Skip to main content

The use of remote sensing techniques for the study of forest fires is a subject that started already several years ago and whose possibilities have been increasing as new sensors were incorporated into earth observation international programmes and new goals were reached based on the improved techniques that have been introduced. In this way, three main lines of work can be distinguished in which remote sensing provides results that can be applied directly to the subject of forest fires: risk of fire spreading, detection of hot spots and establishment of fire parameters and, finally, cartography of affected areas.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Menzel, W. P, Seemann, S. W., Li, J., and Gumley, L. E., MODIS atmospheric profile retrieval algorithm theoretical basis document, Algorithm technical background document.

    Google Scholar 

  • Huete, A., Justice, C., and Leeuwen, W. V., MODIS vegetation index, Algorithm theoretical basis document.

    Google Scholar 

  • Wan, Z., MODIS land-surface temperature, Algorithm theoretical basis document.

    Google Scholar 

  • Alonso, M., Camarasa, A., Chuvieco, E., Kyun, I. A., Martín, M. P., and Salas, F. J., 1996, Estimating temporal dynamic of fuel moisture content of Mediterranean Species from NOAA-AVHRR dataEARSeL Advances in Remote Sensing 4(4):9–21.

    Google Scholar 

  • Al-Rawi, K. L., Casanova, J. L., and Calle, A., 2001, Burned area mapping system and fire detection system, based on neural networks and NOAA-AVHRR imageryInternational Journal of Remote Sensing 22:2015–2032.

    Article  Google Scholar 

  • Arino, O. and Rosaz, J. M., 1997, 1998 and 1999, World ATSR FIRE Atlas using ERS-2 ATSR-2 DataProceedings of the Joint Fire Science Conference, Boise (15–17 June 1999).

    Google Scholar 

  • Boyd D. S. and Ripple W. J., 1997, Potential vegetation index for determining global forest coverInternational Journal of remote Sensing 18:1395–1401.

    Article  Google Scholar 

  • Briess, K., Jahn, H., Lorenz, E., Oertel, D., Skrbek, W., and Zhukov, B., 2003, Fire recognition potential of the bi-spectral detection (BIRD) satelliteInternational Journal of Remote Sensing 24:865–872.

    Article  Google Scholar 

  • Burgan, R. E., 1995, Use of remotely sensed data for fire danger estimation. Proceedings of EARSeL International Workshop on Remote Sensing and GIS Applications to Forest Fire Management, Alcal¬ de Henares, Spain, pp. 87–95.

    Google Scholar 

  • Calle, A., Casanova, J. L., and Romo, A., 2003, CDMC Project. WP-2210: Fire Risk Mapping. Founded by ESA.

    Google Scholar 

  • Calle, A., Romo, A., Sanz, J., and Casanova, J. L., 2004, An¬lisis of forest fire parametres using BIRD, MODIS and MSG-SEVIRI sensors. EARSeL Symposium (Dubrovnik, May 2004).

    Google Scholar 

  • Calle, A., Casanova, J. L., and Romo, A., 2006, Fire detection and monitoring using MSG Spinning Enhanced Visible and Infrared Imager (SEVIRI) dataJournal of Geophysical Research 11, G04S06, doi:10.1029.

    Google Scholar 

  • Casanova, J. L., Calle, A., and Gonz¬lez-Alonso F., 1998, A Forest Fire Risk Assessment obtained in real time by means of NOAA satellite images. Forest Fire Research, III International Conference on Forest Fire Research and 14th Conference on Fire and Forest Meteorology1:1169–1179.

    Google Scholar 

  • Chuvieco, E., Riaño, D., Aguado, I., and Cocero, D., 2002, Estimation of fuel moisture content from multitemporal an¬lisis of Landsat Thematic Mapper reflectance data: applications in fire danger assessmentInternational Journal of Remote Sensing 23(11):2145–2162.

