Introduction
Biomass burning is one of the key processes affecting vegetation
productivity, land cover, soil erosion, hydrological cycles and atmospheric
emissions (Kloster and Lasslop, 2017; Forkel et al., 2017; Gaveau et al.,
2014; van der Werf et al., 2017). It has social implications as well,
impacting people's lives and properties (Roos et al., 2016), particularly
in the wildland–urban interface, where urban areas are intermixed with
forests (Bowman et al., 2017).
Fire is affected by climate, as burning is associated with high to extreme
weather conditions (particularly droughts and heatwaves) (Forkel et
al., 2017). However, fire affects climate too, due to its impacts on carbon
budgets and greenhouse gas emissions (van der
Werf et al., 2017). These mutual influences between fire and climate explain
why fire disturbance is considered one of the Essential Climate Variables
(ECVs) by the Global Climate Observing System (GCOS) programme (GCOS,
2016). Several space agencies are working on developing systematic
assessments of fire occurrence and fire impacts, as part of their efforts to
improve the use of satellite data in climate modelling. This is the main
purpose of the Fire_cci project
(https://www.esa-fire-cci.org/, last access: July 2018), which is part of
the European Space Agency's (ESA) Climate Change Initiative (CCI) programme
(Hollmann et al., 2013; Plummer at al., 2017). The
Fire_cci project aims to develop long-term time series of
burned area (BA) products adapted to the needs of climate modellers
(Chuvieco et al., 2016). In addition to global BA products, the
Fire_cci project is generating a small fire database for the
African continent based on medium-resolution sensors on board the Sentinel-2
and Sentinel-1 satellites.
Global BA information is required for many different applications
(Mouillot et al., 2014). First, it helps to assess fire risk assessment,
by comparing estimated risk conditions with actual fire occurrence
(Marlier et al., 2013; Chuvieco et al., 2014). Burned area information is
also a critical input of dynamic global vegetation models (DGVMs), as it
provides an estimation of carbon emissions and vegetation succession
(Lasslop et al., 2014). In conjunction with other human and physical
variables, BA datasets are required to understand factors controlling fire
activity (Forkel et al., 2017) and particularly those affecting
changes in fire regimes (Hantson et al., 2015; Andela et al., 2017), with
an increasing interest in fire patch identification derived from higher
resolution pixel-level information (Laurent et al., 2018).
Atmospheric emission models require precise information on spatio-temporal
patterns of fire occurrence, as well as combustion characteristics (van
der Werf et al., 2017; Knorr et al., 2016). Finally, BA information is
needed to model fire impacts on human health (Reid et al., 2016) and the
safety of properties (Moritz et al., 2014; Sturtevant et al.,
2009).
A few global BA products have been developed in the last years. Two sensors
have been particularly used to develop these global BA time series datasets:
VEGETATION (VGT), on board the SPOT satellites since 1998, and Moderate
Resolution Imaging Spectroradiometer (MODIS), on board the Terra and Aqua
satellites since 2000 (https://modis.gsfc.nasa.gov/about/, last
access: October 2018). The former images have been used to create BA products funded
under different European projects: L3JRC (Tansey et al.,
2008), Globcarbon (Plummer et al., 2006) and Copernicus GIO_
GL1_BA burned area products, all at 1000 m spatial resolution
(after 2013 the Copernicus product is available at 333 m resolution and is
derived from Proba-V data: http://proba-v.vgt.vito.be/en, last access:
October 2018). The MODIS sensor has been used to generate two global BA time
series, the MCD45A1 and the MCD64A1. Both provide BA detections at 500 m from
2000 to the present. The MCD45A1 was derived from a change detection
algorithm based on a bidirectional reflectance model (Roy et al.,
2008), while the MCD64A1 is derived from a hybrid algorithm that uses both
the reflectance changes and the thermal anomalies associated with biomass
burning (Giglio et al., 2009, 2018). The
latest version of this algorithm is the basis for the now standard MODIS BA
product, which is the MCD64A1 collection 6 (c6)
(https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mcd64a1_v006,
last access: October, 2018). From the
BA detections of the MCD64A1 product with additional variables on fuel
properties and emission coefficients, the Global Fire Emissions Database
(GFED) was first created in 2005. The current version is named GFED4, and
includes data from the MCD64A1 collection 5 (c5) product, as well as from
the Along Track Scanning Radiometer (ATSR) sensor (on board the European
Remote Sensing satellites) for the pre-MODIS era (1995–2000). The GFED4s
adds an estimation of the area burned by small fires (not detected by the
MCD64A1), which is derived from the relation between the number of hotspots and
burned patches (van der Werf et al., 2017).
Generally, small fires are considered as those below the detection threshold
of coarse-resolution sensors (for instance, smaller than 100 ha).
Finally, ENVISAT MERIS images have also been used for generating global BA
products, with a shorter time series than VGT and MODIS (just for the period
when MERIS archives provide enough input data: 2005–2011), but with higher
spatial resolution (300 m) (Chuvieco et al., 2016; Alonso-Canas and
Chuvieco, 2015). This product was named FireCCI41, and is the main precursor
of the one presented in this paper. Previous versions of Fire_cci BA products, based on this
or other ESA sensors, were only prototypes and are not publicly available.
