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Remote Sensing of the Canadian Arctic

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Remote Sensing of the Canadian Arctic REMOTE SENSING OF THE CANADIAN ARCTIC: MODELLING BIOPHYSICAL VARIABLES by Nanfeng Liu A thesis submitted to the Department of Geography and Planning In conformity with the requirements for the degree of Doctor of Philosophy Queen’s University Kingston, Ontario, Canada (June, 2017) Copyright © Nanfeng Liu, 2017 Abstract It is anticipated that Arctic vegetation will respond in a variety of ways to altered temperature and precipitation patterns expected with climate change, including changes in phenology, productivity, biomass, cover and net ecosystem exchange. Remote sensing provides data and data processing methodologies for monitoring and assessing Arctic vegetation over large areas. The goal of this research was to explore the potential of hyperspectral and high spatial resolution multispectral remote sensing data for modelling two important Arctic biophysical variables: Percent Vegetation Cover (PVC) and the fraction of Absorbed Photosynthetically Active Radiation (fAPAR). A series of field experiments were conducted to collect PVC and fAPAR at three Canadian Arctic sites: (1) Sabine Peninsula, Melville Island, NU; (2) Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, NU; and (3) Apex River Watershed (ARW), Baffin Island, NU. Linear relationships between biophysical variables and Vegetation Indices (VIs) were examined at different spatial scales using field spectra (for the Sabine Peninsula site) and high spatial resolution satellite data (for the CBAWO and ARW sites). At the Sabine Peninsula site, hyperspectral VIs exhibited a better performance for modelling PVC than multispectral VIs due to their capacity for sampling fine spectral features. The optimal hyperspectral bands were located at important spectral features observed in Arctic vegetation spectra, including leaf pigment absorption in the red wavelengths and at the red-edge, leaf water absorption in the near infrared, and leaf cellulose and lignin absorption in the shortwave infrared. At the CBAWO and ARW sites, field PVC and fAPAR exhibited strong correlations (R2 > 0.70) with the NDVI (Normalized Difference Vegetation Index) derived from high-resolution WorldView-2 data. Similarly, high spatial resolution satellite-derived fAPAR was correlated to MODIS fAPAR (R2 = 0.68), with a systematic overestimation of 0.08, which was attributed to PAR absorption by soil that could not be excluded from the fAPAR calculation. This research clearly demonstrates that high spectral and spatial resolution remote sensing VIs can be used to successfully model Arctic biophysical variables. The methods and results presented in this research provided a guide for future studies aiming to model other Arctic biophysical variables through remote sensing data. Co-Authorship This dissertation is based on the following three manuscripts: Chapter 2: Liu, N., Budkewitsch, P., and Treitz, P. 2017. Examining spectral reflectance features related to Arctic percent vegetation cover: Implications for hyperspectral remote sensing of Arctic tundra. Remote Sensing of Environment 192, 58-72. doi: 10.1016/j.rse.2017.02.002 Chapter 3: Liu, N. and Treitz, P. 2016. Modelling high arctic percent vegetation cover using field digital images and high resolution satellite data. International Journal of Applied Earth Observation and Geoinformation 52, 445-456. doi: 10.1016/j.jag.2016.06.023 Chapter 4: Liu, N. and Treitz, P. 2017. Multi-scale remote sensing of Arctic percent vegetation cover and fAPAR. Submitted to International Journal of Applied Earth Observation and Geoinformation (in review). For manuscript 1, Sarah Allux, Paul Treitz and Paul Budkewitsch developed the field sampling design and Sarah Allux and Paul Budkewitsch collected the field data. I developed the research goals and objectives, and developed and implemented the analysis design. For manuscripts 2 and 3, I was responsible for the design and implementation of the study. I was also responsible for the writing of all manuscripts. My supervisor, Dr. Paul Treitz provided helpful guidance on the study and was consulted on the sampling designs and analysis methods. He also reviewed and edited the manuscripts. iii Acknowledgements First, I would like to thank my supervisor Dr. Paul Treitz for your excellent supervision. Thank you for the invaluable patience, advice and guidance on my PhD study and research. Thank you for sharing your experiences of Arctic field work with me. Thank you for your detailed and critical comments which highly improved my manuscripts. Most importantly, your continuous encouragement has always boosted my confidence when I got stuck. I am very thankful for the opportunity to work with you. I would also like to thank the committee members of my qualifying examination: Dr. Ryan Danby, Dr. Paul Martin, Dr. Dongmei Chen and Dr. Neal Scott. Your insightful comments and suggestions helped narrow down my research questions and shape this final thesis. Special thanks to Dr. Dongmei Chen and Dr. Neal Scott for their field sampling design advice. I would also like to thank Dr. Greg Henry for being my external reviewer. Many thanks to my colleagues in the Laboratory for Remote Sensing of Earth and Environmental Systems (LaRSEES). Especially, Amy Blaser and Rebecca Edwards: thank you for all your help during my Arctic field work; Karin van Ewijk: thank you for sharing the remote sensing lecture materials with me, which helped my teaching a lot. Many thanks to the staff of the Geography and Planning Department. John Bond, thank you for your IT support and help. Sheila-Rae MacDonald, Joan Knox, Sharon Mohammed and Kathy Hoover, thank you for your patience and help. Joan, wish you get well soon. I would also like to thank my Chinese friends at Queen’s University. Chen Shang, Pengpeng Ni, Tikang Li, Mengqi Yang and Yao Feng, thank you for your help and encouragement when I needed it. I enjoyed the great time spent with you at Queens. Financial support for this work was provided by NCE ArcticNet, the Norther Science Training Program (NSTP), Polar Knowledge Canada, Polar Continental Shelf Project (PCSP), the Natural Sciences and Engineering Research Council (NSERC), and Queen’s University. I would like to thank Dr. Scott iv Lamoureux and Dr. Melissa Lafreniere for their support of this research. I would also like to thank Sarah Allux and Dr. Paul Budkewitsch for collecting the field data for Chapter 2. Finally, I would like to thank my family – my parents and girlfriend, Zhihui Wang. I am most grateful to my parent’s support and unconditional love. Zhihui, I am sincerely grateful to you for patience, tolerance and most importantly your love. I love you all so much. v Statement of Originality I hereby certify that all the work described within this thesis is the original work of the author. Any published (or unpublished) ideas and/or techniques from the work of others are fully acknowledged in accordance with the standard referencing practices. (Nanfeng Liu) (June, 2017) vi Table of Contents REMOTE SENSING OF THE CANADIAN ARCTIC: MODELLING BIOPHYSICAL VARIABLES .... i Abstract ......................................................................................................................................................... ii Co-Authorship.............................................................................................................................................. iii Acknowledgements ...................................................................................................................................... iv Statement of Originality ............................................................................................................................... vi Table of Contents ........................................................................................................................................ vii List of Figures ............................................................................................................................................... x List of Tables ............................................................................................................................................. xiii List of Abbreviations .................................................................................................................................. xv Chapter 1 Introduction .................................................................................................................................. 1 1.1. Arctic Vegetation and Environmental Change ............................................................................. 1 1.2. Remote Sensing of Arctic Vegetation ........................................................................................... 2 1.2.1. Arctic Vegetation Classification ........................................................................................... 2 1.2.2. Spatial and Temporal Dynamics of Arctic Vegetation ......................................................... 4 1.2.3. Arctic Biophysical Variable Estimation ................................................................................ 4 1.3. Research Issues ............................................................................................................................. 5 1.3.1. Research Objective 1 ............................................................................................................ 6 1.3.2. Research Objective 2 ...........................................................................................................
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