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Article
Estimation of Byram’s Fire Intensity and Rate of Spread from
Spaceborne Remote Sensing Data in a Savanna Landscape
Gernot Ruecker * , David Leimbach and Joachim Tiemann †
Citation: Ruecker, G.; Leimbach, D.;
Tiemann, J. Estimation of Byram’s
Fire Intensity and Rate of Spread from
Spaceborne Remote Sensing Data in a
Savanna Landscape. Fire 2021,4, 65.
https://doi.org/10.3390/fire4040065
Academic Editor: Aziz Ballouche
Received: 20 July 2021
Accepted: 23 September 2021
Published: 29 September 2021
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Copyright: © 2021 by the authors.
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This article is an open access article
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Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
ZEBRIS Geo-IT GmbH, 81373 Munich, Germany; dleimbach@zebris.com (D.L.); joachim.tiemann@web.de (J.T.)
*Correspondence: gruecker@zebris.com
† Now at Landratsamt Breisgau-Hochschwarzwald, 79106 Freiburg, Germany.
Abstract:
Fire behavior is well described by a fire’s direction, rate of spread, and its energy release rate.
Fire intensity as defined by Byram (1959) is the most commonly used term describing fire behavior
in the wildfire community. It is, however, difficult to observe from space. Here, we assess fire
spread and fire radiative power using infrared sensors with different spatial, spectral and temporal
resolutions. The sensors used offer either high spatial resolution (Sentinel-2) for fire detection, but
a low temporal resolution, moderate spatial resolution and daily observations (VIIRS), and high
temporal resolution with low spatial resolution and fire radiative power retrievals (Meteosat SEVIRI).
We extracted fire fronts from Sentinel-2 (using the shortwave infrared bands) and use the available
fire products for S-NPP VIIRS and Meteosat SEVIRI. Rate of spread was analyzed by measuring the
displacement of fire fronts between the mid-morning Sentinel-2 overpasses and the early afternoon
VIIRS overpasses. We retrieved FRP from 15-min Meteosat SEVIRI observations and estimated
total fire radiative energy release over the observed fire fronts. This was then converted to total
fuel consumption, and, by making use of Sentinel-2-derived burned area, to fuel consumption per
unit area. Using rate of spread and fuel consumption per unit area, Byram’s fire intensity could be
derived. We tested this approach on a small number of fires in a frequently burning West African
savanna landscape. Comparison to field experiments in the area showed similar numbers between
field observations and remote-sensing-derived estimates. To the authors’ knowledge, this is the
first direct estimate of Byram’s fire intensity from spaceborne remote sensing data. Shortcomings of
the presented approach, foundations of an error budget, and potential further development, also
considering upcoming sensor systems, are discussed.
Keywords:
fire intensity; fire rate of spread; fire radiative power; fuel consumption; remote sensing
of active fires
1. Introduction
Fire behavior is a general descriptive term comprising different parameters used to
characterize actively burning fires. Often fire behavior is described by a fire’s rate of spread
and its energy release rate. These are combined into fire intensity or fireline intensity as
defined by Byram, which is the most commonly used single parameter describing fire
behavior in the wildfire community [
1
–
3
]. Adequately describing fire behavior is important
to estimate a fire’s ecological impact as well as to decide upon an adequate management
response and, in the case a fire is to be suppressed, to ensure the safety of fire-fighting
operations (see, e.g., [
4
]). Fireline intensity is widely used in savanna fire ecology [
1
,
5
,
6
],
and it has been hypothesized that shifting African fire regimes to early season burns—
which would supposedly be less intense—has an important climate mitigation potential [
7
],
a claim which has recently been disputed [8].
Indeed, despite its popularity and widespread use, fire intensity and rate of spread are
difficult to assess on a large scale or from satellite remote sensing data [
9
]. Following [
3
],
frontal fire intensity can be interpreted as “the energy output (kilowatts) being generated
Fire 2021,4, 65. https://doi.org/10.3390/fire4040065 https://www.mdpi.com/journal/fire
Fire 2021,4, 65 2 of 14
from a strip of the active combustion area, 1 m wide, extending from the leading edge
of the fire front to the rear of the flaming zone”. In its classical formulation, Byram’s fire
intensity is expressed as [2]:
I=Hwr (1)
where Iis the fire intensity (in metric units following [
3
], it is expressed in kW/m), His
the heat yield (kJ/kg), wis the weight of available fuel (kg/m
2
) which is assumed to be
combusted, and ris the forward rate of spread of the fire (m/s). The product Hw is the
available fuel energy. H—called “low heat of combustion”, the heat yield of the fuel—is
the least variable of the parameters making up fire intensity and is usually taken from the
literature for common fuel types. To calculate rate of spread, the speed of propagation
of the fire front needs to be measured, whereas to obtain energy release rate, the fuel
consumption in the actively burning area needs to be assessed [
3
]. While fuel varies over
a relatively narrow range (about 10-fold), rate of spread varies over a large range (about
100-fold) and hence has a stronger influence on fire intensity [3].
Assessing fire behavior from space requires the observation and characterization of
the actively burning fire which is possible through observations in the infrared spectral
domain [10].
Quantitative remote sensing of actively burning fires from satellites became possi-
ble with the beginning of the Moderate-Resolution Imaging Spectroradiometer (MODIS)
([
11
,
12
] and the Bi-Spectral Infrared Detection (BIRD) missions [
10
,
13
] in 2000 and 2001,
respectively (see Table 1for a list of sensors used in this study). The sensors flown on these
missions were the first to feature non-saturating mid- and long-wave infrared channels
(MWIR, LWIR) suitable for characterizing fires. Following a method invented by [
14
,
15
],
effective fire area and temperature could be retrieved through the bi-spectral method from
BIRD data [
10
], and these could be combined to estimate the radiative power of a fire
(Fire Radiative Power, FRP). The authors of [
13
,
16
] developed an alternative approach to
derive FRP from MWIR band data only (single channel method) and showed in small-scale
experiments that FRP is linearly related to fuel consumption rates and can be used to
derive fuel consumption totals. This latter, single-channel approach has the advantage of
being more robust than the bi-spectral method, especially for coarser resolution sensors
where the errors of the bi-spectral retrievals become very large [
10
,
17
], although it suffers
from the limitation that it does not yield fire area and temperature and is not well suited
for cooler, smoldering fires. Despite this limitation, the single-channel method algorithm
has become the de facto standard for the quantitative characterizations of active fires and
has been implemented for both geostationary and polar-orbiting missions and is now
available for the geostationary Meteosat Spinning Enhanced Visible Infra-Red Imager
(SEVIRI) sensor [
18
], Geostationary Operational Environmental Satellite (GOES) -E (East)
and GOES-W (West) [
19
,
20
] and Himawari-8 [
21
] as well as the polar orbiting sensors
Visible Infrared Imaging Radiometer Suite (VIIRS) ([
22
,
23
], MODIS [
24
] and Sea and Land
Surface Temperature Radiometer (SLSTR) [25,26]. The relationship between FRP and fuel
consumption is used to drive the fire emissions component of the Copernicus Global Atmo-
sphere Monitoring Services (CAMS) [
27
] and has been used to estimate fuel consumption
per unit area burned [
28
–
30
]. This latter approach can be implemented through integration
of (ideally continuous) FRP observations to yield the total fire radiative energy (FRE) over
a fire event, which is then converted to fuel consumption (FC) through application of
scaling factors, and finally divided by the burned area associated with the event. Despite its
widespread operational and semi-operational use, the FRP retrievals themselves, and the
relationship between FRP/FRE and fuel consumption have not been thoroughly validated,
and only few studies exist that contribute to this validation effort ([31,32]).
