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Quantifying Grassland Non-Photosynthetic Vegetation Biomass Using Remote Sensing Data

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Quantifying Grassland Non-Photosynthetic Vegetation Biomass Using Remote Sensing Data QUANTIFYING GRASSLAND NON-PHOTOSYNTHETIC VEGETATION BIOMASS USING REMOTE SENSING DATA A Thesis Submitted to the College of Graduate and Postdoctoral Studies In Partial Fulfillment of the Requirements For the Degree of Doctor of Philosophy In the Department of Geography and Planning University of Saskatchewan Saskatoon, Canada By Zhaoqin Li Copyright Zhaoqin Li, August 2017. All rights reserved. PERMISSION TO USE In presenting this thesis in partial fulfillment of the requirements for a Postgraduate degree from the University of Saskatchewan, I agree that the Libraries of this University may make it freely available for inspection. I further agree that permission for copying of this thesis in any manner, in whole or in part, for scholarly purposes may be granted by the professor or professors who supervised my thesis work or, in their absence, by the Head of the Department or the Dean of the College in which my thesis work was done. It is understood that any copying or publication of use of this thesis or parts thereof for financial gain shall not be allowed without my written permission. It is also understood that due recognition shall be given to me and to the University of Saskatchewan in any scholarly use which may be made of any material in my thesis. Requests for permission to copy or make to other use of material in this thesis in whole or part should be addressed to: Head of the Department of Geography and Planning 117 Science Place University of Saskatchewan Saskatoon, Saskatchewan S7N5C8 Canada OR Dean College of Graduate and Postdoctoral Studies University of Saskatchewan 105 Administration Place Saskatoon, Saskatchewan S7N 5A2 Canada i ABSTRACT Non-photosynthetic vegetation (NPV) refers to vegetation that cannot perform a photosynthetic function. NPV, including standing dead vegetation and surface plant litter, plays a vital role in maintaining ecosystem function through controlling carbon, water and nutrient uptake as well as natural fire frequency and intensity in diverse ecosystems such as forest, savannah, wetland, cropland, and grassland. Due to its ecological importance, NPV has been selected as an indicator of grassland ecosystem health by the Alberta Public Lands Administration in Canada. The ecological importance of NPV has driven considerable research on quantifying NPV biomass with remote sensing approaches in various ecosystems. Although remote images, especially hyperspectral images, have demonstrated potential for use in NPV estimation, there has not been a way to quantify NPV biomass in semiarid grasslands where NPV biomass is affected by green vegetation (PV), bare soil and biological soil crust (BSC). The purpose of this research is to find a solution to quantitatively estimate NPV biomass with remote sensing approaches in semiarid mixed grasslands. Research was conducted in Grasslands National Park (GNP), a parcel of semiarid mixed prairie grassland in southern Saskatchewan, Canada. Multispectral images, including newly operational Landsat 8 Operational Land Imager (OLI) and Sentinel-2A Multi- spectral Instrument (MSI) images and fine Quad-pol Radarsat-2 images were used for estimating NPV biomass in early, middle, and peak growing seasons via a simple linear regression approach. The results indicate that multispectral Landsat 8 OLI and Sentinel-2A MSI have potential to quantify NPV biomass in peak and early senescence growing seasons. Radarsat-2 can also provide a solution for NPV biomass estimation. However, the performance of Radarsat- 2 images is greatly affected by incidence angle of the image acquisition. This research filled a critical gap in applying remote sensing approaches to quantify NPV biomass in grassland ecosystems. NPV biomass estimates and approaches for estimating NPV biomass will contribute to grassland ecosystem health assessment (EHA) and natural resource (i.e. land, soil, water, plant, and animal) management. Keywords: non-photosynthetic vegetation, biomass, green vegetation, biological soil crust, bare soil, multispectral image, Landsat 8, Sentinel-2A, Radarsat-2, ecosystem health, vegetation phenology ii ACKNOWLEDGEMENTS First of all, I would like to thank my supervisor, Dr. Xulin Guo, for her invaluable mentoring and encouragement during my Ph.D. study. Her constant guidance and support have been indispensable to the completion of my dissertation. I would also like to express my gratitude to my advisory committee members, Dr. Bram Noble, Dr. Dirk deBoer, and Dr. Longhai Li for their valuable comments on this research. I also want to thank Dr. Abraham Akkerman, who served as my committee chair, for his valuable assistance. I would like to acknowledge the Natural Sciences and Engineering Research Council of Canada (NSERC), the Department of Geography and Planning, University of Saskatchewan, the Saskatchewan Innovation & Opportunity Graduate Scholarship, the Saskatchewan Environment Ministry, and the Canadian Federation of University Women (CFUW) Saskatoon Inc. for providing funding to support my study and life. My gratitude goes to Mr. Adam Harrison, Dr. Eric Lamb, and Dr. Cherie Westbrook for providing facilities for drying my samples. Many thanks are also given to Dr. Xiaohui Yang, Dr. Dandan Xu, Carmen Finnigan and other field crew members for collecting some of the field data, and to the staff in Grasslands National Park (GNP) for their logistical support. I would like to thank MacDonald Dettwiler and Associates Ltd. - Geospatial Services Inc. (MDA GSI), the Canadian Space Agency (CSA), and Natural Resources Canada's Centre for Remote Sensing (CCRS) for providing Radarsat-2 data. Thank you to the United States Geological Survey (USGS) for distributing Landsat 8 and Sentinel-2A images and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation (GDEM) data. ASTER GDEM data are a product of the Ministry of Economy, Trade, and Industry (METI) of Japan and the United States National Aeronautics and Space Administration (NASA). I also want to thank the European Space Agency (ESA) for providing free software for processing Sentinel-2A images. iii Finally, I would like to thank my colleagues and visiting professors in Dr. Guo’s research group for their comments on my research and their help with field work. Special thanks are given to my husband (Dr. Zimu Yu) and my daughters (Hope and Frieda) for their unconditional love. iv TABLE OF CONTENTS PERMISSION TO USE .................................................................................................................. i ABSTRACT .................................................................................................................................... ii ACKNOWLEDGEMENTS ........................................................................................................... iii TABLE OF CONTENTS ................................................................................................................ v LIST OF FIGURES .................................................................................................................... viii LIST OF TABLES ......................................................................................................................... x LIST OF ABBREVIATIONS ....................................................................................................... xi CHAPTER 1: INTRODUCTION .................................................................................................. 1 1.1 Preface ........................................................................................................................................ 1 1.2 Ecological Importance of NPV ................................................................................................. 1 1.3 Remote Sensing of NPV ............................................................................................................ 4 1.3.1 Passive optical remote sensing data for NPV estimation ....................................................................... 4 1.3.2 LiDAR for NPV estimation ................................................................................................................. 20 1.3.3 SAR for NPV estimation ..................................................................................................................... 22 1.3.4 The integration of passive and active remote sensing data .................................................................. 27 1.3.5 Advantages and disadvantages of remote sensing data for NPV estimation ........................................ 28 1.4 Summary and Research Gaps ................................................................................................ 31 1.5 Research Hypothesis and Objectives ..................................................................................... 32 1.6 Study Area and Field Data Collection ................................................................................... 32 1.6.1 Study area ............................................................................................................................................ 32 1.6.2 Field data sampling .............................................................................................................................. 35 1.7 Dissertation Structure ............................................................................................................. 37 1.8 Addendum ...............................................................................................................................
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