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.
Fire is an important process in the Earth system, with a global
burned area of 3.0–3.8 million
The characteristics of the vegetation and the environmental conditions affecting the fuels are considered the primary factors in fire initiation, behavior, and effects (Rothermel, 1983). Variables such as fuel loading, fuel depth, stand structure, fuel moisture, etc., will determine fire behavior parameters such as rate of spread, fire intensity, or fuel consumption, amongst others (Cohen and Deeming, 1985). Fuel variables are commonly grouped in fuel types, following different classification systems. Fuel types are frequently created to account for the vegetation characteristics of a particular region, such as the case of the fuels created for Southeast Asia (Dymond et al., 2004), or for the Mediterranean ecosystems (PROMETHEUS S.V. Project, 1999; Riaño et al., 2002). When fuel types are used as input to fire behavior models they are converted to fuel models, which include the specific parameters necessary to run fire simulation programs. Such is the case of the 13 fuel models of the Northern Forest Fire Laboratory (NFFL) (Rothermel, 1972), the 20 fuel models of the National Fire Danger Rating System (NFDRS) (Cohen and Deeming, 1985), or the 17 fuel types of the Canadian Fire Behavior Prediction System (FBP) (Stocks et al., 1989). Other fuel type classifications were created with a broader scope. The Fuel Characteristic Classification System (FCCS) (Ottmar et al., 2007), for example, uses the concept of fuelbed to represent a relatively homogeneous unit in the landscape with a distinct combustion environment (Riccardi et al., 2007), and includes information on physical and biological variables that allow for both fire behavior (through an adaptation of the Rothermels' equations) and effects (emissions) calculations, which can be used for fuel management at different scales (McKenzie et al., 2007).
Maps including information on fuel types are a necessary input for fire risk and fire effects assessment. At local or regional scale, fuel maps are useful for spatial modeling of fire risk assessment (Finney et al., 2011; Chuvieco et al., 2014) and real-time analysis of fire behavior (Dymond et al., 2004; McKenzie et al., 2007). Continental or global fuel maps, meanwhile, are usually used for carbon-cycle or air-quality modeling (Keane et al., 2001; McKenzie et al., 2007; San Miguel-Ayanz et al., 2012), and they can also be used for the estimation of continental to global fire danger (Sebastián-Lopez et al., 2001; Pettinari et al., 2014).
Different approaches can be used to create fuel maps. Field surveys have been used to provide detailed information on fuel characteristics, but they are costly to implement, and thus are only useful for small areas (Keane et al., 2001; Rollins et al., 2004; McKenzie et al., 2007). Ecological modeling employs environmental gradients such as climate and topography, as well as ecosystem dynamic models, to create vegetation and fuel maps (Keane et al., 2001; Rollins et al., 2004). Remote-sensing approaches are sound alternatives to fuel type mapping, as they provide updated spatial coverage and are sensitive to some of the critical variables for fuel type definition: fuel loads, horizontal and vertical continuity, fuel moisture, etc., particularly when using lidar observations (Riaño et al., 2004).
Previous fuel maps created at continental scales have relied on the use of
remote-sensing information, usually reclassified to land cover classes, and
ancillary data from other sources, such as potential vegetation, canopy
cover, etc. Some examples of continental or sub-continental fuel maps are the
National Fuel-type map for Canada (Nadeau et al., 2005), the LANDFIRE fuel
maps for the United States, which include several fuel type classifications
(
The objective of this paper is presenting the methods to generate a global fuel map based on the FCCS approach. Our goal was to deliver a global product to the international community interested in improving the modeling of fire danger and fire effects assessment. To our knowledge, global fuel maps are not yet available, thus this paper is a first attempt to generate a planetary fuel data set that is based on consistent inputs. In addition, since the FCCS is the base for the fuel typology, quantitative estimations of fire risk and behavior parameters can be generated from the final product. In a previous study, we created a fuel map for South America using the FCCS methodology (Pettinari et al., 2014). In this study, we have extended that methodology to create a global fuel data set using FCCS, which required the inclusion of new sources of data to reflect the characteristics of biomes and ecosystems not present in South America. Also, the methodology was expanded, adding more spatial variability to the fuelbeds and updating some sources of information, amongst other improvements. In addition, we have undertaken a first assessment of our product by comparing the biomass estimations provided by the FCCS outputs of our fuelbeds with existing regional or global biomass products.
