Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/42534
Title: Remote Sensing of Larch Disease and Acute Oak Decline Outbreaks in Britain
Authors: Barnes, Chloe
Supervisors: Balzter, Heiko
Barrett, Kirsten
Award date: 8-Jun-2018
Presented at: University of Leicester
Abstract: In UK forest environments, infection from phytopathogens presents a significant risk to tree health and an increasingly pressing concern for forest management. This thesis has considered Phytophthora ramorum, the causal agent of larch disease, and the most recent episode of acute oak decline (AOD) resulting from multiple bacterial agents. The specific focus of the research project concerned the automated isolation of individual tree crowns (ITCs) in forests subject to phytopathogen infection, which facilitated an ITC-scale assessment of tree disease from remotely sensed datasets. The potential applications of airborne laser scanning (ALS) and unmanned aerial vehicle (UAV) based multispectral imagery were assessed in relation to P. ramorum and AOD outbreaks respectively. The ITC segmentation results demonstrated the successful isolation of partially and wholly defoliated larch crowns (>70%) from ALS through the application of a pit-free canopy height model generation methodology. However, the photogrammetrically-derived surface elevation from the UAV-based imagery facilitated a poor overall segmentation of individual oak crowns for all severities of crown decline (<30%). The disease detection capabilities of the two remote sensing technologies reported significant results in the case of both studies. The application of ALS for the assessment of P. ramorum infection reported significant isolation (p < 0.01) of moderate and severely infected individual trees. Larch disease presence/absence and severity was also classified at the ITC-scale with overall accuracies of 72% and 65% respectively. In the application of UAV-based multispectral imagery for AOD assessment, significant differences (p < 0.10) were observed between all five categories of crown decline. The crown decline severity classification at the ITC-scale yielded accuracies of 91% and 55% for the three and five severity classes respectively. Overall, the research results demonstrate the capabilities of remote sensing in the targeted assessment of phytopathogens, adding value to both scientific understanding and the management of forest environments.
Links: http://hdl.handle.net/2381/42534
Type: Thesis
Level: Doctoral
Qualification: PhD
Rights: Copyright © the author. All rights reserved.
Appears in Collections:Leicester Theses
Theses, Dept. of Geography

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