5. Type 3: Dwelling/byre house with opposed, recessed entrances at the middle of the house
5.3 Comparative houses from Norway
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ANA C. L. SA´ *{, JOSE´ M. C. PEREIRA{{ and JOA˜O M. N. SILVA{ {Department of Forestry, Instituto Superior de Agronomia, Tapada da Ajuda, 1349-017
Lisbon, Portugal
{Tropical Research Institute, Travessa do Conde da Ribeira 9, 1300-142 Lisbon, Portugal
An experimental burn was performed in a dambo grassland, in the Western Province of Zambia, during the SAFARI 2000 Third Intensive Field Campaign. The main goal of this study was to analyse the possibility of estimating combustion completeness based on fire-induced spectral reflectance changes in surface. Inverse, nonlinear relationships were obtained between combustion completeness and pre-fire to post-fire spectral reflectance changes, in the green, red, and near-infrared spectral domains (equivalent to Landsat 7 ETM+ channels 2, 3, and 4). The coefficient of determination (R2) varied from 0.50 for channel 4, to 0.57 for channel 3, and all the regressions were significant at the 95% confidence level. Thus, it may be feasible to treat combustion completeness as a variable whose values can be remotely estimated. However, its relationship with fire-induced spectral reflectance changes is expected to exhibit some dependence on vegetation structure. The experimental burn was performed simultaneously with overpasses from the Terra satellite, and from the NASA ER- 2 research airplane carrying the 50-channel MODIS Airborne Simulator (MAS) image spectrometer. Our results may be used in conjunction with imagery from these sensors, to support the development of operational approaches for combustion completeness estimation from remotely sensed data.
1. Introduction
Biomass burning is an important source of trace gases and aerosols emissions to the atmosphere (Crutzen and Carmichael 1993), estimated to contribute about 38% of the tropospheric ozone (O3), 32% of global carbon monoxide (CO), 10% of global
methane (CH4), and up to 40% of the gross carbon dioxide (CO2) emissions
(Lindesay 1997, Justice and Korontzi 2001). Savannah fires play a central role in these processes and are responsible for over 90% of the biomass burnt in Africa (Delmas et al. 1991). At a global scale, the burning of African savannahs accounts for almost one-third of annual gross emissions from biomass burning (Andreae 1991, 1997).
The quantification of biomass burning is usually carried out with the following equation:
where A denotes the area of land affected by fire (km2), B is the biomass density (t km22), and CC is the combustion completeness, or the fraction of biomass consumed by fire. In the present study, we are primarily concerned with CC, and with assessing the feasibility of its estimation from remotely sensed data. The reasons for this concern are the recent questioning of the adequacy of CC values typically used in calculations of biomass burning (McNaughton et al. 1998), and the highlighting of the strong seasonal cycle of CC in tropical savannahs (Hoffa et al. 1999).
McNaughton et al. (1998) conducted 18 grass fires at Serengeti NP, Tanzania, at the beginning of the dry season, when fire frequency exhibits a regional peak. Combustion completeness varied between 20% and 75%, leading the authors to consider CC values of 90–95% typically used in biomass burning studies, and derived from laboratory experiments, as unrealistically high.
Hoffa et al. (1999) carried out 13 experimental burns, of which six were in dambos and seven in miombo woodlands in the Western Province of Zambia, between June and August 1996. Combustion completeness increased as the dry season advanced, and consequently fuels progressively dried out. Between June and August, CC increased from 44% to 98% in dambos, and from 1% to 47% in the miombo woodlands. These results highlight the fact that using a fixed value for CC for the entire season can lead to some overestimation on the amount of emissions released, especially in the early dry season where there are emissions of incomplete combustion products. Thus, it is important to treat CC not as a parameter, but as a seasonal variable, in the calculation of biomass burning.