This paper presents a new global BA product based on the highest
spatial-resolution bands of the MODIS sensor (R, red, and NIR, near-infrared), at
approximately 250 m pixel size (6.25 ha). We have named this product
FireCCI50 throughout the paper. The number 50 stands for version 5.0 of the
Fire_cci BA products. The former versions were derived from
MERIS images (Chuvieco et al., 2016). Therefore, FireCCI50 is the first
version of Fire_cci BA products based on the MODIS sensor.
The goal of generating this product was to complement existing global BA
datasets using higher spatial-resolution bands. The paper describes the BA
algorithm used for generating the FireCCI50 product, presents a
spatio-temporal validation of the results, compares estimations with
existing BA products (FireCCI41, GFED4 and MCD64A1 c6) and describes an
example of using our product for fire-related climate models.
Methods
Algorithm design
The main inputs for developing the FireCCI50 product are the MOD09GQ and the
MCD14ML datasets, both from collection 6. The MOD09GQ reflectance product
includes the daily red (R) and near-infrared (NIR) corrected reflectances of
the Terra-MODIS bands 1 and 2, respectively, both at approximately 250 m
resolution in sinusoidal projected tiles of 1200×200 km. Pixel quality
information was obtained from the MOD09GA product, as recommended by the MODIS
Science team (https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod09ga_v006,
last access: July 2018). Additionally,
our algorithm used the MODIS thermal anomalies product (MCD14ML:
http://modis-fire.umd.edu/pages/ActiveFire.php?target=MCD14ML, last
access: July 2018), which includes active fires (named here hotspots, HSs),
detected from the sensor's middle infrared and thermal channels at 1 km
resolution (Giglio et al., 2016).
Daily images were aggregated into monthly composites to select the pixels
most clearly associated with a burned signal. This approach has been commonly
applied in BA mapping studies (Barbosa et al., 1998; Chuvieco
et al., 2005) as it reduces potential confusion caused by clouds, cloud
shadows or angular effects (Roy et al., 2005). As the MOD09GQ 250 m
product does not contain bidirectional reflectance distribution function (BRDF) corrections, we tested several correction
models, but none reported satisfactory results, as they greatly smoothed the
burned signal in some biomes. The monthly composites of daily reflectance
are less affected by angular effects, although this to some extent compresses
the burned signal.
To create the monthly composites, first pixels with low-quality observations
(cloud, cloud shadows) taken from MOD09GA QA (quality assessment) were
discarded, as well as pixels with land covers considered not to be burnable.
These comprised water, bare areas, permanent snow and ice and urban areas.
This information was derived from the Land Cover CCI product version 1.6.1,
using three epochs corresponding to 2000, 2005 and 2010, and at a similar
spatial resolution of approx. 300 m (Kirches et al., 2013:
https://www.esa-landcover-cci.org/, last access: July 2018).
The composite criterion was based on the HS dating. It was assumed that the
closest day to the nearest hotspot would be the most adequate to obtain the
post-fire reflectances. Although composites include 30 days, for areas with
HSs dated on the latest days of the monthly period, 10 extra running dates
(including images from the next month) were used to have enough post-fire
observations for creating the composites. After creating Thiessen polygons
with the HS location and labelling them with the HS date, the algorithm
selected the closest date among the three daily images with the minimum
NIR monthly reflectance. The actual date was selected based on whether the
three minimum NIR values occurred before or after the HS date. If all three
were acquired before or after the HS date, the date of the second lowest was
selected. In this way, clouds' shadows or smoke not included in the QA would
not affect the composite. When one or two were acquired after the HS, the
closest date to the HS was selected. Finally, the monthly composites were
generated taking the R and NIR reflectances for the selected dates. From
these two bands, the Global Environmental Monitoring Index (GEMI: Pinty
and Verstraete, 1992) was computed as
GEMI=η1-0.25η-(ρRed-0.125)1-ρRed,η=2ρNIR2-ρRed2+1.5ρNIR+0.5ρRedρRed+ρNIR+0.5,
where ρNIR is the NIR reflectance (band 2 of the MOD09GQ product)
and ρR the R reflectance (band 1). This index has been previously
used in burned area detection from R and NIR channels (Barbosa et al.,
1999; Chuvieco et al., 2002; Pereira, 1999).
The BA algorithm followed a two-phase approach, detecting in the first phase
the most clearly burned pixels (named seeds) and improving the burn shape
detection in the second phase, using a growing procedure around the seed
pixels. The former phase aims to reduce the commission errors, while the
latter the omission errors. This approach has been widely used in BA
detection algorithms (Alonso-Canas and Chuvieco, 2015; Bastarrika et al.,
2011; Chuvieco et al., 2008). In addition, the algorithm is spatially
adaptable, as it collects statistics from each MODIS tile to generate the
detection thresholds.
The seed phase began with positioning the HS within a 5×5 pixel window of
the MOD9GQ product, as the HS product included the centre coordinates of a 1 km2
area. The pixel with the minimum NIR within each window around a HS
was selected as the best candidate for having a recent burn. Cumulative
distribution functions (CDFs) of unburned and potentially burned areas were
created to define adaptive thresholds to cope with the wide variety of
global fire conditions. To reduce potential noise, HSs were only considered
as potential burns when the NIR was lower than the previous month and the
NIR value was lower than the 10 % threshold of the unburned pixels' CDF.
Seeds were considered as those having a low NIR value (extracted from the
CDF of potential burns), a decrease in NIR value, and at least one HS in the
surrounding 3×3 pixel window.
