Demonstrating the Validity of a Wildfire DDDAS

  • Craig C. Douglas
  • Jonathan D. Beezley
  • Janice Coen
  • Deng Li
  • Wei Li
  • Alan K. Mandel
  • Jan Mandel
  • Guan Qin
  • Anthony Vodacek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3993)


We report on an ongoing effort to build a Dynamic Data Driven Application System (DDDAS) for short-range forecast of weather and wildfire behavior from real-time weather data, images, and sensor streams. The system changes the forecast as new data is received. We encapsulate the model code and apply an ensemble Kalman filter in time-space with a highly parallel implementation. In this paper, we discuss how we will demonstrate that our system works using a DDDAS testbed approach and data collected from an earlier fire.


Data Assimilation Ensemble Member Wildland Fire Observation Function Approximate Inverse 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Clark, T.L., Coen, J., Latham, D.: Description of a coupled atmosphere-fire model. Intl. J. Wildland Fire 13, 49–64 (2004)CrossRefGoogle Scholar
  2. 2.
    Coen, J.L.: Simulation of the Big Elk Fire using using coupled atmosphere-fire modeling. International J. of Wildland Fire 14(1), 49–59 (2005)CrossRefGoogle Scholar
  3. 3.
    Rothermel, R.C.: A mathematical model for predicting fire spread in wildland fires. USDA Forest Service Research Paper INT-115 (1972)Google Scholar
  4. 4.
    Albini, F.A.: PROGRAM BURNUP: A simulation model of the burning of large woody natural fuels. Final Report on Research Grant INT-92754-GR by U.S.F.S. to Montana State Univ., Mechanical Engineering Dept. (1994)Google Scholar
  5. 5.
    Anderson, H.: Aids to determining fuel models for estimating fire behavior. USDA Forest Service, Intermountain Forest and Range Experiment Station, INT-122 (1982)Google Scholar
  6. 6.
    Mandel, J., Chen, M., Franca, L.P., Johns, C., Puhalskii, A., Coen, J.L., Douglas, C.C., Kremens, R., Vodacek, A., Zhao, W.: A note on dynamic data driven wildfire modeling. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3038, pp. 725–731. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Linn, R., Reisner, J., Colman, J.J., Winterkamp, J.: Studying wildfire behavior using FIRETEC. Int. J. of Wildland Fire 11, 233–246 (2002)CrossRefGoogle Scholar
  8. 8.
    Serón, F.J., Gutiérrez, D., Magallón, J., Ferragut, L., Asensio, M.I.: The evolution of a WILDLAND forest FIRE FRONT. Visual Computer 21, 152–169 (2005)CrossRefGoogle Scholar
  9. 9.
    Evensen, G.: The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dynamics 53, 343–367 (2003)CrossRefGoogle Scholar
  10. 10.
    Evensen, G.: Sampling strategies and square root analysis schemes for the EnKF. Ocean Dynamics, 539–560 (2004)Google Scholar
  11. 11.
    Tippett, M.K., Anderson, J.L., Bishop, C.H., Hamill, T.M., Whitaker, J.S.: Ensemble square root filters. Monthly Weather Review 131, 1485–1490 (2003)CrossRefGoogle Scholar
  12. 12.
    Burgers, G., van Leeuwen, P.J., Evensen, G.: Analysis scheme in the ensemble Kalman filter. Monthly Weather Review 126, 1719–1724 (1998)CrossRefGoogle Scholar
  13. 13.
    Johns, C.J., Mandel, J.: A two-stage ensemble Kalman filter for smooth data assimilation. In: Environmental and Ecological Statistics. Conference on New Developments of Statistical Analysis in Wildlife, Fisheries, and Ecological Research, Columbia, MI, October 13-16 (2004) (in print) Google Scholar
  14. 14.
    Kremens, R., Faulring, J., Gallagher, A., Seema, A., Vodacek, A.: Autonomous field-deployable wildland fire sensors. International J. of Wildland Fire 12, 237–244 (2003)CrossRefGoogle Scholar
  15. 15.
    Li, Y., Vodacek, A., Kremens, R.L., Ononye, A., Tang, C.: A hybrid contextual approach to wildland fire detection using multispectral imagery. IEEE Trans. Geosci. Remote Sens. 43, 2115–2126 (2005)CrossRefGoogle Scholar
  16. 16.
    Dozier, J.: A method for satellite identification of surface temperature fields of subpixel resolution. Remote Sens. Environ. 11, 221–229 (1981)CrossRefGoogle Scholar
  17. 17.
    Wooster, M.J., Zhukov, B., Oertel, D.: Fire radiative energy for quantitative study of biomass burning: derivation from the BIRD experimental satellite and comparison to MODIS fire products. Remote Sensing of Environment 86, 83–107 (2003)CrossRefGoogle Scholar
  18. 18.
    Smith, A.M.S., Wooster, M., Drake, N., Perry, G., Dipotso, F., Falkowski, M., Hudak, A.: Testing the potential of multi-spectral remote sensing for retrospectively estimating fire severity in African savanna environments. Remote Sens. Environ. 97, 92–115 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Craig C. Douglas
    • 1
    • 2
  • Jonathan D. Beezley
    • 4
  • Janice Coen
    • 3
  • Deng Li
    • 1
  • Wei Li
    • 1
  • Alan K. Mandel
    • 1
  • Jan Mandel
    • 4
  • Guan Qin
    • 5
  • Anthony Vodacek
    • 6
  1. 1.Department of Computer ScienceUniversity of KentuckyLexingtonUSA
  2. 2.Department of Computer ScienceYale UniversityNew HavenUSA
  3. 3.National Center for Atmospheric ResearchBoulderUSA
  4. 4.Department of Mathematical SciencesUniversity of Colorado at Denver and Health Sciences CenterDenverUSA
  5. 5.Institute for Scientific ComputationTexas A&M UniversityCollege StationUSA
  6. 6.Center for Imaging ScienceRochester Institute of TechnologyRochesterUSA

Personalised recommendations