Satellite Monitoring of Urbanization and Indicator-based Assessment of Environmental Impact
Dorothy Furberg
Doctoral Thesis in Geoinformatics
KTH Royal Institute of Technology Stockholm, Sweden 2019
TRITA-ABE-DLT-1944 ISBN 978-91-7873-386-6
KTH Royal Institute of Technology School of Architecture and the Built Environment Department of Urban Planning and Environment Division of Geoinformatics 100 44 Stockholm, Sweden
Academic Dissertation which, with due permission of KTH Royal Institute of Technology, is submitted for public defence for the Degree of Doctor of Philosophy on Friday the 6th of December 2019 at 9:00 a.m. in the Visualization Studio, D Building, Lindstedtsvägen 5, KTH, Stockholm.
Dorothy Furberg
Printed by Universitetsservice US AB Stockholm, Sweden, 2019
ii Abstract
As of 2018, 55% of the world population resides in urban areas. This proportion is projected to increase to 68% by 2050 (United Nations 2018). The Stockholm region is no exception to this urbanizing trend: the population of Stockholm City has risen by 28% since the year 2000. One of the major consequences of urbanization is the transformation of land cover from rural/natural environments to impervious surfaces that support diverse forms of human activity. These transformations impact local geology, climate, hydrology, flora and fauna and human-life supporting ecosystem services in the region where they occur. Mapping and analysis of land-cover change in urban regions and monitoring their environmental impact is therefore of vital importance for evaluating policy options for future growth and promoting sustainable urban development.
The overall objective of this research is to investigate the extent of urbanization and analyze its environmental impact in and around selected major cities in North America, Europe and Asia by evaluating change in relevant environmental indicators, from local to regional scales. The urban regions examined are the Greater Toronto Area (GTA) in Canada, Stockholm City, metropolitan area and County in Sweden and Shanghai in China. The analyses are based on classifications of optical satellite imagery at medium to high spatial resolutions (i.e. Landsat TM/ETM+, SPOT-1/5, Sentinel-2A MSI and QuickBird-2/WorldView-2) between 1985 and 2018. Various classification techniques (maximum likelihood under urban/rural masks, object-based image analysis with rule-based or support vector machine classifiers) were used with combinations of spectral, shape and textural input features to obtain high accuracy classifications. Environmental indicators such as landscape metrics, urbanization indices, buffer/edge/proximity analysis, ecosystem service valuation and provision bundles as well as habitat connectivity were calculated based on the classifications and used to estimate environmental impact of urbanization.
The results reveal urban growth and environmental impact to varying degrees in each of the study sites. Urban areas in the GTA grew by nearly 40% between 1985 and 2005. There, change in landscape metrics and urban compactness measures indicated that low-density built-up areas increased significantly, mainly at the expense of agricultural areas. Urban land cover increasingly surrounded the majority of environmentally significant areas during the examined time-period, furthering their isolation from other natural areas. The study comparing Shanghai and Stockholm County between 1990 and 2010 revealed that urban areas increased ten times as much in Shanghai
iii as they did in Stockholm, at 120% and 12% respectively. Fragmentation in both study regions occurred mainly due to the growth of high-density built-up areas in previously more natural environments, while the expansion of low- density built-up areas was mostly in conjunction with pre-existing patches. The growth in urban areas resulted in ecosystem service value losses of approximately 445 million USD in Shanghai, largely due to the decrease in natural coastal wetlands, while in Stockholm the value of ecosystem services changed very little. The remotely sensed data for these studies had the same resolution (30 m) at roughly the same study area extent, which allowed cross- site comparison of regional urbanization and environmental change trends.
Analysis of classifications of SPOT data at 20/10 m resolution indicated urban areas in the greater Stockholm metropolitan area increased by 10% between 1986 and 2006. The landscape metrics indicated that natural areas became more isolated or shrank whereas new small urban patches appeared. Large forested areas in the northeast dropped the most in terms of environmental impact ranking, while the most improved analysis units were close to central Stockholm. Land-cover change analysis in Stockholm County between 2005 and 2015 using Sentinel-2 and SPOT-5 data at 10 m resolution indicated that urban areas increased by 15% and non-urban land cover decreased by 4%. This data’s higher spatial resolution combined with the county study area extent allowed for analysis of regional ecosystem services as well as localized impacts on green infrastructure. In terms of ecosystem services, changes in proximity of forest and low-density built-up areas were the main cause of lowered provision of temperature regulation, air purification and noise reduction. Urban areas near nature reserves increased 16%, with examples of their construction along reserve boundaries. Urban expansion overlapped the deciduous ecological corridor network and green wedge/core areas to a small but increasing degree, often in close proximity to weak but important green links in the landscape. The results from the urban land-cover change analysis based on high-resolution (1 m) data over Stockholm City between 2003 and 2018 revealed that the most significant change occurred through the expansion of the transport network, paved surfaces and construction areas, which increased by 12%, mainly at the expense of grass fields and coniferous forest. Examination of urban growth within ecologically significant green infrastructure indicated that most land area was lost in ecological dispersal zones while the highest percent change was within habitat for species of conservation concern (14%). The high-resolution data made it possible to perform connectivity analysis of the habitat network for the European crested tit, representing small coniferous forest-dependent bird species in Stockholm. Habitat network analysis in both years revealed that overall probability of connectivity decreased slightly through patch fragmentation and shrinkage mainly caused by road expansion at the outskirts of the city.