The development of the global fuelbed data set is based on the Fuel
Characteristic Classification System (FCCS), which is both a conceptual
framework and a software tool for quantifying fuels (Ottmar et al., 2007).
The fuel characteristics are organized into six strata including trees,
shrubs, grasses, woody surface fuels, litter and soil organic matter (duff),
and are referred to as fuelbeds. We have used version 3.0 of the FCCS
software, which is integrated into the Fuel and Fire Tools (FFT, available at
FCCS was selected to develop the fuel data set because it has the advantage that it includes a wide set of physical characteristics of the fuels, and not only the ones required by a particular fire model such as NFFL or NFDRS. The NFFL models were developed for uniform continuous fuels and for the severe period of the fire season (Anderson, 1982; Rothermel, 1983), and they do not describe fuels with higher live fuel moisture or that burn well at high humidity (Scott and Burgan, 2005). FCCS, meanwhile, allows creating fuelbeds for environments not contemplated by other models, such as moist ecosystems that are found in several parts of the world. Also, the parameters included in the FCCS fuelbeds also provide information on the crown and ground fuels, not included in most models only developed for surface fuels (Cohen and Deeming, 1985; Scott and Burgan, 2005). This extends its use to other applications beyond fire behavior estimations, allowing also estimating crown fire potentials, the amount of available fuel or predicting fuel consumption.
General flowchart of the methodology used for the generation of the global fuel data set. More detailed steps are shown in Figs. 2 and 3.
Flow chart of the steps performed for the generation of the fuelbeds.
Flow chart of the steps performed for the parameterization of the fuelbeds. CC: canopy cover.
The fuelbeds to create our global fuel type data set were developed in two stages: first land cover products and a biome map were used to identify fuelbed categories, along with their geographic location, creating a fuelbed map. Then, each fuelbed was given a set of parameters that determine their fire behavior and effects. The fuelbed parameters can be input in the FCCS software, and the results can be mapped joining the results to the fuelbed map (Fig. 1). An example is given on estimated biomass, which is compared with external data bases.
The first stage of the development of the fuel map comprises the delineation of the fuelbeds, and the creation of the map itself. A flow chart summarizing the steps to obtain the fuelbeds is included in Fig. 2.
The land cover information was extracted primarily from the GlobCover 2005
V2.2 product (Bicheron et al., 2008), developed from a temporal series of
MERIS (Medium-Resolution Imaging Spectrometer) images acquired between
December 2004 and June 2006. This product has a spatial resolution of
10
Another important adaptation of the global land cover map was linked to the
Australian eucalyptus class, which was included in the standard GlobCover
with the broadleaved evergreen or semi-deciduous (BE) forests. However, it is
well known that
Regarding the crops, even though the GlobCover V2.2 product only
distinguishes between rainfed and irrigated croplands, fuel conditions and
biomass are very different in some of the most extended crops. To assign
individual crops species to the cropland classes the “Harvested Area and
Yield of 175 crops” map was used
(
Once the global land cover classes were complemented with the ancillary information, some of the classes were combined. Both rainfed and irrigated were grouped when they corresponded to the same crop, because they did not represent a difference in vegetation characteristics for the objective of the fuelbed classification. Also, the classes that differed only in their vegetation density (close or open) were merged.
The biomes description was extracted from the Map of Terrestrial Ecoregions (Olson et al., 2001), as it is widely used by different international organizations, including the World Wildlife Fund (WWF). The description includes 14 global vegetated biomes and more than 800 ecoregions. In order to decrease the total number of fuelbeds, we considered that it was possible to eliminate biomes 9 (Flooded Grasslands and Shrublands) and 10 (Montane Grasslands and Shrublands), as they shared many vegetation characteristics with other fuelbeds in nearby biomes. The different patches of these two biomes were reclassified to the biomes that limited with them. As a result, a total of 12 vegetated biomes were considered for the combination with the land cover classes.