Fire 2021,4, 65 3 of 14
Table 1. Overview of satellite sensors used in this study.
Platform Sensor Spatial Resolution 1Observation Times Use in the Study
Sentinel 2A and 2B MSI 20 m ~10:40, every five days ROS, 1st obs.; burned area
S-NPP and NOAA 20 VIIRS 375 m ~13:30–15:00 and
01:30–3:00, daily ROS, 2nd obs.
Meteosat SEVIRI 3 km Every 15 min Fuel consumption
1Spatial resolution varies depending on the viewing angle for VIIRS and Meteosat.
While the aforementioned sensors offer the possibility to characterize a fire and assess
its fuel consumption within the mentioned limitations, their resolution (ranging from
750 m to a few kilometers) is in general not accurate enough to assess the displacement
of the fire front over shorter distances and time spans. The VIIRS sensor (flying on the
Suomi National Polar Orbiting Partnership, S-NPP, and National Oceanic and Atmospheric
Administration, NOAA-20, satellites) is equipped with two MWIR bands, one—called the I
band (I for imaging)—at a spatial resolution of 375 m, and the other—called the M-band
(M for measuring)—with a spatial resolution of 750 m. Only the M band is designed
for characterizing fires (i.e., non-saturating), but the I-band can be used to detect fires
at the higher spatial resolution of 375 m [
33
] and may therefore be marginally adequate
to monitor fire front displacement. The BIRD and FireBird missions featured a higher
spatial resolution MWIR sensor (also about 350 m) but did not provide data on a more
operational basis, and their usability is therefore limited to case studies [
10
,
34
]. The Optical
Line Imager (OLI) and Multispectral Imager (MSI) sensors flying on board of the Landsat 8
and Sentinel-2 (S-2) missions, respectively, are equipped with Short Wave Infrared (SWIR)
bands at 30 m (Landsat) and 20 m (S-2) resolution. SWIR is more sensible to flaming
fires than to smoldering fires due to the higher temperatures of the former, a fact that has
been exploited to monitor industrial gas flares [
35
–
37
]. The OLI and MSI sensors are not
suitable for fire characterization, since the SWIR bands saturate quickly, but they are able to
detect very small fires and hence to identify the position of the fire front with the accuracy
constrained by the (relatively high) spatial resolution of these sensors [38–40].
Following the advent of quantitative satellite remote sensing of fires, investiga-
tions on measures of fire behavior using these newly available sensors commenced.
Wooster et al. [
41
] first discussed the retrieval of fireline intensity form satellite data.
Using the relatively high-resolution BIRD data, they defined the term “radiative fireline
intensity”, which was defined as the FRP (then still termed “FRE” by the authors) of an
identified fire front, divided by the length of the fire front. Although yielding the same
units (kW/m), this radiative fireline intensity was two orders of magnitude lower than
average ‘expected’ fireline intensities. The authors attributed this to the fact that, on the one
hand, only a small fraction of the released energy during a fire goes to radiation, and that,
on the other hand, “traditional” estimates of fireline intensity do not sufficiently consider
residual combustion happening after the fire front passed and may not correct the low
heat of combustion adequately. The radiative fireline intensity approach was later used
to compare the intensities of fires in Russia and North America [
42
] and to assess fire
behavior of head and back fires from space [
43
]. Johnston et al. [
9
] reviewed and tested
approaches to derive fireline intensity in small-scale experiments. These authors examined
three FRP/FRE-based approaches for deriving Byram’s fire intensity based on different
formulations of the original equation. In the authors terminology, these are:
•
FRED-ROS: Fire Radiative Energy Density (FRED)-ROS: this method uses (pixel-wise)
fire radiative power integrated over the pixel’s burn time to provide pixel FRE and
combines this with a pixel based estimate of rate of spread following [44].
•
FRP-FD: Fire Radiative Power (FRPD)-Flame Depth (FD): this approach is based on a
different formulation of Byram’s equation conceptualizing ROS as the depth of the
flaming zone multiplied by the flame residence time.
•
FRP-FFL: FRP-Fire Front Length (FFL): this is the method based on the FRP measured
along the fire front length as described above and in [42–44].
Fire 2021,4, 65 4 of 14
All of these approaches return radiative fireline intensity, which needs to be converted
to Byram’s fireline intensity by division through the radiative fraction of the total fire
energy release. The study concludes that both the FRED-ROS and FRPD-FD methods
successfully predicted fireline intensity obtained by the ‘traditional method’ and may be
equally accurate, while FRP-FFL did not provide an improved relationship with Byram’s
fireline intensity when compared with FRP alone.
Here, we demonstrate the assessment of fire rate of spread and FRED-ROS-derived fire
intensity using spaceborne infrared sensors with different spatial, spectral and temporal
resolutions in a frequently burning African savanna landscape.
2. Materials and Methods
2.1. Identification of fire Fronts and Estimation of Rate of Spread (ROS)
A fire front’s local rate of spread is calculated by the displacement distance of the fire
front per unit time along a line perpendicular to the local fire front line. Ononye et al. [
45
]
extracted these directions from multispectral airborne data and estimation of the normal to
the fire front, and Paugam et al. [
44
] followed this approach with a handheld IR camera
flying on a helicopter hovering over the fire.
We applied a similar method to S-2 and VIIRS data which we used to measure dis-
placement distance of the fronts, from which we derive rate of spread by dividing the
displacement distance by the time elapsed between the overpasses of the two satellite
sensors over the burning areas. The S-2 MSI sensor features short-wave infrared bands
which are sensitive to flaming fires and can be used to develop active fire detection algo-
rithms [
46
]. Due to the similarities in the relevant bands on the Sentinel-2 MSI and the
Landsat OLI sensor, we implemented the algorithm developed by Schroeder et al. [
38
]
for Landsat to detect fires using Sentinel 2. This algorithm employs (like many other fire
detection algorithms) a series of fixed threshold and contextual tests to detect fires in an
image. The first test detects unambiguous fire pixels (adapted for the corresponding bands
in S-2):
R12 8A>a AND ρ12 −ρ8A>b AND ρ8A>c(2)
where
ρi
is the reflectance in MSI band i,
Rij
is the ratio between channel iand jreflectances,
and a,band care thresholds (a= 2.5, b= 0.3, c= 0.5).
The second rule used by Schroeder to discriminate unambiguous fire pixels is based
on folding of the digital numbers over intensely radiating fires (i.e., high values become
low values), which is an effect that was not observed for S-2, and hence, this test is
not applied.
The third rule is a relaxation of the first rule and is implemented by lowering the
thresholds a,band c(to a= 1.8, b= 0.17 and c= 1.6).
These potential fire pixels undergo a further series of contextual and fixed threshold tests
based upon a 61 by 61 window around the potential fire pixel to be confirmed. These are:
R12 8A>R12 8A+max3σR12 8A, 0.8ANDρ12 >ρ12 +max[ρ12, 0.08]ANDR12 11 >1.6 (3)
where
Rij
and
σRi j
and
ρ12
and
ρ12
are the mean and standard deviation calculated
for the band ration or single-channel reflectance using valid background pixels within the
contextual window. Valid background pixels are showing channel 12 reflectance greater
than zero and excluding water and unambiguous fire pixels. The tests for water are
described in more detail in [38].