The broad geographical range and complex temporal dynamics of the areas affected by biomass burning require the use of remote sensing for quantitative assessment of the process. Thus, it is important to explore the adequacy of remote sensing, already widely used for burned area mapping, to estimate CC. McNaughton et al. (1998), and Stronach and McNaughton (1989) discussed the possibility of retrospectively estimating fire behaviour characteristics based on colour analysis of combustion residues. Ash colour-lightness was shown to be well correlated with the integral of fire time–temperature curves, and with the loss of weight of ash on ignition (Stronach and McNaughton 1989). McNaughton et al. (1998) suggested the possibility of estimating fire properties related to emissions, from satellite remote sensing data.
White et al. (1996) classified forest fire severity into three classes, using Landsat Thematic Mapper data. Light, moderate, and high levels of fire severity were discriminated as a function of litter consumption, soil colour alteration, surface fuel consumption, and the scorching and killing of trees and shrubs. Fire-induced land surface changes increase middle-infrared reflectance, and Landsat TM channel 7 (2.1 mm) was identified as the best discriminator of fire severity. Accuracy of satellite-based fire severity mapping was assessed with aerial photo interpretation, yielding an overall agreement of 63%.
and Yool (1998) that the strongest spectral change over burned areas is an increase in middle infrared reflectance, but consider that the fraction of biomass consumed by fire cannot be measured directly from remote sensing. However, inferences concerning combustion completeness can be derived from knowledge of the timing of fire and type of forest affected, which are useful inputs to ground-based models of fire effects. Maps of burn severity based on fire-induced spectral reflectance changes can be combined with ground data, to produce maps of combustion completeness (Kasischke et al. 2000).
Our objective was to quantify the relationship between CC and fire-induced spectral reflectance changes observed at an experimental burn carried out in a grassland of the Western Province of Zambia, during the SAFARI 2000 Third Intensive Field Campaign. The burn coincided with overpasses of the Terra satellite and of the NASA ER-2 high altitude research aircraft, which carried the 50-channel MODIS airborne simulator (MAS). Thus, this burn is documented with data on pre-fire fuel loading, fire behaviour, fuel consumption (Pereira et al. 2005), spectroradiometric data acquired in the field, and satellite imagery at high (MAS), and low (MODIS) spatial resolution.
2. Study area
The experimental burn was carried out near Kaoma, in the Western Province of Zambia. The study area is located in the wetter Zambezian miombo woodland mapping unit of White (1983), characterized by having rainfall higher than 1000 mm per year, but less when occurring on Kalahari sand. The mean annual rainfall in the Kaoma District is 800 mm, most of which falls during the wet season, between late November to April. The mean daily temperature in July, during the middle of the dry season, varies between 17.5 and 20.0uC (Hoffa et al. 1999). Miombo woodlands cover approximately 2.7 million km2in Africa, in regions with mean annual rainfall between about 700 mm and 1400 mm (Frost 1996). Interspersed within the woodlands are broad, hydromorphic grasslands, called dambos, which can cover up to 40% of the landscape in some areas (Desanker et al. 1997). Dambos are distinctive features of the miombo region, which occupy seasonally waterlogged shallow valley depressions (Campbell et al. 1996). The dominant grass of dambos in the study area is Loudetia simplex (Nees) C. E. Hubb. The burn was performed at the Namalazi dambo (14.81u S, 24.51u E), on 25 August 2000.
3. Methods
function of disk settling height, at an additional 64 locations within the 1506150 m plot. Pre-fire grass fuel loading estimated from the 80 (16 destructive and 64 non- destructive) measurements was 5.63 t ha21with a standard error of the estimate of 0.40 t ha21 (Pereira et al. 2005). Post-fire fuel sampling was performed on 16 quadrats adjacent to those clipped prior to the fire. The sampling procedures used before burning were repeated after the fire. The only significant difference was that charcoal and ash present in the quadrats were collected with a portable vacuum cleaner. Combustion completeness (CC) was calculated at each of the 16 adjacent quadrats as:
CC~Bpre{Bpost Bpre
, ð2Þ
where Bpre is pre-fire biomass, and Bpost is post-fire biomass. The latter includes
uncombusted plant materials, and also charcoal and ash residues. The mean CC value thus obtained is the mean of 16 CC ratios, and differs from the CC value given by Pereira et al. (2005) for the Namalazi dambo, which is a ratio of mean pre-fire and post-fire biomass values. The approach used by Pereira et al. (2005) results from using the entire dambo as the unit of analysis thus contrasting with the present study whose units of analysis are the individual plots, where fuel consumption and reflectance changes were measured.