    Article  Google Scholar 

  • Delabraze, P. and Valete, J. CH., 1977, Etude de l'inflamabilité et de la combustibilité Consultation technique FAO sur les incendies de forets en pays méditerranneéns.

    Google Scholar 

  • Desbois, N. and Vidal, A., 1996, Real time monitoring of vegetation flammability using NOAA-AVHRR thermal infrared dataEARSeL Journal Advance in Remote Sensing 4(4):25–32.

    Google Scholar 

  • Dozier, J., 1981, A method for satellite identification of surface temperature fields of subpixel resolutionRemote Sensing of Environment 11:221–229.

    Article  Google Scholar 

  • Eidenshink, J. C., Burgan, R. E., and Haas, R. H., 1990, Monitoring fire fuels condition by using time series composites of Advanced Very High Resolution Radiometer (AVHRR) data, Proceedings of Resource Technology 90, ASPRS, Washington, DC., pp. 68–82.

    Google Scholar 

  • Fern¬ndez, A., Illera, P., and Casanova, J. L., 1997, Automatic mapping of surfaces affected by forest fires in Spain using AVHRR NDVI composite image dataRemote Sensing of Environment 60:153–162.

    Article  Google Scholar 

  • Giglio, L. and Kendall, J. D., 2001, Application of the Dozier retrieval to wildfire characterization, A sensitivity analysisRemote Sensing of Environment 77:34–49.

    Article  Google Scholar 

  • Giglio, L. and Justice, C. O., 2003, Effect of wavelength selection on characterisation of fire size and temperatureInternational Journal of Remote Sensing 24:3515–3520.

    Article  Google Scholar 

  • Giglio, L., Descloitres, J., Justice, C. O., and Kaufman, Y. J., 2003, An enhanced contextual fire detection algorithm for MODISRemote Sensing of Environment.87:273–282.

    Article  Google Scholar 

  • Huete, A. R., 1988, A soil adjusted vegetation index ( SAVI).Remote Sensing of Environment 27:47–57.

    Google Scholar 

  • Hunt, E. R. and Rock, C. R., 1989, Detection of changes in leaf water content using near and medium infrared reflectances.Remote Sensing of Environment 30:43–54.

    Article  Google Scholar 

  • Ichoku, C., Kaufman, Y. J., Giglio, L., Li, Z., Fraser, R. H., Jin, J. Z., and Park, W. M., 2003, Comparative analysis of daytime fire detection algorithms using AVHRR data for the 1995 fire season in Canada: perspective for MODISInternational Journal of Remote Sensing 24:1669–1690.

    Article  Google Scholar 

  • Illera, P., Fern¬ndez, A., Calle, A., and Casanova, J. L., 1996, Evaluation of fire danger in Spain by means of NOAA-AVHRR images.EARSeL Journal Advance in Remote Sensing 4(4): 33–43.

    Google Scholar 

  • Justice, C. O. and Malingreau, J. P. (editors), 1993, The IGBP satellite fire detection algorithm workshop technical report, IGBP-DIS Working paper 9, NASA/GSFC (Greenbelt, Maryland, USA, February, 1993).

    Google Scholar 

  • Kasischke, E. S., French, N. H. F., Harrell, P., Christensen, N. L. Jr., Ustin, S. L., and Barry, D., 1993, Monitoring of wildfires in the boreal forest using large area AVHRR NDVI composite image dataRemote Sensing of Environment 45:61–71.

    Article  Google Scholar 

  • Kaufman, Y. and Justice, C., 1998, MODIS Fire Products, MODIS Science Team, EOS ID#2741.

    Google Scholar 

  • Kaufman, Y. J., Tucker, C. J., and Fung, I., 1990, Remote sensing of biomass burning in the tropicsJournal of Geophysical Research 95:9927–9939.

    Article  Google Scholar 

  • Kaufman, Y. J., Justice, C., Flyn, L., Kendall, J., Prins, E., Ward, D. E., Menzel, P., and Setzer, A., 1998, Potencial global fire monitoring from EOS-MODISJournal of Geophysical Research.103:32215–32238.

