Review of MTG FDHSI Mission Requirements
Regarding Fire Applications
Dr M.J. Wooster and Dr G. Roberts
Environmental Monitoring and Modelling Group,
Department of Geography
King’s College London
Document is based on the Statement of Work (SOW) EUM/PPS/SOW/04/0055 provided by
Eumetsat, dated 28 June 2004.
1. Potential Requirements for the ‘Fire’ Community
Vegetation fires are widespread on Earth and occur in many parts of the FDHSI footprint at
various times of the year (Figure 1). Potential users of satellite-derived information on active
fires can be considered to fall into two key classes, ‘operational’ users and ‘science’ users.
Figure 1. Middle of the fire season in different areas of the Earth, as derived from AVHRR
‘Global Fire Product’ hotspots by Dwyer et al. (1999), superimposed with the approximate
footprint of the FDHSI.
1.1. Operational Users of Fire Information
‘Operational’ users are composed of those requiring rapid and timely information on fire
initiation and development, most ideally from the time when fire conditions make suppression
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methods feasible if desired. The most important features of a spaceborne-active fire detection
system to this user community are therefore (i) the rapidity of accurate information delivery
(i.e. as close to real-time as possible), (ii) the ability to state the ground location of the fire to
sufficient precision (so any fire suppression or damage mitigation methods can be quickly and
precisely targeted), (iii) the ability to identify even relatively small fires early in their lifetime
(so that they are capable of being realistically tackled) whilst minimising errors of
commission, and (iv) the ability to provide information on the intensity and/or temperature
of a fire and its spatial and temporal variation (such that potential sites for suppression and/or
damage mitigation can be prioritised, and also to inform post-fire damage assessment).
The Fuego Programme (Fuego, 2004), initially supported via the European Commission
Framework IV and European Space Agency funding, relates very closely to such users since
its ultimate stated objective was the development of a dedicated constellation of small
satellites for identifying fires early in their lifecycle and to allow frequent monitoring of their
development. The Fuego Programme focused on the approximately 30 Mha region
potentially affected by severe forest fires in Southern Europe (France, Greece, Italy, Portugal
and Spain), but the data would potentially also be available over certain other countries
having an interest in such a capability (e.g. USA, Canada, Chile, Australia).
The system requirements for the Fuego programme were derived via discussions at relevant
conferences, through questionnaires and via interviews with potential users. The resulting
requirements are available at Fuego (2004) and are reproduced in Tables 1a and 1b. These
provide some indication of the types of capability desired by the operational users of active
fire information and which are potentially derivable from a spaceborne system such as that
proposed by Fuego, though it is likely to be the case that a relaxation of certain of these
requirements would could still provide very useful information.
Key Data
Rapid
identification
of new fires
Detection Time
Detection
Size
Average alert time Minimum
of 10 – 20 mins
area ~ 50 m2
False Alarms
False alarm rate
of ~ 5%.
Table 1a: Requirements for fire detection (Fuego, 2004)
Data Required
Monitoring information (data
on spatial variations in fire
intensity) for fires > 25 ha in
area for locations currently
burning and surroundings.
Temporal
Resolution
In large fires (more
than 25 ha burned),
data on fire line
temperature every
90 minutes.
Spatial
Accuracy
Geo-location of
products
to
around 35-50 m
accuracy.
Table 1b: Requirements for fire monitoring (Fuego, 2004)
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Hot Spot
Identification
Within the fire
scene, a map of the
hot spots that could
re-ignite new fires.
1.2. Science Users
Potential science users of active fire products require access to such data in order to better
estimate fire-related effects on the natural system, for example the flux of gases and aerosols
emitted or the ecosystem alteration induced by fire, and to assist parameterisation, calibration
and validation of environmental processes models incorporating fire effects. Such is the
magnitude of fire activity on Earth, particularly in the tropics and the boreal regions, that
pyrogenic gaseous and particulate emissions are now recognized to be highly significant at
the global scale, where Earth’s radiative budget, atmospheric chemistry, cloud and rainfall
characteristics are all influenced by the products of combustion (Rosenfeld, 1999; Andreae
and Merlet, 2001; Chou, 2002). Clearly, for the above reasons, biomass burning and
pyrogenic emissions must be rigorously considered when analysing the affect of wildfires on
Earth’s surface and atmosphere (Beniston et al., 2000), and satellite-derived active fire data
have proved extremely valuable for this process, despite the sensors use not being generally
designed for such studies.
For science users, the timeliness requirements of the active fire data are certainly less
stringent than for operational users, and it is also that case that the requirement to be able to
identify fires early in their lifecycle can be somewhat relaxed since small fires, in general,
have a relatively lesser effect on the surface and atmosphere than do larger fires. However,
smaller fires are generally far more numerous than larger fires, so care must be taken before
fires below a certain size can be considered unimportant to a particular application.
Individual fires vary in their lifetime from less than an hour to potentially several months in
duration (Yong et al., 2003), but fires that are significant at a regional scale typically have
lifetimes of hours to days and can exhibit both smoldering to flaming combustion.
Smoldering fires burn at lower temperatures and in general produce a larger range of chemical
species and copious amounts of fine organic particles in the smoke plume. Flaming fires are
hotter and varying concentrations of light-absorbing black carbon are emitted as a greater
proportion of the fuel undergoes complete combustion.
Lobert and Warnatz (1993) reviewed fire properties, and the temperatures quoted there for
smoldering and flaming combustion were used to define characteristic temperatures involved
in generating the MODIS fire products (Kaufman et al., 1998). Flaming combustion is
specified as 800 to 1200 K, though with very intense combustion potentially occurring up to
1800 K, whilst smouldering combustion occurs around 450 to 850 K, though the actual range
maybe somewhat smaller than this. If spaceborne data were able to provide information
capable of discriminating the type of combustion occurring (i.e. smouldering of flaming) as
well as detecting the fire event itself it would be of considerable importance since somewhat
different emissions result from the two processes. However, this has yet to be reliably
demonstrated.
