Up-scaling estimates of forest fuel structure using terrestrial remote sensing
Creators: name: Taneja, Ritu
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 300706
subject: Forestry fire management
classificationCode: 401304
subject: Photogrammetry and remote sensing
classificationCode: 460306
subject: Image processing

description: Wildfires are increasing in frequency and impact worldwide, in both natural and anthropogenic systems. For example, the 2018 California wildfires, the 2019 Amazon forest fires, and the 2019/2020 Australia wildfires have become more destructive, causing significant changes in ecosystems and loss of life and property. The ability to effectively and efficiently mitigate, prepare, and respond to wildfires can be assisted by having good-quality fire behaviour information. Increasing computational power, new types of empirical observations of fire-related attributes, and advances in fire behaviour modelling techniques are continually improving predictions of fire spread and behaviour in the landscape. Such models are used to characterise fire behaviour under varying fuel, weather, and atmospheric conditions and predict fire spread during a fire event. Of the factors that influence fire behaviour, fuel is the only element of a landscape that can be modified to influence the behaviour of future fires. Land managers, therefore, undertake substantial efforts to treat fuel with the aim of reducing risks. However, for the impact of these efforts to be understood, accurate and high-resolution maps of the current fuel structure and hazards at landscape scales are required.

The plot-based visual assessments, in conjunction with machine learning frameworks, have shown the capability to model fuel hazards at landscape scales. These approaches demonstrate the importance of landscape variables in predicting fuel hazards across the landscape. However, the lack of precision in the original visual-based assessments utilised as training data limits the models’ accuracy, potentially leading to misleading outputs and disparities in a range of management applications. Recent advances in remote sensing tools and data acquisition methods, such as Airborne and Terrestrial Laser Scanning ("ALS" and "TLS" respectively), can help capture high-resolution 3D fuel structures. A barrier to the wide-scale adoption and application of the technology for operational practices is the plot-based nature of the data captured by ground-based technologies like TLS. This is in contrast to the scale at which fire and fuel management decisions are undertaken (up to thousands of hectares). Given the acknowledged limits of visual-based fuel hazard assessment, it is critical to study how 3D TLS datasets could be utilised to map fuel hazards to extents relevant to wildfire behaviour and control. The aim of this thesis is to develop objective ways to map fuel parameters acquired from high-resolution TLS at the landscape scale. Four research questions were investigated to address this aim.

The first research question investigated the impact of varying spatial resolutions of input fuel data on the performance of a predictive wildfire model. Fire behaviour was modelled to investigate and quantify the impact that varying spatial resolutions of fuel data had on the model outputs. Airborne laser scanning data was used to derive the key fuel inputs, canopy height, and percentage canopy cover in grids at spatial resolutions 2 m, 5 m, 7 m, 10 m, 30 m and 50 m. Metrics including fire extent, shape, burnt area/unburnt area within the fire extent were calculated and compared. The fire extent is defined as the total perimeter up to which the fire can spread, which includes both burnt and unburnt areas. The burnt area was calculated as the sum of the grid cells burnt within the fire perimeter at the end of the simulation. It was observed that the model outputs were strongly affected by the spatial resolution of the input fuel data. Results also showed that both canopy height and percentage canopy cover are spatially correlated up to 10 m. At finer spatial resolutions (5 m and 10 m) breaks in the fuel were well resolved, resulting in a significant reduction in the predicted size of the fire. In contrast, the coarse spatial resolution input data (30 m and above) missed fine fuel breaks in the landscape, allowing the modelled fire to spread smoothly without interruption. Fine fuels in this context include twigs, leaves, grasses, and other light-weight materials having a small diameter (less than 6 mm) [127]. When a fire reaches an area with little fine fuel, it slows; when it reaches an area with no fine fuel, it stops spread-ing and acts as a fine fuel break. A plateau was observed at 30 m suggesting that changing spatial resolution above 30 m has minimal to no effect on the predicted fire behaviour outputs.

