HIGH TEMPORAL RESOLUTION FIRE RADIATIVE ENERGY AND BIOMASS COMBUSTION ESTIMATES DERIVED FROM MSG SEVIRI

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1 HIGH TEMPORAL RESOLUTION FIRE RADIATIVE ENERGY AND BIOMASS COMBUSTION ESTIMATES DERIVED FROM MSG SEVIRI G. Roberts 1, M. J. Wooster 1 & G. W. Perry 1,2 1) Department of Geography, Kings College London, Strand, London, WC2R 2LS, England gareth.j.roberts@kcl.ac.uk, martin.wooster@kcl.ac.uk 2) Now at School of Geography & Environmental Science, Tamaki Campus, University of Auckland, Private Bag 92019, Auckland, New Zealand, george.perry@auckland.ac.nz ABSTRACT Vegetation fires in southern Africa are a major source of trace gas and aerosol emissions. Estimates of the amount of biomass combusted annually in southern Africa vary between 18 and 2719 Tg, values that are highly significant but poorly constrained. A new approach to potentially improve the uncertainty on such estimates is via use of Fire Radiative Power (FRP) and its temporal integral Fire Radiative Energy (FRE), which are respectively related to the rate and total amount of biomass combustion. The August 2002 launch of the SEVIRI radiometer onboard MSG (METEOSAT-8) presents a unique opportunity to obtain FRP estimates at 15-minute intervals over Africa, the continent where the majority of biomass burning occurs. Here we present the first FRP retrievals from SEVIRI over southern Africa, and compare these to those from near-coincident MODIS data. Good agreement is achieved on a per-fire basis (r 2 =0.83, bias = -20 MW, scatter = 213 MW). Correlation on a regional (e.g. ~ 2300 x 2300 km) basis is also strong (r 2 =0.92), however SEVIRI underestimates regional FRP with respect to MODIS by, on average, 18% due to many small and/or weakly emitting fires remaining undetected. Using relationships developed during ground-based experiments, SEVIRI-derived FRP is converted into estimates of the rate of biomass burning. Integrating this over the 4.5 days of data available here, it is estimated that 23 Tg of biomass are combusted over southern Africa. INTRODUCTION Biomass burning is a key component of the biogeochemical cycle in southern Africa, and is a major source of trace gas and aerosol emissions to the atmosphere ([1]). African savannah fires are believed to account for almost one third of emissions from global biomass burning ([1]). Therefore, accurate estimates of biomass burning emissions from these environments are necessary to assess their contribution to global change. Techniques applied for estimating the amount of biomass combusted during vegetation fires, from which emissions can be calculated, have evolved over the past 20 years with the advent of new satellite instruments and biogeochemical models. Remotely sensed observations have also improved the spatial and temporal sampling of biomass burning events. Typically, remotely sensed data are used to identify and estimate burned area using either thermal emitted energy observed from active fires, reflected radiation to detect burn scars, or a combination of both. Estimates of the amount of biomass combusted can then derived using an equation of the following form: M A! B! C = (1) where M is the amount of dry biomass combusted (g/m 2 ), A is the burnt area (m 2 ), B is the biomass density (g/m 2 ) and C is the combustion factor (unitless). The components forming Equation (1) vary both spatially and temporally, making their estimation difficult. Furthermore, whilst the estimation of burned area has received much attention and is now a comparatively mature field, remote sensing methods for deriving estimates of biomass density and combustion efficiency are, comparatively, still in their infancy. In the context of modeling carbon and other emissions from vegetation fires, a commonly applied method for extrapolating estimates of above ground biomass to a wide area is through the classification approach. In this case biomass estimates obtained from field studies or through the literature are stratified according to landcover type ([3], [4]). However, a number of limitations exist with this approach. The accuracy of the landcover classification is critical since this is the basis for ascribing biomass loadings and emission factors. Furthermore, the classification approach assumes within class homogeneity and therefore fails to account for variations in vegetation density. Reference [5] attempted to overcome some of the limitations inherent with the classification approach by incorporating a modelling strategy in the assignment of Proc. Second MSG RAO Workshop, Salzburg, Austria 9 10 September 2004 (ESA SP-582, November 2004)