2. Brief Review of Spaceborne Active Fire Detection
Detailed reviews of the application of remote sensing to the observation of active fires are
provided by (Robinson, 1991; Fuller, 2000 and Cahoon, 2000). In summary, most fires are far
smaller than the spatial resolution of the instrument, more so for geostationary imaging
systems with their generally large pixel sizes, yet fire detection is still possible because of the
highly non-linear nature of the relationship between emitted spectral radiance and temperature
(as expressed by the Planck function). This means that smouldering or flaming fires can emit
radiance at intensities sufficient to significantly affect the pixel-integrated radiance and
brightness temperature in key wavelength regions, even though the fire itself may cover a
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very small fraction of the ground pixel area, for example far less than one percent. Since the
relationship between increasing temperature and increasing intensity of spectral radiant
energy emission is strongly wavelength dependent, the ability of spaceborne remote sensing
instrument to detect active fires is strongly related to their imaging wavelengths. The MIR
atmospheric window (3 – 5 µm) is most useful for the detection of active fires since the
wavelength of peak spectral emittance from fires is contained within this region, or is close to
it, and the background spectral radiance is lowest. The background signal increases at shorter
wavelengths due to daytime solar radiation, and at longer wavelengths due to thermal
emission from the ambient background, which peaks in the thermal infrared atmospheric
window (8 – 14 µm). Calculations indicate that active fires can affect the MIR signal
sufficient for detection to be feasible if their proportion in a pixel exceeds ~ 10-4 - 10-3,
depending on their exact temperature (the hotter the fire, the more feasible its detection at a
smaller pixel proportion). In fact, radiation from fires in the MIR spectral region is so intense
that even modestly sized fires are capable of saturating the MIR channel of instruments such
as the Advanced Very High Resolution (AVHRR), whose dynamic range is set for the
observation of purely ambient temperature surfaces rather than those containing active fires
(Robinson, 1991). As such AVHRR and similar instruments have proved very useful for fire
detection applications.
Active fires are therefore normally detected via their increased MIR spectral signature,
assessed relative to that of the surrounding (ambient background) pixels and/or to the TIR
signal in the same pixel. The latter method is useful because any subpixel active fire has a
substantially lesser effect on the pixel-integrated radiance at this longer wavelength and so the
difference in the signal retrieved from the two channels rises markedly for pixels containing
fires (Robinson, 1991; Fuller, 2000). Generally therefore, multi-spectral fire detection
algorithms are employed, making use of the differential fire pixel signal in the MIR and TIR
spectral channels, and using cloud and water masks to remove potential areas where errors of
commission could occur due to, for example, the reflection of MIR radiation by clouds or
sunglint from water surfaces.
2.1 Polar Orbiting Data
Most work on active fire detection has been carried out using data from the AVHRR. More
recently ATSR onboard ERS-1 and ERS-2 has also been used. Whilst data from such polar
orbiting platforms are extremely valuable, they are limited in their temporal coverage by the
satellites near-polar orbit. This is a particularly relevant fact for rapidly evolving phenomena
such as fires, which show a strong diurnal cycle where fire numbers may change greatly over
the daily cycle. Therefore, studies comparing fire counts derived from sensors observing at
different times of day, or that are required to couple active fire detections to emissions
modeling efforts or atmospheric models, will likely need to take such variations into account
(Moula et al., 1996). A more recent sensor, the TRMM VIRS is similar in functionality to
AVHRR and has a MIR imaging ability, but since the TRMM platform has a non-sun
synchronous orbit the VIRS provides some characterisation of the fire diurnal cycle for
tropical regions (e.g. Ji and Stocker, 2002; Giglio et al., 2003), though data have to be
composited over one month to sample the full 24 hour cycle. Despite their limitations,
AVHRR, ATSR and VIRS active fire data have been successfully used to improve knowledge
of the spatial and temporal distribution of biomass burning emissions (e.g. Schultz, 2002;
Streets et al., 2003), with the emissions magnitude generally being estimated via other data
sources.
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2.2 Geostationary Data
Clearly the coupling of geostationary observing and a MIR spectral channel as is found on
MSG SEVIRI and the proposed MTG system potentially allows fire detection over the full
range of the diurnal cycle. Until the launch of MSG SEVIRI, the only geostationary system
with a fire detection capability of this sort was GOES. In the case of the most recent GOES
system, fire detection is possible once every 15 minutes for North America and half-hourly
for the full Earth disk. Under special situations it is also possible to scan terrestrial areas with
more frequent intervals (for example every minute). GOES data have been used to examine
trends in biomass burning over both the diurnal and seasonal cycles (e.g. Prins et al., 1998)
and GOES detected hotspots are now updated in near real-time on the web via this same
method (GOES Automated Biomass Burning Algorithm, ABBA). The algorithm incorporates
an estimate of the fire size and temperature using an adaptation of the Dozier (1981) method,
which is parameterized by the signal in the MIR and TIR spectral regions. However, Giglio
and Kendall (2001) indicate that saturation thresholds in the MIR band of GOES confines
application of the Dozier (1981) technique to a range of fire fractions for which the resultant
errors in retrieved fire properties are likely to be very large, and this is without considering
the potential for inter-channel co-registration errors. Thus the value of the temperature/area
retrievals made from GOES is somewhat in doubt, though it is clear the ability of GOES to
locate fires (providing they are large enough) and to track changes in the relative number of
fires affecting locations within its footprint on a sub-hourly basis is highly valued (Prins et al.,
1998).
SEVIRI has characteristics with regard to its potential for active fire detection that are
somewhat similar to those of GOES, but improved with regard to imaging frequency and
saturation effects. Therefore its uses in active fire detection are expected to be similar to those
of the existing GOES instruments, and this is also expected to be the case with MTG FDHSI.