To further improve the prediction of fire behaviour, it is important to understand how different methods to estimate fuel inputs, such as canopy cover, impact the outputs from fire behaviour models. The second research question examined the effect of different algorithms to estimate canopy cover on fire predictions such as burnt area, burnt/unburnt area ratio, and fire extent. With the results from research question one suggesting that canopy cover is spatially correlated at 10 m, five different methods (three Lidar return-based and two geometry-based) were used to derive canopy cover estimates from ALS data at a spatial resolution of 10 m. The ground estimates of canopy cover were downloaded from the TERN data portal and compared with cover estimates derived from ALS data. Results showed that the different algorithms used to derive cover from ALS data produce different canopy cover estimates and, in turn, different fire behaviour predictions. Geometric-based cover estimates neglect canopy gaps and provide connectivity between fuel elements used in the model, allowing the fire to propagate. When the canopy cover estimate was lower (for example, when using the return-based approach), features such as gaps and patchy data in the fuel led to the predicted fire simulations being slowed or stopped. Results also showed that geometric-based estimates were more similar to field-based estimates of canopy cover in comparison to return-based estimates. This suggests that the methods for determining fuel inputs for fire behaviour models should be chosen based on the properties of the data, the vegetation structure present in an area, and how the input was determined in developing the model.

The third research question investigated how high-resolution TLS 3D point clouds could be used to predict and upscale fuel hazard metrics from the site scale (meters) up to the landscape scale (kilometers), noting the importance of large area extents for wildfire mitigation and response activities. In this study, fuel hazard metrics were derived from TLS point clouds and visual fuel hazard assessments. These metrics were examined and compared for their utility in upscaling for seven sites across south-eastern Australia. A Random Forest modelling approach was used to upscale the point-based metrics to a continuous, landscape-scale map of fuel hazard, utilising predictor variables derived from ALS, Sentinel 2A images, and other environmental and climatic datasets. The model trained and validated using TLS data (R^2 = 0.51 for near-surface fuel cover and 0.31 for elevated fuel cover) was found to have higher predictive power than the model trained with visual fuel assessments (R^2 = -0.1 for the cover of both fuel layers). Models for height derived from TLS observations showed low to moderate performance for the near surface (r = 0.47) and canopy layers (r = 0.6). It was also found that the under-sampling of ranges and conditions can be an issue with TLS datasets, as higher variation was observed in cover and height for each fuel layer for those sites where fewer plots were used for up-scaling a larger area.

With previous research demonstrating that the strategies to collect data should ultimately be driven according to environmental differences across the landscape, the fourth research question aimed to evaluate the effect of sampling designs on the estimation of fine fuel attributes of eucalypt forests using plot-based terrestrial remote sensing. TLS point clouds were used to generate three fuel metrics: point proportion, fuel strata gap, and a 3D connectivity index for two dry eucalypt forests in south-eastern Australia. For both sites, a variety of TLS sample designs, including transect, cross, and random patterns, were used. To process the TLS point clouds, two approaches were used: merged and unmerged. The merged approach combines multiple scans into a single point cloud, whereas the unmerged approach analyses each scan individually. The results demonstrated that the merged approach produced a more accurate proportion of points and fuel strata. However, estimations of the 3D connection index were shown to be more accurate when the unmerged approach was used. The study suggested that systematic sampling, whether cross or transect, has no effect on accuracy at these study sites. It can, however, aid with scan inter-visibility, allowing scans to be merged and standard sampling procedures to be established.

The increase in frequency and severity of fires has emphasised the importance of measuring fuel (including fuel structure and fuel load) as one of the only drivers of wildfires that can be managed. Having accurate and sufficiently high-resolution fuel maps across the landscape is fundamental to successful management of fuel and understanding its role in fire behaviour. Therefore, it is critically important that the research community and fire managers work together to measure fuel characteristics pertinent to fire behaviour, which will help guide the choice of sampling strategy to collect the fuel data, algorithms to derive fuel inputs, the spatial resolution at which these fuel inputs need to be processed, and workflows to map fuel metrics across an area relevant to operational fire management decisions. Without this level of rigour, the use of remote sensing data as inputs into a fire behaviour model will likely not provide the expected results or performance from that model. The results presented in this thesis enable incorporation of high resolution fuel information into next generation fire behaviour modelling and could be utilised by fire managers to assess fuel treatment effectiveness and validate existing fuel maps.