School of Agriculture, Food and Ecosystem Sciences - Theses

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    Predicting future fire regimes and the implications for biodiversity in temperate forest ecosystems
    McColl-Gausden, Sarah Catherine ( 2022)
    Fire regimes are changing around the world. Fire seasons are lengthening, high severity fires are occurring more often and in unexpected places. Relationships among fire, climate, and vegetation are varied, dynamic, and under-examined in many ecosystems. While some studies have explored links between fire, climate, and vegetation such as species distributions or future fire weather under changing climate, relatively few have considered the dynamic interactions among all three simultaneously. In this thesis, I develop and apply modelling approaches to predict future fire regimes in south-eastern Australia and explore the implications for fire-responsive functional plant types. In the first quantitative chapter of my thesis (Chapter 2), I develop a new fuel model for south-eastern Australia. I use edaphic, climatic, and fire variables to build a predictive fuel model that is independent of vegetation classes and their future distributions. In Chapter 3, I use my fuel model in a landscape fire regime simulator, alongside multiple predictions of future climate, to examine the immaturity risk to an obligate seeder tree species (Eucalyptus delegatensis). My simulations indicate that this species will be under increased immaturity risk under future fire regimes, particularly for those stands located on the periphery of the current distribution, closer to roads or surrounded by a drier landscape at lower elevations. In Chapter 4, I expand the application of the above simulation approach to examine the relative importance of future fuel and future climate to changing fire regimes in six case study areas across temperate south-eastern Australia. My results indicate that the direct influence of climate on fire weather will be the principal driver of changes in future fire regimes (most commonly involving increased extent, decreased intervals, and an earlier start to the fire season). The indirect influence of climate on vegetation and therefore fuel was also important, acting synergistically or antagonistically with weather depending on the area and the fire regime attribute. Finally, in my fifth chapter, I consider future climate and fire impacts on plant persistence by combining the landscape fire regime simulator with spatially explicit population viability analyses. Obligate seeder species were at risk of population extinction or reduction in more simulation scenarios than facultative resprouters. However, my approach highlighted that the resilience of facultative resprouters might also be tested by climate related changes in demographic processes and fire regimes. Overall, my research has provided new methods and scientific insights into the changing nature of fire regimes in temperate south-eastern Australia. Some negative impacts on biodiversity from a changing fire regime, particularly on more vulnerable plant functional types like obligate seeders, appear inevitable. Further understanding of the complex interactions among fire, climate, and vegetation will enable improved integration of risks to people, property, and biodiversity into land and fire management planning.
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    Quantifying fire-severity patterns using optical remote sensing data in temperate eucalypt forests of south-eastern Australia
    Tran, Bang Nguyen ( 2020)
    Wildfires have significant biophysical and ecological impacts on ecosystems worldwide from local to regional and national scales. The magnitude of such impacts is related to wildfire severity. Recent increases in wildfire occurrence have been associated with climate change, however whether there has also been a change in fire severity remains underexamined in many biomes. Better understanding of fire-severity patterns is required for effective wildfire management, particularly in the fire-prone landscapes of temperate south-eastern Australia, which support a diversity of forests varying in species composition, structure, and post-fire regeneration strategies. Thus, the overarching aims of my Thesis were to accurately quantify wildfire severity at landscape scales and to examine spatial and temporal variation in wildfire severity across a range of forest types in Victoria, south-eastern Australia. To meet the overarching aims, my Thesis involves: (1) identification of optimal optical spectral indices for mapping fire severity across the dominant and most fire-prone forest types in Victoria; (2) a comparison of the accuracy of two different fire-severity mapping approaches, namely single spectral indexing thresholding and machine learning; (3) using the acquired knowledge, the development of fire-severity maps for large (>1000 ha) wildfires occurring in Victoria between 1987 and 2017, and a retrospective analysis of changes in spatial patterns of high-severity fires over that period; and (4) an analysis of the relative importance of four groups of environmental variables (namely fire weather, fuel, topography and climate) as predictors of high-severity fire extent and landscape configuration. My evaluation of remote sensing based spectral indices indicated that the best-performing indices of fire severity varied with forest type and forest functional group, but that there is scope to group forests by structure and fire-regeneration strategy to simplify fire-severity classification in heterogeneous forest landscapes. Results from my comparative analysis confirmed that machine learning outperformed the spectral index thresholding approach for mapping fire severity in most cases, increasing overall accuracy by 11% on a forest-group basis, and 16% on an individual wildfire basis. My results also confirmed that the accuracy achieved with a reduced set of predictor variables that included the previously identified optimal indices of fire severity was not improved by adding more variables. Greater overall accuracies (by 12% on average) were achieved when in-situ data (rather than data from other fires) were used to train the machine-learning algorithm. As such, my study demonstrates the utility of machine-learning algorithms for streamlining a robust fire-severity mapping approach across heterogeneous forested landscapes. Analysis of spatial patterns highlighted that high-severity wildfires in temperate Australian forests have increased in extent and aggregation in recent decades. The total and proportional high-severity burned area increased through time from 1987 to 2017. While the number of high-severity patches per year remained unchanged in that period, the variability in high-severity patch size increased, and high-severity patches became more aggregated and more irregular in shape. Finally, key findings from my models on the relative importance of environmental drivers (climate, fire weather, fuel, and topography) were that fuel type and fire weather were the most important predictors of the extent and configuration of high-severity fires in Australian temperate forests. My Thesis presents one of the most comprehensive analyses of fire-severity patterns from remote sensing data in Australia. My research results support the reliable estimation of wildfire severity from optical images using machine-learning algorithms once optimal spectral indices are identified and when in-situ training data are available for individual fires. Importantly, the quantified shifts in fire regimes across Victoria’s forested landscapes may have critical consequences for ecosystem dynamics, as fire-adapted temperate forests are more likely to be burned at high severities relative to historical ranges, a trend that seems set to continue under projections of a hotter, drier climate in south-eastern Australia. It is therefore critical that forest scientists and land managers continue to acknowledge and quantify changing wildfire-severity patterns so that they are better informed to address the ecological consequences.