3. Specific Characteristics of MTG with Regard to Active Fire Detection
[RD.1] provides the general characteristics of the HRFI and FDHSI imaging missions
anticipated for MTG, which each include a MIR spectral channel. These missions are the
next evolution from the current MSG SEVIRI mission, and as such analyses of SEVIRI
imagery can provide useful information in terms of assessing the likely characteristics of
HRFI an FDHSI for active fire analysis. Based on these requirements, and on an initial
analyses of a limited sample (< 1 week) of commissioning phase MSG SEVIRI imagery
supplied by EUMETSAT, the potential for detection and quantification of active fires via
HRFI and FDHSI will be assessed here.
The HRFI and FDHSI mission spectral channels are provided in [RD.1]. Both proposed
missions possess infrared channels in the MIR (3.8 µm) and TIR (11.2 µm and 10.8 µm
respectively) spectral regions, and are therefore capable of being used for fire detection.
Other channels may also be useful, for example in the absence of solar radiation at night, the
emission from fires in the SWIR spectral region means that their signal maybe detectable in
the HRFI 2.1 µm and FDHSI 1.6 and 2.1 µm spectral channels. Of course, since fires are
generally far less active at night, the daytime fire detection capability is generally considered
most important.
As already stated, the MIR spectral channel is considered the basis of any active fire detection
method and so it is the capabilities offered by this channel that must be studied in most detail.
The proposed spectral response function envelope for the HRFI and FDHSI MIR (3.8 µm)
5
channel is provided in [RD.1]. One of the key parameters for a fire detection system is the
minimum detectable fire size. Another is the fire size that will saturate the instrument in the
MIR spectral channel. This later parameter is important because the elevation of the MIR
spectral signal above that of the background provides information on the amount of energy
being emitted by the fire at this wavelength, thus providing information on the fire intensity.
In fact, the rate of MIR spectral radiant energy emission from fires (in W/m2/sr/µm) has been
shown to be well related to the rate of emission of the so-called ‘fire radiative energy’ (FRE),
also termed the Fire Radiative Power (FRP, expressed in MWatts), which is the rate of energy
emission by the fire over all wavelengths. FRP relates closely to the rate at which fuel is
being consumed by the combustion process, since this heat is being liberated in the process of
combustion (Kaufman et al., 1998; Wooster, 2002; Wooster et al., 2003). Estimates of the
rates of emission of aerosol and trace gas species could therefore potentially be derived from
FRP-based measures. However, if the MIR channel saturates then this prevents full
assessment of these parameters.
In order to inform calculation of minimum fire detectability, and assessment of the maximum
size/temperature of active fires able to be detected prior to sensor saturation, Figure 2 shows
the spectral radiance from solar reflection and ambient background emission in the MIR
spectral region, along with the normalised spectral response function of the SEVIRI 3.9 µm
channel and the proposed FDHSI/HRFI 3.8 µm channel.
Figure 2. The top-of-atmosphere 3–5 µm spectral radiance derived from surface emission
and solar reflection from the Earth, calculated via MODTRAN 3.7 (nadir atmospheric path).
Calculations assume mid-latitude summer atmosphere (rural aerosol, 23 km visibility), 20°
solar zenith angle, 300 K surface temperature and surface reflectance of 0.15 and emissivity
0.85. The normalised spectral response function of the SEVIRI 3.9 µm channel is show, along
with a normalised spectral response function fitting the FDHSI/HRFI response function
envelope shown in [RD.1].
A key point to note from Figure 2 is that in the middle of the MIR spectral region, the top-of­
atmosphere (TOA) signals from surface emission and surface reflection are of similar
magnitudes. Thus daytime MIR observations consist of a mixture of reflected and emitted
radiation (though are still commonly expressed in units of brightness temperature). During
nighttime observations only the Earth-emitted signal is present. Across the 3-5 µm region, the
reflected signal decreases rapidly with increasing wavelength, whilst the surface emitted
6
signal simultaneously increases. Therefore the exact placement of the sensor spectral
response function in this region controls the proportions of solar reflected and surface emitted
radiation the channel is sensitive to. This can be important with regard to fire detection and
characterisation, and the exact spectral response function can have significant effects on:
(i)
Fire detectability; by its control on the ambient signal within the MIR spectral
channel and thus the ability of this channel to record elevated radiances due to
fires. Over highly reflective, hot surfaces, daytime MIR observations can become
strongly elevated and, depending on the sensors dynamic range, may even saturate
in the absence of fire.
(ii)
Fire characterisation; by affecting the accuracy to which the fire pixel background
can be assessed via analysis of neighbouring pixels (which in practice may have
different surface reflectances, and perhaps surface temperatures, due to the widely
differing albedo of burned and unburned surfaces).
Together with the MIR spectral channel spectral response function, the other two properties of
most relevance to the sensors ability to detect and quantify fires is the spatial resolution and
dynamic range, in particular the maximum signal able to be sensed without saturation.
3.1 Fire Detectability and Saturation Effects
Using the data shown in Fig. 2, and the dynamic ranges of the HRFI and FDHSI sensors
quoted in [RD.1], the minimum detectable fire hotspot size based on the MIR channel
signature was calculated, along with the fire hotspot size that would saturate the MIR
detectors of these instruments as currently specified (thus preventing quantitative observations
of fire-emitted MIR spectral radiance). Calculations were performed for the range of fire
hotspot temperatures spanning the flaming and smouldering stages discussed previously, and
for simulated daytime and night-time observations.