iv This research demonstrates the utility of urban and environmental indicators combined with remote sensing data to assess the spatio-temporal dynamics of urbanization and its environmental impact in different urban regions. Landscape-metric based bundles were effective for monitoring ecosystem service provision in a moderately urbanizing region. Habitat network analysis based on high-resolution urban land-cover classifications, which has not often been undertaken in previous research, provided informative results. A complementary dual-level analysis approach worked well in several studies. Appropriate indicators at the landscape level yielded an estimation of overall impacts on ecosystem value or service provision for the whole region. More specific indicator analysis at a local level pertaining to green infrastructure highlighted impacted ecological areas as localized manifestations of the regional trends. In addition, comparison of classified remotely sensed urban land-cover data with administrative boundaries and significant green infrastructure can reveal transboundary “hotspots” where environmental impact occurs and where further investigation and coordinated conservation or restorative management efforts may be needed. The combination of study results pertaining to Stockholm allowed comparison of classifications of differing spatial resolutions over the same spatial extent, highlighting advantages and challenges in satellite-based urban land-cover mapping for estimation of environmental impact.
Keywords: Urbanization, remote sensing, land-cover classification, landscape metrics, environmental indicators, environmental impact, ecosystem services, green infrastructure, habitat network analysis, Greater Toronto Area, Stockholm, Shanghai
v Sammanfattning
År 2050 förväntas 68 % av världens befolkning bo i urbana områden, jämfört med dagens 55 % (FN 2018). Stockholmsregionen utgör inget undantag i urbaniseringstrenden: befolkningen i Stockholms stad har ökat med 28 % sedan år 2000. En av de största följderna av urbanisering är markförvandling från lantliga och naturliga miljöer till bebyggda ytor, ämnade för olika former av mänskligt aktivitet. Förvandlingen påverkar lokal geologi, klimat, hydrologi, flora, fauna och ekosystemtjänster som bidrar till att understödja mänskligt liv. Kartläggning och analys av förändringar av marktäcket i urbana regioner och att hålla deras miljöpåverkan under uppsikt är därför kritiskt för att kunna utvärdera politiskt alternativ för framtida tillväxt och stödja hållbar storstadsutveckling.
Det övergripande målet med denna forskning är att undersöka urbaniseringens utbredning och dess potentiella miljöpåverkan i regioner i och omkring utvalda storstäder i Nordamerika, Europa och Asien, genom förändringsanalys av relevanta miljöindikatorer, från lokal till regional nivå. De utvalda urbana regionerna är Toronto och dess omgivning i Kanada (Greater Toronto Area), Stockholms stad, region och län i Sverige och Shanghai i Kina. Analyserna baseras på klassificeringar av optiska satellitbilder (Landsat TM/ETM+, SPOT 1/5, Sentinel-2A MSI och Quickbird-2/WorldView-2) från mellan 1985 och 2018. Olika klassificeringstekniker (”maximum likelihood” klassificering under urbana/agrar maskeringar, objektorienterad analys med regelbaserad eller stödvektormaskin klassificering) användes med kombinerade spektral-, form- och texturdrag som indata för att höja noggrannheten. Miljöindikatorer så som landskapsnyckeltal, urbaniseringsindex, buffer-/kant-/närhetsanalys, ekosystemtjänstvärdering eller försöjningsgrupperingar och habitatnätverkskonnektivitet har beräknats baserad på klassificeringarna och har använts för att uppskatta urbaniseringens miljöpåverkan.