The growing phase included several criteria to add new burned pixels to each
burned patch created in the first phase. Within each iteration, candidate
pixels had to be neighbours of seed burns (or pixels detected as burned in
the previous iteration), with an NIR value below a CDF threshold of the
potentially burned pixels, and showing a decrease in NIR values from the
previous month. In this growing phase, an additional threshold based on the
CDFs of GEMI values (Eq. 1) for burned and unburned areas was also used. The
process was iterated until no further pixels were added to each burned patch.
Finally, a morphological filter with two steps of erosion–dilation was
applied to eliminate small patches of burned pixels or small islands of
unburned pixels within burned patches.
Product specifications
As the result of a user requirement analysis, two BA datasets (pixel and
grid) were generated for the FireCCI50 product. Both were provided in
geographical coordinates. The pixel products include the date of detection
(0–366), the uncertainty of the estimation (0–100) and the land cover
affected by the fire (taken from the Land Cover CCI product of a similar
time period). Pixel products had the full resolution of the MODIS input
images. Monthly files were generated to avoid missing double burns in a
single year. The output datasets were created in a GeoTIFF format and
comprised quasi-continental tiles (Table 1).
Geographical distribution of BA tiles for the pixel product.
Areas
Name
Upper left
Lower right
1
North America
180∘ W
83∘ N
50∘ W
19∘ N
2
South America
105∘ W
19∘ N
34∘ W
57∘ S
3
Europe–North Africa
26∘ W
83∘ N
53∘ E
25∘ N
4
Asia
53∘ E
83∘ N
180∘ E
0∘ N
5
Sub-Saharan Africa
26∘ W
25∘ N
53∘ E
40∘ S
6
Australia and New Zealand
95∘ E
0∘ N
180∘ E
53∘ S
The grid product was produced for biweekly periods and incorporated 23 layers
at 0.25∘ resolution: sum of burned area (m2), standard
deviation of the estimation (m2), fraction of burnable area (0–1),
fraction of observed area (0–1), number of burned patches (0-N) and sum of
burned area in 18 land cover classes (again taken from the Land Cover CCI
product). The output datasets are NetCDF CF files (http://www.unidata.ucar.edu/software/netcdf/docs, last access: July 2018).
The uncertainty layer represents the probability that each pixel was
correctly classified as burned. It was derived from four input layers
related to the BA detection algorithm: the number of daily images to build
the monthly composite, the percentile of the NIR reflectance in the CDF of
burned and unburned pixels, the CDF of the GEMI differences and the distance
to the closest HS. These variables were normalized and combined to obtain
the pixel confidence level, from which the standard error of the grid
product was calculated. The fraction of burnable area was computed from the
land cover CCI product, while the fraction of observed area from the
monthly integrated values of the MODIS QA data. The burned patches were
computed considering as a patch a group of contiguous burned pixels with
burn detection date differences shorter than 15 days. This criterion has
been commonly used for the global characterization of burned patches
(Hantson et al., 2015).
Validation methods
The accuracy assessment was based on a stratified random sample of reference
data distributed across the globe and across 12 years, 2003–2014. This
is the most comprehensive assessment of burned area estimation accuracy ever
reported and aims to meet the requirements of the CEOS CalVal
Level 3 criteria. For comparison purposes, additionally to the product
version presented here, the validation analysis included the FireCCI41
product (Chuvieco et al., 2016), based on MERIS images, and the most
recent NASA BA product (MCD64A1,c6).
Reference data were generated from pairs of medium-resolution (20–50 m)
satellite images as recommended by the CEOS CalVal protocol
(Boschetti et al., 2009). Burned patches observed between
pairs of Landsat 5, 7 and 8 images were mapped with a semi-automatic
classification algorithm. The algorithm was based on a random forest
classifier (Breiman, 2001; Pedregosa et al., 2011), which is a robust
classifier used for land cover change detection (Wessels et al., 2016) and
is increasingly being used in burned area mapping (Ramo and Chuvieco,
2017). The classification made by one interpreter was systematically
reviewed by another. Any discrepancies were rectified and the revision
repeated iteratively until no new differences were detected.
The sampling units were defined spatially by Thiessen scene areas (TSAs)
constructed by Cohen et al. (2010) and Kennedy et
al. (2010), and temporally by the dates of Landsat imagery
available. The population of sampling units was stratified by calendar
years, the biomes as defined by Olson et al. (2001) and BA as detected by MCD64A1 c5
(collection 6 was not yet released when validation started). Each year-biome
stratum was divided into two parts using BA thresholds designed to minimize
the expected variance of BA given the available sample size. The yearly
sampling size was fixed to 100 (for 12 years, 2003 to 2014, 1200
in total) and was allocated across strata proportionally to MCD64A1 BA data.
Details on the sampling strategy and comparisons between several options of
stratification and sample allocation can be found in Padilla et al. (2017).
Each sampling unit selected was subsampled by a spatial cluster of pixels on
a 30 km × 20 km window. This subsampling stage allowed the amount of
reference data generated to be reduced for a given year. This, in turn, allowed the
probability sampling design to be expanded to a multi-year period (12
years). This is the first time the temporal variation of global BA products
accuracy is reported.