Figure 3 shows results for FDHSI as compared to the current SEVIRI imager, and Figure 4
shows results for HRFI. Fire hotspots sizes are expressed as pixel fractions since the actual
ground pixel size changes across the imager footprint away from the sub-satellite point. It can
be seen from Figure 3 that the situation with regard to the minimum hotspot pixel fraction
potentially detectable by the FDHSI imager is close to that of SEVIRI. However due to the
extended maximum signal specified for the FDHSI imager MIR channel, the fire pixel
fractions that will saturate the instrument are significantly larger than for SEVIRI. Actual
values of each of these parameters are detailed in Table 2, and these fire pixel fractions can be
multiplied by the ground pixel size in order to calculate the minimum fire area in square
metres. The improved spatial resolution proposed for FDHSI over SEVIRI acts to increase
the ability of FDHSI over that of SEVIRI in terms of fire detection (i.e. FDHSI will be able to
detect fires smaller than the minimum detectable by SEVIRI, by a factor of 2.8? 0.4).
However the improved spatial resolution will mean that the actual fire areas (in square
meters) that saturate the FDHSI MIR spectral channel will only be larger than those which
saturate the current SEVIRI MIR channel by a factor of 2.2? 0.4.
These results indicate that FDHSI will therefore offer improvements over SEVIRI in terms of
fire detectability and quantification. As an example, using the pixel sizes at the sub-satellite
point indicates that FDHSI will be able to detect a daytime 1000 K fire of size 660 – 1470 m²,
compared the equivalent for SEVIRI of 1810 - 4000 m² (the range depending upon whether a
6 – 12 K MIR brightness temperature increase threshold is used to specify the minimum fire
7
detectable). Furthermore, the MIR channel of FDHSI will saturate once the 1000 K fire
reaches around 35,000 m2 in area, as compared to a value of 15,760 m² for SEVIRI.
Figure 3: Estimate of the minimum fire size detectable from the current SEVIRI imager
and the proposed FDHSI instrument (using the spectral response function shown in Figure
2, derived from that in [RD.1] and the specified 400 K maximum measurable signal) under
daytime and nighttime conditions, along with an estimate of the fire size that would
saturate the MIR detectors of these systems, calculated for a 400 – 1400 K fire temperature
range. Fire sizes are expressed as pixel fraction occupied by the fire. Minimum detectable
fire size/temperature is shown by the lower bar limit, calculated as that which raises MIR
pixel brightness temperature 12 K above that of the non-hotspot background. The vertical
line extending below the bar shows the minimum detectable fire size/temperature if this
MIR brightness temperature threshold is reduced to 6K (using, for example, a more finely
tuned fire detection algorithm; though potentially at the expense of increased errors of
commission). The fire size/temperature that saturates the sensor is shown by the upper bar
limit. Calculations assume mid-latitude summer atmosphere (rural aerosol, 23 km
visibility), and fixed surface reflectance of 0.15 and emissivity 0.85. Daytime simulations
assume a 300 K surface temperature and a 20° solar zenith angle, whilst nighttime
simulations assuming a 285 K surface temperature and no solar reflection.
8
Nighttime Simulation
Temperature 6 K above
of fire (K) background
400
600
800
1000
1200
7.6E-03
3.3E-04
6.7E-05
2.6E-05
1.3E-05
Daytime Simulation
12 K above
background
Saturation
1.7E-02
7.4E-04
1.5E-04
5.8E-05
3.0E-05
unsaturated
5.2E-02
1.1E-02
4.1 E-03
2.2 E-03
6 K above 12 K above
background background Saturation
2.2E-02
9.4E-04
1.9E-04
7.4E-05
3.9E-05
4.8E-02
2.1E-03
4.3E-04
1.6E-04
8.5E-05
unsaturated
5.0E-02
1.0E-02
3.9 E-03
2.0 E-03
Table 2: The minimum detectable fire hotspot sizes for the FDHSI sensor (assuming
detectability based on the MIR signal raised 6K and 12 K above that of the ambient
background), along with the fire size that would saturate the MIR channel. All sizes are
expressed as the pixel fraction occupied by the fire and calculations are based on the
parameters of described in the caption of Figure 3.
The same calculations were performed for the HRFI imager, which has the same spectral
response function envelope as the FDHSI but a significantly lower maximum signal (350 K).
Figure 4 and shows the parameters calculated for HRFI, and they are tabulated in Table 3.
Because of the nine times smaller pixel area of HRFI than FDHSI, the former sensor will be
able to detect fires nine times smaller (in terms of their square meter area) than those
detectable by FDHSI, but due to this and to the reduced dynamic range, the MIR channel will
saturate over even rather modestly sized fires. As an example, using the pixel sizes at the
sub-satellite point, HRFI will be able to detect daytime 1000 K fires of area 75 – 160 m²
(depending upon whether a 6 – 12 K MIR brightness temperature increase threshold used for
fire detection), but will saturate once a fire reaches around 800 m2.
Nighttime Simulation
Temperature 6 K above 12 K above
of fire (K) background background Saturation
400
600
800
1000
1200
7.65E-03
3.28E-04
6.72E-05
2.57E-05
1.34E-05
1.73E-02
7.40E-04
1.52E-04
5.81E-05
3.03E-05
3.01E-01
1.29E-02
2.65E-03
1.01E-03
5.28E-04
Daytime Simulation
6 K above 12 K above
background background Saturation
2.20E-02
9.41E-04
1.93E-04
7.39E-05
3.85E-05
4.84E-02
2.07E-03
4.26E-04
1.63E-04
8.49E-05
2.40E-01
1.03E-02
2.11E-03
8.07E-04
4.20E-04
Table 3: The minimum detectable fire hotspot sizes for the HRFI sensor (assuming
detectability based on the MIR signal raised 6K and 12 K above that of the ambient
background), along with the fire size that would saturate the MIR channel. All sizes are
expressed as the pixel fraction occupied by the fire and calculations are based on the
parameters of described in the caption of Figure 4.
