Register      Login
International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH FRONT

Development and mapping of fuel characteristics and associated fire potentials for South America

M. Lucrecia Pettinari A D , Roger D. Ottmar B , Susan J. Prichard C , Anne G. Andreu C and Emilio Chuvieco A
+ Author Affiliations
- Author Affiliations

A Environmental Remote Sensing Research Group, Department of Geography and Geology, University of Alcala, Calle Colegios 2, Alcalá de Henares, 28801 Madrid, Spain.

B USDA Forest Service, Pacific Wildland Fire Sciences Laboratory, 400 North 34th Street, Suite 201, Seattle, WA 98103, USA.

C College of Forest Resources, University of Washington, Box 352100, Seattle, WA 98195-2100, USA.

D Corresponding author. Email: mlucrecia.pettinari@uah.es

International Journal of Wildland Fire 23(5) 643-654 https://doi.org/10.1071/WF12137
Submitted: 3 September 2012  Accepted: 26 February 2013   Published: 25 July 2013

Abstract

The characteristics and spatial distribution of fuels are critical for assessing fire hazard, fuel consumption, greenhouse gas emissions and other fire effects. However, fuel maps are difficult to generate and update, because many regions of the world lack fuel descriptions or adequate mapped vegetation attributes to assign these fuelbeds spatially across the landscape. This paper presents a process to generate fuel maps for large areas using remotely sensed information and ancillary fuel characteristic data. The Fuel Characteristic Classification System was used to build fuelbeds for South America and predict potential fire hazard using a set of default environmental variables. A land-cover map was combined with a biome map to define 98 fuelbeds, and their parameters were assigned based on information from global datasets and existing Fuel Characteristic Classification System fuelbeds or photo series. The indices of potential surface fire behaviour ranged from 1.32 to 9, whereas indices of potential crown fire and available fuel for combustion had low to medium values (0–6). This paper presents a geospatial fuels map for South America. This map could be used to assess fire hazard, predict fire behaviour under defined environmental conditions or calculate fuel consumption and greenhouse gas emissions. It could also be easily updated as new remotely sensed information on vegetation becomes available.

Additional keywords: FCCS, fuel map, fuelbeds.


References

Anderson, HE (1982) Aids to determining fuel models for estimating fire behavior. USDA Forest Service, Intermountain Forest and Range Experiment Station, General Technical Report INT-122. (Ogden, UT)

Arroyo LA, Pascual C, Manzanera JA (2008) Fire models and methods to map fuel types: the role of remote sensing. Forest Ecology and Management 256, 1239–1252.
Fire models and methods to map fuel types: the role of remote sensing.Crossref | GoogleScholarGoogle Scholar |

Bicheron P, Defourny P, Brockmann C, Schouten L, Vancutsem C, Huc M, Bontemps S, Leroy M, Achard F, Herold M, Ranera F, Arino O (2008) GlobCover: products description and validation report. (MEDIAS-France/POSTEL) Available at http://ionia1.esrin.esa.int/ [Verified July 2012]

Bontemps S, Defourny P, Van Bogaert E, Arino O, Kalogirou V (2010) GlobCover 2009: product description manual, version 1.0. (ESA and UCLouvain) Available at http://dup.esrin.esa.it/files/p68/GLOBCOVER2009_Product_Description_Manual_1.0.pdf [Verified July 2012]

Bossard M, Feranec J, Otahel J (2000) CORINE land cover technical guide – Addendum 2000. (European Environmental Agency: Copenhagen) Available at www.dmu.dk/fileadmin/Resources/DMU/Udgivelser/CLC2000/technical_guide_addenum.pdf [Verified July 2012]

Burgan RE, Klaver RW, Klaver JM (1998) Fuel models and fire potential from satellite and surface observations. International Journal of Wildland Fire 8, 159–170.
Fuel models and fire potential from satellite and surface observations.Crossref | GoogleScholarGoogle Scholar |