Accuracy measures, based on the error matrix approach (Congalton and Green,
1999), were computed. Since the burned category is much less frequent than
the unburned, the overall accuracy was not considered, as it is strongly
influenced by the proportion of the unburned pixels. Instead, the omission and
commission error ratios were computed, as well as the Dice coefficient (DC)
and the relative bias of the burned category. The DC is defined as the
probability that one classifier (product or reference data) identifies a
pixel as burned given that the other classifier also identifies it as burned
(Fleiss, 1981).
DC=2E11E1++E+1,
where E11 is the area correctly classified according to the reference and
E1+ and E+1 are the BA reported by the product and reference data
respectively. Relative bias is computed as the ratio of the difference
between omission and commission errors and the true burned pixels. The
formulae of accuracy measures can be found in Padilla et al. (2015).
The details of the sampling design and the formulae
used to infer accuracy at the global scale, for the whole time period of study
and on a yearly basis, can be found in the validation report of the
Fire_cci project (see technical documents at
https://www.esa-fire-cci.org/documents, last access: October 2018). As
explained above, each year is delimited by a group of strata (delimited by
biomes); therefore the same formulae used for the whole population can be
used to infer accuracy for each year. Estimates of accuracy were computed
taking into account the stratified sampling design, using a stratified
combined ratio estimator, and taking into account the fact that sampling units are of
unequal sizes and they are subsampled (Cochran, 1977).
In addition to the spatio-temporal validation sample, a different sample of
52 Sentinel-2 MultiSpectral Instrument (MSI) scenes was processed as a first
assessment on whether the product was more sensitive than MCD64A1 c6 product
in the detection of small fires (<100 ha). This fire size
validation sample was initially derived to evaluate a BA algorithm for MSI
images in Africa (Roteta et al., 2018; Bastarrika and Roteta, 2018).
The 52 MSI images were acquired in the fire season from December 2015 to
September 2016. The scenes were selected by systematic sampling of
sub-Saharan Africa, excluding areas that are not burnable. The reference
fire perimeters were produced by a semi-automatic algorithm (Bastarrika et
al., 2014) and later visually revised by another interpreter. The detected
fire perimeters were dated with Visible Infrared Imaging Radiometer Suite
(VIIRS) hotspots to compare fire patches with those detected by the global
products. Fire perimeters were classified into five fire size categories:
<1, 1–5, 5–25, 25–100 ha and more than 100 ha.
Average global annual BA from the FireCCI50 product.
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Product intercomparison
Spatial and temporal trends of the FireCCI50 were compared with existing
global BA products. We selected products that are currently operational: the
BA of the GFED4, widely used for fire emissions, and the MCD64A1 c6, now the
standard NASA BA product. For comparison with outputs from the CCI project,
the MERIS Fire_cci v4.1, with its shorter time series
(2005–2011), was also assessed. Global temporal trends of these products were
generated. Spatial comparison between products was performed at 0.25∘
resolution.
In addition, BA detections were compared with existing databases of fire
perimeters, given the importance of understanding performance against
established approaches by national fire agencies. Three sites representative
of different fire regimes were selected: boreal forest, selecting the h12v03
MODIS tile located in Manitoba and Saskatchewan (fire perimeters were
downloaded from the Canadian Wildland Fire information System:
http://cwfis.cfs.nrcan.gc.ca/ha/nfdb/, last access: July 2018); tropical woodlands
and savannas, selecting the h30v10 MODIS tile located in northern Australia
(fire perimeters were downloaded from the North Australia and Rangelands
Fire Information database: http://www.firenorth.org.au/nafi3/, last
access:
July 2018); and temperate forest, including the h08v05 MODIS tile
intersecting the state of California (fire perimeters were obtained from the
Fire and Resource Assessment program's web page: http://frap.fire.ca.gov,
last access: July 2018). These perimeters were mainly obtained from
Landsat images for the Canadian and Californian databases and MODIS 250 m
resolution for the Australian one. In all cases, fire perimeters were
verified by visual interpretation and field reports. We downloaded data from
four different years, covering the full fire season. Comparison of national
fire perimeters with satellite BA products has been widely used as a first
assessment of product performance (Mangeon et al., 2016; Moreno Ruiz et
al., 2014; Boschetti et al., 2015). However, we used national fire perimeters in this paper to
compare our results with existing official fire statistics, rather than as a
validation strategy.
Product assessment: fire patch analysis
As an example of the potential uses of the FireCCI50 product in fire
modelling, a fire patch morphological analysis was carried out. The input
data were the pixel-level products, which were converted to fire patches
based on a spatio-temporal aggregation process. The same methods used to
generate the FRY database (Laurent et al., 2018) were
applied. Individual fire patch indices, such as the fire patch area and the
so-called shape index (SI), defined by the ratio between the perimeter P
and the square root of the of the patch area A (SI =
0.25×P/(A)0.5), were considered. The shape index allows the
investigation of (i) the ability of the product to identify individual fire
patches and capture their power-law size distribution following the self-criticality hypothesis (Hantson et al., 2015), and (ii) how the
resulting fire patch shape improves with finer resolution to better capture
spreading processes and impacts on post-fire vegetation dynamics as proposed
by Nogueira et al. (2017) and Chuvieco et al. (2016) for
pixel-level global remote sensing product assessment. This analysis was
based on a comparison of metrics derived from the FireCCI50 and the MCD64A1 c6
products globally. In addition, a regional comparison with the same methods
was performed between both global products and a Sentinel-2 (20 m) BA
product generated within the Fire_cci project for northern hemispheric tropical Africa for 2017 (Roteta et al., 2018; Bastarrika
and Roteta, 2018).