Parameters assigned to each fuelbed.
The intersection of the land cover classes and the biomes was performed at
the spatial resolution of the land cover map. An area map was developed to
represent the area of each 10 arcsec pixel of the GlobCover map, and it was
used to calculate the total area of each possible combination of land cover
class and biome. The combinations with low representation (
Once the spatial distribution of the fuelbeds was defined, a set of parameters that affect fire behavior and effects was assigned to each fuel stratum (tree, shrubs, grasses, woody surface fuels, litter, and ground fuels). These parameters are listed in Table 1. A flow chart of the steps followed is shown in Fig. 3.
Percentage cover of trees was extracted from the MODIS vegetation continuous
field (VCF), Collection 5 (DiMiceli et al., 2011) corresponding to the year
2005, to be coetaneous with the base land cover product. This product has a
spatial resolution of 250
Percentage canopy cover, derived from the MODIS VCF Collection 5 product. The maps show the subdivision of the CC into the three classes considered for classification.
Canopy height was extracted from the global canopy height map developed by
Simard et al. (2011), which was created using lidar data and ancillary data
corresponding to slope, climate and vegetation characteristics. The lidar
data was acquired in 2005 by the Geoscience Laser Altimeter System (GLAS) on
board the ICESat mission (
To assign the main species of trees, shrubs, and grasses to each fuelbed, the
representative plant species for each biome were extracted from the
description of the Terrestrial Ecoregions of the World Wildlife Fund (WWF)
(
The remaining variables for each fuelbed (Table 1) were assigned based on information from existing fuelbeds in the FCCS database or from the Natural Fuels Photo Series from Mexico (Morfín-Ríos et al., 2008) and Brazil (Ottmar et al., 2001). The existing FCCS database, which includes fuelbeds in most biomes, from the Alaskan Tundra to the tropical forests of Florida and Hawaii, was used if possible, because its fuelbeds have all the necessary parameters required to calculate the fire potentials. For each global fuelbed, the existing similar FCCS fuelbeds were selected based on the biome in which they appear and their vegetation form, foliage type, and plant species, and the mean values of their parameters were used to populate the global fuelbed variables, with some adjustments in the tree layer if necessary due to the differences in canopy cover and/or height. The Natural Fuels Photo Series were used primarily for the tropical fuelbeds because they most accurately represent the vegetation found in those biomes. Some variables were assigned based on expert opinion whenever there was no other information available.
Strict validation of our product was not feasible as it would imply a huge groundwork effort, particularly to obtain average fuel parameters. Comparison with other fuel products was also problematic, as regional fuel types use many different classification systems (Rollins, 2009; San Miguel-Ayanz et al., 2012). For these reasons, as a first assessment of the fuelbed data set we decided to compare estimations produced by FCCS with existing databases. We selected the carbon biomass, since this variable has been modeled at global and regional scales by different research groups.
FCCS estimates the amount of total biomass and carbon load per stratum based
on the parameters assigned to each strata and a set of biomass equations for
different types of vegetation (Prichard et al., 2013). This
biomass is used for the calculation of the available fuel potential and
biomass consumption in the Consume Module inside FFT
( Global biomass from the Orchidee Dynamic Global Vegetation Model (DGVM)
(Krinner et al., 2005), as estimated from Yue et al. (2015). The biomass was
obtained from a vegetation distribution map classified into 13 plant
functional types based on the IGBP vegetation map (Loveland et al., 2000). Northern boreal and temperate above-ground biomass (AGB) from the carbon
stock and density map developed by Thurner et al. (2014). This map is based
on the growing stock volume (GSV) estimates obtained with the Biomasar
algorithm (Santoro et al., 2011) using ENVISAT ASAR images. Tropical biomass from the above-ground live woody vegetation carbon
density map developed by Baccini et al. (2012). Tropical biomass from the forest carbon stocks map developed by Saatchi et
al. (2011).
