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Satellite Observation of Biomass Burning

Implications in Global Change Research
  • Emilio Chuvieco
Chapter

Abstract

Biomass burning, which involves wildland fires as well as agricultural and grassland burnings, plays a critical role in the environmental equilibrium of our planet, since it is a major driving force in land cover transformations and contributes significantly to greenhouse gas emissions. Several satellite missions provide critical information required to better understand the temporal and spatial distribution of biomass burning. Satellite images provide objective and comprehensive information on global patterns of fire occurrence, as well as data on factors affecting fire ignition and propagation. Recent improvements in spatial, temporal, and spectral resolution of satellite remote sensing systems reduce past uncertainties – systems can now be used to obtain a more precise evaluation of burned areas and post-fire effects on soils and plants. Greater efforts are required to operationally use Earth Observation data in fire prevention and early warning. Longer time series data are required to acquire a better understanding of fire regimes, and their mutual relationships with global warming.

Keywords

Remote Sensing Biomass Burning Burned Area Advanced Very High Resolution Radiometer Advanced Very High Resolution Radiometer 
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.

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References

  1. Ahern, F. J., Goldammer, J. G., & Justice, C. O. (Eds.). (2001) Global and regional vegetation fire monitoring from space: Planning a coordinated international effort. The Haghe, The Netherlands: SPB Academic Publishing.Google Scholar
  2. Allgöwer, B., Carlson, J. D., & van Wagtendonk, J. W. (2003) Introduction to fire danger rating and remote sensing. Will remote sensing enhace wildland fire danger rating? In E. Chuvieco (Ed.), Wildland fire danger estimation and mapping. The role of remote sensing data (pp. 1–19). Singapore: World Scientific Publishing.Google Scholar
  3. Alonso, M., Camarasa, A., Chuvieco, E., Cocero, D., Kyun, I., Martín, M. P., & Salas, F. J. (1996) Estimating temporal dynamics of fuel moisture content of Mediterraneam species from NOAA-AVHRR data. EARSEL Advances in Remote Sensing, 4, 9–24.Google Scholar
  4. Ambrosia, V. G., & Brass, J. A. (1988) Thermal analysis of wildfires and effects on global ecosystem cycling. Geocarto International, 1, 29–39.CrossRefGoogle Scholar
  5. Ambrosia, V. G., Wegener, S. S., Sullivan, D. V., Buechel, S. W., Dunagan, S. E., & Brass, J. A. et al. (2003) Demostrating UAV-Acquired real-time thermal data over fires. Photogrammetric Engineering and Remote Sensing, 69, 391–402.Google Scholar
  6. Anderson, G. L., Hanson, J. D., & Haas, R. J. (1993) Evaluating Landsat Thematic Mapper derived vegetation indices for estimating above-ground biomass on semiarid rangelands. Remote Sensing of Environment, 45, 165–175.Google Scholar
  7. Andreae, M. O. (1991) Biomass burning: Its history, use and distribution and its impacts on environmental quality and global climate. In J. S Levine, (Ed.), Global biomass burning : Atmospheric, climatic, and biospheric implications (pp. 3–21). Cambridge, Mass: MIT Press.Google Scholar
  8. Arroyo, L. A., Healey, S. P., Cohen, W. B., Cocero, D., & Manzanera, J. A. (2006) Using object-oriented classification and high-resolution imagery to map fuel types in a Mediterranean region. Journal of Geophysical Research-Biogeosciences, 111, doi:10.1029/2005JG000120.Google Scholar
  9. Barbosa, P. M., Grégoire, J. M., & Pereira, J. M. C. (1999a). An algorithm for extracting burned areas from time series of AVHRR GAC data applied at a continental scale. Remote Sensing of Environment, 69, 253–263.Google Scholar
  10. Barbosa, P. M., Stroppiana, D., Gregoire, J. M., & Pereira, J. M. C. (1999b). An assessment of vegetation fire in Africa (1981–1991): Burned areas, burned biomass, and atmospheric emissions. Global Biogeochemical Cycles, 13, 933–950.Google Scholar
  11. Beaudoin, A., Vidal, A., Desbois, N., & Debaux- Ros, C. (1995) Monitoring the water status of Mediterranean forests using ERS-1 to support fire risk prevention. In International Geoscience and Remote Sensing Symposium, IGARSS ’95. ‘Quantitative Remote Sensing for Science and Applications’, (pp. 963–966). Firenze, Italy.Google Scholar
  12. Boschetti, L., Eva, H. D., Brivio, P. A., & Gregoire, J. M. (2004) Lessons to be learned from the comparison of three satellite-derived biomass burning products. Geophysical Research Letters, 31, L21501, doi:21510.21029/22004GL021229.Google Scholar
  13. BourgeauChavez, L. L., Kasischke, E. S., & Rutherford, M. D. (1999) Evaluation of ERS SAR data for prediction of fire danger in a boreal region. International Journal of Wildland Fire, 9, 183–194.Google Scholar
  14. Briess, K., Lorenz, E., Oertel, D., Skrbek, W., & Zhukov, B. (2001) Fire recognition potential of the Bi-spectral Infrared Detection (BIRD) Satellite. Berlin: Institute of Space Sensor Technology and Planetary Exploration, 2.Google Scholar
  15. Burgan, R. E., & Rothermel, R. C. (1984) BEHAVE: Fire behavior prediction and fuel modeling system. Fuel subsystem. Ogden, Utah: USDA Forest Service, GTR INT-167.Google Scholar
  16. Caetano, M. S., Mertes, L. A. K., & Pereira, J. M. C. (1994) Using spectral mixture analysis for fire severity mapping. Proceedigns of 2nd International Conference on Forest Fire Research (pp. 667–677). Coimbra.Google Scholar
  17. Calle, A., Casanova, J. L., & Romo, A. (2006) Fire detection and monitoring using MSG Spinning Enhanced Visible and Infrared Imager (SEVIRI) data. Journal of Geophysical Research – Biosciences, 111, doi:10.1029/2005JG000116.Google Scholar
  18. CarmonaMoreno, C., Belward, A., Malingreau, J. P., Hartley, A., Garcia Alegre, M., & Antonovskiy, M., et al. (2005) Characterizing interannual variations in global fire calendar using data from Earth observing satellites. Global Change Biology, 11, 1537–1555.Google Scholar
