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Title
Carbon emissions from fires in tropical and subtropical ecosystems
Permalink
https://escholarship.org/uc/item/6gr3t4fb
Journal
Global Change Biology, 9(4)
ISSN
1354-1013
Authors
Van der Werf, GR
Randerson, JT
Collatz, GJ
et al.
Publication Date
2003-04-01
DOI
10.1046/j.1365-2486.2003.00604.x
Copyright Information
This work is made available under the terms of a Creative Commons Attribution License,
availalbe at https://creativecommons.org/licenses/by/4.0/
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Carbon emissions from fires in tropical and subtropical
ecosystems
GUIDO R. VAN DER WERF*,JAMES T. RANDERSON{,G. JAMES COLLATZ{ and
LOUIS GIGLIO§
*USDA-FAS, NASA Goddard Space Flight Center, Code 923, Greenbelt Road, Greenbelt, MD 20771, USA, {Divisions of Geological
and Planetary Sciences and Engineering and Applied Science, California Institute of Technology, Mail stop 100-23, Pasadena, CA
91125, USA, {NASA Goddard Space Flight Center, Code 923, Greenbelt Road, Greenbelt, MD 20771, USA, §Science Systems and
Applications Inc., NASA Goddard Space Flight Center, Code 923, Greenbelt Road, Greenbelt, MD 20771 USA
Abstract
Global carbon emissions from fires are difficult to quantify and have the potential to
influence interannual variability and long-term trends in atmospheric CO
2
concentra-
tions. We used 4 years of Tropical Rainfall Measuring Mission (TRMM) Visible and
Infrared Scanner (VIRS) satellite data and a biogeochemical model to assess spatial and
temporal variability of carbon emissions from tropical fires. The TRMM satellite data
extended between 3888N and 3888S and covered the period from 1998 to 2001. A relationship
between TRMM fire counts and burned area was derived using estimates of burned area
from other satellite fire products in Africa and Australia and reported burned areas from
the United States. We modified the Carnegie-Ames-Stanford-Approach (CASA) biogeo-
chemical model to account for both direct combustion losses and the decomposition from
fire-induced mortality, using both TRMM and Sea-viewing Wide Field of view Sensor
(SeaWiFS) satellite data as model drivers. Over the 1998±2001 period, we estimated that
the sum of carbon emissions from tropical fires and fuel wood use was 2.6 Pg C yr
21
.An
additional flux of 1.2 Pg C yr
21
was released indirectly, as a result of decomposition of
vegetation killed by fire but not combusted. The sum of direct and indirect carbon losses
from fires represented 9% of tropical and subtropical net primary production (NPP). We
found that including fire processes in the tropics substantially alters the seasonal cycle of
net biome production by shifting carbon losses to months with low soil moisture and low
rates of soil microbial respiration. Consequently, accounting for fires increases growing
season net flux by 12% between 3888N and 3888S, with the greatest effect occurring in
highly productive savanna regions.
Keywords: biomass burning, fire ecology, global carbon cycle, net primary production, TRMM
Received 9 August 2002; revised version received and accepted 10 July 2002
Introduction
Biomass burning and wildfires play an important role in
the cycling of carbon in many ecosystems and represent a
significant source of aerosols and trace gas emissions,
particularly in tropical and boreal regions (Crutzen &
Andreae, 1990; Hao et al., 1990; Kasischke et al., 1995).
Within the tropics, fire return times (FRTs) vary widely,
and depend on climate, vegetation type, and human
activity (Menaut et al., 1991). Some savannas burn every
year, while moist tropical forests rarely burn. The large
fires in Brazil and Indonesia during the 1997±1998 El
Nin
Ä
o-Southern Oscillation (ENSO) event demonstrate,
however, that under extreme climatic conditions even
closed canopy rainforests can burn extensively, especially
when promoted by humans (Cochrane et al., 1999;
Nepstad et al., 1999; Siegert et al., 2001).
Approaches used for estimating emissions from fires
have evolved over the last two decades to take advantage
of new satellite data products, improved GIS capabilities,
and more comprehensive biogeochemical models. Early
studies estimated carbon losses from fires on a continental
or global scale by developing inventories of aggregated
Correspondence: G. R. van der Werf, USDA-FAS, NASA God-
dard Space Flight Center, Code 923, Greenbelt Road, Greenbelt,
MD 20771, USA, fax 1 301 614 6695,
e-mail: guido@ltpmail.gsfc.nasa.gov
Global Change Biology (2003) 9, 547±562
ß 2003 Blackwell Publishing Ltd 547
fuel, combustion factors, and fire return times for different
biome types, and then extrapolating regional emissions
estimates using global vegetation maps. Initial estimates
of emissions from this inventory approach varied between
1.5 and 5.0 Pg C yr
1
(Seiler & Crutzen, 1980; Crutzen &
Andreae, 1990; Hao et al., 1990; Hao & Liu, 1994).