Both of these tropical biomass data sets (from now on referred to as the Baccini
and Saatchi maps) use similar remote-sensing inputs, mainly the lidar data
from the ICESat GLAS, but they use different ground-based data sets and
modeling methods to extend the GLAS footprints to full-coverage AGB maps.
The differences between the two maps are described in Mitchard et al. (2013).
Global fuelbed map. The color legend details the number of the fuelbeds, and the land cover and biome that they represent.
The final fuelbed map contains 274 main fuelbeds. As some of them were subdivided considering their canopy cover, the value increased to 359 when the sub-fuelbeds were considered. The resulting fuelbed map is shown in Fig. 5. Each fuelbed is identified by a number where the first two digits correspond to the biome, and the following three identify the land cover type associated with a pixel. For example, fuelbed 13140 is in the Desert and Xeric Shrublands biome (13) and associated with grassland vegetation (140).
The inclusion of the regional GlobCover maps of Eastern Europe and Central
Asia resulted in the creation of 30 dedicated ND fuelbeds or sub-fuelbeds in
biomes 11 (tundra), 8 (temperate grasslands, savannas, and shrublands), 6
(boreal forest/taiga), 5 (temperate coniferous forests), and 4 (temperate
broadleaf and mixed forests). Similarly, 25 fuelbeds or sub-fuelbeds were
created specifically for Australia, in biomes 4, 8, 7 (tropical and
subtropical grasslands, savannas, and shrublands), 12 (Mediterranean forests,
woodlands, and scrub) and 13 (desert and xeric shrublands). The fuelbed with
the largest area is 1040 (broadleaved evergreen or semi-deciduous forest
vegetation in a tropical and/or subtropical moist broadleaf forest biome), with
10.4 million
Carbon biomass obtained as mean values of the 0.5 degree pixels with
at least 80 % of the land cover analyzed, in units of
References to the data:
Figure 6 shows the FCCS estimations of carbon biomass values computed from
our product. There were 11 fuelbeds with biomass higher than
200
Estimated global carbon biomass obtained from the global fuelbed data set.
The comparison between the biomass results in this study and the other
products used for our comparison exercise (also aggregated at
Box plots of the carbon biomass obtained for each product for the
different land covers:
The tropical forest carbon biomass shows the highest consistency between our estimations and the external products used for comparison, with a Spearman's coefficient of 0.79 between this study and the Baccini product. Only the Orchidee estimations are clearly above the others (by 40 %). The box plot distribution (Fig. 7a) also shows a similar biomass distribution amongst the fuelbeds and the Baccini and Saatchi map, with the Orchidee one having the biggest discrepancies.
Regarding the boreal forest fuelbeds, the values obtained in this study for
total carbon biomass are 3.5 to 3.7 times higher than the other biomass
products, which is easily appreciable in Fig. 7b. As described earlier in
this section, in some of the fuelbeds with highest biomass located in boreal,
tundra or temperate biomes, a significant proportion of biomass for these
regions is stored in the ground fuel stratum. The Biomasar product includes
above-ground biomass (AGB) and root biomass, but does not have a duff
component. For this reason, the values of carbon biomass corresponding only
to the tree stratum of the fuelbeds were used for comparison. In that case,
the tree carbon biomass from this study was similar to the Biomasar for the
boreal forest (only 5 % lower). The Spearman's coefficient
(
The mean biomass of the grasses fuelbeds is similar to the one obtained from
the Orchidee biomass map, but the correlation is poor (
The fuelbed map developed in this study is the first global product that describes the characteristics of the vegetation related to fire behavior and effects, and should be useful for studies modeling fire impacts on the climate system as well as fire risk and fire management analysis. While different global land cover maps are available (e.g., Loveland et al., 2000; Bartholomé and Belward, 2005; Bicheron et al., 2008), none of these products can be directly used to determine fire behavior, because they lack the required parameters to run fire behavior models. The fuelbeds, on the other hand, include the necessary information on fuel characteristics to be input in FCCS, and can provide estimations of fuel potentials, biomass, and surface fire behavior.
