{"@context":{"@language":"en","Affiliation":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","AggregatedSourceRepository":"http:\/\/www.europeana.eu\/schemas\/edm\/dataProvider","Campus":"https:\/\/open.library.ubc.ca\/terms#degreeCampus","Creator":"http:\/\/purl.org\/dc\/terms\/creator","DateAvailable":"http:\/\/purl.org\/dc\/terms\/issued","DateIssued":"http:\/\/purl.org\/dc\/terms\/issued","Degree":"http:\/\/vivoweb.org\/ontology\/core#relatedDegree","DegreeGrantor":"https:\/\/open.library.ubc.ca\/terms#degreeGrantor","Description":"http:\/\/purl.org\/dc\/terms\/description","DigitalResourceOriginalRecord":"http:\/\/www.europeana.eu\/schemas\/edm\/aggregatedCHO","FullText":"http:\/\/www.w3.org\/2009\/08\/skos-reference\/skos.html#note","Genre":"http:\/\/www.europeana.eu\/schemas\/edm\/hasType","GraduationDate":"http:\/\/vivoweb.org\/ontology\/core#dateIssued","IsShownAt":"http:\/\/www.europeana.eu\/schemas\/edm\/isShownAt","Language":"http:\/\/purl.org\/dc\/terms\/language","Program":"https:\/\/open.library.ubc.ca\/terms#degreeDiscipline","Provider":"http:\/\/www.europeana.eu\/schemas\/edm\/provider","Publisher":"http:\/\/purl.org\/dc\/terms\/publisher","Rights":"http:\/\/purl.org\/dc\/terms\/rights","RightsURI":"https:\/\/open.library.ubc.ca\/terms#rightsURI","ScholarlyLevel":"https:\/\/open.library.ubc.ca\/terms#scholarLevel","Title":"http:\/\/purl.org\/dc\/terms\/title","Type":"http:\/\/purl.org\/dc\/terms\/type","URI":"https:\/\/open.library.ubc.ca\/terms#identifierURI","SortDate":"http:\/\/purl.org\/dc\/terms\/date"},"Affiliation":[{"@value":"Forestry, Faculty of","@language":"en"}],"AggregatedSourceRepository":[{"@value":"DSpace","@language":"en"}],"Campus":[{"@value":"UBCV","@language":"en"}],"Creator":[{"@value":"Czekajlo, Agatha","@language":"en"}],"DateAvailable":[{"@value":"2020-11-12T20:20:33Z","@language":"en"}],"DateIssued":[{"@value":"2020","@language":"en"}],"Degree":[{"@value":"Master of Science - MSc","@language":"en"}],"DegreeGrantor":[{"@value":"University of British Columbia","@language":"en"}],"Description":[{"@value":"Canada\u2019s urban areas experienced extensive growth over the past-quarter century; however, there has been no reliable, spatially explicit approach for quantifying urban vegetation and land use patterns nationally. Satellite imagery, such as Landsat, provide an opportunity to measure large areas frequently, consistently, and accurately over long temporal periods. Using Landsat imagery, this thesis characterizes multi-decadal trends of vegetative greenness and land use transitions from 1984 to 2016 on the pixel and census dissemination area levels for 18 major Canadian urban areas. First, I developed an urban greenness score using spectrally unmixed Landsat imagery to categorize multi-decadal greenness change relative to its current greenness level across Canadian geographic strata and population density groups. Second, using unmixed fractions and land cover information, I derived a dynamic land use classification scheme and applied it across Canadian peri-urban areas to quantify multi-decadal land use transitions and their correlation with current socio-economic states. Most Canadian urban areas sustained a moderate (\u223c 20\u201340%) or low (\u2272 20%) level of greenness between 1984 and 2016, with denser urban areas experiencing the greatest losses (16% of DAs). Eastern urban areas maintained the most greenness over time, while urban areas in the Prairies had the greatest increase in greenness. During the same time, 47% of Canadian peri-urban areas experienced an increase in urban use, converting about 2,000 km\u00b2 of natural and agriculture uses each. The region with the most area transitioned from natural to urban use occurred in the West (39%), whereas the Prairies observed more marked rates of urbanization on natural areas (median of 3.9% per annum per sq-km) and more area with agriculture conversions (41%). Regional differences in correlations between land use trends and current levels of population density, income, new residential construction, and single-detached housing highlight the potential influence of urban policies and\/or socio-economic trajectories. The methods presented take advantage of sub-pixel information from the open and longstanding Landsat archive to provide a comprehensive and accessible framework to understand current and historic urban land dynamics, which supports long-term strategic planning and can be transferred to other regions across spatial and temporal scales.","@language":"en"}],"DigitalResourceOriginalRecord":[{"@value":"https:\/\/circle.library.ubc.ca\/rest\/handle\/2429\/76485?expand=metadata","@language":"en"}],"FullText":[{"@value":"Characterizing multi-decadal vegetative greenness and land use dynamics across Canadian urban areas using satellite remote sensing by Agatha Czekajlo B.Sc., University of British Columbia, 2018A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Forestry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) November 2020 \u00a9 Agatha Czekajlo, 2020 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Characterizing multi-decadal vegetative greenness and land use dynamics across Canadian urban areas using satellite remote sensing submitted by Agatha Czekajlo in partial fulfilment of the requirements for the degree of Master of Science in ForestryExamining Committee: Nicholas Coops, Professor, Forest Resources Management, UBC Supervisor  Michael Wulder, Research Scientist, Canadian Forest Service, Natural Resources CanadaSupervisory Committee Member Matilda van den Bosch, Assistant Professor, Forest and Conservation Sciences, UBCSupervisory Committee Member Kees Lokman, Assistant Professor, Architecture & Landscape Architecture, UBC Additional Examiner  iii Abstract Canada\u2019s urban areas experienced extensive growth over the past-quarter century; however, there has been no reliable, spatially explicit approach for quantifying urban vegetation and land use patterns nationally. Satellite imagery, such as Landsat, provide an opportunity to measure large areas frequently, consistently, and accurately over long temporal periods. Using Landsat imagery, this thesis characterizes multi-decadal trends of vegetative greenness and land use transitions from 1984 to 2016 on the pixel and census dissemination area levels for 18 major Canadian urban areas. First, I developed an urban greenness score using spectrally unmixed Landsat imagery to categorize multi-decadal greenness change relative to its current greenness level across Canadian geographic strata and population density groups. Second, using unmixed fractions and land cover information, I derived a dynamic land use classification scheme and applied it across Canadian peri-urban areas to quantify multi-decadal land use transitions and their correlation with current socio-economic states. Most Canadian urban areas sustained a moderate (\u223c 20\u201340%) or low (\u2272 20%) level of greenness between 1984 and 2016, with denser urban areas experiencing the greatest losses (16% of DAs). Eastern urban areas maintained the most greenness over time, while urban areas in the Prairies had the greatest increase in greenness. During the same time, 47% of Canadian peri-urban areas experienced an increase in urban use, converting about 2,000 km2 of natural and agriculture uses each. The region with the most area transitioned from natural to urban use occurred in the West (39%), whereas the Prairies observed more marked rates of urbanization on natural areas (median of 3.9% per annum per km2) and more area with agriculture conversions (41%). Regional differences in correlations between land use trends and current levels of population density, income, new residential construction, and single-detached housing highlight the potential influence of urban policies and\/or socio-economic trajectories. The methods presented take advantage of sub-pixel information from the  iv open and longstanding Landsat archive to provide a comprehensive and accessible framework to understand current and historic urban land dynamics, which supports long-term strategic planning and can be transferred to other regions across spatial and temporal scales. v Lay summary Over the past quarter-century cities have grown to accommodate rising populations, resulting in vegetation and land use shifts across the urban-rural gradient. However, long-term maps of urban dynamics are limited in Canada and globally. By utilizing the open and historic Landsat satellite imagery archive, urban dynamics can be mapped consistently and accurately since the mid-1980s. In this thesis, I investigated the multi-decadal (1984-2016) dynamics of land use and vegetative greenness across 18 major Canadian urban areas. My findings identified greening in the Prairies and smaller cities despite an overall greenness loss across Canada. Urbanization of natural and agriculture uses had similar national coverage but varied in distribution and intensity. The most land use transitioned in the West, whereas the Prairies observed the greatest rates of change. This research highlights the value of analysing nation-wide land dynamics for improved knowledge of urban environments and has important implications for developing sustainable policy.  vi Preface The body of this thesis consists of two scientific papers written for peer review for which I am the lead author and investigator, as listed below. The research questions and objectives of this work were developed through discussions with Dr. Nicholas Coops and Dr. Michael Wulder. Project oversight and editorial assistance were provided by Dr. Nicholas Coops, Dr. Michael Wulder, Dr. Joanne White, and Dr. Matilda van den Bosch. Co-authors Dr. Txomin Hermosilla and Dr. Yuhao Lu were responsible for providing data and editorial assistance. I was primarily responsible for refining research objectives, developing and implementing the methodology, analyzing the data, interpreting and presenting results, as well as preparing manuscripts for submission.  Text and information in the dissertation were derived from the following sources: Chapter 3: Czekajlo, A., Coops, N.C., Wulder, M A., Hermosilla, T., Lu, Y., White, J.C., van den Bosch, M. 2020. The urban greenness score: A satellite-based metric for multi-decadal characterization of urban land dynamics. International Journal of Applied Earth Observations and Geoinformation, 93, 102210. https:\/\/doi.org\/10.1016\/j.jag.2020.102210. Chapter 4:  Czekajlo, A., Coops, N.C., Wulder, M A., Hermosilla, T., White, J.C., van den Bosch, M. 2020. Mapping dynamic peri-urban land use transitions across Canada using multi-decadal Landsat satellite data \u2013 method development and correlation to socio-demographic characteristics. To be submitted.  This research was also presented as: Czekajlo, A., Coops, N.C., Wulder, M A., Hermosilla, T., Lu, Y., White, J.C., van den Bosch, M. 2020. Characterizing multi-decadal land dynamics of the Vancouver urban area using a novel urban greenness score. 41st Canadian Symposium on Remote Sensing (online).  vii Table of contents Abstract ................................................................................................................................................... iii Lay summary ........................................................................................................................................... v Preface ..................................................................................................................................................... vi Table of contents.................................................................................................................................... vii List of tables............................................................................................................................................. x List of figures ........................................................................................................................................ xiii List of abbreviations............................................................................................................................. xvi Acknowledgements ............................................................................................................................. xviii Dedication .............................................................................................................................................. xx Chapter 1: Introduction ......................................................................................................................... 1 1.1 Phenomenon of urbanization ..................................................................................................... 1 1.2 Urbanization in Canada ............................................................................................................. 3 1.3 Measuring the effects of urbanization ....................................................................................... 5 1.4 Research objectives ................................................................................................................. 14 1.5 Thesis overview....................................................................................................................... 15 Chapter 2: Study area and data ........................................................................................................... 16 2.1 Major Canadian urban areas .................................................................................................... 16 2.2 Data ......................................................................................................................................... 20 2.2.1 Landsat satellite imagery .................................................................................................... 20 2.2.2 Spectrally unmixed fractions ............................................................................................... 20 2.2.3 Land cover data and agricultural mask .............................................................................. 22 2.2.4 Socio-demographic data ..................................................................................................... 23 Chapter 3: Characterization of multi-decadal urban land dynamics using a novel urban greenness score metric .......................................................................................................................... 25 3.1 Introduction ............................................................................................................................. 25 3.2 Methods ................................................................................................................................... 28 3.2.1 Validation of spectrally unmixed greenness fractions ........................................................ 28 3.2.2 Urban greenness score ........................................................................................................ 29  viii 3.3 Results ..................................................................................................................................... 30 3.3.1 Validation of vegetative greenness fractions ....................................................................... 30 3.3.2 Evolution of greenness fraction through time ..................................................................... 32 3.3.3 Urban greenness score ........................................................................................................ 34 3.3.4 Urban greenness score trends across Canada .................................................................... 36 3.4 Discussion ............................................................................................................................... 42 3.4.1 Analysis approach ............................................................................................................... 42 3.4.2 Urban greenness score trends across Canada .................................................................... 45 Chapter 4: Characterization of multi-decadal land use transitions across Canadian peri-urban areas ........................................................................................................................................................ 48 4.1 Introduction ............................................................................................................................. 48 4.2 Methods ................................................................................................................................... 52 4.2.1 Definition of dynamic land use classification scheme ........................................................ 52 4.2.2 Calculation of dynamic peri-urban land use transitions .................................................... 53 4.2.3 Exploratory correlative analysis with current socio-demographic states .......................... 54 4.3 Results ..................................................................................................................................... 55 4.3.1 Dynamic land use classification .......................................................................................... 55 4.3.2 Canadian peri-urban land use trends ................................................................................. 57 4.3.3 Land use transitions of Canadian peri-urban areas ........................................................... 60 4.3.4 Correlations of peri-urban land use trends with current socio-demographic states .......... 68 4.4 Discussion ............................................................................................................................... 70 4.4.1 Analysis approach ............................................................................................................... 70 4.4.2 Multi-decadal patterns of Canadian peri-urban land use .................................................. 72 4.4.3 Links between peri-urban land use trends and current socio-demographics ..................... 74 Chapter 5: Conclusion .......................................................................................................................... 76 5.1 Overview addressing main research goals .............................................................................. 76 5.2 Significance of research and key findings............................................................................... 78 5.3 Urban planning and management implications ....................................................................... 81 5.4 Limitations .............................................................................................................................. 83 5.4.1 Study unit limitations ........................................................................................................... 83 5.4.2 Auxiliary agricultural mask limitations .............................................................................. 84 5.4.3 Study scope limitations ........................................................................................................ 84  ix 5.5 Future work ............................................................................................................................. 85 References .............................................................................................................................................. 88 x List of tables Table 1. Summary of terms that relate to the concept of urbanization, and their generally accepted definitions. ................................................................................................................................................. 2 Table 2. Selection of studies that have developed characterizations of land cover. Abbreviations used in table: ETM+ \u2013 Enhanced Thematic Mapper Plus; EVI \u2013 Enhanced Vegetation Index; MODIS \u2013 Moderate Resolution Imaging Spectroradiometer; NDVI \u2013 Normalized Difference Vegetation Index; TM \u2013 Thematic Mapper, and; TRASP \u2013 Topographic Solar Radiation Aspect. ...................................... 7 Table 3. Selection of studies that have developed characterizations of land use. Abbreviations used in table: CBERS \u2013 China-Brazil Earth Resources Satellite program; ETM+ \u2013 Enhanced Thematic Mapper Plus; MSI \u2013 Multi-spectral Imager; MSS \u2013 Multi-Spectral Scanner; NBR \u2013 Normalized Burn Ratio; NDVI \u2013 Normalized Difference Vegetation Index, NIR \u2013 Near-Infrared; NSLS \u2013 Nighttime Stable-Light Satellite; OLI \u2013 Operational Land Imager; PALSAR \u2013 Phased Array type L-band Synthetic Aperture Radar; SPOT \u2013 Satellite Pour l\u2019Observation de la Terre (French), SWIR \u2013 Shortwave infrared, and; TM \u2013 Thematic Mapper. ..................................................................................................... 9 Table 4. Selection of studies that have quantified urban vegetation. Abbreviations used in table: ETM+ \u2013 Enhanced Thematic Mapper Plus; NDVI \u2013 Normalized Difference Vegetation Index; NIR \u2013 NearInfrared; OLI \u2013 Operational Land Imager; SPOT \u2013 Satellite Pour l\u2019Observation de la Terre (French); RGB \u2013 Red, Green, and Blue, and; TM \u2013 Thematic Mapper. ................................................................. 12 Table 5. Summary of population density groups (high, moderately-high, moderately-low, and low), including the number of 2016 dissemination areas (DAs), as well as their total geometric area (km2) and average 2016 DA level population density (per km2). ..................................................................... 18  xi Table 6. Summary of urban areas, including their respective stratum, province, assigned population density group (high, moderately-high, moderately-low, and low), as well as a summary for total considered DAs (2016 DA count, geometric area (km2), population, and average DA level population density (per km2), as well as a summary for peri-urban DAs (2016 peri-urban DA count, geometric area (km2), average DA level population density (per km2), percentage of recent construction (< 5 years), percentage of single-detached housing, and average of the median total income in 2015 (CAD $). ..... 