Rate of spread and flaming zone velocities of surface fires from visible and thermal image processing
B. Schumacher
A * , K. O. Melnik B , M. Katurji A , J. Zhang A D , V. Clifford D and H. G. Pearce C
+ Author Affiliations
- Author Affiliations
A School of Earth and Environment, University of Canterbury, Christchurch, New Zealand.
B School of Engineering, University of Canterbury, Christchurch, New Zealand.
C Fire and Emergency New Zealand, Wellington, New Zealand.
D New Zealand Forest Research Institute (Scion), Christchurch, New Zealand.
* Correspondence to: benjamin.schumacher@pg.canterbury.ac.nz
International Journal of Wildland Fire 31(8) 759-773 https://doi.org/10.1071/WF21122
Submitted: 27 August 2021 Accepted: 8 June 2022 Published: 22 July 2022
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)
Abstract
This study presents two new remote sensing approaches that can be used to derive rate of spread and flaming zone velocities of a wildfire at very high spatiotemporal resolution. Time sequential image tracking from thermal or visible video collected on uncrewed aerial vehicles is used to estimate instantaneous spatial rate of spread of a surface fire. The techniques were developed using experimental wheat‐stubble burns carried out near Darfield, New Zealand, in March 2019. The thermal tracking technique is based on Thermal Image Velocimetry, which tracks evolving temperature patterns within an infrared video. The visible tracking technique uses colour thresholding, and tracks fire perimeter progression through time at pixel resolution. Results show that the visible perimeter tracking creates a higher mean rate of spread compared to thermal image velocimetry. The visible perimeter tracking provides rate of spread measurements for fire front progression whereas the thermal tracking techniqueis computationally more expensive, but can resolve velocities of thermal structures within the flaming zone and provides spatiotemporal rate of spread measurements. Both techniques are available as open‐source code and providevital scientific data for new studies concerning e.g. fire–atmospheric interactions or model validation. They may be adapted for operational purposes providing rate of spread at high spatiotemporal resolution.
Keywords: Adaptive thermal image velocimetry, fire rate of spread, image velocimetry, perimeter tracking, ROS, thermal imagery, uncrewed aerial vehicles, wildfire.
References
Abouali A, Viegas DX (2019) Fire ROS calculator: A tool to measure the rate of spread of a propagating wildfire in a laboratory setting. Journal of Open Research Software 7, 24–32.| Fire ROS calculator: A tool to measure the rate of spread of a propagating wildfire in a laboratory setting.Crossref | GoogleScholarGoogle Scholar |
Akhloufi MA, Couturier A, Castro NA (2021) Unmanned aerial vehicles for wildland fires: Sensing, perception, cooperation and assistance. Drones 5, 15–40.
| Unmanned aerial vehicles for wildland fires: Sensing, perception, cooperation and assistance.Crossref | GoogleScholarGoogle Scholar |
Beverly JL, Bothwell P Wildfire evacuations in Canada 1980–2007. Natural Hazards 59, 571–596.
| Wildfire evacuations in Canada 1980–2007.Crossref | GoogleScholarGoogle Scholar |
Blender Online Community (2019) ‘Blender - a 3D modelling and rendering package.’ (Blender Foundation, Blender Institute: Amsterdam) Available at http://www.blender.org
Cardona AM, Hartenstein V (2006) Three-dimensional skin reconstruction by vector sequence alignment and morphing. Blender Conference 2006. In ‘Blender Conference Proceedings’. pp. 1–3 (Waag, Amsterdam)
Cheney N, Gould J (1995) Fire growth in grassland fuels. International Journal of Wildland Fire 5, 237–247.
| Fire growth in grassland fuels.Crossref | GoogleScholarGoogle Scholar |
Cheney P, Sullivan A (2008) ‘Grassfires: fuel, weather and fire behaviour.’ (CSIRO Publishing)
Clements CB, Zhong S, Goodrick S, Li J, Potter BE, Bian X, Heilman WE, Charney JJ, Perna R, Jang M, Lee D, Patel M, Street S, Aumann G (2007) Observing the dynamics of wildland grass fires: FireFlux – a field validation experiment. Bulletin of the American Meteorological Society 88, 1369–1382.
