ESD - Dynamic savanna burning emission factors based on satellite data using a machine learning approach

ESD - Dynamic savanna burning emission factors based on satellite data using a machine learning approach
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10 Oct 2023
Research article |
10 Oct 2023
Dynamic savanna burning emission factors based on satellite data using a machine learning approach
Dynamic savanna burning emission factors based on satellite data using a machine learning approach
Dynamic savanna burning emission factors based on satellite data using a machine learning approach
Roland Vernooij et al.
Roland Vernooij
Tom Eames
Jeremy Russell-Smith
Cameron Yates
Robin Beatty
Jay Evans
Andrew Edwards
Natasha Ribeiro
Martin Wooster
Tercia Strydom
Marcos Vinicius Giongo
Marco Assis Borges
Máximo Menezes Costa
Ana Carolina Sena Barradas
Dave van Wees
and
Guido R. Van der Werf
Roland Vernooij
CORRESPONDING AUTHOR
r.vernooij@vu.nl
Department of Earth Sciences, Faculty of Science, Vrije Universiteit
Amsterdam, Amsterdam, the Netherlands
Tom Eames
Department of Earth Sciences, Faculty of Science, Vrije Universiteit
Amsterdam, Amsterdam, the Netherlands
Jeremy Russell-Smith
Darwin Centre for Bushfire Research, Charles Darwin University,
Darwin, 0909 Northern Territory, Australia
International Savanna Fire Management Initiative (ISFMI), Level 4, 346 Kent Street, Sydney, 2000 New South Wales, Australia
Cameron Yates
Darwin Centre for Bushfire Research, Charles Darwin University,
Darwin, 0909 Northern Territory, Australia
International Savanna Fire Management Initiative (ISFMI), Level 4, 346 Kent Street, Sydney, 2000 New South Wales, Australia
Robin Beatty
International Savanna Fire Management Initiative (ISFMI), Level 4, 346 Kent Street, Sydney, 2000 New South Wales, Australia
321 Fire, Praia Do Tofo, Inhambane, 1300, Mozambique
Jay Evans
Darwin Centre for Bushfire Research, Charles Darwin University,
Darwin, 0909 Northern Territory, Australia
International Savanna Fire Management Initiative (ISFMI), Level 4, 346 Kent Street, Sydney, 2000 New South Wales, Australia
Andrew Edwards
Darwin Centre for Bushfire Research, Charles Darwin University,
Darwin, 0909 Northern Territory, Australia
International Savanna Fire Management Initiative (ISFMI), Level 4, 346 Kent Street, Sydney, 2000 New South Wales, Australia
Natasha Ribeiro
Faculty of Agronomy and Forest Engineering, Eduardo Mondlane
University, Maputo, Mozambique
Martin Wooster
Environmental Monitoring and Modelling Research Group, Department of Geography, King's College London, London, UK
National Centre for Earth Observation (NERC), Leicester, UK
Tercia Strydom
South African National Parks (SANParks), Scientific Services, Skukuza, South Africa
Marcos Vinicius Giongo
Center for Environmental Monitoring and Fire Management (CEMAF), Federal
University of Tocantins, Gurupi, Brazil
Marco Assis Borges
Chico Mendes institute for Conservation of Biodiversity (ICMBio), Rio
da Conceição, Brazil
Máximo Menezes Costa
Chico Mendes institute for Conservation of Biodiversity (ICMBio), Rio
da Conceição, Brazil
Ana Carolina Sena Barradas
Chico Mendes institute for Conservation of Biodiversity (ICMBio), Rio
da Conceição, Brazil
Dave van Wees
Department of Earth Sciences, Faculty of Science, Vrije Universiteit
Amsterdam, Amsterdam, the Netherlands
Guido R. Van der Werf
Department of Earth Sciences, Faculty of Science, Vrije Universiteit
Amsterdam, Amsterdam, the Netherlands
Abstract
Landscape fires, predominantly found in the frequently burning
global savannas, are a substantial source of greenhouse gases and aerosols.
The impact of these fires on atmospheric composition is partially
determined by the chemical breakup of the constituents of the fuel into
individual emitted chemical species, which is described by emission factors
(EFs). These EFs are known to be dependent on, amongst other things, the
type of fuel consumed, the moisture content of the fuel, and the
meteorological conditions during the fire, indicating that savanna EFs are
temporally and spatially dynamic. Global emission inventories, however, rely
on static biome-averaged EFs, which makes them ill-suited for the estimation
of regional biomass burning (BB) emissions and for capturing the effects of
shifts in fire regimes. In this study we explore the main drivers of
EF variability within the savanna biome and assess which geospatial proxies
can be used to estimate dynamic EFs for global emission inventories. We made
over 4500 bag measurements of CO
, CO, CH
, and N
O EFs using
a UAS and also measured fuel parameters and fire-severity proxies during 129 individual fires. The measurements cover a
variety of savanna ecosystems under different seasonal conditions sampled
over the course of six fire seasons between 2017 and 2022. We complemented
our own data with EFs from 85 fires with locations and dates provided in the
literature. Based on the locations, dates, and times of the fires we retrieved
a variety of fuel, weather, and fire-severity proxies (i.e. possible
predictors) using globally available satellite and reanalysis data. We then
trained random forest (RF) regressors to estimate EFs for CO
, CO,
CH
, and N
O at a spatial resolution of 0.25
and a
monthly time step. Using these modelled EFs, we calculated their
spatiotemporal impact on BB emission estimates over the 2002–2016 period
using the Global Fire Emissions Database version 4 with small fires
(GFED4s). We found that the most important field indicators for the EFs of
CO
, CO, and CH
were tree cover density, fuel moisture content, and
the grass-to-litter ratio. The grass-to-litter ratio and the nitrogen-to-carbon ratio were important indicators for N
O EFs. RF models using
satellite observations performed well for the prediction of EF variability
in the measured fires with out-of-sample correlation coefficients between
0.80 and 0.99, reducing the error between measured and modelled EFs by
60 %–85 % compared to using the static biome average. Using dynamic EFs,
total global savanna emission estimates for 2002–2016 were 1.8 % higher
for CO, while CO
, CH
, and N
O emissions were, respectively,
0.2 %, 5 %, and 18 % lower compared to GFED4s. On a regional scale we
found a spatial redistribution compared to GFED4s with higher CO, CH
and N
O EFs in mesic regions and lower ones in xeric regions. Over the
course of the fire season, drying resulted in gradually lower EFs of these
species. Relatively speaking, the trend was stronger in open savannas than
in woodlands, where towards the end of the fire season they increased again.
Contrary to the minor impact on annual average savanna fire emissions, the
model predicts localized deviations from static averages of the EFs of CO,
CH
, and N
O exceeding 60 % under seasonal conditions.
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Vernooij, R., Eames, T., Russell-Smith, J., Yates, C., Beatty, R., Evans, J., Edwards, A., Ribeiro, N., Wooster, M., Strydom, T., Giongo, M. V., Borges, M. A., Menezes Costa, M., Barradas, A. C. S., van Wees, D., and Van der Werf, G. R.: Dynamic savanna burning emission factors based on satellite data using a machine learning approach, Earth Syst. Dynam., 14, 1039–1064, https://doi.org/10.5194/esd-14-1039-2023, 2023.
Received: 15 Feb 2023
Discussion started: 06 Mar 2023
Revised: 16 Aug 2023
Accepted: 19 Aug 2023
Published: 10 Oct 2023
Introduction
Landscape fires emit substantial amounts of gases, including the greenhouse
gases CO
, CH
, and N
O, which affect the Earth's climate. To
quantify the impact of these fire emissions and track the role of fire in
the biogeochemical system, fire emission inventories like the Global Fire
Emissions Database
(GFED, van der Werf et al., 2017) and the Global Fire Assimilation System (GFAS, Kaiser
et al., 2012) use satellite observations to monitor global landscape fires.
They estimate that savannas account
for roughly 60 % of the gross (i.e. not considering regrowth) global
carbon emissions from biomass burning (BB) due to their high burning frequency. The impact of fire emissions on
atmospheric radiative forcing is strongly dependent on the partitioning of
consumed biomass into individual emission species, which in part depends on
the combustion efficiency (often simplified as the CO
emissions
divided by the combined CO
and CO emissions, referred to as the
modified combustion efficiency or MCE) during the fire. For this
partitioning, inventories currently use biome-specific emission factors
(EFs), expressed in grams of a molecule emitted for each kilogram of dry
matter (DM) burned. However, measurements from both laboratory and landscape
fires indicate that important drivers of fire intensity and combustion
efficiency, e.g. the moisture content of the fuel
(Chen et al., 2010) and the
curing state of grasses (Korontzi
et al., 2003), are seasonal and that therefore EFs are both spatially and
temporally dynamic.
Earlier studies targeted a most representative EF for individual biomes.