2 fuel load and combustion completeness, varying these as a function of, for example, vegetation functional type and rainfall. Comparison between the modelling approach and the traditional classification method indicated a large discrepancy in estimates of the amount of biomass combusted. For the year 1989, the former indicated Tg dry matter (DM) combusted over southern Africa, whilst the latter indicated Tg [5]. Due to the uncertainties associated with estimating biomass-burning quantities using the methods discussed above, new techniques have been proposed which utilise thermally emitted radiation to retrieve fire properties in addition to simple location and time. Reference [6] first introduced the concept of Fire Radiative Power (FRP) as a means to quantify pollutant emissions from vegetation fires. The FRP (J s -1 ) describes the amount of energy liberated from a fire over all wavelengths at an instantaneous point in time, whilst the FRE (J) is the integration of FRP over time. Reference [7] found a strong relationship (r = 0.97) between FRE and the development of burn scars, in an environment where vegetation was assumed to be relatively homogeneous in density, suggesting that FRP should be a representative measure of the rate of biomass consumption. Further investigation was presented by [8], where results indicated a linear relationship between FRE and total biomass combusted (r 2 = 0.76). The objectives of the current paper are twofold. The first is further investigation of the relationship between FRE and the mass of vegetation combusted, undertaken here using small experimental fires of southern Africa savannah vegetation carried out in the field. The second objective is to apply the relationship derived in these field experiments to FRP data derived from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Estimates of FRP derived from SEVIRI are compared to near coincident estimates from MODIS on a per-fire regional basis, and SEVIRI data are then used to study FRP and biomass combustion rates and totals for a 4.5-day period during the southern African dry season. MSG SEVIRI DATA MSG, launched in August 2002, is a EUMETSAT/ESA geostationary weather satellite positioned at 10.5o Longitude and carrying the GERB and SEVIRI instruments. The latter is a 12-channel imager observing primarily Africa and Europe with a repeat cycle of 15 minutes. SEVIRI has 11 spectral bands located between 0.6 µm and 14 µm, with a spatial sampling interval of 3 km at nadir. The final band is a high-resolution (1km at nadir) visible (HRV) broadband CHANNEL ( µM). The high temporal resolution of SEVIRI, coupled with the inclusion of 12 spectral channels, provides valuable data for monitoring dynamic phenomena. Whilst SEVIRI was not designed with fire monitoring in mind, it has imaging characteristics which are amenable to this task, particularly the inclusion of middle infrared (, 3.9µm) and a thermal infrared (TIR, 10.8µm) channels. These wavelengths are key to fire detection, and in this case fire characterisation. The channel of SEVIRI saturates at 340K and, due to its lower spatial resolution, saturation over fires is less likely than for sensors such as AVHRR and ATSR. MODIS is also widely used for fire detection and characterisation, and largely avoids channel saturation by using a low-gain channel saturating at 500 K. Fig.1 shows an example of a particularly intense fire imaged by SEVIRI during its commissioning phase. Fig 1. SEVIRI imagery over an intense fire (top left 10.8µm, top right 3.9µm). The plot illustrates three transects across the fire in the channel, highlighting the highly radiant fire pixels, but also the depressed brightness temperatures neighbouring the most intense fire pixels along the scan The data of Fig. 1 illustrate that fires are quite easily detectable with SEVIRI, and modelling suggests they need only comprise ~ 10-3 to 10-4 of a pixel to be distinguishable from the background. However, in the case of Fig. 1, an anomaly can also be seen where the brightness temperatures of pixels neighbouring the fire are depressed in the along scan direction. Such problems are believed in part to be associated with image processing methods being applied to the commissioning phase SEVIRI data, and are relatively rare. They are expected to be absent or at least much reduced in the post-commissioning phase imagery, which will not be subject to the same processing techniques.