A detailed examination of the results of the transfer of this fire detection algorithm
originally developed for the Landsat OLI sensor to Sentinel 2 was out of the scope of this
work, but visual examination of the results showed that the algorithm performed very well
in our study area. Experience from applications in other study areas showed that there is a
certain probability of false positives in built-up areas, but this is not the case in the Comoé
national park.
The detected S-2 fire pixels were converted to polygons for further analysis. These S-2
“fire front” polygons were used as starting points to estimate ROS. Sentinel-2 overpasses
Fire 2021,4, 65 5 of 14
occurred at about 10:40 local time (equaling UTC in our West African study area). VIIRS fire
detections that occur before 15:00 were considered as possible endpoints for the ROS mea-
surement. VIIRS 375 m active fire data were downloaded from the FIRMS (Fire Information
for Resource Management System) website. These data represent the center points of the
pixels flagged as active fires by the VIIRS standard fire detection algorithm [
22
,
38
]. To derive
fire fronts for the ROS calculation, VIIRS active fire pixels were clustered together using
the DBSCAN clustering algorithm [
47
] as implemented in the PostGIS software package.
A concave hull envelope was constructed around each cluster based on the concave hull
algorithm implemented in PostGIS. ROS is then estimated as:
ROS =k*
sS2Vk
tv−ts2
(4)
where
k*
sS2Vk
is the magnitude of the spread vector, which is the hypothetical vector the
fire would travel along if the fire located at the point of origin of the vector at the time of
the S-2 overpass (denoted as
tS2
in Equation (4)) would travel along the normal to the front
line until reaching the outer boundary of the concave hull constructed around the VIIRS
fire detections (with
tv
denoting the time of the VIIRS overpass), assuming these VIIRS fire
detections were caused by the fire spreading out from the S-2 front (the outer boundary
is referred to as the boundary of the hull facing away from the S-2 front). To estimate
k*
sS2Vk
, Sentinel- and VIIRS-derived fire fronts need to be paired. The first criterion on
which to base this pairing is distance. The search for candidate fire fronts to pair the initial
S-2-derived fronts with is based on the extension of the S-2 fire front normal. To construct
the fire front normal, the jagged outlines that resulted from the pixel-to-polygon conversion
were smoothed using a k-means smoothing algorithm as implemented in the “smoothr”
package of R, and outward-facing normal vectors were constructed on the line segments
of the resultant polygon. These normals were used to search for possible VIIRS fire front
pairings, and if a VIIRS fire front was found, then a connector—representing a potential fire
spread vector—was established between the start point on the S-2 fire front line segment
and the outmost endpoint of the normal on the VIIRS fire front concave hull. In the case of
large fire fronts, subsampling was applied to reduce the number of line segments used to
save processing time. Thus, fire front pairs are connected through a varying number of
connecting lines that correspond to hypothetical spread vectors representing linear forward
spread of the fire. While this is clearly not a very realistic assumption, the derived ROS does
provide an average displacement speed which may be considered a lower bound of the
actual speed of travel of the fire front. This, of course, is only valid if a causal relationship
between the S-2 and VIIRS fire detections can be established.
Using this approach in a frequently burning landscape such as our study area, where
fires are widespread, results in a large number of front pairs, many of which are not
plausible. Further intelligence is required to reduce this number and obtain a set of more
reliable font pairs for further analysis. Such an intelligence may include the following rules:
1.
Exclusion of connectors that are crossing already-burned area as the fire is not ex-
pected to travel through already-burned areas.
2.
Exclusion of connectors that cross S-2 fronts that are closer to the connecting VIIRS
fronts than the originating S-2 front, as the connections of the spatially closer S-2 front
shall be used for the ROS calculation.
3.
Exclusion of connectors that go through barriers other than burned area which are
not expected to be crossed by a fire.
4. Exclusion of connectors which are associated with different ignition events.
We only tested an algorithmic solution for the rules 1, 2, and 4. Rule 3 requires
additional data processing and is more complicated to formulate since this rule depends
on ecosystem and weather conditions which were beyond the scope of this initial study. To
exclude fire front pairs that violated rule 4, visual screening of the output connector dataset
was used. The implementation of the other rules is described in the following.
Fire 2021,4, 65 6 of 14
To implement rule 1, already-burned areas were derived from the S-2 data. An
important requirement for the burned area map was that the methods applied should be as
robust as possible with regards to pixels contaminated by smoke, which is especially the
case if the short-wave infrared bands of S-2 are used.
Two spectral indices were calculated, the widely used Normalized Burn Ratio (NBR),
e.g., [
48
], and the Mid-Infrared Burn Index (MIRBI) [
49
]. To avoid misclassifications of
cloud shadows and mask clouds, a cloud mask was applied using the FMask algorithm [
50
].
Mapping of burned areas was conducted by (a) detecting changes in the two indices
between two acquisitions and (b) using a single-scene threshold for MIRBI.
The two scenes used for change detection were co-registered, and detection was based
on a threshold value of 0.3 for the difference MIRBI (t2
−
t1) and the difference NBR
(t2
−
t1) of 0.0. Pixels that exceeded the threshold for the MIRBI difference and were below
the threshold for the NBR difference were classified as burned. The single-scene threshold
for MIRBI was 1.4. This threshold was necessary since clouds and smoke may obscure
scenes, and it is then not possible to obtain two observations of the same spot for change
detection. Since this algorithm had to be more conservative to avoid false alarms, the error
of omission is higher in areas where it was applied. The change detection results were
retained only when no clouds or cloud shadows were detected in both scenes, whereas the
single step results were always retained.
Burned areas were derived for the fire season (starting end of October) using all available
S-2 overpasses. S-2 tiles that had an FMask cloud cover above 80% were excluded from the
analysis, and the next available S-2 image for that tile was used for change detection.
Connectors that crossed areas mapped as burned in S-2 before or at the same time
as the S-2 fire fronts for that date were excluded from further analysis depending on the
length of the intersecting line segment.
To implement rule 2, connectors that crossed another S-2 fire front located between
the originating S-2 fire front and the VIIRS fire front were eliminated.
To implement rule 4, connectors were excluded if less than three Meteosat SEVIRI fire
detections were recorded over the area covered by the paired fronts and their connectors in
the time between the S-2 and VIIRS overpasses.
For all remaining connectors, ROS was calculated using Equation (4).
2.2. Estimating Fuel Consumption and Byram’s Fireline Intensity
To derive fuel consumption (FC), we used temporal integration to estimate total fire
radiative energy for a particular fire event following the conversion of FRE to fuel consump-
tion described in [
16
], enhancing the conversion factor from FRE to FC as proposed by [
28
].
This enhancement is applied as FC derived from satellite FRP tends to underestimate
true FC [
28
,
29
]. Temporal integration is derived from Meteosat FRP retrievals, based on
an approach first presented by Boschetti and Roy for MODIS [
30
], and implemented for
Meteosat by Roberts [
29
]. We use FRP derived from fire detections over the areas covered
by the S-2 and VIIRS fire fronts and their connectors. Meteosat SEVIRI fire detections were
obtained from EUMETSAT, where these are routinely produced following an algorithm
developed by [
18
]. The SEVIRI sensor obtains data every 15 min with a spatial sampling
distance of 3 km at the subsatellite point, and FRP is derived using the single channel MIR
method mentioned in the introduction. FRP was summed up for each observation over the
areas of interest spanned by the S-2 and VIIRS fire detections. For the integration of FRP to
FRE, we linearly interpolated between each observation data point. Missing observations
(e.g., due to cloud or low fire activity) were also linearly interpolated. If an analyzed fire
event did not have observations within one whole hour, the event was excluded from
further analysis. Fuel consumption over the fire event was then estimated by calculating
FRE through integration of FRP over the observation time span between the S-2 and VIIRS
overpass times. Total fuel consumption was then estimated by converting FRE to fuel
consumption by applying the aforementioned conversion factor of 0.368 kg/MJ [
16
] and
the enhancing correction factor of 1.56 [28].
