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Environmental Management

, Volume 52, Issue 1, pp 136–150 | Cite as

A Satellite Model of Forest Flammability

  • Marc K. SteiningerEmail author
  • Karyn Tabor
  • Jennifer Small
  • Carlos Pinto
  • Johan Soliz
  • Ezequiel Chavez
Article

Abstract

We describe a model of forest flammability, based on daily satellite observations, for national to regional applications. The model defines forest flammability as the percent moisture content of fuel, in the form of litter of varying sizes on the forest floor. The model uses formulas from the US Forest Service that describe moisture exchange between fuel and the surrounding air and precipitation. The model is driven by estimates of temperature, humidity, and precipitation from the moderate resolution imaging spectrometer and tropical rainfall measuring mission multi-satellite precipitation analysis. We provide model results for the southern Amazon and northern Chaco regions. We evaluate the model in a tropical forest-to-woodland gradient in lowland Bolivia. Results from the model are significantly correlated with those from the same model driven by field climate measurements. This model can be run in a near real-time mode, can be applied to other regions, and can be a cost-effective input to national fire management programs.

Keywords

Tropical forest Fire risk Drought Remote sensing Amazon Bolivia 

Notes

Acknowledgments

This study was supported by a Grant from the National Aeronautics and Space Administration (NASA Grant # NAG13-02008). We thank Geoffrey Blate for his support in compiling the field data, George Huffman and Louis Giglio for providing expert advice during the development of this model, Tim Killeen and the Museo Noel Kempff Mercado for logistical support, the Fundacion Amigos de la Naturaleza (FAN), the Bolivia Forestry project (BOLFOR), and Wildlife Conservation Society (WCS) for access to field sites.