19 Table 7. Summary of data, including their variable(s), type, data range, spatial resolution, producer(s), temporal span, and temporal frequency. Abbreviations used in table: AAFC \u2013 Agriculture and Agri-Food Canada; CFS \u2013 Canadian Forest Service, DA \u2013 dissemination area; NASA \u2013 National Aeronautics and Space Administration (of the United States of America); USGS \u2013 United States Geological Survey, and; VLCE \u2013 Virtual Land Cover Engine. * Treed includes Broadleaf, Coniferous, Mixed wood, Shrubland, and Wetland-treed land cover classes. ............................................................ 24 Table 8. Dynamic land use classes and their function using spectrally unmixed fractions, based on land cover and agricultural mask conditions. .................................................................................................. 53 Table 9. Summary of significant (p < 0.05) positive and negative urban land use trends, including the geometric area (km2 and %), as well as the percentage of DAs (%) and their median annual rate of change scaled by geometric area (% per annum per km2), for peri-urban areas of each urban area, stratum, and for all peri-urban DAs. Abbreviations used in table: AB \u2013 Alberta; BC \u2013 British Columbia; MN \u2013 Manitoba; NB \u2013 New Brunswick; NL \u2013 Newfoundland and Labrador; NS \u2013 Nova Scotia; ON \u2013 Ontario; QC \u2013 Qu\u00e9bec; SK \u2013 Saskatchewan. .................................................................... 59 Table 10. Summary of land use transitions, including the pixel-based geometric area (km2 and %), as well as the percentage of DAs (%) and their median annual rate of change scaled by geometric area (% per annum per km2), for each peri-urban area, stratum, and for all peri-urban DAs. Abbreviations used  xii in table: AB \u2013 Alberta; BC \u2013 British Columbia; MN \u2013 Manitoba; NB \u2013 New Brunswick; NL \u2013 Newfoundland and Labrador; NS \u2013 Nova Scotia; ON \u2013 Ontario; QC \u2013 Qu\u00e9bec; SK \u2013 Saskatchewan. . 67  xiii List of figures Figure 1. Locations of the 18 selected Canadian urban areas used in this study, by geographic stratum. ................................................................................................................................................................. 17 Figure 2. Dendrogram of population density group assignment (A = high; B = moderately-high; C = moderately-low, and; D = low) using Ward\u2019s clustering method. .......................................................... 18 Figure 3. 2016 Landsat BAP composites (top panels; RGB: SWIR-2, NIR, G) and smoothed unmixed vegetative greenness fraction results (bottom panels) of parts of Toronto (high population density; east-center stratum), Calgary (moderately-high population density; west-center stratum), Victoria (moderately-low population density; west stratum), and Halifax (low population density; east stratum). ................................................................................................................................................................. 22 Figure 4. Urban greenness score matrix showing final greenness (rows = low (L), moderate (M), and high (H)) in relation to the change in greenness (columns = decrease (\u2013), zero (0), and increase (+)). . 30 Figure 5. Estimated (i.e. unmixed) vegetative greenness fractions plotted against reference vegetative greenness fractions, with the associated Spearman\u2019s correlation coefficient (\u03c1) and p-value, for select years of a representative urban area of each stratum. Density indicates the amount of sample points (i.e. fraction of vegetated grids per pixel) represented by a point on the plot. ............................................... 31 Figure 6. Maps of greenness change (top-left panel) and final greenness (top-right panel) for a portion of Toronto dissemination areas (DAs). Time series of vegetative greenness fractions for select DAs (graph; identified by Statistics Canada census unique DA identifier code (DAUID) and outlined in black on the maps) of various urban greenness scores as distinguished by colour. ................................ 33  xiv Figure 7. Urban greenness score map for a portion of Toronto dissemination areas (DAs). Examples of each green score are identified by Statistics Canada census unique DA identifier code (DAUID) and outlined in black. ..................................................................................................................................... 35 Figure 8. Urban greenness score maps for portions of select urban areas\u2019 dissemination areas, each representing a stratum and population density group (A = Toronto (east-center stratum; high population density); B = Calgary (west-center stratum; moderately-high population density); C = Victoria (west stratum; moderately-low population density), and; D = Halifax (east stratum; low population density)). ................................................................................................................................................................. 37 Figure 9. Percentage of dissemination areas (DAs) classified by each urban greenness score for each stratum, population density group, as well as for all DAs. ..................................................................... 39 Figure 10. Dynamic land use classification for 1984 (left panels) and 2016 (right panels) of parts of Victoria (west stratum), Calgary (west-center stratum), Toronto (east-center stratum), and Halifax (east stratum).................................................................................................................................................... 56 Figure 11. Maps of dominant DA level land use transitions for select peri-urban areas, each representing a geographic stratum (A = Victoria (west); B = Calgary (west-center); C = Toronto (east-center), and; D = Halifax (east)). Colour   corresponds with the DA level rate of change, scaled by area. ................................................................................................................................................................. 61 Figure 12. Percentage of area for each land use transition, as well as overlaps, grouped by geographic strata as well as for all peri-urban DAs. Total area (km2) for each stratum and all peri-urban DAs provided to the left of bar graph. ............................................................................................................. 63  xv Figure 13. Percentage of area for each land use transition, as well as overlaps, shown for each peri-urban area, grouped by geographic stratum (west, west-center, east-center, and east). Total areas (km2) provided to the left of bar graph. ............................................................................................................. 65 Figure 14. Correlograms of 2016 socio-demographic variables (population density, percentage of single-detached housing, percentage of recent construction (< 5 years), and median total income) and the 33-year land use trends (DA average and area-scaled) for each geographic stratum and all peri-urban DAs. Colour and values represent the strength and direction of correlation using the Spearman\u2019s correlation coefficient (\u03c1), with the level of significance denoted as * p < 0.05, ** p < 0.01, and *** p < 0.001. .................................................................................................................................................... 68  xvi List of abbreviations AAFC Agriculture and Agri-Food Canada AB Alberta BAP Best-Available Pixel algorithm BC British Columbia CBERS China-Brazil Earth Resources Satellite program CFS Canadian Forest Service CMA Census Metropolitan Area CMA-E Census Metropolitan Area-Ecosystem DA Dissemination Area DAUID Dissemination Area Unique Identifier ETM+ Enhanced Thematic Mapper Plus EVI Enhanced Vegetation Index LiDAR Light Detection and Ranging MN Manitoba MNF Minimum Noise Fraction MODIS Moderate Resolution Imaging Spectroradiometer MSS Multi-Spectral Scanner NASA National Aeronautics and Space Administration (of the United States) NB New Brunswick NBR Normalized Burn Ratio NDVI Normalized Difference Vegetation Index NIR Near-infrared NL Newfoundland and Labrador NS Nova Scotia NSLS Nighttime Stable-Light Satellite OLI Operational Land Imager ON Ontario PALSAR Phased Array type L-band Synthetic Aperture Radar PPI Pixel Purity Index QC Qu\u00e9bec  xvii RADAR Radio Detection and Ranging RGB Red, Green, and Blue SK Saskatchewan SPOT Satellite Pour l\u2019Observation de la Terre (French) SWIR Short-wave Infrared TM Thematic Mapper TRASP Topographic Solar Radiation Aspect TS Theil-Sen estimator UAV Uncrewed Aerial Vehicle UN United Nations USGS United States Geological Survey VHR Very-high Resolution VLCE Virtual Land Cover Engine  xviii Acknowledgements First and foremost, I whole heartedly thank Dr. Nicholas C. Coops for seeing my potential since being an undergraduate research assistant and his continued support as my MSc supervisor. His guidance was instrumental to the outcomes of this research but also to my growth as a scientist and human. I am also very thankful to my committee members, Dr. Michael Wulder and Dr. Matilda van den Bosch, for all their thoughtful input, feedback, and encouragement. A special thank you to Dr. Txomin Hermosilla, whose friendship, mentorship, and driving voice of reason has been instrumental to me far beyond this project. My deepest appreciation also goes to Dr. Joanne White and Dr. Yuhao Lu for their insights and suggestions for improvement of the work presented in this thesis. I would also like to acknowledge the generosity and patience of Dr. Chen Shang, Dr. Tristan Goodbody, and Dr. Piotr Tompalski in sharing their processing, programming, and analytical expertise. A big thanks to all past and current members of the Integrated Remote Sensing Studio that I have crossed paths with; being a part of an encouraging and positive group of like-minded peers has provided me many memories that I will always cherish. To my parents, Beata and Rafal, I am eternally grateful for all their support, sacrifices, and hard work to provide me with a great foundation. I wouldn\u2019t be in this position without their selfless dedication, so I share this success with them. To my sister Caroline, who has achieved her own academic and professional milestones, I thank her for being my role model since childhood as well as the most supportive sister and friend. A very special thank you to Tristan for his enduring support; you continued to believe in my abilities even during my lowest points, and for that I will always be grateful. A big thank you to all my friends for their continued support, and a special thanks to Ola, with whom I grew my curiosity and creativity.xix This research was supported by the \u201cEarth Observation to Inform Canada\u2019s Climate Change Agenda (EO3C)\u201d project jointly funded by the Canadian Space Agency (CSA), Government Related Initiatives Program (GRIP), and the Canadian Forest Service (CFS) of Natural Resources Canada (NRCan), and was enabled in part by capacity provided by WestGrid (www.westgrid.ca) and Compute Canada (www.computecanada.ca). An extra thanks to Dr. Michael Wulder, Dr. Joanne White, and Dr. Txomin Hermosilla at CFS for their fundamental role in developing this project and providing financial and technical support. xx Dedication To all the hard working and ambitious women in my life; Thank you for the encouragement. 1 Chapter 1: Introduction 1.1 Phenomenon of urbanization Globally, urban areas have been consistently growing upward and outward since the turn of the 20th century, with urban populations doubling as the overall global population increased (Angel et al., 2011; Seto et al., 2011; United Nations, 2018). More recently, as global urban populations have increased by approximately 25% since 1950 (United Nations, 2018), the spatial coverage of urban areas has expanded on average twice as quickly (Angel et al., 2011; Seto et al., 2011). This pattern of urban development alongside continued population growth is often referred to as \u2018urbanization\u2019 and occurs via gradual land cover and human population change (Anas et al., 1998; Nechyba and Walsh, 2004; Seto et al., 2011) through the mechanisms of urban densification and expansion (Anas et al., 1998; Broitman and Koomen, 2015; Nechyba and Walsh, 2004). Urban densification typically occurs in underdeveloped urban areas, converting open vegetated areas such as street verges, residential gardens, urban forests, or other green spaces into additional residential or commercial infrastructure (Haaland and Konijnendijk van den Bosch, 2015). However, local development policies can greatly influence urban densification patterns and which areas are targeted (Broitman and Koomen, 2015). In the case of urban expansion, natural and\/or agricultural lands in the urban periphery (i.e. peri-urban area) transition to an urban use (Anas et al., 1998; Broitman and Koomen, 2015; Nechyba and Walsh, 2004), which typically results in increased property value (Clonts, 1970). Table 1 provides a summary of terms related to urbanization. 2 Table 1. Summary of terms that relate to the concept of urbanization, and their generally accepted definitions. Term Definition Green space Space in urban areas that includes some vegetation, such as parks, urban forests, wetlands, and street verges (Taylor and Hochuli, 2017) Land cover Vegetation, artificial constructions, and other biophysical state of the Earth\u2019s surface (Burley, 1961; Grekousis et al., 2015; Loveland and DeFries, 2004) Land use Function of land in relation to human activities (Clawson and Stewart, 1965; Loveland and DeFries, 2004) Peri-urban area Periphery of the urban area; a transitional zone between the urban and rural areas with a mix of land uses (Ford, 1999) Urban area An area with a high population and population density (Statistics Canada, 2016a) Urban core Part of the urban area with the highest population; in Canada, at least 50,000 persons for census metropolitan areas, and 10,000 for census aglomerations (Statistics Canada, 2016a) Urban densification Vertical build-up of underdeveloped land already holding an urban land use (Broitman and Koomen, 2015) Urban expansion Lateral growth of an urban area through the conversion of other land use (Anas et al., 1998; Broitman and Koomen, 2015; Nechyba and Walsh, 2004) Urbanization Changes in land cover and distribution of human population, in the form of urban expansion and densification (Seto et al., 2011) The rate of urbanization varies nationally and regionally due to many factors, such as natural landscape characteristics, accessibility, socio-demographics, socio-economics, land value, and municipal or regional policy (Brueckner and Fansler, 1983; Glaeser and Kahn, 2003; Kulish et al., 2012; Saiz, 2010). More recent effects of urbanization may stem from evolving urban planning policies, such as new initiatives concerning sustainable development (Haaland and Konijnendijk van den Bosch, 2015). For example, the importance of green spaces in urban environments and for public wellbeing have been noted by urban planners (Haaland and Konijnendijk van den Bosch, 2015), ecologists (Livesley et al., 2016), and public health researchers (Hartig et al., 2014; van den Bosch and Bird, 2018; van den Bosch and Sang, 2017). Recent attention has also focused on efforts to manage urban expansion in the mixed land use interface of peri-urban areas (Allen, 2003). 3 Several Canadian cities (e.g. Toronto and Vancouver) have developed sustainable and strategic long-term plans, including increased access to green spaces and protections of urban and heritage agricultural pockets (Gregg, 2019; Metro Vancouver Regional District, 2011). A greater understanding of recent and historic urban land dynamics, as well as developing an enhanced strategy for its continued monitoring, is critical for the success of sustainable urban planning initiatives. 1.2 Urbanization in Canada Canada has experienced a steady increase in urban residents, representing 15\u201380% of the total population over the past 150 years (Statistics Canada, 2018). More recently, Canada\u2019s built up area has grown by over 150% since the 1970s, estimated as increasing from 5,651\u201314,546 km2 (Statistics Canada, 2016b). However, urbanization is not consistent across Canada. Although the most populous Canadian census metropolitan areas (CMAs) of Toronto, Vancouver, and Montr\u00e9al saw notable rates of urban expansion (+ 102\u2013220%) as their population density increased since the early 1970s, mid-sized and smaller CMAs also experienced considerable amounts of urban expansion during the same time (e.g. Halifax: + 319 km2; Qu\u00e9bec City: + 292 km2; Ottawa-Gatineau: + 261 km2, and; London: + 247 km2; Statistics Canada, 2016a). Substantial urban growth has also recently occurred (2011\u20132016) in Prairies, namely surrounding Calgary (+ 14.6%), Edmonton (+ 13.9%), and Saskatoon (+ 12.5%); whereas, some cities on the east coast (e.g. St. John\u2019s) experienced little change in its urban area (2006\u20132011: + 8.7%, and; 2011\u20132016: + 4.6%; Statistics Canada, 2018).  4 Like the physical growth of cities, the socio-economic development of Canadian cities has also not been consistent through time nor space. Vancouver, for example, transitioned from a natural resource (predominantly forestry but also agriculture) based economy in the mid-1980s to encompass a greater variety of sectors by 2016, like technology, finance\/insurance\/real estate, film and TV production, and tourism (Barnes and Hutton, 2016). Mixed land use is also very prominent in Canadian cities like Vancouver, Calgary, and Toronto, and particularly so in their peri-urban areas, as this type of development was highly encouraged since the 1980s (Grant, 2004).  Canadian cities also have very different spatial arrangements. Some, especially larger cities like Vancouver, Montr\u00e9al, and Toronto, have several dispersed pockets of highly dense built-up areas (typically corresponding with job density) while others are either monocentric or dispersed (Sweet et al., 2017). Increasing density and effective land use in already built environments is expensive, difficult, and can create its own set of challenges, such as lack of affordable housing (Gerber et al., 2018). As a result, the pressure of providing additional infrastructure is often pushed to the city\u2019s edge. Although recent additions of urban containment strategies in Canada and other countries present an opportunity to manage urban expansion, results in select Canadian cities indicate that these boundaries are extensive or weak-bounding and may not appropriately limit development by individual homeowners (Millward, 2006). As Canadian urban populations are expected to continue increasing (Chagnon et al., 2019), national and regional assessments of historic urban land dynamics can help inform on the development of effective and sustainable urban policy, plans, and management strategies. 5 1.3 Measuring the effects of urbanization Detailed, long-term, and up-to-date spatiotemporal information about the extent, growth, and physical characteristics of urban areas are required to better understand these complex and dynamic environments (Schneider et al., 2009). Remote sensing technologies have the ability to provide frequent, accurate, and cost-effective information from a variety of spectral, spatial, and temporal attributes to map urban areas (Patino and Duque, 2013). The use of remote sensing technology for urban mapping can stem from a variety of optical sensors and can be optimized to serve different functions, from measuring individual buildings or trees to large area urban growth. Satellites are practical for global or large area optical analyses, whereas imagery acquired from an aircraft or uncrewed aerial vehicles (UAVs; commonly known as drones) are suitable for smaller areas. The spatial and temporal resolution of optical satellite imagery is variable and largely influences its accessibility. Fine spatial resolution imagery (0.5\u20135\u202fm) such as Ikonos, RapidEye, Planet, and SkySat are highly detailed but at most cover just over a decade (1999\u20132015) and are only available commercially (Patino and Duque, 2013; Planet Labs Inc., 2020). Similarly, orthophotos and LiDAR provide three-dimensional information of land surfaces that can aid in land cover and use mapping (Patino and Duque, 2013). However, orthophotos may not be detailed enough to warrant their use, while LiDAR can be costly to acquire over large areas frequently and consistently (Goodbody et al., 2019; Leberl et al., 2010; White et al., 2015). Alternatively, imagery from Terra\/Aqua, Landsat, and Sentinel-2 satellites are freely available but have varying spatial (10\u20131000 m) and temporal resolutions (Patino and Duque, 2013; Wulder et al., 2016). Although costs associated with storage and processing remain, Landsat imagery in particular is suitable for studying urban dynamics as it is free to access, consistently covers the longest    6 temporal period of all earth observation data (1972\u2013present; Belward and Sk\u00f8ien, 2015), and has a medium spatial resolution (30 m) that is able to identify various anthropogenic activities (Patino and Duque, 2013; Wulder et al., 2016). Anlayses identifying long-term landscape changes, such as trends in vegetative greenness or land use, are made possible with Landsat\u2019s historic archive that contains reliable, consistent, and frequent imagery with global coverage for over 30 years. As Landsat imagery is available from the United States Geological Survey (USGS) free of charge, municipalities and organizations are not restricted by high costs associated with propreity imagery (Wulder et al., 2016). Given that Landsat imagery is available at various tiers of data quality and preprocessing stages, from real-time aquired raw imagery to analysis-ready data products, users are able to fine tune Landsat data selection to meet their research needs (Wulder et al., 2012). Further, using Landsat imagery may be more cost-effective and produce more accurate results for regional or national scale analyses than long-term administrative records or cadastral data, which may be inconsistent or unavailable. Coupled with recent advancements in data acquisition and processing methods, multi-decadal analyses of fine spatial and temporal scales can provide an insight into historic trends of urban