BIOMASS BURNING
2.5.2 Introduction
2800
2.5 METHODS FOR ESTIMATING GHG’S EMISSIONS FROM
2801
ground fires do not alter the equilibrium of the ecosystem (i.e. do not result in a 2841
conversion from forest to non forest cover), while most crown fires lead to a forest-non 2842
forest temporary transition (i.e. disturbance) or in some cases to a permanent landcover 2843
change 2844
The issue of the definition of forest (described in detail in chapter 2.2) is a particularly 2845
sensitive one when the fire monitoring from satellite data is concerned. Within the 10 to 2846
30 percent tree crown cover range indicated by the Marrakech Accords, most of woody 2847
savannah ecosystems might or might not be considered as forest. These are the 2848
ecosystems where most of the biomass burning occurs (Roy et al., 2008, van der Werf, 2849
2003) and where fire contributes to maintaining the present landcover: for example high 2850
fire frequency (fire return interval of a few years) inhibits young tree growth and blocks 2851
the transition from open to closed woodland ecosystem.
2852
Different fire management practices in different ecosystems can determine the amount 2853
of trace-gas and particulate emissions and changes the forest carbon stocks. In closed 2854
forest, controlled ground fires reduce the amount of biomass in the understory and 2855
reduce the occurrence of high severity, stand replacement fires. Conversely, in open 2856
woodland systems reducing the occurrence of fire allows tree growth with the 2857
subsequent effect of carbon sequestration. Furthermore, emission coefficients do have a 2858
seasonal variability: even assuming that fires affect the same areal extent, shifting the 2859
timing of the burning (early season versus late season) can have a significant effect on 2860
the total emissions. Early season burning when the vegetation is moist is often 2861
recommended as a good fire management practice in savanna woodlands as the fires are 2862
less damaging to the ecosystem.
2863
The purpose of this chapter is to present and explain the IPCC guidelines, list the 2864
available sources of geographically distributed data to be used for the emissions 2865
estimation, illustrate some of the main issues and uncertainties associated with the 2866
various steps of the methodology. Drawing from the experience of GOFC-GOLD Fire 2867
Implementation Team and Regional Fire Networks, the chapter emphasizes the possible 2868
use of satellite derived products and information.
2869
2.5.2.2 Direct and indirect approach to emission estimates 2870
Estimates of atmospheric emissions due to biomass burning have conventionally been 2871
derived adopting ‗bottom up‘ inventory based methods (Seiler and Crutzen, 1980) as:
2872
L = A × Mb × Cf × Gef [Equation 2.5.1]
2873
where the quantity of emitted gas or particulate L [g] is the product of the area affected 2874
by fire A [m2], the fuel loading per unit area Mb [g m-2], the combustion factor Cf, i.e.
2875
the proportion of biomass consumed as a result of fire [g g-1], and the emission factor or 2876
emission ratio Gef, i.e. the amount of gas released for each gaseous specie per unit of 2877
biomass load consumed by the fire [g g-1].
2878
Rather than attempting to measure directly the emissions L, this method requires to 2879
estimate the pre-fire biomass (A x Mb), then estimate what portion of it burned (Cf) and 2880
finally convert the total biomass burned (A x Mb x Cf) into emissions by means of the 2881
coefficient Gef. For this reason, it is defined as an indirect method. The main issue with 2882
the indirect method is that, being L the result of the multiplication of four independent 2883
terms, their uncertainties will propagate into the uncertainty of the estimate L. As a 2884
consequence, a precise estimate of L requires a precise estimate of all the terms of 2885
equation 2.5.1.
2886
The area burned (A) was considered as the parameter with the greatest uncertainty 2887
(Seiler and Crutzen, 1980) but in the last decade significant improvements in the 2888
systematic mapping of area burned from satellite data have been made (Roy et al.