Fire 2021,4, 65 7 of 14
Due to the coarse resolution of the Meteosat data, often several fire front systems
were within the footprint of a cluster of Meteosat fire detections. Since it is not possible to
attribute the FRP of a single-pixel detection to a single fire front S-2–VIIRS pair, FRP for the
whole cluster of detections was analyzed.
Fuel consumption per square meter as required as an input to Byram’s fireline intensity
calculation was then derived by dividing the total fuel consumption by the burned area
under the envelope around all paired fire fronts and their connectors within the Meteosat
fire detection cluster. Byram’s fireline intensity was then estimated for each connector or fire
spread vector based on Equation (1), using a low heat of combustion of 18 700 kW/kg [3].
2.3. Study Area
The ComoéNational Park (CNP) in Côte D’Ivoire is one of the largest national parks
of West Africa and a UNESCO world heritage site due to its rich biodiversity and a unique
pattern of savanna, gallery forest and forest islands [
51
]. CNP lies at the center of one of
three identified fire “hotspots” in Côte D’Ivoire, with the fire season lasting from November
through February with a peak in December and the first half of January [
52
]. Fires occur
mainly in the savanna landscapes and rarely spread into the forest areas where they tend
to quickly go out [
53
]. Large parts of the park support an annual fire regime. Grass and
grass litter are the predominant fuel driving the fires in CNP, and grass phytomass has
been measured in the southern part of CNP to be in the range between about 0.4 kg/m
2
(0.25 quantile) and 0.9 kg/m
2
(0.75 quantile) [
53
]. Work carried out by the first author
of the present paper in cooperation with the National Parks Agency (Office Ivoirien des
Parcs et Réserves, OIPR) includes two fire experiments in the south-central area of the
park during the late fire season in February 2019. Fuel load in these fires was between
0.4 and 0.7 kg/m
2
and fuel consumption between 0.33 and 0.65 kg/m
2
with a combustion
completeness between 83 and 93%. Observed rates of spread in the experimental fires was
0.14 and 0.15 m/s and fire intensity 960 and 1500 kW/m, respectively [54].
The algorithm was tested on all Sentinel 2 acquisitions over the ComoéNational
Park and its vicinity from mid-November to end of December of 2020, a time interval that
covered early and high fire season in Northern Côte D’Ivoire [52].
3. Results
Implementing the tests outlined in the methods section greatly reduced the number of
fire front pairs available for analysis, and it was necessary to relax the criteria for exclusion
of already-burned areas. On the other hand, a substantial number of implausible fire spread
vectors were still present in the resulting dataset, e.g., vectors crossing a river or gallery
forest. For a first quantitative analysis of fire intensity and rate of spread, fire clusters were
therefore visually screened, and analysis limited to samples which were considered to be
most likely associated to a common ignition and the associated connection between the
fronts through spread vectors were considered likely to be correct. This was performed,
e.g., through visual comparison with the false color VIIRS satellite images corresponding
to the VIIRS detections, as well as visual comparison to S-2 false color images showing
the burned areas after the next overpass. A total of 30 fire clusters was analyzed covering
five S-2 acquisitions. For three dates (November 30 and December 10 and 15), no usable
samples could be identified due to clouds or low fire activity, and no usable samples were
found before November 20.
3.1. Illustration of Rate of Spread, Fuel Consumption and Fireline Intensity Retrievals
Figures 1–5illustrate the retrieval of ROS, FC and FI for three fire clusters that burned
in the study area on November 20 and 25, 2020. Figure 1shows the S-2 image subsets which
were used to retrieve the S-2 fire fronts. Figure 2illustrates the spread vectors which were
constructed from the normals on the smoothed S-2 fire front edges, which were extended
until the outer edge of the hull around the VIIRS fire detections from the NOAA 2 or S-NPP
Fire 2021,4, 65 8 of 14
satellites. Depending on the frontal geometry, these hypothetical spread vectors can cross
each other, which sometimes leads to unrealistic spread paths.
Fire2021,4,xFORPEERREVIEW8of15
firespreadvectorswerestillpresentintheresultingdataset,e.g.,vectorscrossingariver
orgalleryforest.Forafirstquantitativeanalysisoffireintensityandrateofspread,fire
clusterswerethereforevisuallyscreened,andanalysislimitedtosampleswhichwere
consideredtobemostlikelyassociatedtoacommonignitionandtheassociated
connectionbetweenthefrontsthroughspreadvectorswereconsideredlikelytobecorrect.
Thiswasperformed,e.g.,throughvisualcomparisonwiththefalsecolorVIIRSsatellite
imagescorrespondingtotheVIIRSdetections,aswellasvisualcomparisontoS‐2false
colorimagesshowingtheburnedareasafterthenextoverpass.Atotalof30fireclusters
wasanalyzedcoveringfiveS‐2acquisitions.Forthreedates(November30andDecember
10and15),nousablesamplescouldbeidentifiedduetocloudsorlowfireactivity,and
nousablesampleswerefoundbeforeNovember20.
3.1.IllustrationofRateofSpread,FuelConsumptionandFirelineIntensityRetrievals
Figures1–5illustratetheretrievalofROS,FCandFIforthreefireclustersthatburned
inthestudyareaonNovember20and25,2020.Figure1showstheS‐2imagesubsets
whichwereusedtoretrievetheS‐2firefronts.Figure2illustratesthespreadvectorswhich
wereconstructedfromthenormalsonthesmoothedS‐2firefrontedges,whichwere
extendeduntiltheouteredgeofthehullaroundtheVIIRSfiredetectionsfromtheNOAA
2orS‐NPPsatellites.Dependingonthefrontalgeometry,thesehypotheticalspread
vectorscancrosseachother,whichsometimesleadstounrealisticspreadpaths.
Figure1.FalsecolorS‐2imagesof20(left)and25November2020(centerandrightpanel)which
wereusedasstartingpointsforestimatingROSfortheexamplespresentedinFigure2ff.The
detectedS‐2firefrontswhichwereusedfortheROSdeterminationaremarkedwithblackoutlines.
CoordinatesontheaxesforallmapsareUTMcoordinates(inm)forUTMzone30N.
Figure2.Rate‐of‐spreadvectorscolorcodedbyROS(m/min)fortheS‐2observationsinFigure1.
RedfilledpolygonscorrespondtoS‐2firedetections,redoutlinedpolygonscorrespondtothe
concavehullshapesconstructedfromtheVIIRSfiredetectionpixelcenterpoints.Theimagesmay
haveVIIRSfiredetectionenvelopesfromdifferentoverpasses(associatedwithNOAA20andS‐
NPPsatellites)andhencespreadvectorsofsimilarlengthmayhavedifferentassociatedROS
measures.ThenumbersonorneartheVIIRSfiredetectionenvelopescorrespondtotheassociated
boxplotsinFigure7.
1748
1646
2658
2716
2704
2559
2634
2780
Figure 1.
