Mapping Plant Communities and Understanding the Landscape Structure of Coastal Barrens Using an Unmanned Aerial Vehicle

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Mapping Plant Communities and Understanding the Landscape Structure of Coastal Barrens Using an Unmanned Aerial Vehicle Mapping plant communities and understanding the landscape structure of coastal barrens using an unmanned aerial vehicle by Michael Buckland-Nicks A Thesis Submitted to Saint Mary's University, Halifax, Nova Scotia, in Partial Fulfillment of the Requirements for the Degree of Master of Science in Applied Science April 20th, 2018, Halifax, Nova Scotia Copyright Michael Buckland-Nicks, 2018 Approved: Dr. Jeremy Lundholm Supervisor Department of Biology Approved: Dr. Danika Van Proosdij External Examiner Department of Geography Saint Mary’s University Approved: Dr. Karen Harper Supervisory Committee Member Department of Biology Approved: Dr. Jeff Barrell Supervisory Committee Member Department of Fisheries and Oceans, Government of Canada Approved: Dr. Sam Veres Chair of Thesis Defence Faculty of Graduate Studies and Research Date: April 20th, 2018 Mapping plant communities and understanding the landscape structure of coastal barrens using an unmanned aerial vehicle by Michael A. Buckland-Nicks Abstract Coastal barrens are landscape mosaics - patchworks of plant communities that exist in harsh environmental conditions created by land-sea interactions and shallow soils. Many rare and uncommon species inhabit these ecosystems, making them a high priority for conservation. In Nova Scotia, coastal barrens are abundant along the coastline of the Halifax region. Little is known of the spatial distributions of plant communities that inhabit them and their overall landscape structure. The purpose of this study was to investigate the use of a UAV to map plant communities and to quantify the landscape structure of coastal barrens. First, high-resolution multispectral UAV imagery was evaluated to discriminate plant communities from three classification levels across three coastal barrens sites in Halifax, Nova Scotia: Chebucto Head, Prospect Bay, and Polly’s Cove. All plant communities were discriminated with 95% confidence except for one pair, showing that plant communities in the coastal barrens could be discriminated with a high level of confidence using UAV imagery. Next, UAV imagery was classified to produce detailed maps of plant communities for the three coastal barrens landscapes. Environmental factors, such as elevation, stream networks and wind exposure were also mapped to help understand landscape structure. Sites were dominated by shrublands and dwarf heath; however, many other types of communities co-occurred on these landscapes, including bogs, salt marshes, and tree islands. The most common plant community across the three sites was Gaylussacia baccata shrubland. Plant community patches varied in i size, shape, abundance, and spatial distribution from one plant community type to another and in many cases from one site to another. Landscape patterns were driven by various combinations of environmental factors, including slope position, proximity to stream networks, elevation, and distance to coastline. Overall differences in landscape structure could be mostly explained by the degree of topographic heterogeneity of each landscape. UAVs are an excellent tool for mapping plant communities and quantifying landscape structure and this information is critical for informing land managers, conservation planners, and policy makers. ii Acknowledgements First, I would like to thank my supervisor Dr. Jeremy Lundholm. Jeremy has been an incredible mentor for me and was so supportive of my interests in geographic information systems. Thank you also to my supervisory committee Dr. Karen Harper and Dr. Jeff Barrell for offering their time in giving valuable feedback and helping steer me towards the finish line. I am also very grateful for the assistance I received from my fellow graduate students, lab mates, and field assistants who have helped me to collect my field data, assisted me with flying the drone, and offered their amazing advice, knowledge, and expertise: Hughstin Grimshaw-Surrette, Maddie Clarke, Amy Heim, Emily Walker, Logan Gray, Jasmine Jamieson, and Graeme Matheson. I would like to thank Greg Baker for always making the time to share his infinite wisdom on geographic information systems and advice on flying the drone. I would also like to acknowledge my uncle, Trevor Hart, for offering his time and help for the statistical analysis of this project. I am indebted to Caitlin Porter for all her time and help with classifying plant communities and helping me to plan this study. I express my gratitude to the teams at MPSPARC and CBWES for sharing knowledge and collaborating on using drones as research tools. Lastly, I am eternally grateful for my supportive friends and family who have kept me