9
Figure 4: Estimate of the minimum fire hotspot size detectable from the proposed HRFI
instrument (using the spectral response function shown in Figure 2, derived from that in
[RD.1] and the specified 350 K maximum measurable signal) under daytime and nighttime
conditions, along with an estimate of the fire hotspot size that would saturate the MIR
detector, calculated for a 400 – 1400 K fire temperature range. Fire sizes are expressed as
pixel fraction occupied by the fire. Minimum detectable fire size/temperature is shown by
the lower bar limit, calculated as that which raises MIR pixel brightness temperature 12 K
above that of the non-hotspot background. The vertical line extending below the bar shows
the minimum detectable fire size/temperature if this MIR brightness temperature threshold
is reduced to 6K (using, for example, a more finely tuned fire detection algorithm; though
potentially at the expense of increased errors of commission). The fire size/temperature
that saturates the sensor is shown by the upper bar limit. Calculations assume mid-latitude
summer atmosphere (rural aerosol, 23 km visibility), and fixed surface reflectance of 0.15
and emissivity 0.85. Daytime simulations assume a 300 K surface temperature and a 20°
solar zenith angle, whilst nighttime simulations assuming a 285 K surface temperature and
no solar reflection.
The same parameters used to compute the data shown in Tables 1 and 2 can also be used to
calculate the equivalent radiative power of each fire temperature/area combination. This
gives a measure of the minimum and maximum amount of fire radiative power output that can
be detected by the instrument, but which will not lead to sensor saturation. Table 4 shows the
results, and in these calculations a nadir view of the fire is assumed. The approximate FRP
values for off-nadir views can be calculated by simply scaling the quoted FRP by the pixel
area at those alternative locations relative to that at nadir (though this does not take into
account addition atmospheric or geometric effects introduced by off-nadir viewing).
Calculations are only shown for daytime conditions (i.e. in the presence of solar radiation)
since this is the much more important period of the diurnal cycle with regards to fire activity.
The results indicate that compared to SEVIRI, FDHSI will be able to detect significantly
more weakly burning fires, emitting approximately 2.5 times less thermal radiation per second
and so burning ~ 2.5 times less biomass per unit time. The results also indicate that,
compared to SEVIRI, the FDHSI sensor will saturate only over significantly more intense
fires, emitting approximately 2.2-2.6 times more thermal radiation per second, i.e. burning
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2.2-2.6 times more biomass per unit time. Therefore FDHSI as currently specified is
significantly more capable for fire detection and quantification than is SEVIRI.
FRP (MW) - SEVIRI Simulation
Temperature 6 K above
of fire (K) background
400
600
800
1000
1200
688
161
108
103
112
12 K above
background
Saturation
1519
355
238
227
248
6484
1404
940
894
977
FRP (MW) - FDHSI Simulation
6 K above 12 K above
background background Saturation
287
62
40
38
41
633
137
89
83
90
unsaturated
3319
2147
2005
2166
Table 4: The minimum detectable fire radiative power output (expressed in MWatts) for
the FDHSI and SEVIRI sensors (assuming detectability based on the MIR signal raised 6K
and 12 K above that of the ambient background), along with the fire radiative power output
that would saturate the MIR channel of these sensors. Parameters as described in the
caption of Figure 3.
3.2 Additional Geometric Parameters
[RD.1] provides details on other geometric parameters relevant for assessing the capabilities
of the FDFSI and HRFI systems with regard to fires. The spatial sampling distance (SSD)
indicates oversampling since the SSD is equal to 0.833 times the spatial resolution of the
thermal channels. This would appear to mean that a highly subpixel feature such as a fire
could be viewed for a differing cumulative amount of time by the imager, and thus its signal
would vary, depending, upon where it exactly fell on the detector array. The effect of this on
any fire signal could be quantified with the information provided.
The accuracy of interchannel co-registration has an effect on fire applications since multi­
spectral pixel-based tests are generally used to detect fires. The data on this is provided, as is
the absolute geometric accuracy, and the relative geometric accuracy between images that
allows calculation of the precision to which any fire can be located on the Earths surface.
The Level 1.5 mission data is to be registered to a reference grid and projection defined
according to the CGMS HRIT/LRIT Global Specification. Consideration must be given to
the exact nature of pixel re-projection, since anything other than a nearest neighbour
resampling scheme could potentially alter the signal at pixels containing active fires (since the
spatial variation in radiometric signal is very strong).
3.3 Other Issues
The data on radiometric accuracy and precision contained in [RD.1] are sufficient to calculate
their effect on active fire characterisation. A cloud mask product is identified in [RD.1;
Table 2, Section 6.3.1] as being of value to active fire products. This is indeed the case.
Since clouds mask the surface from view, any fire burning beneath thick meteorologic cloud
will not be visible by the sensor. Many potential application of active fires will therefore
want information as to the cloud coverage of an area, so that they can assess whether any lack
of fires may have been due to cloud cover rather than to actual fire absence. This is the case
for operational and science users. In some cases, quantitative counts of active fires deduced
from remotely sensed data are already weighted by the cloud fraction of the region in order to
try to normalise the data for temporal variations in cloud-cover (e.g. Giglio et al., 2003). Two
issues impact on this topic however, the first being that intensely burning fires can sometimes
still be detected through thinner meteorologic cloud – so that any cloud mask should not be
11
applied to the data in such a way that it could remove the signature of these fires, and second
that any cloud mask should clearly have the ability to distinguish meteorologic cloud from
thick fire-related smoke and haze.
4. Experience with SEVIRI and MODIS
The current active fire detection capabilities of SEVIRI and MODIS can be used to provide
information useful with regard to assessing the fire-related capabilities of FDHSI. Figures 5a
and 5b graphically demonstrate the ability of SEVIRI to identify fires using multi-spectral fire
detection criteria based on the MIR and TIR spectral channels, and that these data can provide
somewhat similar fire-maps to those of higher spatial resolution systems that have been
designed in part specifically for fire detection, in this case the EOS-MODIS sensor.