Fire size distribution was characterized by the slope β of the fire
number to fire size relationship (log scale). The slope was adjusted using
the same methodology as in Laurent et al. (2018). Grid cell to
grid cell correlations accounting for spatial autocorrelation of both
products were implemented with a modified t test of spatial association
based on Clifford et al. (1989) and Dutilleul et al. (1993), and performed
with the modified.ttest function of the “SpatialPack” R Cran Package
(https://CRAN.R-project.org/package=SpatialPack, last access: July 2018).
In order to assess the level of similarity between the products, it was also
necessary to compare the same fire size category between the two products.
This is why a threshold of 107 ha was applied on fire size: this threshold
corresponds to the size of a 5 pixel fire patch at the Equator for the MCD64A1
c6 product (the Plate Carreé projection product of the GeoTIFF MCD64
product was used for this analysis:
http://modis-fire.umd.edu/files/MODIS_C6_BA_User_Guide_1.0.pdf, last
access: July 2018), corresponding to the minimum size of fire patch provided
by the FRY database.
Results
Global burned area
The FireCCI50 BA product includes the global time series from 2001 to 2016.
Average yearly BA was 3.808 Mkm2. Years with largest BA were 2004,
2005, 2011 and 2012 with more than 4 Mkm2 each, while only 2013 and
2016 had less than 3.5 Mkm2. Figure 1 shows the spatial distribution of
average annual BA of the full time series. The tropical belts of Africa,
South and Central America and Australia were the most extensively burned. Temperate forest in
western Europe and the USA and grasslands and croplands of south-east Asia, far east
Russia and northern Kazakhstan also had significant fire occurrence. The
most persistent biomass burning in the time series occurred in tropical
mainland Africa and Madagascar (excluding the rainforest), northern
Australia and tropical Latin America (both hemispheres), excluding the
central Amazonian region and the Andes. Fire persistency is also observed in
large agricultural areas of southern Russia, south-east USA and south-east Asia (Myanmar,
Laos, Thailand and Vietnam).
Day of the year (DoY) when the pixels are detected as burned in tropical Africa (2008).
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Fire occurs mostly during the dry season, particularly in the tropics.
Figure 2 shows the day of the year when pixels were detected as burned in
the FireCCI50 product. As expected, December to February showed most fire
activity in the northern hemispheric tropical regions, while June to
September showed fire activity in the Southern Hemisphere. Southern Chad, Sudan, the Central African Republic and Ghana were the most affected by fires in
the northern fringe, while Angola, Congo, Zambia, Mozambique and Tanzania
were the most impacted in the southern region.
Product validation results at the global scale
The estimated error matrices and accuracy measures can be found in Table 2
and Fig. 3 for the time periods with available product and reference data
(FireCCI50 and MCD64 c6 for 2003–2014 and FireCCI41 for 2005–2011). The
accuracy of the FireCCI50 product, expressed by a Dice coefficient (DC) value of
0.365 (standard error, SE =0.026), was higher than for the FireCCI41
product (DC =0.248 with SE =0.030), but lower than for the MCD64A1 c6
product (DC =0.478 with SE =0.031). Global commission and omission error
ratios (Ce and Oe) were 0.512 (SE =0.020) and 0.708 (SE =0.30) for the
FireCCI50 product. The FireCCI41 had a total of 0.643 (SE =0.045) omission
and 0.810 (SE =0.030) commission rates, while the MCD64A1 c6 had 0.353
(SE =0.016) and 0.622 (SE =0.038), respectively. While it might be
expected that the higher spatial resolution of FireCCI50 would produce a
more accurate product than the MCD64A1 c6, the limited spectral resolution,
as there are no shortwave bands which are very important for BA detection,
offsets improvement in terms of spatial resolution and means that the two
products are complementary. A detailed fire size and type analysis is needed
to understand this complementarity (see Sect. 3.4 for the first assessment
of fire size).
Estimated error matrices for each product at the global level.
Cells of the error matrices represents areas of 1013 m2.
E^11 is the BA according to both the product and the reference
classifications, E^12 is the commission error area,
E^21 is the omission error area and E^22 is
the unburned area according to both the product and the reference
classifications.
E^11
E^12
E^21
E^22
FireCCI41
1.88 (0.3)
3.4 (0.7)
8.04 (1)
3.34e+3 (5e+2)
FireCCI50
4.35 (0.4)
4.57 (0.4)
10.6 (1)
5.49e+3 (6e+2)
MCD64
5.85 (0.5)
3.19 (0.3)
9.6 (1)
4.41e+3 (6e+2)
Estimates of Dice coefficient (DC), relative bias (relB),
commission error ratio (Ce) and omission error ratio (Oe) at the global scale.
Horizontal segments show the 95 % confidence intervals.
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Estimates of Dice coefficient (DC), relative bias (relB),
commission error ratio (Ce) and omission error ratio (Oe) at the global scale
and for each year. Vertical segments show the 95 % confidence intervals.
Points are slightly moved along the x axis to improve visualization.
![]()
Dice coefficient (DC), relative bias (relB) and reference
burned area (BAref; m2) for FireCCI50 in TSAs. TSAs
with reference data but without accuracy measurements available are represented
by empty polygons (white polygons with grey borders). DC is not available
when there is no BA in the reference data or in the product, and relB is not
available when there is no BA in the reference data.