Resultaten visar olika grader av urban tillväxt och miljöpåverkan i varje studieområde. Urbana områden i den Greater Toronto Area (GTA) växte närmare 40 % mellan 1985 och 2005. Förändring i beräknade landskapsnyckeltal och urbana täthetsindikatorer visar att glest bebyggda områden växte betydligt i GTA mellan 1985 och 2005, på bekostnad av agrara områden. Majoriteten av de betydelsefulla ekologiska områdena omringades allt mer av urbana områden, vilket bidrog till deras isolering från andra naturliga områden. Jämförelsens studie mellan Shanghai och Stockholms län visade att urbana områden växte tio gånger mer i Shanghai än i Stockholm, med 120 % respektive 12 %. Landskapsfragmentering i båda studieområdena
vi skedde på grund av tätbebyggda områdens tillväxt i tidigare mer naturliga miljöer, medan utbredningen av glest bebyggda områden skedde främst i direkt anslutning till redan existerande sådana. Tillväxten av urbana områden ledde till en värderingsförlust i ekosystemtjänster av ungefär 445 miljoner amerikanska dollar i Shanghai, mest på grund av en minskning i naturliga kustvåtmarker, medan ekosystemtjänsters värdering i Stockholm förändrades väldigt lite. Fjärranalysdatan i dessa studier hade samma upplösning (30 m) på ungefär samma rumsliga uträckning, vilket tillät jämförelser av regional urbanisering och miljöförändringstrender.
Analys av klassificeringar av SPOT data på 20/10 m upplösning indikerade att urbana områden i Stockholms region växte med 10 % mellan 1986 och 2006. Resultaten från lansskapsnyckeltalen tyder på att naturliga områden isolerades mer eller krympte medan däremot nya små urbana områden blev till. Större skogsområden i nordöstra delen tappade mest i miljöpåverkans rangordning, medan de mest förbättrade befann sig närmare centrala Stockholm. Marktäckeförändringsanalys i Stockholms län mellan 2005 och 2015, baserad på Sentinel-2 och SPOT-5 data med 10 m upplösning, visade att urbana områden växte med 15 % och att icke urban mark reducerades med 4 %. Denna datas högre upplösning, tillsammans med länstudieområdet, möjliggjorde analys av regionala ecosystemtjänster och den lokala påverkan på grön infrastruktur. En förändring av närliggande skog och glest bebyggda områden hade en påverkan på ekosystemtjänsterna så som temperaturreglering, luftrening och ljudreduktion. Urbana områden nära naturreservat ökade med 16 %, med exempelvis byggnationer längs reservatgränser. Urban utbredning överlappade ädellövnätverkets spridningszoner och gröna kilar/kärnor till en liten men växande grad, ofta i närheten av de redan svaga men dock så viktiga gröna förbindelserna i landskapet. Resultaten från en urban marktäcke förändringsanalys baserad på högupplöst data (1 m) över Stockholms stad mellan 2003 och 2018 visar att den största förändringen skedde genom en expansion av transportnätverket och byggnadsplatser som ökade med 12 %, på bekostnad av öppna gräsfält och barrskog. Undersökningen av urbanisering inom ekologiskt betydelsefulla grön infrastruktur tydde på den största minskningen av markarea skedde inom spridningszonerna, medan största relativa förändringen fanns inom habitat för skyddsvärda arter (14 %). Den högupplösta data möjliggjorde konnektivitetsanalys av habitatnätverk för tofsmesen, representant för barrskogsfåglar i Stockholm. Habitat nätverksanalysen visade att den övergripande sannolikheten för konnektiviteten minskade något till följd av fragmentering och minskning av habitatarea, som i sin tur orsakades av en utbyggnad av vägnätet i utkanten av staden.
Den här forskningsavhandlingen visar på användbarheten av urbaniserings- och miljöindikatorer som erhållits från fjärranalysdata för att utvärdera både
vii rumslig och tidsmässig dynamik av urbanisering och dess miljöpåverkan i olika storstadsregioner. Landskapsnyckeltal-baserade grupperingar visade sig vara effektiva för övervakning av ekosystemtjänstförsörjning i en måttligt växande region. Den sparsamt utforskade kombinationen av nätverksanalys av habitat och högupplösta urbana marktäckedatasklassificeringar, gav informativa resultat. Ett tillvägagångssätt med analys på två nivåer var användbart i flera studier. Relevanta indikatorer på landskapsregionalnivå uppskattade övergripande påverkan på ekosystemvärde eller tjänstförsörjning för hela regionen. Mer specifik indikatoranalys på en lokal nivå rörande grön infrastruktur identifierade påverkade ekologiska områden, som representerade lokaliserade uttryck för de regionala trenderna. Dessutom kan en metodik, där en jämförelse av klassificerade urban marktäckedata med administrativa gränser och ekologiskt betydelsefull grön infrastruktur, avslöja gränsöverskridande problemområden med negativ miljöpåverkan. Kring dessa områden kan det behöva göras vidare studier och koordinerade miljöskyddsinsatser. De olika resultaten för Stockholm möjliggör jämförelse av klassificeringar med olika rumslig upplösning över samma rumsliga utsträckning, och belyser fördelar och utmaningar med satellitbaserad kartläggning av urban marktäckedata för uppskattning av miljöpåverkan.
