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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 5, pp. 9-27

Use of remote sensing data in wildfire modelling

S.A. Khvostikov 1, 2 , S.А. Bartalev 1, 2 
1 Space Research Institute RAS, Moscow, Russia
2 Center for Forest Ecology and Productivity RAS, Moscow, Russia
Accepted: 11.09.2021
DOI: 10.21046/2070-7401-2021-18-5-9-27
Wildfire modelling can be used to evaluate fire threat level and support fire related decisions. State-of-the-art wildfires model produce accurate forecast of fire spread if provided with timely and accurate input data. Model input data can be provided by remote sensing (RS). This article gives short summary on wildfire modelling methods, methods to evaluate fire and fuel characteristics from RS and reviews multiple applications of RS data in wildfire modelling. RS data provides information on fire characteristics for every point on Earth, forming the basis for global or regional (national) wildfire modelling systems. Development of wildfire modelling and RS methods expanded opportunities for model accuracy estimation and model parameters evaluation. Joint use of RS data and model forecast form the basis for data assimilations methods which can further increase model accuracy.
Keywords: wildfire modelling, hotspots, burns, classification, accuracy estimation, data assimilation
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References:

  1. Akimov V. A., Alekseenko Ya. V., Epikhin A. V., Kartashev V. I., Kramintsev A. P., Kudinov A. A., Kurbatov M. Yu., Ledentsev S. A., Rotaru A. N., Savel’ev M. I., Shabunevich A. V., Shabunevich V. I., Nazemno-kosmicheskii monitoring chrezvychainykh situatsii (Remote and ground monitoring of emergency situations), Moscow: FGBU VNII GOChS (FTs), 2016, 128 p. (in Russian).
  2. Bartalev S. A., Egorov V. A., Khovratovich T. S., Krylov A. M., Stytsenko F. V., The evaluation of possibilities to assess forest burnt severity using multi-spectral satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2010, Vol. 7, No. 3, pp. 215–225 (in Russian).
  3. Bartalev S. A., Egorov V. A., Efremov V. Yu., Loupian E. A., Stytsenko F. V., Flitman E. V., Integrated burnt area assessment based on combine use of multi-resolution MODIS and Landsat-TM/ETM+ satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9, No. 2, pp. 9–27 (in Russian).
  4. Bartalev S. A., Loupian E. A., Stytsenko F. V., Panova O. Yu., Efremov V. Yu., Rapid mapping of forest burnt areas over Russia using Landsat data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2014, Vol. 11, No. 1, pp. 9–20 (in Russian).
  5. Bartalev S. A., Egorov V. A., Zharko V. O., Loupian E. A., Plotnikov D. E., Khvostikov S. A., Current state and development prospects of satellite mapping methods of Russia’s vegetation cover, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2015, Vol. 12, No. 5, pp. 203–221 (in Russian).
  6. Volokitina A. V., Sofronova T. M., Korets M. A., Vegetation fire behavior prediction, Lesnoi Zhurnal (Russian Forestry J.), 2020, No. 1, pp. 9–25 (in Russian), DOI: 10.37482/0536-1036-2020-1-9-25.
  7. Grishin A. M., Matematicheskie modeli lesnykh pozharov (Mathematical wildfire models), Tomsk, 1981, 278 p. (in Russian).
  8. Grishin A. M., Penenko V. V., Matematicheskoe modelirovanie lesnykh pozharov i novye sposoby bor’by s nimi (Mathematical modelling of wildfires and new ways of firefighting), Novosibirsk: Nauka, 1992, 404 p. (in Russian).
  9. Dorrer G. A., Dinamika lesnykh pozharov (Dynamic of forest fires), Novosibirsk: Izd. Sibirskogo otdeleniya Rossiiskoi akademii nauk, 2008, 403 p. (in Russian).
  10. Dorrer G. A., Komorovskii V. S., Yakimov S. P., Method for evaluation and forecast of big wildfires based on remote sensing data, Khvoinye boreal’noi zony, 2011, Vol. 28, No. 1, pp. 18–26 (in Russian).
  11. Loupian E. A., Bartalev S. A., Ershov D. V., Kotelnikov R. V., Balashov I. V., Burtsev M. A., Egorov V. A., Efremov V. Yu., Zharko V. O., Kovganko K. A., Kolbudaev P. A., Krasheninnikova Yu. S., Proshin A. A., Mazurov A. A., Uvarov I. A., Stytsenko F. V., Sychugov I. G., Flitman E. V., Khvostikov S. A., Shulyak P. P., Satellite data processing management in Forest Fires Remote Monitoring Information System (ISDM-Rosleskhoz) of the Federal Agency for Forestry, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2015, Vol. 12, No. 5, pp. 222–250 (in Russian).