This single value was based on averaging numerous usually randomly sampled
fires mostly from aircraft at the peak of fire season in the most active
areas. These sophisticated measurements revealed much about the species that
are emitted from fires, but there is little opportunity for detailed
measurements of the actual fire in this approach. Although they quantify
overall variability
(as
summarized in, for example, Akagi et al., 2011, and Andreae, 2019), to date we
cannot quantify how specific factors such as moisture content impact EFs
(van Leeuwen and van der Werf, 2011).
Thus, current global inventories are not designed to quantify any variation
in emissions at local or temporal scales. This results, for example, in the
same EFs being assumed for a savanna woodland and an open grassland. Using
historic averages also means that EFs do not dynamically change, while fire
regimes, weather patterns, and environmental burning conditions can shift as
a result of climate change or human interaction. One additional field of
research that requires a better understanding of spatiotemporal dynamics
involves fire management strategies in savannas to reduce fire-related
greenhouse gas emissions, with the aim of mitigating climate change. Over
the past decade, significant efforts have been directed at shifting the
temporal patterns of savanna fire regimes in order to make them more
sustainable and to abate greenhouse gas emissions (e.g.
Russell-Smith
et al., 2013; Schmidt et al., 2018). EFs used for the accreditation of such
projects currently assume a dichotomy of early and late dry season
averages determined by a cut-off date. However, as discussed by
Laris (2021), the fuel and meteorological conditions thought to
drive EFs vary more gradually over the season and are subjected to
substantial interannual and spatial variability. Incorporating
spatiotemporal variability in inventories makes emission inventories more
dynamic and better equipped for assessing seasonal fluctuations.
Over the past 6 years (2017–2022), a series of savanna burning experiments
measuring EFs using UASs has resulted in a large
amount of new data with broad spatiotemporal coverage (e.g. Vernooij et al.,
2021, 2022b;
Russell-Smith
et al., 2021). While lacking the extensive species coverage and precision of
instruments found in advanced aircraft campaigns, these UAS measurements can
effectively focus on particular vegetation types, facilitating the
connection between ground conditions and emissions. In this study we
describe the variability in over 4500 individual bag-measured EFs of
CO
, CO, CH
, and N
O covering 129 fires. Combined with
the EFs from fires already reported in literature, these new EF measurements
allow us to study the variability in BB EFs in more detail by using
unexplored non-linear statistical methods like decision-tree-based machine
learning algorithms. The non-linear nature of these models makes them
suitable to quantify distinctive dynamics under different conditions in
complex natural processes such as landscape fires. This approach does
require large datasets for training and
validation, which were not available
until now. We first determine the dominant drivers of EF variability based
on field measurements and then apply random forest regression methods to
estimate dynamic EFs for the abovementioned species using globally available
satellite data and geospatial reanalysis data. Depending on the application,
these dynamic EFs can be computed at various spatiotemporal resolutions,
limited by the resolution of the underlying features (i.e. starting from
500 m and with hourly time steps). Finally, we use GFED4s, in combination
with the dynamic EFs – computed on a monthly basis at 0.25
– to
estimate the emission dynamics over the 2002–2016 period.
Methods
The main objectives of this study are (1) to identify the drivers of EF
variability in the savanna biome and (2) to implement this variability into
global emission inventories and assess the implications of using dynamic EFs
instead of static ones. The first objective requires a large dataset of EFs
and a thorough assessment of a wide range of possible drivers, including
direct field measurements of vegetation composition, meteorological
conditions, and fire intensity dynamics. This is described in Sect. 2.1.
The second objective requires a more globalized approach that allows BB EFs
to be predicted based on satellite and reanalysis data with broad
spatiotemporal coverage; see Sect. 2.2 and 2.3.
2.1
Field measurements
2.1.1
Measurement setup
Using a UAS-mounted sampling system we measured BB EFs of CO
, CO,
CH
, and N
O in fresh smoke during savanna fires following the
methodology described by
Vernooij
et al. (2021, 2022b). Fires were lit with the aim of being representative of
early dry season (EDS, often prescribed) fires and late dry season (LDS)
non-prescribed fires. Although some backing fires were sampled during the
initial phase of the fires, the majority of samples were obtained from the
faster heading fires, which consumed most of the biomass. Fire sizes
generally ranged between 2 to 10 ha based on UAS drone imagery
described by Eames et al. (2021), with the exception of some fires that would not light and conversely
some fires that burned several hundred hectares. In the EDS, fire size was
primarily limited by environmental conditions, and fires ceased burning as
humidity increased overnight, whereas in the LDS, fire size was confined by
low-fuel areas like burn scars, roads, and prepared fire breaks. Particularly
in the LDS, this means that a limited fire size does not necessarily indicate
limited fire intensity. Emissions were sampled at altitudes between 5–50 m
depending on flame height for a duration of 35 s, resulting in 0.7 L per gas sample. On average, we took 35 samples per fire. The sampling
methodology involved taking samples from a fire passing a certain point
– while correcting for wind direction and severity – until no more visual
smoke passed the drone anymore. From earlier work
(Vernooij
et al., 2022b), where we compared the average of these measurements to
results using continuous measurements taken at a mast, we have some
confidence in the fidelity of this approach. Within 12 h, the samples
were measured using cavity ring-down spectroscopy for atmospheric mixing
ratios of CO
and CH
(Los Gatos Research, microportable gas
analyser) and CO and N
O (Aeris Technologies, Pico series). We
calculated EFs using the carbon mass balance method (Ward
and Radke, 1993) and using ground measurements of the weighted-average (WA)
carbon content of the combusted fuel and emissions of CO
, CO,
CH
, and N
O. The carbon emitted in non-methane hydrocarbons (NMHCs)
and particulates was estimated based on the linear relations with EFs of CO
(for particulates) and CH
(for NMHCs), which were derived from
previous savanna literature
(Andreae,
2019; Vernooij et al., 2022b). Based on the N
O measurements, we calculated its EF using CO
as the co-emitted carbonaceous reference species.
2.1.2
Sample coverage and literature studies
The dataset obtained using the abovementioned UAS methodology includes both
previously published data collected in Mozambique, South Africa, and Brazil
(Russell-Smith
et al., 2021; Vernooij et al., 2021, 2022b) and new measurements from xeric
and mesic savannas in Botswana, Zambia, and Australia measured during the
fire seasons of 2021 and 2022. The measurements cover three continents and
the full length of the dry season, ranging from early dry season (EDS)
campaigns in which fuel conditions sometimes prevented successful ignition
to late dry season (LDS) campaigns with high-intensity fires. The 129 fires
that we measured using the abovementioned methodology were supplemented with
85 previous savanna fires for which EFs of the measured species were
reported in the updated database by Andreae (2019). This literature
compilation only includes samples taken within minutes after emission to
avoid significant chemical changes during atmospheric ageing. For the
comparison with geospatial data, we only included fires for which the fire
date and coordinates were provided, a prerequisite to get relevant satellite
features. These criteria mean that laboratory studies, satellite studies
covering wider regions, and most aircraft campaigns were excluded. Figure 1
provides an overview of the UAS (red for previously published and orange for
our new measurements) and literature (blue) sample locations included in
the study.
Figure 1
Overview of sampling locations used for the analysis. The
previously published (red) and new (orange) UAS measurements and the
locations of the included literature studies of savanna fire emission
factors listed in Andreae (2019) (blue) are shown. The shaded green area shows the
distribution of savanna and grassland fires over the 2002–2016 period
according to GFED4s.
2.1.3
Fuel measurements
During more recent fieldwork campaigns, we measured not only EFs but also
other parameters including fuel characteristics and fire severity
indicators. Before the fire, we collected fuel load and fuel composition
from various classes
(e.g. grass, litter, coarse woody debris, shrubs, and
trees) and meteorological parameters. After the fire, we revisited the plots
and recorded the combustion completeness of various fuel classes and
fire intensity proxies (e.g. patchiness of the fire and scorch and char
heights) following the methodology outlined by Eames et al. (2021) and
Russell-Smith et al. (2020). Table 1 lists the individual UAS EF measurement
campaigns and whether fuel was collected following the abovementioned
methodology. Fires were lit on the windward side of the plot and generally
burned through two to six individual randomly scattered
50×10
m fuel
transects covering the fuel of a homogenous vegetation type and equal time since
the last fire. We took the average of the affected fuel transects as the
fire-averaged value to correspond to the fire-averaged EF, which is
calculated over all the bag samples taken from that specific fire. Although
the measurements were linearly correlated using the calibration bags for the
individual fires, the standard deviations between the calibration samples
were 2.58 % for CO
, 7.06 % for CO, 2.32 % for CH
, and
4.04 % for N
O, indicating larger measurement uncertainties than
reported by the manufacturers, which possibly arises from the bag
methodology. The difference in the mean calibration value compared to the
calibration gases was
−4.75
% for CO
−1.32
% for CO,
−3.97
for CH
, and
−1.28
% for N
O.
