Prescribed Fire Simulation with Dynamic Ignitions Using Data from UAS-based Sensing

References

• Andrews, P. L. (2012). Modeling wind adjustment factor and midflame wind speed for Rothermel’s surface fire spread model. Gen. Tech. Rep. RMRS-GTR-266. US Department of Agriculture, Forest Service, Rocky Mountain Research Station. 39:–266
   Google Scholar
• Beachly, E. M. (2017). An Unmanned Aerial System for Prescribed Fires, MS Thesis, University of Nebraska –Lincoln.
   Google Scholar
• Bugwood Center. (1989). Firing techniques - a guide for prescribed fire in southern forests. United States department of agriculture, forest service southern region, Technical Publication R8-TP 11. Available from: https://www.bugwood.org/pfire/techniques.cfm.
   Google Scholar
• Clements, C. B., Kochanski, A. K., Seto, D., Davis, B., Camacho, C., Lareau, N. P., Contezac, J., Heilman, W. E., Krueger, S. K., Butler, R. D., Ottmar, B., Vihnanek, R., Flynn, J., Barboni, J., Hall, D. E., Man- Del, T., Jenkins, M. A., O’Brien, J., Hornsby, B. … Teske, C. (2019). The FireFlux II experiment: A model-guided field experiment to improve understanding of fire–atmosphere interactions and fire spread. International Journal of Wildland Fire, 28(4), 308–326. https://doi.org/10.1071/WF18089
   Web of Science ®Google Scholar
• Clements, C. B., Zhong, S., Li, J., Potter, B. E., Bian, W. E., Heilman, X., Charney, J. J., Perna, M., Jang, R., Lee, M., Patel, D., Street, G., Aumann, S., & Aumann, G. (2007). Observing the dynamics of wildland grass fires: fireflux—a field validation experiment. Bulletin of the American Meteorological Society, 88(9), 1369–1382. https://doi.org/10.1175/BAMS-88-9-1369
   Web of Science ®Google Scholar
• Dahl, N., Xue, H., Hu, X., & Xue, M. (2015). Coupled fire–atmosphere modeling of wildland fire spread using DEVS-FIRE and ARPS. Natural Hazards, 77(2), 1013–1035. https://doi.org/10.1007/s11069-015-1640-y
   Web of Science ®Google Scholar
• Fayad, J., Morandini, F., Accary, G., Chatelon, F., Wandon, C., Burglin, A., Rossi, L., Marcelli, T., Cancellieri, D., Cancellieri, V., Morvan, D., Meradji, S., Pieri, A., Planelles, G., Costantini, R., Briot, P., & Rossi, J. (2023). A study of two high intensity fires across Corsican shrubland. Atmosphere, 14, 473. https://doi.org/10.3390/atmos14030473
   Web of Science ®Google Scholar
• Fernandes, P. M., & Botelho, H. S. (2003). A review of prescribed burning effectiveness in fire hazard reduction. International Journal of Wildland Fire, 12(2), 117–128. https://doi.org/10.1071/WF02042
   Web of Science ®Google Scholar
• Filippi, J. -B., Mallet, V., & Nader, B. (2014). Representation and evaluation of wildfire propagation simulations. International Journal of Wildland Fire, 23(1), 46–57.
   Web of Science ®Google Scholar
• Filippi, J. -B., Pialat, X., & Clements, C. B. (2013). Assessment of ForeFire/Meso-NH for wildland fire/atmosphere coupled simulation of the FireFlux experiment. Proceedings of the Combustion Institute, 34, 6233, https://doi.org/10.1016/j.proci.2012.07.022
   Google Scholar
• Finney, M. A., & McAllister, S. S. (2011). A review of fire interactions and mass fires. Journal of Combustion, 2011, 1–14, 548328.