environments (Schneider, 2012; White et al., 2014; Zhao et al., 2015). Studies using Landsat satellite imagery to measure land dynamics are common among remote sensing research, with a variety of case studies from across the globe (e.g. Hermosilla et al., 2018; Homer et al., 2015; Prastacos et al., 2017; Schneider, 2012). However, most remote sensing studies analyzing urban land dynamics to date have focused on physical land changes, such as of built-up area (Reba and Seto, 2020). This may be because land cover can be derived directly from earth observation imagery maps as it describes physical and biological land attributes (Burley, 1961; Grekousis et al., 2015; Loveland and DeFries, 2004). For example, despite the use of 7 different classifiers, spectral attributes from Landsat imagery and auxiliary land characteristic data were used by Hermosilla et al. (2018), Phinn et al. (2002), Schneider (2012), and Zhang and Foody (2001) to derive land cover maps of various environments, including urban, natural, and agricultural areas (Table 2). Table 2. Selection of studies that have developed characterizations of land cover. Abbreviations used in table: ETM+ \u2013 Enhanced Thematic Mapper Plus; EVI \u2013 Enhanced Vegetation Index; MODIS \u2013 Moderate Resolution Imaging Spectroradiometer; NDVI \u2013 Normalized Difference Vegetation Index; TM \u2013 Thematic Mapper, and; TRASP \u2013 Topographic Solar Radiation Aspect. Author(s) Year Imagery Data Methodology Example classes Zhang and Foody 2001 Landsat TM Spectral bands Fuzzy supervised classifier Grass\/parkland, Built-up\/Barren land, Woodland, Shrubland Phinn et al.  2002 Landsat TM Red, NIR, & SWIR 1 bands, NDVI, Vegetation, impervious surface, & soil fractions Unsupervised classifier, Spectral unmixing Developed, Cleared, Grass\/sparse vegetation, Forest\/Woodland Schneider 2012 Landsat TM Spectral bands & metrics,  NDVI Boosted decision trees,  Support vector machines, Maximum likelihood classifier Urban\/built-up, Cropland, Forest Hermosilla et al.  2018 Landsat TM & ETM+ NIR & SWIR 2 bands, EVI,  Tasselled cap greenness, Elevation,  Slope,  TRASP index Random forest classifier, Hidden Markov model, Logical land cover transition rules Exposed\/barren land, Herbs, Bryoids, Shrubs, Coniferous, Broadleaf, Mixedwood Alternatively, understanding land use requires an anthropogenic context, involving the land\u2019s function for humans (Clawson and Stewart, 1965; Loveland and DeFries, 2004). Part of the difficulty in translating land cover to land use is its complexity; a single land use may include several land cover categories and may vary by region and over time. High spatial resolution imagery is helpful for producing detailed and accurate land use maps as more physical details can 8 be identified, however this typically comes at a cost of temporal resolution and\/or span. For example, the European Environment Agency\u2019s Urban Atlas contains comprehensive urban and rural classes, including continuous and multi-level discontinuous urban fabric, commercial\/industrial, urban green spaces, agriculture, as well as natural and semi-natural areas (European Environment Agency, 2016). However, as the Urban Atlas is produced using very high resolution (VHR) imagery and supplementary land characteristic data, it is only available since 2006. Other fine scale land use maps using VHR imagery like RapidEye, aerial, or orthophotos can derive very detailed information, such as types of buildings or green space, but are also limited temporally (Beykaei et al., 2014; Guindon et al., 2004; Haase et al., 2019; Zhang et al., 2018). The use of medium-resolution imagery like Landsat with additional physical land and\/or socio-demographic data, such as land cover, vegetation cover, and population and dwelling density, has been applied successfully to map urban, natural, and\/or agriculture use (e.g. Goodin et al., 2015; Homer et al., 2015; Lu and Weng, 2006; Yu and Ng, 2007). Additionally, Zhu et al. (2012) found that spectral and textural variables from multi-seasonal Landsat imagery alone produced highly accurate land use maps (92.7%) and the addition of PALSAR data minimally increased mapping accuracy (1.1%). Goodin et al. (2015) and Yang and He (2017) also created land use maps differentiating natural, agriculture, and urban areas using only medium spatial resolution imagery (Landsat and CBERS-2, respectively). However, similar to land cover classifications, land use maps typically involve a categorical scheme. These include distinct classes that include both land cover and land use, such as in the National Land Cover Database for the Conterminous United States (Homer et al., 2015), to in depth multi-level classification schemes like in the Urban Atlas by the European Environment Agency (2016). Overall, few studies have optimized the use of continuous    9 land attribute data to derive fractional classes (e.g. Gopal et al., 2016; Haase et al., 2019; Phinn et al., 2002; Schug et al., 2020). Table 3 provides several examples of urban land use classifications derived from various data and methods. Table 3. Selection of studies that have developed characterizations of land use. Abbreviations used in table: CBERS \u2013 China-Brazil Earth Resources Satellite program; ETM+ \u2013 Enhanced Thematic Mapper Plus; MSI \u2013 Multi-spectral Imager; MSS \u2013 Multi-Spectral Scanner; NBR \u2013 Normalized Burn Ratio; NDVI \u2013 Normalized Difference Vegetation Index, NIR \u2013 Near-Infrared; NSLS \u2013 Nighttime Stable-Light Satellite; OLI \u2013 Operational Land Imager; PALSAR \u2013 Phased Array type L-band Synthetic Aperture Radar; SPOT \u2013 Satellite Pour l\u2019Observation de la Terre (French), SWIR \u2013 Shortwave infrared, and; TM \u2013 Thematic Mapper. Author(s) Year Imagery Data Methodology Example classes Phinn et al.  2002 Landsat TM, Aerial Red and SWIR bands, NDVI, Vegetative greenness, impervious surface, and soil fractions Hybrid classifier, Spectral unmixing Developed, Cleared, Grass\/sparse, Forest\/woodland Guindon et al.  2004 Landsat MSS & TM,  Aerial Spectral bands, Shape parameters, NDVI,  Road network, Population density, Dwelling density Clustering, Segmentation, Merged classifier Residential, Commercial\/industrial, Forest, Herbaceous Lu and Weng 2006 Landsat ETM+ Albedo fraction, Impervious surface, Population density Minimum noise fraction transformation, Spectral unmixing, Rule based classifier Residential (4 levels), Commercial, Industrial, Non-urban Yu and Ng 2007 Landsat TM Spectral bands, Road network ISODATA unsupervised classifier, Maximum likelihood classifier Urban built-up,  New development area, Cultivated land, Forest Zhu et al.  2012 Landsat ETM+, PALSAR Spectral bands, Microwave L-band Random forest classifier Residential (2 levels), Commercial\/industrial, Orchards, Mixed forest Beykaei et al.  2014 Orthophotos Shape parameters, Spatial arrangement, Property map, Road network Step-wise binary logistic regression model, Rule based classifier Residential, Non-residential Goodin et al.  2015 Landsat OLI Spectral bands, Shape parameters, Spatial arrangement Segmentation, Support vector machine, Object-based classifier Urban\/artificial, Heterogeneous agriculture, Pasture\/abandoned, Forest  10 Homer et al.  2015 Landsat TM, NSLS Spectral bands,  NBR, NDVI, Elevation,  Soil survey, Crop survey, Wetland survey, Nighttime lights Multi-index integrated change analysis, Random forest classifier Developed (4 levels),  Barren land,  Cultivated crops, Mixed Forest, Grassland\/herbaceous Yang et al. 2015 CBERS-02 Spectral bands Maximum likelihood classifier, Rule-based classifier Urban, Village, Cropland, Deciduous trees European Environment Agency 2016 SPOT 5 & 6, Formosat-2 Topography, Impervious surface, Road network, Street trees, Zoning Segmentation, Clustering, Rule based classifier Urban (5 levels), Industrial\/Commercial, Green urban areas, Agriculture, Natural and semi-natural Gopal et al.  2016 Landsat TM NDVI, Land cover\/use, Population density, Vehicle mileage Fuzzy supervised classifier, Rule based classifier Urban (5 levels) Zhang et al.  2018 Aerial Red, green, blue, & NIR bands Segmentation, Object-based convolution neural network classifier, Rule based classifier Residential (2 levels),  Commercial,  Industrial,  Parks & recreational Haase et al.  2019 RapidEye Spectral bands, Biotope, Road network, Land cover,  Public trees Spectral unmixing, Random forest classifier Front- & back-yard green fraction, Urban parks & forests fraction, Allotment & community gardens fraction Vegetation (or lack thereof) is a common attribute in characterizing land use and identifying green spaces, and the development of reliable methods to map it across spatiotemporal scales can provide a better understanding of land dynamics and the integral role of ecology in cities. Table 4 provides a summary of recent studies that quantify urban vegetation. Photo interpretation is a longstanding and reliable method to assess urban vegetation (McGovern and Pasher, 2016), although time and monetary costs would rise with greater spatial and temporal detail. The two most prominent methods to identify and calculate vegetative greenness for larger areas is using the 11 Normalized Difference Vegetation Index (i.e. NDVI) or using a spectral unmixing model. NDVI utilizes the degree of reflection\/absorption of red and near-infrared (NIR) spectra, and has been applied as a proxy measure of vegetation density across a variety of studies, including those measuring urban green spaces (Atasoy, 2018; Fung and Siu, 2001; Hidayat and Ridwan, 2018; Jin et al., 2019; Li et al., 2015).    However, the accuracy of vegetative greenness estimates using NDVI may be compromised for cross-continental studies as spectral responses can vary greatly across a variety of vegetation types or other surface materials, as well as atmospheric conditions (Liu and Kafatos, 2007).  Using subpixel techniques instead, such as spectral unmixing models, can elevate issues related to mixed spectral responses by assessing the mixture of various different materials of a given pixel (Small, 2001). Spectral unmixing models provide a demonstrated method to extract accurate and reliable values of green vegetation density in urban areas over a range of spatial and temporal scales.  For example, the unmixing of vegetative greenness, darkness, and high albedo characteristics has been applied to accurately quantify the amount of urban vegetation on the pixel level using Landsat (e.g. Lu et al., 2017; Small, 2001; Small and Lu, 2006; Tooke et al., 2009) and VHR imagery (e.g. Tooke et al., 2009). Others, have also applied spectral unmixing to quantify urban green spaces (Haase et al., 2019), land cover (Powell and Roberts, 2010), and land use (Lu and Weng, 2006). 12 Table 4. Selection of studies that have quantified urban vegetation. Abbreviations used in table: ETM+ \u2013 Enhanced Thematic Mapper Plus; NDVI \u2013 Normalized Difference Vegetation Index; NIR \u2013 Near Infrared; OLI \u2013 Operational Land Imager; SPOT \u2013 Satellite Pour l\u2019Observation de la Terre (French); RGB \u2013 Red, Green, and Blue, and; TM \u2013 Thematic Mapper. Author(s) Year Imagery Data Methodology Measure of greenness Small 2001 Landsat TM Spectral bands Minimum noise fraction transformation, Spectral unmixing Vegetative greenness, high albedo, and darkness fractions Small and Lu 2006 Landsat ETM+ Spectral bands Principal component analysis, Spectral unmixing Vegetative greenness, high albedo, and darkness fractions Powell and Roberts 2008 Landsat ETM+ Spectral bands Spectral unmixing Vegetative greenness, impervious surface, and soil fractions Tooke et al. 2009 Quickbird Spectral & panchromatic bands Spectral unmixing Vegetative greenness, high albedo, and darkness fractions Fung and Siu 2010 SPOT Red & NIR bands Spectral ratio NDVI Li et al.  2015 Landsat ETM+ Red & NIR bands Spectral ratio NDVI McGovern and Pasher 2016 Aerial RBG image Point sampling, Photo interpretation Tree canopy cover (coniferous, deciduous), Shrub, Grass, Agriculture,  Building,  Other impermeable surface Lu et al.  2017 Landsat TM & ETM+ Spectral bands Minimum noise fraction transformation, Spectral unmixing Vegetative greenness, high albedo, and darkness fractions Atasoy 2018 Landsat ETM+ Red & NIR bands Spectral ratio NDVI Hidayat and Ridwan 2018 Worldview 2, Landsat OLI Red & NIR bands Spectral ratio NDVI Jin et al. 2019 Landsat TM & ETM+ Red & NIR bands Spectral ratio NDVI While current research using remote sensing technology to map land use and vegetative greenness is available and innovative, there is a lack of systematic and feasible methods suitable to derive fine spatial information for cross-regional scales as well as for historic accounting and long-term monitoring. In Canada, current nation-wide characterizations of urban land use are limited, covering only select years and\/or cities (Guindon et al., 2004; Zhang et al., 2010), or focused on select land uses, such as agriculture (Agriculture and Agri-Food Canada, 2015), or the built-up 13 extent (Statistics Canada, 2016b). Meanwhile, Canadian studies of urban vegetation are limited to local contexts, cover smaller time frames, and\/or use insensitive (e.g. NDVI) or impracticable methods (e.g. aerial photo interpretation) for their extension to multi-decadal national scale analyses. Understanding how vegetative greenness has changed locally, and its comparison on regional and national levels, will provide greater insight into multiple spatial scales of urban land dynamics. In turn, using vegetative greenness and other land characteristics to quantity the fractional amount of land use and its gradual transitions can enhance socio-economic and environmental accounting, and may assist sustainable landscape management. Characterizing multi-decadal vegetative greenness and land use in cities and their peri-urban extension can help with the development of evidence-based policies, offering accurate and reliable measures to more clearly understand the socio-ecological connections with the urban world. 14 1.4 Research objectives Overall, the purpose of this thesis is to investigate how vegetative greenness and land use has changed across Canadian urban areas over the past three decades. This thesis will be comprised of two components. First, I will characterize the multi-decadal change in vegetative greenness across select Canadian urban areas using a novel, spectrally derived, greenness metric. Secondly, I will quantify land use transitions from 1984 to 2016 and assess their relationship with current socio-demographic factors, focusing on the dynamic periphery of each select Canadian urban area. This work will incorporate spectrally unmixed Landsat satellite imagery, as well as Landsat derived land cover and Canadian census derived socio-demographic data. The sub-objectives of my MSc thesis are the following:  1. Describe the current state and recent changes in Canadian urban vegetation using anurban greenness score metric developed from spectrally unmixed Landsat satelliteimagery2. Characterize multi-decadal land use transitions in Canadian peri-urban areas usingspectrally unmixed Landsat satellite imagery, land cover, and socio-demographic data15 1.5  Thesis overview This thesis is made up of five chapters: an introduction, information on the study area and data used, two research chapters, and a conclusion. Chapter 2 describes the study area and data, namely the Landsat satellite imagery and the derived spectrally unmixed fractions, land cover, and socio-demographic data, used for analyses in Chapters 3 and 4. Chapter 3 addresses sub-objective l by developing a multi-decadal urban greenness score metric using medium-resolution Landsat satellite imagery. The urban greenness score framework was applied to select urban areas across Canada to quantify over 30 years of change in vegetative greenness and its relation to the current state of greenness. Chapter 4 addresses sub-objective 2 by building a dynamic, peri-urban focused, land use classification scheme using medium-resolution Landsat satellite data products, including spectrally unmixed fractions and land cover data. Using the derived dynamic land use data, multi-decadal peri-urban land use transitions were quantified and correlated with recent census based socio-demographic variables. Chapter 5 provides a summary and conclusions for the analyses in Chapters 3 and 4. It also discusses management implications, limitations of the study, and avenues for further research. 16 Chapter 2: Study area and data 2.1 Major Canadian urban areas For this thesis 18 major urban areas across Canada were selected (Figure 1) based on their status as a provincial capital or having a population greater than 150,000 persons in 2016. In total, these selected urban areas represented about 60% of Canada\u2019s population at the time and over 40,000\u202fkm2 (Statistics Canada, 2017). An urban area in this thesis is defined as the dissemination areas (DAs) within the urban core (i.e. census metropolitan area or census agglomeration), as well as all directly adjoining DAs. The spatial unit of DAs was chosen as it is the smallest for which Canadian census data are disseminated, typically containing between 400 and 700 people (Statistics Canada, 2011). DAs with more than 50% of pixels encompassed by water masks (and agriculture masks in Chapter 3) were excluded from analysis and did not contribute to respective total geometric areas. Urban areas were grouped into four geographic strata that share similar vegetation types (west, west-center, east-center, and east; Marshall, Schut, and Ballard, 1999), and subsequently distinguished in the spectral unmixing procedure (as outlined in Section 2.2.2). Population density is a key component in Canada\u2019s current urban classification system that used to delineate population centres from rural areas (Statistics Canada, 2011) as Canadian cities typically follow the pattern of decreasing population density with distance from the urban core (Turcotte, 2008). Using Ward\u2019s method of hierarchical clustering (Ward, 1963), I stratified urban areas by their DA level average population densities into four population density based groups: High, moderately-high, moderately-low, and low (Figure 2). Table 5 provides a summary of each population density groups\u2019 DA count 17 (2016), as well as their total geometric area and average DA level population density. A complete summary for each urban area is provided in Table 6, including each the respective stratum and population density group, as well as a summary of the peri-urban area and associated socio-demographic variables used in Chapter 4.  Figure 1. Locations of the 18 selected Canadian urban areas used in this study, by geographic stratum. 18 Figure 2. Dendrogram of population density group assignment (A = high; B = moderately-high; C = moderately-low, and; D = low) using Ward\u2019s clustering method. Table 5. Summary of population density groups (high, moderately-high, moderately-low, and low), including the number of 2016 dissemination areas (DAs), as well as their total geometric area (km2) and average 2016 DA level population density (per km2). Population density group DA count Area (km2) Population density (per km2) High 20,578 10,746 6,148 Moderately-high 5,689 9,541 4,044 Moderately-low 3,296 2,079 3,360 Low 2,464 20,454 2,248 19 Table 6. Summary of urban areas, including their respective stratum, province, assigned population density group (high, moderately-high, moderately-low, and low), as well as a summary for total considered DAs (2016 DA count, geometric area (km2), population, and average DA level population density (per km2), as well as a summary for peri-urban DAs (2016 peri-urban DA count, geometric area (km2), average DA level population density (per km2), percentage of recent construction (< 5 years), percentage of single-detached housing, and average of the median total income in 2015 (CAD $). Stratum Province Urban area Population density group Total Peri-urban DA count Area (km2) Population Population density (per km2) DA count Area (km2) Population density (per km2) Recent construction (%) Single-detached housing (%) Median total income ($ CAD) West British Columbia Victoria Moderately-low 541 633 349,598 3,226 160 116 1,947 4% 57% 40,644 Vancouver High 3,663 3,335 2,536,528 6,011 961 471 3,510 6% 53% 35,787 West-center Alberta Edmonton Moderately-low 1,441 1,033 987,371 3,554 641 1,002 2,927 5% 69% 45,543 Calgary Moderately-high 1,589 775 1,116,102 4,088 392 340 3,746 4% 80% 47,433 Saskatchewan Saskatoon Moderately-low 343 99 214,409 3,302 131 102 3,173 5% 68% 44,773 Regina Moderately-low 385 138 209,250 2,951 29 979 1,224 11% 86% 55,125 Manitoba Winnipeg Moderately-high 1,095 659 652,264 4,158 261 241 2,486 5% 77% 42,627 East-center Ontario Greater Sudbury Low 255 4,289 155,223 1,462 86 44 2,011 2% 58% 38,465 Windsor Low 745 321 409,604 2,713 170 106 2,167 3% 77% 38,297 London Moderately-low 603 176 382,512 3,313 31 186 938 12% 84% 45,597 Toronto High 10,881 5,488 7,653,628 5,704 3,109 1,038 4,901 2% 63% 34,405 Ottawa Moderately-high 1,847 6,007 1,189,369 4,008 751 548 3,460 4% 55% 46,313 Qu\u00e9bec Montr\u00e9al High 6,162 1,923 3,782,524 7,013 1,016 302 3,410 3% 61% 37,973 Qu\u00e9bec City Moderately-high 1,164 2,100 704,327 3,934 472 197 2,810 5% 56% 41,222 East Qu\u00e9bec Sherbrooke Low 273 930 178,040 2,310 97 188 1,733 8% 58% 35,633 New Brunswick Fredericton Low 164 3,483 101,733 961 66 384 798 6% 66% 39,608 Nova Scotia Halifax Low 741 10,700 471,062 2,365 317 146 2,957 3% 48% 36,560 Newfoundland and Labrador St. John\u2019s Low 306 732 190,778 2,123 117 424 1,071 12% 66% 40,872 20 2.2 Data 2.2.1 Landsat satellite imagery Landsat satellite imagery is the world\u2019s largest collection of Earth imagery, available from the US Geological Survey (USGS) at a medium spatial resolution (30 m) and since the mid 1970s (Woodcock et al., 2008). The opening of the historic Landsat archive in 2008 continues to make dense and long-term time series analyses of land dynamics feasible and accessible worldwide (Wulder and Coops, 2014). Landsat Thematic Mapper (TM and ETM+) imagery with < 70% cloud cover, acquired during the summer growing period (August 1st \u00b1 30 days) from 1984 to 2016, were selected for the basis of both analyses in this thesis. For each year of analysis, the best-available pixel algorithm (BAP; White et al., 2014) was employed to choose the highest quality pixel from selected Landsat imagery and create a single annual cloudless composite of each urban area.  2.2.2 Spectrally unmixed fractions All 33 annual Landsat BAP composites for each urban area of a given stratum were analyzed simultaneously in the spectral unmixing procedure to derive spectrally unmixed fractions of darkness, high albedo, and vegetative greenness. The methods for spectral unmixing built upon those of Lu et al. (2017), and in part Small and Lu (2006) and Tooke et al. (2009). The unmixing analysis was conducted for urban areas of the same stratum to ensure vegetative greenness levels were representative of vegetation inherent to each geographic region. First, a minimal noise fraction transformation (MNF; Green et al., 1988) was performed on each stratum to extract three MNF components that explained over 98% of variance. Next, the pixel purity index (PPI) was computed and used to identify image-based endmembers that best describe the mixing volume (Bateson and 21 Curtiss, 1996; Boardman, 1993; Boardman et al., 1995). Using the PPI, three endmembers (i.e. spectrally pure pixels) of darkness, high albedo (i.e. reflected brightness), and vegetative greenness (i.e. greenness) were selected for each stratum. Endmembers were differentiated spectrally by their relative distributions across the first, second and third MNF components. Pixels characterized by the vegetative greenness endmember include highly manicured grass, such as those in golf courses, as well as treed areas like parks or natural forests. The high albedo endmember pixels ranged from impervious urban surfaces to rocky surfaces in natural areas. Dark endmember pixels include shadows, such as those cast by high rise buildings or dense multi-level forests.  