False color S-2 images of 20 (
left
) and 25 November 2020 (
center
and
right
panel) which were used as starting
points for estimating ROS for the examples presented in Figure 2ff. The detected S-2 fire fronts which were used for the
ROS determination are marked with black outlines. Coordinates on the axes for all maps are UTM coordinates (in m) for
UTM zone 30 N.
Fire2021,4,xFORPEERREVIEW8of15
firespreadvectorswerestillpresentintheresultingdataset,e.g.,vectorscrossingariver
orgalleryforest.Forafirstquantitativeanalysisoffireintensityandrateofspread,fire
clusterswerethereforevisuallyscreened,andanalysislimitedtosampleswhichwere
consideredtobemostlikelyassociatedtoacommonignitionandtheassociated
connectionbetweenthefrontsthroughspreadvectorswereconsideredlikelytobecorrect.
Thiswasperformed,e.g.,throughvisualcomparisonwiththefalsecolorVIIRSsatellite
imagescorrespondingtotheVIIRSdetections,aswellasvisualcomparisontoS‐2false
colorimagesshowingtheburnedareasafterthenextoverpass.Atotalof30fireclusters
wasanalyzedcoveringfiveS‐2acquisitions.Forthreedates(November30andDecember
10and15),nousablesamplescouldbeidentifiedduetocloudsorlowfireactivity,and
nousablesampleswerefoundbeforeNovember20.
3.1.IllustrationofRateofSpread,FuelConsumptionandFirelineIntensityRetrievals
Figures1–5illustratetheretrievalofROS,FCandFIforthreefireclustersthatburned
inthestudyareaonNovember20and25,2020.Figure1showstheS‐2imagesubsets
whichwereusedtoretrievetheS‐2firefronts.Figure2illustratesthespreadvectorswhich
wereconstructedfromthenormalsonthesmoothedS‐2firefrontedges,whichwere
extendeduntiltheouteredgeofthehullaroundtheVIIRSfiredetectionsfromtheNOAA
2orS‐NPPsatellites.Dependingonthefrontalgeometry,thesehypotheticalspread
vectorscancrosseachother,whichsometimesleadstounrealisticspreadpaths.
Figure1.FalsecolorS‐2imagesof20(left)and25November2020(centerandrightpanel)which
wereusedasstartingpointsforestimatingROSfortheexamplespresentedinFigure2ff.The
detectedS‐2firefrontswhichwereusedfortheROSdeterminationaremarkedwithblackoutlines.
CoordinatesontheaxesforallmapsareUTMcoordinates(inm)forUTMzone30N.
Figure2.Rate‐of‐spreadvectorscolorcodedbyROS(m/min)fortheS‐2observationsinFigure1.
RedfilledpolygonscorrespondtoS‐2firedetections,redoutlinedpolygonscorrespondtothe
concavehullshapesconstructedfromtheVIIRSfiredetectionpixelcenterpoints.Theimagesmay
haveVIIRSfiredetectionenvelopesfromdifferentoverpasses(associatedwithNOAA20andS‐
NPPsatellites)andhencespreadvectorsofsimilarlengthmayhavedifferentassociatedROS
measures.ThenumbersonorneartheVIIRSfiredetectionenvelopescorrespondtotheassociated
boxplotsinFigure7.
1748
1646
2658
2716
2704
2559
2634
2780
Figure 2.
Rate-of-spread vectors color coded by ROS (m/min) for the S-2 observations in Figure 1. Red filled polygons
correspond to S-2 fire detections, red outlined polygons correspond to the concave hull shapes constructed from the VIIRS
fire detection pixel center points. The images may have VIIRS fire detection envelopes from different overpasses (associated
with NOAA 20 and S-NPP satellites) and hence spread vectors of similar length may have different associated ROS measures.
The numbers on or near the VIIRS fire detection envelopes correspond to the associated boxplots in Figure 7.
Fire2021,4,xFORPEERREVIEW9of15
Figure3.SummedMeteosatSEVIRIFRPoverthefirefrontclustersdepictedinFigures1and2.Red
verticallinesindicatethetimesoftheS‐2(leftline)andVIIRS(rightline)overpasseswhichwere
usedforthetemporalintegrationandcreationoftheburnedareaenvelopes.
Figure4.BurnedareaenvelopesoverthefirefrontclustersdepictedinFigures1–3.Grayareashave
beenmappedasburnedusingS‐2,redpolygonscorrespondtoS‐2firedetectionsandredoutlines
toVIIRSfiredetectionenvelopes.ReddotsarethecentercoordinatesoftheVIIRSfiredetection
pixels.
Figure5.Colorcodedfireintensities(kW/m)derivedforthefirefrontsofFigures1and2(forfurther
detailsrefertoFigure2).
Figure3showsthesummedFRPfromMeteosatSEVIRI.Thissummationisbasedon
aselectionprocessofMeteosatactivefiredetectionsbasedontheirproximitytoanyof
theVIIRSandS‐2firedetectionsthatarejoinedtoeachotherthroughpotentialspread
vectors.Theseenvelopesarethereforeinmanycaseslargerthanthefootprintsofthefire
clustersinFigures2and5.Thisisduetothecoarsespatialresolutionwhichmakesit
impossibletoresolvetheFRPattributedtoaspecificfireclusterinmorespatialdetail.The
relationtoFCperunitareaisillustratedinFigure4,whichshowstheenvelopesbasedon
theS‐2andVIIRSfiredetectionswhichcircumscribetheareathathaspotentiallybeen
burnedbetweentheS‐2andVIIRSoverpasses.Anunknown,thoughprobablynotvery
large,errorisintroducedhereasfiresmayhaveignitedaftertheS‐2overpassandwent
outbeforetheVIIRSoverpassandthereforeleavenotracesintheburnedareaenvelopes
butdocontributetotheFRPdetectedbyMeteosat,whichwouldleadtoanoverestimation
ofFCandconsequentlyFI.FCperunitareaisthenderivedfromtheburnedareathatwas
mappedbyusingS‐2dataobtainedaftertheS‐2overpassusedforROSanalysis.Accuracy
oftheburnedareaproducthencealsoinfluencesFCestimates,ascommissionerrors
contributetounderestimatingFCwhileomissionerrorscontributetooverestimatingFC.
ArtefactscanbeseenforinstanceintheleftpanelofFigure4,wheretheareainfrontof
theS‐2firefrontswasnotmappedashavingbeenburnedaftertheS‐2overpass.
Figure 3.
Summed Meteosat SEVIRI FRP over the fire front clusters depicted in Figures 1and 2. Red vertical lines indicate
the times of the S-2 (left line) and VIIRS (right line) overpasses which were used for the temporal integration and creation of
the burned area envelopes.
Fire2021,4,xFORPEERREVIEW9of15
Figure3.SummedMeteosatSEVIRIFRPoverthefirefrontclustersdepictedinFigures1and2.Red
verticallinesindicatethetimesoftheS‐2(leftline)andVIIRS(rightline)overpasseswhichwere
usedforthetemporalintegrationandcreationoftheburnedareaenvelopes.
Figure4.BurnedareaenvelopesoverthefirefrontclustersdepictedinFigures1–3.Grayareashave
beenmappedasburnedusingS‐2,redpolygonscorrespondtoS‐2firedetectionsandredoutlines
toVIIRSfiredetectionenvelopes.ReddotsarethecentercoordinatesoftheVIIRSfiredetection
pixels.
Figure5.Colorcodedfireintensities(kW/m)derivedforthefirefrontsofFigures1and2(forfurther
detailsrefertoFigure2).