Table 1
Measurement campaigns, including the number of fires for which
emission factors were measured and the number of corresponding
fuel transects.
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2.2
Regression analysis
Field measurements provide the most accurate description of the vegetation
conditions during the fire and yielded the most reliable insights into the
drivers of EF dynamics. However, these measurements are sparse and thus
unsuitable for spatiotemporal extrapolation. We therefore built machine
learning algorithms, for which we selected a subset of satellite and
reanalysis features with global coverage and temporal data availability for
at least the past 20 years.
Table 2
Satellite and reanalysis features assessed for the prediction of
savanna biomass burning emission factors.
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2.2.1
Global feature selection
To avoid the model becoming a black box, we did not include features with no
intuitive significance or cogent link to EFs (e.g. individual satellite
retrieval bands). Table 2 lists the different satellite and reanalysis
products included in this study, along with the observed range for each
feature over the included fires. We used remote sensing products based on
retrievals and reanalysis data with sufficient spatial and temporal
coverage, primarily using products based on the Moderate Resolution Imaging
Spectroradiometer (MODIS). This meant that at this stage, we did not include
data from visible infrared imaging radiometer suite (VIIRS) or geostationary satellites. Based on the coordinates of the
individual samples, we obtained a broad range of features which we then
averaged over the samples from each individual fire in order to obtain the
fire-averaged feature scores. As proxies for the vegetation conditions and
landscape parameters prior to the fire we used fractional tree cover (FTC)
and fractional bare soil cover (FBC) from MOD44BV006
(DiMiceli et al., 2015) and the fraction of absorbed
photosynthetically active radiation (FPAR) and the leaf area index (LAI),
which were retrieved from MCD15A2HC6 (Myneni et al.,
2015). Based on MOD09GAC6 surface spectral reflectance
(Vermote, 2015), we determined the normalized difference
vegetation index (NDVI) before the fire and the Pgreen (calculated as NDVI
before the fire minus the minimum NDVI of the previous year, divided by the
total NDVI range of previous year; Korontzi, 2005).
To estimate the weather conditions during the fire, we used ERA5-Land
meteorological reanalysis data from the European Centre for Medium Range
Weather Forecasts
(ECMWF)
(Muñoz-Sabater et al., 2021).
Hourly meteorological data for air temperature, wind speed, relative
humidity, evapotranspiration, and potential evapotranspiration were used to
obtain the feature score at the UTC-corrected time stamp of each sample.
Based on the timing of the sample, the feature value was obtained using
linear temporal interpolation. Temperature and relative humidity were
subsequently used to derive the vapour pressure deficit (VPD, i.e. the
difference between the saturation vapour pressure and the actual vapour
pressure) following the method described by Tetens (1930). The
evaporative stress index (ESI) was calculated as the actual
evapotranspiration divided by the potential evapotranspiration
(Anderson et al., 2007). We used
ERA5-Land monthly average rainfall data to estimate the mean annual rainfall
(MAR) over the 1990–2022 period, and we also used the cumulative rainfall in the
12 months prior to the fire.
Fire weather comprises combinations of weather and fuel parameters that
determine the risk and behaviour of wildfires. Indices like the globally
available fire weather index (FWI) have been developed with the aim of
estimating the risk of wildfires (De Groot,
1987; Van Wagner, 1987) and are based on global reanalysis data. In this
assessment we have included the daily FWI along with some of the
intermediate parameters used to calculate the FWI. These intermediate
parameters include (1) the fine fuel moisture code (FFMC), designed to
capture changes in the moisture content of fine fuels and leaf litter; (2) the drought code (DC), which captures the moisture content of deep,
compacted organic soils and heavy surface fuels; (3) the buildup index
(BUI), which represents the total fuel availability; and (4) the initial
spread index (ISI), which is driven by wind speed and the FFMC and
represents the ability of a fire to spread immediately after ignition. We
used the global fire weather indices based on ERA5 (Hersbach et al., 2020)
with a 0.25
spatial resolution and 1950–present temporal coverage
(Vitolo et al., 2020) that are calculated as part of
the European Forest Fire Information System (EFFIS). Global fire weather
indices based on ERA5 (Vitolo et al., 2020) showed
significant inconsistencies compared to fire weather indices based on GEOS-5
and MERRA-2 obtained from the Global Fire Weather Database (GFWED;
Field et al., 2015),
meaning these data should not be used as substitutes. Because of the long
temporal coverage and higher spatial resolution, we only included ERA5 in
our analysis.
For fire severity proxies we used the differential normalized burn ratio
(dNBR) and the differential normalized difference vegetation index (dNDVI)
retrieved before and after the fire. These were based on the MODIS surface
spectral reflectance, corrected for atmospheric conditions
(MOD09GAV6; Vermote, 2015). If the scene before or after the
fire was cloud covered, the preceding or successive scene was used with a
limit of 14 d before or after the fire. If no cloud-free scene was
available in that time window, the fire was removed from the dataset.
2.2.2
Machine learning methodology
We tested a variety of different regression methodologies for the prediction
of the fire WA EFs based on the abovementioned satellite and reanalysis
features. Using the scikit-learn library in Python
(Pedregosa et al., 2011), we trained multiple
linear regression, decision tree, random forest, gradient boosting
machine, and neural network regressors to predict the MCE and the EFs of CO,
CO
, CH
, and N
O. Many of the meteorological and fuel
characteristics follow seasonal patterns and exhibit strong co-variation.
While this may be problematic for linear models, it should not negatively
impact the decision-tree-based modes, and therefore these features were
included in the initial modelling stages. We trained the models to
reconstruct the measured EF dynamics using the in situ EF measurements (both
ours and those from literature). We removed measurements with missing values
for any of the included features. The remaining data were divided into
training (70 %) and validation data (30 %), and the training data were
resampled using 10-fold cross-validation. This means that the training
dataset is divided into 10 equal-sized parts or folds. The random forest
model is trained and evaluated 10 times. In each iteration, one fold is used
as the “temporary validation” set (different from the 30 % which is not
included in the training data), and the remaining nine folds are used as the
training set. The folds are created while allowing sample replacement (i.e.
bootstrap method), meaning that for each sample in the dataset, there is an
equal chance of it being selected more than once or not selected at all. All
regression methods were trained to maximize the explained variance in the
data. The hyper parameters (model configurations like number of trees,
minimum samples per leaf, and maximum features) were tuned using the
scikit-learn “GridsearchCV” algorithm
(Pedregosa et al., 2011). Random forest (RF)
regressors gave the best results, closely followed by gradient-boosting
machine (GBM) regressors. We therefore decided to proceed using RF
regressors to predict the MCE and the EFs of CO, CO
, CH
, and
O.
2.3
Spatial extrapolation for global savanna emission estimates
To assess the impact of EF dynamics on emission estimates and study global
spatiotemporal patterns, we developed gridded EF layers that can easily be
incorporated into existing emission inventories. The remote sensing proxies
(“features”) were resampled to the required spatial resolution by simply
averaging the values of the relevant grid cells. For example, to compute the
0.25
fraction tree cover feature, we averaged the fraction tree
cover of all 500 m pixels classified as savanna or grassland. When
computing to a higher resolution, e.g. 500 m EFs, only the higher-resolution (MODIS-based) features exhibit pixel-to-pixel variability, while
meteorological conditions (derived from ERA5-Land at 0.10
resolution) remain consistent across many adjacent 500 m pixels.
However, due to MODIS-derived features like FTC EF, estimates remain distinct
between the grid cells. In contrast, temporal resolution within the models
is more influenced by ERA5-Land-derived fluctuations. While FTC retrievals
remain constant throughout the year, variations in factors like VPD,
temperature, and FWI cause EF estimates to fluctuate on a daily basis.
Figure 2
Estimation of the CO EF at 500 m resolution for MODIS tile
“h20v10” on 1 June 2019
(g)
using a random forest regression
based on
(a)
fractional tree cover (FTC),
(b)
fraction of absorbed
photosynthetically active radiation (FPAR),
(c)
the fire weather index
(FWI),
(d)
vapour pressure deficit (VPD), and
(e)
soil moisture. For grid
cells containing biomes other than savanna
(f)
, GFED4s static EFs for the
respective biome were imposed replacing the savanna EFs. Sources of the
individual features are listed in Table 2.