| Observing the dynamics of wildland grass fires: FireFlux – a field validation experiment.Crossref | GoogleScholarGoogle Scholar |
Finney MA (1998) FARSITE, Fire Area Simulator-model development and evaluation. Number 4. Volume 59. No. 2(USDA, Forest Service, Rocky Mountain Research Station)
Finney MA, Cohen JD, Grenfell IC, Yedinak KM (2010) An examination of fire spread thresholds in discontinuous fuel beds. International Journal of Wildland Fire 19, 163–170.
| An examination of fire spread thresholds in discontinuous fuel beds.Crossref | GoogleScholarGoogle Scholar |
Finney MA, Cohen JD, McAllister SS, Jolly WM (2013) On the need for a theory of wildland fire spread. International Journal of Wildland Fire 22, 25–36.
| On the need for a theory of wildland fire spread.Crossref | GoogleScholarGoogle Scholar |
Finney MA, Cohen JD, Forthofer JM, McAllister SS, Gollner MJ, Gorham DJ, Saito K, Akafuah NK, Adam BA, English JD (2015) Role of buoyant flame dynamics in wildfire spread. Proceedings of the National Academy of Sciences 112, 9833–9838.
| Role of buoyant flame dynamics in wildfire spread.Crossref | GoogleScholarGoogle Scholar |
Finney M, Pearce G, Strand T, Katurji M, Clements C (2018) New Zealand prescribed fire experiments to test convective heat transfer in wildland fires. In ‘Advances in Forest Fire Research 2018. Proceedings of the VII International Conference on Forest Fire Research’. pp. 10–16. (Universidade de Coimbra: Portugal)
Forestry Canada (1992) Development and structure of the Canadian Forest Fire Behavior Prediction System. Information Report ST-X-3. (Forestry Canada, Ottawa) Available at https://cfs.nrcan.gc.ca/publications?id=10068
Gould JS, Sullivan AL, Hurley R, Koul V (2017) Comparison of three methods to quantify the fire spread rate in laboratory experiments. International Journal of Wildland Fire 26, 877–883.
| Comparison of three methods to quantify the fire spread rate in laboratory experiments.Crossref | GoogleScholarGoogle Scholar |
Inagaki A, Kanda M, Onomura S, Kumemura H (2013) Thermal image velocimetry. Boundary-Layer Meteorology 149, 1–18.
| Thermal image velocimetry.Crossref | GoogleScholarGoogle Scholar |
Johnston J, Wheatley M, Wooster M, Paugam R, Davies G, DeBoer K (2018) Flame-front rate of spread estimates for moderate-scale experimental fires are strongly influenced by measurement approach. Fire 10, 16–33.
| Flame-front rate of spread estimates for moderate-scale experimental fires are strongly influenced by measurement approach.Crossref | GoogleScholarGoogle Scholar |
Johnston JM, Wooster MJ, Paugam R, Wang X, Lynham TJ, Johnston LM (2017) Direct estimation of Byrams fire intensity from infrared remote sensing imagery. International Journal of Wildland Fire 26, 668–684.
| Direct estimation of Byrams fire intensity from infrared remote sensing imagery.Crossref | GoogleScholarGoogle Scholar |
Katurji M, Zhang J, Satinsky A, McNair H, Schumacher B, Strand T, Valencia A, Finney M, Pearce G, Kerr J, Seto D, Wallace H, Zawar-Reza P, Dunker C, Clifford V, Melnik K, Grumstrup T, Forthofer J, Clements C (2021) Turbulent thermal image velocimetry at the immediate fire and atmospheric interface. Earth and Space Science Open Archive
| Turbulent thermal image velocimetry at the immediate fire and atmospheric interface.Crossref | GoogleScholarGoogle Scholar |
Lin Z, Liu HHT, Wotton M (2019) Kalman filter-based large-scale wildfire monitoring with a system of UAVs. IEEE Transactions on Industrial Electronics 66, 606–615.
| Kalman filter-based large-scale wildfire monitoring with a system of UAVs.Crossref | GoogleScholarGoogle Scholar |
Littell JS, McKenzie D, Peterson DL, Westerling AL (2009) Climate and wildfire area burned in western U.S. ecoprovinces, 1916–2003. Ecological Applications 19, 1003–1021.
| Climate and wildfire area burned in western U.S. ecoprovinces, 1916–2003.Crossref | GoogleScholarGoogle Scholar |
Liu X, He B, Quan X, Yebra M, Qiu S, Yin C, Liao Z, Zhang H (2018) Near real-time extracting wildfire spread rate from Himawari-8 satellite data. Remote Sensing 10, 1654–1669.
