1 Spatial Modelling of the Terrestrial Environment - Georeferencial

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Fireline Intensity and Biomass Consumption in Wildland Fires 189 Figure 9.6 Fire radiative energy variation for fire fronts 3 and 6 of Figure 9.4, derived from the BIRD HSRS data having a pixel resolution of 370 m and a pixel sampling step of 185 m. FRE varies by more than an order of magnitude over the cluster of pixels making up each fire front only. Figure 9.6 shows an example of the detailed FRE pattern associated with fronts 3 and 6 of Figure 9.4. FRE clearly varies widely along the zone of active combustion, presumably due to variations in the rate of combustion of vegetation within each pixel, and the total FRE for these fire fronts is 210 and 396 MW, respectively. The corresponding radiatively derived mean fireline intensities, calculated simply by dividing FRE by the fire front length, are 70 and 65 kW m −1 , though the variation in fireline intensity along the individual fire fronts mirrors that in FRE. The remaining clusters in Figure 9.4 have radiatively derived mean fireline intensities in the range 15–75 kW m −1 , values all very much lower than the earlier reported ‘average’ fireline intensities of 8000 kW m −1 . However, as discussed earlier, the uncertainties inherent in the calculation of fireline intensity make direct comparison difficult. In many cases ‘traditional’ field-based estimations of this parameter appear to assume complete fuel combustion, with all particles in the fuel bed available to the combustion process and the degree to which the heat yield of the fuel bed is corrected for FMC is unclear (see
190 Spatial Modelling of the Terrestrial Environment section 9.3). Many field-based estimates are also based on head fires, with much lower intensities reported for flanking and backing fires (e.g. Roberts et al., 1988). Analysis of the raw data provided by Roberts et al. for a series of experimental grassland fires shows that the fireline intensity (average ± 1SE; KW m −1 ) of backfires (148.1 ± 23.2; n = 22) was only 6% that of headfires (2306.9 ± 405.0; n = 39). Finally, the extent to which heat may be lost to convective and conductive transfer(s) is somewhat unclear, with Ferguson et al. (2000) quoting convection as providing 40–80% of the total heat loss process (McCarter and Broido, 1975), a range in broad agreement with the statement that only around one-third of the evolved heat may be radiated (Vines, 1981). Clearly these effects may explain some of the difference between the modelled fireline intensities, and the values obtained from remote sensing. However, there may be effects operating that are currently not accounted for, and indeed it may be that a much more complex comparison is needed between the measured and modelled parameters. Further investigations continue on this issue. 9.4.2 Application to Geostationary Satellite Imagery Despite the current level of uncertainty in the relationship between the FRE and the total energy liberated during combustion, the strong agreement between the BIRD- and MODISderived FRE measurements provides confidence in the transportability of the approach between sensors. One limitation, however, is that neither of these polar-orbiting sensors can provide very high frequency observations. The experimental BIRD-HSRS sensor can at best supply only one daily FRE measurement of a fire due to its limited swath width and orbital configuration, and this can continue for perhaps four or five consecutive days before the instrument moves out of observing range. This situation is somewhat improved with the operational MODIS imager where, cloud cover permitting, the two spaceborne sensors now operating can supply four FRE observations per day of any fire large enough to be detected. More frequent FRE measurements can only be provided via a geostationary platform equipped with a suitable detector system having MIR imaging capability. The current generation of US Geostationary Operational Environmental Satellites (GOES) are suitable for this purpose, as is the new European Meteosat Second Generation satellite launched in August 2002. Figure 9.7 provides the first example of remotely sensed FRE derived from geostationary satellite data. The target was a large forest fire that occurred in Miller’s Reach, Alaska (61 ◦ N, 149 ◦ W) on 3–4 June 1996 (see Hufford et al., 2000, for maps of the activity). The Miller’s Reach fire is reported to have burned 1335 ha of forest on the evening of 3 June and the results in Figure 9.7 were derived by adapting equation (6) for use with the GOES imager 3.9 µm channel and applying this to the GOES data presented by Hufford et al. (1999). The values of FRE are of a similar magnitude to the largest obtained for the Australian fires by BIRD and MODIS, which appears reasonable if we recall that GOES pixels at this latitude have a spatial dimension of around 8 km, which is a large proportion of the total length of many of the fire fronts seen in Figure 9.4. Total FRE release for Fire Pixel 1 as measured by GOES is 8.1 × 10 11 J, and for Fire Pixel 2 is 2.8 × 10 12 J, which as a rough guide equates to 0.7 − 2.2× 10 5 kg and 2.5 − 7.5× 10 5 kg of vegetation combusted (in 3 1 / 2 and 5 hours, respectively) if we assume Burgan and Rothermel’s (1984) constant heat yield value and the assumption that between 20 and 60% of the heat is liberated in the form of radiation (see Ferguson et al., 2000). However, as already stated, the relative importance of radiation and convection may vary from fire to
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Spatial Modelling of the Terrestria
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Copyright C○ 2004 John Wiley & So
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Contents List of Contributors Prefa
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Contributors Jonathan L. Bamber Sch
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List of Contributors xi George Perr
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xiv Preface in theory and applicati
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2 Spatial Modelling of the Terrestr
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Editorial: Spatial Modelling in Hyd
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Editorial: Spatial Modelling in Hyd
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2 Modelling Ice Sheet Dynamics with
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Modelling Ice Sheet Dynamics by Sat
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36 Spatial Modelling of the Terrest
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4 Using Coupled Land Surface and Mi
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Coupled Land Surface and Microwave
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100 Spatial Modelling of the Terres
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Editorial: Terrestrial Sediment and
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Editorial: Terrestrial Sediment and
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6 Remotely Sensed Topographic Data
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Remotely Sensed Topographic Data fo
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7 Modelling Wind Erosion and Dust E
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Page 204 and 205: PART III SPATIAL MODELLING OF URBAN
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Page 232 and 233: 11 Modelling the Impact of Traffic
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Modelling the Impact of Traffic Emi
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PART IV CURRENT CHALLENGES AND FUTU
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268 Index Bi-spectral InfraRed Dete
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270 Index floodplain maps, UK 92 fl
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272 Index Long-Term Ecological Rese
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274 Index snow depth measurement ne
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276 Index urban land use in remotel
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1 Spatial Modelling of the Terrestrial Environment - Georeferencial

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