3.2 Spectral reflectance measurements
Spectral reflectance of the 16 quadrats at the Namalazi dambo was measured prior to the fire and immediately after the fire, using a FieldSpec VNIR spectroradiometer (Analytical Spectral Devices, Boulder, CO), over the range 400–900 nm, with a 1.4 nm sampling interval. At the beginning of each set of measurements, the reflectance of a white Spectralon reference plate was measured in order to convert the measured radiance into reflectance factor values. In each sample quadrat, the instrument was positioned approximately at a height of 1 m, and five spectral measurements were collected at a fixed position. Each sample measurement is already an average of 10 instrument pre-defined spectral reflectance measurements. The pre- and post-fire reflectance measurements were converted into band equivalent reflectance using the spectral response functions of Landsat 7 ETM+ channels 2, 3, and 4, to obtain mean reflectance factor values over the spectral range of each channel. The pre-fire vs. post-fire reflectance factor differences were used in a regression analysis to estimate combustion completeness. The blue spectral range (channel 1) was discarded because it is considered inadequate to detect burnt surfaces (Pereira et al. 1999, Trigg and Flasse 2000). A problem with the spectroradiometer is that it invalidated the post-fire reflectance measurement at one of the quadrats, reducing the sample size to 15 observations.
combination of dry grass and bare soil, which have similar, approximately parallel spectral signatures (figure 2).
Figure 3 shows the nonlinear relationships between pre-fire and post-fire spectral reflectance difference values (Dr) in the spectral domain of the Landsat 7 ETM+ sensor green (ETM+ 2), red (ETM + 3), and NIR (ETM + 4) bands, and CC at the 15 clipped quadrats of the Namalazi dambo burn.
The observed range of CC values is large (40% to 93%), and is relatively well explained by the observed Dr, especially in the visible bands. Red reflectance is the better estimator of CC, followed by green and NIR reflectance, as shown by the values of the coefficient of determination (R2). All of these estimated nonlinear
Figure 1. Mean¡1 standard deviation pre-fire and post-fire spectral reflectance signatures for the 15 clipped plots of the Namalazi dambo burn. The x-axis represents three simulated optical bands, equivalent to Landsat 7 ETM+ bands 2, 3 and 4.
Figure 3. Fitted nonlinear regressions between pre-fire and post-fire spectral reflectance difference values in the spectral domain of the Landsat 7 ETM+ sensor green (ETM2) (a); red (ETM3) (b); and NIR (ETM4) (c); bands, and combustion completeness at the 15 clipped plots of the Namalazi dambo burn. The coefficient of determination (R2) value for all the regressions is significant (p-values below 0.015) at the 95% confidence level. Quadratic curve used.
combustion completeness based on nonlinear pre-fire vs. post-fire changes in spectral reflectance of the surface appears to be feasible in grasslands, with reasonably good accuracy. Therefore, it may be possible to reduce uncertainties in the estimation of emissions from biomass burning, by replacing the fixed combustion completeness values typically used to estimate emissions, with values, which vary in time and space. Additional research is required to determine if CC is also predictable from reflectance change in shrublands, woodlands and forests. The relationship found between spectral reflectance change and the severity of fire effects (as represented by combustion completeness) is reversed from that mentioned by Stronach and McNaughton (1989). They used ash colour as the diagnostic feature of fire effects, and proposed that the lighter the colour, the more complete the combustion. In our experiment, a higher decrease in the reflectance values is associated with greater combustion completeness, so that darker surfaces (caused by a more abundant charcoal deposit) indicate higher-severity fires (figure 4).