positive, motivated, and have offered advice and encouragement when I needed it. iii Table of Contents Abstract ............................................................................................................................ i Acknowledgements ........................................................................................................ iii Table of Contents ........................................................................................................... iv List of Tables .................................................................................................................. v List of Figures .............................................................................................................. viii Chapter 1: Introduction ................................................................................................... 1 Chapter 2: Evaluating Multispectral Imagery from an Unmanned Aerial Vehicle for Discriminating Plant Communities in the Coastal Barrens of Halifax, Nova Scotia .. 26 Chapter 3: Landscape Patterns of Plant Communities in the Coastal Barrens of Halifax, Nova Scotia ................................................................................................................... 85 Chapter 4: Synthesis ................................................................................................... 169 Appendix I .................................................................................................................. 177 Appendix II ................................................................................................................. 236 iv List of Tables Chapter 2 Table 2.1. Acquisition results of RGB imagery at the study sites and root-mean-square error of the georeferenced models based on ground control points. Table 2.2. Spectral indices derived by UAV imagery. Table 2.3. Structural indices derived by analysis of 3D point clouds and subsequent 10 cm digital elevation models computed from structure from motion photogrammetric processing of UAV imagery. Table 2.4. The number of field plots of types I, II and III sampled at Chebucto Head, Prospect Bay, and Polly’s Cove. Table 2.5. The top 10 most frequent plant species identified from field plot sampling across all sites. Table 2.6. List of classes from the association level plant community classification from field plot sampling across all three sites. Plant communities with less than three field plots were removed and not used for statistical analysis. Table 2.7. List of classes from the formation class plant community classification from field plot sampling across all three sites. Plant communities with less than three field plots were removed and not used for statistical analysis. Table 2.8. Top 10 indices sorted by their “score” of importance in relation to their contribution to the linear discriminant analysis (LDA) model for each plant community classification. The score was determined by summing the weighted contributions of each index for each discriminatory axis from the LDA model. Chapter 3 Table 3.1. Landscape-level metrics used to describe landscape composition. Table 3.2. Class-level metrics used to describe the spatial configurations of plant community patches within a landscape. Table 3.3. Environmental factors computed for plant community patches. Table 3.4. Classification accuracies of mapped plant communities from the broadened association level classification at Chebucto Head. v Table 3.5. Summary of the spatial configurations of plant community patches at Chebucto Head. *AW = Area-weighted. Table 3.6. The top three most common neighbors of each plant community type at Chebucto Head. Table 3.7. Environmental factors for plant communities at Chebucto Head. *AW = Area- weighted; C.I = Area-weighted 95% confidence interval. Table 3.8. Classification accuracies of mapped plant communities from the broadened association level classification at Prospect Bay. Table 3.9. Summary of the spatial configurations of plant community patches at Prospect Bay. *AW = Area-weighted. Table 3.10. The top three most common neighbors of each plant community type at Prospect Bay. Table 3.11. Environmental factors for plant communities at Prospect Bay. *AW = Area- weighted; C.I = Area-weighted 95% confidence interval. Table 3.12. Classification accuracies of mapped plant communities from the broadened association level classification at Polly’s Cove. Table 3.13. Summary of the spatial configurations of plant community patches at Polly’s Cove. *AW = Area-weighted. Table 3.14. The top three most common neighbors of each plant community type at Polly’s Cove. Table 3.15. Environmental factors for plant communities at Polly’s Cove. *AW = Area- weighted; C.I = Area-weighted 95% confidence interval. Appendix I Table A1.1. Specifications of the unmanned aerial vehicle used in this study. Table A1.2. Flight times
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