References

  1. Albini FA (1979) Spot distance from burning trees—a predictive model. USDA Forest Service general technical report INT-56. Intermountain forest and range experiment station, Forest Service, US Department of Agriculture, Ogden, Utah, USAGoogle Scholar
  2. Albini FA (1985) A model for fire spread in wildland fuels by radiation. Combust Sci Technol 42:229–258CrossRefGoogle Scholar
  3. Anderson H (1982) Aids to determining fuel models for estimating fire behavior. USDA Forest Service general technical report INT-122. Intermountain Forest and Range Experiment Station, Forest Service, US Department of Agriculture, Ogden, Utah, USAGoogle Scholar
  4. Bradshaw LS, Deeming JE, Burgan RE, Cohen JD (1984) The 1978 national fire-danger rating system: technical documentation. General technical report INT-169. US Intermountain Forest and Range Experiment Station, Forest Service, US Department of Agriculture, Ogden, Utah, USAGoogle Scholar
  5. Brown JK (2000) Wildland fire in ecosystems: effects of fire on flora. USDA Forest Service general technical report Gen. Tech. Rep. RMRS-GTR-42-vol 2. Ogden, UT: US Department of Agriculture, Forest Service, Rocky Mountain Research Station, US Department of Agriculture Ogden, UT, USAGoogle Scholar
  6. Burgan RE, Andrews PL, Bradshaw LS, Chase CH, Hartford RA, Latham DJ (1997) WFAS: wildland fire assessment system. Fire Manag Notes 57(2):14–17Google Scholar
  7. Burgan RE, Klaver RW, Klaver JM (2000) Fuel models and fire potential from satellite and surface observations. http://www.fs.fed.us/land/wfas/firepot/fpipap.htm. Accessed online 8 Apr 2009
  8. Burgen RE (1979) Estimating live fuel moisture for the 1978 national fire danger rating system. USDA Forest Service research paper INT-226. Intermountain Forest and Range Experiment Station, Forest Service, US Department of Agriculture, Ogden, Utah, USAGoogle Scholar
  9. Byram GM (1959) Combustion of forest fuels. In: Davis KP (ed) Forest fire control and use, 2nd edn. McGraw-Hill Book Company, New York, pp 113–126Google Scholar
  10. Cardoso MF, Hurtt GC, Moore B III, Nobre CA, Prins EM (2003) Projecting future fire activity in Amazonia. Glob Change Biol 9:656–669CrossRefGoogle Scholar
  11. Chen Y, Randerson JT, Morton DC, DeFries RS, Collatz GJ, Kasibhatla PS, Giglio L, Jin Y, Marlier ME (2011) Forecasting fire season severity in South America using sea surface temperature anomalies. Science 334:787–791CrossRefGoogle Scholar
  12. Chuvieco E, Cocero D, Riaño D, Martin P, Martínez-Vega J, de la Riva J, Pérez F (2004) Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sens Environ 92:322–331CrossRefGoogle Scholar
  13. Cohen JD, Deeming J (1985) The national fire danger rating system: basic equations. US Forest Service technical report PSW-82. Pacific Southwest Forest Range Experimental Station, Berkeley, California, USAGoogle Scholar
  14. Dinku T, Ceccato P, Grover-Kopec E, Lemma M, Connor SJ, Ropelewski CF (2007) Validation of satellite rainfall products over East Africa’s complex topography. Int J Remote Sens 28:1503–1526CrossRefGoogle Scholar
  15. Ebert EE (2005) Satellite versus model rainfall—Which one to use? Fifth international scientific conference on the global energy and water cycle, Orange County. Global Energy and Water Experiment. http://www.gewex.org/5thConfposterT6-7_Ebert.pdf. Accessed 9 Mar 2009
  16. Fosberg MA (1971) Moisture content calculations for the 100-h timelag fuel in fire danger rating. US Department of Agriculture Forest Service Research Note RM-199. US Department of Agriculture Forest Service, Rocky Mountain Forest and Range Experiment Station, Fort Collins, Colorado, USAGoogle Scholar
  17. Fosberg MA (1977) Forecasting the 10-h timelag fuel moisture. USDA Forest Service research paper RM-187. Rocky Mountain Forest and Range Experiment Station, Fort Collins, Colorado, USAGoogle Scholar
  18. Fosberg MA, Rothermel RC, Andrews PL (1981) Moisture content calculations for 1,000-h timelag fuels. For Sci 27:19–26Google Scholar
  19. GADSC (2013) Autonomous government of the Department of Santa Cruz in partnership with FAN implements climate change program. Webpage of the Gobierno Autonimo Departmental de Santa Cruz (GADSC), Bolivia. http://www.santacruz.gob.bo/turistica/medioambiente/cambioclimatico/contenido.php?IdNoticia=3409&IdMenu=30044. Accessed 30 Jan 2013
  20. Goetz SJ, Prince SD, Goward SN, Thawley MM, Small J, Johnston A (1999) Mapping net primary production and related biophysical variables with remote sensing: applications to the Boreas region. J Geophys Res 104:27,719–727,734Google Scholar