| Near real-time extracting wildfire spread rate from Himawari-8 satellite data.Crossref | GoogleScholarGoogle Scholar |
McCaffrey S, Wilson R, Konar A (2018) Should I stay or should I go now? Or should I wait and see? Influences on wildfire evacuation decisions. Risk Analysis 38, 1390–1404.
| Should I stay or should I go now? Or should I wait and see? Influences on wildfire evacuation decisions.Crossref | GoogleScholarGoogle Scholar |
McNamee M, Meacham B, van Hees P, Bisby L, Chow W, Coppalle A, Dobashi R, Dlugogorski B, Fahy R, Fleischmann C, Floyd J, Galea ER, Gollner M, Hakkarainen T, Hamins A, Hu L, Johnson P, Karlsson B, Merci B, Ohmiya Y, Rein G, Trouvé A, Wang Y, Weckman B (2019) IAFSS agenda 2030 for a fire-safe world. Fire Safety Journal 110, 102889
| IAFSS agenda 2030 for a fire-safe world.Crossref | GoogleScholarGoogle Scholar |
Mell W, Maranghides A, McDermott R, Manzello SL (2009) Numerical simulation and experiments of burning douglas fir trees. Combustion and Flame 156, 2023–2041.
| Numerical simulation and experiments of burning douglas fir trees.Crossref | GoogleScholarGoogle Scholar |
Melnik K (2021) Visual fire perimeter tracking: First release. Available at https://zenodo.org/record/5138773
Moran CJ, Seielstad CA, Cunningham MR, Hoff V, Parsons RA, Queen L, Sauerbrey K, Wallace T (2019) Deriving fire behavior metrics from UAS imagery. Fire 2, 36–56.
| Deriving fire behavior metrics from UAS imagery.Crossref | GoogleScholarGoogle Scholar |
Ononye A, Vodacek A, Saber E (2007) Automated extraction of fire line parameters from multispectral infrared images. Remote Sensing of Environment 108, 179–188.
| Automated extraction of fire line parameters from multispectral infrared images.Crossref | GoogleScholarGoogle Scholar |
Paugam R, Wooster MJ, Roberts G (2013) Use of handheld thermal imager data for airborne mapping of fire radiative power and energy and flame front rate of spread. IEEE Transactions on Geoscience and Remote Sensing 51, 3385–3399.
| Use of handheld thermal imager data for airborne mapping of fire radiative power and energy and flame front rate of spread.Crossref | GoogleScholarGoogle Scholar |
Plucinski MP, Sullivan AL, Rucinski CJ, Prakash M (2017) Improving the reliability and utility of operational bushfire behaviour predictions in Australian vegetation. Environmental Modelling & Software 91, 1–12.
| Improving the reliability and utility of operational bushfire behaviour predictions in Australian vegetation.Crossref | GoogleScholarGoogle Scholar |
Ramos TCP, Freymüller-Haapalainen E, Schenkman S (2011) Three-dimensional reconstruction of Trypanosoma cruzi epimastigotes and organelle distribution along the cell division cycle: 3D Electron Microscopy of Trypanosoma cruzi. Cytometry Part A 79A, 538–544.
| Three-dimensional reconstruction of Trypanosoma cruzi epimastigotes and organelle distribution along the cell division cycle: 3D Electron Microscopy of Trypanosoma cruzi.Crossref | GoogleScholarGoogle Scholar |
Rochoux MC, Emery C, Ricci S, Cuenot B, Trouvé A (2015) Towards predictive data-driven simulations of wildfire spread – part II: Ensemble Kalman filter for the state estimation of a front-tracking simulator of wildfire spread. Natural Hazards and Earth System Sciences 15, 1721–1739.