Figure3showsthesummedFRPfromMeteosatSEVIRI.Thissummationisbasedon
aselectionprocessofMeteosatactivefiredetectionsbasedontheirproximitytoanyof
theVIIRSandS‐2firedetectionsthatarejoinedtoeachotherthroughpotentialspread
vectors.Theseenvelopesarethereforeinmanycaseslargerthanthefootprintsofthefire
clustersinFigures2and5.Thisisduetothecoarsespatialresolutionwhichmakesit
impossibletoresolvetheFRPattributedtoaspecificfireclusterinmorespatialdetail.The
relationtoFCperunitareaisillustratedinFigure4,whichshowstheenvelopesbasedon
theS‐2andVIIRSfiredetectionswhichcircumscribetheareathathaspotentiallybeen
burnedbetweentheS‐2andVIIRSoverpasses.Anunknown,thoughprobablynotvery
large,errorisintroducedhereasfiresmayhaveignitedaftertheS‐2overpassandwent
outbeforetheVIIRSoverpassandthereforeleavenotracesintheburnedareaenvelopes
butdocontributetotheFRPdetectedbyMeteosat,whichwouldleadtoanoverestimation
ofFCandconsequentlyFI.FCperunitareaisthenderivedfromtheburnedareathatwas
mappedbyusingS‐2dataobtainedaftertheS‐2overpassusedforROSanalysis.Accuracy
oftheburnedareaproducthencealsoinfluencesFCestimates,ascommissionerrors
contributetounderestimatingFCwhileomissionerrorscontributetooverestimatingFC.
ArtefactscanbeseenforinstanceintheleftpanelofFigure4,wheretheareainfrontof
theS‐2firefrontswasnotmappedashavingbeenburnedaftertheS‐2overpass.
Figure 4.
Burned area envelopes over the fire front clusters depicted in Figures 1–3. Gray areas have been mapped as
burned using S-2, red polygons correspond to S-2 fire detections and red outlines to VIIRS fire detection envelopes. Red
dots are the center coordinates of the VIIRS fire detection pixels.
Fire 2021,4, 65 9 of 14
Fire2021,4,xFORPEERREVIEW9of15
Figure3.SummedMeteosatSEVIRIFRPoverthefirefrontclustersdepictedinFigures1and2.Red
verticallinesindicatethetimesoftheS‐2(leftline)andVIIRS(rightline)overpasseswhichwere
usedforthetemporalintegrationandcreationoftheburnedareaenvelopes.
Figure4.BurnedareaenvelopesoverthefirefrontclustersdepictedinFigures1–3.Grayareashave
beenmappedasburnedusingS‐2,redpolygonscorrespondtoS‐2firedetectionsandredoutlines
toVIIRSfiredetectionenvelopes.ReddotsarethecentercoordinatesoftheVIIRSfiredetection
pixels.
Figure5.Colorcodedfireintensities(kW/m)derivedforthefirefrontsofFigures1and2(forfurther
detailsrefertoFigure2).
Figure3showsthesummedFRPfromMeteosatSEVIRI.Thissummationisbasedon
aselectionprocessofMeteosatactivefiredetectionsbasedontheirproximitytoanyof
theVIIRSandS‐2firedetectionsthatarejoinedtoeachotherthroughpotentialspread
vectors.Theseenvelopesarethereforeinmanycaseslargerthanthefootprintsofthefire
clustersinFigures2and5.Thisisduetothecoarsespatialresolutionwhichmakesit
impossibletoresolvetheFRPattributedtoaspecificfireclusterinmorespatialdetail.The
relationtoFCperunitareaisillustratedinFigure4,whichshowstheenvelopesbasedon
theS‐2andVIIRSfiredetectionswhichcircumscribetheareathathaspotentiallybeen
burnedbetweentheS‐2andVIIRSoverpasses.Anunknown,thoughprobablynotvery
large,errorisintroducedhereasfiresmayhaveignitedaftertheS‐2overpassandwent
outbeforetheVIIRSoverpassandthereforeleavenotracesintheburnedareaenvelopes
butdocontributetotheFRPdetectedbyMeteosat,whichwouldleadtoanoverestimation
ofFCandconsequentlyFI.FCperunitareaisthenderivedfromtheburnedareathatwas
mappedbyusingS‐2dataobtainedaftertheS‐2overpassusedforROSanalysis.Accuracy
oftheburnedareaproducthencealsoinfluencesFCestimates,ascommissionerrors
contributetounderestimatingFCwhileomissionerrorscontributetooverestimatingFC.
ArtefactscanbeseenforinstanceintheleftpanelofFigure4,wheretheareainfrontof
theS‐2firefrontswasnotmappedashavingbeenburnedaftertheS‐2overpass.
Figure 5.
Color coded fire intensities (kW/m) derived for the fire fronts of Figures 1and 2(for further details refer to
Figure 2).
Figure 3shows the summed FRP from Meteosat SEVIRI. This summation is based
on a selection process of Meteosat active fire detections based on their proximity to any
of the VIIRS and S-2 fire detections that are joined to each other through potential spread
vectors. These envelopes are therefore in many cases larger than the footprints of the fire
clusters in Figures 2and 5. This is due to the coarse spatial resolution which makes it
impossible to resolve the FRP attributed to a specific fire cluster in more spatial detail. The
relation to FC per unit area is illustrated in Figure 4, which shows the envelopes based
on the S-2 and VIIRS fire detections which circumscribe the area that has potentially been
burned between the S-2 and VIIRS overpasses. An unknown, though probably not very
large, error is introduced here as fires may have ignited after the S-2 overpass and went
out before the VIIRS overpass and therefore leave no traces in the burned area envelopes
but do contribute to the FRP detected by Meteosat, which would lead to an overestimation
of FC and consequently FI. FC per unit area is then derived from the burned area that
was mapped by using S-2 data obtained after the S-2 overpass used for ROS analysis.
Accuracy of the burned area product hence also influences FC estimates, as commission
errors contribute to underestimating FC while omission errors contribute to overestimating
FC. Artefacts can be seen for instance in the left panel of Figure 4, where the area in front of
the S-2 fire fronts was not mapped as having been burned after the S-2 overpass.
3.2. Characteristics of the Observed Fire Clusters
The analyzed fire clusters had a Meteosat-derived fuel consumption estimate ranging
from 0.14 to 1.17 kg/m
2
with a mean of 0.49 kg/m
2
(sd: 0.27 kg/m
2
). Figure 6shows
boxplots of FC for the different dates analyzed. Late November and early December had
lower fuel consumption than late December, which is consistent with the assumption
that combustion completeness increases as fuel dries out with the progressing fire season.
The ROS and FI estimates are presented as boxplots of summary statistics derived from
the single spread vectors connecting the S-2 and VIIRS fronts analyzed. These measures
were the mean of all connectors associated with one cluster, the means of the 0.75 quan-
tile of the connectors within each analyzed fire cluster, and the mean of the 0.9 quantile
of connectors. The mean of cluster means was 0.07 m/s, minimum cluster mean was
0.02 m/s, and maximum cluster mean was 0.21 m/s. These values include head, flank-
ing and back fires. To obtain a better estimate for ROS attained by head fires, i.e., fires
burning with the wind, the 0.75 and 0.9 quantiles of spread vectors for each fire cluster
were analyzed. Mean of the 0.75 quantile over all days and clusters was 0.09 m/s (max:
0.29 m/s, min: 0.028 m/s), and mean of the 0.9 quantile was 0.11 m/s (min: 0.04 m/s, max:
0.3 m/s). No strong differences could be observed between the days analyzed. Boxplots for
the 90% percentile are given in Figure 6. Mean of the mean fire intensities over all clusters
was 608 kW/m (sd: 350 kW/m, min: 166 kW/m, max: 1510 kW/m). The mean of the 90%
percentile of FI was 928 kW/m (min: 286 kW/m, max: 2316 kW/m, see Figure 6). Fire
intensities were influenced by the fuel consumption estimates and were slightly higher in
the late December observations (December 25 and 30) and lower in the late November and
Fire 2021,4, 65 10 of 14
early December observations with the exception of November 20 that had a similar median
FI as the late December observations, but a lower 0.75 quantile.
