Stefan W. Maier

Improving estimates of savanna burning emissions for greenhouse accounting in northern Australia: limitations, challenges, applications

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.

Fire in Australian savannas: from leaf to landscape

Savanna ecosystems comprise 22% of the global terrestrial surface and 25% of Australia (almost 1.9 million km2) and provide significant ecosystem services through carbon and water cycles and the maintenance of biodiversity. The current structure, composition and distribution of Australian savannas have coevolved with fire, yet remain driven by the dynamic constraints of their bioclimatic niche. Fire in Australian savannas influences both the biophysical and biogeochemical processes at multiple scales from leaf to landscape. Here, we present the latest emission estimates from Australian savanna biomass burning and their contribution to global greenhouse gas budgets. We then review our understanding of the impacts of fire on ecosystem function and local surface water and heat balances, which in turn influence regional climate. We show how savanna fires are coupled to the global climate through the carbon cycle and fire regimes. We present new research that climate change is likely to alter the structure and function of savannas through shifts in moisture availability and increases in atmospheric carbon dioxide, in turn altering fire regimes with further feedbacks to climate. We explore opportunities to reduce net greenhouse gas emissions from savanna ecosystems through changes in savanna fire management.

Modelling the potential for prescribed burning to mitigate carbon emissions from wildfires in fire-prone forests of Australia

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.

Mangrove Species Identification: Comparing WorldView-2 with Aerial Photographs

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.

Impacts of an extreme cyclone event on landscape-scale savanna fire, productivity and greenhouse gas emissions

North Australian tropical savanna accounts for 12% of the world’s total savanna land cover. Accordingly, understanding processes that govern carbon, water and energy exchange within this biome is critical to global carbon and water budgeting. Climate and disturbances drive ecosystem carbon dynamics. Savanna ecosystems of the coastal and sub-coastal of north Australia experience a unique combination of climatic extremes and are in a state of near constant disturbance from fire events (1 in 3 years), storms resulting in windthrow (1 in 5‐10 years) and mega-cyclones (1 in 500‐1000 years). Critically, these disturbances occur over large areas creating a spatial and temporal mosaic of carbon sources and sinks. We quantify the impact on gross primary productivity (GPP) and fire occurrence from a tropical mega-cyclone, tropical Cyclone Monica (TC Monica), which affected 10 400 km 2 of savanna across north Australia, resulting in the mortality and severe structural damage to 140 million trees. We estimate a net carbon equivalent emission of 43 Tg of CO2-e using the moderate resolution imaging spectroradiometer (MODIS) GPP (MOD17A2) to quantify spatial and temporal patterns pre- and post-TC Monica. GPP was suppressed for four years after the event, equivalent to a loss of GPP of 0.5 Tg C over this period. On-ground fuel loads were estimated to potentially release 51.2 Mt CO2-e, equivalent to 10% of Australia’s accountable greenhouse gas emissions. We present a simple carbon balance to examine the relative importance of frequency versus impact for a number of key disturbance processes such as fire, termite consumption and intense but infrequent mega-cyclones. Our estimates suggested that fire and termite consumption had a larger impact on Net Biome Productivity than infrequent mega-cyclones. We demonstrate the importance of understanding how climate variability and disturbance impacts savanna dynamics in the context of the increasing interest in using savanna landscapes for enhanced carbon sinks in emission offset schemes.

Field estimation of ash and char colour-lightness using a standard grey scale

Vegetation fires produce biomass combustion residues, with colour varying from dark black char to white mineral ash. The colour-lightness of char and ash combustion residues is a qualitative parameter describing the post-fire condition of burned areas, and has been correlated with the completeness of combustion, fire intensity, and fire duration. Researchers have suggested that visual comparison of combustion residue samples with a standard grey scale would enable reliable combustion residue colour-lightness estimation. This paper describes an experiment aimed at assessing if colour-lightness can be estimated using a standard grey scale. Fifteen combustion residue samples with colour-lightness ranging from black char to white mineral ash were collected in the Northern Territory, Australia, and visually evaluated by three individuals using a grey scale. The grey-scale scores (0–19) were compared with the mean visible (390 to 830 nm) wavelength combustion residue reflectance (0–1) measured with a portable spectroradiometer. A significant linear relationship between the grey-scale scores and the mean visible combustion residue reflectance was found (R2 = 0.816 with a linear fit, R2 = 0.936 with a logarithmic-transformed fit). This finding suggests that combustion residue colour-lightness can be assessed in the field using inexpensive grey scales, and that this technique is a suitable avenue for future research on the field assessment of fire characteristics and effects.

