Earth Syst. Sci. Data, 9, 697–720, 2017
https://doi.org/10.5194/essd-9-697-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Global fire emissions estimates during 1997–2016
Guido R. van der Werf
1
, James T. Randerson
2
, Louis Giglio
3
, Thijs T. van Leeuwen
4,a
, Yang Chen
2
,
Brendan M. Rogers
5
, Mingquan Mu
2
, Margreet J. E. van Marle
1,b
, Douglas C. Morton
6
,
G. James Collatz
6
, Robert J. Yokelson
7
, and Prasad S. Kasibhatla
8
1
Faculty of Earth and Life Sciences, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
2
Department of Earth System Science, University of California, Irvine, CA 92697, USA
3
Department of Geographical Sciences, University of Maryland, MD 20742, USA
4
SRON Netherlands Institute for Space Research, 3584 CA Utrecht, the Netherlands
5
Woods Hole Research Center, Falmouth, MA 02540, USA
6
Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
7
Department of Chemistry, University of Montana, Missoula, MT 59812, USA
8
Nicholas School of the Environment, Duke University, Durham, NC 27708, USA
a
now at: VanderSat BV, 2011 VK, Haarlem, the Netherlands
b
now at: Deltares, 2629 HV, Delft, the Netherlands
Correspondence to: Guido R. van der Werf (guido.vander.werf@vu.nl)
Received: 9 December 2016 – Discussion started: 12 January 2017
Revised: 6 July 2017 – Accepted: 18 July 2017 – Published: 12 September 2017
Abstract. Climate, land use, and other anthropogenic and natural drivers have the potential to influence fire
dynamics in many regions. To develop a mechanistic understanding of the changing role of these drivers and
their impact on atmospheric composition, long-term fire records are needed that fuse information from different
satellite and in situ data streams. Here we describe the fourth version of the Global Fire Emissions Database
(GFED) and quantify global fire emissions patterns during 1997–2016. The modeling system, based on the
Carnegie–Ames–Stanford Approach (CASA) biogeochemical model, has several modifications from the previ-
ous version and uses higher quality input datasets. Significant upgrades include (1) new burned area estimates
with contributions from small fires, (2) a revised fuel consumption parameterization optimized using field obser-
vations, (3) modifications that improve the representation of fuel consumption in frequently burning landscapes,
and (4) fire severity estimates that better represent continental differences in burning processes across boreal
regions of North America and Eurasia. The new version has a higher spatial resolution (0.25
◦
) and uses a dif-
ferent set of emission factors that separately resolves trace gas and aerosol emissions from temperate and boreal
forest ecosystems. Global mean carbon emissions using the burned area dataset with small fires (GFED4s) were
2.2 × 10
15
grams of carbon per year (Pg C yr
−1
) during 1997–2016, with a maximum in 1997 (3.0 Pg C yr
−1
)
and minimum in 2013 (1.8 Pg C yr
−1
). These estimates were 11 % higher than our previous estimates (GFED3)
during 1997–2011, when the two datasets overlapped. This net increase was the result of a substantial increase in
burned area (37 %), mostly due to the inclusion of small fires, and a modest decrease in mean fuel consumption
(−19 %) to better match estimates from field studies, primarily in savannas and grasslands. For trace gas and
aerosol emissions, differences between GFED4s and GFED3 were often larger due to the use of revised emis-
sion factors. If small fire burned area was excluded (GFED4 without the “s” for small fires), average emissions
were 1.5 Pg C yr
−1
. The addition of small fires had the largest impact on emissions in temperate North America,
Central America, Europe, and temperate Asia. This small fire layer carries substantial uncertainties; improving
these estimates will require use of new burned area products derived from high-resolution satellite imagery. Our
revised dataset provides an internally consistent set of burned area and emissions that may contribute to a better
understanding of multi-decadal changes in fire dynamics and their impact on the Earth system. GFED data are
available from http://www.globalfiredata.org.
Published by Copernicus Publications.
698 G. R. van der Werf et al.: Global fire emissions estimates during 1997–2016
1 Introduction
Fires have occurred naturally since the rise of vascular plants
on land over 400 million years ago (Scott and Glasspool,
2006), shaping biomes and influencing climate through mod-
ulation of the carbon cycle and emissions of greenhouse
gases and aerosols (Edwards et al., 2010; Langmann et
al., 2009; van Langevelde et al., 2003). During the An-
thropocene, humans have become an increasingly important
driver of fire occurrence (Bowman et al., 2011). Human ac-
tivity has enhanced fire activity in locations such as defor-
estation zones, while fire suppression and conversion of fire-
prone landscapes such as savannas to agriculture in Africa,
or of fire-maintained open lands to closed-canopy forests in
the eastern US has generally decreased fire activity (Andela
and van der Werf, 2014; Bowman et al., 2009; Nowacki and
Abrams, 2008). To study how climate influences fires at the
global scale and, in turn, how fires influence the carbon cy-
cle, air quality, and climate we have developed the Global
Fire Emissions Database (GFED).