Figure 2 provides an example for the estimation of the CO EF at 500 m
resolution for MODIS tile “h20v10” (covering parts of Zambia, Botswana,
Angola, Namibia, Zimbabwe, Mozambique, and the Democratic Republic of the
Congo) on 1 June 2019, using the features shown in Fig. 2a–e. The
temporal resolution of the computed gridded EFs in the example of Fig. 2 is
daily, in which the day-to-day EF dynamics are being driven by daily
variations in VPD, FPAR, FWI and soil moisture. Burned area products cannot
differentiate the time of the day at which a grid cell was burned. For
features with a typical diurnal pattern, we therefore weighed the hourly
meteorological data by the average diurnal fire profile for the grid cell in
the respective month of the year. This diurnal fire profile was based on the
3-hourly fractions of daily emissions obtained from GFED4.1s, which is
based on the timing of active fire detections from both MODIS and
geostationary satellites
(Mu
et al., 2011; van der Werf et al., 2017). To study the impact of EF dynamics
in savannas, we calculated monthly global savanna emissions by multiplying
the dynamic EFs computed by our models with dry matter consumption from
GFED4s
(Randerson
et al., 2012; van der Werf et al., 2017) at 0.25
spatial
resolution for the 2002–2016 period (the period for which MCD64A1C5 as used
in GFED4s was available). To classify the land cover type of the cell (Fig. 2f) we used the International Geosphere-Biosphere Program (IGBP)
classification (Loveland and Belward, 1997),
obtained from the MODIS annual MCD12Q1C6 product (Friedl and
Sulla-Menashe, 2019), where the savanna biome comprised land cover type
classes 6–11. We then calculated the dynamic monthly MCE and the EFs for CO,
CH
, N
O, and CO
at 0.25
spatial resolution for
the savanna biome using the RF models. For
burned grid cells that
were partially classified as savanna, the EF of the cell was obtained by
averaging the EFs of the different biomes in the underlying 500 m grid
cells, weighted by their dry-matter consumption. We ran GFED4s using both
static (original) and dynamic (this study) EFs for the savanna biome to
determine the impact on seasonal and spatial emission patterns using our
approach.
Figure 3
EFs (g kg DM
−1
measured in the sampled vegetation types
during the EDS and LDS and the EFs from savanna measurements listed
in savanna literature based on the Andreae (2019) compilation. The green
diamond represents the arithmetic mean, and the red cross represents the
EMR weighted-average value. The colours correspond to the savanna subclasses
at the bottom of the figure. Table 1 lists the time frames of the individual
field campaigns, while Table A1 in the Appendix provides a broad floristic
description of the dominant vegetation types.
Results
3.1
Variability of savanna EF measurements
During six fire seasons we have collected over 4500 bag samples containing
emissions from 129 fires in a variety of savanna ecosystems under different
seasonal conditions. Figure 3 shows the ranges, averages (green diamond), and
WA EFs (red crosses) measured during the campaigns listed in Table 1. For
the calculation of the WA N
O EF we excluded samples that contained
total carbon emissions of less than 10 mol following the findings
described by Vernooij et al. (2021). Table A1 provides a short
geomorphological and floristic description of the savanna ecosystems
included in Fig. 3, including the seasonal behaviour of the dominant
vegetation. The relatively small range in the boxplot describing previous
savanna literature (Fig. 3, red box based on studies listed by Andreae, 2019) may be attributed to the fact that most studies report either
fire averages, vegetation type averages, or even study averages, whereas the
other boxplots based on our measurements show the variability observed
between individual samples.
We observed substantial variability within EF bag samples from different
savanna ecosystems, which was strongly linked to tree-cover density and mean
annual rainfall. EFs of CO and CH
were lower (i.e. higher MCE) in
xeric open savannas compared to woodland savannas. Fire-WA EF measurements
for CO, CH
, and N
O using the UAS method were on average
13 %, 29 %, and 44 % lower, respectively, than estimates listed in previous
inventories. However, this may be largely attributable to the fact that
xeric savannas were overly represented in our measurements in terms of
annual biomass consumption (i.e. sample bias). Our measurements in higher-rainfall savannas were much closer to the previous averages (Fig. 3). In
humid areas like dambos (seasonally inundated grasslands) and riverine
forests, we found large intra-seasonal differences in N
O, CO, and
CH
EFs. Water availability in these landscape features is often
strongly soil type and geomorphology related
(Bullock, 1992; Gonçalves et
al., 2022), making the correlation with seasonal rainfall less direct and
drying patterns over the dry season more diverse. The grasslands with the
highest EFs (found in high-rainfall savanna dambos) were
uncharacteristically green for the time of the season, and under those
conditions fires in these landscapes would therefore not be representative
of more xeric grasslands.
Table 3
Consumption of fuels prone to residual smouldering combustion in the EDS and LDS for xeric open
savannas measured in Botswana and Australia and Miombo woodlands measured in
Mozambique and Zambia.
Weighted average over the consumed contribution of each individual
fuel subclass.
Weighted average over the dominant shrub types found in the plots.
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3.2
EF seasonality, fire intensity dynamics, and fuel consumption in xeric and mesic savannas
Table 3 lists the EDS and LDS pre- and post-fire fuel characteristics
averaged over all the transects we measured in the respective vegetation
type and season. In both xeric and mesic savannas, the moisture content of
the fuel and the relative humidity were substantially lower in the LDS
compared to the EDS. This resulted in increases in fire intensity proxies
over the dry season. Particularly during measurement campaigns in the Miombo
woodlands in Mozambique and Zambia, the fine fuel in the EDS plots
predominantly consisted of tree litter and became even more litter-dominated
with the progression of the dry season. EDS fires were patchy and generally
did not consume coarse woody debris and shrubs. As the dry season
progressed, there was a clear shift towards the combustion of more live
foliage and fuels prone to residual smouldering combustion (RSC) like coarse
woody debris, stems, and densely packed litter, which after months of drying
had become more receptive to combustion. RSC occurs after the passage of a
flame front, and its emissions are not lofted by strong fire-induced
convection (Bertschi et al., 2003).
The increase in the consumption of live and coarse fuels towards the end of
the dry season coincided with higher EFs for CO and CH
in the LDS.
This shift in combusted fuels also results in a seasonal increase in the WA
carbon content of the consumed fuel of woody savannas (Table 3), which
linearly scales the EFs of all measured species. For some characteristics
(e.g. the total fuel load), it is important to note that the average time
since the last fire was not necessarily equal between the listed vegetation
types. The higher fuel loads we found in open savannas in Australia
compared to Botswana may be partially attributed to the longer fuel
build-up.
Figure 4
(a)
Correlation of the predicted and measured fire-integrated
weighted-average MCE for the training (orange) and validation (blue)
datasets. The vertical blue and orange lines represent the standard error of
the mean within the respective fire. The vertical red line is the static MCE
derived from the EFs used in GFED4s. The “improvement” refers to the reduced
mean absolute error compared to prediction using this static GFED4 (red
line) MCE and compared to the average of the input data (magenta line).
(b)
The remote sensing and reanalysis datasets used by the model and the
feature importance (an indication of how strong each feature is used to
differentiate the data) of the respective features.
Overall, our measurements of CO and CH
EFs in xeric, grass-dominated, and shrub-dominated savannas (e.g. Australian
Spinifex
grasslands and open savannas
in the Kalahari) were slightly lower in the LDS compared to the EDS
campaigns but much lower compared to woody savannas (Fig. 3). Contrary to
the mesic savannas, where RSC-prone fuel is readily available and becomes
more flammable with the progression of the fire season, fires in xeric shrub
and grasslands tended to consume much of the available fuel in the EDS
(Table 3). Overall, the WA nitrogen content of the combusted fuel decreased
with the progression of the dry season through curing of grasses and litter
decomposition. This was somewhat compensated for by an influx of leaf litter
and an increased combustion of live shrubs, which were richer in nitrogen
than grasses (that had commodiously already cured in the EDS). Overall,
fires that consumed more litter emitted more N
O than grass-dominated
fires. Between individual fires, the curing stage of the grasses affected
the N
O EF, with green seasonally inundated grasslands emitting more
O compared to fully cured grasslands. In some miombo woodland fires
in Kafue, which were measured in November when the vegetation already
carried its first green flush, we also measured relatively high N
EFs.
3.3
Estimation of BB EFs using random forest regression based on satellite proxies
To extrapolate these relations for use in global emission inventories, we
correlated the field measurements to satellite products. Table 4 lists the
correlations of the individual field-measured ecosystem attributes to the
MCE and fire-averaged EF measurements and global satellite proxies.
Direct correlations between fire-averaged EF measurements and global
satellite proxies and inter-correlations between the satellite and
reanalysis proxies are listed in Table A2. The strongest predictors for the
MCE and the CO and CH
EF were the tree density in the plots, the grass-to-litter ratio, the combustion completeness, and the WA moisture content of
the consumed fuel (Table 4). In turn, these parameters were best correlated
to the remotely sensed FTC, FBC, VPD, and FWI. EFs for CO and CH
are primarily proportionate to the inverse combustion efficiency (i.e. the
not fully oxidized compounds) which had a standard deviation of 90 %
relative to the mean. CO
, on the other hand, is proportionate to the
fully combusted carbon fraction, which is much larger and more stable, with a
relative standard deviation of 4.5 % compared to its mean. Therefore, the
carbon content of the fuel – with a standard deviation of roughly 5 % – becomes a dominant factor explaining the variability in CO
EFs. The
features that most strongly correlated with the N
O EF were the
nitrogen-to-carbon ratio in the combusted fuel and the percentage of grass
in the fine fuel (consisting of grass, litter, and coarse woody debris),
which in turn correlated with the FBC and the VPD.