3 For the purposes of this investigation 4.5 days of SEVIRI imagery, comprising of 432 images, were acquired between 12:00 p.m. on the 3 rd and 23:57 on the 8 th September These level 1.5 data are radiometrically and geometrically corrected by EUMETSAT prior to their use here. Cloud screening is carried out using a series of solar and thermal channel thresholding tests, described in [9] and adapted for use with SEVIRI. No atmospheric correction was applied during this test. We study the southern Africa region between 0 and 32 latitude and 9 and 42 longitude, and the images are timed towards the end of the local dry season when fires are prevalent. FIRE RADIATIVE POWER (FRP) The Fire Radiative Power (FRP) describes the rate of release of radiant energy by fire over all wavelengths. The integration of FRP over time provides estimates of the Fire Radiative Energy (FRE). However, Earth Observation (EO) sensors typically measure IR radiation only in discrete wavebands, rather than over all the wavelengths where fires are emitting. Two approaches which circumvent this problem are the Bispectral method coupled to the Stefan-Boltzmann Law ([10]) and the MODIS method ([7],[8]). A third method for retrieving FRP, and which is used in this paper, is the radiance method developed by [11]. This approach approximates the Planck function using a simple power law and exploits the power law relationship between the emitted (4µm) radiance and the emitter temperature, whose exponent approaches that of Stefan s Law. As a result, the ratio of the total power emitted over all wavelengths to that emitted in the is approximately constant for emitters between 700 and 1500K, which corresponds rather well to the temperature range expected for savannah and other wildfires. FRP can therefore be estimated from the radiance observations via: FRP Asamp"! L a! = (2) where L is the radiance of the fire pixel with the contribution of the ambient background (estimated from surrounding non-fire pixels) removed,! is the emissivity which is assumed to radiate as a grey body A is the pixel sampling area,! =! ) ([12]), samp ( and a is a constant based on the empirical best-fit between emitter temperature and radiance using the Planck function approximation. For a detailed derivation of the radiance method refer to [11]. FIELDWORK METHODOLOGY The methodology employed in this paper builds on the work by [8] and [13]. Reference [8] carried out a series of small experimental fires and indicated that FRE is strongly related to the mass of vegetation combusted. Reference [13] expanded upon this by including a wider range of vegetation types (miscanthus, straw, hay, wood chips and combinations thereof) and a larger fuel mass range. Results confirmed that a strong linear relationship exists between the amount of biomass combusted and FRE, irrespective of vegetation type. In order to utilise SEVIRI data for estimating the amount of biomass combusted over southern Africa, it is necessary to determine whether the relationship between FRE and the amount of biomass combusted is consistent when applied to southern Africa savannah vegetation (grass and leaf litter, woody material). A series of small experimental fires were conducted in Chobe national park (northern Botswana) in September Vegetation samples of known mass were combusted. A digital camera, sampling every second, was used to estimate FRE via (2). Fig 2 presents the strong relationship (r 2 =0.96) between FRE and total biomass combusted using data acquired in the UK ([13]) and Botswana. Importantly, the relationship appears independent of vegetation type, which is critical if the approach is to be successfully applied to spaceborne data where the exact type of vegetation burning would not be known. Fig 2. The linear relationship (r 2 = 0.96) between FRE and the amount of biomass combusted. Agreement between the UK and African experiments is clear. ACTIVE FIRE DETECTION USING SEVIRI Active fire detection using remotely sensed data typically utilises spectral thresholds applied to the signals recorded in the and TIR channels. A simple difference threshold (-TIR) enhances the contribution of active fires at the expense of the ambient background. To improve the certainty that only active fires are enhanced, a range active fire algorithms have been proposed ([14], [15]). Such algorithms typically fall into two categories: fixed threshold or contextual techniques. Algorithms with fixed thresholds ([16]) utilise a set of static thresholds and operate on a per pixel basis. Contextual algorithms incorporate both absolute and relative thresholds, the latter taking into account the signal strength of non-fire pixels surrounding likely potential hotspots ([14]). In