The scientific community has used past releases of GFED
for over a decade. GFED has been used by atmospheric
and biogeochemical modeling groups as an input dataset to
study the impact of fires on biogeochemical cycles (Chen et
al., 2010; Schwietzke et al., 2016), atmospheric chemistry
(Aouizerats et al., 2015; Castellanos et al., 2014), and hu-
man health (Johnston et al., 2012; Marlier et al., 2013), in
assessment reports of the Intergovernmental Panel on Cli-
mate Change (IPCC) to estimate the role of fire and de-
forestation in biogeochemical cycles (Ciais et al., 2013),
in the National Oceanic and Atmospheric Administration
(NOAA’s) CarbonTracker system (Peters et al., 2007), and
in annual updates of the Global Carbon Project (Le Queré
et al., 2015). GFED also serves as a benchmark for optimiz-
ing fire modules in dynamic global vegetation and Earth sys-
tem models (Hantson et al., 2016), and for fire emissions es-
timates derived from fire radiative power (FRP), including
the Global Fire Assimilation System (Kaiser et al., 2012).
Finally, burned area from GFED has provided a means for
building early warning systems of fire season severity (Chen
et al., 2016).
The first version of GFED was released in 2004 and
has since undergone several revisions as improved burned
area estimates became available. GFED2 was released after
Giglio et al. (2006) improved on the mapping of burned area
from active fire data. GFED3 was released when this con-
version was no longer necessary because almost all burned
area in the Moderate Resolution Imaging Spectroradiome-
ter (MODIS) era had been mapped (Giglio et al., 2010),
and the current version follows further improvements in the
burned area algorithm (Giglio et al., 2013). Satellite burned
area is the most important input dataset regulating the spa-
tial and temporal pattern of emissions following the Seiler
and Crutzen (1980) approach, and is complemented in GFED
by a biogeochemical modeling framework that provides esti-
mates of biomass in various carbon “pools” including leaves,
grasses, stems, coarse woody debris, and litter. These pools
are combusted to different degrees during a fire depending on
pool-specific parameters and environmental conditions that
influence fuel moisture and the simulated burn depth in or-
ganic soils of boreal forests and peatlands.
Over the past decade, a parallel line of research has made
considerable progress in estimating emissions using satel-
lite observations of FRP. When continuous observations are
available or the FRP diurnal cycle can be modeled, FRP can
be integrated over time, yielding fire radiative energy (FRE).
FRE is directly related to fire emissions (Wooster, 2002),
and approaches using FRP observations can provide emis-
sions estimates in near-real time (Darmenov and da Silva,
2015; Kaiser et al., 2012). Despite progress (Ichoku and El-
lison, 2014; Schroeder et al., 2014a), there is still substan-
tial uncertainty and some of these FRE approaches apply
a scaling factor to match GFED. Comparisons between the
“classical” burned area approach and the FRP approach, or
approaches based on active fire detections in general, have
indicated there is considerable variability in the amount of
burned area associated with an individual active fire detec-
tion, and thus the two approaches do not always align (Giglio
et al., 2006; Randerson et al., 2012). In general, direct map-
ping of burned area excels when fires are large, but has diffi-
culty in detecting smaller fires, for example, in croplands and
in other areas where many fires have a size below the 21 ha of
an individual 500 m MODIS pixel. Combining both burned
area and active fire data, Randerson et al. (2012) provided ev-
idence that the total area burned by these relatively small fires
could be substantial at the global scale. Therefore, emission
estimates based solely on active fires, including the Fire IN-
ventory from NCAR (Wiedinmyer et al., 2011), may better
capture spatial and temporal variability in regions with many
small fires than emission estimates based solely on burned
area (Reddington et al., 2016). However, approaches based
solely on active fires often do not account for spatial and tem-
poral variability in the amount of burned area per active fire
detection or variability in fuel consumption within biomes.