Chuvieco E, Wagtendonk J, Riaño D, Yebra M, Ustin SL (2009) Estimation of fuel conditions for fire danger assessment. In ‘Earth Observation of Wildland Fires in Mediterranean Ecosystems’. (Ed. E Chuvieco) pp. 83–96. (Springer-Verlag: Berlin)

Chuvieco E, Aguado I, Yebra M, Nieto H, Salas J, Martín MP, Vilar L, Martínez J, Martín S, Ibarra P, de la Riva J, Baeza J, Rodríguez F, Molina JR, Herrera MA, Zamora R (2010) Development of a framework for fire risk assessment using remote sensing and geographic information system technologies. Ecological Modelling 221, 46–58.
Development of a framework for fire risk assessment using remote sensing and geographic information system technologies.Crossref | GoogleScholarGoogle Scholar |

Cohen JD, Deeming JE (1985) The National Fire Danger Rating System: basic equations. USDA Forest Service, Pacific Southwest Forest and Range Experiment Station, General Technical Report PSW-82. (Berkeley, CA)

Deeming JE, Lancaster JW, Fosberg MA, Furman WR, Schroeder MJ (1972) The National Fire Danger Rating System. USDA Forest Service, Rocky Mountain Forest and Range Experiment Station, Research Paper RM-84. (Fort Collins, CO)

Di Gregorio A (2005) Land Cover Classification System. Classification concepts and user manual, Software version 2. FAO, Environment and Natural Resources Series number 8. (Rome)

Dymond CC, Roswintiarti O, Brady M (2004) Characterizing and mapping fuels for Malaysia and western Indonesia. International Journal of Wildland Fire 13, 323–334.
Characterizing and mapping fuels for Malaysia and western Indonesia.Crossref | GoogleScholarGoogle Scholar |

Friedl MA, Sulla-Menashe D, Tan B, Schneider A, Ramankutty N, Sibley A, Huang X (2010) MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sensing of Environment 114, 168–182.
MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets.Crossref | GoogleScholarGoogle Scholar |

Fritz S, McCallum I, Schill C, Perger C, Grillmayer R, Achard F, Kraxner F, Obersteiner M (2009) Geo-Wiki.Org: the use of crowdsourcing to improve global land cover. Remote Sensing 1, 345–354.
Geo-Wiki.Org: the use of crowdsourcing to improve global land cover.Crossref | GoogleScholarGoogle Scholar |

Golluscio R, Sala OE (1993) Plant functional types and ecological strategies in Patagonian forbs. Journal of Vegetation Science 4, 839–846.
Plant functional types and ecological strategies in Patagonian forbs.Crossref | GoogleScholarGoogle Scholar |

Hansen MC, DeFries RS, Townsend JRG, Carroll M, Dimiceli C, Sohlberg RA (2003) Global per cent tree cover at a spatial resolution of 500 metres: first results of the MODIS vegetation continuous fields algorithm. Earth Interactions 7, 1–15.
Global per cent tree cover at a spatial resolution of 500 metres: first results of the MODIS vegetation continuous fields algorithm.Crossref | GoogleScholarGoogle Scholar |

Herold M, Mayaux P, Woodcock CE, Baccini A, Schmullius C (2008) Some challenges in global land-cover mapping: an assessment of agreement and accuracy in existing 1-km datasets. Remote Sensing of Environment 112, 2538–2556.
Some challenges in global land-cover mapping: an assessment of agreement and accuracy in existing 1-km datasets.Crossref | GoogleScholarGoogle Scholar |

Hollis JJ, Matthews S, Ottmar RD, Prichard SJ, Slijepcevic S, Burrows ND, Ward B, Tolhurst KG, Anderson WR, Gould JS (2010) Testing woody fuel consumption models for application in Australian southern eucalypt forest fires. Forest Ecology and Management 260, 948–964.
Testing woody fuel consumption models for application in Australian southern eucalypt forest fires.Crossref | GoogleScholarGoogle Scholar |