Nyckelord: urbanisering, fjärranalys, marktäckeklassificering, landskapsnyckeltal, miljöindikatorer, miljöpåverkan, ekosystemtjänster, grön infrastruktur, habitat nätverksanalys, Greater Toronto Area, Stockholm, Shanghai
viii
Acknowledgements
This research was supported by the project ‘Sentinel4Urban’ funded by the Swedish National Space Agency (PI: Yifang Ban, grant number dnr 155/15), and the project 'Spatial Temporal Patterns of Urban Growth and Sprawl: Monitoring, Analysis, & Modelling' funded by the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (FORMAS) (PI: Yifang Ban, 2007-2010). This research is also part of the project 'Earth Observation for Smart Cities and Sustainable Urbanization’ (European Lead PI: Yifang Ban) within the European Space Agency (ESA) and Chinese Ministry of Science and Technology (MOST) Dragon 4 program. High-resolution satellite images are provided courtesy of the DigitalGlobe Foundation.
I would first of all like to thank my supervisor, Professor Yifang Ban, for giving me the opportunity to pursue doctoral studies in Geoinformatics. Her guidance, resourcefulness, support and flexibility have been much appreciated over the years, especially when circumstances change.
I would also like to thank my co-authors, Jan Haas, Andrea Nascetti and Ulla Mörtberg, for fruitful and instructive research collaborations. Thank you to past and present staff at Geoinformatics and fellow PhD students for open and fun discussions and comradery. Many thanks to Hans Hauska for quality assessment and helpfulness throughout the years. A special thanks also goes to Gunilla Hjorth for interest in the work and taking the time for questions and discussion.
In addition, I would like to thank an anonymous reviewer of the paper examining the Greater Toronto Area. Their thoughtful questions and constructive critique were very instructive and helped to improve the quality of the study. They were regrettably not mentioned in the acknowledgements of the article and so I take the opportunity here.
Finally, I am very grateful for and to my family and friends - for their encouragement, patience, prayers and support through what has been a rather long but rich learning experience.
Dorothy Furberg Stockholm, November 2019
ix Table of Contents
Abstract iii Sammanfattning vi Acknowledgements ix
1 Introduction ...... 16 1.1 Rationale ...... 16 1.2 Research objectives ...... 18 1.3 Thesis structure ...... 19 1.4 Statement of contributions ...... 20
2 Background and literature review ...... 21 2.1 Multispectral remote sensing data for urban land-cover mapping: classification techniques ...... 22 2.1.1 Pixel-based classification ...... 23 2.1.2 Object-based image analysis and classification ...... 25 2.2 Indicator-based assessment of environmental impact of landscape changes with remote sensing and GIS ...... 27 2.2.1 Landscape ecology metrics as indicators of landscape change and environmental impact ...... 30 2.2.2 Remote sensing and the use of ecosystem service indicators ...... 32 2.2.3 Connectivity analysis to assess environmental impact... 34 2.3 Stockholm green infrastructure monitoring ...... 35
3 Study areas and data description ...... 38 3.1 Greater Toronto Area ...... 40 3.2 Stockholm County, metropolitan area and City ...... 41 3.2.1 Stockholm metropolitan area ...... 42 3.2.2 Stockholm County ...... 44 3.2.3 Stockholm City ...... 46 3.3 Shanghai ...... 48
4 Methodology ...... 48 4.1 Image processing ...... 49
x 4.1.1 Image pre-processing ...... 49 4.1.2 Texture analysis ...... 50 4.2 Image classification ...... 50 4.2.1 Maximum likelihood classification ...... 51 4.2.2 Object-based image analysis and rule-based classification 51 4.2.3 Support vector machine classification ...... 52 4.2.4 OBIA SVM classification ...... 52 4.3 Accuracy assessment ...... 53 4.4 Landscape metrics ...... 53 4.5 Urban and environmental indicators and indices ...... 56 4.5.1 Greater Toronto Area ...... 56 4.5.2 Stockholm metropolitan area ...... 57 4.5.3 Stockholm and Shanghai comparison ...... 59 4.5.4 Stockholm County ...... 59 4.5.5 Stockholm City ...... 60 4.6 Monitoring of ecosystem services ...... 60 4.6.1 Stockholm and Shanghai comparison ...... 60 4.6.2 Stockholm County ...... 61
5 Results and discussion ...... 62 5.1 Image classifications ...... 62 5.2 Landscape change and potential environmental impact analysis using landscape metrics ...... 69 5.2.1 Urban growth and landscape change trends ...... 69 5.2.2 Greater Toronto Area ...... 72 5.2.3 Stockholm metropolitan area ...... 73 5.2.4 Stockholm and Shanghai Comparison ...... 74 5.2.5 Stockholm County ...... 75 5.2.6 Stockholm City ...... 76 5.3 Assessing impact with urban form, ecosystem service and green infrastructure indicators ...... 76 5.3.1 Greater Toronto Area ...... 76 5.3.2 Stockholm metropolitan area ...... 77 5.3.3 Stockholm and Shanghai comparison ...... 78 5.3.4 Stockholm County ...... 79 5.3.5 Stockholm City ...... 79