    Article  Google Scholar 

  • Kaufman, Y. and Justice, C., 1998, MODIS Fire Products, Algorithm Theoretical Basis Document, MODIS Science Team, EOS ID#2741.

    Google Scholar 

  • Langaas, S., 1993, A parametrised bispectral model for savana fire detection using AVHRR night imagesInternational Journal of Remote Sensing 14:2245–2262.

    Article  Google Scholar 

  • Langaas, S., 1995, A critical review of sub-resolution fire detection techniques and principles using thermal satellite data, Ph.D. thesis, Department of Geography, University of Oslo, Norway.

    Google Scholar 

  • Lasaponara, R., Cuomo, V., and Tramutoli, V., 1998, Satellite forest fire detection in the Italian ecosystems using AVHRR data, XII International Conference on Forest Fire Research Luso (16–20 November 1998)2:2013–2028.

    Google Scholar 

  • Lee, T. M. and Tag, P. M., 1990, Improved detection of hotspots using the AVHRR 3.7 μm channel.Bulletin of the American Meteorological Society 71:1722–1730.

    Article  Google Scholar 

  • Li, Z., Nadon, S., Chilar, J., and Stocks, B., 2000, Satellite mapping of Canadian boreal forest fires: Avaluation and comparison of algorithmsInternational Journal of Remote Sensing 21:3071–3082.

    Article  Google Scholar 

  • López, S., Gonz¬lez, F., Llop, R., and Cuevas M., 1991, An evaluation of the utility of NOAA-AVHRR images for monitoring forest fire risk in SpainInternational Journal of Remote Sensing 12:1841–1851.

    Article  Google Scholar 

  • Lorentz, E. and Skrbek, W., 2001, Calibration of a bi-spectral infrared push-broom imager, Proceedings of SPIE, Infrared Spaceborne Remote Sensing IX, San Diego, 29 july–3 August 2001, 4486, 90–103

    Google Scholar 

  • Matson, M. and Dozier, J., 1981, Identification of sub-resolution high temperatures sources using a thermal IR sensorPhotogrammetric Engineering and Remote Sensing 47(9):1311–1318.

    Google Scholar 

  • Matson, M. and Holben, B., 1987, Satellite detection of tropical burning in BrazilInternational Journal of Remote Sensing 8:509–516.

    Article  Google Scholar 

  • Moran, M. S., Clarke, T. R., Inoue, Y., and Vidal, A., 1994, Estimating crop water deficit using the relation between surface air temperature and spectral vegetation indexRemote Sensing of Enviroment 49(3):246–263.

    Article  Google Scholar 

  • Nemani, R. R. and Running, S. W., 1989, Estimation of regional surface resistance to evapotranspiration from NDVI and thermal IR AVHRR dataJournal of Applied Meteorology 28(4):276–274.

    Article  Google Scholar 

  • Norman, J. M., Divakarla, M., and Goel, N. S., 1995, Algorithms for extracting information from remote thermal IR observations of the Earth surfaceRemote Sensing of Environment 51(1):157–168.

    Article  Google Scholar 

  • Patterson, M. W. and Yool, S. R., 1998, Mapping fire-induced vegetation mortality using Landsat Thematic Mapper data: A comparison of linear transformation techniquesRemote Sensing of Environment 65:132–142.

    Article  Google Scholar 

  • Pinty, B. and Verstraete, M. M., 1992, GEMI: a non linear index to monitor global vegetation from satellitesVegetatio 101:15–20.

    Article  Google Scholar 

  • Pereira, J. M. C. and Setzer, A. W., 1993, Spectral characteristics of fire scars in Landsat-5 TM images of AmazoniaInternational Journal of Remote Sensing 14:2061–2078.

    Article  Google Scholar 

  • Pereira, J. M., Chuvieco, E., Beandoin, A., and Desbois, N., 1997, Remote sensing of burned areas: A review of remote sensing methods for study of large wildland fires (Chuvieco, ed.)Megafires project, ENV-CT96-0256, pp. 127–183.