The use of satellite observations has generally led to a
decrease in emission estimates, and to greater recognition
of the importance of spatial and temporal variability in
fire frequency. The two primary ways of assessing
burned area with remote sensing are (1) indirectly by
detecting active fires (hot spots or smoke) and converting
these to burned areas, and (2) directly by measuring fire
scars, based on changes in surface reflectance (or vegeta-
tion indices) before and after fire (Eva & Lambin, 1998).
Sometimes the two approaches are combined, with hot
spot algorithms applied over large regions, and validated
with burned scar information from high-resolution satel-
lite imagery from a few specific locations. For example,
Setzer & Pereira (1991) assessed burned area in the
Amazon using Advanced Very High Resolution Radio-
meter (AVHRR) fire counts scaled to burned area derived
from Landsat/TM data. Using an indirect satellite
method, Scholes et al. (1996) showed that the inventory-
based methods overestimated burned areas when FRTs
from productive savanna and other ecosystems that burn
frequently were assumed to represent larger regions. In
the Scholes et al. (1996) analysis, satellite measurements
showed that fire frequency was reduced in many areas
that had bare patches, cities, and lower productivity eco-
systems. Using a direct satellite-based measurement of
fire scars, Barbosa et al. (1999a) obtained burned areas for
southern Africa that had considerable interannual vari-
ability, but with a mean comparable to the earlier esti-
mates from Scholes et al. (1996). During the 1980s in
Africa, they found that emissions in a high fire year
were double those in a low fire year.
While satellite data has vastly improved our capability
to assess heterogeneity in burned areas in non-forested
ecosystems, many remote sensing approaches have had
limited success in detecting fire in or under closed
canopy forests. Pereira et al. (1999) addressed the need
for accurately assessing burned areas in tropical forest
and other forested areas; although the area burned annu-
ally in tropical forest is much smaller than in savannas,
the amount of fuel is large so emissions from forested
areas can be significant. For example, Potter et al. (2001)
used fire counts scaled to satellite-derived deforestation
rates (indirect method) to obtain an emissions estimate of
0.71 Pg C yr
1
for the Legal Amazon, nine times higher
than the estimate for southern Africa by Scholes et al.
(1996). In comparing southern Africa and Amazon emis-
sions, it is important to note that less than 15% of the fires
detected in the Amazon occurred in the tropical forests
and that the fuel loads and combustion factors varied
considerably between the two studies.
While burned area estimates have improved with the
use of satellite data, large uncertainties remain. The fire
count approach using hot spot detection may miss fires
that occur mostly outside of the overpass time and
ground fires under closed canopy forests and woodlands.
Smoke and clouds may also interfere with the detection
of hot spots. More fundamentally, the nature of the rela-
tionship between the number of fires and their sizes may
be variable and other types of information may be neces-
sary to specify that relationship with useful accuracy.
Direct measurement of burn scars from satellites can
also miss small fires and understorey fires and may
have difficulty in attributing changes in surface reflect-
ance to burned area.
Some of the uncertainty in estimating large-scale emis-
sions using inventory methods or satellite based methods
comes from the difficulty of scaling local measurements
to larger regions such as grid cells in global models.
Many field campaigns focusing partly or completely on
biomass burning in the tropics [including the Southern
Africa Fire-Atmosphere Research Initiative (SAFARI,
Lindesay et al., 1992), and Experiment for Regional
Sources and Sinks of Oxidants (EXPRESSO, Delmas
et al., 1999)] have provided key information on combus-
tion factors and fuel loads. However, applying this infor-
mation to regional or continental scales remains difficult
because of limited information on the distribution of
fuels, and on the fire-induced mortality of woody vegeta-
tion in tropical regions.
Here we explore the effects of fire on carbon cycling in
the global tropics and subtropics (between 388Nand388S)
along a precipitation-driven vegetation gradient. In our
analysis, we partition the tropics into 6 classes based on
precipitation and tree cover. Within each vegetation class,
we then estimate the effects of fire on ecosystem processes,
including FRT, emissions, indirect losses (from fire-
induced mortality), herbivory (competing with fire as a
loss pathway), and fuel wood consumption. We use satel-
lite datasets from the Tropical Rainfall Measuring Mission
(TRMM) and Sea-viewing Wide Field of view Sensor (Sea-
WiFS) as inputs to a global biogeochemical model (CASA,
Carnegie-Ames-Stanford Approach) that accounts for the
effects of fire on carbon stocks, on fluxes from fires and
respiration and on net biome productivity. Other processes
considered include fuel wood consumption and herbivory.