From this three-endmember model, a spectral library was built for each geographic stratum and then a sub-pixel linear spectral unmixing algorithm was applied to extract the fractions of darkness, high albedo, and vegetative greenness for each individual urban area. This was done using the following equation: \ud835\udc45\ud835\udc45 =  \ufffd\ud835\udc53\ud835\udc53\ud835\udc56\ud835\udc56\ud835\udc52\ud835\udc52\ud835\udc56\ud835\udc56 +  \ud835\udf00\ud835\udf00\ud835\udc5b\ud835\udc5b\ud835\udc56\ud835\udc56=1 where R is the unmixed surface reflectance; fi is the endmember image fraction; ei is the endmember\u2019s surface reflectance value; n is the number of endmembers, and; \u03b5 is the root mean square error. The extracted fraction images were subset and temporally smoothed using locally weighted regression method (LOESS; Cleveland and Devlin, 1988) to minimize intra-seasonal fluctuations. Figure 3 shows the comparison between the Landsat BAP composites and the resulting vegetative greenness fraction for four example urban areas (Toronto, Calgary, Victoria, and Halifax), each representing a different stratum and population density group. The vegetative greenness fraction generally follows the pattern of impervious\/vegetated surfaces. 22 Figure 3. 2016 Landsat BAP composites (top panels; RGB: SWIR-2, NIR, G) and smoothed unmixed vegetative greenness fraction results (bottom panels) of parts of Toronto (high population density; east-center stratum), Calgary (moderately-high population density; west-center stratum), Victoria (moderately-low population density; west stratum), and Halifax (low population density; east stratum). 2.2.3 Land cover data and agricultural mask Land cover derived from the Virtual Land Cover Engine framework (VLCE) is generated at an annual time step across Canada using an integrated workflow that includes BAP gap-free annual image composites produced from Landsat imagery, elevation information, and a logical ecological-based land cover transition model (Hermosilla et al., 2018). Yearly VLCE land cover maps were used to identify a water mask and other vegetation-related land covers. The agriculture mask used in Chapters 3 and 4 represents agricultural zones circa 2011 as identified by Agriculture and Agri-23 Food Canada (AAFC) and was also used in the development of the VLCE land cover product (Hermosilla et al., 2018). The VLCE based land cover groups generated for analysis in Chapter 4 (further details in Section 4.2) include Herb, Treed (including the classes of Broadleaf, Coniferous, Mixed Wood, Shrubland, or Wetland-Treed), and all other classes. Overall accuracy of the VLCE dataset is 70.3% (\u00b1 2.5%), and 82.5% (\u00b1 2.1%) at the subsequent land-cover level (non-vegetated, vegetated non-treed, vegetated treed). 2.2.4 Socio-demographic data Select recent socio-demographic data, namely population density, percentage of single-detached housing, percentage of recent construction (< 5 years), and median total income (2015; CAD $), were acquired from 2016 census data on the DA level for an exploratory correlative analysis with peri-urban land use trends in Chapter 4 (Table 6; Statistics Canada, 2017). As noted previously, population density is an important factor related to physical and socio-economic urban structure in Canada (Statistics Canada, 2011; Turcotte, 2008). The percentages of single-detached housing and recent construction illustrate the type of residential development and its current demand, respectively, and may provide insight about current land development strategies (Landry and Pu, 2010). Level of income is typically used to quantify material resources of individuals and their community (Chan et al., 2015; Freeman et al., 2016; Lawson et al., 2018), and its correlation with land use changes may illuminate relationships between individual or local investment and urban development. Although census data is available on the DA level since 2001, the shape and distribution of these areas may change through time between censuses because they are designed to consistently constitute between 400 and 700 people (Statistics Canada, 2011). Therefore, as 2016 DAs were 24 chosen as the spatial unit in both research chapters, only census data from the same year was applied to assess correlations between the current state of select socio-demographic variables and multi-decadal land use trends in peri-urban areas (Chapter 4). Table 7 provides a summary of data used in the analyses presented in this thesis. The population density of several smaller DAs (e.g. with area < 1 km2) was exaggerated because of the aggregated spatial unit (i.e. km2). Table 7. Summary of data, including their variable(s), type, data range, spatial resolution, producer(s), temporal span, and temporal frequency. Abbreviations used in table: AAFC \u2013 Agriculture and Agri-Food Canada; CFS \u2013 Canadian Forest Service, DA \u2013 dissemination area; NASA \u2013 National Aeronautics and Space Administration (of the United States of America); USGS \u2013 United States Geological Survey, and; VLCE \u2013 Virtual Land Cover Engine. * Treed includes Broadleaf, Coniferous, Mixed wood, Shrubland, and Wetland-treed land cover classes. Data Variable(s) Type Data range Spatial resolution Producer(s) Temporal span Temporal frequency Landsat TM & ETM+ imagery Spectral bands Raster \u2013 continuous 0\u2013255 30 m NASA & USGS 1984\u20132016 (33 years) Annual Spectrally unmixed fractions Darkness Raster \u2013 continuous 0\u20131 30 m Czekajlo et al. (2020) 1984\u20132016 (33 years) Annual High albedo Vegetative greenness VLCE land cover Herb Raster \u2013 categorical  NA 30 m CFS 1984\u20132016 (33 years) Annual Treed* Water Other Agricultural mask Agricultural Raster \u2013 categorical NA 30 m AAFC \u2013  used in VLCE 2011 Annual Non-agricultural Census data Population density (per km2) Vector \u2013 continuous  0\u2013141,389 Peri-urban DAs \u2013 Variable Statistics Canada 2016 Annual Recent construction  (< 5 years; %) 0\u2013100 Single-detached housing (%) 0\u2013100 Median total income (CAD $) 11,136\u201393,440 25 Chapter 3: Characterization of multi-decadal urban land dynamics using a novel urban greenness score metric 3.1 Introduction While cities are centers of prosperity, education, and culture, unmanaged urban development contributes to high levels of air pollution, noise, and crowding, all with negative effects on human health (Block et al., 2018; Krefis et al., 2018; Muzet, 2007). One way for urban planners to mitigate the negative impacts of urbanization and create more sustainable and healthy cities is by incorporating more vegetation, such as through green spaces (UN-Habitat, 2016). Green spaces include public or private vegetated areas, such as parks, street trees, natural areas like wetlands or grasslands, and residential gardens (Haaland and Konijnendijk van den Bosch, 2015; Kumagai, 2011). They provide a range of regulating ecosystem services to urban areas, such as heat reduction, flood mitigation, and air purification (Livesley et al., 2016), as well as direct public health benefits like improving the physical, mental, and social wellbeing of individuals (Hartig et al., 2014; van den Bosch and Bird, 2018; van den Bosch and Sang, 2017). However, despite the benefits of green spaces for environmentally sustainable cities, they are typically not evenly distributed within urban areas (Heynen et al., 2006; Landry and Chakraborty, 2009; Tooke et al., 2010) and are threatened by continued urban growth (Fragkias et al., 2013). Planning for and managing urban green spaces is key to ensuring their functionality and potential for providing ecosystem services, as well as the general sustainability of cities. Remote sensing technologies have the potential to support the information needs of planners and civic managers by providing frequent, accurate, and cost-effective spatial information at an appropriate 26 scale and expedited rate (Patino and Duque, 2013). Across a variety of spatial resolutions, spectral unmixing methods have been used extensively in remote sensing research to derive spectrally differentiated characteristics of a surface. During a spectral unmixing procedure, spectral characteristics of determined pure pixels in an image are extracted and compared across remaining pixels to determine the fractional component of each characteristic (Small, 2001). Using spectral unmixing, various forms of vegetation have been distinguished from other surfaces, such as bare soil (Asner and Heidebrecht, 2002), impervious surface (Lu and Weng, 2006), or snow (Vikhamar and Solberg, 2003). The method has also been used to quantify the fraction of vegetation across international urban environments by differentiating between darkness, high albedo, and green vegetated surfaces (Lu et al., 2017).  Using a multitude of spectral unmixing variations, the proportion of urban vegetation greenness can be quantified at the pixel level for extracting area-based green space information. Unlike the Normalized Difference Vegetation Index (NDVI), which has been extensively used to measure vegetation in many contexts, including urban green spaces and at various spatial scales (e.g. Atasoy, 2018; Fung and Siu, 2001; Hidayat and Ridwan, 2018; Li et al., 2015), spectral unmixing avoids errors caused by interactions between detected spectral signatures (Liu and Kafatos, 2007). Additionally, sensor-specific band characteristics and unexplained atmospheric interactions may contribute to the overall uncertainty of NDVI (van Leeuwen et al., 2006). This may pose problems for the analysis of large spatial extents, such as vegetation greenness across Canadian cities. Spectral unmixing has been demonstrated to produce accurate and reliable information regarding urban vegetation greenness and its changes over time, with informative examples over limited extents of time and space (Haase et al., 2019; Lu et al., 2017; Okujeni et al., 27 2013; Phinn et al., 2002; Powell and Roberts, 2010, 2008; Small, 2001; Tooke et al., 2009), offering insights towards investigations over longer time periods and areal extents. Given the trajectory of urbanization globally, effective spatial monitoring of urban growth and green space dynamics is critical to achieve sustainable urban development. Medium spatial resolution (10\u2013100\u202fm) remote sensing platforms, such as the series of Landsat satellites, provide freely-available and long-term data appropriate for studying urban dynamics around the world. Landsat satellite imagery, in particular, is suitable to utilize for urban landscape analyses as it has been systematically acquired over several decades (Schneider et al., 2009; White et al., 2014; Woodcock et al., 2008; Wulder and Coops, 2014). Given that urban planning and management is conducted on a range of spatial scales, information attained from medium-resolution satellite imagery can be appropriately applied for a variety of needs. Additionally, recent advancements in data acquisition and processing methods have facilitated the creation of accurate, medium spatial resolution time series products at annual intervals from large datasets (Schneider, 2012; White et al., 2014; Zhu et al., 2019). With advanced computing abilities, and access to reliable long-term satellite imagery, it is now possible to develop a standardized approach to map past and current urban vegetation greenness in a reproducible fashion. The overall goal of this chapter is to develop an urban greenness score that incorporates the current state of urban vegetative greenness with historic trends. My objective was to describe and temporally assess Canadian urban greenness using the score, which integrates the final year and overall change of unmixed vegetative greenness fractions derived from annual gap-free Landsat surface reflectance products. To address this objective I: (i) applied spectral unmixing for extracting the pixel level vegetative greenness fraction; (ii) assessed the accuracy of extracted vegetative 28 greenness fractions; (iii) developed an urban greenness score that combines status and trends in vegetative greenness fractions over time, and; (iv) applied the urban greenness score to major urban areas in Canada and demonstrate the utility of the urban greenness score for national level urban monitoring.  3.2 Methods 3.2.1 Validation of spectrally unmixed vegetative greenness fractions Spectrally unmixed vegetative greenness fractions were validated for corresponding years throughout the 33-year time series using high spatial resolution images provided by Google Earth. Validation images were chosen based on two criteria: (i) being cloud-free, and (ii) collected during the Canadian growing season. Since cloud-free Google Earth images during the growing season vary in quality and availability, one representative urban area was chosen per stratum: Vancouver (west), Edmonton (west-center), Toronto (east-center), and Fredericton (east). Three validation images from varying years were chosen for each of the four urban areas: Vancouver (2003, 2008, and 2015), Toronto (2002, 2009, and 2015), Edmonton (2010, 2012, and 2015) and Fredericton (2005, 2010, and 2011). For each of the twelve images (i.e. three images per year per urban area), up to 100 pixels per image were selected using stratified random sampling (with 10% vegetative greenness fraction classes) within each urban area boundary, and then subdivided into 6 \u00d7 6 m grids (i.e. 25 grids per pixel). An interpreter, blind to the vegetative greenness fraction of each pixel, assessed the presence\/absence of green vegetation for each grid. The reference vegetative greenness fractions were calculated using the proportion of presence to absence values of all 25 grids per pixel. Poor quality pixels of imagery shared in Google Earth were excluded from the validation 29 results after closer inspection. Spearman\u2019s correlation coefficients (\u03c1) were employed to compare the non-parametric estimated (i.e. unmixed) and reference vegetative greenness fractions of all pixels of a given year of each representative urban area. 3.2.2 Urban greenness score On the DA level, average vegetative greenness (referred to simply as \u2018greenness\u2019) was calculated as a percentage from the time series of annual, smoothed, unmixed greenness fractions (ranging between 0\u20131). The urban greenness score matrix, which categorizes the change in greenness during the time series in relation to the final greenness level of a given year, is shown as Figure 4. For this study, the final greenness indicated the value from the year 2016, and change in greenness was calculated using the Theil-Sen estimator (i.e. TS; Sen, 1968; Theil, 1992) of all 33 years of the time series. Change in greenness was categorized into three levels (decrease (\u2013), zero (0), and increase (+)) using a positive and negative unit of standard deviation for all DAs. The final greenness of all DAs was categorized into three levels (low (L), moderate (M), and high (H)) using natural breaks (Jenks), which identifies class breaks by minimizing within-group variation and maximizing between-group variation (Coulson, 1987; Jenks and Caspall, 1971). The natural breaks classification was chosen in part for its common usage, data-driven determination of breaks, as well as its successful use in a similar circumstance of classifying a remotely sensed vegetation index by Anchang et al. (2016). As the urban greenness score framework is meant to provide a practical yet reliable method to identify areas with relatively higher or lower greenness, which may subsequently be assessed at a finer scale for management purposes, the natural breaks classification harnesses the inherent distribution of greenness across all DAs and avoids subjective class distinctions.  30 3.3 Results 3.3.1 Validation of vegetative greenness fractions The estimated vegetative greenness fractions largely correlated with the reference values (Figure 5), with Spearman\u2019s correlation coefficients (\u03c1) between estimated (i.e. unmixed) and reference vegetative greenness fractions of 0.85, 0.67, 0.71, and 0.80 for the cities of Vancouver, Edmonton, Toronto, and Fredericton, respectively. Although outliers were present for all cities, Edmonton exhibited the most outliers and had the lowest rank correlation (\u03c1 = 0.67). Edmonton also showed higher estimated median values compared to its reference values than Vancouver even though both share similar estimated and reference median vegetative greenness fractions (Edmonton = 0.54 and 0.50, and; Vancouver = 0.53 and 0.52, respectively). Toronto and Fredericton both showed lower estimated median vegetative greenness fractions (0.50 and 0.52, respectively) in comparison to their reference values (0.56 for both). Figure 4. Urban greenness score matrix showing final greenness (rows = low (L), moderate (M), and high (H)) in relation to the change in greenness (columns = decrease (\u2013), zero (0), and increase (+)). 31 Figure 5. Estimated (i.e. unmixed) vegetative greenness fractions plotted against reference vegetative greenness fractions, with the associated Spearman\u2019s correlation coefficient (\u03c1) and p-value, for select years of a representative urban area of each stratum. Density indicates the amount of sample points (i.e. fraction of vegetated grids per pixel) represented by a point on the plot. 32 3.3.2 Evolution of greenness fraction through time A crucial component of the urban greenness score is the change in greenness extracted from the entire time series of greenness fraction data. Figure 6 shows a portion of Toronto DAs classified by their change in greenness over the 33-year period, the final (2016) level of greenness, as well as the time series of select DAs\u2019 greenness fractions that achieve different urban greenness scores. The integration of greenness change provides a temporal context to the single-year current greenness level that differentiates areas of varying urban greening trends during the time series. For example, although DAs identified by unique census IDs (i.e. DAUIDs) 35202159 and 35202989 are both classified with moderate final greenness (Figure 6B), DA 35202159 reached that state after losing greenness since 1984 (Figure 6A), whereas DA 35202989 gained greenness (Figure 6A). The time series of these two DAs demonstrate that despite having the same greenness level in 2016, the trajectory each DA took over the preceding time period examined herein is markedly different (Figure 6C).  33 Figure 6. Maps of greenness change (top-left panel) and final greenness (top-right panel) for a portion of Toronto dissemination areas (DAs). Time series of vegetative greenness fractions for select DAs (graph; identified by Statistics Canada census unique DA identifier code (DAUID) and outlined in black on the maps) of various urban greenness scores as distinguished by colour. 34 3.3.3 Urban greenness score Utilizing a multi-decadal time series of vegetative greenness data, the urban greenness score describes the current level of greenness in relation to its change in greenness over time using a single class. A map showing the urban greenness score for a portion of Toronto is provided as Figure 7, indicating several DAs of different urban greenness scores. Areas that exhibited a gain in greenness and resulted in a moderate or high final level of greenness in 2016 are classified as [+ M] or [+ H], respectively (e.g. DAUID = 35202989 and 35204856 in Figure 7, respectively). Areas that remained with a moderate or high amount of greenness throughout the 33 years are classified as [0 M] (e.g. DAUID = 35200746 in Figure 7) or [0 H] (e.g. DAUID = 35201495 in Figure 7),respectively. Built-up urbanized areas that resulted in low levels of greenness in 2016 but had experienced, since 1984, an increase in greenness are classified as [+ L] (e.g. DAUID = 35201407 in Figure 7). Built-up areas that neither gained nor lost greenness, and generally maintained a low level of greenness, are classified with the urban greenness score [0 L] (e.g. DAUID = 35204793 in Figure 7). Areas that have lost greenness during the study time, and have resulted in either low, moderate, or high amounts of urban greenness would be classified as [\u2013 L] (e.g. DAUID = 35204047 in Figure 7), [\u2013 M] (e.g. DAUID = 35204047 in Figure 7), or [\u2013 H] (no example DA in Figure 7), respectively. 35 Figure 7. Urban greenness score map for a portion of Toronto dissemination areas (DAs). Examples of each green score are identified by Statistics Canada census unique DA identifier code (DAUID) and outlined in black. 