xi 5.4 Comparison of the GTA, Stockholm and Shanghai investigations ...... 80 5.5 Comparison of the Stockholm investigations ...... 83
6 Conclusions and future research ...... 86 6.1 Conclusions ...... 86 6.2 Future research ...... 90
References: ...... 92
Appendix: Environmental Impact Indicator Results...... 123
xii List of figures
Figure 1 The DPSIR framework for Reporting on Environmental Issues ...... 28 Figure 2 Specific concepts addressed in the thesis and their role within the DPSIR framework ...... 29 Figure 3 2005 Landsat TM images and study extent of the Greater Toronto Area...... 41 Figure 4 Study area extents from Papers II, III, IV and V...... 43 Figure 5 Study area from Paper IV...... 45 Figure 6 The Stockholm City study area used in Paper V...... 47 Figure 7 Flowchart comparing image processing and classification methodologies for each study area ...... 49 Figure 8 Flowchart comparing environmental indicator calculations for each study area ...... 53 Figure 9 Land-cover classification results for the Greater Toronto Area in 1985 and 2005 ...... 64 Figure 10 Land-cover classification result for Stockholm metropolitan area in 2006 ...... 65 Figure 11 Land-cover classification results for Shanghai and Stockholm County in 1989/90, 2000 and 2010 ...... 66 Figure 12 Land-cover classification result for Stockholm County in 2015 ...... 68 Figure 13 Land-cover classification result for Stockholm City in 2018 ...... 69 Figure 14 Growth trends for urban vs. non-urban land cover in the GTA, Shanghai, Stockholm County and region during the period 1985-2015 ...... 71 Figure 15 Contagion trends for the GTA, Shanghai, Stockholm County and City during the period 1985-2018 ...... 72
xiii List of tables
Table 1 Overview and characteristics of the multispectral data used in each study ...... 39 Table 2 Overview of the geographic datasets used in each study ... 39 Table 3 Compiled set of landscape metrics used in the current research ...... 54 Table 4 Table indicating in which studies each landscape metric was used ...... 56 Table 5 Urban indicators used in the Greater Toronto Area study (Paper I) ...... 57 Table 6 Environmental impact indicator specifications used in Paper II ...... 58 Table 7 Comparison of overall classification accuracies and kappa statistics ...... 63 Table 8 Outline of dual levels of analysis in each study ...... 83 Table 9 Comparison of urban vs. non-urban land-cover percentages for the Stockholm County classifications between 2000 and 2015 based on 30 m and 10 m resolution data ...... 84 Table 10 Comparison of Stockholm City green structure land area percentages per district as estimated from the 2005-2015 county classifications and 2003-2018 city classifications ...... 86
xiv
List of abbreviations
DPSIR - Driving forces, Pressure, State, Impact, Response EI - Environmental Impact ESA - Environmentally/Ecologically Significant Area ES(V) - Ecosystem Service (Value) (E)TM - (Enhanced) Thematic Mapper GLCM - Gray-Level Co-occurrence Matrix GTA - Greater Toronto Area HDB - High-Density Built-up LDB - Low-Density Built-up LFA - Large Forested Area MLC - Maximum Likelihood Classifier OBIA - Object-Based Image Analysis ORM - Oak Ridges Moraine SPOT - Satellite Pour l’Observation de la Terre SVM - Support Vector Machine TRCA - Toronto Region and Conservation Authority
xv
1 Introduction
“You have to know the past to understand the present.” - Carl Sagan
1.1 Rationale
Urbanization poses numerous challenges for those working towards sustainable development. While cities may experience internal problems as they grow, their impact on the surrounding natural environment upon which they depend is of critical importance. Lambin et al. (2001) describe this relationship as follows:
In reality, urbanization affects land change elsewhere through the transformation of urban-rural linkages. For example, urban inhabitants within the Baltic Sea drainage depend on forest, agriculture, wetland, lake and marine systems that constitute an area about 1000 times larger than that of the urban area proper (Folke et al., 1997). Given that urban life-styles tend to raise consumption expectations and that 60% of the world’s population will be urban by 2025 (United Nations Population Fund, 1991), the rural–urban linkage or the urban ‘‘ecological footprint’’ is critical to land change assessments.
