Generation of a global fuel data set using the Fuel Characteristic Classification System

Abstract. This study presents the methods for the generation of the first global fuel data set, containing all the parameters required to be input in the Fuel Characteristic Classification System (FCCS). The data set was developed from different spatial variables, both based on satellite Earth observation products and fuel databases, and is comprised by a global fuelbed map and a database that includes the parameters of each fuelbed that affect fire behavior and effects. A total of 274 fuelbeds were created and parameterized, and can be input into FCCS to obtain fire potentials, surface fire behavior and carbon biomass for each fuelbed. We present a first assessment of the fuel data set by comparing the carbon biomass obtained from our FCCS fuelbeds with the average biome values of four other regional or global biomass products. The results showed a good agreement both in terms of geographical distribution and biomass loads when compared to other biomass data, with the best results found for tropical and boreal forests (Spearman's coefficient of 0.79 and 0.77). This global fuel data set may be used for a varied range of applications, including fire danger assessment, fire behavior estimations, fuel consumption calculations and emissions inventories.

[1]  E. Chuvieco,et al.  Integrating geospatial information into fire risk assessment , 2014 .

[2]  G. Powell,et al.  Terrestrial Ecoregions of the World: A New Map of Life on Earth , 2001 .

[3]  Pierre Hiernaux,et al.  Savanna Vegetation-Fire-Climate Relationships Differ Among Continents , 2014, Science.

[4]  M. E. Alexander,et al.  Canadian Forest Fire Danger Rating System: An Overview , 1989 .

[5]  C. Justice,et al.  The spatial and temporal distribution of crop residue burning in the contiguous United States. , 2009, The Science of the total environment.

[6]  M. L. C. Ripoli,et al.  Energy potential of sugar cane biomass in Brazil , 2000 .

[7]  D. Riaño,et al.  Generation of fuel type maps from Landsat TM images and ancillary data in Mediterranean ecosystems , 2002 .

[8]  Donald McKenzie,et al.  Mapping fuels at multiple scales: landscape application of the fuel characteristic classification system. , 2007 .

[9]  D. Nepstad,et al.  Positive feedbacks in the fire dynamic of closed canopy tropical forests , 1999, Science.

[10]  I. C. Prentice,et al.  A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system , 2005 .

[11]  W. Barthlott,et al.  Patterns of vascular plant diversity at continental to global scales , 2007 .

[12]  M Lucrecia Pettinari Global Fuelbed Dataset , 2015 .

[13]  Y. Finegold,et al.  Land Cover Classification System Classification concepts Software version 3 , 2016 .

[14]  H. Anderson Aids to Determining Fuel Models for Estimating Fire Behavior , 1982 .

[15]  Keith W. Oleson,et al.  Landscapes as patches of plant functional types: An integrating concept for climate and ecosystem models , 2002 .

[16]  J. Randerson,et al.  Influence of tree species on continental differences in boreal fires and climate feedbacks , 2015 .

[17]  Charles W. McHugh,et al.  Numerical Terradynamic Simulation Group 10-2011 A simulation of probabilistic wildfire risk components for the continental United States , 2017 .

[18]  Joe H. Scott,et al.  Standard Fire Behavior Fuel Models: A Comprehensive Set for Use with Rothermel?s Surface Fire Spread Model , 2015 .

[19]  J. Pereira,et al.  Global wildland fire emissions from 1960 to 2000 , 2008 .

[20]  Holly K. Gibbs,et al.  New IPCC Tier-1 Global Biomass Carbon Map for the Year 2000 , 2008 .

[21]  N. French A US national fuels database and map for calculating carbon emissions from wildland and prescribed fire , 2013 .

[22]  P. Strobl,et al.  Comprehensive Monitoring of Wildfires in Europe: The European Forest Fire Information System (EFFIS) , 2012 .

[23]  J. Loisel,et al.  Global peatland dynamics since the Last Glacial Maximum , 2010 .

[24]  J. Townshend,et al.  MODIS Vegetative Cover Conversion and Vegetation Continuous Fields , 2010 .

[25]  A. Baccini,et al.  Mapping forest canopy height globally with spaceborne lidar , 2011 .

[26]  Johann G. Goldammer,et al.  Developing a global early warning system for wildland fire , 2006 .

[27]  R. Rothermel A Mathematical Model for Predicting Fire Spread in Wildland Fuels , 2017 .

[28]  F. Achard,et al.  Can recent pan-tropical biomass maps be used to derive alternative Tier 1 values for reporting REDD+ activities under UNFCCC? , 2014 .

[29]  M. Finney FARSITE : Fire Area Simulator : model development and evaluation , 1998 .

[30]  S. Running,et al.  Generalization of a forest ecosystem process model for other biomes, Biome-BGC, and an application for global-scale models. Scaling processes between leaf and landscape levels , 1993 .

[31]  Limin Yang,et al.  Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .

[32]  J. Bastow Properties of ecotones: Evidence from five ecotones objectively determined from a coastal vegetation gradient , 2003 .

[33]  C. Justice,et al.  Global characterization of fire activity: toward defining fire regimes from Earth observation data , 2008 .

[34]  J. Kesselmeier,et al.  Biogenic Volatile Organic Compounds (VOC): An Overview on Emission, Physiology and Ecology , 1999 .

[35]  J. Keeley,et al.  A Burning Story: The Role of Fire in the History of Life , 2009 .

[36]  Roger D. Ottmar,et al.  The fuelbed: a key element of the Fuel Characteristic Classification System. , 2007 .