In this paper we describe the emissions estimates asso-
ciated with the GFED4 burned area product from Giglio et
al. (2013), with or without additional burned area from small
fires based on a revised version of the Randerson et al. (2012)
small-fire estimation approach. The main focus of our anal-
ysis will be on the model version that includes small fires
(GFED4s), while the emissions estimates based on burned
area without small fires will be referred to as GFED4. We
also used a recent meta-analysis (van Leeuwen et al., 2014)
to constrain our modeled estimates of fuel consumption. Fuel
consumption is the amount of biomass, coarse and fine lit-
ter, and soil organic matter consumed per unit area burned
Earth Syst. Sci. Data, 9, 697–720, 2017 www.earth-syst-sci-data.net/9/697/2017/
G. R. van der Werf et al.: Global fire emissions estimates during 1997–2016 699
and is the product of fuel load and combustion completeness.
Besides these two main improvements over earlier versions,
we made a number of additional modifications including up-
dated input datasets, the use of satellite-derived estimates of
parameters governing fuel consumption and tree mortality in
the boreal region (Rogers et al., 2015), and application of a
new emission factor methodology that separates temperate
and boreal forest ecosystems (Akagi et al., 2011). In Sect. 2
we provide more detail on these input datasets, followed by a
description of the modeling framework in Sect. 3. Results are
given in Sect. 4 followed by a discussion in Sect. 5 that in-
cludes a description of the main differences with GFED3 and
an assessment of the primary sources of uncertainty in esti-
mating fire emissions. In the conclusions (Sect. 6) we sum-
marize the main points of our analysis and describe several
important directions for future work.
2 Input datasets
Our version of the Carnegie–Ames–Stanford Approach
(CASA) model described in Sect. 3 requires input datasets
on vegetation characteristics, meteorology, and fire param-
eters. Most of these datasets are somewhat different from
those used in previous versions of GFED, in part from a need
for shorter latency in our updates. We re-gridded all of the
input datasets to 0.25
◦
spatial resolution and a monthly tem-
poral resolution. We took additional steps to create estimates
of fire dynamics on daily and 3-hourly time steps.
2.1 Vegetation characteristics
In CASA, the fraction of absorbed photosynthetically active
radiation (fAPAR) is used to estimate net primary production
(NPP), fractional tree cover (FTC) is used in the allocation
of NPP between living carbon pools, and land cover (LC)
is used to set turnover rates for stems and leaves, applying
emission factors, and for categorizing fire carbon emissions
into various fire types.
We calculated fAPAR based on the Global Inventory Mod-
eling and Mapping Studies (GIMMS) normalized difference
vegetation index (NDVI) version 3g (Pinzon and Tucker,
2014) and relations established by Los et al. (2000). This
dataset is derived from the Advanced Very High Resolution
Radiometer (AVHRR) sensor flying on board several satel-
lites. We capped fAPAR at 0.95, corresponding to an NDVI
value of 0.9. Data were not available for several remote is-
lands, including Hawaii and Fiji, and we do not report emis-
sions for these locations.
FTC was derived by aggregating the annual MODIS
MOD44B vegetation continuous fields (250 m, V051;
Hansen et al., 2005) to 0.25
◦
. In order to provide consis-
tency over the full time period, we used the last year available
(2013) and increased FTC in prior years using the fire-driven
deforestation rates. These fire-driven deforestation rates were
based on the amount of burned area within tropical forests at
an annual time step. We used land cover maps from the an-
nual MODIS MCD12C1 land cover type product and Univer-
sity of Maryland (UMD) classification scheme (Friedl et al.,
2010). The climate modeling grid (CMG, 0.05
◦
) dataset was
resampled to 0.25
◦
based on the most abundant land cover
type. This dataset was available for 2001–2012; data from
2001 were applied to earlier years in the time series, and 2012
land cover data were used for years after 2012.
2.2 Meteorological datasets
We now use air temperature (t2m), soil moisture (swvl), and
solar radiation (ssrd) from the ERA-Interim dataset (Dee et
al., 2011) produced by the European Centre for Medium-
Range Weather Forecasts (ECMWF). We calculated the
monthly mean for all datasets and regridded the 0.75
◦
dataset
to our 0.25
◦
resolution without interpolation.
These datasets are somewhat different from inputs for
earlier GFED versions but are now internally consis-
tent. Interannual and seasonal variability was relatively
similar to datasets previously used in GFED, and these
variations have the largest impact on our calculations.