Figure 5. (a) A SEVIRI 400 x 400 pixel scene of southern Africa (September 4th, 12:12pm)
containing numerous vegetation fires. From top left (clockwise) 10.8 µm channel, 3.9 µm
channel, 10.8 - 3.9 µm brightness temperature difference, and mask of detected fire pixels.
12
Figure 5 (b). Near simultaneous MIR channel image subsets collected by (left) SEVIRI at
11:57am and (right) MODIS at 12:05pm over the Okavango Delta of southern Africa (a
subset of the area shown in Figure 5a). The increased MIR pixel signal due to active fires
is clearly evident in both the SEVIRI and MODIS datasets.
Examining SEVIRI data over the full 24-hour cycle indicates the very large temporal
variation in detected fire pixel number (Figure 6a). This pattern is essentially the same as that
seen from the TRMM VIRS polar orbiting sensor, and is caused primarily by the very strong
fire diurnal cycle. It results from the fact that many fires die out at night, and those that
remain active may burn less intensely than during the daytime such that they are more
difficult to detect using spaceborne imagery of moderate to low spatial resolution. As
previously stated, saturation of the MIR channel hinders fire characterisation in that a fire can
be detected but its actual intensity cannot be measured. Figure 6b indicates the number of
saturated (and near-saturated) pixels present in the data used to construct Figure 6a, expressed
as a percentage of the total number of fire pixels detected in each SEVIRI scene. Clearly the
vast majority of the pixels are unsaturated, but the effect is seen in the data and, since the
saturated pixels are located at the locations of the most intensely burning fires, it occurs at
pixels that are generally producing the greatest pollutant emissions.
Additionally, on closer examination some particularly intense fires appear to be subject to
other effects, as indicated in Figure 7. Non-zero saturation and pixel bleeding over high
intensity targets has been a feature of AVHRR data (Setzer and Verstrate, 1994; Harris et al.,
1995). Discussions with EUMETSAT have indicated that certain of the effects seen in Figure
6 are due to data pre-processing procedures used to convert the raw observations made by the
SEVIRI imager into calibrated, geolocated radiances present in the level 1.5 product shown
here. They therefore represent the normal operation of the instrument. However, it maybe
that such processing is not optimum for the purpose of fire applications, and this requires
further investigation. Any tendency for the design of the FDHSI imager to suffer similar
effects should be assessed for its impact on fire detection and characterisation. Furthermore,
there is the consideration as to whether a deconvolution process should be used on the data to
adjust for the effects of the instrument PSF. Consideration of this would again require careful
assessment. Essentially, since the relationship between Fire Radiative Power and MIR
radiance signal above the background is approximately linear (Wooster et al., 2003), then the
effect of the instrument point spread function (PSF) in ‘smearing’ the fire signal out over a
13
number of contiguous pixels may not be greatly significant to the calculation of the total FRP
from that fire, provided all the fire-affected pixels were included in the FRP calculation.
However, if the fire detection algorithm fails to correctly identify all the pixels influenced by
the fire radiance, then the proportion of the total FRP that is represented by the undetected fire
pixels will remain unaccounted for. It seems very likely that pixels where the fire resides in
the PSF wings will remain undetected, and this would lead to some underestimation of the
total FRP output from that fire. However, it is possible that the contribution to the total FRP
signal represented by these pixels is insignificant (since by definition they cannot be too
greatly affected by the fire, as otherwise they would have been successfully detected by the
fire detection algorithm). A dedicated modelling study using the instrument PSF data and
applying a fire detection and characterisation algorithm to simulated imagery is required to
assess these effects further.
Figure 6. Data on fires in southern-Africa extracted from SEVIRI during a five-day period
in 2003. (a) indicates the number of fire pixels and fires (contiguous clusters of individual
fire pixels), whilst (b) shows the percentage of fire pixels that saturated, and almost
saturated, the SEVIRI MIR channel.
14
Figure 7. SEVIRI image data of a Southern Africa fire (top left is 10.8 µm, right is 3.9 µm).
MIR channel data appear affected by anomalies, namely depression of the signal at alongscan pixels neighbouring the most intense fire pixels, and along-scan bleeding of this
pattern. An along scan brightness temperature transect of the MIR channel data for the
three scan lines containing the majority of the active fire pixels is shown (bottom).
From the calculations undertaken earlier we know that as currently specified, FDHSI should
suffer from fewer saturation effects than does SEVIRI. Nevertheless, saturation is still likely
to occur. One variable that would be useful to know would therefore be the maximum fire
signal that might be expected to be contained within an FDHSI pixel, since this would
theoretically allow adjustment of the sensors dynamic range to prevent saturation occurring.
We can get some idea of this through analysis of 1 km MODIS imagery. MODIS has a low
gain 3.9 µm channel with a 500 K maximum measurable signal. A series of MODIS Aqua
afternoon pass images (equator crossing time 13:30 GMT) were collected for the southern
African region shown in Figure 5a over a similar timescale. Using a multi-spectral fire
detection algorithm based on Kaufman et al. (1998), fire pixels were identified and their MIR
channel signal used to calculate the fire radiative power (FRP) via the MIR-radiance method
presented in Wooster et al. (2003). Figure 8 shows the frequency-magnitude plot of the
resulting dataset, indicating that fires of lower FRP are significantly more common that those
of higher FRP. Given the potential 3 km spatial resolution of the FDHSI imager, it is feasible
15
that a fire cluster containing up to nine MODIS pixels could fit within one FDHSI pixel.
Analysis of the data represented in Figure 8 indicates that for MODIS fire pixel clusters
containing up to nine pixels in which fires are identified, the maximum FRP is 2900 MW.