Table 4
Spearman correlation matrix for the field-measured ecosystem
attributes, the fire-averaged emission factors and MCE, and the
satellite products used in the study. Positive correlations are presented in
blue, while negative correlations are presented in red.
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Figure 5
Pearson correlation of the predicted and measured fire-integrated
WA MCE
(a)
, CH
EF
(b)
, N
O EF
(c)
, and CO EF
(d)
for the
training (orange) and validation (blue) datasets using a limited set of
features. The boxes in the bottom right of the panels list the remote
sensing and reanalysis datasets used by the model and the feature importance
(an indication of how strong each feature is used to differentiate the
data). The red line represents the static biome average used in GFED4s, while
the magenta line represents the average of the training and validation data. The abbreviation
“improv. GFED” refers to the reduced mean absolute error compared to the
static average used by GFED4s, and the abbreviation “improv. Avg.” refers to the reduced
mean absolute error compared to the static average of the input data.
For the global estimation of MCE and EFs, we found that RF models performed
well with respective out-of-sample correlation coefficients ranging between
0.80 and 0.99. In Figs. 4 and 5, feature importance represents the mean
accumulation of the impurity decrease within each tree and is an indication
of how much the variability in each feature is used as a split criteria by
the models to explain the variability in the EF data. The average MCE in the
measurements was slightly higher compared to earlier assessments that were
used by GFED4s (red line in Fig. 4), which may again be attributable to
the dominance of relatively dry savannas. Overall, we found that by using only
globally available features covering a large (
>20
year)
time span, we could estimate the field-measured MCE of the fires in the
validation set with a mean absolute error (MAE) of 0.006. Using the static
MCE in GFED4 (MAE of 0.015 compared to the measurements) as a baseline, this
meant a MAE reduction of 60 %.
Although the features listed in Fig. 4 all have sufficient spatiotemporal
coverage for global emission modelling, some features exhibited strong
co-variation. Other retrievals were hampered by LDS cloud cover (e.g. dNBR
and Pgreen), which meant we could not use consistent quality retrievals or
had to remove samples from the data. Further simplification using a subset
of features that are not directly correlated reduced the data dependency
and computational intensity of the model and the loss of training
data due to cloud cover without losing much explained variance. When using
a five-feature subset, we found that RF regressors still predicted much of the
variability in the MCE and EFs. Figure 5a shows the predictive performance
of a RF regression model that uses VPD, FTC, FWI, FPAR, and soil moisture
(SM) to estimate the MCE, which was relatively similar to the model
predicting MCE using all features (
of 0.80 versus 0.86).
Figure 6
Difference in savanna and grassland fire emissions for CO
(a)
CH
(b)
, and N
(c)
between emission computation using dynamic EFs
versus static biome reference EFs (dynamic minus static) calculated using
GFED4s for the 2002–2016 period.
We found that spatial variability dominated the total variability in the MCE
within the savanna biome with higher combustion efficiency in more xeric and
open savannas. To isolate the effect of combustion efficiency in the
prediction of individual species and make the model more transparent, we
added the computed MCE to the predictor features. Both the models that were
trained using the full set of features in Table 1 and the five-feature models
identified the computed MCE as one of the primary features explaining of the
variability in other EFs. The largest deviation from static EFs (vertical
red line in Fig. 5d) was predicted for N
O. This is partially due to
the large number of new fires, which on average (vertical magenta line) were
lower than the static
reference used in GFED4s. The modelled MCE was the
main predictor of the N
O EF, followed by the soil moisture in the top
layer (0–7 cm depth). Somewhat surprisingly, we found soil moisture to
correlate more strongly with the tree density in the plot rather than the
fuel moisture content (Table 4).
Figure 7
Seasonality of fire carbon emissions (black) and the computed CO
EF (orange) for different savanna subclasses in Southern Hemisphere Africa,
averaged over the 2002–2016 period. The savanna classes are based on the
International Geosphere-Biosphere Program (IGBP) classification
(Loveland and Belward, 1997). The shaded
areas represent the timing of our measurements in Southern Hemisphere
African savannas, indicating that our LDS campaigns in particular may not be
representative of the bulk of the fires. The horizontal red bar on the
right represents the static EF used for savannas by GFED4s.
3.4
Impact on global emission estimates using variable savanna emission factors
Figure 6 shows the relative impact of using variable EFs on annual global
savanna fire emissions of CO (Fig. 6a), CH
(Fig. 6b), and N
O (Fig. 6c), averaged
over the 2002–2016 period based on GFED4s. The map only shows cells for
which the partial coverage of savannas exceeds 50 %. In grid cells that
are partially (50 %–99 %) covered by savanna, the total impact on emissions
is to some degree diluted as the EFs of the non-savanna biomes remained
constant. For CO and CH
, the dominant effect is a spatial
redistribution with higher CO and CH
EFs in mesic, high-tree-cover
savannas and lower EFs in xeric savannas compared to previous estimates. For
CO
(not shown), we find the opposite pattern to CO. Relatively
speaking, however, changes in CO
emission are much smaller because
most carbon is emitted as CO
, even when MCE values are low. Although
CO and CH
followed the same spatial pattern, we found that MCE
affected the CH
EF more strongly than the CO EF, which resulted in
lower CH
to MCE ratios in savannas with lower tree density. Global
savanna emissions of CO were 2 % higher compared to the GFED4s reference
scenario, whereas CO
, N
O, and CH
emissions were, respectively,
0.2 %, 18 %, and 5 % lower. N
O emissions were lower for the
entire savanna biome (Fig. 6c).
Figure 7 shows the seasonal patterns in the average CO EF for different
savanna vegetation classes in Southern Hemisphere Africa. The IGBP savanna
subclasses are only used here to indicate the average patterns and are not
involved in the EF calculation. Using the IGBP classification, our samples
were classified as “Woody savannas” (24 %),
“Savannas” (42 %),
“Open shrubland” (21 %), “Grassland” (4 %), “Cropland/Natural
vegetation mosaic” (6 %), and “Croplands” (1 %). The latter two
classes are misclassifications and were all situated in protected areas with
no crops. These classes are listed in the accompanied dataset
(Vernooij, 2023). We found a stronger and more persistent
seasonal decline in the CO EF in xeric grasslands and shrublands compared to
woody savannas. N
O EFs showed a similar pattern characterized by a
decline over the dry season in the more xeric grass and shrubland savannas,
while EFs in woody savannas are more stable. The model indicates a reversal
of the seasonal trend in woody savannas around August–September, long before
these rains start. The coloured areas represent the timing of our field
campaigns in this region. Although LDS campaigns were conducted before the
first seasonal rains, the graph indicates they may not be indicative of
peak-season fires. Figure 8 shows an overview of the relative changes in
emissions for the various savanna-rich GFED regions. Many of these regions
contain both xeric and mesic savannas with contrasting spatial patterns,
meaning local differences may be much larger (Fig. 6).
Table 5
Emission factor averages for the global savanna.
Averaged over the fires measured using the drone methodology (skewed
towards xeric savannas)
Averaged over the fires measured using the drone methodology and the
included literature studies.
Dynamic EFs weighted by the consumed biomass at time and location of
fires as calculated using GFED4s.
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Both our measurements and the savanna biome averages in literature
compilations (e.g.
Akagi et
al., 2011; Andreae, 2019) are subject to sampling bias when representing
global savannas. A disproportionate number of field studies are clustered
around reactively accessible locations with a well-developed research
infrastructure, whereas other fire-prone areas lack direct field
measurements. Rather than comparing the average of our savanna measurements
to the literature averages, we computed the dynamic EFs globally using the
RF model and subsequently calculated the emissions for the entire savanna
biome. We then divided these annual emissions by the consumed biomass from
GFED4s to get the annual consumed-biomass weighted-average EFs, which we
will further refer to as the “effective” EFs. Over the 2002–2016 period,
the effective EFs over the savanna biome were 1685 (
±5
) for CO
64.3 (
±0.6
) for CO, 1.9 (
±0.0
) for CH
, and 0.16 (
±0.00
) for N
O, with the number in the parentheses indicating the
interannual standard deviation. In Table 5, we compare the effective average
EFs over the 2002–2016 period calculated by our model to the static average
EFs for savanna and grassland vegetation used by GFED4s and those suggested
by Andreae (2019) and
Wiedinmyer et al. (2023). Table 5 also lists the average EFs of the UAS-measured fires and the average EFs of all included fires (including
literature studies). Except for N
O, the differences between the
effective EFs compared to more recently updated static EFs from Andreae
(2019) were larger (
+1.3
% for CO
−7.1
% CO,
−31.4
% CH
and
−3.7
% N
O) than when using GFED4s EFs as the static reference.