The advantage of embedding fire emission processes
within a biogeochemical model is that we can compare
fire with other ecosystem processes in terms of their impact
on carbon fluxes. The disadvantage is that our global mod-
elling framework may misrepresent key regional scale pro-
cesses that affect emissions, including the relationship
between burned area and satellite fire index.
ß 2003 Blackwell Publishing Ltd, Global Change Biology, 9, 547±562
548 G. R. va n d e r WERF et al.
We obtain fire location and timing from the analysis of
hot spots detected by the Visible and Infrared Scanner
(VIRS) instrument on board the TRMM satellite platform
(Giglio et al., 2002) and compare these data with other
published estimates. We show that fires consume 6% of
annual NPP within 388N and 388S, substantially alter the
seasonal cycle of net biome production, and have the
greatest impact on carbon fluxes in biomes with inter-
mediate levels of moisture.
Methods
We calculated the amount of carbon combusted during
fires, C at each grid cell, and month (t) using the following
relationship (modified from Seiler & Crutzen, 1980):
C
t
A
t
f
c
X
d
E
d
D
t; d
X
b
E
b
M
b
B
t; b
"#
f
c
E
F
F 1
where A is the area burned, D is the dead plant material
(detritus), B is the living plant material (biomass), f
c
is the
fraction of carbon in the fuel (0.45), M is the mortality
factor (which only operates on biomass), and E the com-
bustion factor (the fraction of the fuel that was com-
busted). E, M, D and B vary among the different detritus
(d) and biomass (b) pools (i.e. surface litter, coarse woody
debris, living leaf, and living stem pools). The last term in
Eqn (1) accounts for the amount (F) and combustion effi-
ciency (E
F
, set to 1.0) of fuel wood. We obtained A
t
by
deriving a relationship between TRMM satellite fire
counts and burned area using regionally based estimates
(described below). The formation of black carbon was not
explicitly treated in our model because it is relatively inert
on the seasonal and interannual timescales that were the
focus of this study. We solved Eqn (1) using the CASA
biogeochemical model modified for fires and fuel wood
consumption. E was estimated from plant tissue depend-
ent combustion factors, and the fraction of wood, leaves,
and detritus in a grid cell. F was estimated from maps of
population density and per capita estimates of consump-
tion (described below). With CASA, we were able to use
other ecological and satellite data to constrain NPP, allo-
cation of NPP, and decomposition. CASA allowed us also
to track the decomposition of biomass from fire-induced
mortality (hereafter referred to as an indirect loss).
TRMM fire counts and burned area
The TRMM satellite was launched in November 1997 and
is currently projected to collect data for about 8 years. The
orbit of the TRMM satellite is inclined at 358 so that the
sensors on board of the satellite observe the earth be-
tween 388N and 388S (Kummerow et al., 1998). The fre-
quency of overpasses at any location within this region
depends on the latitude; at equatorial latitudes two ob-
servations are made every other day, at higher latitudes
two to three observations are made daily (Giglio et al.,
2002). Local overpass time progresses over the 24-h di-
urnal cycle roughly once per month. The TRMM satellite
carries the Visible and Infrared Scanner (VIRS), an im-
aging radiometer with five channels between 0.6 and
12 mm, and a nadir spatial resolution of 2.1 km. An
existing AVHRR algorithm was modified to detect fires
with VIRS, based on the brightness temperature of
channel 3 (3.75 mm) and channel 4 (10.8 mm) and the
surface reflectance of channel 2 (1.6 mm); the detected
fire pixels are then used to produce a monthly, coarse-
resolution (0.58 0.58) fire product that includes correc-
tions for missing data and the variable number of
observations made at different latitudes (Giglio et al.,
2002, available online at http://daac.gsfc.nasa.gov/CAM-
PAIGN_DOCS/hydrology/TRMM_VIRS_Fire.shtml). One
advantage of TRMM's orbital characteristics over
platforms in sun-synchronous orbits is that, because the
TRMM overpass time varies on a daily basis, diurnal
patterns of burning may be observed (Giglio et al.,
2000). Sun-synchronous platforms on the other hand,
which include most polar orbiters, restrict observations
to the same local times each day.
