Pitfalls and possible solutions for using geo-referenced site data to inform vegetation models

Most predictive models rely on 'the known' to infer 'the unknown'. Geo-referenced, on-ground observational data are the 'point of truth' upon which many vegetation models are built. We focus on some of the enigmatic errors that we have uncovered when using vegetation plot data. Using a case study, we sourced 9362 sites to examine the prevalence of spatial errors. We found that an incorrect datum was recorded for 5% of sites; less than 2% of sites were duplicated and up to 34% of sites were located within 1000. m of each other. Whilst sites within a 1000. m neighbourhood are not necessarily errors, they do need to be considered within the context of using spatial environmental layers and predictive modelling. We offer solutions for identifying and managing spatial locations of point data to ensure that the information-rich resource held in data repositories is not compromised by unidentified spatial error

Remote sensing tools for the objective quantification of tree structural condition from individual trees to landscape scale assessment

Tree management is the practice of protecting and caring for trees for sustainable, defined objectives. However, there are often conflicts between maintaining trees and the obligation to protect targets, such as people or infrastructure, from the risks associated with the failure of trees and major limbs. Where there are targets worthy of protection, tree structural condition is typically monitored relative to the prescribed management objectives. Traditionally, field methods for capturing data on tree structural condition are manual with a tree surveyor taking very limited direct measurements, and only from parts of the tree that are within reach from the ground. Consequently, large sections of the tree remain unmeasured due to the logistical complications of accessing the aerial structure. Therefore, the surveyor estimates tree part sizes, approximates counts of relevant tree features and uses personal interpretation to infer the significance of the observations. These techniques are temporally and logistically demanding, and largely subjective. This thesis develops solutions to the limitations of traditional methods through the development of remote sensing (RS) tools for assessing tree structural condition, in order to inform tree management interventions. For individual trees, a proximal photogrammetry technique is developed for objectively quantifying tree structural condition by measuring the self-affinity of tree crowns in fractal dimensions. This can identify the individual tree crown complexity along a structural condition continuum, which is more effective than the traditional categorical approach for monitoring tree condition. Moving out in scale, a framework is developed which optimises the matchpairing agreement between ground reference tree data and RS-derived individual tree crown (ITC) delineations in order to quantify the accuracy of different ITC delineation algorithms. The framework is then used to identify an optimal ITC delineation algorithm which is applied to aerial laser scanning data to map individual trees and extract a point cloud for each tree. Metrics are then derived from the point cloud to classify a tree according to its structural condition, a process which is then applied to the tree population across an entire landscape. This provides information with which to spatially optimise tree survey and management resources, improve the decision making process and move towards proactive tree management. The research presented in this thesis develops RS tools for assessing tree structural condition, at a range of investigative scales. These objective, data-rich tools will enable resource-limited tree managers to direct remedial interventions in an optimised and precise way