Kaptué Tchuenté AT, Roujean J-L, De Jong SM (2011) Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land-cover data sets at the African continental scale. International Journal of Applied Earth Observation and Geoinformation 13, 207–219.
Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land-cover data sets at the African continental scale.Crossref | GoogleScholarGoogle Scholar |

Kasischke ES, Hoy EE (2012) Controls on carbon consumption during Alaskan wildland fires. Global Change Biology 18, 685–699.
Controls on carbon consumption during Alaskan wildland fires.Crossref | GoogleScholarGoogle Scholar |

Keane RE, Burgan RE, van Wagtendonk J (2001) Mapping wildland fuels for fire management across multiple scales: integrating remote sensing, GIS, and biophysical modeling. International Journal of Wildland Fire 10, 301–319.
Mapping wildland fuels for fire management across multiple scales: integrating remote sensing, GIS, and biophysical modeling.Crossref | GoogleScholarGoogle Scholar |

Lasaponara R, Lanorte A (2007) Remotely sensed characterization of forest fuel types by using satellite ASTER data. International Journal of Applied Earth Observation and Geoinformation 9, 225–234.
Remotely sensed characterization of forest fuel types by using satellite ASTER data.Crossref | GoogleScholarGoogle Scholar |

Morfín-Ríos JE, Alvarado-Celestino E, Jardel-Peláez EJ, Vihnanek RE, Wright DK, Michel-Fuentes JM, Wright CS, Ottmar RD, Sandberg DV, Nájera-Díaz A (2008) Photo series for quantifying forest fuels in Mexico: montane subtropical forests of the Sierra Madre del Sur and temperate forests and montane shrubland of the northern Sierra Madre Oriental. University of Washington, College of Forest Resources, Pacific Wildland Fire Sciences Laboratory Special Publication number 1. (Seattle, WA)

Nadeau LB, McRae DJ, Jin J-Z (2005) Development of a national fuel-type map for Canada using fuzzy logic. Canadian Forest Service, Natural Resources Canada, Information Report NOR-X-406. (Edmonton, AB) Available at http://dsp-psd.pwgsc.gc.ca/Collection/Fo133-1-406E.pdf [Verified July 2012]

Olson DM, Dinerstein E, Wikramanayake ED, Burgess ND, Powell GVN, Underwood EC, D’Amico JA, Itoua I, Strand HE, Morrison JC, Loucks CJ, Allnutt TF, Ricketts TH, Kura Y, Lamoreux JF, Wettengel WW, Hedao P, Kassem KR (2001) Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51, 933–938.
Terrestrial ecoregions of the world: a new map of life on Earth.Crossref | GoogleScholarGoogle Scholar |

Oswald BP, Fancher JT, Kulhavy DL, Reeves HC (1999) Classifying fuels with aerial photography in east Texas. International Journal of Wildland Fire 9, 109–113.
Classifying fuels with aerial photography in east Texas.Crossref | GoogleScholarGoogle Scholar |

Ottmar RD, Vihnanek RE, Miranda HS, Sato MN, Andrade SMA (2001) Stereo-photo series for quantifying cerrado fuels in Central Brazil – Volume I. USDA Forest Service, Pacific Northwest Research Station, General Technical Report PNW-GTR-519. (Seattle, WA)

Ottmar RD, Sandberg DV, Riccardi CL, Prichard SJ (2007) An overview of the fuel characteristic classification system – quantifying, classifying, and creating fuelbeds for resource planning. Canadian Journal of Forest Research 37, 2383–2393.
An overview of the fuel characteristic classification system – quantifying, classifying, and creating fuelbeds for resource planning.Crossref | GoogleScholarGoogle Scholar |

Riaño D, Chuvieco E, Salas J, Palacios-Orueta A, Bastarrika 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.
Generation of fuel type maps from Landsat TM images and ancillary data in Mediterranean ecosystems.Crossref | GoogleScholarGoogle Scholar |