  19. Ceccato, P., Flasse, S., Tarantola, S., Jacquemoud, S., & Grégoire, J. M. (2001) Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sensing of Environment, 77, 22–33.Google Scholar
  20. Ceccato, P., Leblon, B., Chuvieco, E., Flasse, S., & Carlson, J. D. (2003) Estimation of live fuel moisture content. In E. Chuvieco (Ed.), Wildland fire danger estimation and mapping. The role of remote sensing data (pp. 63–90). Singapore: World Scientific Publishing.Google Scholar
  21. Chen, D. (2005) Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands. Remote Sensing of Environment, 98, 225–236.Google Scholar
  22. Cheng, Y. B., ZarcoTejada, P. J., Riaño, D., Rueda, C. A., & Ustin, S. (2006) Estimating vegetation water content with hyperspectral data for different canopy scenarios: Relationships between AVIRIS and MODIS indexes. Remote Sensing of Environment, 105, 354–366.Google Scholar
  23. Chladil, M. A., & Nunez, M. (1995) Assessing grassland moisture and biomass in Tasmania. The application of remote sensing and empirical models for a cloudy environment. International Journal of Wildland Fire, 5, 165–171.Google Scholar
  24. Chuvieco, E. (Ed.). (1999) Remote sensing of large wildfires in the european mediterranean basin. Berlin: Springer-Verlag.Google Scholar
  25. Chuvieco, E. (Ed.). (2003) Wildland fire danger estimation and mapping. The role of remote sensing data. Singapore: World Scientific Publishing.Google Scholar
  26. Chuvieco, E., Aguado, I., Cocero, D., & Riaño, D. (2003a). Design of an empirical index to estimate fuel moisture content from NOAA-AVHRR analysis in forest fire danger studies. International Journal of Remote Sensing, 24, 1621–1637.Google Scholar
  27. Chuvieco, E., Allgöwer, B., & Salas, F. J. (2003b). Integration of physical and human factors in fire danger assessment. In E. Chuvieco (Ed.), Wildland fire danger estimation and mapping. The role of remote sensing data (pp. 197–218). Singapore: World Scientific Publishing.Google Scholar
  28. Chuvieco, E., Cocero, D., Aguado, I., PalaciosOrueta, A., & Prado, E. (2004a). Improving burning efficiency estimates through satellite assessment of fuel moisture content. Journal of Geophysical Research – Atmospheres, 109, D14S07, doi:10.1029/2003JD003467, 1–8.Google Scholar
  29. Chuvieco, E., Cocero, D., Riaño, D., Martín, M. P., MartínezVega, J., de la Riva, J., & Pérez, F. (2004b). Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sensing of Environment, 92, 322–331.Google Scholar
  30. Chuvieco, E., & Congalton, R. G. (1988) Mapping and inventory of forest fires from digital processing of TM data. Geocarto International, 4, 41–53.Google Scholar
  31. Chuvieco, E., De Santis, A., Riaño, D., & Halligan, K. (2007) Simulation approaches for burn severity estimation using remotely sensed images. Journal of Fire Ecology, in press.Google Scholar
  32. Chuvieco, E., & Martín, M. P. (1994) Global fire mapping and fire danger estimation using AVHRR images. Photogrammetric Engineering and Remote Sensing, 60, 563–570.Google Scholar
  33. Chuvieco, E., Martín, M. P., & Palacios, A. (2002a). Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination. International Journal of Remote Sensing, 23, 5103–5110.Google Scholar
  34. Chuvieco, E., Riaño, D., Aguado, I., & Cocero, D. (2002b). Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: Applications in fire danger assessment. International Journal of Remote Sensing, 23, 2145–2162.Google Scholar
  35. Chuvieco, E., Riaño, D., Danson, F. M., & Martín, M. P. (2006) Use of a radiative transfer model to simulate the post-fire spectral response to burn severity. Journal of Geophysical Research – Biosciences, 111, doi:10.1029/2005JG000143.Google Scholar
  36. Chuvieco, E., Riaño, D., Van Wagtendok, J., & Morsdof, F. (2003c). Fuel Loads and Fuel Type Mapping. In E. Chuvieco (Ed.), Wildland fire danger estimation and mapping. The role of remote sensing data (pp. 119–142). Singapore: World Scientific Publishing.Google Scholar
  37. Chuvieco, E., Ventura, G., Martín, M. P., & Gomez, I. (2005) Assessment of multitemporal compositing techniques of MODIS and AVHRR images for burned land mapping. Remote Sensing of Environment, 94, 450–462.Google Scholar
  38. Cocero, D., Chuvieco, E., & Salas, J. (2001) El sensor SPOT-Vegetation, una nueva alternativa en la estimación de la humedad de la vegetación. In J. I. Rosell & J. A. Martínez-Casasnovas (Eds.), Teledetección. Medioambiente y Cambio Global (pp. 179–182). Lleida: Universitat de Lleida y Editorial Milenio.Google Scholar
  39. Cochrane, M. A., Alencar, A., Schulze, M. D., Souza, C. M., Nepstad, D. C., Lefebvre, P., & Davidson, E. A. (1999) Positive feedbacks in the fire dynamic of closed canopy tropical forests. Science, 284, 1832–1835.Google Scholar
  40. Cocke, A. E., Fule, P. Z., & Crouse, J. E. (2005) Comparison of burn severity assessments using Differenced Normalized Burn Ratio and ground data. International Journal of Wildland Fire, 14, 189–198.Google Scholar
  41. Couturier, S., Taylor, D., Siegert, F., Hoffmann, A., & Bao, M. Q. (2001) ERS SAR backscatter. A potential real-time indicator of the proneness of modified rainforests to fire. Remote Sensing of Environment, 46, 410–417.Google Scholar
  42. Csiszar, I., Denis, L., Giglio, L., Justice, C. O., & Hewson, J. (2005) Global fire activity from two years of MODIS data. International Journal of Wildland Fire, 14, 117–130.Google Scholar
  43. Csiszar, I. A., Morisette, J. T., & Giglio, L. (2006) Validation of active fire detection from moderate-resolution satellite sensors: The MODIS example in Northern Eurasia. IEEE Transactions on Geoscience and Remote Sensing, 44, 1757–1764.Google Scholar
  44. Danson, F. M., & Bowyer, P. (2004) Estimating live fuel moisture content from remotely sensed reflectance. Remote Sensing of Environment, 92, 309–321.Google Scholar
  45. De Santis, A., & Chuvieco, E. (2007) Burn severity estimation from remotely sensed data: Performance of simulation versus empirical models. Remote Sensing of Environment, doi:10.1016/j.rse.2006.1011.1022.Google Scholar