  12. Loupian E. A., Bartalev S. A., Balashov I. V., Egorov V. A., Ershov D. V., Kobets D. A., Senko K. S., Stytsenko F. V., Satellite monitoring of forest fires in the 21st century in the territory of the Russian Federation (facts and figures based on active fires detection), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, Vol. 14, No. 6, pp. 158–175 (in Russian), DOI: 10.21046/2070-7401-2017-14-6-158-175.
  13. Ponomarev E. I., Kharuk V. I., Yakimov N. D., Current results and perspectives of wildfire satellite monitoring in Siberia, Sibirskii lesnoi zhurnal, 2017, No. 5, pp. 25–36 (in Russian), DOI: 10.15372/SJFS20170503.
  14. Stytsenko F. V., Bartalev S. A., Egorov V. A., Loupian E. A., Post-fire forest tree mortality assessment method using MODIS satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2013, Vol. 10, No. 1, pp. 254–266 (in Russian).
  15. Khvostikov S. A., Balashov I. V., Bartalev S. A., Efremov V. Yu., Loupian E. A., Regional scale optimization of wildfire model parameters and modelling of wildfire dynamic using remote sensing data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9, No. 3, pp. 91–100 (in Russian).
  16. Khvostikov S. A., Bartalev S. A., Loupian E. A., Stochastic wildfire model based on Monte-Carlo method and remote sensing data integration, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2016, Vol. 13, No. 5, pp. 145–156 (in Russian), DOI: 10.21046/2070-7401-2016-13-5-145-156.
  17. Shihov A. N., Zaripov A. S., Long-term dynamics of fire- and wind-related forest losses in northeast European Russia from satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 7, pp. 114–128 (in Russian), DOI: 10.21046/2070-7401-2018-15-7-114-128.
  18. Allaire F., Filippi J.-B., Mallet V., Generation and evaluation of an ensemble of wildland fire simulations, Intern. J. Wildland Fire, 2020, Vol. 29, No. 2, pp. 160–173, DOI: 10.1016/j.rse.2009.12.019 DOI: 10.1071/WF19073.
  19. Amraoui M., DaCamara C. C., Pereira J. M. C., Detection and monitoring of African vegetation fires using MSG-SEVIRI imagery, Remote Sensing of Environment, 2010, Vol. 114, No. 5, pp. 1038–1052, DOI: 10.1016/j.rse.2009.12.019.
  20. Anderson K. R., Englefield P., Little J. M., Reuter G., An approach to operational forest fire growth predictions for Canada, Intern. J. Wildland Fire, 2009, Vol. 18, No. 8, pp. 893–905, DOI: 10.1071/WF08046.
  21. Andrews P. L., Do you BEHAVE? — Application of the BehavePlus fire modeling system, Proc. 3rd Fire Behavior and Fuels Conf., 25–29 Oct. 2010, Spokane; Birmingham: Intern. Association of Wildland Fire, 2010, 17 p.
  22. Cai S., Liu D., Sulla-Menashe D., Friedl M. A., Enhancing MODIS land cover product with a spatial-temporal modeling algorithm, Remote Sensing of Environment, 2014, Vol. 147, pp. 243–255, DOI: 10.1016/j.rse.2014.03.012.
  23. Cardil A., Monedero S., Ramírez J., Silva C. A., Assessing and reinitializing wildland fire simulations through satellite active fire data, J. Environmental Management, 2019, Vol. 231, pp. 996–1003, DOI: 10.1016/j.jenvman.2018.10.115.
  24. Chuvieco E., Mouillot F., van der Werf G. R., San Miguel J., Tanase M., Koutsias N., García M., Yebra M., Padilla M., Gitas I., Heil A., Hawbaker T. J., Giglio L., Historical background and current developments for mapping burned area from satellite Earth observation, Remote Sensing of Environment, 2019, Vol. 225, pp. 45–64, DOI: 10.1016/j.rse.2019.02.013.
  25. Cruz M. G., Alexander M. E., Uncertainty associated with model predictions of surface and crown fire rates of spread, Environmental Modelling and Software, 2013, Vol. 47, pp. 16–28, DOI: 10.1016/j.envsoft.2013.04.004.