Mangrove Tree Crown Delineation from High-Resolution Imagery

Mangroves are very dense, spatially heterogeneous, and have limited height variations between neighboring trees. Delineating individual tree crowns is thus very challenging. This study compared methods for isolating mangrove crowns using object based image analysis. A combination of WorldView-2 imagery, a digital surface model, a local maximum filtering technique, and a region growing approach achieved 92 percent overall accuracy in extracting tree crowns. The more traditionally used inverse watershed segmentation method showed low accuracy (35 percent), demonstrating that this method is better suited to homogeneous forests with reasonable height variations between trees. The main challenges with each of the methods tested were the limited height variation between surrounding trees and multiple upward pointing branches of trees. In summary, mangrove tree crowns can be delineated from appropriately parameterized object-based algorithms with a combination of high-resolution satellite images and a digital surface model. We recommend partitioning the imagery into homogeneous species stands for best results.

Changes in surface reflectance from wildfires on the Australian continent measured by MODIS

Atmospherically corrected Moderate Resolution Imaging Spectroradiometer (MODIS) data have been used to measure the changes in surface reflectance induced by fires. To account for observation geometry effects a kernel driven bi-directional reflectance factor model was applied. Whereas the blue, green, red and shortwave infrared bands show no consistent behaviour, the near-infrared bands almost always show a strong reduction in reflectance. An angular dependence of the change in reflectance was not found in this study. Different bio-geographical regions exhibit different spectral reflectance changes due to the different types of fuel being burnt (green/living versus dry/dead vegetation). This difference is also reflected in the seasonality of the green, red, near-infrared and shortwave infrared bands for the tropics. The conclusion of this study is that the near-infrared bands are the most suitable bands for an automatic burnt area mapping algorithm using optical, reflective remote sensing data. The results also suggest that satellite remote sensing might be able provide additional information about burning conditions which are strongly affecting greenhouse gas emissions.

Extraction of Tree Crowns from High Resolution Imagery over Eucalypt Dominant Tropical Savannas

High spatial resolution satellite imagery provides data that enable the analysis of detailed landscape information, including tree crowns. The inherent characteristics of Eucalypt crowns are challenging to remotely sensed tree crown extraction. This paper develops and applies an object-based tree crown delineation approach suitable for estimating canopy cover of Eucalypts in tropical savannas. A two level segmentation was undertaken upon QuickBird data. Firstly, a broad segmentation masked out non-Eucalypt dominant communities; and secondly, a finer segmentation and ruleset identified seed objects within crowns and then expanded these objects to cover entire crown extents. Of the 1604 tree crowns manually observed within the scene 84.3% were detected by the automated seed identification process. 75% of tree crowns extracted through the region growing process show strong overlap with their corresponding reference crowns. Results indicate the potential of this method for delineating tree crowns from Eucalypt dominant savanna and the use of this information to estimate canopy cover and tree distribution patterns.

Monitoring mangrove forests: Are we taking full advantage of technology?

Abstract Mangrove forests grow in the estuaries of 124 tropical countries around the world. Because in-situ monitoring of mangroves is difficult and time-consuming, remote sensing technologies are commonly used to monitor these ecosystems. Landsat satellites have provided regular and systematic images of mangrove ecosystems for over 30 years, yet researchers often cite budget and infrastructure constraints to justify the underuse this resource. Since 2001, over 50 studies have used Landsat or ASTER imagery for mangrove monitoring, and most focus on the spatial extent of mangroves, rarely using more than five images. Even after the Landsat archive was made free for public use, few studies used more than five images, despite the clear advantages of using more images (e.g. lower signal-to-noise ratios). The main argument of this paper is that, with freely available imagery and high performance computing facilities around the world, it is up to researchers to acquire the necessary programming skills to use these resources. Programming skills allow researchers to automate repetitive and time-consuming tasks, such as image acquisition and processing, consequently reducing up to 60% of the time dedicated to these activities. These skills also help scientists to review and re-use algorithms, hence making mangrove research more agile. This paper contributes to the debate on why scientists need to learn to program, not only to challenge prevailing approaches to mangrove research, but also to expand the temporal and spatial extents that are commonly used for mangrove research.