School of Agriculture, Food and Ecosystem Sciences - Research Publications
Permanent URI for this collection
Search Results
1 - 5 of 5
-
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 (
-
ItemPerformance of GEDI Space-Borne LiDAR for Quantifying Structural Variation in the Temperate Forests of South-Eastern Australia(MDPI, 2022-08)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.
-
ItemForest Structure Drives Fuel Moisture Response across Alternative Forest States(MDPI, 2021-09)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.
-
ItemClimate Dictates Magnitude of Asymmetry in Soil Depth and Hillslope Gradient(AMER GEOPHYSICAL UNION, 2018-07-16)Abstract Hillslope asymmetry is often attributed to differential eco‐hydro‐geomorphic processes resulting from aspect‐related differences in insolation. At midlatitudes, polar facing hillslopes are steeper, wetter, have denser vegetation, and deeper soils than their equatorial facing counterparts. We propose that at regional scales, the magnitude in insolation‐driven hillslope asymmetry is sensitive to variations in climate, and investigate the fire‐prone landscapes in southeastern Australia to evaluate this hypothesis. Patterns of asymmetry in soil depth and landform were quantified using soil depth measurements and topographic analysis across a contemporary rainfall gradient. Results show that polar facing hillslopes are steeper, and have greater soil depth, than equatorial facing slopes. Furthermore, we show that the magnitude of this asymmetry varies systematically with aridity index, with a maximum at the transition between water and energy limitation, suggesting a possible long‐term role of climate in hillslope development.
-
ItemThe Role of Fire in the Coevolution of Soils and Temperate Forests(AMER GEOPHYSICAL UNION, 2020-08-01)Climate drives the coevolution of vegetation and the soil that supports it. Wildfire dramatically affects many key eco‐hydro‐geomorphic processes, but its potential role in coevolution of soil‐forest systems has been largely overlooked. The steep landscapes of southeastern Australia provide an excellent natural laboratory to study the role of fire in the coevolution of soil and forests, as they are characterized by temperate forest types, fire frequencies, and soil depths that vary systematically with aridity. The aims of this study were (i) to test the hypothesis that in Southeastern Australia, fire‐related processes are critical to explain the variations in coevolved soil‐forest system states across an aridity gradient and (ii) to identify the key processes and (iii) feedbacks involved. To achieve these aims, we developed a numerical model that simulates the coevolution of soil‐forest systems which employ eco‐hydro‐geomorphic processes that are typical of the flammable forests of southeastern Australia. A stepwise model evaluation, using measurements and published data, confirms the robustness of the model to simulate eco‐hydro‐geomorphic processes across the aridity gradient. Simulations that included fire replicated patterns of observed soil depth and forest cover across an aridity gradient, supporting our hypothesis. The contribution of fire to coevolution increased in magnitude with aridity, mainly due to the higher fire frequency and lower post‐fire infiltration capacity, increasing the rates of fire‐related surface runoff and erosion. Our results show that critical feedbacks between soil depth, vegetation, and fire frequency dictate the trajectory and pace of the coevolution of flammable temperate forests and soils.