  46. De Santis, A., Vaughan, P., & Chuvieco, E. (2006) Foliage moisture content estimation from 1-D and 2-D spectroradiometry for fire danger assessment. Journal of Geophysical Research – Biosciences, 111, doi:10.1029/2005JG000149.Google Scholar
  47. Deeming, J. E., Burgan, R. E., & Cohen, J. D. (1978) The national fire-danger rating system – 1978. Ogden, UT: USDA Forest Service, GTR INT-39..Google Scholar
  48. Dennison, P. E., Roberts Dar, A., Peterson, S. H., & Rechel, J. (2005) Use of normalized difference water index for monitoring live fuel moisture content. International Journal of Remote Sensing, 26, 1035–1042.Google Scholar
  49. DíazDelgado, R., Pons, X., & Lloret, F. (2001) Fire severity effects on vegetation recovery after fire. The Bigues I Riells wildfire case study. In E. Chuvieco & M. P. Martín (Eds.), Third International workshop on remote sensing and gis applications to forest fire management. New methods and sensors (pp. 152–155). Paris: EARSeL.Google Scholar
  50. Dimitrakopoulos, A., & Papaioannou, K. K. (2001) Flammability assessment of Mediterranean forest fuels. Fire Technology, 37, 143–152.Google Scholar
  51. Dobson, J. E., Bright, E. A., Coleman, P. R., Purfee, R. C., & Worley, B. A. (2000) Landscan: A global population database for estimating populations at risk. Photogrammetric Engineering and Remote Sensing, 66, 849–857.Google Scholar
  52. Dozier, J. (1981) A method for satellite identification of surface temperature fields of subpixel resolution. Remote Sensing of Environment,11, 221.Google Scholar
  53. Dwyer, E., Pereira, J. M. C., Grégorie, J.-M., & DaCamara, C. C. (2000) Characterization of the spatio-temporal patterns of global fire activity using satellite imagery for the period April 1992 to March 1993. Journal of Biogeography, 27, 57–69.Google Scholar
  54. Eidenshink, J. C., & Faundeen, J. L. (1994) The 1 km AVHRR Global Land Data Set – 1st Stages in implementation. International Journal of Remote Sensing, 15, 3443–3462.Google Scholar
  55. Elvidge, C. D. (2001) DMSP-OLS estimation of tropical forest area impacted by surface fires in Roraima, Brazil: 1995 versus 1998. International Journal of Remote Sensing, 22, 2661–2673.Google Scholar
  56. Epting, J., Verbyla, D. L., & Sorbel, B. (2005) Evalation of remotely senses indices for assessing burn severity in interior Alaska using Landsat TM and ETM+. Remote Sensing of Environment, 96, 328–339.Google Scholar
  57. Everitt, J. H., & Nixon, P. R. (1986) Canopy Reflectance of two drought-stressed shrubs. Photogrammetric Engineering and Remote Sensing, 52, 1189–1192.Google Scholar
  58. Fazakas, Z., Nilsson, M., & Olsson, H. (1999) Regional forest biomass and wood volume estimation using satellite data and ancillary data. Agricultural and Forest Meteorology, 98–99, 417–425.Google Scholar
  59. Fearnside, P. M., Lima de Alencastro Graça, P. M., & Alves Rodriguez, F. J. (2001) Burning of Amazonian rainforests: Burning efficiency and charcoal formation in forest cleared for cattle pasture near Manaus, Brazil. Forest Ecology and Management, 146, 115–128.Google Scholar
  60. Ferek, R. J., Reid, J. S., Hobbs, P. V., Blake, D. R., & Liousse, C. (1998) Emission factors of hydrocarbons, halocarbons, trace gases and particles from biomass burning in Brazil. Journal of Geophysical Research-Atmosphere, 103, 32107–32118.Google Scholar
  61. Ferrare, R. A., Fraser, R. S., & Kaufman, Y. J. (1990) Satellite measurements of large-scale air pollution: Measurements of forest fire smoke. Journal of Geophysical Research, 95, 9911–9925.Google Scholar
  62. Flannigan, M. D., & Vonder Haar, T. H. (1986) Forest fire monitoring using NOAA satellite AVHRR. Canadian Journal of Forest Research, 16, 975–982.Google Scholar
  63. Flasse, S. P., & Ceccato, P. (1996) A contextual algorithm for AVHRR fire detection. International Journal of Remote Sensing, 17, 419–424.Google Scholar
  64. Fourty, T., & Baret, F. (1997) Vegetation water and dry matter contents estimated from top-of-the atmosphere reflectance data: A simulation study. Remote Sensing of Environment, 61, 34–45.Google Scholar
  65. França, H., & Setzer, A. W. (2001) AVHRR analysis of a savanna site through a fire season in Brazil. International Journal of Remote Sensing, 22, 2449–2461.Google Scholar
  66. Fraser, R. H., Li, Z., & Cihlar, J. (2000) Hotspot and NDVI Differencing Synergy (HANDS): A new technique for burned area mapping over boreal forest. Remote Sensing of Environment, 74, 362–376.Google Scholar
  67. Friedl, M. A., McIver, D. K., Hodges, J. C. F., Zhang, X. Y., Muchoney, D., & Strahler, A. H., et al. (2002) Global land cover mapping from MODIS: Algorithms and early results. Remote Sensing of Environment, 83, 287–302.Google Scholar
  68. Fuller, D. O., & Fulk, M. (2000) Comparison of NOAA-AVHRR and DMSP-OLS for operational fire monitoring in Kalimantan, Indonesia. International Journal of Remote Sensing, 21, 181–187.Google Scholar
  69. García Haro, F. J., Gilabert, M. A., & Meliá, J. (2001) Monitoring fire-affected areas using Thematic Mapper data. International Journal of Remote Sensing, 22, 533–549.Google Scholar
  70. Garcia, M., & Chuvieco, E. (2004) Assessment of the potential of SAC-C/MMRS imagery for mapping burned areas in Spain. Remote Sensing of Environment, 92, 414–423.Google Scholar
  71. Giglio, L., Csiszar, I., & Justice, C. O. (2006a). Global distribution and seasonality of active fires as observed with the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Journal of Geophysical Research-Biogeosciences, 111, doi:10.1029/2005JG000142.Google Scholar
  72. Giglio, L., Descloitres, J., Justice, C. O., & Kauffmam, J. B. (2003a). An enhanced contextual fire detection algorithm for MODIS. Remote Sensing of Environment, 87, 273–282.Google Scholar
  73. Giglio, L., Kendall, J. D., & Mack, R. (2003b). A multi-year active fire dataset for the tropics derived from the TRMM VIRS. International Journal of Remote Sensing, 24, 4505–4525.Google Scholar
