Although biomass burning of savannas is recognised as a major global source of greenhouse gas emissions, quantification remains problematic with resulting regional emissions estimates often differing markedly. Here we undertake a critical assessment of Australia’s National Greenhouse Gas Inventory (NGGI) savanna burning emissions methodology. We describe the methodology developed for, and results and associated uncertainties derived from, a landscape-scale emissions abatement project in fire-prone western Arnhem Land, northern Australia. The methodology incorporates (i) detailed fire history and vegetation structure and fuels type mapping derived from satellite imagery; (ii) field-based assessments of fuel load accumulation, burning efficiencies (patchiness, combustion efficiency, ash retention) and N : C composition; and (iii) application of standard, regionally derived emission factors. Importantly, this refined methodology differs from the NGGI by incorporation of fire seasonality and severity components, and substantial improvements in baseline data. We consider how the application of a fire management program aimed at shifting the seasonality of burning (from one currently dominated by extensive late dry season wildfires to one where strategic fire management is undertaken earlier in the year) can provide significant project-based emissions abatement. The approach has wider application to fire-prone savanna systems dominated by anthropogenic sources of ignition.

Remote sensing plays a critical role in mapping and monitoring mangroves. Aerial photographs and visual image interpretation techniques have historically been known to be the most common approach for mapping mangroves and species discrimination. However, with the availability of increased spectral resolution satellite imagery, and advances in digital image classification algorithms, there is now a potential to digitally classify mangroves to the species level. This study compares the accuracy of mangrove species maps derived from two different layer combinations of WorldView-2 images with those generated using high resolution aerial photographs captured by an UltraCamD camera over Rapid Creek coastal mangrove forest, Darwin, Australia. Mangrove and non-mangrove areas were discriminated using object-based image classification. Mangrove areas were then further classified into species using a support vector machine algorithm with best-fit parameters. Overall classification accuracy for the WorldView-2 data within the visible range was 89%. Kappa statistics provided a strong correlation between the classification and validation data. In contrast to this accuracy, the error matrix for the automated classification of aerial photographs indicated less promising results. In summary, it can be concluded that mangrove species mapping using a support vector machine algorithm is more successful with WorldView-2 data than with aerial photographs.

Current good practice guidelines for national greenhouse gas inventories requires that seasonal variation in emission factors from savanna fires be considered when compiling national accounts. African studies concluded that the emission factor for methane decreases during the dry season principally due to curing of the fuels. However, available data from Australian tropical savannas shows no effect of seasonality on emission factors, consistent with observations that the fine fuels appear to cure fully soon after the start of the fire season. To test whether the seasonality in greenhouse gas emission factors reported for Africa also occurs in Australia, methane and nitrous oxide emission factors were measured in early and in late dry season fires in Western Arnhem Land, a region typical of much of the northern Australia savanna zone. We found no significant seasonality in methane emission factors, but there was substantial variation in emission factors associated with inter‐fire differences in vegetation and fuel. This variation could be explained almost completely by combustion efficiency. Nitrous oxide emission factors were not related to combustion efficiency but showed some variation across vegetation and fuel size class. Both methane and nitrous oxide emission factors were consistent with previous work in northern Australia and with some published values from Africa. The absence of a significant seasonal trend in emission factors indicates that savanna fire emissions in northern Australia can be managed by strategic prescribed burning.

Prescribed fire can potentially reduce carbon emissions from unplanned fires. This potential will differ among ecosystems owing to inherent differences in the efficacy of prescribed burning in reducing unplanned fire activity (or ‘leverage’, i.e. the reduction in area of unplanned fire per unit area of prescribed fire). In temperate eucalypt forests, prescribed burning leverage is relatively low and potential for mitigation of carbon emissions from unplanned fires via prescribed fire is potentially limited. Simulations of fire regimes accounting for non-linear patterns of fuel dynamics for three fuel types characteristic of eucalypt forests in south-eastern Australia supported this prediction. Estimated mean annual fuel consumption increased with diminishing leverage and increasing rate of prescribed burning, even though average fire intensity (prescribed and unplanned fires combined) decreased. The results indicated that use of prescribed burning in these temperate forests is unlikely to yield a net reduction in carbon emissions. Future increases in burning rates under climate change may increase emissions and reduce carbon sequestration. A more detailed understanding of the efficacy of prescribed burning and dynamics of combustible biomass pools is required to clarify the potential for mitigation of carbon emissions in temperate eucalypt forests and other ecosystems.