Mapping and analysis of land-cover change in urban regions is therefore crucial to tracking this “ecological footprint” and deciding policy options and/or remedies for future growth and environmental conservation. Wentz et al. (2009) also emphasize the importance of this task: “Urbanization represents one of the most significant alterations that humankind has made to the surface of the Earth… It is essential that we document, to the best of our ability, the nature of land transformations and the consequences to the existing environment.”
Past and present research demonstrates the utility of remote sensing data and geographic information systems to capture and map urban land-cover change (e.g. Cihlar 2000; Barnsley and Barr 2000; Franklin and Wulder 2002; Zhou et al. 2008; Yang 2011; Qin et al. 2013; Weng 2014; Ban et al. 2014 and 2015; Haas and Ban 2017). In their review of trends and knowledge gaps in urban
16 remote sensing, Wentz et al. (2014) underscore the importance of understanding environmental impacts from urbanization in cities in support of planning policies, to which studies based on remotely sensed data can significantly contribute. They highlight the search for an appropriate scale of measurement with regard to pairing sensor resolutions and level of urban investigation. Recent improved access to remote sensing data coupled with higher spatial resolutions, through the European Space Agency’s Sentinel program, for example, is furthering opportunities for environmental assessment in urban areas and multi-scale analysis (Pesaresi et al. 2016; Gómez et al. 2016).
Tracking the environmental impact of urban land cover changes has often been undertaken with the help of environmental indicators such as landscape ecology metrics (Forman and Godron 1986; O’Neill et al. 1988; Turner 1990; Haines-Young et al. 1993; Hargis et al. 1998; Botequilha Leitao and Ahern 2002; and e.g. McGarigal and McComb 1995; Narumalani et al. 2004; Kamusoko and Aniya 2007; Li et al. 2010; Haas and Ban 2014 and 2018; Hernández-Moreno et al. 2018). But the use of environmental indicators particularly relating to green infrastructure and ecosystem services based on remote sensing data and geographic information techniques for estimation of environmental impact in urban regions is a newer and growing research area (e.g. Derkzen et al. 2015; Calderón-Contreras and Quiroz-Rosas 2017; Banzhaf et al. 2019). Maes et al. (2015) emphasize the importance of green infrastructure in maintaining ecosystem services and state that there is an expected decrease of 10-15% in ecosystem services across Europe by 2050 relative to the 2010 baseline. Monitoring of green infrastructure is therefore vital and landscape pattern affects ecosystem service provision (Duarte et al. 2018). Yet changes in the spatial attributes of ecosystem service-providing land cover, such as green infrastructure, in urban areas are not often examined and there is opportunity for further research in this area (Frank et al. 2012; Haas and Ban 2018; Duarte et al. 2018).
Turner et al. (2015) and Pettorelli et al. (2014a) have pointed out that remote sensing data is underused in and holds great potential for studies monitoring biodiversity. The use of remote sensing at medium resolution to monitor biodiversity has seen extensive development in recent years (Pettorelli et al. 2014b). Mairota et al. (2015) highlight the high-resolution data that remote sensing can deliver on habitat quantity and quality but note hindrances that have limited its use for this purpose. Boyle et al. (2014) demonstrate the utility of high-resolution optical remotely sensed data for monitoring land-cover change for conservation but also found that its use was very limited (approximately 10% of the land-cover studies used satellite imagery of 5 m or less in the research published by three major conservation biology journals).
17 There are to date relatively few high-resolution remote sensing-based land- cover assessments of impacts on habitat for biodiversity from urbanization. This has usually been undertaken based on maps or information derived from aerial photos or surveying and with the aid of information from aerial scanners (Nagendra et al. 2013 and, e.g., Löfvenhaft et al. 2004; Hartfield et al. 2011).
Newton et al. (2009) have noted the greater potential for use of remote sensing within landscape ecology but also draw attention to a traditional divide between the remote sensing and ecological science research communities. Lopez and Frohn (2017) state that the value of research that involves monitoring and assessment of ecosystems at a variety of scales through the integration of remote sensing, geographic information systems and landscape ecology metrics has yet to be fully realized. This research integrates data and techniques from the fields of remote sensing and landscape ecology by exploring the utility of environmental indicators for impact assessment derived from classified remote sensing data over urban regions in Europe, North America and Asia. Studies on several spatial scales at the European site are performed to estimate benefit from multi-scale analysis, including local- level impact analysis on a habitat network based on high-resolution urban land cover classifications. The spatial scales examined in the research can be termed regional (approximately 6 000 – 7 000 km2), metropolitan (approximately 2 000 km2) and municipal (approximately 200 km2). By evaluating changes in landscape patterns and environmental impact of urban growth in different regions and at different scales, the results highlight data, methodological approaches and indicators likely to be useful for urban and environmental assessment and planning in many different locations.