The use of soil moisture is new; previously, we used a
bucket model based on rainfall and potential evapora-
tion to calculate the wetness of soils, a key input dataset
for calculating heterotrophic respiration (R
h
) rates and
combustion completeness (see Sect. 3). Soil moisture is
now transformed to a soil moisture index (SMI) based on
soil-type-specific permanent wilting point (PWP) and field
capacity (FC) values as described in http://www.ecmwf.
int/en/forecasts/documentation-and-support/evolution-ifs/
cycles/change-soil-hydrology-scheme-ifs-cycle and is
capped at 1. This was done for all four different soil layers
(0–7, 8–28, 29–100, 101–255 cm). The SMI for the 0–7 cm
layer replaced the scalar used previously for combustion
completeness. The average SMI of the top two layers was
used to down-regulate NPP in herbaceous vegetation in
the light use efficiency model when moisture was limiting,
whereas the average of the top four layers was used for
NPP in woody vegetation. The average SMI for the upper
two layers was also used to represent the influence of soil
moisture on the abiotic scalar regulating rates of R
h
. Finally,
the average SMI of all layers was used in the allocation of
assimilated carbon to above- and belowground pools (see
Sect. 3).
2.3 Fire processes
We derived burned area (both mapped burned area and ac-
tive fire detections scaled to burned area) and metrics that
can be used to assess fire-induced tree mortality and combus-
tion completeness from satellite. Our burned area time series
is based on MODIS data for the August 2000 onwards pe-
riod (the “MODIS era”) and based on other sensors before
that period. In Sect. 2.3.1 we briefly describe the MODIS
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700 G. R. van der Werf et al.: Global fire emissions estimates during 1997–2016
burned area data for which a more detailed description is de-
scribed in Giglio et al. (2013). In Sect. 2.3.2 we then explain
how the small fire burned area estimates for the MODIS era
were derived based on Randerson et al. (2012). This is the
GFED4s burned area time series and complemented with
other sensors to compute the full 1997–2016 time period
dataset (Sect. 2.3.3).
2.3.1 Burned area from MODIS
For the MODIS era we used the MODIS Collection 5.1
MCD64A1 burned area product (Giglio et al., 2013).
Compared with Collection 5 and earlier versions of the
MCD64A1, the Collection 5.1 product reduces the uninten-
tional removal of small burns and eliminates some systematic
omission errors (Giglio et al., 2013). The MCD64A1 prod-
uct maps daily burned area at 500 m spatial resolution; these
data are then aggregated to a 0.25
◦
grid (both monthly and
daily) to produce the MODIS-era GFED4 burned area prod-
uct (Fig. 1a).
2.3.2 Small fire burned area during the MODIS era
In the MODIS era, we combined 500 m burned area (see
above), 1 km thermal anomalies (active fires) from Terra and
Aqua MODIS, and 500 m surface reflectance observations to
statistically estimate burned area associated with small fires,
BA
sf
, in each 0.25
◦
grid cell ( i), month (t), and aggregated
vegetation type (v):
BA
sf
(
i, t, v
)
= FC
out
(
i, t, v
)
× α
r, s, v, y
× γ
r, s, v, y
, (1)
where FC
out
is the number of active fire pixels outside of
the perimeter of the MCD64A1 burned area, α is a ratio of
burned area to active fires within MCD64A1 burned areas,
and γ is a correction factor derived by comparing difference
normalized burned area (dNBR) of active fires observed out-
side (dNBR
out
) and inside (dNBR
in
) of MCD64A1 burned
areas with unburned control areas (dNBR
control
; see Eq. 4 of
Randerson et al., 2012). α and γ scalars were estimated each
year (y), as a function of region (r), seasonal interval (s),
and aggregated vegetation type (v). Our method was similar
to that described in Randerson et al. (2012), but with several
important modifications to each of the three factors on the
right-hand side of Eq. (1) as described below.
First, we used the MCD64A1 product from Collection
5.1, replacing Collection 5 that was used in Randerson
et al. (2012). Second, instead of using a single source of
level 3 composited thermal anomaly/fire product from Terra
(MOD14A1), here we used individual active fire detec-
tions from both Terra and Aqua. Third, to improve geolo-
cation accuracies, we used the MODIS fire location product
(MCD14ML) instead of the gridded composite fire product
(MOD14A1). To further reduce geolocation uncertainties,
we only retained active fire detections with small or moderate
Figure 1. Average burned area over 2003–2016 from (a) MODIS
surface reflectance imagery (MCD64A1) and (b) small fire burned
area. Panel (c) shows the small fire percentage of total burned area.