  21. Goetz SJ, Bunn AG, Fiske GJ, Houghton RA (2005) Satellite-observed photosynthetic trend across boreal North America associated with climate and fire-disturbance. Proc Natl Acad Sci 102:13521–13525CrossRefGoogle Scholar
  22. Grégoire JM, Tansey K, Silva JMM (2003) The GBA2000 initiative: developing a global burned area database from SPOT-VEGETATION imagery. Int J Remote Sens 24:1369–1376CrossRefGoogle Scholar
  23. Hansen M, DeFries RS, Townshend JRG, Carroll M, Dimiceli C, Sohlberg RA (2003) Global percent tree cover at a spatial resolution of 500 meters: first results of the MODIS vegetation continuous fields algorithm. Earth Interact 7:1–15CrossRefGoogle Scholar
  24. Heinsch FA, Andrews PL (2010) BehavePlus fire modeling system, version 5.0: Design and features. General technical report RMRS-GTR-249. Fort Collins: US Department of Agriculture, Forest Service, Rocky Mountain Research Station. (10,487 KB; p 111)Google Scholar
  25. Hirpa FA, Gebremichael M (2010) Evaluation of high-resolution Satellite precipitation products over very complex terrain in Ethiopia. J Appl Meteorol Climatol 29:1044–1051CrossRefGoogle Scholar
  26. Huffman GJ, Adler RF, Bolvin DT, Gu G, Nelkin EF, Bowman KP, Hong Y, Stocker EF, Wolff DB (2007) The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8:38–55CrossRefGoogle Scholar
  27. Illera P, Fernández A, Delgado JA (1996) Temporal evolution of the NDVI as an indicator of forest fire danger. Int J Remote Sens 5(17):1093–1105CrossRefGoogle Scholar
  28. INPE (2013) Fire Monitoring Program, Instituto Nacional de Pesquisas Espaciais, Brazil. http://pirandira.cptec.inpe.br/queimadas/#. Accessed 23 Jan 2013Google Scholar
  29. INPE-Instituto Nacional de Pesquisas Espaciais (2011) Portal do Monitoramento de Queimadas e Incêndios. Disponível em. http://www.inpe.br/queimadas. Accessed 30 Nov 2011
  30. Justice CO, Giglio L, Korontzi S, Owens J, Morisette JT, Roy D, Descloitres J, Alleaume S, Petitcolin F, Kaufman Y (2002) The MODIS fire products. Remote Sens Environ 83:244–262CrossRefGoogle Scholar
  31. Katsanos D, Lagouvardos K, Kotroni V, Huffman GJ (2004) Statistical evaluation of MPA-RT high-resolution precipitation estimates from satellite platforms over the central and eastern Mediterranean. Geophys Res Lett 31:L06116. doi: 10.1029/2003GL019142 CrossRefGoogle Scholar
  32. Keetch J, Byram GM (1968) A drought index for forest fire control. USDA Forest Service research paper SE-38. US Department of Agriculture-Forest Service, Ashville, NC, USAGoogle Scholar
  33. Killeen TJ, Chavez E, Peña-Claros M, Toledo M, Arroy L, Caballero J, Correa L, Guillén R, Quevedo R, Saldias M, Soria L, Uslar Y, Vargas I, Steininger M (2006) The Chiquitano dry forest, the transition between humid and dry forest in Eastern lowland Bolivia. In: Pennington T, Lewis GP, Ratter JA (eds) Neotropical savannas and dry forests: plant diversity, biogeography and conservation. Taylor & Francis, London, p 213–234Google Scholar
  34. Kummerow C, Simpson J, Thiele O, Barnes W, Chang ATC, Stocker E, Adler RF, Hou A, Kakar R, Wentz F, Ashcroft P, Kozu T, Hong Y, Okamotok Iguchi T, Kuroiwa H, Im E, Haddad Z, Huffman G, Ferrier B, Olson WS, Zipser E, Smith EA, Wilheit TT, North G, Krishnamurti T, Nakamura K (2000) The status of the tropical rainfall measuring mission (TRMM) after 2 years in orbit. J Appl Meteorol 39:1965–1982CrossRefGoogle Scholar
  35. Lewis SL, Brando PM, Phillips OL, van der Heijden GMF, Nepstad D (2011) The 2010 Amazon drought. Science 331:554CrossRefGoogle Scholar
  36. Machado, LAT, Ceballos J (2000) Satellite based products for monitoring weather in South America: winds and trajectories. 5th international winds workshop, SaannenmoserGoogle Scholar
  37. Matson M, Holben B (1987) Satellite detection of tropical burning in Brazil. Int J Remote Sens 8:509–516CrossRefGoogle Scholar
  38. Monteith JL, Unsworth MH (1990) Principles of environmental physics, 2nd edn. Edward Arnold Publishers, New YorkGoogle Scholar
  39. Myneni RB, Hoffman S, Knyazikhin Y, Privette JL, Glassy J, Tian Y, Wang Y, Song X, Zhang Y, Smith GR, Lotsche A, Friedl M, Morisette JT, Votava P, Nemani RR, Running SW (2002) Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens Environ 83:214–231CrossRefGoogle Scholar