To generate a global fuel data set product several generalizations and assumptions had to be made, which prevent the comparison of our product with regional more-detailed products. In addition, the uncertainty of each input variable to generate the final database should also be taken into account if using our product for regional-scale studies. A few thoughts on our product limitations and strengths follow.
The development of the global fuelbed map includes several improvements compared to the previous product elaborated using this methodology, corresponding to the fuel map of South America (Pettinari et al., 2014). Supplementary information was added to the canopy stratum, which now includes a secondary layer of trees, and also duff information was incorporated, which is particularly relevant in the temperate and boreal biomes of the Northern Hemisphere. This information adds to the total fuel and biomass information, and affects both the behavior outputs and total available carbon biomass. The canopy cover data were also improved. On the one hand, a more recent version of the MODIS VCF was used (collection 5 vs. collection 3), which has a higher accuracy compared to previous versions (Townshend et al., 2011). And on the other hand, the subdivision of the canopy cover into three groups, as well as the creation of sub-fuelbeds according to percentage of canopy cover, allowed obtaining more realistic results than before, because it allowed keeping a higher variability of canopy structure than in the case of using one mean value for the whole fuelbed.
Another improvement for this global map was the use of mean values from several existing fuelbeds or Photo Series, instead of using only one existing value as representative of each of the global fuelbeds. The use of different existing data of the same land cover and biome combination, but from separate locations, provided a better characterization of the diverse ecosystems, generalized by the use of the mean values. With this approach, each global fuelbed represents the mean conditions that could be found in different ecosystems of the same land cover–biome combination.
The disaggregation of the cropland land cover, addressed as the selection of crop species with highest cultivated area per administrative division, also improved the characterization of the crops' fuelbeds compared to the previous product. While the viability of different crops is dependent on biophysical parameters (Sacks et al., 2010), it is also affected by socio-economic factors (Rasul and Thapa, 2003; Olesen et al., 2011). Distinct crops have different biomass, react differently to fire, and also the period and conditions in which crops are usually burned are not the same. For example, most of the crops are burned after harvest, to eliminate crop residue and for pest and weed control (Jenkins et al., 1992; McCarty et al., 2009). Sugar cane, on the other hand, is usually burned previous to harvesting, to remove trash, kill pests and facilitate the harvesting process (Cannavam Rípoli et al., 2000); and for this reason the biomass is live, and its amount is high compared to other crops. The inclusion of different crop fuelbeds in different geographic regions of the same land cover–biome combination tackles these issues, and will be able to provide more realistic results when fire behavior or effects are calculated from the fuelbed map.
The FCCS fuelbed database and the Photo Series from which the global fuelbeds
were created, while including data from the different existing biomes,
reflect the conditions of American ecosystems, and do not have information
from other continents. Many studies have shown continental differences within
biogeographical regions, including species richness (Barthlott et al., 2007;
Kreft and Jetz, 2007), total biomass (Saatchi et al., 2011; Baccini et
al., 2012; Banin et al., 2014; Thurner et al., 2014), and fire behavior
(Lehmann et al., 2014; Rogers et al., 2015). Some of the most evident
differences regarding vegetation behavior to fire were addressed with the
inclusion of the regional GlobCover map to account for needle-leaved deciduous
trees (
At this point, only the existing FCCS fuelbeds and the Photo Series were used to populate the global fuelbed parameters, because they include all (in the case of the FCCS fuelbeds) or most (in the case of the Photo Series) of the required variables. Many other vegetation databases exist, but they only have information for some of the parameters required. For instance, there are few field databases that include information on dead woody fuels, such as some in the Brazilian Amazon (Cochrane et al., 1999) or in South African and Zambian savannas (Shea et al., 1996). This fuel stratum is critical in determining surface fire behavior, and as such should be included in the information used for the creation of the fuelbeds. But many databases, while having detailed information on tree characteristics, do not specify the dead woody fuels or other surface fuels such as shrubs or grasses (Prasad et al., 2001; Muche et al., 2012). Also, information on litter, lichen, moss, and duff loadings (which affect the total combustible biomass and the fire emissions) is usually published without including detailed data on the rest of the fuels present in the site (Harden et al., 2006). Future improvements of the fuelbed map will involve the inclusion of fuel data from other continents, developing methods to homogenize the information from different sources into fuelbed variables.