  26. Cruz M. G., Sullivan A. L., Leonard R., Malkin S., Matthews S., Gould J. S., McCaw W. L., Alexander M. E., Fire Behaviour Knowledge in Australia, CSIRO Ecosystems Sciences and CSIRO Digital Productivity and Services Flagship Client Report No. EP145189, 2014, 171 p.
  27. Faggian N., Bridge C., Fox-Hughes P., Jolly P., Jacobs H., Ebert B., Bally J., Final Report: An evaluation of fire spread simulators used in Australia, Melbourne, Australia: Bureau of Meteorology, 2017, 92 p.
  28. Filippi J. B., Mallet V., Nader B., Evaluation of forest fire models on a large observation database, Natural Hazards and Earth System Science, 2014, Vol. 14, No. 11, pp. 3077–3091, DOI: 10.5194/nhess-14-3077-2014.
  29. Filizzola C., Corrado R., Marchese F., Mazzeo G., Paciello R., Pergola N., Tramutoli V., RST-FIRES, an exportable algorithm for early-fire detection and monitoring: description, implementation, and field validation in the case of the MSG-SEVIRI sensor, Remote Sensing of Environment, 2016, Vol. 186, pp. 196–216, DOI: 10.1016/j.rse.2016.08.008.
  30. Finney M. A., Efforts at Comparing Simulated and Observed Fire Growth Patterns: Final Report, 2000, 20 p., available at: https://www.firelab.org/sites/default/files/2021-05/Finney_2000_FarsiteValidation_FinalReport.pdf.
  31. Finney M. A., Grenfell I. C., McHugh C. W., Seli R. C., Trethewey D., Stratton R. D., Brittain S., A Method for Ensemble Wildland Fire Simulation, Environmental Modeling and Assessment, 2011, Vol. 16, No. 2, pp. 153–167, DOI: 10.1007/s10666-010-9241-3.
  32. Giglio L., Schroeder W., Justice C. O., The collection 6 MODIS active fire detection algorithm and fire products, Remote Sensing of Environment, 2016, Vol. 178, pp. 31–41, DOI: 10.1016/j.rse.2016.02.054.
  33. Jahdi R., Salis M., Darvishsefat A. A., Mostafavi M. A., Alcasena F., Etemad V., Lozano O., Spano D., Calibration of FARSITE simulator in northern Iranian forests, Natural Hazards and Earth System Sciences, 2015, Vol. 15, No. 3, pp. 443–459, DOI: 10.5194/nhess-15-443-2015.
  34. Kalabokidis K., Ager A., Finney M., Athanasis N., Palaiologou P., Vasilakos C., AEGIS: a wildfire prevention and management information system, Natural Hazards and Earth System Sciences, 2016, Vol. 16, No. 3, pp. 643–661, DOI: 10.5194/nhess-16-643-2016.
  35. Kurbanov E., Vorobyev O., Leznin S., Polevshikova Y., Demisheva E., Assessment of burn severity in Middle Povozhje with Landsat multitemporal data, Intern. J. Wildland Fire, 2017, Vol. 26, No. 9, pp. 772–782, DOI: 10.1071/WF16141.
  36. Mandel J., Beezley J. D., Coen J. L., Kim M., Data Assimilation for Wildland Fires — Ensemble Kalman filters in coupled atmosphere-surface models, IEEE Control Systems Magazine, 2009, Vol. 29, Issue 3, pp. 47–65, DOI: 10.1109/MCS.2009.932224.
  37. Mandel J., Kochanski A. K., Vejmelka M., Beezley J. D., Data Assimilation of Satellite Fire Detection in Coupled Atmosphere-Fire Simulation by WRF-SFIRE, arXiv preprint, arXiv:1410.6948, 2014, 9 p.
  38. Milne G. J., Kelso J. K., Mellor D., Murphy M. E., Evaluating wildfire simulators using historical fire data, Advances in forest fire research. Imprensa da Universidade de Coimbra, 2014, pp. 1366–1375, DOI: 10.14195/978-989-26-0884-6_150.
  39. Noonan-Wright E. K., Opperman T. S., Finney M. A., Zimmerman G. T., Seli R. C., Elenz L. M., Calkin D. E., Fiedler J. R., Developing the US Wildland Fire Decision Support System, J. Combustion, 2011, Vol. 2011, pp. 1–14, DOI: 10.1155/2011/168473.
  40. Oliva P., Schroeder W., Assessment of VIIRS 375m active fire detection product for direct burned area mapping, Remote Sensing of Environment, 2015, Vol. 160, pp. 144–155, DOI: 10.1016/j.rse.2015.01.010.