Riccardi CL, Ottmar RD, Sandberg DV, Andreu A, Elman E, Kopper K, Long J (2007a) The fuelbed: a key element of the fuel characteristic classification system. Canadian Journal of Forest Research 37, 2394–2412.
The fuelbed: a key element of the fuel characteristic classification system.Crossref | GoogleScholarGoogle Scholar |

Riccardi CL, Prichard SJ, Sandberg DV, Ottmar RD (2007b) Quantifying physical characteristics of wildland fuels using the fuel characteristic classification system. Canadian Journal of Forest Research 37, 2413–2420.
Quantifying physical characteristics of wildland fuels using the fuel characteristic classification system.Crossref | GoogleScholarGoogle Scholar |

Rollins MG (2009) LANDFIRE: a nationally consistent vegetation, wildland fire, and fuel assessment. International Journal of Wildland Fire 18, 235–249.
LANDFIRE: a nationally consistent vegetation, wildland fire, and fuel assessment.Crossref | GoogleScholarGoogle Scholar |

Rothermel RC (1972) A mathematical model for predicting fire spread in wildland fuels. USDA Forest Service, Intermountain Forest and Range Experiment Station, Research Paper INT-115. (Odgen, UT)

San Miguel-Ayanz J, Schulte E, Schmuck G, Camia A, Strobl P, Liberta G, Giovando C, Boca R, Sedano F, Kempeneers P, McInerney D, Withmore C, Santos de Oliveira S, Rodrigues M, Durrant T, Corti P, Oehler F, Vilar L, Amatulli G (2012) Comprehensive monitoring of wildfires in Europe: the European Forest Fire Information System (EFFIS). In ‘Approaches to Managing Disaster – Assessing Hazards, Emergencies and Disaster Impacts’. (Ed. J Tiefenbacher) (InTech) Available at http://www.intechopen.com/books/approaches-to-managing-disaster-assessing-hazards-emergencies-and-disaster-impacts/comprehensive-monitoring-of-wildfires-in-europe-the-european-forest-fire-information-system-effis- [Verified 12 June 2013]

Sandberg DV, Riccardi CL, Schaaf MD (2007a) Fire potential rating for wildland fuelbeds using the Fuel Characteristic Classification System. Canadian Journal of Forest Research 37, 2456–2463.
Fire potential rating for wildland fuelbeds using the Fuel Characteristic Classification System.Crossref | GoogleScholarGoogle Scholar |

Sandberg DV, Riccardi CL, Schaaf MD (2007b) Reformulation of Rothermel’s wildland fire behaviour model for heterogeneous fuelbeds. Canadian Journal of Forest Research 37, 2438–2455.
Reformulation of Rothermel’s wildland fire behaviour model for heterogeneous fuelbeds.Crossref | GoogleScholarGoogle Scholar |

Scott JH, Burgan RE (2005) Standard fire behavior fuel models: a comprehensive set for use with Rothermel’s surface fire spread model. USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-153. (Fort Collins, CO) Available at http://www.fire.org/downloads/behaveplus/3.0.0/rmrs_gtr153.pdf [Verified 12 June 2013]

Schaaf MD, Sandberg DV, Schreuder MD, Riccardi CL (2007) A conceptual framework for ranking crown fires potential in wildland fuelbeds. Canadian Journal of Forest Research 37, 2464–2478.
A conceptual framework for ranking crown fires potential in wildland fuelbeds.Crossref | GoogleScholarGoogle Scholar |

Sebastián-Lopez A, San Miguel-Ayanz J, Libertá G (2001) An integrated forest fire risk index for Europe. In ‘A Decade of Trans-European Remote Sensing Cooperation.’ (Ed. MF Buchroithner) pp. 83–88. (Balkema: Rotterdam)