[37]  J. Randerson,et al.  Analysis of daily, monthly, and annual burned area using the fourth‐generation global fire emissions database (GFED4) , 2013 .

[38]  L. You,et al.  COMPARING AND SYNTHESIZING DIFFERENT GLOBAL AGRICULTURAL LAND DATASETS FOR CROP ALLOCATION MODELING , 2008 .

[39]  Chengquan Huang,et al.  Integrating global land cover products for improved forest cover characterization: an application in North America , 2014, Int. J. Digit. Earth.

[40]  J. Terborgh,et al.  Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites , 2014, Global ecology and biogeography : a journal of macroecology.

[41]  Susan J. Prichard,et al.  An overview of the fuel characteristic classification system—quantifying, classifying, and creating fuelbeds for resource planning. , 2007 .

[42]  D. Deryng,et al.  Crop planting dates: an analysis of global patterns. , 2010 .

[43]  C. Dymond,et al.  Characterizing and mapping fuels for Malaysia and western Indonesia , 2004 .

[44]  Roger D. Ottmar,et al.  Fuel Characteristic Classification System version 3.0: technical documentation , 2013 .

[45]  Jack D. Cohen,et al.  The national fire-danger rating system: basic equations , 1985 .

[46]  M. Trnka,et al.  Impacts and adaptation of European crop production systems to climate change , 2011 .

[47]  J. E N N I F E,et al.  Effects of wildfire and permafrost on soil organic matter and soil climate in interior Alaska , 2006 .

[48]  R. Rothermel,et al.  How to Predict the Spread and Intensity of Forest and Range Fires , 2017 .

[49]  R. Keane,et al.  MAPPING FUELS AND FIRE REGIMES USING REMOTE SENSING, ECOSYSTEM SIMULATION, AND GRADIENT MODELING , 2004 .

[50]  S. Goetz,et al.  Uncertainty in the spatial distribution of tropical forest biomass: a comparison of pan-tropical maps , 2013, Carbon Balance and Management.

[51]  N. Jürgens,et al.  BIOTA Southern Africa Biodiversity Observatories Vegetation Database , 2012 .

[52]  Gopal B. Thapa,et al.  Shifting cultivation in the mountains of South and Southeast Asia: regional patterns and factors influencing the change , 2003 .

[53]  P. Ciais,et al.  Modelling the role of fires in the terrestrial carbon balance by incorporating SPITFIRE into the global vegetation model ORCHIDEE – Part 2: Carbon emissions and the role of fires in the global carbon balance , 2014 .

[54]  B. Jenkins,et al.  Atmospheric emissions from agricultural burning in California: Determination of burn fractions, distribution factors, and crop-specific contributions , 1992 .

[55]  C. Schmullius,et al.  Carbon stock and density of northern boreal and temperate forests , 2014 .

[56]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[57]  Maurizio Santoro,et al.  Global covariation of carbon turnover times with climate in terrestrial ecosystems , 2014, Nature.

[58]  E. Chuvieco,et al.  Global burned area mapping from ENVISAT-MERIS and MODIS active fire data , 2015 .

[59]  Rick Mueller,et al.  Mapping global cropland and field size , 2015, Global change biology.

[60]  Emilio Chuvieco,et al.  Development and mapping of fuel characteristics and associated fire potentials for South America , 2014 .

[61]  Urs Wegmüller,et al.  Retrieval of growing stock volume in boreal forest using hyper-temporal series of Envisat ASAR ScanSAR backscatter measurements , 2011 .

[62]  Berien Elbersen,et al.  Semi-natural vegetation in agricultural land: European map and links to ecosystem service supply , 2014, Agronomy for Sustainable Development.

[63]  N. Ramankutty,et al.  Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000 , 2008 .

[64]  W. Jetz,et al.  Global patterns and determinants of vascular plant diversity , 2007, Proceedings of the National Academy of Sciences.

[65]  A. Belward,et al.  GLC 2000 : a new approach to global land cover mapping from Earth observation data , 2005 .

[66]  Bicheron Patrice,et al.  GlobCover - Products Description and Validation Report , 2008 .

[67]  M. Finney An Overview of FlamMap Fire Modeling Capabilities , 2006 .

[68]  M. Rollins LANDFIRE: a nationally consistent vegetation, wildland fire, and fuel assessment , 2009 .

[69]  S. Goetz,et al.  Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps , 2012 .

[70]  C. Sharma,et al.  Biomass and combustion characteristics of secondary mixed deciduous forests in Eastern Ghats of India , 2001 .

[71]  S. Ustin,et al.  Generation of crown bulk density for Pinus sylvestris L. from lidar , 2004 .

[72]  R. Keane,et al.  Mapping wildland fuels for fire management across multiple scales: Integrating remote sensing, GIS, and biophysical modeling , 2001 .

[73]  O. Phillips,et al.  Tropical forest wood production: a cross‐continental comparison , 2014 .

[74]  Niklaus E. Zimmermann,et al.  Plant functional type mapping for earth system models , 2011 .

[75]  D. Ward,et al.  Fuel biomass and combustion factors associated with fires in savanna ecosystems of South Africa and Zambia , 1996 .

[76]  D. V. Sandberg,et al.  Fire potential rating for wildland fuelbeds using the Fuel Characteristic Classification SystemThis article is one of a selection of papers published in the Special Forum on the Fuel Characteristic Classification System. , 2007 .

[77]  W. Salas,et al.  Benchmark map of forest carbon stocks in tropical regions across three continents , 2011, Proceedings of the National Academy of Sciences.