Figure 8
Relative difference in the landscape fire emissions of CO
CO, CH
, and N
O for the 2002–2016 period when using dynamic EFs
versus static EFs using GFED4s (dynamic minus static) over the different
savanna-rich GFED regions. Note that many of these regions encompass both
xeric and mesic savannas with contrasting patterns that balance each other
out. Differences may therefore be much larger on a regional scale.
Discussion
4.1
Comparison with previous studies
The largest difference compared to previous savanna burning emission
estimates is the reduction in N
O emissions. Rather than being the
effect of including spatiotemporal dynamics, this reduction resulted from a
substantial influx of new N
O EF measurements that exhibited
significantly lower values than the averages found in EF compilations. Our
field measurements yielded an average EF of 0.11 g kg
−1
, while EF
compilations reported averages of 0.21 g kg
−1
(Andreae and Merlet,
2001), 0.20 g kg
−1
(Akagi et al., 2011), and 0.17 g kg
−1
(Andreae,
2019). However, in our measurements, xeric savannas are over-represented.
When using the global RF model to extrapolate the measurements over the
entire savanna biome, the effective average N
O EF – for savanna
grid cells at the time of their burning – was 0.16 g kg
−1
, which is
similar to the value listed in Andreae (2019). It is known that older
studies might overestimate N
O, due to N
O formation in stainless steel sample containers (Muzio and Kramlich, 1988).
Particularly compared to more recent studies, our EFs were in line with
other savanna measurements from South America (0.05–0.07 g kg
−1
Hao et al., 1991; Susott et al., 1996),
Australia (0.07–0.12 g kg
−1
Hurst
et al., 1994; Meyer et al., 2012; Surawski et al., 2015), and Africa (0.16 g kg
−1
; Cofer et al., 1996). In accordance with
Winter et al. (1999b), we found N
O EFs to be closely
correlated with the nitrogen content of the fuel. Through this relation, we
can explain both the spatial distribution observed in Fig. 6c and the
different seasonal trends. In line with Susott et al. (1996) and Ward et al. (1992), we found that
woody vegetation has higher nitrogen content contained in the foliage (Table 3), causing higher N
O emissions from tree-dominated areas. We found
relatively low nitrogen content for Australian open woodland savannas, which
was in line
with previous studies
(Bustamante et al., 2006). The seasonal
reduction in the nitrogen content of the fuel as the vegetation cures (Table 3) coincides with a reduction in the N
O EF over the dry season
(Yokelson et al., 2011; Vernooij et al.,
2021). This tends to happen quicker in xeric grasslands and shrublands compared
to more mesic and tree-covered areas. On the other hand, as fuels get more
receptive over the dry season, fires consume increasingly greater amounts of litter,
coarse fuels, and live foliage, provided these fuels are available (Table 3).
This increases the WA carbon and nitrogen contents of the fuel.
For carbonaceous species our model predicts a spatial redistribution,
characterized by higher combustion efficiency in lower-tree-cover savannas
and lower combustion efficiencies in more woody savannas. Previous research
by van Leeuwen and van der Werf (2011) identified multi-linear correlations between EFs of CO
, CO, and
CH
and environmental drivers resulting in coefficients of
determination (
ranging from 0.48 to 0.62. In accordance with their
study, as well as many other field studies (e.g. Laris et al., 2021;
Sinha et al., 2004), we found the FTC to be a strong predictor of the MCE
and the EFs of CO and CH
(Fig. 9). When denoted in grams per kilogram
of dry biomass consumed, EFs of carbonaceous species are dependent on both
the combustion efficiency and the carbon content of the fuel. The carbon
content is often fixed in global studies of EFs, e.g. 45 %
in Andreae (2019) and Andreae and Merlet (2001) or 50 %
in
Akagi et al. (2011), with the latter forming the basis of the EFs used in
GFED4s that represent the static EF references in this study. However, both
the combustion efficiency and the carbon content have a spatial component
with higher carbon contents in shrubs and trees compared to grasses (Table 3). For the studied fires, the WA carbon content of the fuel ranged from
40.3 % to 49.3 %, which linearly scales to a 22 % difference in EFs
between those extremes. In line with Andreae (2019), we assigned a carbon
content of 45 % to literature studies for which the carbon content was not
reported that was close to our average measured value of
45.8±2.3
%. Contrary to previous research, which indicated that dryer conditions
in the LDS would lead to higher-MCE fires in both grasslands and savanna
woodlands (Korontzi, 2005), we found lower MCE in these
regions under late-LDS conditions (Fig. 3). One potential explanation is
that although the LDS fires were more intense, they consumed much more
RSC-prone fuels (Table 3), which may explain the higher CH
and CO EFs.
An alternative explanation to this fuel-driven MCE reduction is that in
certain areas our measurement campaigns missed the peak season when fires
are driven by stronger winds (Laris et al., 2021; N'Dri
et al., 2018) and that fire intensity and MCE in these areas would already
be on the decline. Eck et al. (2013) studied seasonal
changes in BB particles during 15 annual fire seasons in xeric (e.g. Etosha
Pan and Kruger National Park) and mesic (e.g. Mongu) savannas in southern
Africa using the Aerosol Robotic Network (AERONET). They found a linear
trend in the single scattering albedo (SSA), increasing throughout the dry
season, which would support a late dry-season decrease in MCE
(Liu
et al., 2014; Pokhrel et al., 2016). We found that in the xeric savannas
the composition of the fuel in LDS fires did not significantly differ from
EDS fires, as most of the available fuel was consumed in both the EDS and
LDS fires. In these areas, we did observe a slight seasonal decline in CO
and CH
EFs.
Figure 9
The non-linear regression between the CH
EF and the MCE for
the individual bag samples (green circles) and the fire-averaged values
(orange diamonds). In the box on the bottom left,
refers to
Spearman's rank correlation coefficient for the bag samples.
In accordance with previous studies (e.g. Korontzi et al., 2003b; van
Leeuwen and van der Werf, 2011; Barker et al., 2020), we found steeper
CH
EF-to-MCE regression slopes in woodlands compared to grasslands.
Our data indicated a positive correlation of the CH
EF-to-MCE slope
with the FTC based on MOD44Bv006. The MCE is a simplified form of the
combustion efficiency and only calculated using CO and CO
emissions.
Being less oxidized than CO (which is still common in flaming combustion),
CH
emissions have a stronger dependency on the actual combustion
efficiency (CO
divided by all carbon emissions). While most studies
describe the relationship between the CH
EF and the MCE as being
linear
(Korontzi
et al., 2003; van Leeuwen and van der Werf, 2011; Selimovic et al., 2018;
Yokelson et al., 2003), we found that for individual bag samples it was
better described using a non-linear function (Fig. 9), in line with findings
by Meyer et al. (2012) for Australian savanna measurements. Figure 9
represents individual bag measurements rather than fire averages (for which
the spread in MCE is much lower). Laboratory experiments described by
Selimovic et al. (2018) and others showed that the CH
-to-CO ratio is
more complex and variable in real time than at the fire-average level.
Individual bag samples sampled over a concise 35 s time frame thus
exhibit a broader range and more pronounced variation in comparison to fire
averages. Stable carbon isotopes also point to CH
emissions being more
depleted in heavy carbon (
13
C) compared to CO in both mixed (C
and C
and single-fuel-type experiments using wooden logs, indicating a stronger
dominance of RSC and the pyrolysis of lignin in its total emissions
(Vernooij et al., 2022a). Mainly within woody savannas, this clarifies why studies focused on
either smouldering- or flaming-phase emissions exhibit diverse slopes for
CH
EF-to-MCE when employing linear regressions. Additionally, this
phenomenon accounts for the inclination of the slope to intensify in
fuel types characterized by higher lignin content.
Although higher MAR generally coincides with high FTC, this was not the case
for our measurements from Brazil. The measured areas in the Estação
Ecológica Serra Geral do Tocantins (EESGT) received relatively high MAR
(1250–1600  mm yr
−1
compared to 850–1250  mm yr
−1
for Miombo woodlands and
890–1100  mm yr
−1
for Mozambican Miombo woodlands. Nonetheless,
despite being strictly protected from logging and other land clearing
practices, the MOD44BV006 FTC in the measured areas in EESGT was very low
(1 %–10 %, with an average of 2 %) compared to 7 %–32 %, with an average of
19 %, for Zambian Miombo woodlands and 3 %–43 %, with an average of 22 %, for Mozambican
Miombo woodlands. Our measurements in the EESGT being skewed towards
open savannas (that typically burn with higher MCE) may explain the
relatively low CH
EF-to-MCE slope discussed in
Vernooij et al. (2021). For the whole Cerrado, the average MOD44BV006 FTC is 17 %,
indicating that the measurements in EESGT may be underestimating the MCE in
other parts of the Cerrado. According to its classification, MCD44BV006 FTC
only includes canopies of trees exceeding 5 m in height
(Adzhar et al., 2022), which may be why some
common Cerrado species are classified as shrubs. However, the EFs observed
from these areas were similar to those observed in low-tree-cover savannas.