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FireCCI50 underestimates a fraction of 0.402 (0.058) of the BA reported in
the reference sample, as expressed by the relative bias (relB). This value
is slightly less than MCD64A1 c6, 0.415 (0.056), and lower than MERIS
Fire_cci v4.1, 0.468 (0.094). These results indicate that
globally the errors are more balanced between omission and commission in FireCCI50 than in the MCD64c6 product.
The temporal trends of accuracies are remarkably similar among BA products.
Relative differences between product accuracies observed in 1 year are
similarly replicated in the other years (Fig. 4). Yearly accuracies are
estimated with relatively low precision (large 95 % confidence intervals)
and show relatively high inter-annual variability without clear monotonic
trends. For example, the relative bias (relB) of MERIS Fire_cci
v4.1 was much closer to zero in 2008 than in the other years, but with a
very large intra-annual variability, which led to a very large confidence
interval (expressed by the vertical bars). This reflects that the low bias
was obtained by large overestimations and underestimations across sampling
units that were cancelled out in the global estimate of relative bias.
The spatial variation of accuracy of FireCCI50 is shown in Fig. 5. Accuracy
tended to be highest across the tropical and subtropical savanna of Africa,
South America and Australia, where fire occurrence was high, and decreased
in temperate forest and croplands, where fires are generally less intense
and smaller.
Comparison with existing products
Figure 6 shows the temporal trends of global BA (in km2) estimated from
the BA products analysed. The FireCCI50 product provided an intermediate estimation
between the MCD64A1 c6 and the GFED4 BA products (based on MCD64A1 c5). It
was similar to the FireCCI41 product for the 2007–2009 years, but this latter
product showed lower estimations for 2010 and 2011 than the FireCCI50
because of the limited availability of MERIS images for those 2 years.
Annual trends in BA derived from these products are remarkably similar
across years, with a stable behaviour at the beginning of the time series
and a trend towards a slight decrease starting from 2007. The FireCCI41
product showed a high decline in 2010, caused by the lack of observations in
the last years of the time series. For this reason, annual estimates of
FireCCI41 were poorly correlated with the FireCCI50 product (r2=0.12)
by comparison with those for the other MODIS-based products
(r2>0.79 in both cases). The annual estimations of the
FireCCI50 product were lower than for the MCD64A1 c6 by 11 % and higher
than for FireCCI41 (10 %) and GFED4 (12 %) products.
Temporal trends of yearly BA for different global products.
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In terms of global spatial patterns of annual BA, FireCCI50 showed a higher
agreement with NASA products than with the FireCCI41 (Table 3). The average
determination coefficient (r2) of the pairwise correlation of FireCCI50
with the MCD64A1 c6 and GFED4 was 0.70 and 0.67, respectively, and exhibited
little inter-annual variability from 2005 to 2011. The spatial agreement
with the FireCCI41 product was strongly dependent on the year. While r2
values ranged between 0.63 and 0.65 in the years 2005 to 2009, they were
distinctly lower in 2010 (r2=0.44) and 2011 (r2=0.54), which
again should be related to the lack of MERIS observations in those years.
Table 3 also highlights that the global spatial BA patterns of GFED4 and
MCD64A1 c6 were largely similar (average r2=0.93) despite global
annual burned area having increased by 27 % with the update of MCD64A1 from c5 to
c6.
The comparison between the FireCCI50 product and national fire perimeters
for the three sites and the 4 years selected exhibited high agreement
between the two products. Assuming fire perimeters to be the reference
information, commission errors for the FireCCI50 product were generally
below 0.2 (Table 4), with the exception of California. The commission errors
were related to the total BA, as small overestimations had a significant
weight in the final proportion. This affects both the geographical and
temporal variation of ratio errors between the two sources. California in
fact had much less area burned than the other two sites (annual average 1241 km2
versus 229 187 km2 of Australia or 13 680 of Canada). The higher
commission errors of California were actually found in the years with less
burned area (353 km2 in 2010 versus 2059 km2 in 2015). Omission
errors were generally higher than the commission errors (with the exception
of California, where they were much lower), with values commonly in the
range of 0.2 to 0.35. Summing up the three sites (more than 2.5 Mkm2),
the overall disagreements between the MODIS Fire_cci product
and the fire perimeters were in the range of 0.12–0.23 for commissions and
0.20–0.33 for omissions (Table 4).
Fire size accuracy
The assessment of the impact of the higher spatial resolution of the MODIS
Fire_cci product with reference to the MCD64A1 product
conducted over the sampled African sites confirms the limited performance of
both global products in detecting fires smaller than 100 ha. Both FireCCI50
and MCD64A1 products show omission values larger than 85 %, mostly caused
by the coarse resolution of the input images. However, for smaller fire
patches, the FireCCI50 product shows a better performance than MCD64A1, with
3.5 % less omission error in both the 5–25 and the 25–100 ha fire size
ranges (97.39 % and 92.13 % for the MCD64A1 and 93.91 % and 86.93 % for the
FireCCI50, respectively). For fires larger than 100 ha, the MCD64A1 presents
less omission than the FireCCI50 product (55.58 % and 60.79 %,
respectively). Figure 7 shows examples of two of the test Sentinel-2 scenes
in tropical Africa. The FireCCI50 product identifies more small patches,
while larger ones are better defined by the MCD64A1 product.