36 3.3.4 Urban greenness score trends across Canada The large area and long-term greenness time series data used in this study enabled the development of a comparative framework to be used across a range of spatial and temporal scales, as well as a national multi-decadal assessment of urban greenness. Figure 8 shows urban greenness score maps of Toronto, Calgary, Victoria, and Halifax DAs as an example for each population density group, as well as each stratum. The distribution of urban greenness scores varied greatly between urban areas across population density groups and cross-continentally. For example, Victoria (Figure 8C) and Halifax (Figure 8D), coastal urban areas from lower population density groups, tended to have more peripheral DAs that increased or did not change in greenness and resulted in either high or moderate levels in 2016 (i.e. [+ H], [+ M], or [0 H]). The urban core of Halifax, representing the lowest population density group and the east stratum, was mostly comprised of DAs that decreased in greenness resulting in low or moderate levels in 2016 (i.e. [\u2013 L] or [\u2013 M]; Figure 8D). In contrast, DAs in Toronto (Figure 8A) and Calgary (Figure 8B) that decreased or did not change in greenness and resulted in either low or moderate levels in 2016 (i.e. [\u2013 L], [\u2013 M], or [0 L]) were found in the urban periphery. These two densely populated urban areas are both found within Canada\u2019s interior (west-center and east-center stratums), which tend to be less restricted (geographically and\/or legislatively) to urban spread.37 Figure 8. Urban greenness score maps for portions of select urban areas\u2019 dissemination areas, each representing a stratum and population density group (A = Toronto (east-center stratum; high population density); B = Calgary (west-center stratum; moderately-high population density); C = Victoria (west stratum; moderately-low population density), and; D = Halifax (east stratum; low population density)). The percentage of DAs classified with a given urban greenness score for each stratum, population density group, as well as for all DAs is shown in Figure 9. For DAs across all urban areas, most were classified as unchanged with a moderate final level of greenness ([0 M] = 34.5%), followed closely by those that were unchanged with a low final level of greenness ([0 L] = 30.9%). The lowest percentage of DAs increased in greenness and resulted in a low level of greenness ([+ L] = 0.4%). Over the 33 years, more DAs decreased in greenness ([\u2013 L] and [\u2013 M] and [\u2013 H] =14.8%) than increased ([+ L] and [+ M] and [+ H] = 6.9%). Across Canada, most DAs that 38 decreased in greenness resulted in a low level ([\u2013 L] = 7.1%), and most that increased in greenness resulted in either a moderate or high level ([+ M] = 2.9%, and; [+ H] = 3.6%). Regardless of the change, most DAs exhibited a moderate or low level of greenness in 2016 (43.3% and 38.4%, respectively).   Unique distributions of urban greenness scores were evident regionally. Generally, coastal urban areas maintained the same level of greenness over the 33-year time period for the largest percentage of DAs (west = 86.5%, and; east = 83.9%). Moving from west to east, the proportion of unchanged, moderate level greenness decreased (i.e. [0 M]; west = 59.9%; west-center = 36.7%; east-center = 29.7%, and; east = 24.7%). Urban areas of the east-center stratum showed the largest percentage of DAs classified as no-change, low greenness ([0 L] = 39.2%). This is demonstrated in Figure 8A, which shows the urban core and expansive urban area of Toronto, much of which is classified as [0 L]. In contrast, the east strata had only a small percentage of unchanged, low greenness DAs ([0 L] = 2.8%), distinctly lower than the percentage for DA\u2019s across all strata ([0 L] = 30.9%). The relative lack of [0 L] in eastern urban areas is illustrated in Figure 8D, which shows the less densely populated urban core (i.e. fewer small DAs) of Halifax and its expansive urban periphery, much of which is classified as either [0 M], [0 H], or [+ H]. 39 Figure 9. Percentage of dissemination areas (DAs) classified by each urban greenness scores for each stratum, population density group, as well as for all DAs. 40 The west stratum showed the lowest percentage of increased greenness scores ([+ L] and [+ M] and [+ H] = 0.3%), whereas the west-center stratum had proportionally the most ([+ L] and [+M] and [+ H] = 16.9%). For the west-center\u2019s DAs classified with an increase in greenness score,most resulted in a moderate level of greenness (i.e. [+ M] = 8.2%). In the case of decreased greenness scores, the east-center stratum had the highest percentage ([\u2013 L] and [\u2013 M] and [\u2013 H] = 16.0%), and the east stratum the lowest ([\u2013 L] and [\u2013 M] and [\u2013 H] = 9.0%). For the east-center\u2019s DAs classified with a decrease in greenness score, most resulted in a low level of greenness (i.e. [\u2013 L] = 9.4%). The east stratum exhibited the most DAs with a high greenness level in 2016 (65.5%),in relation to the remaining strata (west-center = 22.2%; west = 21.9%, and; east-center = 13.4%). On the other hand, the east-center stratum had the most DAs that resulted in a low final greenness level (49.1%) compared to the other strata (west-center = 26.4%; west = 8.8%, and; east = 4.9%).  More distinct patterns in proportions of urban greenness scores are discerned between population density groups than between strata (Figure 9). Densely populated urban areas showed the highest percentage of DAs classified as unchanged and remaining with moderate or low levels of greenness ([0 M] and [0 L] = 69.1%; individually representing 32.3% and 36.9%, respectively). The percentage of these two urban greenness scores decreases as the average urban area population density decreases, whereby the low population density group exhibits only 44.0% ([0 M] = 28.5%, and; [0 L] = 15.5%). In contrast, the lower the average population density of an urban area the greater percentage of DAs that maintained a high level of greenness since 1984 (low population density [0 H] = 38.8%, and; high population density [0 H] = 9.0%). This distinction is apparent when urban greenness score maps of Toronto (Figure 8A, high population density) and Halifax (Figure 8D, low population density) are compared. As seen in the figure, there is a stark contrast 41 between Toronto\u2019s urban greenness scores [0 M] and [0 L], respectively and its relatively smaller amount of [0 H]. The opposite pattern is seen for Halifax.  Differences between population density groups were also evident between opposing change in urban greenness scores. Densely populated urban areas, such as Toronto, showed the largest percentage of DAs that decreased in greenness ([\u2013 L] and [\u2013 M] and [\u2013 H] = 16.2%) and the smallest percentage that increased ([+ L] and [+ M] and [+ H] = 5.6%). In the case of decreased greenness in high population density urban areas, most DAs resulted in a low level of greenness ([\u2013 L] = 9.0%). On the other hand, the greatest percentage of DAs that increased in greenness werefound in moderately-low population density urban areas, such as Victoria ([+ L] and [+ M] and [+ H] = 14.1%), as well as the smallest percentage that decreased in greenness ([\u2013 L] and [\u2013 M] and [\u2013H] = 11.2%). In the case of increased greenness in moderately-low population density urban areas,most DAs resulted in a high level of greenness ([+ H] = 7.9%). Halifax and other urban areas of the low population density group showed the largest percentage of DAs that decreased in greenness and still resulted in high levels of greenness ([\u2013 H] = 2.6%), whereas Toronto and other densely populated urban areas showed the smallest percentage ([\u2013 H] = 1.5%). Conversely, urban areas with a high population density had the largest percentage of DAs classified as increased, low final level of greenness ([+ L] = 0.5%), whereas low population density urban areas had the smallest ([+ L] = 0.1%). Of all the population density groups, the moderately-high population density group showed the largest percentage of DAs classified as a decrease in greenness that resulted in a moderate level ([\u2013 M] = 7.6%), and the low population density group showed the smallest ([\u2013 M] = 4.4%). For the case of a greenness increase that resulted in a moderate greenness level, DAs of moderately-low population density urban areas had the most ([+ M] = 6.1%), but both the high and low population 42 density groups had the least ([+ M] = 2.1% and 1.9%, respectively). Low population density urban areas exhibited the most DAs that resulted in a high final greenness level (45.2%), in relation to the remaining population density groups (moderately-low = 25.1%; moderately-high = 19.6%, and; high = 13.6%). Conversely, densely populated urban areas presented the most DAs with a low level of greenness in 2016 (46.3%) compared to the other population density groups (moderately-high = 29.0%; low = 20.1%, and; moderately-low = 19.0%).   3.4 Discussion 3.4.1 Analysis approach The urban greenness score developed in this analysis, using final greenness and the TS-derived overall change in greenness, provides a new framework for urban change and is suitable to apply across spatial levels, such as at the DA level or on a national scale. Depending on the year from which greenness data is derived, the urban greenness score can be tuned to relate to a different greenness state within the time series. If greenness information from the initial year was used, the urban greenness score would show how the quantity of urban vegetation has changed since the start of the time series. Although this provides an interesting glimpse into the past, planners and developers require accessible and relevant data in order to prioritize goals and allocate resources efficiently and effectively for long-term strategic plans (Haaland and Konijnendijk van den Bosch, 2015). The final level of greenness from a time series presents the most up-to-date information that enables urban planners to better understand current green areas and their recent histories. As greenness data continues to be collected, the urban greenness score can be updated accordingly and comparisons between iterations would inform about potential shifts in single-year greenness as well 43 as its ongoing trends. Additionally, since the urban greenness score framework was developed using administrative boundaries (census DAs), investigations using additional municipal, regional, or census data with the urban greenness score may help illuminate potential factors related to urban greenness losses or gains. The developed urban greenness score simply yet thoroughly describes urban greenness and its change over time. Prior to the availability of dense time series data, standard practice for trend analysis considered the relative change of values extracted from satellite imagery of different times. However, as relative change only considers data from two time steps, it does not harness the full potential of long, high frequency time series data that are currently available. In addition, relative change analyses may skew results in favour of anomalies found in the initial and\/or final years. Instead, TS-derived change, as used in the urban greenness score, is robust against outliers and uneven non-normal distributions of input data as it is computed using pairwise slopes between all of the time series data points (Ohlson and Kim, 2015; Wilcox, 2010). Due to these advantages, TS has become a common method for quantifying change of remotely sensed data (Fernandes and Leblanc, 2005; Liu et al., 2015; Mishra and Mainali, 2017) and is particularly beneficial for a high-frequency, multi-decadal urban land dynamics characterizations such as the urban greenness score metric. Spectral unmixing successfully captured the level of urban greenness across 18 urban areas in four Canadian geographic-based strata. From the validation procedure, the estimated vegetative greenness fractions largely correlated with the reference vegetative greenness fractions derived from imagery in Google Earth. All cities had rank correlation coefficients (\u03c1) > 0.70 except for Edmonton (\u03c1 = 0.67). Lower correlations between estimated and reference vegetative greenness 44 fractions can be attributed to some spatial and temporal mismatch as a result of comparing imagery of different sources. Despite careful spatial alignment of estimated Landsat-based greenness fraction data with reference Google Earth imagery, some pixel misregistration is a common, yet minor, source of error.  Although both Landsat and Google Earth imagery used in this analysis were acquired during the growing season of the same year, a maximum two-month temporal mismatch between estimated and reference data was still possible. For example, Edmonton\u2019s lower correlation may be due to temporal mismatch of imagery combined with the phenology of herbaceous vegetation typically found in the prairie ecozone (i.e. west-center stratum; Rochon et al., 2010). The phenology of herbaceous vegetation that thrives in this geographic region, particularly grasses, is closely related to drought conditions that advance senescence and loss of greenness (Cui et al., 2017). It may be the case that Landsat and Google Earth imagery were acquired at a large enough temporal gap that the discrepancy in phenology contributed to inconsistencies between estimated and reference vegetative greenness fractions. It may also be the case that real changes in greenness occurred during the temporal gap between data from Landsat and Google Earth Imagery, such as greenness loss due to construction.  Some inaccuracy in estimated vegetative greenness fractions can also be attributed to the dark endmember chosen for the spectral unmixing technique, particularly for densely forested pixels such as those found in the west stratum. However, these pixels tend to be found in peripheral DAs and thus should not greatly influence the average trend of the entire urban area. Since the validation procedure was conducted for a representative urban area of each geographic stratum, the 45 remaining urban areas would show similar high correlations between estimated and reference vegetative greenness fractions. 3.4.2 Urban greenness score trends across Canada In this analysis I compare over 30 years of satellite data systematically across major Canadian urban centers to spatially identify and temporally characterize urban greenness both geographically and by population density. Overall, my results correspond with studies examining how greenness of various Canadian and international cities has changed within the past 25 years. For example, the results presented in this chapter are in line with those of Jin et al. (2019), who indicated that three of five selected North American cities (all found on the western half) experienced greenness loss between 1992 and 2011. In the same study, four of nine Asian cities also showed a negative temporal trend of greenness, whereas the two major Australian cities investigated (Melbourne and Sydney) indicated an increase in greenness. Kabisch and Haase (2013) found that European cities have also experienced greening trends (between 1990 and 2006), with variations between cities of different climatic regions (i.e. western, eastern, southern, and northern Europe). Similar to McGovern and Pasher (2016), who found that the national urban tree canopy did not substantially change between 1990 and 2012, this analysis found that, on average, Canadian urban areas maintained a similar level of greenness between 1984 and 2016.  McGovern and Pasher (2016) found a similar trend in urban tree canopy loss by Canadian ecozones between 1990 and 2012, where the majority of loss (6%) occurred in the Ontario mixedwoods and British Columbia Pacific maritime regions. In this analysis, the east-center stratum (which would include most of Ontario\u2019s mixedwoods ecozone) only experienced a marginal 46 greenness loss, and the west coast urban areas (which would be part of the British Columbia Pacific maritime ecozone) did not show much more greenness loss than the other geographic regions. Alternatively, this analysis found for both Vancouver and Victoria (west stratum) that recent greenness levels were relatively high despite an overall loss since 1992, in agreement with the findings by Jin et al. (2019). The high incidence of peripheral areas that decreased in greenness in this chapter is similar to the findings of McGovern and Pasher (2016), who noted that the loss of urban tree canopy in Ontario was predominantly a result of continued urban expansion. For both studies the greatest increase in greenness was found in Prairie urban areas (i.e. west-center stratum), which are relatively younger cities since early settlement in this region heavily focused on the development of agriculture in rural areas (Thomas, 1975). As Prairie cities are younger, with low population densities, and have not undertaken much urban infilling, the time series of satellite imagery used in this analysis captured their sustained green-up. In densely populated urban areas, this study identified only 6% of DAs that increased in greenness, but just over 16% of DAs that showed the largest reduction in greenness. This pattern may be present because Canadian cities are much younger and with relatively lower urban densities than many other cities internationally. Only since the mid-1970s, Toronto and other industrialized Canadian cities turned to the urban periphery to expand industrial and commercial facilities, leaving open lots in the urban core available for development (Gertler, 1995). By the mid- to late-1990s many of these cities took action to remediate these abandoned lots and develop them residentially and\/or commercially, and only in some cases into public green spaces (De Sousa, 2002). Now, as the urban core is relatively stable, development of Canadian cities most often occurs along the urban fringe where space is available.  47 Ultimately, the noted green-up trends following new urban expansion projects across Canada point towards urban greenness trends acting as a function of inherent urban form. Areas that lost greenness between 1984 and 2016 within the metropolises of Toronto, Vancouver, and Montr\u00e9al were mostly located at the urban edges, which agrees with previous work that has noted the trade-off between urban greenness and expansion around the periphery of cities around the world (Catal\u00e1n et al., 2008; Jin et al., 2019; Zhao et al., 2016). Despite the consistent loss of greenness occurring in the urban periphery, many areas on the edge of the urban landscape exhibited higher levels of greenness by the end of the analysis period than those deeper within the core. It is possible that recent urban developments have adopted a greater focus on integrating green spaces into their plans, like in the case of many Asian cities (Jin et al., 2019), as well as the metropolitan area of Vancouver (Jarvis et al., 2020). Although trends in vegetative greenness area able to describe urban patterns, research about land use transitions and their association with key socio-demographic variables would provide a fuller picture Canada\u2019s urban landscape and its recent evolution.  48 Chapter 4: Characterization of multi-decadal land use transitions across Canadian peri-urban areas 4.1 Introduction Characterizing urban land dynamics using a vegetative greenness lens is a straightforward yet informative approach. For example, agriculture and natural areas are for the most part dominated by various forms of vegetation, such as trees, herbs and grasses, whereas the intensity of urban use can be understood by the limitation of vegetative greenness. However, the purpose and benefits of vegetation vary depending on the land use. For example, natural areas that contain trees and\/or a variety of other vegetation may provide ecosystem services such as heat reduction, flood mitigation, and native habitat (Alberti, 2005), as well as enhance public wellbeing (van den Bosch and Sang, 2017). On the other hand, agriculture land use focuses primarily on the production of specific food or other resources, but has the potential to include other ecosystem services (Sanderson et al., 2013). Studying land use dynamics in particular, with a focus on land conversions in the urban periphery, provides a more comprehensive understanding of the current urban socio-ecological system and how it has changed. Although there are ample land use classifications available, current studies are limited to categorical schemes, short temporal lengths, and\/or few geographic regions (Reba and Seto, 2020). Since long-term and freely available satellite imagery like Landsat can provide information about historic land characteristics, a more nuanced land use classification can be developed to better describe the mixture of land use and its change in the peri-urban realm.  