   Google Scholar
• Gowravaram, S., Chao, H., Zhao, T., Parsons, S., Hu, X., Xin, M., Flanagan, H., & Tian, P. (2022). Prescribed grass fire evolution mapping and rate of spread measurement using orthorectified thermal imagery from a fixed-wing UAS. International Journal of Remote Sensing, 43(7), 2357–2376.
   Web of Science ®Google Scholar
• Hiers, J. K., O’Brien, J. J., Varner, J. M., Butler, B. W., Dickinson, M., Furman, J., Gallagher, M., Godwin, D., Goodrick, S. L., Hood, S. M., Hudak, A., Kobziar, L. N., Linn, R., Loudermilk, E. L., McCaffrey, S., Robertson, K., Rowell, E. M., Skowronski, N., Watts, A. C., & Yedinak, K. M. (2020). Prescribed fire science: The case for a refined research agenda. Fire Ecology, 16, 1–15, 11.
   Web of Science ®Google Scholar
• Hu, X., & Ge, M. (2021). Modeling and simulating prescribed fire ignition techniques. In 2021 Annual Modeling and Simulation Conference (ANNSIM’21), Fairfax, VA, USA.
   Google Scholar
• Hu, X., Sun, Y., & Ntaimo, L. (2012). DEVS-FIRE: Design and application of formal discrete event wildfire spread and suppression models. Simulation, 88(3), 259–279.
   Google Scholar
• Johansen, R. W. (1987). Ignition patterns & prescribed fire behavior in southern pine stands. Georgia Forestry Commission-Georgia Forest Research Paper, 72, 1–8.
   Google Scholar
• Kelso, J. K., Mellor, D., Murphy, M. E., & Milne, G. J. (2015). Techniques for evaluating wildfire simulators via the simulation of historical fires using the Australis simulator. International Journal of Wildland Fire, 24(6), 784–797.
   Web of Science ®Google Scholar
• Kim, E. H., Yang, K., & Tran, M. N. (2012). Thermal-image-based wildfire spread simulation using a linearized model of an advection-diffusion-reaction equation. Simulation, 88(9), 1093–1115.
   Google Scholar
• Kochanski, A. K., Jenkins, M. A., Mandel, J., Beezley, D., Clements, C. B., & Krueger, S. (2013). Evaluation of WRF- SFIRE performance with field observations from the FireFlux experiment. Geoscientific Model Development, 6, 109–1126. https://doi.org/10.5194/gmd-6-1109-2013
   Web of Science ®Google Scholar
• Linn, R. R., Goodrick, S. L., Brambilla, S., Brown, M. J., Middleton, R. S., O’Brien, J. J., & Hiers, J. K. (2020). QUIC-fire: A fast-running simulation tool for prescribed fire planning. Environmental Modelling & Software, 125, 104616. https://doi.org/10.1016/j.envsoft.2019.104616
   Web of Science ®Google Scholar
• Martin, R. E., & Dell, J. D. (1978). Planning for prescribed burning in the inland northwest. Department of Agriculture, Forest Service. Pacific Northwest Forest and Range Experiment Station.
   Google Scholar
• Matthias, B., Sadler, R., Wittkuhn, R. S., Mccaw, L., & Grierson, P. (2009). Long-term impacts of prescribed burning on regional extent and incidence of wildfires - Evidence from 50 years of active fire management in SW Australian forests. Forest Ecology and Management, 259(1), 132–142.
   Web of Science ®Google Scholar
• Morandini, F., Silvani, X., Rossi, L., Santoni, P. A., Simeoni, A., Balbi, J. H., Rossi, J. L., & Marcelli, T. (2006). Fire spread experiment across Mediterranean shrub: Influence of wind on flame front properties. Fire Safety Journal, 41(3), 229–235. https://doi.org/10.1016/J.FIRESAF.2006.01.006
   Web of Science ®Google Scholar
• Ntaimo, L., Hu, X., & Sun, Y. (2008). DEVS-FIRE: Towards an integrated simulation environment for surface wildfire spread and containment. Simulation, 84(4), 137–155.