Fire2021,4,xFORPEERREVIEW10of15
3.2.CharacteristicsoftheObservedFireClusters
TheanalyzedfireclustershadaMeteosat‐derivedfuelconsumptionestimateranging
from0.14to1.17kg/m²withameanof0.49kg/m²(sd:0.27kg/m²).Figure6showsboxplots
ofFCforthedifferentdatesanalyzed.LateNovemberandearlyDecemberhadlowerfuel
consumptionthanlateDecember,whichisconsistentwiththeassumptionthat
combustioncompletenessincreasesasfueldriesoutwiththeprogressingfireseason.The
ROSandFIestimatesarepresentedasboxplotsofsummarystatisticsderivedfromthe
singlespreadvectorsconnectingtheS‐2andVIIRSfrontsanalyzed.Thesemeasureswere
themeanofallconnectorsassociatedwithonecluster,themeansofthe0.75quantileof
theconnectorswithineachanalyzedfirecluster,andthemeanofthe0.9quantileof
connectors.Themeanofclustermeanswas0.07m/s,minimumclustermeanwas0.02m/s,
andmaximumclustermeanwas0.21m/s.Thesevaluesincludehead,flankingandback
fires.ToobtainabetterestimateforROSattainedbyheadfires,i.e.,firesburningwiththe
wind,the0.75and0.9quantilesofspreadvectorsforeachfireclusterwereanalyzed.Mean
ofthe0.75quantileoveralldaysandclusterswas0.09m/s(max:0.29m/s,min:0.028m/s),
andmeanofthe0.9quantilewas0.11m/s(min:0.04m/s,max:0.3m/s).Nostrong
differencescouldbeobservedbetweenthedaysanalyzed.Boxplotsforthe90%percentile
aregiveninFigure6.Meanofthemeanfireintensitiesoverallclusterswas608kW/m(sd:
350kW/m,min:166kW/m,max:1510kW/m).Themeanofthe90%percentileofFIwas
928kW/m(min:286kW/m,max:2316kW/m,seeFigure6).Fireintensitieswere
influencedbythefuelconsumptionestimatesandwereslightlyhigherinthelate
Decemberobservations(December25and30)andlowerinthelateNovemberandearly
DecemberobservationswiththeexceptionofNovember20thathadasimilarmedianFI
asthelateDecemberobservations,butalower0.75quantile.
Figure6.BoxplotsofROS(left),FC(middle)andFI(right)oftheanalyzedfireclusters.ROSandFI
valuesrepresentthemeansofthe90%quantilesofthefirespreadvectorsoverthedifferentfire
clusters.Numberofobservationsare:11‐20:8,11‐25:6,12‐05:3,12‐25:4,12‐30:9.
Forsomefires,itwaspossibletoassociatetheheadandflankingfireswithVIIRSfire
clusters,andinrarecases,headandbackfires’spreadcouldbeobservedandassociated
withdifferentVIIRSfireclusters.InFigure7,boxplotsofthefireintensityofthespread
vectorsoftheclustersofFigures2and4showthattheflankingfireshavesubstantially
lowerintensities(duetotheirlowerspreadrates)thantheheadfires.Equally,theback
firehasasignificantlylowerintensitythantheassumedheadfires.
Figure 6.
Boxplots of ROS (
left
), FC (
middle
) and FI (
right
) of the analyzed fire clusters. ROS and FI values represent the
means of the 90% quantiles of the fire spread vectors over the different fire clusters. Number of observations are: 11-20:8,
11-25:6, 12-05:3, 12-25:4, 12-30:9.
For some fires, it was possible to associate the head and flanking fires with VIIRS fire
clusters, and in rare cases, head and back fires’ spread could be observed and associated
with different VIIRS fire clusters. In Figure 7, boxplots of the fire intensity of the spread
vectors of the clusters of Figures 2and 4show that the flanking fires have substantially
lower intensities (due to their lower spread rates) than the head fires. Equally, the back fire
has a significantly lower intensity than the assumed head fires.
Fire2021,4,xFORPEERREVIEW10of15
3.2.CharacteristicsoftheObservedFireClusters
TheanalyzedfireclustershadaMeteosat‐derivedfuelconsumptionestimateranging
from0.14to1.17kg/m²withameanof0.49kg/m²(sd:0.27kg/m²).Figure6showsboxplots
ofFCforthedifferentdatesanalyzed.LateNovemberandearlyDecemberhadlowerfuel
consumptionthanlateDecember,whichisconsistentwiththeassumptionthat
combustioncompletenessincreasesasfueldriesoutwiththeprogressingfireseason.The
ROSandFIestimatesarepresentedasboxplotsofsummarystatisticsderivedfromthe
singlespreadvectorsconnectingtheS‐2andVIIRSfrontsanalyzed.Thesemeasureswere
themeanofallconnectorsassociatedwithonecluster,themeansofthe0.75quantileof
theconnectorswithineachanalyzedfirecluster,andthemeanofthe0.9quantileof
connectors.Themeanofclustermeanswas0.07m/s,minimumclustermeanwas0.02m/s,
andmaximumclustermeanwas0.21m/s.Thesevaluesincludehead,flankingandback
fires.ToobtainabetterestimateforROSattainedbyheadfires,i.e.,firesburningwiththe
wind,the0.75and0.9quantilesofspreadvectorsforeachfireclusterwereanalyzed.Mean
ofthe0.75quantileoveralldaysandclusterswas0.09m/s(max:0.29m/s,min:0.028m/s),
andmeanofthe0.9quantilewas0.11m/s(min:0.04m/s,max:0.3m/s).Nostrong
differencescouldbeobservedbetweenthedaysanalyzed.Boxplotsforthe90%percentile
aregiveninFigure6.Meanofthemeanfireintensitiesoverallclusterswas608kW/m(sd:
350kW/m,min:166kW/m,max:1510kW/m).Themeanofthe90%percentileofFIwas
928kW/m(min:286kW/m,max:2316kW/m,seeFigure6).Fireintensitieswere
influencedbythefuelconsumptionestimatesandwereslightlyhigherinthelate
Decemberobservations(December25and30)andlowerinthelateNovemberandearly
DecemberobservationswiththeexceptionofNovember20thathadasimilarmedianFI
asthelateDecemberobservations,butalower0.75quantile.
Figure6.BoxplotsofROS(left),FC(middle)andFI(right)oftheanalyzedfireclusters.ROSandFI
valuesrepresentthemeansofthe90%quantilesofthefirespreadvectorsoverthedifferentfire
clusters.Numberofobservationsare:11‐20:8,11‐25:6,12‐05:3,12‐25:4,12‐30:9.