49 In the previous chapter I created a novel urban greenness score framework to relate an area\u2019s current greenness level with its multi-decadal change. In this chapter I quantify the intensity of urban, agriculture, and natural land use in the urban periphery, how they have changed since the mid-1980s, and how they relate to the current state of select socio-demographic variables. Part of the challenge in assessing recent land use changes is deciphering their relation to physical and socio-demographic factors, anticipating the cumulative regional and national impacts, and managing for sustainable urban development. However, the development of cities is not consistent through space or time. Across Canada, only a handful of cities have become metropolises (i.e. Toronto, Montr\u00e9al, and Vancouver) with several dispersed pockets of high-density built-up areas, while others have become singular monocentric or dispersed entities (Sweet, Bullivant and Kanaroglou 2017). Additionally, the consequence of variable urban developments in relation to socio-demographic distribution patterns is poorly understood. The spatial extent of peri-urban areas in particular is temporally dynamic and can substantially vary across regions as a result of several related factors, including socio-demographic, socio-economic, geography, history, and policy variables (Kasanko et al., 2006; Storper and Manville, 2006). Despite a global trend of urbanization and the need for effective and efficient management, spatial information about land use and its changes is limited. Conventional methods of urban mapping on the municipal or regional level using cadastral and other administrative data can be inconsistent, costly, and time-consuming if applied over large areas. A remote sensing approach provides a cost-effective, reliable, and feasible method to enhance national level land use inventories that include multi-decadal patterns (Patino and Duque, 2013). In particular, 30-m resolution Landsat satellite imagery has been continuously acquired since the mid-1980s, is freely 50 available, and at a suitable spatial resolution for national level urban mapping (Wulder et al., 2012; Zhu et al., 2019). Studies using remotely sensed data for characterizing urban land changes and their implications cover a wide range of topics, including urban greenness (Dou and Kuang, 2020; Lu et al., 2016; Wellmann et al., 2020b), built-up\/impervious surface (Melchiorri et al., 2018; Yang and He, 2017), and the urban environment\u2019s impact on human health and wellbeing (Kabisch et al., 2019; Rugel et al., 2017). However, the integration of standardized remote sensing products in the fields of urban planning and ecology are limited, particularly in supporting evidence-based policy solutions (Wellmann et al., 2020a). While current research using remote sensing technology to map urban land use is plentiful and innovative (e.g. Gholoobi and Kumar, 2015; Gopal et al., 2016; Hu et al., 2016; Huang et al., 2018; Yang et al., 2017), systematic and feasible methods suitable for cross-regional applications with long and high-frequency temporal periods, as well as fine spatial information, are lacking. In Canada, current nation-wide characterizations of urban land use are limited, covering only select years and\/or cities (Guindon et al., 2004; Zhang et al., 2010), or focused on select land uses, such as agriculture (Agriculture and Agri-Food Canada, 2015), or the built-up extent (Statistics Canada, 2016b). Part of the difficulty in mapping peri-urban land use is its complexity; a single land use may include several land covers that may vary by region and over time. The association between land use changes and population variables, such as sociodemographic factors, is also poorly understood, likely in part due to this complexity. Most remote sensing studies analyzing urban land dynamics to date have focused on physical land changes, such as built-up area, and very few have considered multi-decadal changes (Reba and Seto, 2020). Additionally, the standard method of characterizing spatiotemporal dynamics of land cover\/use involves categorical classification 51 schemes (e.g. Agriculture and Agri-Food Canada, 2015; European Environment Agency, 2016a; Homer et al., 2015), which restricts the ability to map mixed land use dynamically through time. In response to this, fractional land characteristics and cover information can be applied to identify physical differences between land uses and enhance classification. However, understanding land use requires a socio-demographic context for holistic urban planning and regional management (Long and Qu, 2018).  As urban areas are expected to continue expanding, national and regional accounting of long-term land use dynamics would provide greater insight into recent historical patterns and inform strategic planning. The objective of this chapter was to characterize multi-decadal dynamic land use transitions in peri-urban areas across Canada using Landsat satellite imagery and other available geospatial data. To accomplish this I: (i) created a dynamic land use classification scheme to quantify urban, agriculture, and natural land use for 33 years (1984-2016) across 18 major Canadian cities; (ii) characterized urban, agricultural, and natural land use transitions in peri-urban areas of select Canadian cities, and; (iii) explored the correlation between multi-decadal land use trends and the current state of select socio-demographic variables. This chapter applies established remote sensing techniques in an innovative application for an enhanced national characterization of peri-urban land use transitions. Through this approach, new insights on land use transitions are gained that may be useful to inform future planning, or for retrospectively investigating the impacts of existing or historic land use policies.  52 4.2 Methods The methodological approach of this analysis is outlined in three steps, including the definition of the dynamic land use classification scheme, calculation of dynamic peri-urban land use transitions, and the correlation of peri-urban land use trends with current socio-demographic states. 4.2.1 Definition of dynamic land use classification scheme Spectrally unmixed fractions, along with VLCE land cover data and agricultural mask conditions, were used to develop a dynamic land use classification scheme that derived fractional values for three classes of land use: Urban, Agriculture, and Natural. These classes were chosen as they are the key land uses in Canadian peri-urban areas (Statistics Canada, 2016). Table 8 provides functions using spectrally unmixed fractions to calculate each dynamic land use based on land cover and agricultural mask conditions. Land cover conditions determined which spectrally unmixed fraction function was applied that either maximized or minimized the intensity of each dynamic land use as appropriate. Similarly, the agricultural mask provided a recent snapshot of agriculture lands that assisted with differentiating between crops and naturally occurring herbaceous vegetation. Thus, the value of dynamic Urban land use for built-up urban areas is characterized by the lack of greenness. In contrast, the Urban value for treed land covers is calculated using a function of greenness and darkness (i.e. shadows) absence. Agriculture land use was calibrated using a function of greenness and high albedo, which represents the matrix of crops, bare soil, and anthropogenic infrastructures. Naturally herbaceous or treed land covers attained their Natural land use value based on a function of greenness and darkness. 53 Table 8. Dynamic land use classes and their function using spectrally unmixed fractions, based on land cover and agricultural mask conditions. Dynamic land use class Land cover & agricultural mask conditions Spectrally unmixed fractions function Urban Agricultural mask OR Herb 1  \u2212   \ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc52\ud835\udc52\ud835\udc52\ud835\udc52\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc52\ud835\udc52\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc3a  \u2212   \ud835\udc3b\ud835\udc3b\ud835\udc3b\ud835\udc3b\ud835\udc3b\ud835\udc3b\u210e \ud835\udc4e\ud835\udc4e\ud835\udc4e\ud835\udc4e\ud835\udc4e\ud835\udc4e\ud835\udc52\ud835\udc52\ud835\udc4e\ud835\udc4e\ud835\udc4e\ud835\udc4e Treed 1  \u2212   \ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc52\ud835\udc52\ud835\udc52\ud835\udc52\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc52\ud835\udc52\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc3a  \u2212   \ud835\udc37\ud835\udc37\ud835\udc4e\ud835\udc4e\ud835\udc3a\ud835\udc3a\ud835\udc37\ud835\udc37\ud835\udc3a\ud835\udc3a\ud835\udc52\ud835\udc52\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc3aOther 1  \u2212   \ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc52\ud835\udc52\ud835\udc52\ud835\udc52\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc52\ud835\udc52\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc3a Agriculture Herb \ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc52\ud835\udc52\ud835\udc52\ud835\udc52\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc52\ud835\udc52\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc3a Agricultural mask OR Treed OR Other \ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc52\ud835\udc52\ud835\udc52\ud835\udc52\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc52\ud835\udc52\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc3a  +    \ud835\udc3b\ud835\udc3b\ud835\udc3b\ud835\udc3b\ud835\udc3b\ud835\udc3b\u210e \ud835\udc4e\ud835\udc4e\ud835\udc4e\ud835\udc4e\ud835\udc4e\ud835\udc4e\ud835\udc52\ud835\udc52\ud835\udc4e\ud835\udc4e\ud835\udc4e\ud835\udc4e Natural Herb OR Treed \ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc52\ud835\udc52\ud835\udc52\ud835\udc52\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc52\ud835\udc52\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc3a  +    \ud835\udc37\ud835\udc37\ud835\udc4e\ud835\udc4e\ud835\udc3a\ud835\udc3a\ud835\udc37\ud835\udc37\ud835\udc3a\ud835\udc3a\ud835\udc52\ud835\udc52\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc3aAgricultural mask OR Other \ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc52\ud835\udc52\ud835\udc52\ud835\udc52\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc52\ud835\udc52\ud835\udc3a\ud835\udc3a\ud835\udc3a\ud835\udc3a 4.2.2 Calculation of dynamic peri-urban land use transitions I calculated multi-decadal land use transitions by summarizing significant (p < 0.05) changes in dynamic land use classes by peri-urban DAs of each selected urban area. Peri-urban DAs were defined as having at least one third of pixels with a significant positive Urban trend and not being dominant in DA-average Urban, Agriculture, or Natural land use throughout the 33-year time period. As follows, annual dynamic fractions of Urban, Agriculture, and Natural land uses were summarized by mean on the DA level and their 33-year land use trends were calculated using the Theil-Sen estimator (TS; Sen, 1968; Theil, 1992) and Mann-Kendall significance test (Mann, 1945) on the pixel and DA levels. Land use transitions were derived using the difference in corresponding significant positive and negative land use trends; transitions from Natural to Urban, Agriculture to Urban, and Natural to Agriculture were investigated in particular given their association with increased anthropogenic activity. Given that each peri-urban DA varied in size, I summarized the area (and its percentage) of land use trends and transitions on the pixel level, as 54 well as the percentage of DAs and their corresponding rates of change (scaled by geometric area). As multiple land use transitions could be present within a given pixel, transitions were summarized in terms of their total coverage, as well as their exclusive and overlapping coverages.  4.2.3 Exploratory correlative analysis with current socio-demographic states In an exploratory analysis I assessed the correlation between peri-urban land use trends and 2016 socio-demographic census variables using Spearman\u2019s correlation analysis (due to non-normal distributions). The resulting significant correlations (p-value < 0.05) describe the correlation between urbanization trends (i.e. increase in Urban and corresponding decrease in Agriculture and\/or Natural use) and final year (2016) socio-demographic variable states. For example, a positive correlation between the Urban land use trend and the 2016 population density would indicate that a peri-urban area with a currently high population density is associated with increasing urban land use over the preceding years. Software used for data processing includes ENVI 5.5, ArcMap 10.6, FETEX 2.0 (Ruiz et al., 2011), and R (ver. 3.5.2; R Core Team, 2018) for statistical analysis. Within R, the wq package was used to calculate Theil-Sen estimators and conduct the Mann-Kendall test for land use trends (Jassby and Cloern, 2017). 55 4.3 Results 4.3.1 Dynamic land use classification Examples of the annual dynamic land use classification results are shown in Figure 10 for representative urban areas of each geographic stratum. In this figure, the tri-colour representation shows distinct urban cores that shift into a mosaic of urban, agriculture, and natural land uses. At the edge of these cities, natural land uses and agriculture dominate, leaving a patchwork of smaller urban clusters. The varying land use composition of peri-urban areas across Canada demonstrates differences in the dominance of dynamic land use classes. Urban land use in the west-center (e.g. Calgary) and east-center strata (e.g. Toronto) is the most distinct across the urban-rural gradient, whereas smaller cities (e.g. Halifax in the east stratum) exhibit less pronounced urban cores and clusters. Urban expansion is evident across Canada as the representative cities\u2019 peripheral regions move towards higher Urban land use values from the 1984 to 2016 maps.56 Figure 10. Dynamic land use classification for 1984 (left panels) and 2016 (right panels) of parts of Victoria (west stratum), Calgary (west-center stratum), Toronto (east-center stratum), and Halifax (east stratum).57 4.3.2 Canadian peri-urban land use trends The results of land use trends for each peri-urban area, as well as geographic stratum are summarized in Table 9. Across Canada, 3,194 km2 (47%) of peri-urban areas experienced a positive Urban trend. This amounted to 35% of peri-urban DAs that experienced urbanization at a median rate of 1.3% per annum per km2. The west stratum exhibited one of the largest increases of urban land use, covering about half of its peri-urban areas (49%; 291 km2). 64% DAs in this stratum exhibited an increase in urban land use with a relatively high median rate of change (1.4% per annum) that was mainly driven by Vancouver (1.7% per km2). The west coast had the fewest DAs decrease in urban land use (8%) with the lowest median annual rate of change (-0.7% per annum per km2), but with the greatest area coverage (27%). Peri-urban areas of the Prairies (i.e. west-center stratum) also experienced a large increase of urban land use, which occurred on about half of its area (49%; 1,297 km2). The west-center stratum exhibited the greatest median rate of urbanization nationally (1.6% per annum per km2) despite relatively fewer DA cases than the west coast (42%). The DA level median rate of urbanization varied the most across peri-urban areas of the west-center stratum (< 0.1\u20132.8% per annum per km2), with Calgary exhibiting the greatest median rate of change and Regina exhibiting the lowest. All Prairie peri-urban areas, but especially Winnipeg, showed the most DA cases of urban land use loss (28% and 54%, respectively). Ontarian and Qu\u00e9b\u00e9cois peri-urban areas (i.e. east-center stratum) observed the lowest proportion of area that urbanized nationally (44%; 1,059 km2), which was driven by Qu\u00e9bec City (29%). Although urbanization was observed for only 25% of peri-urban DAs in this stratum, its annual rate of change was relatively high (1.5% per annum per km2). However, like in the west-58 center stratum, median annual rates of positive Urban trends varied greatly (< 0.1\u20132.4% per annum per km2). Additionally, like in other strata, the percentage of peri-urban DAs that increased in urban land use, and their median annual rate of change, were not related. For example, London, Greater Sudbury, and Windsor exhibited relatively large percentages of peri-urban DAs with a positive Urban trend but varied in their median annual rates of change (London = < 0.1% per annum per km2; Greater Sudbury = 0.4% per annum per km2, and; Windsor = 1.2% per annum per km2). Additionally, despite no cases of Urban decrease in London\u2019s peri-urban DAs, the east-center stratum exhibited the greatest rate of change for negative Urban trends (-2.6% per annum per km2).  In contrast, urbanization was generally homogenous across peri-urban areas of the east stratum. The coverage of increased urban land use was moderate apart from Fredericton, which was the national leader (Fredericton = 54%, and; 42\u201346% for others). Most Eastern peri-urban DAs urbanized (71\u201379%), except for in Halifax (27%). However, median rates of change for positive Urban trends were relatively low, ranging from 0.1\u20130.7% per annum per km2. Again, apart from Halifax, negative trends in urban land use were uncommon on the DA level (Halifax = 21%, and; 3\u20136% for other peri-urban areas) and with relatively low median annual rates of change (Halifax =   -1.4% per annum per km2, and; -0.8\u2013 -0.3% per annum per km2 for other peri-urban areas).59 Table 9. Summary of significant (p < 0.05) positive and negative urban land use trends, including the geometric area (km2 and %), as well as the percentage of DAs (%) and their median annual rate of change scaled by geometric area (% per annum per km2), for peri-urban areas of each urban area, stratum, and for all peri-urban DAs. Abbreviations used in table: AB \u2013 Alberta; BC \u2013 British Columbia; MN \u2013 Manitoba; NB \u2013 New Brunswick; NL \u2013 Newfoundland and Labrador; NS \u2013 Nova Scotia; ON \u2013 Ontario; QC \u2013 Qu\u00e9bec; SK \u2013 Saskatchewan. Stratum Province Urban area Peri-urban DA count Peri-urban area (km2) Positive Urban trend Negative Urban trend Area (km2) Area (%) DA (%) Rate of change (% per annum per km2) Area (km2) Area (%) DA (%) Rate of change (% per annum per km2) West BC Victoria 160 116 58 50% 75% 0.7 33 28% 9% -0.5 Vancouver 961 471 233 49% 63% 1.7 125 27% 7% -0.7 1,121 588 291 49% 64% 1.4 158 27% 8% -0.7 West-center AB Edmonton 641 1,002 453 45% 44% 1.4 271 27% 24% -2.4 Calgary 392 340 177 52% 58% 2.8 77 23% 14% -1.8 SK Saskatoon 131 102 37 36% 17% 1.5 40 39% 39% -2.3 Regina 29 979 523 53% 45% < 0.1 191 20% 38% -0.8 MN Winnipeg 261 241 109 45% 22% 0.6 68 28% 54% -1.0 1,454 2,665 1,297 49% 42% 1.6 647 24% 28% -1.7 East-center ON Greater Sudbury 86 44 17 39% 64% 0.4 14 32% 20% -0.2 Windsor 170 106 46 43% 56% 1.2 28 26% 18% -1.9 London 31 186 89 48% 81% < 0.1 37 20% 0% 0.0 Toronto 3,109 1,038 475 46% 12% 1.5 250 24% 27% -3.9 Ottawa 751 548 247 45% 46% 2.4 159 29% 16% -1.7 QC Montr\u00e9al 1,016 302 127 42% 37% 1.9 78 26% 17% -1.2 Qu\u00e9bec City 472 197 58 29% 32% 0.9 73 37% 23% -0.7 5,635 2,422 1,059 44% 25% 1.5 638 26% 23% -2.6 East QC Sherbrooke 97 188 87 46% 79% 0.3 50 27% 3% -0.4 NB Fredericton 66 384 207 54% 71% 0.1 76 20% 6% -0.8 NS Halifax 317 146 62 42% 27% 0.7 44 30% 21% -1.4 NL St. John\u2019s 117 424 192 45% 74% 0.2 113 27% 6% -0.3 597 1,142 547 48% 50% 0.3 283 25% 14% -1.1 All 8,807 6,817 3,194 47% 35% 1.3 1,726 25% 21% -2.1 60 4.3.3 Land use transitions of Canadian peri-urban areas Figure 11 shows the dominant (i.e. largest difference between annual rates of change) peri-urban land use transitions for portions of Victoria, Calgary, Toronto, and Halifax as examples of each geographic stratum. DA level dominant land use transitions and corresponding area-scaled annual rates of change varied by stratum. The west-center and east-center peri-urban areas observed greater DA level rates of change for all transitions, but particularly for the urbanization of natural use (Figure 11B & C), than coastal urban areas (i.e. west and east strata; Figure 11A & D). Across Canada, the greatest rates of change predominantly corresponded with smaller DAs (i.e. with higher population densities). Although patches of multiple DAs with land use transitions were identified (e.g. west and east strata; Figure 11A & D), many were also unevenly distributed across the peri-urban gradient (e.g. east-center; Figure 11C).61 Figure 11. Maps of dominant DA level land use transitions for select peri-urban areas, each representing a geographic stratum (A = Victoria (west); B = Calgary (west-center); C = Toronto (east-center), and; D = Halifax (east)). Colour   corresponds with the DA level rate of change, scaled by area. 62 Peri-urban land use transitions were investigated using the percentage of pixel-based area with corresponding transitions (total, as well as exclusive and overlapping) across geographic strata as well as nationally (Figure 12). In total, 32% (2,183 km2) of Canadian peri-urban area transitioned from an agriculture to urban use, while 29% (1,988 km2) urbanized from a natural use. More specifically, 8% (574 km2) transitioned exclusively from an agriculture to urban use, 14% (922 km2) exclusively from natural to urban use, and 19% (1,262 km2) exhibiting both transitions (with a small amount of overlap remaining). Much less peri-urban area observed a natural to agriculture land use transition across Canada (4%; 301 km2) and geographic strata (3\u20137%; 26\u2013108 km2). Although very similar, the greatest relative area with land use transitions occurred in the west stratum (71%; 418 km2), followed by a tie between the east (66%; 758 km2) and west-center strata (66%; 1,757 km2), and the east-center stratum with the least (63%; 1,526 km2). For the west stratum, the urbanization of natural and agriculture land uses both covered relatively large areas (Natural to Urban = 39% in total and 9% exclusively; Agriculture to Urban = 36% in total and 9% exclusively), with a considerable amount of area that observed both transition types (27% of total area). The east coast observed a similar pattern, but with lower proportions (Natural to Urban = 35% in total and 12% exclusively; Agriculture to Urban = 25% in total and 8% exclusively) and less area that observed both transitions (17% of total area). However, the east stratum observed the most areas convert from a natural to agriculture use (7%; 2% exclusive), as well as to both an agriculture and urban use (17%). Urbanization in the Prairies (i.e. west-center stratum) was also substantial, with much more area that urbanized from an agriculture (41%) than natural (22%) use. The proportion of west-center peri-urban area that observed both a natural and agriculture to urban transition was comparable to other strata (19%), while the exclusive transition 63 of Agriculture to Urban covered ten times more area than exclusively Natural to Urban (22% and 2%, respectively). The east-center stratum observed a similar amount of area urbanize from natural use (32%; 13% exclusive) and agriculture use (25%; 9% exclusive), with 17% of its total peri-area observing both transitions. Figure 12. Percentage of pixel-based area for each land use transition, as well as overlaps, grouped by geographic strata as well as for all peri-urban DAs. Total area (km2) for each stratum and all peri-urban DAs provided to the left of bar graph. 