    Google Scholar 

  • Prins, E. M. and Menzel, W. P., 1992, Geostationary satellite detection of biomas burning in South AmericaInternational Journal of Remote Sensing 13(2):783–2789.

    Google Scholar 

  • Prins, E. and Schmetz, J., 1999, Diurnal fire active detection using a suite of International geoestationaty satellites, GOFC Forest Fire Monitoring and Mapping Workshop, JRC, Ispra.

    Google Scholar 

  • Prins, E., Govaerts, Y., and Justice, C. O., 2004, Report on the Joint GOFC/GOLD Fire and CEOS LPV Working Group Workshop on Global Geostationary Fire Monitoring ApplicationsGOFC/GOLD ReportNo. 19. (23–25 March 2004). EUMETSAT, Darmstadt, Germany.

    Google Scholar 

  • Price, J. C., 1984, Land surface temperature measurements from the split-window channels of the NOAA-7 Advanced Very High Resolution Radiometer.Journal of Geophysical Research 89(D5):7231–7237.

    Article  Google Scholar 

  • Prosper-Laget, V., Douguedroit, A. and Guinot, J.P., 1995, Mapping the risk of forest fire occurrence using NOAA satellite information.EARSeL Advance in Remote Sensing 4(3-XII):30–38.

    Google Scholar 

  • Robinson, J. M., 1991, Fire from space: Global fire evaluation using infrared remote sensing.International Journal of Remote Sensing, 12:3–24.

    Article  Google Scholar 

  • Seguin, B., Lagouarde, J.P. and Savane, M., 1991, The assessment of regional crop water conditions from meteorological satellite thermal infrared data.Remote Sensing of Environment 35:141–148.

    Article  Google Scholar 

  • Schmetz, J., Pili, P., Tjemkes, S., Just, D., Kerkmann, J, Rota, S., and Ratier, A., 2002, An Introduction to Meteosat Second Generation (MSG)BAMS 83:977–992.

    Article  Google Scholar 

  • Schultz, M., 2002, On the use of ATSR fire count data to estimate the seasonal and inter-annual variability of vegetation fire emissions.Atmospheric Chemistry and Physics 2:387–395.

    Article  Google Scholar 

  • Synder, W. and Wan, Z., 1998, BRDF models to predict spectral reflectance and emissivity in the infrared, IEEE Trans. Geosci. Remote Sens.36: 214–225.

    Article  Google Scholar 

  • Van de Griend, A.A. and Owe, M., 1993, On the relationship between thermal emissivity and the normalized difference vegetation index for nature surfaces.International Journal of Remote Sensing 14(6):1119–1131.

    Article  Google Scholar 

  • Vidal, A., Pinglo, F., Durand, H., Devaux-Ros, C., and Maillet, A., 1994, Evaluation of a temporal risk index in Mediterranean forest from NOAA thermal IR.Remote Sensing of Environment 49:296–303.

    Article  Google Scholar 

  • Wan, Z., 1999, MODIS Land-Surface temperature algorithm theoretical basis document. MODIS Land Team.

    Google Scholar 

  • Wooster, M. J., Zhukov, B, and Oertel, D., 2003, 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.

    Article  Google Scholar 

  • Zhukov, B. and Oertel, D., 2001, Hot Spot Detection and analysis algorithm for the BIRD mission. Algorithm Theoretical Basis.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to A. Calle or J. -L. Casanova .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer Science+Business Media B.V

About this paper

Cite this paper

Calle, A., Casanova, J.L. (2008). Forest Fires And Remote Sensing. In: Coskun, H.G., Cigizoglu, H.K., Maktav, M.D. (eds) Integration of Information for Environmental Security. NATO Science for Peace and Security Series C: Environmental Security. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6575-0_19

Download citation

Publish with us

Policies and ethics