  74. Giglio, L., van der Werf, G. R., Randerson, J. T., Collatz, G. J., & Kasibhatla, P. S. (2006b). Global estimation of burned area using MODIS active fire observations. Atmospheric Chemistry and Physics, 6, 957–974.Google Scholar
  75. Gillon, D., Dauriac, F., Deshayes, M., Vallette, J. C., & Moro, C. (2004) Estimation of foliage moisture content using near infrared reflectance spectroscopy. Agricultural and Forest Meteorology, 124, 51–62.Google Scholar
  76. Grégoire, J. M., Cahoon, D. R., Stroppiana, D., Li, Z., Pinnock, S., & Eva, H., et al. (2001) Forest fire monitoring and mapping for GOFC: Current products and information networks based on NOAA-AVHRR, ERS-ATSR, and SPOT-VGT systems. In F. Ahern, J. Goldammer & C. O. Justice (Eds.), Global and regional fire monitoring from space: Planning a coordinated international effort (pp. 105–124). The Hague: SPB Academic.Google Scholar
  77. Hao, W. M., Ward, D. E., Olbu, G., & Baker, S. P. (1996) Emissions of CO2, CO, and Hydrocarbons from fires in diverse african savanna ecosystems. Journal Of Geophysical Research-Atmospheres, 101, 23577–23584.Google Scholar
  78. Harding, D. J., & Carabajal, C. C. (2005) ICESat waveform measurements of within-footprint topographic relief and vegetation vertical structure. Geophysical Research Letters, 32, doi: 10.1029/2005GL023471, 023474Google Scholar
  79. Hardy, C. C., & Burgan, R. E. (1999) Evaluation of NDVI for monitoring live moisture in three vegetation types of the Western U.S. Photogrammetric Engineering and Remote Sensing, 65, 603–610.Google Scholar
  80. Hirsch, K. G. (1996) Canadian Forest Fire Behavior Prediction (FBP) System : User’s guide. Edmonton, Canada: Northern Forestry Centre.Google Scholar
  81. Hirsch, S. N., Kruckeberg, R. F., & Madden, F. H. (1971) The bispectral forest detection system. In, 7th Inter. Symp. on Remote Sensing of Environment (pp. 2253–2259). Ann Arbor, MI.Google Scholar
  82. Hoffa, E. A., Ward, D. E., Hao, W. M., Susott, R. A., & Wakimoto, R. H. (1999) Seasonality of carbon emissions from biomass burning in a Zambian savanna. Journal of Geophysical Research-Atmosphere, 104, 13841–13853.Google Scholar
  83. Holben, B. N., Schutt, J. B., & McMurtrey, J. (1983) Leaf water stress detection utilizing thematic mapper bands 3, 4 and 5 in soybean plants. International Journal of Remote Sensing, 4, 289–297.Google Scholar
  84. Houghton, R. A. (2005) Tropical deforestation as a source of greenhouse gas emisions. In P. Moutinho & S. Schwartzman (Eds.), Tropical deforestation and climate change (pp. 13–21). Belem: Amazon Institute for Environmental Research.Google Scholar
  85. Houghton, R. A., Boone, R. D., Melillo, J. M., Palm, C. A., Woodwell, G. M., & Myers, N., et al. (1985) Net flux of carbon dioxide from tropical forests in 1980. Nature, 316, 617–620.Google Scholar
  86. Hunt, E. R., Rock, B. N., & Nobel, P. S. (1987) Measurement of leaf relative water content by infrared reflectance. Remote Sensing of Environment, 22, 429–435.Google Scholar
  87. Hyyppa, J., Hyyppa, H., Inkinen, M., Engdahl, M., Linko, S., & Zhu, Y. H. (2000) Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management, 128, 109–120.Google Scholar
  88. Jackson, R. D., Idso, S. B., Reginato, R. J., & Pinter, P. J. (1981) Canopy temperature as a crop water stress indicator. Water Resources Research, 17, 1133–1138.Google Scholar
  89. Jain, T. B., Pilliod, D., & Graham, R. T. (2004) Tongue-tied. Confused meanings for common fire terminology can lead to fuels mismanagement. A new framework is needed to clarify and communicate the concepts. Wildfire, 4, 22–26.Google Scholar
  90. Jakubauskas, M. E., Lulla, K. P., & Mausel, P. W. (1990) Assessment of vegetation change in a fire-altered forest landscape. Photogrammetric Engineering and Remote Sensing, 56, 371–377.Google Scholar
  91. Johnson, E. A., & Miyanishi, K. (2001) Forest fires: Behavior and ecological effects. San Diego, Calif.: Academic Press.Google Scholar
  92. Jones, D. A. (1992) Nomenclature of hazard and risk assessment in the process industries. Rugby, Warwickshire, UK: Institution of Chemical Engineers.Google Scholar
  93. Justice, C. O., Giglio, L., Korontzi, S., Owens, J., Morisette, J. T., & Roy, D., et al. (2002a). The MODIS fire products. Remote Sensing of Environment, 83, 244–262.Google Scholar
  94. Justice, C. O., Townshend, J. R. G., Vermote, E. F., Masuoka, E., Wolfe, R. E., & Saleous, N., et al. (2002b). An overview of MODIS Land data processing and product status. Remote Sensing of Environment, 83, 3–15.Google Scholar
  95. Kasischke, E., & French, N. H. (1995) Locating and estimating the areal extent of wildfires in Alaskan boreal forest using multiple-season AVHRR NDVI composite data. Remote Sensing of Environment, 51, 263–275.Google Scholar
  96. Kasischke, E. S., Melack, J. M., & Dobson, M. C. (1997) The use of imaging radars for ecological applications – A review. Remote Sensing of Environment, 59, 141–156.Google Scholar
  97. Kasischke, E. S., & Turetsky, M. R. (2006) Recent changes in the fire regime across the North American boreal region – Spatial and temporal patterns of burning across Canada and Alaska. Geophysical Research Letters, 33, 1–5.Google Scholar
  98. Kaufman, Y. J., Setzer, A., Ward, D., Tanre, D., Holben, B. N., Menzel, P., et al. (1992) Biomass burning airborne and spaceborne experiment in the Amazonas (Base-A). Journal of Geophysical Research, 97, 14581–14599.Google Scholar
  99. Kennedy, P. J., Belward, A. S., & Grégoire, J. M. (1994) An improved approach to fire monitoring in West Africa using AVHRR data. International Journal of Remote Sensing, 15, 2235–2255.Google Scholar
  100. Key, C. (2005) Remote Sensing sensitivity fo fire severity and fire recovery. In J. Riva, F. Pérez-Cabello & E. Chuvieco (Eds.), Proceedings of the 5th International Workshop on Remote Sensing and GIS applications to Forest Fire Management: Fire Effects Assessment (pp. 29–39). Zaragoza: Universidad de Zaragoza, GOFC-GOLD, EARSeL.Google Scholar