64 Distributions of area with each land use transition greatly varied across the country, as well as within strata (Figure 13). Leaders in relative coverage of peri-urban land use transitions were Victoria and Fredericton (west and east strata, respectively; 52% for each), Calgary (west-center; 51%), as well as Vancouver and Regina (west and west-center strata, respectively; 49% for each). Additionally, Fredericton observed the largest proportion of area with a Natural to Urban transition (46%; 19% exclusive), followed closely by Calgary (42%; 3% exclusive), Victoria (41%; 9% exclusive), and Vancouver (38%; 9% exclusive). Regina (west-center stratum) exhibited the largest amount of relative area that transitioned from an agriculture to urban use (48%; 46% exclusive) and very little of the other transition types (< 1\u20133%). Calgary also observed a large amount of area that transitioned from an agriculture to urban use (44%), however much of it was shared with the Natural to Urban transition (only 6% exclusively Agriculture to Urban). The largest amounts of peri-area that transitioned from both a natural and agriculture to urban use were noted in Calgary (38%), Edmonton (30%), Victoria (27%), and Vancouver (27%). London (east-center stratum) experienced the greatest amount of Natural to Agriculture transition (13%; 9% exclusive), followed by St. John\u2019s (east stratum; 9%; 2% exclusive) and Victoria (west stratum; 9%; 4% exclusive). 65 Figure 13. Percentage of pixel-based area for each land use transition, as well as overlaps, shown for each peri-urban area, grouped by geographic stratum (west, west-center, east-center, and east). Total areas (km2) provided to the left of bar graph.Table 10 provides a summary of DA level rates of change for each land use transition (using median), as well as their total pixel-based coverage, for each peri-urban area, geographic strata, and at the national level. Patterns of coverage and rates of change for land use transitions across Canada followed corresponding results of Urban trends in Section 4.3.2. Despite similar coverage, 66 transitions from natural, rather than agriculture, to urban use occurred at overall greater rate of change on the national level (3% and 2% per annum per km2, respectively). Across peri-urban areas, no relationship was observed between the relative area of land use transitions and their rate of change. The highest median rates of change for transitions from natural or agriculture to urban use occurred in the Prairies (i.e. west-center stratum; 3.9% and 2.7% per annum per km2, respectively). The same peri-urban areas also experienced the largest ranges in their rates of transition; Calgary observed the largest rates of change for both urban-related transitions nationally (Natural to Urban = 6.8% per annum per km2, and; Agriculture to Urban = 5.5% per annum per km2), while Regina had the lowest for both (0.1% per annum per km2). In the east-center stratum, conversions of natural use occurred at a greater rate than conversions of agriculture (3.5% and 1.5% per annum per km2, respectively), and were led by Ottawa and Montr\u00e9al (5.8% and 4.2% per annum per km2). This stratum also exhibited the greatest rate or change for natural to agriculture transitions (2.1% per annum per km2), which was also driven by Ottawa and Montr\u00e9al (3.1% and 3.7% per annum per km2). On the west coast, median rates of change were moderately high for both natural and agriculture land conversions in this stratum (2.5% and 2.7% per annum per km2, respectively), and were mainly driven by Vancouver (Natural to Urban = 2.9% per annum per km2, and; Agriculture to Urban = 3.2% per annum per km2). Peri-urban areas of the east coast observed the lowest median rates of change (Natural to Urban = 0.3% per annum per km2, and; Agriculture to Urban = 0.8% per annum per km2), while Halifax observed the highest rates of urban-related change in its stratum (Natural to Urban = 0.7% per annum per km2, and; Agriculture to Urban = 2.0% per annum per km2) despite less area converted (23% and 28%, respectively). 67 Table 10. Summary of land use transitions, including the pixel-based geometric area (km2 and %), as well as the percentage of DAs (%) and their median annual rate of change scaled by geometric area (% per annum per km2), for each peri-urban area, stratum, and for all peri-urban DAs. Abbreviations used in table: AB \u2013 Alberta; BC \u2013 British Columbia; MN \u2013 Manitoba; NB \u2013 New Brunswick; NL \u2013 Newfoundland and Labrador; NS \u2013 Nova Scotia; ON \u2013 Ontario; QC \u2013 Qu\u00e9bec; SK \u2013 Saskatchewan. Stratum Province Peri-urbanarea Total DA count Total area (km2) Natural to Urban Agriculture to Urban Natural to AgricultureArea (km2) Area (%) DAs (%)  Rate of change  (% per annum per km2) Area (km2) Area (%) DAs (%) Rate of change (% per annum per km2) Area (km2) Area (%) DAs (%) Rate of change (% per annum per km2) West BC Victoria 160 116 48 41% 40% 1.3 40 34% 42% 1.6 10 9% 18% 0.2 Vancouver 961 471 179 38% 39% 2.9 171 36% 40% 3.2 15 3% 21% 0.6 1,121 588 227 39% 52% 2.5 211 36% 54% 2.7 26 4% 27% 0.5 West-center AB Edmonton 641 1,002 349 35% 35% 2.6 376 37% 29% 1.7 61 6% 35% 0.9 Calgary 392 340 142 42% 32% 6.8 151 44% 29% 5.5 14 4% 35% 1.9 SK Saskatoon 131 102 24 23% 33% 3.9 22 21% 24% 2.2 4 4% 43% 1.3 Regina 29 979 27 3% 8% 0.1 469 48% 58% 0.2 3 <1% 33% 0.2 MN Winnipeg 261 241 52 22% 31% 2.0 73 30% 27% 0.4 6 2% 42% 0.8 1,454 2,665 594 22% 39% 3.9 1,091 41% 34% 2.7 87 3% 45% 1.4 East-center ON Greater Sudbury 86 44 13 29% 48% 0.9 9 21% 40% 0.8 1 2% 12% 0.3 Windsor 170 106 37 35% 56% 2.7 21 20% 20% 1.5 5 5% 24% 1.3 London 31 186 49 26% 50% 0.1 20 11% 17% 0.2 24 13% 33% 0.2 Toronto 3,109 1,038 363 35% 37% 3.5 304 29% 17% 1.6 34 3% 45% 2.2 Ottawa 751 548 167 31% 56% 5.8 153 28% 37% 2.4 24 4% 19% 3.1 QC Montr\u00e9al 1,016 302 102 34% 53% 4.2 57 19% 24% 1.4 11 4% 23% 3.7 Qu\u00e9bec City 472 197 42 21% 56% 2.5 31 16% 23% 0.9 9 5% 22% 1.4 5,635 2,422 773 26% 3% 3.5 595 25% 12% 1.5 108 4% 17% 2.1 East QC Sherbrooke 97 188 40 22% 36% 0.6 62 33% 52% 0.4 5 3% 12% 0.1 NB Fredericton 66 384 178 46% 53% 0.1 96 25% 32% 0.2 28 7% 15% < 0.1 NS Halifax 317 146 34 23% 33% 0.7 40 28% 52% 2.0 7 5% 16% 0.3 NL St. John\u2019s 117 424 142 33% 41% 0.3 88 21% 44% 0.6 40 9% 15% 0.1 597 1,142 394 35% 27% 0.3 286 25% 32% 0.8 80 7% 10% 0.1 All 8,807 6,817 1,988 29% 32% 3.0 2,183 32% 22% 2.0 301 4% 23% 1.3 68 4.3.4 Correlations of peri-urban land use trends with current socio-demographic states Figure 14 demonstrates correlations between peri-urban land use trends and the 2016 status of select socio-demographic variables. Across Canada, the strongest correlation observed was between the loss of natural use and current population density (\u03c1 = -0.57, p < 0.001), with a much weaker positive correlation between multi-decadal agriculture use and single-detached housing (\u03c1 = 0.14, p-values < 0.001, respectively). Current income levels had the strongest (and only significant) correlation with national level urbanization (\u03c1 = 0.12, p-values < 0.001, respectively). The type and strength of correlations between sociodemographic variables and dynamic peri-urban land use trends varied across geographic strata.  Figure 14. Correlograms of 2016 socio-demographic variables (population density, percentage of single-detached housing, percentage of recent construction (< 5 years), and median total income) and the 33-year land use trends (DA average and area-scaled) for each geographic stratum and all peri-urban DAs. Colour and values represent the strength and direction of correlation using the Spearman\u2019s correlation coefficient (\u03c1), with the level of significance denoted as * p < 0.05, ** p < 0.01, and *** p < 0.001. 69 On the west coast, which experienced the greatest amount of urbanization in peri-urban areas, observed a strong and positive correlation between the Urban trend and 2016 population density (\u03c1 = 0.65, p < 0.001). Urbanization was also negatively correlated with the current proportion of single-detached housing as well as income level (\u03c1 = -0.21 and -0.35, p < 0.001, respectively). In contrast, both the Agriculture and Natural land use trends showed negative correlations with current population density (\u03c1 = -0.68 and -0.56, p-values < 0.001, respectively). In this geographic stratum, the peri-urban Agriculture trend was most strongly, positively correlated with the current income level and the amount of recent construction (\u03c1 = 0.29 and 0.21, p-values < 0.001, respectively), whereas the peri-urban Natural trend was most strongly, positively correlated with income and single-detached housing (\u03c1 = 0.28 and 0.22, p-values < 0.001, respectively).  Moving eastward, correlations between current socio-demographic states and land use trends for peri-urban areas in the Prairies (i.e. west-center stratum) vary from those on the west coast. Urbanization in west-center peri-urban areas was only correlated with population density (\u03c1 = 0.22, p < 0.001), although with a much weaker association than in the west stratum. The Natural trend of Prairie peri-urban areas had a strong, negative correlation with current population density (\u03c1 = -0.55, p < 0.001) and a much weaker negative correlation with single-detached housing (\u03c1 = -0.14, p < 0.01). The loss of agriculture in the west-center stratum was moderately correlated with high levels of current income and population density (\u03c1 = -0.21 and -0.18, p-values < 0.001, respectively). Unlike in the west and west-center strata, multi-decadal urbanization in the provinces of Ontario and Qu\u00e9bec (i.e. east-center stratum) was not strongly correlated with current population density (\u03c1 = -0.08, p < 0.001). Instead, the Urban trend was most strongly correlated with income 70 level and the amount of recent construction in 2016 (\u03c1 = 0.28 and 0.15, p-values < 0.01, respectively). However, like peri-urban areas in the west coast and Prairies, the west-center stratum exhibited a strong negative correlation between the Natural trend and current population density (\u03c1 = -0.62, p < 0.001). Much weaker correlations were also noted between the loss of natural use and the low levels of recent construction and income (\u03c1 = -0.17 and -0.13, p-values < 0.001, respectively). The Agriculture trend of the east-center stratum was only mildly, positively correlated with single-detached housing (\u03c1 = 0.16, p < 0.001).  On the east coast, most correlations between current socio-demographic states and peri-urban land use trends were not significant and\/or very weak. The strongest correlation in this stratum was between the loss of agriculture use and current levels of population density (\u03c1 = -0.64, p < 0.001). Moderate, positive correlations were also observed between the Agriculture trend and recent construction as well as income level in 2016 (\u03c1 = 0.37 and 0.27, p-values < 0.05, respectively). 4.4 Discussion 4.4.1 Analysis approach The dynamic land use classification developed in this analysis, which utilizes spectrally unmixed fractions and land cover data derived from Landsat satellite imagery, provides a mechanism for understanding nuanced changes that may not be captured by categorical classifications. I calculated land use trends (and transitions) for over 33 years and about 7,000 km2 of peri-urban area across Canada using the developed dynamic land use classification scheme. Although the land use changes were aggregated for census dissemination areas (DA) to expose 71 neighborhood level spatial and temporal trends as well as socio-demographic correlations, the 30-m dynamic land use classification (Figure 10) may provide highly detailed spatiotemporal information (e.g. area coverage). Using the dynamic land use classification, I was able to identify subtle as well as multiple urbanization-related land use transitions for a given area. Gopal et al. (2016) provided a similar land use classification using fuzzy sets whereby membership to a class is a gradual transition from non- to full membership. In this study I incorporated a similar concept using conditions based on relationships between spectrally unmixed fractions to assign land use \u201cmembership\u201d, but also included a multidirectional approach to map several land use changes related to increased anthropogenic activities (Pandey et al., 2018). Because each pixel contains a fractional value of each land use class and may exhibit multiple land use transitions, the dynamic land use classification scheme presented in this chapter can produce highly detailed multi-temporal information about mixed land use for improved urban planning and management frameworks. While the dynamic land use classification developed in this study has the potential to be a robust multi-temporal approach for historic and continued monitoring of national urban land dynamics, a few limitations should be noted. Higher Agriculture values in some urban cores is likely due to the greater spectrally unmixed high albedo fraction, which can be influenced by building materials and other surfaces. The use of a recent agricultural mask for the differentiation of crops and natural vegetation may also contribute to less accurate classification of agricultural areas not captured in the mask or non-treed natural areas incorrectly included in the mask. For example, an area that was correctly identified with an agricultural land use in 2011, but subsequently transitioned into an urban land use with grassy vegetation, may have overestimated values of 72 Agriculture as per the dynamic land use classification. Another consequence may be that some areas with a land use transition from Natural to Agriculture may instead have increased in their herbaceous vegetation cover, for example through the addition of residential gardens and\/or parks. However, since this land use transition was least represented across all strata and nationally, potential error from overestimated or misclassified agriculture land use should be minimal. Providing additional spatial information, such as object-based metrics like in Goodin, Anibas, and Bezymennyi (2015) or Zhang et al. (2018), throughout the time series or for specified time steps should be tested for potential improvement of the dynamic peri-urban land use classification. 4.4.2 Multi-decadal patterns of Canadian peri-urban land use Across Canada, higher rates of land use transitions typically coincided within smaller DAs, which may be a magnification effect from area-scaling. However, as larger DAs have lower population densities and thus mostly include non-urban elements, it is likely that any land use transitions that occur in these areas are not dominant enough to be classified as urbanization. Many small DAs, such as those surrounding Toronto, also did not exhibit any land use transitions. As these small DAs with stable land use are densely populated, it is likely they are saturated with an urban use and may only experience additional urbanization through densification. Although pockets of urban expansion on natural and agriculture areas were identified in each geographic stratum, the many unevenly distributed DAs that experienced a land use transition may not be associated with strategic urban development plans. Overall the results of this chapter are in line with information provided in a recent report by Statistics Canada (2016) about major urban area land dynamics between 1970 and 2011, which indicates extensive outward growth across all cities since the 1970s. While large CMAs (population 73 > 1 million people) experienced substantial rates of urbanization along their urban gradient,Statistics Canada (2016) identified mid-sized and smaller CMAs as exhibiting considerable amounts of urban expansion in particular (e.g. Halifax = +319 km2 and Qu\u00e9bec City = +292 km2). Similarly, moderate and small sized urban areas in the same Statistics Canada study showed large expansions of urban land use, such as Regina (+523 km2), Edmonton (+452 km2), and Ottawa (+247 km2). Similar results were also noted in a global analysis between 1970 and 2010 by G\u00fcneralp et al. (2020), where small-medium sized urban areas (population of less than 2 million people) led the trend in urban expansion. While there are some similarities in land use transitions between our results and information provided by Statistics Canada (2016), varying spatial extents make comparisons complex. For example, Statistics Canada identified Census Metropolitan Area-Ecosystems (i.e. extended CMA that includes environmental geography) of both the Prairies and southern Ontario to exhibit some of the largest amounts of urban expansion on agriculture, as well as the greatest rates of change. However, this was only the case for the Prairies (i.e. west-center) in Chapter 4. Results for Edmonton and Calgary correspond with Statistics Canada (2016), which indicated substantial urban expansion on agricultural land (Statistics Canada = +402 km2 and 214 km2, respectively, and; Chapter 4 = +376 km2 and +151 km2, respectively). In contrast, other results of this chapter indicate that peri-urban areas on the west coast experienced substantial urbanization of agriculture, with regards to area (+211 km2) and the rate of change (2.7% per annum per km2). Montr\u00e9al and Vancouver experienced some of the most conversions of natural and semi-natural land (e.g. forested or pastoral land) in the country in both studies (Statistics Canada (2016) = +462km2 and +296 km2, respectively, and; Chapter 4 = +102 km2 and +179 km2, respectively). 74 Like other cities in North America (mainly Eastern United States) and Oceania, as identified by G\u00fcneralp et al. (2020), many Canadian peri-urban areas of this study also experienced more natural than agriculture area transition into an urban use. However, more peri-urban area on the national level urbanized from an agriculture than natural use (32% and 29%, respectively), mainly due to the domination of the Agriculture to Urban transition in Regina (west-center stratum). This trend corresponds more so with cities in Europe, China, and Southeast Asia, where agriculture\u2013urban land use transitions are more common (G\u00fcneralp et al., 2020). Additionally, considering DA-level rates of land use transitions identifies conversions of natural to urban use as more drastic than conversions of agriculture. Depending on the scale of analysis, the summary of multi-decadal Canadian land use transitions may or may not follow the global pattern of primary conversion of agriculture (60%) and a secondary conversion of natural areas (33%; G\u00fcneralp et al., 2020).  4.4.3 Links between peri-urban land use trends and current socio-demographics Other studies analyzing relationships between land use dynamics and socio-demographic variables predominantly focused on the entire urban area rather than specific to the urban periphery. However, some inferences can be drawn about how socio-demographic factors relate to long-term land use changes. For example, Fertner, J\u00f8rgensen, Nielsen, and Nilsson (2016) noted that economic and population growth were consistently important factors in relation to national and regional urbanization (respectively) for select cities in Europe and western United States, however the relationship was consistently weaker further away from the urban core. Similarly in China (Shenzhen and Shanghai), both population and economic levels were highly correlated with urban land cover changes, but with varying associations of grouping variables through time (Tian et al., 2014; Yin et al., 2011).  75 Chapter 4 found that current population density was the most strongly correlated socio-demographic factor across all land use trends nationally, but most consistently with agriculture and\/or natural loss. However, relationships between land use trends and population density, as well as to a lesser extent income level and the amounts of single-detached housing and recent residential construction, varied across strata. Weaker correlations between urbanization trends and population density have also been noted, particularly for small-medium urban areas (population of less than 2 million people; 15 of the 18 urban areas studied in thesis), which are global leaders in urban expansion despite decreases in population density (G\u00fcneralp et al., 2020). In support, Statistics Canada (2016) has identified a drop in Canadian urban population densities (~ 65%; from 3,460 persons\/km2 to close to 2,250 persons\/km2) between 1971 and 2011 despite continuous population growth. The results of this chapter in conjunction with previous literature may point towards an increase in peri-urban lifestyles across Canada as city dwellers move to continuously expanding urban peripheries in search of more affordable and\/or larger homes (Ottensmann, 1977). Regional differences in peri-urban land use trends and their correlations with socio-demographic factors may also be attributed to various stages along historic socio-economic trajectories. For example, Toronto and other older industrialized cities of Central Canada began shifting industrial and commercial activities to the urban periphery as early as the mid-1970s (Gertler, 1995), while urbanization in the west materialized as an increase in the number of cities rather than the expansion of existing urban hubs (Abbott, 2008). The historic dependence on fluctuating natural resource sectors that fuel regional growth may have contributed to the variable current physical and socio-demographic structure of Canada\u2019s urban areas (Broadway, 2013). Coupled with more recent local and regional policies, more in depth investigations of historic urban land and social dynamics require place-based contextualization. 