To obtain a relationship between fire counts and burned
area, we used burned area estimates derived from the
MODIS (MODerate resolution Imaging Spectrometer) in-
strument, on board the NASA Terra satellite, using a newly
developed algorithm which is based on detecting changes
in vegetation indexes at 500-meter resolution. We also used
burned area estimates provided by the National Intera-
gency Coordination Center (NICC), which gives monthly
burned areas for regions within the US (available online:
http://www.cidi.org/wildfire/index.html). The TRMM
fire counts capture seasonal variability of burned area
within the state of California (Fig. 1).
We made the relationship between burned area and
fire counts a linear function of tree cover in regions with
less than 40% tree cover based on a comparison of TRMM
fire counts with MODIS burned area (Fig. 2). In this
analysis we used four MODIS tiles, each 108 108
from Australia (2), West Africa (1), and southern Africa
(1). Within each tile, the 500-meter resolution MODIS
burned area was aggregated to 0.58 0.58 resolution and
compared with the TRMM fire count product, which is
also aggregated to 0.58 0.58 resolution. The ratio bet-
ween burned area and fire counts was then related to
the percentage tree cover (DeFries et al., 1999), which
was the average value for the corresponding 0.58 0.58
grid cell. As shown in Fig. 2 the burned area per fire
count decreases with increasing percentage tree cover.
One possible reason for this trend is the difference in
fire spreading rate: a fire in a grassland spreads quickly
ß 2003 Blackwell Publishing Ltd, Global Change Biology, 9, 547±562
CARBON EMISSIONS FROM TROPICAL FIRES 549
and burns for a short period. This results in a lower
detection probability of hot spots and thus a higher
burned area per detected fire count. Another explanation
may be that the drop in vegetation index in a grassland
may be bigger than the drop in a more wooded area; the
trees, which may cause most of the vegetation index
signal may be unaffected by the fire.
To serve as input data in our model, the 0.58 0.58
TRMM fire counts were binned to 18 18 and the burned
area was then calculated using the tree cover dependant
fire count to burned area ratio as presented in Fig. 3.
Further testing, refinement and validation of the algo-
rithm for transforming TRMM fire counts into burned
area is underway (Giglio et al. in preparation) and will
likely lead to improved accuracy in fire emission esti-
mates. One factor likely to be important in estimating
emissions that is not explicitly addressed in this paper is
the actual area burned within the boundaries of an identi-
fied fire scar. Within a fire perimeter, patches of land may
undergo varying degrees of combustion completeness as a
result of variability in moisture, fuel load, and topo-
graphy. For the burned area calculation we assumed that
a MODIS 500 meter grid cell was either completely burned
or was completely unburned. In some areas, this may have
led to an overestimation of actual area burned. This bias
may have been compensated for in other areas where the
fire scar was not large enough to be detected.
We investigated the sensitivity of predicted burned
area and emissions to our assumptions about the rela-
tionship between burned area per fire count and percent-
age tree cover with two additional scenarios (Fig. 3). The
sensitivity of our results to a possible underestimation of
burned area in closed woody canopies was assessed by
prescribing burned area per fire pixel as independent of
amount of tree cover when this value exceeded 35%
(scenario 1 in Fig. 3). In a second test (scenario 2), we
scaled our original function so that the total area burned
for Africa matched the total area burned from the mean
of the upper and lower scenarios given by Barbosa et al.
(1999b) and we applied this relationship to the entire
model domain. In these alternative scenarios we
6
5
4
3
2
1
0
CIDI burned area (ha month
−1
)
x10
4
0
20
40
60
80
100
120
TRMM fire counts month
−
1
Jan. '98 Jul. '98 Jan. '99 Jul. '99 Jan. '00 Jul. '00 Jan. '01 Jul. '01
= TRMM fire counts
= CIDI burned area
+
Fig. 1 Burned area as reported by NICC
compared with TRMM fire counts for the
southern part of California.
20 000
18 000
16 000
14 000
12 000
10 000
8000
6000
4000
2000
0
0 5 10 15 20 25 30 35 40 45
Burned area per fire count (ha (fire count
−1
))
Percentage tree cover
90 perc. >
75 perc. >
mean >
median >
25 perc. >
10 perc. >
Fig. 2 The relationship between burned area and TRMM fire
counts depends on tree cover. Burned area is derived from a
change in MODIS vegetation index. Both TRMM fire counts
and MODIS burned area were aggregated to 0.58 0.58 for this
comparison. Percentage tree cover (average value for the corres-
ponding 0.58 0.58 grid cells) from DeFries et al. (1999). Perc. is
percentile.
ß 2003 Blackwell Publishing Ltd, Global Change Biology, 9, 547±562
550 G. R. va n d e r WERF et al.