Figure 10
Detection rate of the fires measured using the UAS methodology by
different satellite algorithms in the EDS (green) and LDS (orange). The
darker area represents the cases where a fire was observed in the actual
pixel within the listed time frame. The lighter areas represent fires that
were not detected in the same pixel as the samples but were detected in
adjacent pixels. Time frames are listed below the product labels. For the
VIIRS detections, the distance limits between the detection point and closest
sample of the fire were 1 km for the darker-shaded area and 3.5 km for the
lighter-shaded area.
Measurements of fuel loads were higher than previous measurements from
African savannas described by Shea et al. (1996). They
found average fine-fuel loads (litter and grass) of 3.8 t ha
−1
in
moist Miombo woodland. In semiarid Miombo woodland they found 3.1 t ha
−1
. In comparison we found 5.6 t ha
−1
in Mozambican Miombo
woodland and 5.6 t ha
−1
in Zambian Miombo woodland. The percentage
of grasses in these fuels was similar; Shea et al. (1996)
reported 24 % in moist Miombo woodland and 18 % in semi-arid Miombo
woodland, whereas we found 37 % in
Mozambican Miombo
woodlands and 18 % in Zambian Miombo
woodlands. The combustion completeness of these fuels was slightly lower in
our fires at 50 %–80 % versus 80 %–92 % reported by Shea et
al. (1996), although the lower values in this range occurred in the EDS.
Combustion completeness of shrub leaves and coarse woody debris was in the
same range. For dambo grasslands our fuel loads were also much higher at 6.2
±2.16
) t ha
−1
of which 99 % was grass versus 3.1 t ha
−1
from Shea et al. (1996). Although these differences are large,
they may be attributed to the significant natural variability in
productivity and decay related to water availability, fire frequency, and
termite and grazing activities in these natural landscapes.
4.2
Model representativeness
This is the first study to quantify the spatial distribution of GHG EFs over
the entire savanna biome using field measurements from a variety of savanna
ecosystems and their relation to global data mainly from satellites.
Although spatiotemporal coverage has improved, there are still many
understudied savanna and grassland areas for which we have derived EFs based
on our model. Figure 1 clearly illustrates the gaps in the spatial
distribution of the training data. Savannas bordering the
tropical rainforest, Northern Hemisphere Africa, Central America, South-East
Asia, and temperate grassland ecosystems are particularly understudied. Due to the
lack of measurements in these ecosystems, EFs are presently computed based
on measurements primarily taken in Southern Hemisphere Africa. Nevertheless,
EF trends in other regions might considerably differ from those observed in
extensively studied savannas. To guarantee the model's relevance to specific
regions, it remains essential to calibrate and evaluate the model using
supplementary in situ emission factor measurements.
Most of the fires used to train the models were prescribed fires set by
scientists or park rangers in protected areas in order to facilitate
collection of data pre- and post-burn on site. It is common practice to
extrapolate these measurements in relatively undisturbed savanna vegetation
to the wider savanna. Even though these protected natural areas tend to burn
more frequently, they represent a minority of the area that is currently
modelled using savanna and grassland emission factors by global inventories
(e.g. Fig. 1). Most of this area is to some degree affected by humans though
cattle ranging, wood harvesting, slash and burn agriculture, etc. This means
fires in this study may not always represent the burning practices by local
farmers, and representativeness of our work for the larger savanna area
therefore remains uncertain. The samples were predominantly collected over
heading fires, which in the measured fires typically represented most of the
burned area. A common approach for prescribed fires is burning against the
wind (backing fire) to minimize both the impact on vegetation and risk of
spread. In a heading fire, RSC can be increased because the high rate of
spread and patchiness leaves fuels smouldering further from the convection
associated with the advancing flame front. In accordance with
Wooster et al. (2011) and Laris et al. (2021), we found higher MCE in
samples from backing fires, indicating less RSC and thus CH
and CO
emissions in these types of fires. Another possible explanation for the
higher MCE in the backing fire samples is that more slowly lofting RSC smoke does
not mix with the flaming combustion emissions in these measurements like it
does in heading fires.
4.3
Spatial resolution and model considerations
For this research, we computed the average attributes within the
0.25
grid cell before calculating the savanna EF. This spatial
resolution was selected because the GFED4s burned area data, including
assumptions for small fires, is generated at a 0.25
resolution. Nonetheless, there are potential advantages to future EF
estimations at greater spatial resolutions. Recent studies indicate that
higher-resolution modelling yields different emissions than that based on
aggregated data, due in part to improved representation of landscape
heterogeneity (van Wees and van
der Werf, 2019). Enhancing the resolution of meteorological data would
further amplify the precision of these models. These advancements anticipate
that future global emission inventories will adopt higher spatial
resolutions, enabling better representation of local or regional dynamics.
We found the highest variability in EFs within smaller landscape features
that are bound to geomorphological niches, typically along rivers and
valleys. While these features are likely to have low significance for global
emission patterns, they represent vital ecosystems that may require special
fire protection. In its current form, the model may not always pick up on
those landscape features. High-resolution modelling allows for a better
understanding of localized fire regimes, especially in areas with relatively
heterogeneous land cover.
The model is limited by the accuracy and spatial resolution of the
underlying products. Using the features included in the current models, EFs
can be calculated up to the native spatial resolution of the included
MODIS-based products (500
500 m), which is also the resolution of
globally available burned area products. New high-resolution burned area
products, however, indicate that these global products, including the GFED4s
data used for global emission analyses in this study, grossly underestimate
burned area due to omission of small fires
(Chen
et al., 2023; Roteta et al., 2021; Roy et al., 2019). This also pertains to
a substantial proportion of the fires we measured. Of the UAS-measured fires
in this study, only 5 of the 45 EDS fires (11 %) and 13 of the 65 LDS fires
(20 %) were registered by MCD64A1 as burned area (including adjacent
pixels and a 4 d time lag). Only 4 (9 %) of the 45 EDS fires and
just 32 (49 %) of the 65 LDS fires were detected by VIIRS S-NPP as
thermal anomalies, with the hotspot's centre point (accounting for a 1 d
time lag) falling within a 3.5 km radius of the sample. Depending on
the
spatiotemporal nature of these omissions, this may affect some of the
results in this study concerning the effects of the EF dynamics on total
emissions. Chen et al. (2023) indicate that disproportionately more burned area is added in higher-tree-cover
areas when using higher-resolution satellite imagery in the
savannas. Giving more weight to
these areas would mean our savanna-wide effective EFs of CO, CH
, and
O would increase. The Sentinel-2-based burned area product from
Roteta et al. (2021) performed much
better and registered 8 of our 14 EDS fires (57 %) and all of our 16 LDS
fires (100 %) in Botswana and Mozambique in 2019 (including adjacent
pixels and up to a 21 d time lag). Due to there being fewer overpasses present, the
temporal allocation of this product is less precise, with an average time lag
of 5.5 d. Figure 10 shows the portion of our EDS and LDS fires that were
detected by various satellite algorithms.
Fire intensity proxies (dNDVI and dNBR from MODIS) were considered by the
models to be poor predictors for the EFs. A potential explanation is that
these features were not always representative, as many of the fires only
affected part of the pixel. Similar misrepresentation errors can be expected
for the NDVI before the fire, FPAR, and Pgreen. Particularly in the LDS,
we were often limited to areas that were enclosed by recent fire scars (0–2 years old) or other non-flammable boundaries like roads or bare areas.
Although the burnt areas were sizable (several hectares), many of the
retrievals in these pixels may poorly represent the burned vegetation. Along
with inconsistent retrievals related to cloud cover, this may contribute to
these features being deemed poor predictors by the models. Enhanced-resolution features could improve the accuracy of pixel representations for
the actual burned vegetation.
The meteorological parameters obtained from the ERA5-Land dataset carry
uncertainty. This uncertainty increases when examining earlier time periods
or remote regions due to diminished validation data availability. To what
extent uncertainty propagates to the EF predictions varies depends mostly on
whether there is a bias that was also present in the training data or
misinterpretation or uncertainty in general. As this model is trained using
specific datasets, these datasets should not be replaced by other sources
without evaluating the consistency of that source with the feature training
data. FTC and FBC based on MOD44Bv006 were found to be strong predictors of
BB EFs. However, intercomparison with Tropical Biomes in Transition (TROBIT)
field sites in African, Brazilian, and Australian savannas has shown that
this product consistently underestimates canopy cover in tropical savannas
by between 9 % and 15 % (Adzhar et al.,
2022). Products based on higher-resolution satellite retrievals (e.g.
LandSat and Sentinel) have the potential to further enhance the spatial
resolution of the EF estimates to include small landscape features and thus
become more representative. Although all satellite data come with some
uncertainty, we feel the errors are small enough to have high confidence in
the key findings, such as lower EFs in dry regions and higher EFs in wetter
regions.