76 Chapter 5: Conclusion 5.1 Overview addressing main research goals The overall objective of this thesis was to characterize multi-decadal vegetative greenness and land use across major Canadian urban areas from 1984 to 2016 using satellite remote sensing. To do so, I first developed a spectrally based urban greenness score metric from Landsat satellite imagery and applied it to select urban areas across Canada to investigate recent trends in vegetative greenness and relate it to its current greenness state. Secondly, I assessed multi-decadal land use transitions and their socio-demographic relationships in Canadian peri-urban areas using spectrally unmixed Landsat satellite fractions of darkness, high albedo, and vegetative greenness, as well as land cover and recent census data. Chapter 3 describes the development of a satellite time series based urban greenness score framework using the total trend of and final year vegetative greenness fraction derived from 33 years of spectrally unmixed, annual, gap-free Landsat image composites. Recent historic greenness trends in relation to current greenness levels via the urban greenness score were summarized on the national and regional levels, as well as per population density group, to census dissemination areas (DAs) of 18 major Canadian urban areas. My findings indicate that over the past 30 years vegetative greenness of major Canadian urban areas has generally decreased alongside continued urbanization, resulting in moderate to low levels of greenness. However, regional and socio-demographic differences were apparent, such as greenness gains greenness in Prairie or lower density urban areas. In Chapter 4, I describe the derivation and application of a dynamic land use classification using spectrally unmixed fractions of vegetative greenness, high albedo, and darkness fractions 77 from Landsat satellite imagery, as well as available land cover data. Using the dynamic land use classification, I quantified the occurrence and rates of change for urban, agriculture, and natural land use on the DA and pixel levels for 18 major Canadian peri-urban areas across four geographic regions and over 33 years. Across Canada about 3,000 km2 (47%) of peri-urban area urbanized, with comparable coverage but at varying rates of change across geographic strata. Similar amounts of area converted from a natural or agriculture use (~2,000 km2), but transitions from a natural to urban use were typically more dominant on the DA level (i.e. had greater rates of change). Peri-urban areas of the west and west-center strata generally exhibited the greatest rates and largest area of natural and\/or agriculture conversions. An exploratory correlative analysis between multi-decadal peri-urban land use trends and select recent socio-demographic states highlighted great geographic variability, likely stemming from local policy and historic socio-economic trajectories. In addressing my research purpose, I determined that vegetative greenness and other fractional characteristics from Landsat satellite imagery can be used to better understand multi-decadal land use dynamics. The urban greenness score developed in Chapter 3 identified long-term changes in urban greenness relative to its current state on the neighborhood level, which varied across geographic strata. Using Landsat-based darkness, high albedo, and vegetative greenness fractions, as well as auxiliary land cover and census data, Chapter 4 shows the quantification of fractional land use, its multi-decadal transitions, and its correlations with recent socio-demographic states for 18 Canadian peri-urban areas. My results provide a better understanding of land use transitions in peri-urban areas across Canada, as well as enable future work to build upon streamlining methods presented in this thesis to enhance national and regional reporting on land dynamics. 78 5.2 Significance of research and key findings This thesis offers a number of new insights and innovations through the developed approaches and data products presented. The utilization of the open Landsat satellite archive in both chapters provides a glimpse into long-term patterns of urbanization and promotes development of data products for general utility both within and outside of the remote sensing community. The unique combination of fine spatial detail and high temporal resolution of Landsat imagery enables multi-year assessments of individual urban areas that can be conducted systematically, transparently, efficiently, and at a relatively low cost for studying urban land dynamics at multiple management scales for long-term analyses.  I believe the development of the urban greenness score framework in this thesis is one of the preliminary examples of both applying spectral unmixing across extremely large spatial scales, as well as exploiting the long-term archival data made available by the Landsat program to extract endmembers\u2019 fractions annually for multi-decadal time series. Similarly, the dynamics land use classification scheme, generated using sub-pixel Landsat information, provides a primary example of mapping fractional land use intensity and its change over multiple decades. While urban studies using Landsat imagery are not uncommon, the condensing of high dimensional spectral data in a simple and interpretable greenness score, or employed dynamically to assess fractional land use and its changes, shows promise for robust and highly detailed analyses of regional, national, and potentially global land dynamics. Through their application across a range of urban environments in Canada, the urban greenness score and dynamic land use classification scheme presented in this thesis can help inform future applications globally. The methods presented take advantage of the open and long-term Landsat archive, as well as multiple spatial scales, including sub-pixel 79 unmixing techniques, pixel level data products, urban greenness and land use results aggregated for management units, and summarized results on regional and national scales. The results in Chapter 3 show major trends of urban greenness over 30 years, particularly greater increases in the Prairies than in Ontario, and greater declines in denser urban areas. However, urban greenness dynamics are closely linked to historical and ongoing place-based land use factors (Chauvin et al., 2017; Deinlnger and Binswanger, 1999; \u00d6zg\u00fcner, 2011; Zhao et al., 2015). For example, McGovern and Pasher (2016) indicate that Canadian urban trees were typically lost as a result of infilling or expansion, but in some cases urban tree canopy also increased alongside tree planting initiatives of new developments. In another case, in which urban areas were surrounded by agriculture, the same authors noted that the conversion of these lands into suburban housing translated into the addition of tree cover. This pattern may be persistent in the Prairies and may help explain its substantial urban greenness growth over the past few decades as identified by McGovern and Pasher (2016) as well as this study. On another note, greening international cities, identified by Jin et al. (2019), also maintained medium-dense vegetation cover while North American cities in the same study that lost greenness overall were unable to maintain their medium-dense vegetation. The results of Chapter 3 translate to a similar conclusion; DAs that lost greenness typically resulted in low greenness levels in 2016, whereas those that gained greenness attained high levels. Yet, the DAs in high population density urban areas of this analysis that gained greenness over the 33 years for the most part resulted in high levels of greenness in 2016. These areas are found interspersed in the urban fabric, such as those just outside Toronto\u2019s core, which implies that the complexity of intra-urban greenness dynamics 80 may be influenced by several factors, likely including but not limited to municipal and\/or regional planning, demand for densification, and\/or individual homeowner decision making. Chapter 4 indicates that, since 1984, 47% of peri-urban area across 18 major Canadian cities become more urban. Land use transitions from natural or agriculture to urban use covered similar areas nationally (29% and 32%, respectively), but the rate of natural use conversions dominated on the DA level. This difference may be an effect of spatial characteristics that are entangled with land use types. For example, the rate at which agriculture is converted to an urban use may be subject to greater restrictions (e.g. agricultural land reserve regulations) as well as individual property owner decision making. Additionally, because DAs with high agriculture use are typically larger (due to population-focused DA delineation guidelines), agricultural changes would require large coverage and\/or drastic changes within a given DA to be impactful at that scale. On the other hand, because the dynamic land use classification can identify pockets of natural use at varying fractional values, such as residential gardens, vacant lots, or recreational areas, there is greater opportunity for minimal changes of natural areas to accumulate on the DA level. As such, the multiple spatial scales used in Chapter 4 provides insight into the potential effects of spatial scale in accounting of land use transitions. The exploratory analysis of Chapter 4 showed current levels of population density, as well as to a lesser extent income and the amounts of single-detached housing and recent residential construction, were generally correlated to multi-decadal land use trends in Canadian peri-urban areas. However, regional inconsistencies in the strength and type of correlation may stem from variable socio-economic trajectories (Abbott, 2008; Broadway, 2013; Gertler, 1995). This work echoes other observations that recent urban expansion of North American cities follow a 81 fragmented, rather than the presumed sprawling, pattern (Irwin and Bockstael, 2007; Leyk et al., 2020; MacGregor-Fors, 2010). For example, strong and positive correlations between urbanization and population density, as well as weak\/negative correlations with single-detached housing, may indicate peri-urban densification (e.g. west and west-center strata). On the other hand, despite large coverage and high rates of change of urbanization, the east-center stratum observed weak correlations between its Urban trend and current levels of both population density and single detached housing. This relationship may suggest that some Canadian cities, such as those in Central Canada, may be effectively developing their peri-urban areas to include mixed uses despite continuous population growth (Wellmann et al., 2020), although further investigation is needed to support such a claim. 5.3 Urban planning and management implications The methodology presented in both chapters of this thesis take advantage of multiple spatial scales, from sub-pixel unmixing techniques and the production of 30-m vegetation greenness and land use fraction data products, to results summarized on the neighborhood (i.e. DA), city, regional, and national levels. Open access to long-term satellite imagery is also an integral component of this work, enabling investigations of current, historic, and multi-decadal time series analyses of urban land dynamics. The methodology, results, and data presented in this thesis provides researchers, practitioners, and residents a comprehensive and accessible way to better understand historic urban greenness and land use changes. Embracing accessible remote sensing technology for urban landscape management will provide robust and comparable information for more fruitful collaboration to shape sustainable land use in an urbanizing world (Haaland and Konijnendijk van den Bosch, 2015). 82 Chapter 3 offers a characterization framework of urban change using the urban greenness score, developed using regionally calibrated, spectrally unmixed vegetation greenness across major Canadian urban areas and over three decades. The fusion of open access remotely sensed data and an operational urban greenness score framework as presented in this thesis provides decision makers with otherwise unavailable information about long-term and detailed spatial patterns of urban vegetation for strategic, evidence-based, and sustainable urban policies. For example, using the urban greenness score metric, urban planners and managers can identify key areas in need of improvement and then locate fine scale potential areas for street trees or green space additions using the current 30-m greenness fraction and other data. The results from Chapter 4 provide baseline information for environmental accounting, as well as further inter-disciplinary analysis relating to anthropogenic land states and changes. With an increased interest in sustainable urban planning and adoption of policies that mitigate urban expansion challenges (UN-Habitat, 2016), solutions like densification (Daneshpour and Shakibamanesh, 2011; Gordon and Richardson, 1997; Stevenson et al., 2016) and smart growth (i.e. developing walkable neighbourhoods with mixed land use; Dong and Zhu, 2015; Mandpe and Meyer, 2005) can be adequately assessed using fractional land use through the dynamic land use classification. Multi-decadal and spatially explicit information about peri-urban land use dynamics are crucial for enhancing regional, national, and global reporting as it relates to carbon budgeting, as well as other social, economic, and environmental implications (Bai et al., 2016; Maes et al., 2019). For example, the conversion of agricultural lands into an urban use has often occurred on some of Canada\u2019s most productive soils, particularly in southern Ontario (Statistics Canada, 2016), which should be closely monitored to ensure sufficient good quality agricultural land is maintained near urban centres for sustainable local food production. 83 5.4 Limitations The work presented in this thesis provides foundational characterizations of urban land dynamics, with an opportunity to better understand long-term urban greenness and land use trends for individual administrative units to city, regional, and national scales. However, the work presented in this thesis is limited by several components, including the study unit (i.e. dissemination areas (DAs)), auxiliary agricultural mask, and study scope. 5.4.1 Study unit limitations Using administrative study units such as census DAs to summarize urban greenness and land use results is practical for further planning, management, or research applications, however the use of an aggregated study unit disables the identification of fine scale observations. For example, in Chapter 4, land use summarized on the DA level diminishes the effects of very localized land use changes (e.g. for Agriculture to Urban as noted Section 5.2) and makes it difficult to accurately summarize area-based changes. The additional accounting of pixel-based area provides a deeper understanding of the true coverage of land use transitions, however regional accounting and planning at such high spatial resolutions is difficult.  Different study units and\/or area extents between the analyses of this thesis and other Canadian studies made for poor comparisons, making conclusions broad. The identification of more fine scale land changes would enable observations of unique patterns that more could provide a better understanding of regional and\/or local land use transitions or vegetative greenness trends. Additionally, because some census data at fine scales like the DA level are only available since 2001 and do not maintain spatial boundaries through time (Statistics Canada, 2011), analyses using census based socio-demographic variables across the entire time series were not possible. While this 84 thesis presents correlations between fractional land use trends and current socio-demographic variables on the neighborhood level in Canada, fine scale time series population data is needed to better predict and understand drivers and consequences of land use development. 5.4.2 Auxiliary agricultural mask limitations Reliable and accurate land cover data is a crucial component of delineating areas of interest in the urban greenness score metric and the dynamic land use classification scheme. However, the static agricultural mask used in both analyses of this thesis was for a single recent year of the time series (i.e. 2011). Potential consequences of this decision may be a mismatch between identified and true agricultural areas, resulting in inappropriate exclusion of non-agricultural areas (Chapter 3) and incorrectly applied spectral fraction function to determine land use fractions (Chapter 4). Additionally, this may have contributed to any agricultural land use transitions in the last 5 years of the 33-year study period (2011-2016) to be improperly accounted for in Chapter 4. 5.4.3 Study scope limitations Although work presented in this thesis accounts for regional and national patterns of multi-decadal vegetative greenness and land use trends, important components of sustainable land management were not considered for analysis in this thesis. As Chapter 3 focused on the quantification of urban greenness, other key elements in assessing the success of urban green spaces (including but not limited to vegetation type and quality, ecosystem services, accessibility, and user experiences) were not considered. For example, greenness of private urban green spaces was not distinguished, but would be crucial in green space equity analyses (Nesbitt et al., 2019; Wolch et al., 2014). The dynamic land use classification scheme in Chapter 4 is restricted to three broad classes (i.e. Urban, Agriculture, and Natural), which limits the understanding of local and more 85 specific land uses. Additionally, as socio-demographic data was restricted to a single current year, inferences cannot be made about long-term drivers of land use change from the exploratory analysis. 5.5 Future work The methods applied in this thesis provide insight into multi-decadal trends of vegetative greenness and land use across 18 major Canadian cities using newly developed comprehensible frameworks. The extension of this work to include future changes and additional cities will provide a more detailed outlook into the current and historic state of Canada\u2019s cities and their continued changes. Additionally, the application of the data products developed in this thesis across multiple spatial scales and\/or types of communities (e.g. urban, peri-urban, and rural) can provide greater insight into their differences and impacts of various land dynamics. Through the analyses of Chapters 3 and 4, I have identified the need for a robust Canadian peri-urban demarcation strategy, a refined methodology for comparable long-term monitoring of Canadian urban land dynamics, as well as long-term and fine scale comparisons of urban greenness and peri-urban land use change with specific policies and initiatives as well as socio-economic and other important factors.  Although the method to select peri-urban areas in Chapter 4 was data-driven and locally focused, the use and local-contextualization of current and relevant socio-demographic variables would help refine peri-urban area selection (Mortoja et al., 2020). Providing regional and local urban practitioners with a consistent and repeatable peri-urban demarcation strategy that utilizes fine scale and multi-year population and land cover data would enable more guided and forward-planning land use policy (Silva, 2018). A standardized peri-urban area delineation for Canada, and    86 potentially other comparable countries, would enable more consistent regional and national accounting of land dynamics. The land use classification scheme developed in Chapter 4 provides a foundational method for mapping dynamic land use on regional and national scales and monitoring long-term patterns. However, I acknowledge that improvements in calculating dynamic land use should be made in the continuation of this work. Incorporating other remote sensing data, such as fine spatial resolution imagery (e.g. European Environment Agency, 2016; Haase et al., 2019; Zhang et al., 2018) or LiDAR (e.g. Hermosilla et al., 2012; Man et al., 2015), can help identify key land cover\/use characteristics in more detail and refine land use classes. For example, since land cover data and an agricultural mask were critical in dynamic land use classification scheme, using multiple time stamps of these data could enable a more accurate representation of all land use patterns. As demonstrated by other land use classifications, additional population and infrastructure data, such as road networks (e.g. Beykaei et al., 2014; European Environment Agency, 2016; Guindon et al., 2004; Haase et al., 2019; Yu and Ng, 2007), building type and density (e.g. Beykaei et al., 2014; European Environment Agency, 2016; Guindon et al., 2004), as well as population density (e.g. Gopal et al., 2016; Guindon et al., 2004; Lu and Weng, 2006), could also assist in fine-tuning the dynamic land use classes. Future work on urban land dynamics should incorporate explicit intra-urban area spatial analyses to better understand the current patterns and recent historic drivers of change. For example, an analysis applying the 30-m vegetative greenness fraction and the neighborhood level urban greenness score separately would illuminate how the urban greenness score captures different types and degrees of localized vegetative greenness change. Similarly, the urban 87 greenness score could be examined with current or historic trends of dynamic land use to inform on whether the score could directly serve in identifying areas undergoing gradual and long-term land use changes. Continued work about spatial and temporal trends of urban greenness and land use in Canada should also consider relative associations with governance and socio-demographic factors, such as land use policies, urban planning, wealth, housing types, and prices (as identified previously by Lepczyk et al. (2017)), as well as explore specific cases of greening of land use policies that may be key to long-term urban dynamics (Broitman and Koomen, 2015).  Research in other fields, such as ecology, public health, or urban planning, would greatly benefit from incorporating reliable, continuous, and historic data related to the state and change of urban greenness and land use (Newbold et al., 2016; Spyra et al., 2019; Stevenson et al., 2016; Zhou et al., 2016). The data products created through the application of Landsat imagery in this thesis are useful tools for immediate implementation in interdisciplinary research, including epidemiological studies analyzing changes in health status related to the variation in urban vegetation and\/or land use. For example, most epidemiological studies on land use in relation to public health have applied relatively coarse and static land cover measurements, risking spatial and temporal inaccuracies that can result in exposure misclassification (Helbich, 2019; Reid et al., 2018). Likewise, the urban greenness score can better inform public health research as it provides a more comparable and appropriate description of urban vegetation cover than frequently used remotely sensed vegetation indexes (Liu and Kafatos, 2007). 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