  41. Opperman T., Gould J., Finney M., Tymstra C., Applying Fire Spread Simulators in New Zealand and Australia: Results from an International Seminar, Fuels Management-How to Measure Success, Proc. Conf., Portland, 28–30 March 2006, Fort Collins, U. S.: Department of Agriculture, Forest Service, Rocky Mountain Research Station, 2006, pp. 201–212.
  42. Ponomarev E., Yakimov N., Ponomareva T., Yakubailik O., Conard S. G., Current Trend of Carbon Emissions from Wildfires in Siberia, Atmosphere, 2021, Vol. 12, No. 5, Art. No. 559, 15 p., DOI: 10.3390/atmos12050559.
  43. Reeves M. C., Ryan K. C., Rollins M. G., Thompson T. G., Spatial fuel data products of the LANDFIRE Project, Intern. J. Wildland Fire, 2009, Vol. 18, No. 3, pp. 250, DOI: 10.1071/WF08086.
  44. Rochoux M. C., Emery C., Ricci S., Cuenot B., Trouvé A., Towards predictive data-driven simulations of wildfire spread, Part 2: Ensemble Kalman Filter for the state estimation of a front-tracking simulator of wildfire spread, Natural Hazards and Earth System Sciences Discussions, 2014, Vol. 2, No. 5, pp. 3769–3820, DOI: 10.5194/nhessd-2-3769-2014.
  45. Rothermel R. C., A mathematical model for predicting fire spread in wildland fuels, USDA Forest Service, Research paper INT-115, 1972, 48 p.
  46. Sá A. C. L., Benali A., Fernandes P. M., Pinto R. M. S., Trigo R. M., Salis M., Russo A., Jerez S., Soares P. M. M., Schroeder W., Pereira J. M. C., Evaluating fire growth simulations using satellite active fire data, Remote Sensing of Environment, 2017, Vol. 190, pp. 302–317, DOI: 10.1016/j.rse.2016.12.023.
  47. Salis M., Arca B., Alcasena F., Arianoutsou M., Bacciu V., Duce P., Duguy B., Koutsias N., Mallinis G., Mitsopoulos I., Moreno J. M., Pérez J. R., Urbieta I. R., Xystrakis F., Zavala G., Spano D., Predicting wildfire spread and behaviour in Mediterranean landscapes, Intern. J. Wildland Fire, 2016, Vol. 25, No. 10, pp. 1015–1032, DOI: 10.1071/WF15081.
  48. Schroeder W., Oliva P., Giglio L., Quayle B., Lorenz E., Morelli F., Active fire detection using Landsat-8/OLI data, Remote Sensing of Environment, 2016, Vol. 185, pp. 210–220, DOI: 10.1016/j.rse.2015.08.032.
  49. Sullivan A. L., A review of wildland fire spread modelling, 1990-present, 1: Physical and quasi-physical models, Intern. J. Wildland Fire, 2009, Vol. 18, No. 4, pp. 349–368, DOI: 10.1071/WF06143.
  50. Szpakowski D. M., Jensen J. L. R., A Review of the Applications of Remote Sensing in Fire Ecology, Remote Sensing, 2019, Vol. 11, No. 22, Art. No. 2638, DOI: 10.3390/rs11222638.
  51. Toukiloglou P., Eftychidis G., Gitas I., Tompoulidou M., ArcFuel methodology for mapping forest fuels in Europe, Proc 1st Intern. Conf. Remote Sensing and Geoinformation of Environment, 2013, Vol. 8795, Art. No. 87951J, 19 p., DOI: 10.1117/12.2028213.
  52. Tymstra C., Bryce R., Wotton B. M., Armitage O. B., Development and structure of Prometheus the Canadian Wildland Fire Growth Simulation Model, Information Report NOR-X-417, Edmonton: Northern Forestry Centre, 2010, 102 p.
  53. Valero M. M., Rios O., Mata C., Pastor E., Planas E., An integrated approach for tactical monitoring and data-driven spread forecasting of wildfires, Fire Safety J., 2017, Vol. 91, pp. 835–844, DOI: 10.1016/j.firesaf.2017.03.085.
  54. Zhang C., Collin A., Moireau P., Trouvé A., Rochoux M. C., Front shape similarity measure for data-driven simulations of wildland fire spread based on state estimation: Application to the RxCADRE field-scale experiment, Proc. Combustion Institute, 2019, Vol. 37, No. 3, pp. 4201–4209, DOI: 10.1016/j.proci.2018.07.112.