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Remote Sensing Tools for the Objective Quantification of Tree Structural Condition from Individual Trees to Landscape Scale Assessment

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Remote Sensing Tools for the Objective Quantification of Tree Structural Condition from Individual Trees to Landscape Scale Assessment Remote Sensing Tools for the Objective Quantification of Tree Structural Condition from Individual Trees to Landscape Scale Assessment Jon Murray This thesis is submitted for the degree of Doctor of Philosophy October 2018 Lancaster Environment Centre Faculty of Science and Technology For Sue, Beth, Erin, Henry and Ozzy. i “The importance of the forest canopy to forestry and forest research has been reflected in the ingenuity of foresters in devising methods and instruments to measure it.” (Jennings, Brown et al. 1999) ii Declaration This thesis has not been submitted in support of an application for another degree at this or any other university. It is the result of my own work and includes nothing that is the outcome of work done in collaboration except where specifically indicated. Many of the ideas in this thesis were the product of discussion with my supervisory team; Prof. Alan Blackburn, Dr. Duncan Whyatt and Dr. Chris Edwards. Excerpts of this thesis have been published to the following academic publications: Murray, J., G.A. Blackburn, J.D. Whyatt and C. Edwards (2018). "Using Fractal Analysis of Crown Images to Measure the Structural Condition of Trees." Forestry: An International Journal of Forest Research 91(4): 480- 491. Murray, J., Gullick, D., Blackburn, G. A., Whyatt, J. D., & Edwards, C. (2019). ARBOR: A new framework for assessing the accuracy of individual tree crown delineation from remotely-sensed data. Remote Sensing of Environment, 231, 111256. Jon Murray MSc, PGCE, BSc (Hons) Lancaster University, UK iii Abstract 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 match- pairing agreement between ground reference tree data and RS-derived individual tree crown (ITC) delineations in order to quantify the accuracy of different ITC iv 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. v Acknowledgements There are always many people to thank for their individual help during the course of a project like this, too many to name everyone individually, but I extend genuine gratitude to all those who have helped me on this journey. Especially to all the office dwellers of B31a, and in particular to Miss B for, you know, absolutely everything! Hopefully, these brief words will go some small way in conveying my appreciation to everyone who has helped over the years. To my supervisors Alan, Duncan and Chris, I am of course, so very grateful for all the help and insight you have provided over the years. In particular during difficult times where your support and guidance was most needed, and was freely given. Thank you all. To Vassil, my thanks for the many hours of help with the fieldwork, always with a genuine interest and willingness to shoulder whatever task (and equipment!) you could. A special thanks to Mr. Oswald Copperpot for the many hours spent surveying in the woods and never complaining or wanting to go home early, no matter how grotty the days were! Sadly, you passed before seeing me get to the end of this but I know I couldn’t have got here without your company and friendship along the way. To my three amazing children, Beth, Erin and Henry. I am so indebted to all of you for putting up with my endless hours on the computer, and for cheering me up when things went wrong. I know as a result I missed a lot of your growing up and vi irreplaceable family time. I can’t make any of that lost time back, but I promise you that things will be better now this PhD is finally finished! Finally, to Sue. Although it has turned out not to have been the journey that we originally hoped for, I offer you my thanks for all your support which ultimately helped me to reach the end, and to finish this ‘little job’. Jon Murray October 2018 vii Contents 1 INTRODUCTION .................................................................................................. 1 1.1 Thesis Aim and Objectives ..................................................................................... 3 1.2 Thesis Structure ...................................................................................................... 3 2 LITERATURE REVIEW...................................................................................... 6 2.1 The Problem of Subjectivity ................................................................................... 6 2.1.1 Subjectivity and Tree Management .................................................................. 9 2.2 The Development of Tree Surveying .................................................................... 13 2.2.1 Plot Establishment for Site Surveys ............................................................... 15 2.3 Decision Support Systems for Tree Management ................................................ 16 2.4 Tree Structure........................................................................................................ 18 2.4.1 Dynamic Change in Tree Structure ............................................................... 18 2.4.2 Phenotypic Tree Structure ............................................................................. 19 2.5 The General Principles of Remote Sensing in the Environment .......................... 21 2.5.1 Remote Sensing and Trees ............................................................................. 22 2.5.2 LiDAR ............................................................................................................ 23 2.6 Photogrammetry .................................................................................................... 30 2.6.1 Proximal Hemispheric Imagery ..................................................................... 31 2.6.2 Landscape Scale Photogrammetric Investigations ........................................ 32 2.7 Unique Programming Requirements of RS Data .................................................. 33 2.7.1 Canopy Height Model Data Pits .................................................................... 34 2.7.2 Data Threshold Manipulation ........................................................................ 36 2.7.3 Weighting as a Data Filter ............................................................................ 37 2.8 Aerial Laser Scanning ........................................................................................... 38 2.8.1 ALS Tree Investigation ................................................................................... 39 2.8.2 Individual Tree Crown Delineation ............................................................... 40 2.9 From the Past to the Future ................................................................................... 43 2.9.1 Predicted Remote Sensing Trends ................................................................. 45 2.10 Summary ............................................................................................................. 47 3 FIELD SITES, METHODS & LIDAR DATA SPECIFICATION ................. 50 3.1 Field Sites.............................................................................................................. 51 3.1.1 Eaves Wood, Lancashire, UK .......................................................................
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