The main combustion product was charcoal, in agreement with the results of Trigg and Flasse (2000). Therefore, more complete combustion yields more char, resulting in a darker surface. Low combustion completeness leaves a post-fire surface whose spectral signal is dominated by bare soil and uncombusted grass, both of which are much brighter than char (figure 2). Evidently, if it were feasible to unmix the spectral signatures of soil, dry grass, charcoal, and ash, we could focus only on the combustion products, charcoal and ash, and then a higher CC would indeed be associated with more ash, and brighter post-fire surfaces. However, considering the actual spectral signatures of the relevant materials (figure 2 and Pereira et al. 1999), this unmixing appears problematic.
The approach we used has various limitations. It may be unfeasible, or at least less accurate for understory burns, such as in miombo fires, and it requires acquisition of spectral data shortly after the fire, before the combustion products are scattered by wind. The use of multitemporal image compositing procedures adequate for burned area mapping (Sousa et al. 2002, Stroppiana et al. 2002) will guarantee the selection of pixels corresponding to dates immediately, or shortly after the fire. Our analysis was restricted to the 400–900 nm spectral range due to equipment limitations, while Justice et al. (1993) and Pereira et al. (1999) consider that the 1.6 mm region may be more effective to characterize the spectral variability of burnt surfaces. Trigg and Flasse (2000) studied spectral reflectance changes caused by a grass fire in Namibia, and found that the variance of post-fire reflectance increases with wavelength. It remains to be shown if this variance does represent sensitivity to CC, or if it is due to other variables dependent on fire behaviour.
(a)
(b)
Figure 4. Burnt and unburnt area at the Namalazi dambo, showing (a) dry grass in the background and charcoal and bare soil in the foreground and (b) detail of the area burned, with an intact patch of dry grass.
We analysed the possibility of estimating CC based on fire-induced spectral reflectance change (Dr) of the surface, in an experimental burn at a dambo grassland site. Reflectance changes at the spectral range of Landsat 7 ETM+ channels 2–4 showed a nonlinear relationship with CC values, with a reasonably good degree of fit. We conclude that it appears feasible to replace the fixed CC values typically used to estimate emissions by values that vary in time and space, and that may be estimated from remotely sensed data. Additional research is required to determine the dependence of the relationship between Dr and CC on vegetation-type fire severity, and to explore the sensitivity to CC of the mid-infrared spectral range. Acknowledgements
We thank the Luso-American Foundation for the Development, which provided financial support to the participation of J.M.C.P., A.C.L.S., and J.M.N.S. in the Third Intensive Field Campaign of SAFARI 2000. We are also grateful to Dr Darold Ward, USDA Forest Service, for the logistic support to our activities. He and Natasha Ribeiro (University Eduardo Mondlane, Mozambique) also helped with fieldwork. A.C.L.S. and J.M.N.S. were funded by the Foundation for Science and Technology, Ministry for Science and Technology, through doctoral grants SFRH/BD/891/2000 and SFRH/BD/1026/2000, respectively. This research was performed under Project POCTI/CTA/33582/99 (Reduction of uncertainties in estimates of atmospheric emissions from fires in southern Africa), Foundation for Science and Technology, Ministry for Science and Technology, Portugal. We are grateful to the Government of Zambia for hosting our activities. This study was part of the SAFARI 2000 Southern African Regional Science Initiative.
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Contribuição para a
Redução da Incerteza
nas
Contribuição para a
Redução da Incerteza
nas
A. C. L. SA´ 1*, J. M. C. PEREIRA1, 2, M. J. P. VASCONCELOS2, J. M. N. SILVA1, N. RIBEIRO3 and A. AWASSE4
1Department of Forestry, Instituto Superior de Agronomia, Tapada da Ajuda, 1349-017 Lisboa, Portugal; e-mail: anasa,jmcpereira, [email protected] 2Tropical Research Institute, Travessa do Conde da Ribeira 9, 1300-142 Lisboa, Portugal; e-mail: [email protected]
3Faculty of Agronomy and Forestry, Universidade Eduardo Mondlane, Campus Universita´rio edificio 1, C.P.257, Maputo, Mozambique;
e-mail: [email protected]