1.2 Research objectives
The overall objective of this research is to investigate the extent of urbanization and its potential environmental impact in and around selected major cities in North America, Europe and Asia using multi-temporal multi- resolution remote sensing data and environmental indicators to assess landscape changes.
Primary scientific questions are:
(1) When and where has urbanization occurred in these cities and regions in recent decades and what are the probable impacts on the natural and semi-natural environment as a result of this urban expansion?
18 (2) Is there a widely applicable approach to evaluating probable environmental impact from urbanization in metropolitan regions based on optical remote sensing data and the use of indicators, and which indicators are effective in capturing the environmental impact?
(3) What is the benefit associated with impact analysis at multiple scales?
The specific research objectives are:
(1) To monitor urban growth in recent decades in Toronto, Canada, Stockholm, Sweden and Shanghai, China using optical remote sensing imagery;
(2) To analyze landscape change in and around the cities and to evaluate probable environmental impact from urban growth using information from landscape metrics and other environmental indicators;
(3) To evaluate the methods used in terms of finding a widely applicable approach to estimating environmental impact from urbanization at multiple scales (metropolitan landscape regions to local levels) based on optical remotely sensed data.
1.3 Thesis structure
The thesis is structured as follows: Chapter 1 gives an overview of the research, including the rationale, research objectives and organization of the thesis. Chapter 2 reviews related literature and the state-of-the-art of research in terms of classification of medium to high-resolution optical satellite imagery and the use of landscape metrics and indicators for assessing environmental impact in urban areas. Chapter 3 describes the study areas and data used for the research and Chapter 4 outlines the methodologies employed. Chapter 5 presents and compares the results from the various studies. Chapter 6 draws conclusions and discusses the potential for future research.
This thesis is based on the following papers, which are referred to in the text by their Roman numerals.
19 I. Furberg, D. and Ban, Y., 2012. Satellite Monitoring of Urban Sprawl and Assessment of its Potential Environmental Impact in the Greater Toronto Area between 1985 and 2005. Environmental management 50(6): 1068-1088.
II. Furberg, D. and Ban, Y., 2013. Satellite Monitoring of Urban Land Cover Change in Stockholm Between 1986 and 2006 and Indicator-Based Environmental Assessment. In Earth Observation of Global Changes (EOGC): 205-222. Springer: Berlin Heidelberg.
III. Haas, J., Furberg, D. and Ban, Y., 2015. Satellite Monitoring of Urbanization and Environmental Impacts: A Comparison of Stockholm and Shanghai. International Journal of Applied Earth Observation and Geoinformation 38: 138-149.
IV. Furberg, D., Ban, Y. and Nascetti, A., 2019. Monitoring of Urbanization and Analysis of Environmental Impact in Stockholm with Sentinel-2A and SPOT-5 Multispectral Data. Remote Sensing 11: 2408.
V. Furberg, D., Ban, Y. and Mörtberg, U., 2019 (manuscript). Monitoring Urban Green Infrastructure Changes using High-resolution Satellite Data.
1.4 Statement of contributions
Paper I All analyses and methodologies of paper I were developed and performed by the first author under the supervision of Professor Ban, the second author. Professor Ban initiated the ideas for this paper and has been involved in the development of the paper.
Paper II All methodologies and analyses of paper II were developed and performed by the first author under the supervision of Professor Ban, the second author, who initiated the ideas for this paper and has been involved in its development.
Paper III Professor Ban, the third author, proposed the topic for this paper. Methodology development was performed by the first author together with the second author under the supervision of Professor Ban. Study area description, image processing, classifications, post-processing, accuracy assessment, landscape metric analysis and the discussion part for Shanghai were performed by the first author and for Stockholm by the second author, with
20 the exception of the SVM classification which was performed by a departmental colleague, Martin Sjöström. Urbanization indices and ecosystem service valuation were calculated by the first author. The abstract, introduction and data description parts were mainly written by the first author with editorial input from the second author. The selection and interpretation of landscape metrics are mainly based on the knowledge and previous research experience of the second author. The third author reviewed and edited the paper.
Paper IV Professor Ban, the second author, proposed the topic for this paper and its conceptualization was developed together with the first author. Methodology development and research analysis was performed by the first author under the supervision of the second and third authors. The first author drafted the paper and the second and third authors reviewed and edited the paper.
Paper V Professor Ban, the second author, proposed the topic for this paper and its conceptualization was developed together with the first and third authors. The habitat connectivity assessment method and tool were proposed by the third author. All methodologies were applied and analyses performed by the first author under the supervision of the second and third authors. The first author drafted the paper and the second and third authors reviewed and edited the paper.