Forsomefires,itwaspossibletoassociatetheheadandflankingfireswithVIIRSfire
clusters,andinrarecases,headandbackfires’spreadcouldbeobservedandassociated
withdifferentVIIRSfireclusters.InFigure7,boxplotsofthefireintensityofthespread
vectorsoftheclustersofFigures2and4showthattheflankingfireshavesubstantially
lowerintensities(duetotheirlowerspreadrates)thantheheadfires.Equally,theback
firehasasignificantlylowerintensitythantheassumedheadfires.
Figure 7. Boxplots of FI for VIIRS fire clusters that could visually be associated with head fires and flanking fires (left and
right
panel) and head and backing fires (
middle
panel). The fire clusters correspond to those depicted in Figure 2ff (in the
same left-to-right order). The numbers of the VIIRS subclusters on the x-axis correspond to the numbers on or near these
clusters in Figure 2.
4. Discussion
We have developed and tested a method to directly estimate rate of spread and
Byram’s fire or fireline intensity from satellite remote sensing observations using high
(20 m), medium (375 m) and coarse resolution (~3 km) active fire data. Our methods differ
from previous studies on spaceborne fire intensity [
41
–
43
] in that these works provided
“radiative” fireline intensity, which is usually an order of magnitude lower than fireline
intensity estimated in the field, while our approach tries to directly derive the main
parameters of Byram’s classical equation from remote sensing data and hence yields fire
intensity in similar numbers as those observed in the field. It is possible to independently
validate the single input parameters to establish an error budget, e.g. through airborne
experiments. Furthermore, rate of spread and fuel consumption, which are first-order
measures of fire behavior [55], are estimated in addition to fire intensity.
Our fire intensity, rate of spread and fuel consumption estimates are similar to those
obtained from field experiments in the region; they fall within the range found in CNP [
54
]
and in the Lamto reserve which is located further South and has higher fuel loads and
hence somewhat higher fire intensities than those observed in CNP [
56
]. The fire intensities
and rate of spread observed in this study are in turn a bit higher than those measured in
the field in a “working landscape” in southern Mali which is further north than our study
site [57].
Fire 2021,4, 65 11 of 14
While these first results seem promising, application on a larger scale would still
require a considerable research investment in developing a stable algorithm which would
allow one to process a larger amount of data to, e.g., develop a fire intensity time series and
fire climatology on the landscape scale. Such a data product could possibly complement
existing products such as those defined for fire in the Essential Climate Variables concept
(ECVs) [
58
], which are active fires, burned areas and FRP. Among the main issues that need
to be addressed to produce an automated product in the future is the correct pairing of the
Sentinel and VIIRS-derived fire fronts. Despite implementing the different tests described
in the methods section, implausible connections between fronts have been selected, while at
the same time, plausible connections had been removed by the original algorithm. Methods
based on artificial intelligence such as pattern recognition could potentially help to establish
a more robust method to perform the pairing. Regarding the establishment of the spread
vectors themselves, the approach to simply extend the fire front normal until it hits a VIIRS
front can possibly also be improved through other methods that find more likely pathways.
To establish an error budget, a prerequisite for a potential future product, errors in
ROS and FC need to be constrained. ROS errors stem from locational error (influenced,
i.a., by pixel size and point spread function of the sensors) in the position of the fire
front and by the error in estimating the distance travelled by the fire front, whereas the
error in the time difference (resulting from the sensor overpass times) can be neglected.
Reference [
59
] showed for small-scale experiments that fire front rate of spread estimates are
strongly influenced by measurement approach, and this also holds true for satellite-based
observations. To evaluate errors stemming from the measurement approach, airborne
experiments on experimental fires could be used to develop and test different approaches.
According to Byram [
2
,
3
], FC in fire intensity is the fuel consumed in the active flaming
zone. However, in “traditional” fire experiments to derive fire intensity, FC in the active
flaming zone cannot be separated from total FC, and this is in part used to explain the
large differences between fire intensity and radiative fireline intensity [
41
]. On the other
hand, FRP derived through the single-band MWIR method [
15
] is more sensitive to flaming
combustion, and we do not assume that in predominantly flaming grass fires, a large error
on FC can be attributed to the missing distinction between FRP generated in the flaming
zone from total FRP. A substantially larger error stems from the conversion of FRP to FC,
and as outlined in the introduction section of this paper, this is an area of active research,
and there is a need for better constraining these relationships which are, e.g., used in the
Copernicus Atmosphere Monitoring Service (CAMS) [
27
]. A further error is introduced
by missing observations of the fire by the SEVIRI sensor, as well as by missing weaker
radiating fires in general. We tried to reduce this error by excluding clusters with too
many missing observations (see methods section). For the establishment of an error budget,
resampling techniques, e.g., jacknife resampling [
60
], could be used to better constrain this
error. To arrive at FC per unit area as needed in the fire intensity calculation, the division by
the area that burned between the two observations of the fire front is needed. This quantity
is influenced (a) by the accuracy of the assessment of burned area (in our test case derived
from S-2 data), and (b) by the burned area envelope which is formed by the area enclosed
by the connect S-2 and VIIRS fire fronts and their connecting spread vectors.
The approach demonstrated here can be carried to other sensors and other areas of the
globe, ideally where geostationary satellites (currently, apart from Meteosat, the GOES [
19
]
and Himawari [
21
] satellites) with a high temporal resolution and FRP retrievals provide
the option to estimate fuel consumption over the Americas, parts of Asia and Australia as
does Meteosat for Africa and parts of Europe and South America. For other parts of the
world, our approach would still be feasible if coarser FC estimates can be performed from
polar orbiting satellites or other sources. Within the next couple of years, the Meteosat
Third Generation satellites will be equipped with a 1 km spatial resolution fire detection
and characterization capability every ten minutes over Europe, Africa and parts of South
America [
61
], which will substantially improve the ability to characterize fires and track
single-fire events, e.g., for derivation of fire behavior parameters such as discussed here.
Fire 2021,4, 65 12 of 14
Other upcoming instruments such as the planned Canadian WildFireSat mission [
62
] and
the proposed DIEGO mission on the International Space Station [
63
] will provide additional
opportunities to derive wildfire ROS at high spatial resolution at varying overpass times
(DIEGO) and medium spatial resolution fire detection at a currently under-observed late
afternoon overpass time (WildFireSat).
Author Contributions:
Conceptualization, G.R.; methodology, G.R.; software, G.R. and D.L.; formal
analysis, G.R., D.L. and J.T.; data curation, G.R., D.L. and J.T.; writing—original draft preparation,
G.R.; writing—review and editing, G.R.; project administration, G.R.; funding acquisition, G.R. All
authors have read and agreed to the published version of the manuscript.
Funding:
This research was partially funded by the ZIM program of the German Ministry of Economy,
grant number 16KN052420.
Data Availability Statement:
Sentinel-2 data are available at https://scihub.copernicus.eu/, ac-
cessed on 1 July 2021, VIIRS data are available through FIRMS (https://earthdata.nasa.gov/firms,
accessed on 1 July 2021) and Meteosat through EUMETSAT (https://landsaf.ipma.pt/en/, accessed
on 1 July 2021).
Acknowledgments:
The authors are grateful for the provision of free and open data, namely from
NASA’s Fire Information for Resource Management System (FIRMS), part of NASA’s Earth Observing
System Data and Information System (EOSDIS), the Copernicus program of the European Union, the
NOAA polar orbiting satellite system programs and EUMETSAT, the operator of Meteosat.
Conflicts of Interest: The authors declare no conflict of interest.
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