Simard M, Pinto N, Fisher JB, Baccini A (2011) Mapping forest canopy height globally with spaceborne LiDAR. Journal of Geophysical Research 116, G04021
Mapping forest canopy height globally with spaceborne LiDAR.Crossref | GoogleScholarGoogle Scholar |

Stocks BJ, Lawson BD, Alexander ME, Van Wagner CE, McAlpine RS, Lynham TJ, Dubé DE (1989) Canadian Forest Fire Danger Rating System: an overview. Forestry Chronicle 65, 258–265.

ter Steege H, Pitman NCA, Phillips OL, Chave J, Sabatier D, Duque A, Molino J-F, Prévost M-F, Spichiger R, Castellanos H, von Hildebrand P, Vásquez R (2006) Continental-scale patterns of canopy tree composition and function across Amazonia. Nature 443, 444–447.
Continental-scale patterns of canopy tree composition and function across Amazonia.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28XhtVSns7fL&md5=f2b757ce069ba5a11fd7ec7711099545CAS | 17006512PubMed |

Van Wagtendonk JW, Root RR (2003) The use of multi-temporal Landsat Normalized Difference Vegetation Index (NDVI) data for mapping fuel models in Yosemite National Park, USA. International Journal of Remote Sensing 24, 1639–1651.
The use of multi-temporal Landsat Normalized Difference Vegetation Index (NDVI) data for mapping fuel models in Yosemite National Park, USA.Crossref | GoogleScholarGoogle Scholar |

Verburg PH, van de Steeg J, Veldkamp A, Willemen L (2009) From land-cover change to land function dynamics: a major challenge to improve land characterization. Journal of Environmental Management 90, 1327–1335.
From land-cover change to land function dynamics: a major challenge to improve land characterization.Crossref | GoogleScholarGoogle Scholar | 18809242PubMed |

Vignolio OR, Laterra P, Fernández ON, Linares MP, Macira NO, Giaquinta A (2003) Effects of fire frequency on survival, growth and fecundity of Paspalum quadrifarium (Lam.) in a grassland of the Flooding Pampa (Argentina). Austral Ecology 28, 263–270.
Effects of fire frequency on survival, growth and fecundity of Paspalum quadrifarium (Lam.) in a grassland of the Flooding Pampa (Argentina).Crossref | GoogleScholarGoogle Scholar | 23878864PubMed |

Walker S, Wilson JB, Steel JB, Rapson GL, Smith B, King WM, Cottam YH (2003) Properties of ecotones: evidence from five ecotones objectively determined from a coastal vegetation gradient. Journal of Vegetation Science 14, 579–590.
Properties of ecotones: evidence from five ecotones objectively determined from a coastal vegetation gradient.Crossref | GoogleScholarGoogle Scholar |

Wiedinmyer C, Quayle B, Geron C, Belote A, McKenzie D, Zhang X, O’Niell S, Klos Wynne K (2006) Estimating emissions from fires in North America for air quality modeling. Atmospheric Environment 40, 3419–3432.
Estimating emissions from fires in North America for air quality modeling.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28XltVyhtrY%3D&md5=8f1d52054237765d9a7f428a784b64deCAS |

Wooster MJ, Roberts G, Perry GLW, Kaufman YJ (2005) Retrieval of biomass combustion rates and totals from fire radiative power observations: FRP derivation and calibration relationships between biomass consumption and fire radiative energy release. Journal of Geophysical Research 110, D24311
Retrieval of biomass combustion rates and totals from fire radiative power observations: FRP derivation and calibration relationships between biomass consumption and fire radiative energy release.Crossref | GoogleScholarGoogle Scholar |

Xiao-Rui T, McRae DJ, Li-fu S, Ming-yu W (2005) Fuel classification and mapping from satellite imagines. Journal of Forest Research 16, 311–316.
Fuel classification and mapping from satellite imagines.Crossref | GoogleScholarGoogle Scholar |