2889
2008). Fuel load (Mb) remains an uncertain parameter and has been variously estimated 2890
from sample field data, satellite data and models (including those partially driven by 2891
satellite data) calculating Net Primary Production to provide biomass increments and 2892
partitioning between fuel classes (Van der Werf et al., 2003). Emission factors (Gef) are 2893
largely well-determined from laboratory measurements, although aerosol emission 2894
factors and the temporal dynamics of emission factors as a function of fuel moisture 2895
content are less certain. The burning efficiency (Cf) is a function of fire 2896
condition/behavior, the relative proportions of woody, grass, and leaf litter fuels, the fuel 2897
moisture content and the uniformity of the fuel bed. Dependencies on cover type can 2898
potentially be specified by the use of satellite-derived land cover classifications or related 2899
products such as the percentage tree cover product of Hansen et al. (2002)40, used by 2900
Korontzi et al. (2004) to distinguish grasslands and woodlands in Southern Africa.
2901
Korontzi et al. (2004) modeled a term related to Cf (combustion completeness, CC) as a 2902
weighted proportion of fuel types and emission factor database values. Roy and 2903
Landmann (2005)41 stated that there is no direct method to estimate CC from remote 2904
sensing data, although they demonstrated a near linear relationship between the product 2905
of CC and the proportion of a satellite pixel affected by fire and the relative change in 2906
short wave infrared reflectance.
2907 2908
Rather than estimate A × Mb × Cf independently, a recently proposed alternative is to 2909
directly measure the power emitted by actively burning fires and from this estimate the 2910
total biomass consumed. The radiative component of the energy released by burning 2911
vegetation can be remotely sensed at mid infrared and thermal infrared wavelengths 2912
(Ichoku and Kaufman, 200542, Wooster et al. 2005, Smith and Wooster 200543). This 2913
instantaneous measure, the Fire Radiative Power (FRP) expressed in Watts [W], has 2914
been shown to be related to the rate of consumption of biomass [g/s]. Importantly this 2915
method provides accurate (i.e. ± 15%) estimates of the rate of fuel consumed (Wooster 2916
et al 2005) and the integral of the FRP over the fire duration, the Fire Radiative Energy 2917
(FRE) expressed in Joules [J], has been shown to be linearly related to the total biomass 2918
consumed by fire [g] (Smith and Wooster, 2005, Wooster et al., 2005, Freeborn 200844).
2919
However, the accuracy of the integration of FRP over time to derive FRE depends on the 2920
spatial and temporal sampling of the emitted power. Ideally, the integration requires 2921
high spatial resolution and continuous observation over time, while the currently 2922
available systems provide low spatial resolution an high temporal resolution 2923
(geostationary satellites) or moderate spatial resolution and low temporal resolution 2924
(polar orbiting systems). For this reason, direct methods have yet to transition from the 2925
research domain to operational application, and at this stage they are not a viable 2926
alternative to indirect methods for GHG inventories in the context of REDD.
2927 2928
40 Hansen, M.C., DeFries R.S., Townsend, JG.R, Carroll, M., Dimiceli, C. and Sohlberg, R.A, Percent Tree Cover at a Spatial Resolution of 500 Meters: First Results of the MODIS Vegetation Continuous Field Algorithm, Earth Interactions, 7:1-15.
41 Roy, D.P. and Landmann, T., (2005), Characterizing the surface heterogeneity of fire effects using multi-temporal reflective wavelength data, International Journal of Remote Sensing, 26:4197-4218
42 Ichoku, C and Kaufman, Y., (2005), A method to derive smoke emission rates from MODIS Fire Radiative Energy Measurments, IEEE Transaction on Geosciences and Remote Sensing, 43(11), 2636-2649DOI 10.1109/TGRS.2005.857328
43 Smith A.M.S., and Wooster, M.J., (2005), Remote classification of head and backfire types from MODIS fire radiative power observations, International Journal of Wildland Fire, 14, 249-254.
44 Freeborn, P.H., Wooster, M.J., Hao, W.M., Ryan, C.A.,Nordgren, B.L. Baker, S.P. and Ichoku, C.(2008) Relationships between energy release, fuel mass loss, and trace gas and aerosol emissions during laboratory biomass fires, J. Geophys. Res., 113, D01102, doi:10.1029/2007JD008489