Introduction

2.5.2.1 REDD+ and emissions due to fire in forest environments

Fire is a complex biophysical process with multiple direct and indirect effects on the atmosphere, the biosphere and the hydrosphere. Moreover, it is now widely recognized that, in some fire prone environments, fire disturbance is essential to maintain the ecosystem in a state of equilibrium.

Reducing the emissions from deforestation and degradation (REDD) from fire requires an understanding of the process of fire in forest systems (either as an ecological change agent, a disturbance, a forest management tool, or as a process associated with land cover conversion) and how fire emissions are calculated. Fire can be seen both as a threat to REDD, in the measure in which it is a disturbance affecting areas where programs aimed at reducing deforestation and degradation are in place, but also as an integral component of REDD+ if the emissions due to fire are directly addressed through

integrated fire and forest management programs. The specific details of how REDD+ will be implemented with respect to fire are still in development.

This chapter focuses on above-ground fires in forest environments and how to calculate greenhouse gas emissions due to vegetation fires, using available satellite-based fire monitoring products, biomass estimates and coefficients. Below-ground fires, for example, those that occur in the peat forests of Indonesia, Alaska or Canada are beyond the scope of this sourcebook version, although it is envisaged that in the future, below-ground fires will be accounted for.

The effects of fire in forests are widely variable. It is possible to refer to fire severity as a term to indicate the magnitude of the effects of the fire on the ecosystem⁴⁰ which in turn is strongly related to the post-fire status of the ecosystem. As a broad categorization, low severity surface fires affect mainly the understory vegetation rather than the trees, while high severity crown fires directly affect the trees. The latter are sometimes referred to as stand replacement fires. Consequently, at the broad scale, ground fires generally do not alter the equilibrium of the ecosystem (i.e. do not result in a conversion from forest to non forest cover), but increased fire frequency and intensity can lead to forest transition, starting with degradation before complete conversion. Crown fires can lead to a forest-non-forest temporary transition followed by regrowth (i.e. fire is a disturbance), or to a permanent change where human activities inhibit forest regeneration.

The issue of the definition of forest (described in detail in chapter 1.2) is a particularly sensitive one when the fire monitoring from satellite data is concerned. Within the 10 to 30 percent tree crown cover range indicated by the Marrakech Accords, most of woody savannah ecosystems might or might not be considered as forest. These are the ecosystems where most of the biomass burning occurs (Roy et al., 2008, van der Werf, 2003) and where fire is an important process contributing to the maintenance of the present land cover. Typically, high fire frequency in savannas (fire return interval of a few years or less) inhibits young tree growth and succession from open to closed woodland ecosystems. These fire-prone ecosystems are characterized by a cycle of recurring fires and natural regeneration of the vegetation to its original state; therefore, the presence of fire is not per se regarded as a component of the climate change process. Instead, there is a need to establish baseline data on the current fire regimes, in order to assess any changes and trends in fire and emission patterns.

Different fire management practices in different ecosystems can determine the amount of trace-gas and particulate emissions and changes to forest carbon stocks. In closed forests, controlled ground fires reduce the amount of biomass in the understory but, over a period of time, may lead to increase in carbon stock by reducing the occurrence of high severity, stand replacement fires, and under certain circumstances, by promoting the growth of fast growing shade intolerant tree species. Conversely, in open woodland systems, reducing the occurrence of fire allows tree growth with the subsequent effect of carbon sequestration. Furthermore, emission coefficients do have a seasonal variability (Korontzi et al., 2004): even assuming that fires affect the same areal extent, shifting the timing of the burning (early season versus late season) can have a significant effect on the total emissions. Wildfires are characterised by two main forms of combustion–

flaming and smouldering combustion; which implies that variable emission coefficients should be used. It is the relative mix of these two types of combustion that generate the mix of species emitted from biomass burning. Flaming combustion or oxidation-type combustion reactions (e.g. production of CO₂, NOx) proceed at a faster rate when the fuel is dry and has a large surface-area-to-volume (SAV) ratio. The converse holds for smoldering combustion or reduction-type reactions (CO, CH4 etc). A good example is the tropical savannas in which early dry season burns produce a higher CO/CO2 ratio than

40 De Santis A, Chuvieco E, Vaughan P (2009) Short-term assessment of burn severity using the inversion of PROSPECT and GeoSail models. Remote Sensing of Environment. 113: 126-136.

those during the late dry season. Early season burning when fuels tend to be moist is often recommended as a good fire management practice in savanna woodlands as the fires are less intense, thus less damaging to the trees, the ecosystem and hence the carbon stock. In order to fully quantify the implications in terms of emissions of early versus late season fires, more research is needed to characterize fully the seasonal variability of the emission coefficients. The purpose of this chapter is to present and explain the IPCC guidelines, list the available sources of geographically distributed data to be used for the emissions estimation, illustrate some of the main issues and uncertainties associated with the various steps of the methodology. Drawing from the experience of GOFC-GOLD Fire Implementation Team and Regional Fire Networks, the chapter emphasizes the possible use of satellite derived products and information.

