This research proposes to build upon earlier published works where high-end technology is used as a decision support tool to aid operational tree management. Specifically focussing on developing RS methods to capture tree data at a variety of scales, then categorise the tree structure using a combination of novel field and analysis techniques. This includes both proximal data capture and distant observations of the landscape, ultimately to develop methods that can be used to characterise the condition of many trees that are observable at the wide-landscape scale.
This research is informed by the limitations of the current and traditional tree observation and assessment practices, that are used extensively throughout forest management, arboriculture and forest science, that are based on outdated, historical field techniques. These techniques are shown to be frequently dependent on user subjectivity and the
consideration of the significance of the observations by the individual field surveyor. Therefore, it is likely that the subjectivity will lead to a range of different conclusions being reached by different assessors. The requirement is therefore, to develop observation and analytical techniques that are removed, insofar as it is possible, from the subjective process.
In order to develop solutions to this research problem, additional questions and technical considerations will also be addressed. During the initial experimental research phases, the research problem is considered; does the structural condition of trees change in a way that can be independently quantified and be used to discriminate between different types of structural condition? Furthermore, can it be demonstrated that the structural condition of a tree changes in such a way that can inform an objective assessment of the tree condition? These issues relate to the first objective which is considered in Chapter 4, which demonstrates whether it is possible to quantify the complexity of tree structural condition using proximal photogrammetry.
Following the investigation of tree structure, the next research problem addressed considers the technical issue of confirming exactly where trees are located when RS data is compared to field-captured, ground reference (GR) data. Tree crown delineation alignment is a frequently overlooked issue in RS investigations, yet it is commonly used in the validation phases during analysis. GR tree measurements are taken in the field to describe the geospatial location (Euclidean space) and physical attributes of the tree, such as crown location, crown orientation and crown extent. These biophysical attributes can also be modelled from a 3D point cloud and the ITC attributes delineated from the point data using specific delineation algorithms. However, there are frequently discrepancies between the GR data and the ITC delineations of tree location and attributes, and many
previous investigations rely heavily on the acceptance of arbitrary, linear distance thresholds or similar assumptions, to confirm ITC delineation agreement between the datasets without any further validation of how well ITC delineated tree crowns match what is expected in the GR data. Correspondingly Chapter 5, which addresses the second objective, describes the development of a new framework that can quantify the extent of the similarity between tree delineations in two types of delineation data, providing the opportunity to measure the amount of agreement, and therefore, provide a way for researchers to make informed choices regarding the most suitable delineation method to provides the highest level of ITC agreement between different datasets.
The final research problem being considered is the development of a method for assessing and categorising tree structure conducted from a remote perspective, and to consider the individual biophysical characteristics of the many trees that occupy the landscape scale view. This research problem is driven by the requirement to upscale and optimise limited resources into individual tree assessment, for all the relevant trees within the field of view. These trees would be typically surveyed by individual field operatives, at exponential financial and logistical costs dependant on the extent of the survey. The solution will need to consider how to assess individual trees at the landscape scale, compare the individual to an ideal reference model dataset, and subsequently classify these trees into groups that will identify trees both in good structural condition, while also identifying other trees that are in much poorer condition. This work will provide a landscape scale assessment technique that will allow field operatives to make informed decisions about where to concentrate remedial interventions, such as an area of identified poor condition trees. This work is considered in Chapter 6, which also addresses objective 3, and describes the development of a method to use ALS LiDAR data for the optimised, remote classification of tree structure.
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Literature Review
This literature review is to provide an in-depth evaluation of the current research landscape relating to the RS of trees. Within this context, the review will focus on operational problems faced in tree management and the complications that arise from fieldwork methodologies, including practitioner influence on tree assessments. Furthermore, there is commentary on what tree managers or researchers need to help facilitate their decision making process, and how the development of different RS technologies and techniques are being used to understand the complexities of tree assessment and tree management. These issues will be addressed through the independent review of scientific and investigative works, where there will be a critical examination of the publications, including both academic and relevant grey literature, identifying the key research themes that are relative to this project. Finally, this literature review will also enable the identification and definition of pertinent terms and technical procedures that are of relevance to or used within this project, and that are referred to throughout this thesis.