Correlation matrix showing the coefficients of determination
(r2) calculated for different BA product pairs based on annual data
(see Fig. 5). The average and the range calculated from
individual years in 2005–2011 are given.
r2 mean (min–max)
FireCCI41
GFED4
MCD64A1 C6
FireCCI50
0.59 (0.44–0.65)
0.67 (0.64–0.69)
0.70 (0.68–0.72)
FireCCI41
0.47 (0.33–0.54)
0.49 (0.35-0.54)
GFED4
0.93 (0.92–0.94)
Agreement between FireCCI50 and national fire perimeters for the
selected test areas. Ce denotes commission errors, and Oe denotes omission errors, assuming
fire perimeters to be the reference.
Year
Australia
California
Canada
Global
Ce
Oe
Ce
Oe
Ce
Oe
Ce
Oe
2002
0.147
0.200
0.499
0.146
0.184
0.388
0.153
0.206
2010
0.192
0.339
0.602
0.299
0.100
0.273
0.191
0.330
2011
0.130
0.191
0.493
0.362
0.060
0.377
0.128
0.202
2015
0.089
0.251
0.324
0.158
0.292
0.324
0.231
0.268
Examples of small fire detections by the FireCCI50 and MCD64A1 c6
products in two Sentinel-2 images located in the west of the Central African
Republic (a) and north of Angola (b). UTM coordinates are included in
both maps.
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Fire patch analysis
Figure 8 displays the global pattern of fire patch density (number of fire
patches km-2) calculated from the FireCCI50 and MCD64A1 c6 products. A
similar global pattern was observed for both datasets, with the savannas and
temperate grasslands yielding higher fire patch density. When plotting the
difference between the two products, it was observed that MODIS Fire_cci
v5.0 detected more fire patches in woody savannas and open
shrubland/grasslands worldwide (+52 %), while it detected fewer fire
patches in savannas (-42 %). In other biomes, no clear spatial pattern was
observed, with a mixture of fire patch densities varying ±70 %. The
total number of fire patches without and with consideration of a 107 ha fire size
threshold estimated globally for FireCCI50 was 1.20×106 and
0.54×106, respectively. This last number reached 0.56×106 for
MCD64A1 c6. Therefore, when considering a minimum fire patch size threshold
of 107 ha, a difference of 3.7 % was observed between the two
products, indicating that the 250 m resolution doubles the fire patch number by
accounting for smaller ones, and kept the total number of large
fire patches almost constant, with only local differences.
Global pattern of fire patch number
(km2) obtained from the pixel-level information of
FireCCI50 (a) and MCD64A1 c6 (b) for the period 2000–2016.
The difference between the two products is also presented (c). A
minimum fire size of 107 ha has been chosen to prevent resolution effects.
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Slope (β) of the fire number–fire size
relationship (log scale) and its uncertainty (σ) for
FireCCI50 (a, c) and MCD64A1 c6 (b, d). The
difference between the two products is also presented (e), with
the level of agreement between the two products (f).
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The grid cell to grid cell correlation between the two products was high for
fire patch numbers with r2=0.932 (p value ≪0.01).
In the comparison with Sentinel-2 data over northern hemispheric Africa, the
obtained values were r2=0.839 (p≪0.01) for
FireCCI50 and r2=0.836 for MCD64A1 c6 (p≪0.01).
The slope β of the fire number–fire size relationship (log scale) is
presented in Fig. 9 for each product for the period 2001–2016, alongside
their uncertainties. A similar global spatial pattern was observed
between the two products as well as values varying between 0.4 and 1.8. However, a
steeper slope was also observed (higher beta, Δ=+0.15 on
average) of the fire size distribution in FireCCI50, indicating that small
fires were more frequent. This observation is particularly important and
systematic in savannas, with the highest positive differences observed in
South America (Δ=+0.4). A reverse pattern was observed in
grasslands with a flatter slope in FireCCI50 (Δ=-0.25). As a
result, the level of agreement is good at the global scale, except for the
regions mentioned above.
Grid cell to grid cell correlations of the β slope between FireCCI50
and MCD64A1 c6 were high, with r2=0.84 (p≪0.01)
when considering fire sizes larger than 107 ha and grid cells with
p value <0.01 for the significance of the fire size–fire number
relationship in each product. In the comparison with Sentinel-2 data for
northern hemispheric Africa (Roteta et al., 2018; Bastarrika and
Roteta, 2018), the obtained grid cell to grid cell relationship showed r2=0.49 (p=0.0073) for FireCCI50, and this was a little lower with
r2=0.36 (p=0.033) for MCD64A1 c6 when selecting a fire size larger
than 107 ha and grid cells with a p value <0.025 for the
significance of the fire size–fire number relationship. Lower correlations
with Sentinel-2 might result from the smaller time frame (2016–2017) used
for this sensor.