Back-calculating the pixel brightness temperature observed by FDHSI over such a fire
(assuming it fell within one FDHSI pixel of 3 km spatial resolution) gives an estimate of 418
K, somewhat above the 400 K maximum signal range currently specified and above the
maximum measurable FRP calculated for FDHSI (Table 4; ~2000 – 2170 MW; for fires
800 K and above). This indicates that, even given its improved performance over SEVIRI,
FDHSI will still saturate over the most intense fires observed. Raising the maximum MIR
channel signal to, say, 450 K would allow fires of significantly greater intensity (specifically
around twice that observed in the current MODIS dataset) to be observed without saturating,
as indicated in Table 5. Of course it is quite possible that fires greater than this intensity do
exist at a size that would be encompassed within a single FDHSI pixel, but such fires are not
present in the limited MODIS dataset currently examined.
Figure 8: A frequency-magnitude plot of MODIS per-pixel FRP using 45 near coincident
scenes. The FRP estimates are binned into 50MW bins. The frequency-magnitude
distribution is a function of the regional fire regime and the sensor spatial resolution.
Temperature
of fire (K)
400
600
800
1000
1200
FRP (MW) at
Saturation
unsaturated
9934
6426
6001
6482
Table 5. Fire Radiative Power for the FDHSI
sensor at MIR channel saturation, assuming an
increased MIR channel brightness temperature
saturation level of 450 K. Other parameters as
described in the caption of Figure 3.
MODIS operates at a higher spatial resolution than SEVIRI and so can detect fire pixels
whose FRP is ~ an order of magnitude lower. Furthermore the high (500 K) MIR channel
saturation temperature of MODIS allows quantification of all but the most intensely burning
events. Therefore, MODIS represents a standard with which geostationary instruments such
as SEVIRI and FDHSI can be compared. Thus a comparison between MODIS and SEVIRI
FRP provides a useful metric for assessing SEVIRI data quality in relation to fires. The
16
comparison is performed on a per-fire basis, rather than a per-pixel basis. This is necessary
due to differences in the spatial resolution between instruments where, for example, a fire
comprising of six along-scan pixels in MODIS may only affect two along-scan pixels in
SEVIRI. Using five daytime MODIS (Terra and Aqua) and SEVIRI images, closely spaced
in time, 139 fires in each dataset were manually located and their per-fire FRP (MW)
retrieved with each sensor compared. Figure 9 presents the results, showing strong agreement
between SEVIRI and MODIS per-fire FRP (r2 = 0.83, bias = -20 MW, scatter = 213 MW). On
a per-fire basis SEVIRI is therefore able to accurately retrieve FRP with a low bias with
respect to MODIS, though the degree of scatter is relatively high (e.g. only 75% of the
SEVIRI FRP observations are within 40% of the co-incident MODIS value). One factor that
will contribute to this degree of scatter is the fact that there are temporal differences between
the MODIS and SEVIRI datasets of the same fire and, though these are limited to a maximum
of 12 minutes, changes in FRP output from individual fires do occur on this timescale. Other
factors, for example an increased divergence between the MIR background temperatures of
the larger SEVIRI fire and non-fire pixels, and the fact that a given fire size in square meters
will form a smaller proportion of the SEVIRI pixel than the MODIS pixel, so will be subject
to greater influence from such background effects, will also influence the scatter. The full
range of influences and effects remains to be fully assessed via further MODIS/SEVIRI
comparisons and simulation modelling, however these early results clearly indicate the ability
of SEVIRI to provide data that, on an individual per-fire basis, performs reasonably well
when compared to MODIS. Furthermore, in terms of science applications, it is likely to be
the SEVIRI-derived time-series and time-integrated FRP observations that are of most value
(e.g. Figure 10) as opposed to instantaneous FRP for an individual fire pixel. Therefore the
overall low bias of the SEVIRI FRP retrievals suggests that SEVIRI will be highly valuable
for this work, and FDHSI should offer further improved characteristics due to its enhanced
detection and non-saturation abilities highlighted previously.
Figure 9. SEVIRI and MODIS per-fire FRP comparison using 139 fires taken from 5 nearcoincident MODIS and SEVIRI scenes. Fires are designated as contiguous clusters of
active fire pixels detected by each sensor, and FRP was calculated using the MIR-radiance
method outlined in Wooster et al. (2003). Error bars characterise the standard deviation of
per-pixel FRP (MW) in the fire, based on the variability in the ambient temperature
background signal immediately surrounding the fire. The two-tailed P value is less than
17
0.0001, and by conventional criteria this is considered to be extremely statistically
significant
Figure 10: The temporal dynamics of biomass burning over southern Africa recorded by
SEVIRI-derived fire radiative power during a 4.5 day period in 2003. The FRP calculated
and summed from all detected fire pixels in each image is shown at 15 minute timesteps.
Raw FRP data are shown, along with the FRP measure adjusted by the proportion of cloud
covered pixels in 0.1 and 0.5 degree grid cells (to estimate the additional FRP potentially
emitted by cloud-covered fires that the sensor is not able to detect). This adjustment
assumes that fires are as likely to occur under clouds as in clear skies, and in fact makes
little difference to the FRP time series since fires are mainly occurring in grid cells where
clouds are wholly absent. The time of peak biomass burning clearly occurs between
midday and 3pm local time, although large daily variations in fire activity are evident.