The global fuelbed map maintains some of the same limitations as the South American map. Modeling terrestrial ecosystems at a global scale implies the use of a generalized representation of their characteristics (Running and Hunt, 1993). This necessary generalization of the fuelbeds loses much of the complexity of the ecosystems, as mean values of the fuel parameters are assigned globally. Also, only one representative species (or two in the case of mixed forests) was assigned for each vegetation stratum. For this reason, while it is appropriate for global or continental applications, it should be used carefully when working at more detailed scales. Adjustments to the fuelbed parameters should be applied to approximate them to particular regions if possible.
The map also carries the uncertainties and limitations of the original products from which it is based. The GlobCover product, as any other land cover map, includes some misclassification of pixels in certain regions, which has been addressed in their validation report (Bicheron et al., 2008). Also, the Olson biomes' map has sharp boundaries between biomes, while gradual transitions of environmental variables and vegetation cover between adjacent biomes are more realistic (Walker et al., 2003).
Even though the objective of our study was not estimating carbon biomass, we considered comparing this output of FCCS with other products as a first assessment of our results. The comparison can be considered successful, as the main spatial trends and actual values of our product agree quite acceptably with existing ones, particularly when considering the differences in methods and scopes between the products that were compared.
Terrestrial biomass is an essential indicator for the monitoring of Earth's ecosystems and climate and for studying biogeochemical cycles, and has promoted the development of many biomass maps in the past few years. We selected diverse products for the comparison of the fuelbeds' biomass, which were generated employing different methods. As a global biomass product, we used the map obtained by the Orchidee DGVM (Yue et al., 2015), because the biomass is calculated separately for different fuel strata, and we were able to select the layers that corresponded to the fuel strata from the fuelbeds, hence obtaining comparable results. Although there is a global biomass product currently available (Ruesch and Gibbs, 2008), it includes data of both living above and below (root) ground biomass. Since the fuelbeds do not include root biomass information, while they do include information on dead ground fuels, the two products were not analogous. We also compared the biomass from the most important forested regions of the world (tropical forests, and Northern Hemisphere temperate and boreal forests) with products developed using remote-sensing technology: Envisat ASAR in the case of the Biomasar product (Santoro et al., 2011), and GLAS in the tropical forest maps (Saatchi et al., 2011; Baccini et al., 2012).
The carbon biomass values for the boreal forests obtained in this study
(considering only the tree stratum) were very similar to those obtained for
the Biomasar map, with only a 5 % difference in their mean (30.9 vs.
32.4
In the case of the temperate forests, our estimations were approximately 30 % higher than the Biomasar biomass. This divergence can be explained by different
reasons. First, it should be noted that both products are based on different
land cover maps: while the fuelbeds are based on the GlobCover 2005, the land
cover map used to determine the forest pixels in Biomasar was the GLC2000
(Bartholomé and Belward, 2005), with a different spatial resolution (1 km
vs.
For the tropical forests, the value obtained as the mean biomass from all the
pixels with homogeneous forest cover was within the values found for the
other three maps, and closest to the Baccini map (110.0
The differences between the savanna and shrub biomass results between the
Orchidee and the fuelbed maps (15.5 vs. 8.1
In all, the carbon biomass obtained for the fuelbeds shows acceptable results compared to the other products analyzed. The results also show consistency between the diverse approaches used to develop the different maps. Future work could include further information to the assessment of the biomass results, such as an estimation of soil carbon biomass using data extracted from the Harmonized World Soil Database (FAO/IIASA/ISRIC/ISSCAS/JRC, 2012), as was done by Carvalhais et al. (2014).