   Google Scholar
• Rothermel, R. C. (1972). A mathematical model for predicting fire spread in wildland fuels. Intermountain forest & range experiment station, forest service. US Department of Agriculture.
   Google Scholar
• Salis, M., Arca, B., Alcasena, F., Arianoutsou, M., Bacciu, V., Duce, P., Duguy, B., Koutsias, N., Mallinis, G., Mitsopoulos, I., Moreno, J. M., Perez, J. R., Rodriguez, I., Xystrakis, F., Zavala, G., & Spano, D. (2016). Predicting wildfire spread and behavior in Mediterranean landscapes. International Journal of Wildland Fire, 25(10), 1015–1032.
   Web of Science ®Google Scholar
• Santoni, P. A., Simeoni, A., Rossi, J. L., Bosseur, F., Morandini, F., Silvani, X., Balbi, J. H., Cancellieri, D., & Rossi, L. (2006). Instrumentation of wildland fire: Characterisation of a fire spreading through a Mediterranean shrub. Fire Safety Journal, 41(3), 171–184. https://doi.org/10.1016/J.FIRESAF.2005.11.010
   Web of Science ®Google Scholar
• Sargent, R. G. (2013). Verification and validation of simulation models. Journal of Simulation, 7(1), 12–24.
   Web of Science ®Google Scholar
• Scott, J. H., & Burgan, R. E. (2005). Standard fire behavior fuel models: A comprehensive set for use with Rothermel’s surface fire spread model. Gen. Tech. Rep. RMRS-GTR-153. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station.
   Google Scholar
• Sullivan, A. L. (2009a). Wildland surface fire spread modelling, 1990–2007. 1: Physical and quasi-physical models. International Journal of Wildland Fire, 18(4), 349–368.
   Web of Science ®Google Scholar
• Sullivan, A. L. (2009b). Wildland surface fire spread modelling, 1990–2007. 2: Empirical and quasi-empirical models. International Journal of Wildland Fire, 18(4), 369–386.
   Web of Science ®Google Scholar
• Toivanen, J., Engel, C. B., Reeder, M. J., Lane, T. P., Davies, L., Webster, S., & Wales, S. (2019). Coupled atmosphere‐fire simulations of the black Saturday Kilmore east wildfires with the unified model. Journal of Advances in Modeling Earth Systems, 11(1), 210–230.
   Web of Science ®Google Scholar
• Trunfio, G. A., Ambrosio, D., Rongo, R., Spataro, W., & DiGregorio, S. (2011). A new algorithm for simulating wildfire spread through cellular automata. ACM Translation Modeling and Computer Simulation, 22(1), 1–26, 6.
   Web of Science ®Google Scholar
• Twidwell, D., Allen, C. R., Detweiler, C., Higgins, J., Laney, C., & Elbaum, S. (2016). Smokey comes of age: Unmanned aerial systems for fire management. Frontiers in Ecology and the Environment, 14(6), 333–339.
   Web of Science ®Google Scholar
• USDA. (n.d.). Prescribed Fire. https://www.fs.usda.gov/managing-land/prescribed-fire.
   Google Scholar
• USGS. (n.d.). Science Data Catalog (SDC). https://data.usgs.gov/datacatalog/data/USGS:b7e353d2-325f-4fc6-8d95-01254705638a
   Google Scholar
• Waldrop, T. A., & Goodrick, S. L. (2012). Introduction to prescribed fires in Southern ecosystems. Science Update SRS-054. US Department of Agriculture Forest Service, Southern Research Station.
   Google Scholar
• Xue, H., Gu, F., & Hu, X. (2012). Data assimilation using sequential monte carlo methods in wildfire spread simulation. The ACM Transactions on Modeling and Computer Simulation (TOMACS), 22(4), 1–25, 23.
   Web of Science ®Google Scholar
• Zeigler, B. P., Praehofer, H., & Kim, T. G. (2000). Theory of modeling and simulation (2nd ed.). Academic Press.
   Google Scholar