| Towards predictive data-driven simulations of wildfire spread – part II: Ensemble Kalman filter for the state estimation of a front-tracking simulator of wildfire spread.Crossref | GoogleScholarGoogle Scholar |
Rothermel RC (1972) A mathematical model for predicting fire spread in wildland fuels. Research Paper INT-RP-115.(Intermountain Forest & Range Experiment Station, USDA forest Service)
Schumacher B (2021) bschumac/ativ: A-tiv starting release. Available at https://zenodo.org/record/4741550
Schumacher B, Katurji M, Zhang J, Stiperski I, Dunker C (2019) Evolution of micrometeorological observations instantaneous spatial and temporal surface wind velocity from thermal image processing. In ‘GeoComputation 2019 ’. (University of Auckland, NZ). Available at
| Crossref |
Schumacher B, Melnik K, Katurji M, Clifford V, Zhang J, Mcnair H, Pearce G (2021) Instantaneous spatio-temporal rate of spread of fast spreading wildfires - a new approach from visible and thermal image processing. EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6502. Available at
| Crossref |
Stow D, Riggan P, Schag G, Brewer W, Tissell R, Coen J, Storey E (2019) Assessing uncertainty and demonstrating potential for estimating fire rate of spread at landscape scales based on time sequential airborne thermal infrared imaging. International Journal of Remote Sensing 40, 4876–4897.
| Assessing uncertainty and demonstrating potential for estimating fire rate of spread at landscape scales based on time sequential airborne thermal infrared imaging.Crossref | GoogleScholarGoogle Scholar |
Stow DA, Riggan PJ, Storey EJ, Coulter LL (2014) Measuring fire spread rates from repeat pass airborne thermal infrared imagery. Remote Sensing Letters 5, 803–812.
| Measuring fire spread rates from repeat pass airborne thermal infrared imagery.Crossref | GoogleScholarGoogle Scholar |
Sun R, Krueger SK, Jenkins MA, Zulauf MA, Charney JJ (2009) The importance of fire–atmosphere coupling and boundary-layer turbulence to wildfire spread. International Journal of Wildland Fire 18, 50–60.
| The importance of fire–atmosphere coupling and boundary-layer turbulence to wildfire spread.Crossref | GoogleScholarGoogle Scholar |
Tymstra C, Bryce R, Wotton B, Taylor S, Armitage O, et al. (2010) Development and structure of Prometheus: the Canadian wildland fire growth simulation model. Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Information Report NOR-X-417. (Edmonton, AB)
Valero MM, Rios O, Pastor E, Planas E (2018) Automated location of active fire perimeters in aerial infrared imaging using unsupervised edge detectors. International Journal of Wildland Fire 27, 241–256.
| Automated location of active fire perimeters in aerial infrared imaging using unsupervised edge detectors.Crossref | GoogleScholarGoogle Scholar |
Valero MM, Verstockt S, Butler B, Jimenez D, Rios O, Mata C, Queen L, Pastor E, Planas E (2021) Thermal infrared video stabilization for aerial monitoring of active wildfires. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, 2817–2832.
| Thermal infrared video stabilization for aerial monitoring of active wildfires.Crossref | GoogleScholarGoogle Scholar |
Viseras A, Meissner M, Marchal J (2021) Wildfire front monitoring with multiple UAVs using deep Q-learning. IEEE Access
| Wildfire front monitoring with multiple UAVs using deep Q-learning.Crossref | GoogleScholarGoogle Scholar |
Vivo FD, Battipede M, Johnson E (2021) Infra-red line camera data-driven edge detector in UAV forest fire monitoring. Aerospace Science and Technology 111, 106574
| Infra-red line camera data-driven edge detector in UAV forest fire monitoring.Crossref | GoogleScholarGoogle Scholar |
Wardihani E, Ramdhani M, Suharjono A, Setyawan TA, Hidayat SS, Helmy SW, Triyono E, Saifullah F (2018) Real-time forest fire monitoring system using unmanned aerial vehicle. Journal of Engineering Science and Technology 13, 1587–1594.
Wong SD, Broader JC, Shaheen SA (2020) Review of California wildfire evacuations from 2017 to 2019. UC Office of the President: University of California Institute of Transportation Studies. (Berkeley, CA) Available at https://escholarship.org/uc/item/5w85z07g.
Wotton BM, Gould JS, McCaw WL, Cheney NP, Taylor SW (2011) Flame temperature and residence time of fires in dry eucalypt forest. International Journal of Wildland Fire 21, 270–281.
| Flame temperature and residence time of fires in dry eucalypt forest.Crossref | GoogleScholarGoogle Scholar |
Zhuang J, Payyappalli VM, Behrendt A, Lukasiewicz K (2017) Total cost of fire in the United States. (Fire Protection Research Foundation Quincy: MA, USA)