  101. Key, C., & Benson, N. (2002) Landscape Assessment, in Fire effects monitoring (FireMon) and inventory protocol: integration of standardized field data collection techniques and sampling design with remote sensing to assess fire effects. In, NPS-USGS National Burn Severity Mapping Project.Google Scholar
  102. Key, C. H., & Benson, N. (2004) Ground Measure of Severity: The Composite Burn Index. FIREMON Landscape Assessment V4. http://burnseverity.cr.usgs.gov/methodology.aspGoogle Scholar
  103. Korontzi, S., Roy, D. P., Justice, C. O., & Ward, D. E. (2004) Modeling and sensitivity analysis of fire emissions in southern Africa during SAFARI 2000. Remote Sensing of Environment, 92, 255–275.Google Scholar
  104. Koutsias, N., Karteris, M., Fernández, A., Navarro, C., Jurado, J., Navarro, R., & Lobo, A. (1999) Burnt land mapping at local scale. In E. Chuvieco (Ed.), Remote sensing of large wildfires in the european mediterranean basin (pp. 123–138). Berlin: Springer-Verlag.Google Scholar
  105. Langaas, S. (1992) Temporal and spatial distribution of Savanna fires in Senegal and the Gambia, West Africa, 1989–1990, derived from multi-temporal AVHRR night images. International Journal of Wildland Fire, 2, 21–36.Google Scholar
  106. Leblon, B., Kasischke, E. S., Alexander, M. E., Doyle, M., & Abbott, M. (2002) Fire danger monitoring using ERS-1 SAR images in the case of northern boreal forests. Natural Hazards 27, 231–255.Google Scholar
  107. Lefsky, M. A., Harding, D. J., Keller, M., Cohen, W. B., Carabajal, C. C., & Espirito-Santo, F. D. et al. (2005) Estimates of forest canopy height and aboveground biomass using ICESat. Geophysical Research Letters, 32, doi:10.1029/2005GL023971.Google Scholar
  108. Lentile, L. B., Holden, Z. A., Smith, A. M. S., Falkowski, M. J., Hudak, A. T., & Morgan, P. et al. (2006) Remote sensing techniques to assess active fire characteristics and post-fire effects. International Journal of Wildland Fire, 15, 319–345.Google Scholar
  109. Levine, J. S. (2000) Global biomass burning: A case study of the looseness-1 gaseous and particulate emissions released to the atmosphere during the 1997 Fires in Kalimantan and Sumatra, Indonesia. In J. L. Innes, M. Beniston & M. M. Verstraete (Eds.), Biomass burning and its inter-relationships with the climate system (pp. 15–31). Dordrecht – Boston – London: Kluwer Academic Publishers.Google Scholar
  110. Li, R. R., Kaufman, Y. J., Hao, W. M., Salmon, J. M., & Gao, B. C. (2004) A technique for detecting burn scars using MODIS Data. IEEE Transactions on Geoscience and Remote Sensing, 42, 1300–1308.Google Scholar
  111. Li, Z., Nadon, S., & Cihlar, J. (2000) Satellite-based detection of Canadian boreal forest fires: Development and application of the algorithm. International Journal of Remote Sensing, 21, 3057–3069.Google Scholar
  112. Liousse, C., Andreae, M. O., Artaxo, P., Barbosa, P., Cachier, H., & Grégoire, J. M. et al. (2004) Deriving global quantitative estimates for spatial and temporal distributions of biomass burning emissions. In C. Granier, P. Artaxo & C. E. Reeves (Eds.), Emissions of atmospheric trace compounds (pp. 77–120). Dordrecht: Kluwer Academic Publishers.Google Scholar
  113. López García, M. J., & Caselles, V. (1991) Mapping burns and natural reforestation using thematic mapper data. Geocarto International, 1, 31–37.Google Scholar
  114. Maggi, M., & Stroppiana, D. (2002) Advantages and drawbacks of NOAA–AVHRR and SPOT–VGT for burnt area mapping in a tropical savanna ecosystem. Canadian Journal of Remote Sensing, 28, 231–245.Google Scholar
  115. Malingreau, J. P., Stevens, G., & Fellows, L. (1985) Remote sensing of forest fires: Kalimantan and North Borneo in 1982–1983. Ambio, 14, 314–321.Google Scholar
  116. Martín, M. P., Ceccato, P., Flasse, S., & Downey, I. (1999) Fire detection and fire growth monitoring using satellite data. In E. Chuvieco (Ed.), Remote sensing of large wildfires in the european mediterranean basin (pp. 101–122). Berlin: Springer-Verlag.Google Scholar
  117. Martín, M. P., & Chuvieco, E. (1995) Mapping and evaluation of burned land from multitemporal analysis of AVHRR NDVI images. EARSeL Advances in Remote Sensing, 4(3), 7–13.Google Scholar
  118. Martín, M. P., Díaz Delgado, R., Chuvieco, E., & Ventura, G. (2002) Burned land mapping using NOAA-AVHRR and TERRA-MODIS. In D. X. Viegas (Ed.), IV International conference on forest fire research. 2002 Wildland fire safety summit (p. 45). Luso, Coimbra, Portugal: Millpress.Google Scholar
  119. Martínez, S., Tourné, I., Gonzalo de Grado, J., & Casanova, J. L. (2000) Programa FUEGO: Detección y seguimiento de incendios desde el espacio. In IX Simposio Latinoamericano de Percepción Remota. Iguazú.Google Scholar
  120. Matson, M., & Holben, B. (1987) Satellite detection of tropical burning in Brazil. International Journal of Remote Sensing, 8, 509–516.Google Scholar
  121. Matson, M., Schneider, S. R., Aldridge, B., & Satchwell, B. (1984) Fire detection using the NOAA-Series satellites. Washington, DC: NOAA, NESDIS 7.Google Scholar
  122. Matson, M., Stephens, G., & Robinson, J. (1987) Fire detection using data from the NOAA-N satellites. International Journal of Remote Sensing, 8, 961–970.Google Scholar
  123. Merrill, D. F., & Alexander, M. E. (1987) Glossary of forest fire management terms. Ottawa: National Research Council of Canada, Committee for Forest Fire Management.Google Scholar
  124. Miller, H. J., & Yool, S. R. (2002) Mapping forest post-fire canopy consumption in several overstory types using multi-temporal Landsat TM and ETM data. Remote Sensing of Environment, 82, 481–496.Google Scholar
  125. Minick, G. R., & Shain, W. A. (1981) Comparison of satellite imagery and conventional aerial photography in evaluating a large forest fire. In Seventh International Symposium Machine Processing of Remotely Sensed Data (pp. 544–546). West Lafayette.Google Scholar
  126. Mollicone, D., Eva, H. D., & Achard, F. (2006). Human role in Russian wild fires. Nature, 440, 436–437.Google Scholar