4 the current analysis a contextual algorithm similar to that proposed by [7] for use in the MODIS MOD14 fire products is applied to both MODIS and SEVIRI radiance/brightness temperature imagery. Adjustments were made to both the absolute and contextual thresholds used, since the spatial and radiometric characteristics of MODIS and SEVIRI are different. In particular with respect to SEVIRI, the whole Earth disc is imaged every 15 minutes and the solar zenith angle can vary greatly across the scene. Thus three different absolute and contextual threshold sets were implemented, varying with time of day (06:00-09:00, 09:00-15:00,15:00-06:00). The thresholds were defined via inspection of a number of differently timed training data images. SEVIRI AND MODIS FRP COMPARISON Once fire pixels are detected in either MODIS or SEVIRI, the active fire pixel and ambient background pixel radiances are used to retrieve estimates of FRP via (2), on both a per-pixel and per-fire basis. Fires are defined as spatially contiguous clusters of individual active fire pixels, typically up to perhaps a few dozen pixels in the case of MODIS and fewer with SEVIRI. A statistical comparison between SEVIRI and nearcoincident MODIS FRP estimates is presented here. The comparison must be made on a per-fire basis since the differing spatial resolutions of the instruments mean direct per-pixel comparisons make little sense. Since MODIS operates at higher spatial resolution than SEVIRI, the contribution of a sub-pixel fire to the pixel-integrated signal will be relatively greater for MODIS, and thus the per-fire MODIS FRP measurement should lie closer to the truth due to the better signal-to-noise. However MODIS can only provide FRP observations of any particular fire a few times per day, whereas SEVIRI provides such data every 15 minutes provided the fire is sufficiently large enough to be discriminated from the background. A total of five day and night MODIS level 1B scenes (MOD021KM) were used in the comparison. MOD021KM data contain calibrated and geo-located radiances in 36 spectral bands ( µm) at a spatial resolution of 1km at nadir. The data were cloud screened using an adaptation of the algorithm of [9] and the cloud mask available with the MOD14 product derived from the radiance images. Of the fire detections apparent in the SEVIRI imagery, 139 fires were manually selected for FRP comparison to MODIS and the results are shown in Fig. 3. The agreement between SEVIRI and MODIS per-fire FRP is quite strong (r 2 = 0.83, bias = -20 MW, scatter = 213 MW) and, on average, SEVIRI underestimates FRP when compared to MODIS by only 5%. On a per-fire basis SEVIRI is therefore clearly able to retrieve FRP with a low bias when compared to MODIS, though the degree of scatter is quite high (e.g. only 75% of the SEVIRI FRP observations are within 40% of the co-incident MODIS value). This is presumed to result largely from temporal differences between the datasets (a maximum of 7 minutes) and from variations in retrieved FRP caused by the exact location of the fire within the MODIS or SEVIRI pixels (which when coupled with the sensor point-spread function will cause the observed per-pixel and per-fire FRP to vary between each instrument). Nevertheless, since it is likely to be SEVIRI-derived FRE observations (i.e. accumulations of FRP over time) that are of most value, rather than FRP snap-shots of individual fires, the overall low bias of the SEVIRI FRP retrievals may mean that the degree of scatter (due to the random effects discussed above) is relatively unimportant. Fig 3. SEVIRI/MODIS FRP comparison for individual fires. A comparison between SEVIRI- and MODIS-derived FRP calculated on a regional basis was also undertaken. This was carried out by calculating the cumulative FRP for all fires in one MODIS image, this being compared to the cumulative FRP calculated over the same geographical region but from the nearsimultaneous SEVIRI image. In this comparison, the maximum time difference between the MODIS and SEVIRI regional matchups was 10 minutes, which though small will again result in some differences due to the dynamic nature of biomass burning. Furthermore, only MODIS pixels imaged within a 45 view zenith angle were used in the comparison, since outside of this limit the impact of the edge-of-swath bow-tie effect has a substantial influence with, for example, the same fire appearing twice in the image on different scan lines. Fig 4 presents the results of the comparison, and indicates a strong correlation between the two datasets (r 2 = 0.92). However, SEVIRI underestimates the FRP with respect to MODIS by on average 18%. The key reason for this is that the reduced spatial resolution of SEVIRI when compared with MODIS means that it misses a greater proportion of small and/or weakly emitting fires. In regions where such fires are numerous, the fact that the FRP from such fires will be included in the MODIS regional FRP