2.5.2.2 Direct and indirect approach to emission estimates

Estimates of atmospheric emissions due to biomass burning have conventionally been derived adopting ‘bottom up’ inventory based methods (Seiler & Crutzen, 1980) as:

L = A × Mb × Cf × Gef [Equation 2.5.1]

where the quantity of emitted gas or particulate L [g] is the product of the area affected by fire A [m2], the fuel loading per unit area Mb [g m^-2], the combustion factor Cf, i.e.

the proportion of biomass consumed as a result of fire [g g^-1], and the emission factor or emission ratio Gef, i.e. the amount of gas released for each gaseous specie per unit of biomass load consumed by the fire [g g^-1].

Rather than attempting to measure directly the emissions L, this method estimates the pre-fire biomass (A x Mb), then estimate what portion of it burned (Cf) and finally converts the total biomass burned (A x Mb x Cf) into emissions by means of the coefficient Gef. For this reason, it is defined as an indirect method. A precise estimate of L requires a precise estimate of all the terms of equation 2.5.1.

In the past, the area burnt (A) was considered to be the variable with the greatest uncertainty, however, in the last decade significant improvements in the systematic mapping of area burned from satellite data have been made (Roy et al. 2008). Fuel load (Mb) remains an uncertain variable and has been generally estimated from sample field data, and/or simulation models of plant productivity driven by satellite-derived estimates of plant photosynthesis. The CASA model is a good example of this approach where by satellite data is used to calculate Net Primary Production to provide biomass increments and partitioning between fuel classes⁴¹. Emission factors (Gef) have been fairly precisely estimated from laboratory measurements⁴². However it is by no means certain how these translate to different conditions outside those measured in the laboratory and at the ecosystem level. Aerosol emission factors and the temporal dynamics of emission factors as a function of fuel moisture content remain uncertain (e.g. those of CO2 versus CO, see above). The burning efficiency (Cf) is a function of fire condition/behavior, the relative proportions of woody, grass, and leaf litter fuels, the fuel moisture content and the uniformity of the fuel bed. Dependencies on cover type can potentially be specified by the use of satellite-derived land cover classifications or related products such as the percentage tree cover product⁴³, used by Korontzi et al. (2004) to distinguish grasslands and woodlands in Southern Africa through a model related to Cf (combustion completeness, CC) as a weighted proportion of fuel types and emission factor database

41 van der Werf GR et al. (2006) Interannual variability in global biomass burning emissions from 1997 to 2004. Atmospheric Chemistry and Physics. 6: 3423-3441.

42 Andreae MO, Merlet P (2001) Emission of trace gases and aerosols from biomass burning, Global Biogeochemical Cycles, 15: 955-966.

43 Hansen MC et al. (2002) Percent Tree Cover at a Spatial Resolution of 500 Meters: First Results of the MODIS Vegetation Continuous Field Algorithm. Earth Interactions, 7:1-15.

values. Roy and Landmann⁴⁴ stated that there is no direct method to estimate CC from remote sensing data, although for savannas they demonstrated a near linear relationship between the product of CC and the proportion of a satellite pixel affected by fire and the relative change in short wave infrared reflectance.

Rather than estimate A × Mb × Cf independently, a more recently proposed alternative is to directly measure the power emitted by actively burning fires and from this estimate the total biomass consumed. The radiative component of the energy released by burning vegetation can be remotely sensed at mid infrared and thermal infrared wavelengths^45,46. This instantaneous measure, the Fire Radiative Power (FRP) expressed in Watts [W], has been shown to be related to the rate of consumption of biomass [g/s]. Importantly this method provides accurate (i.e. ± 15%) estimates of the rate of fuel consumed (Wooster et al 2005) and the integral of the FRP over the fire duration, the Fire Radiative Energy (FRE) expressed in Joules [J], has been shown to be linearly related to the total biomass consumed by fire [g]⁴⁷. However, the accuracy of the integration of FRP over time to derive FRE depends on the spatial and temporal sampling of the emitted power. Ideally, the integration requires high spatial resolution and continuous observation over time, while the currently available systems provide low spatial resolution and high temporal resolution (geostationary satellites) or moderate spatial resolution and low temporal resolution (polar orbiting systems). For this reason, direct methods have yet to transition from the research domain to operational application, and at this stage they are not a viable alternative to indirect methods for GHG inventories in the context of REDD.