  127. Moran, M. S., Clarke, T. R., Inoue, Y., & Vidal, A. (1994) Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote Sensing of Environment, 49, 246–263.Google Scholar
  128. Moreno, J. M., & Oechel, W. C. (1989) A simple method for estimating fire intensity after a burn in California Chaparral. Acta Ecologica (Ecologia plantarum), 10, 57–68.Google Scholar
  129. Morisette, J. T., Giglio, L., Csiszar, I., & Justice, C. O. (2005) Validation of the MODIS active fire product over Southern Africa with ASTER data. International Journal of Remote Sensing, 26, 4239–4264.Google Scholar
  130. Morisette, J. T., Privette, J. L., & Justice, C. O. (2002) A framework for the validation of MODIS Land products. Remote Sensing of Environment, 83, 77–96.Google Scholar
  131. Morsdorf, F., Meier, E., Kotz, B., Itten, K. I., Dobbertin, M., & Allgower, B. (2004) LIDAR-based geometric reconstruction of boreal type forest stands at single tree level for forest and wildland fire management. Remote Sensing of Environment, 92, 353–362.Google Scholar
  132. Morton, D., DeFries, R., Giglio, L., Schroeder, W., Csiszar, I., & Morisette, J. et al. (2006) Distinguishing between conversion and maintenance fires in the Amazon. In Tenth LBA-ECO Science Team Meeting. Brasilia, Brazil.Google Scholar
  133. Nelson, R. M. (2001) Water relations of forest fuels. In E. A. Johnson & K. Miyanishi (Eds.), Forest fires : Behavior and ecological effects (pp. 79–149). San Diego, Calif.: Academic Press.Google Scholar
  134. Omi, P. N. (2005) Forest fires : A reference handbook. Santa Barbara, Calif.: ABC-CLIO.Google Scholar
  135. PalaciosOrueta, A., Chuvieco, E., Parra, A., & Carmona-Moreno, C. (2005) Biomass burning emissions: A review of models using remote-sensing data. Environmental Monitoring and Assessment, 104, 189–209.Google Scholar
  136. PalaciosOrueta, A., Parra, A., Chuvieco, E., & Carmona, C. (2004) Remote sensing and geographic information system methods for global spatiotemporal modelling of biomass burning emissions: Assessment in the African continent. Journal of Geophysical Research – Atmospheres, 109, 1–12.Google Scholar
  137. Paltridge, G. W., & Barber, J. (1988) Monitoring grassland dryness and fire potential in Australia with NOAA/AVHRR data. Remote Sensing of Environment, 25, 381–394.Google Scholar
  138. Parra, A., & Chuvieco, E. (2005) Assessing burn severity using Hyperion data. In J. Riva, F. Pérez-Cabello & E. Chuvieco (Eds.), Proceedings of the 5th International workshop on remote sensing and GIS applications to Forest Fire Management: Fire Effects Assessment (pp. 239–244). Paris: Universidad de Zaragoza, GOFC-GOLD, EARSeL.Google Scholar
  139. Peñuelas, J., Piñol, J., Ogaya, R., & Filella, I. (1997) Estimation of plant water concentration by the reflectance Water Index WI (R900/R970). International Journal of Remote Sensing, 18, 2869–2875.Google Scholar
  140. Pereira, J. M. C. (1999) A comparative evaluation of NOAA/AVHRR Vegetation indexes for burned surface detection and mapping. IEEE Transactions on Geoscience and Remote Sensing, 37, 217–226.Google Scholar
  141. Pereira, J. M. C., Mota, B., Privette, J. L., Caylor, K. K., Silva, J. M. N., Sa, A. C. L., & Ni-Meister, W. (2004) A simulation analysis of the detectability of understory burns in miombo woodlands. Remote Sensing of Environment, 93, 296–310.Google Scholar
  142. Pereira, J. M. C., Sa, A. C. L., Sousa, A. M. O., Silva, J. M. N., Santos, T. N., & Carreiras, J. M. B. (1999) Spectral characterisation and discrimination of burnt areas. In E. Chuvieco (Ed.), Remote sensing of large wildfires in the European mediterranean basin (pp. 123–138). Berlin: Springer-Verlag.Google Scholar
  143. Pérez, B., & Moreno, J. (1998) Methods for quantifying fire severity in shrubland-fires. Plant Ecology, 139, 91–101.Google Scholar
  144. Peters, A. J., WalterShea, E. A., Ji, L., Viña, A., Hayes, M., & Svodoba, M. D. (2002) Drought monitoring with NDVI-based standardized vegetation index. Photogrammetric Engineering and Remote Sensing, 62, 71–75.Google Scholar
  145. Piccolini, I., & Arino, O. (2000) Towards a global burned surface world Atlas. Earth Observation Quartely, 65, 14–18.Google Scholar
  146. Pinnock, S., & Grégoire, J. M. (Eds.). (1999) World fire web: A global fire observation system. Luxembourg: Publications of the European Communities.Google Scholar
  147. Price, J. C. (2003) Comparing MODIS and ETM+ data for regional and global land clasification. Remote Sensing of Environment, 86, 491–499.Google Scholar
  148. Prins, E. M., Feltz, J. M., Menzel, W. P., & Ward, D. E. (1998) An overview of GOES-8 diurnal fire and smoke results for SCAR-B and 1995 fire season in South America. Journal of Geophysical Research, 103, 31821–31836.Google Scholar
  149. Prins, E. M., & Menzel, W. P. (1992) Geostationary satellite detection of biomass burning in South America. International Journal of Remote Sensing, 13, 2783–2799.Google Scholar
  150. Pyne, S. J. (1995) World fire. The culture of fire on earth. Seattle and London: University of Washington Press.Google Scholar
  151. Radeloff, V. C., Hammer, R. B., Stewart, S. I., Fried, J. S., Holcomb, S. S., & McKeefry, J. F. (2005) The wildland-urban interface in the United States. Ecological Applications, 15, 799–805.Google Scholar
  152. Randerson, J. T., Liu, H., Flanner, M. G., Chambers, S. D., Jin, Y., & Hess, P. G. et al. (2006) The impact of boreal forest fire on climate warming. Science, 314, 1130–1132.Google Scholar
  153. Randerson, J. T., van der Werf, G. R., Collatz, G. J., Giglio, L., Still, C. J., & Kasibhatla, P. et al. (2005) Fire emissions from C 3 and C 4 vegetation and their influence on interannual variability of atmospheric CO 2 and D13 CO2. Global Biogeochemical Cycles, 19, doi:10.1029/2004GB002366.Google Scholar
  154. Randriambelo, T., Baldy, S., & Bessafi, M. (1998) An improved detection and characterization of active fires and smoke plumes in south-eastern Africa and Madagascar. International Journal of Remote Sensing, 19, 2623–2638.Google Scholar