  40. Nepstad D, Lefebvre P, da Silva UL, Tomasella J, Schlesinger P, Solorzano L, Moutinho P, Ray D, Benito JG (2004) Amazon drought and its implications for forest flammability and tree growth: a basin-wide analysis. Glob Change Biol 10:704–717CrossRefGoogle Scholar
  41. NWCG (2013) National wildfire coordinating group (NWCG), Glossary of wildland fire terminology. http://www.nwcg.gov/pms/pubs/glossary/f.htm. Accessed 30 Jan 2013
  42. Onset (2012) http://www.onsetcomp.com/products. Accessed 20 Feb 2012
  43. Palmer WC (1965) Meteorological drought. US Department of Commerce research paper no. 45. US government printing office, Washington, DC, USAGoogle Scholar
  44. Phillips OL, Aragão LEOC, Lewis SL, Fisher JB, Lloyd J, López-González G, Malhi Y et al (2009) Drought sensitivity of the Amazon rainforest. Science 323:1344–1347CrossRefGoogle Scholar
  45. Prince SD, Goward SN (1995) Global primary production: a remote sensing approach. J Biogeogr 22:815–835CrossRefGoogle Scholar
  46. Ray D, Nepstad D, Moutinho P (2005) Micrometeorological and canopy controls of fire susceptibility in a forested amazon landscape. Ecol Appl 15:1664–1678CrossRefGoogle Scholar
  47. Roy DP, Lewis PE, Justice CO (2002) Burned area mapping using multi-temporal moderate spatial resolution data- a bi-directional reflectance mode-based expectation approach. Remote Sens Environ 83:263–286CrossRefGoogle Scholar
  48. Running SW, Nemani RR, Heinsch FA, Zhao M, Reeves M, Hashimoto H (2004) A continuous satellite-derived measure of global terrestrial primary production. Bioscience 56:547–560CrossRefGoogle Scholar
  49. Schroeder MJ (1969) Ignition probability. Office report 2106-1. US Department of Agriculture Forest Service, Rocky Mountain Forest and Range Experiment Station, Fort Collins, Colorado, USAGoogle Scholar
  50. Scott JH, Burgan RE (2005) Standard fire behavior fuel models: a comprehensive set for use with Rothermel’s surface fire spread model. General technical report RMRS-GTR-153. US Department of Agriculture Forest Service, Rocky Mountain Forest and Range Experiment Station, Fort Collins, Colorado, USAGoogle Scholar
  51. Seemann SW, Li J, Menzel WP, Gumley LE (2003) Operational retrieval of atmospheric temperature, moisture, and ozone from MODIS infrared radiances. J Appl Meteorol 42:1072–1091CrossRefGoogle Scholar
  52. Seemann SW, Borbas EE, Li J, Menzel WP, Gumley LE (2006) MODIS atmospheric profile retrieval algorithm theoretical basis document version 6. http://modis.gsfc.nasa.gov/data/atbd/atbd_mod07.pdf. Accessed 1 Jun 2009
  53. Setzer AW, Sismanoglu RA (2009) Fire risk: summary of calculations. INPE. http://www.cptec.inpe.br/queimadas/documentos/doc_RF_2007.pdf. Accessed 9 Apr 2009
  54. USFS (2013) US Forest Service Wildland Fire Assessment System. http://www.wfas.net. Accessed 23 Jan 2013
  55. USGS (2004) Shuttle radar topography mission, 3 arc second scene SRTM_u03_n008e004, Unfilled Unfinished 2.0, Global Land Cover Facility, University of Maryland, College Park, Maryland, February 2000Google Scholar
  56. Wan Z (2009) MODIS land surface temperature products users’ guide. http://www.icess.ucsb.edu/modis/LstUsrGuide/MODIS_LST_products_Users_guide.pdf. Accessed 6 Apr 2009
  57. Wan Z, Li Z-L (1997) A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data. IEEE Trans Geosci Remote Sens 35:980–996CrossRefGoogle Scholar
  58. Wan Z, Zhang Y, Zhang Q, Li Z-L (2004) Quality assessment and validation of the MODIS global land surface temperature. Int J Remote Sens 25:261–274CrossRefGoogle Scholar
  59. Wang J, Wolff DB (2010) Evaluation of TRMM ground-validation radar-rain errors using rain gauge measurements. J Appl Meteorol Climatol 49:310–324CrossRefGoogle Scholar
  60. Wang W, Liang S, Meyers T (2007) MODIS land surface temperature products using long-term nighttime ground measurements. Remote Sens Environ 112:623–635CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Marc K. Steininger
    • 1
    Email author
  • Karyn Tabor
    • 1
  • Jennifer Small
    • 2
    • 3
  • Carlos Pinto
    • 4
    • 5
  • Johan Soliz
    • 4
  • Ezequiel Chavez
    • 4
  1. 1.Conservation InternationalArlingtonUSA
  2. 2.Department of GeographyUniversity of Maryland at College ParkCollege ParkUSA
  3. 3.National Aeronautics and Space Administration Goddard Space Flight CenterGreenbeltUSA
  4. 4.Departamento de Geografia, Museo Noel Kempff MercadoUniversidad Autonomia Gabriel Rene MorenoSanta Cruz de la SierraBolivia
  5. 5.Fundación Amigos de la NaturalezaSanta Cruz de la SierraBolivia

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