Using 3D remote sensing to observe the structure of forest fuel
Creators: name: HILLMAN, Samuel
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 401304
subject: Photogrammetry and remote sensing

description: Attributes describing below canopy vegetation are important for a range of forest ecosystem applications including fuel hazard and fire behaviour modelling. As fires become more frequent and severe, it is increasingly important to be able to accurately assess the fire hazard risk that fuels pose in environments around the world. However, current methodologies to measure fuel properties have been shown to be subjective, lack repeatability and are unsuitable for direct input into next-generation fire-behaviour models. This poses a challenge: to investigate accurate, repeatable, robust methods of assessing fuel hazard utilising 3D data. Whilst satellite and airborne remote sensing methods are able to measure canopy based properties, they are not always able to measure below-canopy structure characteristics, which are important for fuel hazard estimates. The aim of this thesis is to investigate the utility of image-based and Light Detection And Ranging (LiDAR) 3D data representations captured from terrestrial and Unoccupied Aircraft Systems (UAS) platforms to measure fuel properties for hazard assessment within the context of Australian eucalypt forests.

The first research question examined the utility of point clouds derived from Terrestrial Laser Scanners (TLS) and terrestrial Structure from Motion (SfM) workflows to represent fine fuel structure. To do this, an in-field frame was designed to co-register point intercept measurements with point cloud data and act as a validation source. Results showed a high correlation of point matching in forests with understory vegetation elements with large mass and/or surface area, typically consisting of broad leaves, twigs and bark \SI{0.02}{\meter} diameter or greater in size (SfM: Matthews Correlation Coefficient(MCC) 0.51 - 0.66, TLS: MCC 0.37 - 0.47). In contrast, complex environments with understory vegetation elements with low mass and low surface area, showed lower correlations between validation measurements and point clouds (SfM: MCC 0.40 and 0.42, TLS: MCC 0.25 and 0.16). The results of this study demonstrated that the validation frame provided a suitable method for comparing the relative performance of different point cloud generation processes and whilst both technologies showed potential to provide a representation of fine fuel structure, point clouds derived from TLS provided a more complete and reliable representation of fine fuel structure.

The second research question investigated the ability of point clouds derived from UAS platforms to describe forest structure properties. To bridge the gap in scale between terrestrial observations and large-area fixed-wing airborne observations, point clouds derived from UAS LiDAR and UAS SfM workflows were collected and analysed in a dry sclerophyll forest in south-eastern Australia. Results indicated that UAS LiDAR point clouds contained information that could describe fuel properties in all strata. Similar estimates of canopy cover (TLS: 68.27% and UAS LiDAR: 64.20%) and sub-canopy cover (elevated cover TLS: 44.94%, UAS LiDAR: 32.27%, combined surface and near-surface cover TLS: 96.10%, UAS LiDAR: 93.56%) to TLS were achieved using this technology. It was also shown that the UAS SfM photogrammetric technique significantly under performed in the representation of the canopy and below canopy structure (canopy cover: 20.31%, elevated cover: 10.09%) by comparison. A method for classifying fuel hazard layers was also developed to facilitate the exploitation of this structural information for identifying vegetation vertical connectivity.

With evidence that point clouds derived from UAS platforms with compact imaging and LiDAR sensors are capable of capturing vegetation structure, the third research question investigated the ability of these sensors to classify land cover and subsequently map fire severity. UAS imagery and UAS LiDAR data were captured pre and post-fire over a sedgeland to forest transition plot in Tasmania, Australia. Three streams of data inputs were prepared; predictor variables derived from an image-based information only platform (Phantom 4 Pro)(point cloud and imagery), LiDAR only (M600 with Reigl Minivux-1) (point cloud and CHM) and a combined stream (using both LiDAR and image-based products). It was shown that both image- and LiDAR-based technologies were able to map vegetation and severity to a similar accuracy (within 5%). However, analysis of the point clouds showed an improved representation of the vertical structural information from using UAS LiDAR point clouds.

With previous research demonstrating that UAS platforms carrying laser scanning sensors have an ability to describe general vegetation distribution, the fourth research question addressed the utility of these sensors to distinguish between vegetation elements that contribute to fire spread and those that do not (such as large woody elements). This study evaluated the ability of UAS LiDAR and TLS point clouds to provide a description of crown fuel elements in eucalypt trees. Using the TLSeparation package, TLS and UAS LiDAR point clouds achieved 84.6% and 81.1% overall accuracy respectively in the separation of crown fuel and wood. The analysis showed that TLS and UAS LiDAR point clouds were both capable of accurately quantifying crown fuel volume and produced highly correlated estimates of crown volume at the individual tree and plot scale. Visual inspection and a greater distance between clumps in TLS point clouds demonstrated that internal features of crowns were reconstructed to a greater extent in comparison to UAS LiDAR. However, despite these differences it is likely that a low precision model of canopy fuel such as that generated from UAS LiDAR point clouds will suitably represent the crown and canopy fuel objects effectively for fire behaviour modelling purposes.

The challenges of increasing frequency and severity of wildfires have emphasised the importance of being able to accurately model fire behaviour to understand the potential impact of fires, and to be able to plan and implement activities to reduce their size, spread and intensity. Fuel is the only element of wildfire behaviour that can be readily managed by human intervention. Therefore, it is critically important that fire and land agencies understand how fuels change in space and time (fuel dynamics). Emerging remote sensing technologies offer opportunities to capture and characterise fuel dynamics at a scale needed by land and fire managers. With the application of novel classification methods presented in this thesis, a potential paradigm shift in the assessment of fine fuel is made possible enabling high resolution fuel information integration into next-generation fire behaviour modelling.