  155. Ranson, K. J., Sun, G., Kharuk, V. I., & Kovacs, K. (2001) Characterization of forests in Western Sayani Mountains, Siberia from SIR-C SAR data. Remote Sensing of Environment, 75, 188–200.Google Scholar
  156. Riaño, D., Chuvieco, E., Condés, S., GonzálezMatesanz, J., & Ustin, S. L. (2004) Generation of crown bulk density for Pinus sylvestris L. from lidar. Remote Sensing of Environment, 92, 345–352.Google Scholar
  157. Riaño, D., Chuvieco, E., Salas, J., PalaciosOrueta, A., & Bastarrica, A. (2002) Generation of fuel type maps from Landsat TM images and ancillary data in Mediterranean ecosystems. Canadian Journal of Forest Research, 32, 1301–1315.Google Scholar
  158. Riaño, D., Chuvieco, E., Ustin, S. L., Salas, J., Rodríguez-Pérez, J. R., & Ribeiro, L. M. et al. (2007a). Estimation of shrub height for fuel type mapping combining airborne LiDAR and simultaneous color infrared ortho image. International Journal of Wildland Fire, 16, 341–348.Google Scholar
  159. Riaño, D., Meier, E., Allgöwer, B., Chuvieco, E., & Ustin, S. L. (2003) Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behavior modeling. Remote Sensing of Environment, 86, 177–186.Google Scholar
  160. Riaño, D., Ruiz, J. A. M., Isidoro, D., Ustin, S. L., & Riaño, D. (2007b). Global spatial patterns and temporal trends of burned area between 1981 and 2000 using NOAA-NASA Pathfinder. Global Change Biology, 13, 40–50, doi: 10.1111/j.1365–2486.2006.01268.Google Scholar
  161. Riaño, D., Vaughan, P., Chuvieco, E., ZarcoTejada, P., & Ustin, S. L. (2005) Estimation of fuel moisture content by inversion of radiative transfer models to simulate equivalent water thickness and dry matter content: Analysis at leaf and canopy level. IEEE Transactions on Geoscience and Remote Sensing, 43, 819–826.Google Scholar
  162. Roberts, D. A., Peterson, S., Dennison, P. E., Sweeney, S., & Rechel, J. (2006) Evaluation of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Moderate Resolution Imaging Spectrometer (MODIS) measures of live fuel moisture and fuel condition in a shrubland ecosystem in southern California. Journal of Geophysical Research, 111, G04S02, doi:10.1029/2005JG000113.Google Scholar
  163. Rogan, J., & Franklin, J. (2001) Mapping wildfire burn severity in Southern California Forests and shrublands using enhanced Thematic Mapper imagery. Geocarto International, 16, 89–99.Google Scholar
  164. Rollins, M. G., Keane, R. E., & Parsons, R. A. (2004) Mapping fuels and fire regimes using remote sensing, ecosystem simulation, and gradient modeling. Ecological Applications, 14, 75–95.Google Scholar
  165. Roy, D., Frost, P., Justice, C., Landmann, T., Roux, J., & Gumbo, K. et al. (2005a). The Southern Africa Fire Network (SAFNet) regional burned area product validation protocol. International Journal of Remote Sensing, 26, 4265–4292.Google Scholar
  166. Roy, D., Jin, Y., Lewis, P., & Justice, C. (2005b). Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data. Remote Sensing of Environment, 97, 137–162.Google Scholar
  167. Roy, D., & Landmann, T. (2005) Characterizing the surface heterogeneity of fire effects using multi-temporal reflective wavelength data. International Journal of Remote Sensing, 26, 4197–4218.Google Scholar
  168. Roy, D., Lewis, P. E., & Justice, C. O. (2002) Burned area mapping using multi-temporal moderate spatial resolution data—a bi-directional reflectance model-based expectation approach. Remote Sensing of Environment, 83, 263–286.Google Scholar
  169. Roy, D. P., Boschetti, L., & Trigg, S. N. (2006) Remote sensing of fire severity: Assessing the performance of the normalized burn ratio. IEEE Transactions on Geoscience and Remote Sensing, 3, 112–116.Google Scholar
  170. Roy, D. P., Giglio, L., Kendall, J. D., & Justice, C. O. (1999) Multi-temporal active-fire based burn scar detection algorithm. International Journal of Remote Sensing, 20, 1031–1038.Google Scholar
  171. Sá, A. C. L., Silva, J. M. N., Pereira, J. M. C., & Vasconcelos, M. J. (2001) Burned area detection in the Miombo of Northern Mozambique using MODIS and Landsat Data. In E. Chuvieco & M. P. Martín (Eds.), Third international workshop on remote sensing and GIS applications to Forest Fire Management. New methods and sensors (pp. 156–160). Paris: EARSeL.Google Scholar
  172. Salvador, R., Valeriano, J., Pons, X., & Díaz-Delgado, R. (2000) A semi-automatic methodology to detect fire scars in shrubs and evergreen forests with Landsat MSS time series. International Journal of Remote Sensing, 21, 655–671.Google Scholar
  173. Sandholt, I., Rasmussen, K., & Andersen, J. (2002) A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment, 79, 213–224.Google Scholar
  174. Saunders, R. W., & Kriebel, K. T. (1988) An improved method for detecting clear sky and cloudy radiances from AVHRR data. International Journal of Remote Sensing, 9, 123–150.Google Scholar
  175. Seiler, W., & Crutzen, P. J. (1980) Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning. Climatic Change, 2, 207–247.Google Scholar
  176. Setzer, A. W., & Pereira, M. C. (1991) Operational detection of fires in Brazil with NOAA-AVHRR. In Twenty-fourth. International Symp. on Remote Sensing of Environment (pp. 469–482). Rio de Janeiro.Google Scholar
  177. Siljeström, P., & Moreno, A. (1995) Monitoring burnt areas by principal components analysis of multi-temporal TM data. International Journal of Remote Sensing, 16, 1577–1587.Google Scholar
  178. Simon, M., Plummer, S., Fierens, F., Hoelzemann, J. J., & Arino, O. (2004) Burnt area detection at global scale using ATSR-2: The GLOBSCAR products and their qualification. Journal of Geophysical Research – Atmospheres, 109, D14S02, doi:10.1029/2002JD003622, 1–16.Google Scholar
  179. Sims, D. A., & Gamon, J. A. (2003) Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: A comparison of indices based on liquid water and chlorophyll absorption features. Remote Sensing of Environment, 84, 526–537.Google Scholar
