Fire ember production from wildland and structural fuels (2019)

    Google Scholar 

  117. S. Prohanov, A. Filkov, D. Kasymov, M. Agafontsev, V. Reyno, Determination of firebrand characteristics using thermal videos. Fire 3(4), 1–17 (2020). https://doi.org/10.3390/fire3040068

    Article  Google Scholar 

  118. N. Bouvet, E.D. Link, S.A. Fink, A new approach to characterize firebrand showers using advanced 3D imaging techniques. Exp. Fluids 62(9), 1–14 (2021). https://doi.org/10.1007/s00348-021-03277-6

    Article  Google Scholar 

  119. D.K. Thompson et al., Quantifying firebrand production and transport using the acoustic analysis of in-fire cameras. Fire Technol. 58(3), 1617–1638 (2022). https://doi.org/10.1007/s10694-021-01194-y

    Article  Google Scholar 

  120. A. Filkov, S. Prohanov, Particle tracking and detection software for firebrands characterization in wildland fires. Fire Technol. 55(3), 817–836 (2019). https://doi.org/10.1007/s10694-018-0805-0

    Article  Google Scholar 

  121. M.Z. Naser, Mechanistically informed machine learning and artificial intelligence in fire engineering and sciences 57(6); Springer US (2021)

    Google Scholar 

  122. G. Rebala, A. Ravi, S. Churiwala, in An Introduction to Machine Learning (Springer, 2019)

    Google Scholar 

  123. M.Z. Naser, Demystifying ten big ideas and rules every fire scientist & engineer should know about blackbox, whitebox and causal artificial intelligence. Fire Technol. 58(3), 1075–1085 (2022). https://doi.org/10.1007/s10694-021-01210-1

    Article  Google Scholar 

  124. J.K. Shuman et al., Reimagine fire science for the anthropocene. PNAS Nexus 1(3), 1–14 (2022). https://doi.org/10.1093/pnasnexus/pgac115

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge support from the UC Merced School of Engineering for startup funding used to support work by Jeanette Cobian-Iñiguez and her research group.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, University of California, Merced, USA

    Jeanette Cobian-Iñiguez, Shusmita Saha, Joseph Avalos & Ehsan Ameri

  2. Department of Mechanical Engineering, University of California, Berkeley, USA

    Michael Gollner

Authors
  1. Jeanette Cobian-Iñiguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Gollner
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shusmita Saha
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Joseph Avalos
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ehsan Ameri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jeanette Cobian-Iñiguez .

Editor information

Editors and Affiliations

  1. Department of Building Environment and Energy Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong

    Xinyan Huang

  2. Fire Research Division, National Institute of Standards and Technology, Gaithersburg, MD, USA

    Wai Cheong Tam

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Cobian-Iñiguez, J., Gollner, M., Saha, S., Avalos, J., Ameri, E. (2024). Improved Fire Safety in the Wildland-Urban Interface Through Smart Technologies. In: Huang, X., Tam, W.C. (eds) Intelligent Building Fire Safety and Smart Firefighting. Digital Innovations in Architecture, Engineering and Construction. Springer, Cham. https://doi.org/10.1007/978-3-031-48161-1_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-48161-1_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48160-4

  • Online ISBN: 978-3-031-48161-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation