Full-waveform (FW) LiDAR is a particularly useful source of information in forestry since it samples data between tree branches, but compared to discrete LiDAR, there are very few researchers exploiting this due to the increased complexity. DASOS, an open source program, was developed, along with this thesis, to improve the adoption of FW LiDAR. DASOS uses voxelisation for interpreting the data and this approach is fundamentally different from state-of-art tools. There are three key features of DASOS, reflecting the key contributions of this thesis:Firstly, visualisation of a forest to improve field work planning. Polygonal meshes are generated using DASOS, by extracting an iso-surface from the voxelised data. Additionally, six data structures are tested for optimising iso-surface extraction. The new structure, `Integral Volumes', is the fastest but the best choice depends on the size of the data.Secondly, the FW LiDAR data are efficiently aligned with hyperspectral imagery using a geo-spatial representation stored within a hashed table with buckets of points. The outputs of DASOS are coloured polygonal meshes, which improves the visual output, and aligned metrics from the FW LiDAR and hyperspectral imagery. The metrics are used for generating tree coverage maps and it is demonstrated that the increased amount of information improves classification.The last feature is the extraction of feature vectors that characterise objects, such as trees, in 3D. This is used for detecting dead standing Eucalypt trees in a native Australian forest for managing biodiversity without tree delineation. A random forest classifier, a weighted-distance KNN algorithm and a seed growth algorithm are used to predict positions of dead trees. Improvements in the results from increasing numbers of training samples was prevented due to the noise in the field data. It is nevertheless demonstrated that forest health assessment without tree delineation is possible. Cleaner training samples that are adjustable to tree heights would have improved prediction.
|Date of Award||11 Dec 2017|
|Sponsors||Plymouth Marine Laboratory|
|Supervisor||Matthew Brown (Supervisor), Neill Campbell (Supervisor), Darren Cosker (Supervisor) & Michael Grant (Supervisor)|