Future work will also analyze the continental differences in biomass from other products, in order to improve the spatial distribution of biomass worldwide. This is related to the incorporation of fuel data from different continents, as stated in the previous section.
The global fuelbed data set developed in this study can be used for different applications, as the FCCS includes a wide set of characteristics of the fuels, and not only the ones required for a particular fuel model. For example, FCCS calculates three fuel potentials (surface fire behavior potential, crown fire potential, and available fuel potential) using benchmark environmental variables, which can be used to evaluate fire danger based solely on fuel characteristics (Sandberg et al., 2007; Prichard et al., 2013). Also, specific environmental variables (fuel moisture, slope, and wind speed) can be assigned to calculate expected surface fire behavior for different weather conditions, as it provides results on rate of spread, flame length, and reaction intensity. Furthermore, the available fuel and carbon results obtained for each fuelbed can be used to calculate fuel consumption and pollutant emissions using tools such as Consume (Prichard et al., 2005).
All these results provide information for different applications. The fuelbed
map could be used for global or continental fire danger assessment, using the
values of fire potentials or fire behavior to complement existing early
warning systems, such as EFFIS (
Finally, our product could also be used to calculate emissions from wildland
fires at country or continental scale from Consume or other fire emission
models, complementing information supplied by other products as the Global
Fire Emissions Database (GFED,
Due to the resolution of the map and the global characteristics of the fuelbeds, all of these applications are intended for regional to global studies and are not intended for the local scale. For example, this map is not intended to predict “real-world” fire behavior at a local scale, which would need a much finer spatial resolution of the fuelbeds and equally detailed weather information. For this purpose, other systems such as FlamMap (Finney, 2006) or FARSITE (Finney, 2004) would be a more appropriate option.
To obtain a more detailed fuelbed map for a local region (such as a country or province) we would suggest to use the methodology described in this article to create a custom fuelbed map, using local vegetation information if possible. If no local information is available, it would be possible to create a data set with the same data sources used in this article, but assigning mean information on canopy cover, height, and fuelbed parameters related only to the study area, thus describing better the local conditions.
Future research will focus on the application of this fuelbed data set to different fire management issues, particularly obtaining fire behavior and potential values for fire danger estimation.
This study developed the first global fuel data set for modeling wildland fire danger and fire effects. The data set is based on the Fuel Characteristic Classification System (FCCS), and includes parameters that may be used to obtain quantitative estimations of fire behavior variables. The geographical distribution of the fuelbeds was created by combining the GlobCover 2005 V2.2 land cover map and the Olson biomes' map, with the aid of some ancillary information for particular land cover types or regions. A total of 274 fuelbeds were created (359 if the sub-fuelbeds are considered). Each fuelbed was assigned a set of parameters related to fire behavior, extracted from global or regional databases. With these parameters, FCCS can be run to obtain fire potentials, surface fire behavior, and carbon biomass for each fuelbed.
A comparison between the carbon biomass obtained for our fuelbeds and four
other regional or global biomass products showed reasonable agreement both
in terms of geographical distribution and biomass load. The highest
Spearman's rho coefficients were found for tropical and boreal forests
(
The resulting global fuelbed map in GeoTIFF format, as well as a spreadsheet
containing all the variables assigned to each fuelbed and the sources of the
information used for their creation, is available from Pettinari (2015),
The authors thank Susan Prichard, Paige Eagle, Anne Andreu, and Roger Ottmar for their help in the use of FCCS, and Ernesto Alvarado and Nancy French for providing the information on the Mexican and agricultural fuelbeds, respectively.
We would also like to thank Chao Yue and Martin Thurner for supplying their biomass data sets and for their useful comments regarding the carbon biomass results, and Edward Mitchard and Alessandro Baccini for providing the tropical biomass data sets.
Finally, we would like to thank the anonymous authors and the editor of the article for their suggestions, which helped us improve the document. Edited by: K. Thonicke