5 estimate but not the corresponding SEVIRI estimate will lead to significant differences. A second factor is that the SEVIRI channel will saturate over the most intense fires, but this is not a large effect since out of the 432 SEVIRI scenes analysed, less than 0.1% (231) of the 31,158 active fire pixels detected by SEVIRI were saturated. Fig 4. A comparison of FRP estimates derived via SEVIRI and MODIS on a regional (~2300 x 2300 km) basis. The correlation between the two datasets is good (r 2 = 0.92) although SEVIRI in general underestimates FRP with respect to MODIS. Further investigations of SEVIRIs fire related capabilities were carried out using frequencymagnitude analysis. Similar fire-related assessments have been carried out by [17] and [18] using frequency-magnitude burned area and FRP distributions. Fig. 5 shows the per-pixel FRP estimates from 45 SEVIRI and MODIS scenes, binned into 50 MW intervals. The most notable feature is the large frequency difference between the SEVIRI- and MODIS-derived data at low FRP magnitudes (1-100 MW). Fig 5. Frequency-magnitude plot of SEVIRI and MODIS pixel-based FRP, binned into 50MW intervals This supports the contention that numerous small and/or weakly emitting fires are remaining undetected by SEVIRI. At low FRP, the departure from the generally linear trend shown in the MODIS dataset also indicates that MODIS is likely to be missing some of the smallest/weakest fires. However, these data suggest that regional FRP underestimation by SEVIRI could, perhaps, be adjusted through statistical extrapolation of the relevant MODIS FRP frequency distribution. It should also be remembered that, on a per-fire basis, the smaller/weakest emitting fires are those that are burning least biomass, though their high frequency makes their cumulative contribution significant. QUANTITY OF BIOMASS COMBUSTED OVER SOUTHERN AFRICA The previous analysis has not yet leveraged off the key strength of SEVIRI, namely its high temporal resolution. In Fig. 6 we show a full time-series of FRP derived from SEVIRI, and the corresponding estimates of the biomass combustion rate derived from these data, calculated over a 4.5-day period for southern Africa. Attempts have been made to reduce the impact of cloud cover on regional-scale FRP estimation by weighting the FRP observations by the cloud fraction in 0.5 grid cells. (~15x15 SEVIRI pixels). Fig 6: SEVIRI-derived temporal dynamics of biomass burning over southern Africa. Using these data it is estimated that a minimum of 23 Tg of vegetation is combusted in this period. The plot emphasizes the diurnal variability of biomass burning, with peak fire activity occurring between midday and 15:00 GMT. This variation further emphasizes the importance of high temporal resolution data for quantifying these dynamics. During the peak, approximately 50 tonnes of biomass is combusted per second. Temporal integration of the FRP indicates that 23 Tg of vegetation are combusted during this timeframe, though this is a minimum estimate due to the limitations on the observation of smaller/weaker fires discussed previously. Hypothetically, if it is assumed that this time period is representative of fire activity throughout the May October dry season, this leads to an estimate of 949 Tg of biomass combusted over the whole southern Africa throughout this period. Comparison with other studies suggests this is not an unreasonable figure. Over the whole of Africa, [19] suggest that between 1835 to 2705 Tg of biomass are

6 combusted annually (year 2000 estimate). Over southern Africa during the 1989 dry season, [5] estimate that Tg of biomass are combusted using an advanced modelling approach, and Tg using the classification approach. Recently, using the MODIS burned area product derived for the dry season (year 2000 estimate), [20] estimate that 79 Tg of biomass were combusted over southern Africa. The estimates cited above are all based on variations of the classification approach and are therefore heavily reliant on the accuracy of the burned area estimates and/or the landcover map and locally sampled biomass and combustion factors. An illustration of this dependence is provided by [20], where three different burned area products are seen to produce estimates of the amount of biomass combusted that vary by 30 Tg between products. Currently, there is no means of easily quantifying which method is most reliable. (a) (b) Fig 7. (a) Temporal profile of active fire counts and active fires over southern Africa (b) a plot of the ratio of active fire pixels and FRP, illustrating the temporal variation in perpixel FRP. Fig 7 (a) presents a temporal trajectory of the number of active fires and active fire pixels over