scan angles (equal to or less than 0.5 radians). This thresh-
old was somewhat arbitrary and future research is required
to identify how a balance between sample size and area of
view is best achieved. Even with the above adjustments to
improve georegistration, some remaining resampling error
was introduced in the process of projecting the variable-
size MODIS fire pixels onto the 500 m sinusoidal grid on
which the MCD64A1 burned area product is generated. To
partially correct this known bias, we applied region-specific
factors ranging from 0.88 in Africa north of the Equator to
1.12 for temperate and boreal Asia. These correction factors,
which were derived using a rigorous model of the sample-
dependent MODIS pixel shape and size, partially compen-
sated for the simplified, fixed 1 km radius initially used to
determine whether an active fire pixel was co-located (inside)
or outside of the MCD64A1 burn area pixels. Finally, to esti-
mate dNBR for active fires inside of MCD64A1 burned area,
we only used active fire detections for which each of the four
overlapping 500 m pixels were classified as burned. This was
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G. R. van der Werf et al.: Global fire emissions estimates during 1997–2016 701
0.00
0.08
0.16
(a) NHAF
Original
(b) CEAS
Original
-0.5 0 0.5 1
dNBR
0.00
0.08
0.16
Normalized pdf
(c) NHAF
Modified
-0.5 0 0.5 1
dNBR
(d) CEAS
Modified
Figure 2. The distribution of difference normalized burn ra-
tio (dNBR) for active fires detected within burned areas from
MCD64A1 (red), outside of burned areas (orange), and for control
areas (blue) within Northern Hemisphere Africa (NHAF) and Cen-
tral Asia (CEAS). The distributions, generated using observations in
2001–2012, were constructed during the peak fire month for each
region. The improved approach (see Sect. 2.3.2 for details) com-
pressed the distributions in unburned control areas and increased
the separation between the three categories.
a stricter criterion than in Randerson et al. (2012) that in-
creases dNBR
in
and its separation from dNBR
out
and other
areas used as controls (Fig. 2).
It was not possible to apply the same constraint in the cal-
culation of dNBR
out
, so this adjustment usually had the effect
of lowering γ . We note that dNBR
out
in particular is strongly
affected by resampling error; thus, the individual γ correc-
tion factors are in turn also influenced by resampling error.
The net effect is to limit the range of values that may be at-
tained by γ , in a sense leaving an “imprint” of resampling
error on the resulting small fire burned area estimates. This
imprint is an unavoidable outcome of using relatively coarse
1 km and 500 m gridded time series data to track small, sub-
pixel fires. At the same time, we raised the filtering standard
for control pixels (Eq. 4 of Randerson et al., 2012) so that
pixels within a 1 km buffer area of active fire detections by
either Terra or Aqua MODIS were excluded in the calcula-
tion of dNBR for non-burning areas (dNBR
control
). During
the regional aggregation of dNBR, we excluded 500 m pix-
els that were marked as “water” by MODIS land cover type
product (MCD12Q1).
During the time both Terra and Aqua fire detections
were available (January 2003–December 2016), we calcu-
lated BA
sf
separately for Terra (MOD) and Aqua (MYD).
BA
sf
was then estimated as the arithmetic mean of the two
estimates. A climatological ratio of BA
sf−MYD
/ BA
sf−MOD
was used to estimate BA
sf−MYD
during periods when Aqua
MODIS observations were not available (August 2000–
Figure 3. Map of the 14 regions used in this study, after Giglio et
al. (2006) and van der Werf et al. (2006).
December 2002). The final GFED4s burned area during the
MODIS era was the sum of GFED4 burned area (Sect. 2.3.1;
Fig. 1a) and burned area from small fires (BA
sf
, Fig. 1b).
As expected, burned area from small fires is more preva-
lent in areas with extensive agriculture and in other human-
dominated landscapes (Fig. 1c).
2.3.3 Estimating burned area prior to the MODIS era
(1997–2000) for GFED4s
For the pre-MODIS era, we used monthly active fire data
from the Visible and Infrared Scanner (VIRS) aboard the
Tropical Rainfall Measuring Mission (TRMM) or the Along
Track Scanning Radiometers (ATSR) on board multiple plat-
forms to estimate burned area. Two steps of optimization
were used to derive total burned area, starting with the
GFED4s product described above. The first step was to de-
velop a relationship between aggregated active fires (from
VIRS or ATSR) and burned area during the MODIS era in
each GFED region, with the aim of using this relationship to
estimate regional burned area during 1997–2000. The second
step involved distributing the aggregated burned area within
each region to individual 0.25
◦
grid cells.
To calculate the regional sum of BA during the pre-
MODIS era, we first performed regression analyses be-
tween ATSR or VIRS active fires and the regional sum of
GFED4s burned area during the MODIS era. We developed
linear regression models for each GFED region (Fig. 3),
for each month, and for each of the five aggregated veg-
etation classes (see Randerson et al., 2012, for a descrip-
tion of the vegetation classes). When ATSR and VIRS active
fire data were both available (January 1998–July 2000), the
highest performing regression from these two datasets was
used to estimate the burned area in each region. Among the
14 continental-scale regions, we used VIRS data in Africa,
Southeast Asia, Equatorial Asia, and Australia and ATSR
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