Whilst the data of Figure 9 indicate the relative agreement between the FRP observations of
individual fires detectable by both SEVIRI and MODIS, it should be remembered that
SEVIRI can only reliably detect fire pixels whose FRP is > 100 MW, around an order of
magnitude more than the FRP required for reliable detection by MODIS. The effect of this is
illustrated in Figure 11, where the frequency of SEVIRI-recorded fire pixels having FRP <
100 MW falls dramatically compared to that recorded by MODIS. Therefore, on a regional
basis, SEVIRI will certainly underestimate total FRP due its inability to detect the lower
intensity fires. Whilst these smaller fires will by definition have a relatively modest FRP
individually (i.e. < 100 MW), they maybe sufficiently numerous to contribute significantly to
the FRP cumulative total for the area under study. This was examined via a comparison of
the total FRP observed in 45 near-coincident, co-located MODIS and SEVIRI sub-images of
southern Africa, where the SEVIRI image was subset to match the geographic coverage of
each MODIS scene. Only MODIS data less than 45° view zenith angle were used, since this
limits any impact of the so-called ‘bow-tie’ effect where certain features such as fires a
18
replicated in MODIS edge-of-swath imagery. The maximum time difference between the
MODIS and SEVIRI images used was 10 minutes, and the correlation between the SEVIRI
and MODIS per-region FRP is strong (r2 = 0.92, n=45). However, SEVIRI underestimates the
regional FRP by a mean of 18% with respect to MODIS. The effect of SEVIRI sensor
saturation contributes to this underestimation, but as seen from Figure 6 this is in fact a
relatively minor occurrence. Far more important with regards to cumulative FRP
underestimation is the fact that SEVIRI fails to reliably detect fire pixels whose FRP < 100
MW, and such fires are relatively numerous, as indicated by the MODIS FRP frequencymagnitude relationship shown in Figure 11.
It is likely that the extent of FRP underestimation due to non-detected low-FRP fires varies
somewhat with season and geographic region, since it is dependent upon the fire regime and
burning conditions. Therefore, ongoing comparisons between fire data from SEVIRI and
MODIS-type sensors seem sensible. However, it is also likely that some estimate of the
extent of the FRP underestimation can be gained from the SEVIRI data itself, via
extrapolation of the SEVIRI FRP frequency magnitude statistics of the sort shown in Figure
11. This allows an estimate of the frequency of the smaller, undetected fires to be made. In
the case of Figure 11, such an extrapolation suggests that SEVIRI-derived FRP is
underestimated by 19.5%, which is close to the true 18% mean underestimation found with
respect to MODIS. Of course, as indicated in Table 4, FDHSI will not suffer so greatly from
this problem due to its ability to detect fires whose FRP is between one half and one third that
of the minimum detectable FRP with SEVIRI. Nevertheless, extrapolation of the FRP
frequency-magnitude relations provided by FDHSI to estimate the FRP provided by
undetected low intensity fires may still be warranted.
Figure 11: A frequency-magnitude plot of SEVIRI and MODIS per-pixel FRP using 45
near coincident scenes. The FRP estimates are binned into 50MW bins. The frequencymagnitude distribution is a function of the fire regime of the region and differences in the
spatial resolution of the sensors. The impact of spatial resolution is illustrated by the fact
that as cluster size decreases, the disparity between MODIS and SEVIRI increases since
SEVIRI is unable to detect fires whose FRP < 100 MW (Table 4).
19
5. Key Recommendations for HDFSI Specifications
Consideration of the spatial resolution and maximum signal of the of the MIR 3.8 µm channel
with respect to the effect on:
(i)
the smallest fire that can be detected (controlled by the sensor spatial resolution)
(ii)
the incidence of saturation over the most intense fires. Raising the maximum MIR
signal measurable for any given spatial resolution will reduce the incidence of
saturation (though potentially at the expense of radiometric resolution, whose
stated specification is set in [RD.1]), whilst increasing the spatial resolution for a
given maximum MIR signal will increase the incidence of saturation.
If the spatial resolution is set at 3 km then an increase in the maximum MIR signal
measurable to at least 450 K would be advantageous in terms of assessing Fire Radiative
Power output. However, a 3 km spatial resolution is insufficient for the operational fire
community, and would also likely not meet the full requirements of the scientific community
since the majority of fires will be missed, and these missed fires maybe so numerous that they
are responsible for a significant portion (though very likely not the majority) of pyrogenic
emissions, as discussed above. The true significance of these undetected fires to the overall
FRP could be examined via a study using MODIS FRP data, similar to the one undertaken
here but with data taken over a more representative timeframe (e.g. a full dry season).
The stated requirements of the operational community mean that a spatial resolution
significantly higher than even the current maximum quoted for the HRFI mission (i.e. 1 km)
would be required, though it is in fact likely that a geostationary imager with a spatial
resolution of this magnitude would be very useful for operational purposes.
6. Suggestions for Further Research
In order to be able to better assess the effect of FDHSI spatial resolution and dynamic range
on fire detectability and characterisation, further work should be carried out to assess the
percentage of fires of different sizes and FRP intensities within different fire-affected regions
of the FDHSI footprint at different times in the dry season. In this way the sensor
characteristics required to be able to accurately detect a set proportion (for example 75%) of
all fires, and characterise those responsible for a set percentage, for example 90%, of fire
radiative power (and thus gaseous and aerosol) emissions could be quantitatively assessed.
The effect of the imaging process on the ability of the sensors to accurately characterise fires
should be assessed using the convolution of the SEVIRI and FDHSI PSF with a range of
individual fire scenarios. The requirement for the use of deconvolution methods to attempt to
best reconstruct the original fire signal, and thus more accurately quantify FRP, should also be
assessed as discussed in Section 4. Ideally this should be combined with an assessment of the
effect of the oversampling, interchannel co-registration, and geometric resampling procedures
mentioned in Section 3.3.
The ability of the data from the FDHSI sensor to be subject to the analysis method of Dozier
et al. (1981) should be examined if the spatial resolution of the sensor is decreased
significantly towards the 1 km size that Giglio and Kendall (2001) suggests maybe the upper
limit for meaningful application of the technique.
20
The ability of the FDHSI short wave infrared bands to provide additional data for fire
characterisation should be investigated, for daytime situations over the most intense fires and
for nighttime observations of lower intensity fires where the solar radiation component of the
signal is absent.
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