The interdependence among features led to varying feature importance scores
(depicted in Fig. 4) across different model runs, driven by the test–train
data division and bootstrap resampling. For instance, a decision tree split
based on VPD might closely resemble soil moisture or RH, and FTC in national
parks often exhibits strong correlation with the MAR, with the exception of
our measurement sites in Brazil. While we conducted model runs considering
different subsets of features and selected the optimal one, it is important
to note that various features might also effectively account for a
significant portion of the variance. In cases where features had substantial
co-variation (such as FPAR and LAI or FWI and ISI), this resulted in the
selection of only one feature for the simplified model, even if both
features demonstrated high initial scores.
The models are currently trained using meteorological features obtained from
ERA5-Land (Muñoz-Sabater et al.,
2021), which is available from 1950 to present and has a 2- to 3-month delay.
When interested in longer time periods or for near-real-time (NRT)
applications, these features may be substituted with ERA5
(Hersbach et al., 2020), which is available from 1940 to
present with a shorter latency period of 5 d, or even CMIP climate
projections. Although supplementing the datasets on which the models are
trained with alternative data always comes with additional uncertainty, we
found meteorological parameters obtained from ERA5-Land to be in close
accordance with ERA5, indicating the two may also be substituted. This means
that the EFs computed using the methodology outlined in this paper could
potentially also be used to improve NRT biomass burning emission estimates
like those from CAMS-GFAS
(Andela et al.,
2015; Di Giuseppe et al., 2016).
Conclusions
Over the last decade, substantial progress has been made in increasing the
spatiotemporal coverage of savanna fire emission factor measurements (EFs).
In this study we described the variability in GHG EFs measured during 18 new
field campaigns over the 2017–2022 period during which we sampled 129 fires
in different parts of the savanna biome using a UAS platform. On average, CO,
CH
, and N
O EFs in these UAS measurements were, respectively,
13 %, 29 %, and 44 % lower compared to the biome-averaged EFs used in
previous inventories. However, from a global savanna perspective, xeric
savannas with relatively low EFs were over-represented in our measurements,
which could explain part of the mismatch. The measured fires were
predominantly intentional burns conducted by scientists or park rangers in
protected areas for data collection, and while these measurements are
extended to undisturbed savanna, the majority of the broader savanna used in
emission models is influenced by human
activities such as cattle grazing and
agriculture, raising some uncertainty about the representativeness of our
findings for global savannas. Measurements of the pre- and post-fire fuel load
and the fuel conditions during the fire indicated significant changes in
fuel receptiveness, resulting in increased fire intensity over the dry
season. Particularly for mesic savannas, an increase in the combustion of
RSC-prone fuels resulted in higher EFs of CO and CH
during LDS fires.
The main drivers of variability in CO and CH
EFs were tree cover,
fuel moisture content, and the prevalence of grasses, while EFs for N
strongly correlated with the nitrogen content of the fuel, which in turn is
strongly linked to the grass-to-litter ratio. Although these correlations
are consistent with previous savanna EF studies, quantifying their impact on
EFs for the use in global emission studies has so far been hampered by a
lack of measurements.
We developed a random forest regressor that estimates dynamic EFs (monthly
EFs at 0.25
) based on satellite products to replace the use of
static biome-averaged EFs in global emission inventories or the use of a
dichotomy of EDS versus LDS EFs (based on a cut-off date). The model-produced
data resulted in significant fire-specific improvements compared to static
biome-averaged EFs, reducing the mean absolute error in the modelled versus
measured predictions by 64 % for CH
, 58 % for N
O, 85 % for
CO, and 79 % for CO
. Except for N
O EFs, our study does not
indicate that savanna averages have large errors, but it instead shows that temporal
and especially spatial variability is large and better accounted for by
using a more sophisticated model. We used the dynamic EF models to calculate
the emissions for global savanna emissions over the 2002–2016 period, which
is more indicative of the effective EF differences. This resulted in a
spatial redistribution of emissions over the savanna biome, characterized by
increases in average annual emissions of CO and CH
in woody savannas
and reductions in open savannas. While the model indicates an initial
seasonal decrease in combustion efficiency as the vegetation dried out,
there was a reversal for woody savannas towards the end of the dry season,
occurring before the first seasonal rains. This shift coincides with the
increased consumption of live vegetation and RSC-prone fuels (like densely
packed litter and coarse woody debris). Xeric savannas had much lower EFs with
a longer and more profound seasonal decrease in CO and CH
. Although
O EFs were lower for the entire savanna biome, they followed a
similar spatiotemporal pattern.
The proposed dynamic EF method resulted in a 18 % reduction in the
estimated annual global N
O emissions from savanna fires compared to
static averages, with emission reductions of up to 60 % in xeric regions.
The impact on the global savanna emission estimates for CO
(decrease
of 0.2 %), CO (increase of 1.8 %), and CH
(decrease of 2.1 %) was
low, indicating the use of static EFs did not lead to biases for studies
focusing on global emissions. However, the regional impact on these EF
estimates was as high as 60 % and even 80 % under extreme seasonal
conditions, highlighting its variability at a more local level. Overall, the
model results are a first step towards more dynamic and area-specific
emission inventories, which we plan to make available in monthly and daily
resolution at 0.25
and will further improve as more measurements
and better remote sensing products become available.
Appendix A
Table A1
Floristic and geomorphological description of the different
vegetation types measured in this study.
Life cycle of the dominant grass species: PE indicates perennial or
>2
years, and AN indicates annual grasses.
Deciduousness of the dominant trees: D is deciduous,
SD is semi-deciduous, and EG is evergreen.
Download Print Version
Download XLSX
Table A2
Spearman correlation matrix for the field measurements and the
globally available satellite products. Positive correlations are presented
in blue, while negative correlations are presented in red.
Download Print Version
Data availability
The data table containing the training data used for this article, along
with an explanatory table, is available online at
(Vernooij, 2023). Model results are available upon request.
Supplement
The supplement related to this article is available online at:
Author contributions
RV and GRvdW designed the study. RV, TE, JRS, CY, RB, JE, AE, NR, MW, TS,
MVGA, MAB, MMC, and ACSB conducted the field measurements. RV conducted the
analyses of the samples. RV performed the random forest modelling and global
analyses and wrote the manuscript with help from DvW and GRvdW.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This research was supported by the Netherlands organization for Scientific
Research (NWO) (Vici scheme research programme, grant no. 016.160.324) and the
Ammodo Science Award (2017) for Natural Sciences. The measurement campaigns
in Botswana received funding from the International Savanna Fire Management
Initiative (ISFMI) and Australia's Department of Foreign Affairs and Trade,
while the field campaigns in Zambia in 2021 and 2022 were partially funded
by the United Nations Green Climate Fund. We owe great thanks for the
contributions of countless individuals and institutions that provided the
permissions, oversight, logistics, and expertise needed to perform the field
measurements in a safe and coordinated fashion. Among others, this has been
made possible thanks to 321 Fire, the Brazilian Instituto Chico Mendes de
Conservação da Biodiversidade, South African National Parks, the
Botswana Department of Forestry and Range Resources, the Tsodilo Community
Development Trust, the Zambian Department of Forestry, the Wildlife
Conservation Society, the Administração Nacional das Áreas de
Conservação in Mozambique, the Australian Central Land Council and
the Yanunijarra Aboriginal Corporation.
Financial support
This research was supported by the Netherlands organization for Scientific
Research (NWO) (Vici scheme research programme, grant no. 016.160.324) and the
Ammodo
Science Award (2017) for Natural Sciences. The measurement campaigns
in Botswana received funding from the International Savanna Fire Management
Initiative (ISFMI) and Australia's Department of Foreign Affairs and Trade,
while the field campaigns in Zambia in 2021 and 2022 were partially funded
by the United Nations Green Climate Fund.
Review statement
This paper was edited by Anping Chen and reviewed by Robert Yokelson and one anonymous referee.
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Articles
Abstract
Introduction
Methods
Results
Discussion
Conclusions
Appendix A
Data availability
Author contributions
Competing interests
Disclaimer
Acknowledgements
Financial support
Review statement
References
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Short summary
Savannas account for over half of global landscape fire emissions. Although environmental and fuel conditions affect the ratio of species the fire emits, these dynamics have not been implemented in global models. We measured CO
, CO, CH
, and N
O emission factors (EFs), fuel parameters, and fire severity proxies during 129 individual fires. We identified EF patterns and trained models to estimate EFs of these species based on satellite observations, reducing the estimation error by 60–85 %.
Savannas account for over half of global landscape fire emissions. Although environmental and...
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Sections
Abstract
Introduction
Methods
Results
Discussion
Conclusions
Appendix A
Data availability
Author contributions
Competing interests
Disclaimer
Acknowledgements
Financial support
Review statement
References
Supplement