southern Africa. The trajectory of active fire pixels follows a similar trend to that of FRP, with a peak of ~4800 fire pixels detected. Fig 7 (b) presents a temporal plot of the ratio of FRP and the number of active fire pixels. Active fire pixel counts have been applied to characterise fire activity for the purposes of inventorying and studying the atmospheric transport of biomass burning emissions ([21], [22]). The varying relationship between FRP and active fire pixel count shown here suggests that, in this case, such an approach will be prone to substantial error since the relationship fluctuates between 50 and 250 MW per pixel. This implies that fire pixel count alone is insufficient for fire characterisation with, in this case, some fire pixels burning at least five times the biomass per unit time than others. However, the data shown in Fig. 7 are early results and should be viewed as such. Further, whilst we believe that the derivation of FRP from geostationary satellites offers an important new method of deriving estimates of the rates and totals of biomass combustion in Africa, and one that is perhaps closer to a direct physical linkage than many of the previous methods, it is important to stress some of the uncertainties associated with FRP estimation using spaceborne data. These uncertainties are in addition to the limitations of the algorithm methodology detailed in [11], due to the approximations made therein. Two factors that may act to reduce observed FRP to below that actually emitted by the fire are (i) the unknown amount of radiant energy intercepted (scattered and absorbed) by the forest canopy above surface fires occurring in forests and woodlands, and (ii) the impact of absorptive black carbon released during combustion [23] on the observed radiances. These effects have yet to be quantified. Moreover, whilst the field experiments have indicated that a strong relationship exists between FRE and total biomass combusted, the degree to which this relationship, derived with relatively small fires, holds over larger and more intense fires has not yet been studied. CONCLUSION Work by [8] and [13] have indicated a strong relationship between FRE and the amount of biomass combusted. This study has demonstrated that this relationship is maintained for natural savannah vegetation, providing confidence in applying the technique to EO data over southern Africa. Fire detection and characterisation studies indicate that SEVIRI is capable of estimating FRP accurately on a per-fire basis, when compared to near-simultaneous observations made by MODIS. However, the analysis also indicates that SEVIRI underestimates FRP regionally due to many small/weakly-emitting fires not being detected, and low levels of sensor saturation. Consequently, the estimates of FRP and biomass combustion rates from SEVIRI should be treated as minimum values.

7 This study has also demonstrated the potential for retrieving estimates of the amount of biomass combusted using SEVIRI time series to derive FRE estimates via integration of FRP. Results indicate that, over a 4.5-day period, 23 Tg of biomass were combusted over southern Africa in September Extrapolating this result to the entire dry season provides an estimate of ~950 Tg of vegetation combusted. Estimates in the literature vary between Tg, so SEVIRI provides an estimate within these, admittedly wide, limits. Processing of an entire SEVIRI fire-season will provide improved comparisons. Ultimately, it is likely that a combined approach, utilising both high spatial resolution burned area maps and high temporal resolution FRP estimates, will provide the most accurate means to estimate biomass burning combustion totals. ACKNOWLEDGEMENTS This study was supported by NERC grant NER/Z/S/2001/ MSG SEVIRI data were provided by EUMETSAT. MODIS data were provided by NASA GSFC and EROS DAACs. The authors would like to thank the Botswana National Parks Service and the NERC Equipment Pool for Field Spectroscopy (EPFS) for their invaluable support. REFERENCES [1] Andreae, M. O. (1991) Biomass Burning : Its history, use and distribution and its impact on the environmental quality and global climate, in Global Biomass Burning : Atmospheric, Climatic and Biospheric Implications, edited by J. S. Levine, pp 2-21, MIT Press, Cambridge, Massachusetts. [2] Seiler, W and Crutzen, P. J. (1980) Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning. Climate Change [3] Kasischke, E. S., French, N. H. F., Bourgeau-Chavez, L. L., and Christiansen, N. L. (1995) Estimating release of carbon from 1990 and 1991 forest fires in Alaska. Journal of Geophysical Research [4] Pereira, J. M. C., Pereira, B. S. Barbosa, P., Stroppiana, D., Vasconcelos, M. J. P., and Gregoire, J-M. 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