The pixel-level characterization of fire patches was also used to extract
their shape index (SI) information. As an example of pixel-level product use
and the derived fire patches, fire shapes show the drivers of fire spread as
wind-driven fires would be more elongated than fire spreading under mild
conditions. Fire shape complexity can also show the difficulty of the fire
to spread over fragmented landscapes or complex topography (Hargrove et
al., 2000; Cary et al., 2006). On the other side, fire shape complexity can
provide keystone information on fire refuges of unburned islands within or
at the boundary of fire patches (Román-Cuesta et al., 2009). Figure 10
illustrates the mean fire shape index (SI) from Fire_cci
v5.0 and MCD64A1 c6 for the 2000–2016 period. We did not show the difference
between the two products because the aliasing bias depends on the spatial
resolution of each product. The global pattern of fire complexity is
conserved between the two products, with a systematic higher and more
precise characterization of the complexity in the Fire_cci
v5.0 product because of its higher spatial resolution. A minimum shape fire
complexity was observed in savannas and tropical forests, and the highest
complexity in the boreal forests and grasslands. The global
grid cell to grid cell correlation analysis of SI reached r2=0.528
(p≪0.01) between the two products. When correlating SI
values from the FireCCI50 product with the Sentinel-2 BA product for
northern hemispheric Africa, a value of r2=0.111 (p=0.017) was
obtained when the fire patch size was larger than 107 ha. For this fire class
size, a similar correlation (r2=0.111, p=0.01) was obtained for
MCD64A1 c6. When selecting all fire patches from the products (i.e. without
applying any fire size threshold), we obtained a higher correlation between
Sentinel-2 and FireCCI50 SI (r2=0.3, p<0.01), while the
correlation without this fire size threshold was no more significant for
MCD64A1 c6 (r2=-0.004, p=0.9). This result indicates a similar
spatial conservation of mean fire SI for fires >107 ha by
both BA products when compared to fine-resolution Sentinel-2 data at the
continental scale in northern hemispheric Africa. When adding small fires to
the comparison, the spatial pattern of mean SI is no more conserved for
MCD64A1 c6, while the correlation increases for Fire_cci
v5.0. This could suggest a loss of fire shape characterization of small
fires in MCD64A1 c6, with the coarsest resolution as observed in Nogueira et
al. (2017) for South American savannas, but this conclusion should be
considered carefully as it is also highly driven by the higher fire number
in Sentinel-2 than MCD64A1 c6, so the mean SI does not cover the same fire
size panel. The poor correlation in mean SI is in turn more the result of
missing small fires than bias in fire shapes. Regarding MODIS
Fire_cci v.5.0, the number of fire patches is higher than
MCD64A1 c6 and closer to Sentinel-2 due to its finer resolution, and the
mean SI correlation is improved. This suggests that additional small fires
were identified in this product and that they conserved the shape obtained
at the finer Sentinel-2 resolution.
Shape index for FireCCI50 (a) and MCD64A1 c6 (b).
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Discussion
This paper has presented a new global BA product aimed to improve the
information available to climate modellers on spatio-temporal patterns of
fire occurrence. The FireCCI50 is the first global BA product derived from
the two highest spatial-resolution channels (R, NIR) of this sensor, with
approximately 250 m pixel size. This product was generated as theoretically
it provides an improved representation of small fire patches and is thus
more suitable to analyse spatial properties of burned patches than other
existing global BA datasets. A first assessment of the product indicates it
is more sensitive to small fire patches for the African continent than the
MCD64A1 c6 product. However, further assessment is needed to determine to
what extent this observation applies to other continents.
With respect to spatial and temporal variability, the FireCCI50 BA product
showed the highest fire occurrence in dry tropical regions of Africa,
Australia and South America. Global estimates of BA for FireCCI50 product
range from 3.24 Mkm2 in 2013 to 4.16 Mkm2 in 2011, with a slight
trend towards a decline in the total BA in the whole period (2001–2016). This
follows other observations of global BA trends
(Andela et al., 2017), particularly evident in
the last 4 years.
Global average accuracy estimated for the FireCCI50 product was lower than
for the MCD64A1 c6 product, particularly in terms of omission errors. It
should be pointed out that the recently released c6 version has
significantly increased the detection of fire patches with respect to
previous MCD64A1 versions (around 30 % more BA than c5, which is the basis
of GFED4). The main advantage of the MCD64A1 over our product is the use of
short-wave infrared (SWIR) bands, which have been widely reported to be very sensitive to BA
detection (Bastarrika et al., 2011; Giglio et al., 2009; Martín et
al., 2005). However, this implies a reduction in the spatial resolution
of the FireCCI50 product by 4 times. Further efforts are required for achieving higher
accuracy metrics for BA products based on the R–NIR bands, while keeping
their higher spatial resolution. In addition to the MODIS sensor, BA
algorithms based on just NIR bands would also benefit from having higher
spatial resolution than SWIR bands in other sensors, such as the Ocean and Land Colour Instrument (OLCI) or VIIRS.
Improved resolution of BA products is clearly relevant for fire modellers,
as has been shown with the example of the fire patch analysis.
Compared with other existing products, the FireCCI50 product provided higher
accuracy values than FireCCI41, as well as other European BA products based
on SPOT-VEGETATION images (Padilla et al., 2015).
Comparison of our results with the national fire perimeters in three
different fire regimes (boreal, tropical and temperature areas) showed very
satisfactory results, with lower errors than those estimated by the
statistical validation. This may be caused by the longer period the national
fire perimeters refer to (the full year), which should mitigate the impact
of temporal detection accuracy when using short periods of time in between
Landsat pairs (the standard procedure for BA validation). Extending
validation periods to the full year would help to decouple detection and
temporal reporting errors in future validation efforts.