School of Ecosystem and Forest Sciences - Research Publications
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ItemA forest fuel dryness forecasting system that integrates an automated fuel sensor network, gridded weather, landscape attributes and machine learning models(Imprensa da Universidade de Coimbra, 2022)Accurate and timely forecasting of forest fuel moisture is critical for decision making in the context of bushfire risk and prescribed burning. The moisture content in forest fuels is a driver of ignition probability and contributes to the success of fuel hazard reduction burns. Forecasting capacity is extremely limited because traditional modelling approaches have not kept pace with rapid technological developments of field sensors, weather forecasting and data-driven modelling approaches. This research aims to develop and test a 7-day-ahead forecasting system for forest fuel dryness that integrates an automated fuel sensor network, gridded weather, landscape attributes and machine learning models. The integrated system was established across a diverse range of 30 sites in south-eastern Australia. Fuel moisture was measured hourly using 10-hour automated fuel sticks. A subset of long-term sites (5 years of data) was used to evaluate the relative performance of a selection of machine learning (Light Gradient Boosting Machine (LightGBM) and Recurrent Neural Network (RNN) based Long-Short Term Memory (LSTM)), statistical (VARMAX) and process-based models. The best performing models were evaluated at all 30 sites where data availability was more limited, demonstrating the models' performance in a real-world scenario on operational sites prone to data limitations. The models were driven by daily 7-day continent-scale gridded weather forecasts, in-situ fuel moisture observation and site variables. The model performance was evaluated based on the capacity to successfully predict minimum daily fuel dryness within the burnable range for fuel reduction (11 – 16%) and bushfire risk (
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ItemPerformance of GEDI Space-Borne LiDAR for Quantifying Structural Variation in the Temperate Forests of South-Eastern Australia(MDPI, 2022-08-01)Monitoring forest structural properties is critical for a range of applications because structure is key to understanding and quantifying forest biophysical functioning, including stand dynamics, evapotranspiration, habitat, and recovery from disturbances. Monitoring of forest structural properties at desirable frequencies and cost globally is enabled by space-borne LiDAR missions such as the global ecosystem dynamics investigation (GEDI) mission. This study assessed the accuracy of GEDI estimates for canopy height, total plant area index (PAI), and vertical profile of plant area volume density (PAVD) and elevation over a gradient of canopy height and terrain slope, compared to estimates derived from airborne laser scanning (ALS) across two forest age-classes in the Central Highlands region of south-eastern Australia. ALS was used as a reference dataset for validation of GEDI (Version 2) dataset. Canopy height and total PAI analyses were carried out at the landscape level to understand the influence of beam-type, height of the canopy, and terrain slope. An assessment of GEDI’s terrain elevation accuracy was also carried out at the landscape level. The PAVD profile evaluation was carried out using footprints grouped into two forest age-classes, based on the areas of mountain ash (Eucalyptus regnans) forest burnt in the Central Highlands during the 1939 and 2009 wildfires. The results indicate that although GEDI is found to significantly under-estimate the total PAI and slightly over-estimate the canopy height, the GEDI estimates of canopy height and the vertical PAVD profile (above 25 m) show a good level of accuracy. Both beam-types had comparable accuracies, with increasing slope having a slightly detrimental effect on accuracy. The elevation accuracy of GEDI found the RMSE to be 10.58 m and bias to be 1.28 m, with an R2 of 1.00. The results showed GEDI is suitable for canopy densities and height in complex forests of south-eastern Australia.
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ItemChange in fire frequency drives a shift in species composition in native Eucalyptus regnans forests: Implications for overstorey forest structure and transpiration(WILEY, 2022-04-01)The world's most iconic forests are under threat from climate change. Climate-fire-vegetation feedback mechanisms are altering the usual successional trajectories of forests. Many obligate seeder forests across the globe are experiencing regeneration failures and subsequent alterations to their recovery trajectories. For example, the persistence of Eucalyptus regnans F. Muell. forests in southeast Australia is highly vulnerable to the effects of climate-driven increases in wildfire frequency. Shortening of the wildfire return interval from >100 years to < 20 years would inhibit or entirely stop regeneration of E. regnans, leading to replacement with understorey species such as Acacia dealbata Link. In this study, it is hypothesised that following such replacement, forest overstorey structure and transpiration will diverge. An experiment was designed to test this hypothesis by measuring and comparing overstorey transpiration and structural properties, including sapwood area and leaf area, between E. regnans and A. dealbata over a chronosequence (10-, 20-, 35- and 75-/80-year-old forests). We found that overstorey structure significantly diverged between the two forest types throughout the life cycle of A. dealbata after age 20. The study revealed strikingly different temporal patterns of water use, indicating a highly significant eco-hydrologic change as a result of this species replacement. Overall, the results provide a strong indication that after age 20, overstorey transpiration in Acacia-dominated forests is substantially lower than in the E. regnans forests they replace. This difference may lead to divergence in water yield from forested catchments where this species replacement is widespread.
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ItemProbability and Consequence of Postfire Erosion for Treatability of Water in an Unfiltered Supply System(AMER GEOPHYSICAL UNION, 2021-01-01)
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ItemScale-dependency of effective hydraulic conductivity on fire-affected hillslopes(AMER GEOPHYSICAL UNION, 2016-07-01)
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ItemEffects of aridity in controlling the magnitude of runoff and erosion after wildfire(AMER GEOPHYSICAL UNION, 2016-06-01)
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ItemForest Structure Drives Fuel Moisture Response across Alternative Forest States(MDPI, 2021-09-01)Climate warming is expected to increase fire frequency in many productive obligate seeder forests, where repeated high-intensity fire can initiate stand conversion to alternative states with contrasting structure. These vegetation–fire interactions may modify the direct effects of climate warming on the microclimatic conditions that control dead fuel moisture content (FMC), which regulates fire activity in these high-productivity systems. However, despite the well-established role of forest canopies in buffering microclimate, the interaction of FMC, alternative forest states and their role in vegetation–fire feedbacks remain poorly understood. We tested the hypothesis that FMC dynamics across alternative states would vary to an extent meaningful for fire and that FMC differences would be attributable to forest structural variability, with important implications for fire-vegetation feedbacks. FMC was monitored at seven alternative state forested sites that were similar in all aspects except forest type and structure, and two proximate open-weather stations across the Central Highlands in Victoria, Australia. We developed two generalised additive mixed models (GAMMs) using daily independent and autoregressive (i.e., lagged) input data to test the importance of site properties, including lidar-derived forest structure, in predicting FMC from open weather. There were distinct differences in fuel availability (days when FMC < 16%, dry enough to sustain fire) leading to positive and negative fire–vegetation feedbacks across alternative forest states. Both the independent (r2 = 0.551) and autoregressive (r2 = 0.936) models ably predicted FMC from open weather. However, substantial improvement between models when lagged inputs were included demonstrates nonindependence of the automated fuel sticks at the daily level and that understanding the effects of temporal buffering in wet forests is critical to estimating FMC. We observed significant random effects (an analogue for forest structure effects) in both models (p < 0.001), which correlated with forest density metrics such as light penetration index (LPI). This study demonstrates the importance of forest structure in estimating FMC and that across alternative forest states, differences in fuel availability drive vegetation–fire feedbacks with important implications for forest flammability.
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ItemClimate Dictates Magnitude of Asymmetry in Soil Depth and Hillslope Gradient(AMER GEOPHYSICAL UNION, 2018-07-16)
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ItemQuantifying relations between surface runoff and aridity after wildfire(WILEY, 2018-08-01)
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