  180. Skole, D., & Tucker, C. (1993) Tropical deforestation and habitat fragmentation in the Amazon. Satellite data from 1978 to 1988. Science, 260, 1905–1910.Google Scholar
  181. Sousa, A. M. O., Pereira, J. M. C., & Silva, J. M. N. (2003) Evaluating the performance of multitemporal image compositing algorithms for burned area analysis. International Journal of Remote Sensing, 24, 1219–1236.Google Scholar
  182. Souza, C. M., Roberts, D. A., & Cochrane, M. A. (2005) Combining spectral and spatial information to map canopy damage from selective logging and forest fires. Remote Sensing of Environment, 98, 329–343.Google Scholar
  183. Spencer, J. E. (1966) Shifting cultivation in Southeastern Asia. Berkeley: University of California Press.Google Scholar
  184. Stow, D., Niphadkar, M., & Kaiser, J. (2005) MODIS-derived visible atmospherically resistant index for monitoring chaparral moisture content. International Journal of Remote Sensing, 26, 3867–3873.Google Scholar
  185. Stroppiana, D., Brivio, P. A., & Grégorie, J.-M. (2000a). Modelling the impact of vegetation fires, dectected from NOAA-AVHRR data, on tropospheric chemistry in Tropical Africa. In J. L. Innes, M. Beniston & M. M. Verstraete (Eds.), Biomass burning and its inter-relationships with the climate system (pp. 193–213). Dordrecht, Boston, London: Kluwer Academic Publishers.Google Scholar
  186. Stroppiana, D., Pinnock, S., & Gregoire, J. M. (2000b). The global fire product: Daily fire occurrence from April 1992 to December 1993 derived from NOAA AVHRR data. International Journal of Remote Sensing, 21, 1279–1288.Google Scholar
  187. Sukhinin, A. I., French, N. H. F., Kasischke, E. S., Hewson, J. H., Soja, A. J., & Csiszar, I. A. et al. (2004) AVHRR-based mapping of fires in Russia: New products for fire management and carbon cycle studies. Remote Sensing of Environment, 93, 546–564.Google Scholar
  188. Tansey, K., Grégoire, J M., Stroppiana, D., Sousa, A., Silva, J., & Pereira, J. M. et al. (2004) Vegetation burning in the year 2000: Global burned area estimates from SPOT VEGETATION data. Journal of Geophysical Research – Atmospheres, 109, D14S03, doi:10.1029/2002JD003598, 2–22.Google Scholar
  189. Thompson, O. R., & Wehmanen, O. A. (1979) Using Landsat digital data to detect moisture stress. Photogrammetric Engineering and Remote Sensing, 45, 201–207.Google Scholar
  190. Tian, Q., Tong, Q., Pu, R., Guo, X., & Zhao, C. (2001) Spectroscopic determination of wheat water status using 1650–1850 nm spectral absorption features. International Journal of Remote Sensing, 22, 2329–2338.Google Scholar
  191. Toutin, T., & Amaral, S. (2000) Stereo RADARSAT data for canopy height in Brazilian forests. Canadian Journal of Remote Sensing, 26, 189–199.Google Scholar
  192. van der Werf, G. R., Randerson, J., T., Collatz, G. J., Giglio, L., Kasibhatla, P. S., & Arellano, A. F. et al. (2004) Continental Scale-partitioning of fire emissions during the 1997 to 2001 El Niño/La Niña period. Science, 303, 73–76.Google Scholar
  193. van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Kasibhatla, P. S., & Arellano, A. F. (2006) Interannual variability in global biomass burning emissions from 1997 to 2004. Atmospheric Chemistry and Physics, 6, 3423–3441.CrossRefGoogle Scholar
  194. van Wagtendonk, J. W., Root, R. R., & Key, C. H. (2004) Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity. Remote Sensing of Environment, 92, 397–408.Google Scholar
  195. Van Wilgen, B. W. (1997) Fire in southern African savannas : Ecological and atmospheric perspectives. Johannesburg, South Africa: Witwatersrand University Press Thorold’s Africana Books [distributor].Google Scholar
  196. Vázquez, A., Cuevas, J. M., & González-Alonso, F. (2001) Comparison of the use of WiFS and LISS images to estimate the area burned in a large forest fire. International Journal of Remote Sensing, 22, 901–907.Google Scholar
  197. VegaGarcia, C., & Chuvieco, E. (2006) Applying local measures of spatial heterogeneity to Landsat-TM images for predicting wildfire occurrence in Mediterranean landscapes. Landscape Ecology, 21, 595–605.Google Scholar
  198. Venkataraman, C., Habib, G., Kadamba, D., Shrivastava, M., Leon, J. F., Crouzille, B., Boucher, O., & Streets, D. G. (2006) Emissions from open biomass burning in India: Integrating the inventory approach with high-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) active-fire and land cover data. Global Biogeochemical Cycles, 20. GB2013, doi: 2010.1029/2005GB002547Google Scholar
  199. Vidal, A., Pinglo, F., Durand, H., DevauxRos, C., & Maillet, A. (1994) Evaluation of a temporal fire risk index in Mediterranean forest from NOAA thermal IR. Remote Sensing of Environment, 49, 296–303.Google Scholar
  200. Wheatherspoon, C. P., & Skiner, C. N. (1995) An assessment of factors associate with damage to tree crowns from the 1987 wildfires in Northern California. Forest Science, 41, 430–451.Google Scholar
  201. White, J. D., Ryan, K. C., Key, C. C., & Running, S. W. (1996) Remote sensing of forest fire severity and vegetation recovery. International Journal of Wildland Fire, 6, 125–136.Google Scholar
  202. Yebra, M., Chuvieco, E., & Riaño, D. (2007) Estimation of live Fuel Moisture Content from MODIS images for fire risk assessment. Agricultural and Forest Meteorology, in press.Google Scholar
  203. ZarcoTejada, P. J., Rueda, C. A., & Ustin, S. L. (2003) Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sensing of Environment, 85, 109–124.Google Scholar
  204. Zheng, D. L., Prince, S. D., & Wright, R. (2001) NPP Multi-Biome: Gridded estimates for selected regions worldwide, 1989–2001. Available on-line [http://www.daac.ornl.gov/]. Oak Ridge, Tennessee, U.S.A: Oak Ridge National Laboratory. Distributed Active Archive Center.Google Scholar
  205. Zhu, Z., & Evans, D. L. (1994) U.S. forest types and predicted percent forest cover from AVHRR data. Photogrammetric Engineering and Remote Sensing, 60, 525–531.Google Scholar

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  • Emilio Chuvieco

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