2 Background and literature review
In recent decades, numerous studies have made use of remote sensing data over urban areas for landscape change analysis. Various studies (e.g. Herold et al. 2005; Liu et al. 2015; Haas 2016) have demonstrated that the combination of remote sensing and landscape metrics data provide more detailed and spatially consistent information on urban structure and change than either technique can separately. Researchers have highlighted the need for development and use of environmental and ecosystem condition and service indicators from remote sensing data for conservation, mitigation and planning purposes (e.g. Revenga 2005; Rose et al. 2015; Dawson et al. 2016). The combined use of indicators and remote sensing data holds great potential to further knowledge on landscape change due to urbanization and the results derived can inform and improve urban planning. The following sections describe the research context and recent developments in terms of classification of optical remote sensing data over urban areas and the use of indicators, including landscape metrics, ecosystem service bundles and habitat
21 connectivity indices to monitor landscape changes and assess environmental impact. The derivation of reliable metrics and indicators is dependent upon the generation of high-accuracy land-cover maps from remotely sensed data; classification techniques are therefore the focus of the first section.
2.1 Multispectral remote sensing data for urban land- cover mapping: classification techniques
Remote sensing data is a valuable source of information for the study of urban areas. It can be obtained over large regions with accurate spatial and geometric detail at high-temporal frequency (Herold et al. 2005; Gamba and Herold 2009; Belward and Skøien 2015). Thematic information such as land-cover change can be obtained from remote sensing data once it has been classified. Land-cover classification of multispectral remote sensing data based on statistical pattern recognition techniques is one of the most commonly used methods of information extraction and forms the information base for many socioeconomic and environmental analyses (Narumalani et al. 2002; Lu and Weng 2007; Gómez et al. 2016).
A number of classification methods are available in order to generate land- cover maps from optical remote sensing data, including algorithms based on parametric and non-parametric statistics, supervised or unsupervised classifiers, hard or fuzzy set classification logic, semi-supervised or active learning techniques, per-pixel or object-oriented classification and hybrid approaches (Jensen 2005; Bruzzone and Demir 2014). All classifiers are subject to a three-way compromise between the spectral information content of the imagery, the method of making class decisions and the information classes that are desired (Franklin and Wulder 2002). The choice of classification method will depend on physical characteristics and prior knowledge of the study area, the spatial and spectral resolution and distribution of the remote sensing data and the nature of the classification problem itself (Gómez et al. 2016).
The focus of this research is mainly on the regional or metropolitan scale (thousands of square kilometers) in order to take into account the whole of an urban area and its surrounding natural environment. This is also an important extent for assessing environmental impact since detrimental effects of fragmentation on biodiversity and ecosystem services are generally found at intermediate (regional) spatial scales (Olff and Ritchie 2002). While there have been numerous studies conducted on a local, municipal or district level (e.g., Löfvenhaft et al. 2004; Lundberg et al. 2008; Li et al. 2010; Haas and Ban 2017; Kotharkar and Bagade 2018) and the global, continental or national
22 level (e.g. Petit et al. 2001; Gerard et al. 2005; Halada et al. 2009; Ståhl et al 2011; Seto et al. 2012; Padilla et al. 2015; Moreno-Martinez et al. 2018), there are fewer studies on a regional metropolitan level linking urbanization to its region-specific environmental impact, and more that include a landscape perspective are needed (Seto et al. 2012; Colding 2013; Haas and Ban 2014 and 2018; Lopez and Frohn 2017). This research examines study sites at this particular scale, with one exception.
The choice of remote sensing data must be well-suited to the spatial extent of the study area (Ban et al. 2015). Low-resolution, large extent data such as MODIS (Moderate Resolution Imaging Spectroradiometer) or AVHRR (Advanced Very High Resolution Radiometer) work well for studies over continental or global regions. High-resolution data (or very high-resolution data as it is sometimes referred to) such as QuickBird or IKONOS favor more detailed, smaller-scale studies, such as at the sub-city level or for examining specific habitat types. However, high-resolution data is not as widely utilized due to its often commercial nature, limited geographical coverage and demand for computational and storage resources (Lu and Weng 2007). For intermediate regional spatial scales, medium resolution satellite data such as Landsat, SPOT and Sentinel-2 offer the best compromise between spatial coverage and level of detail, as well as accessibility and ease of use.
Gómez et al. (2016) reviewed strengths and weaknesses of algorithms for large area land-cover classification based on optical remotely sensed time- series data. The algorithms were Artificial Neural Network (ANN), clustering, decision trees (DT), maximum likelihood, support vector machine (SVM), random forests (RF), bagging and boosting classifiers. Lu and Weng (2007) grouped advanced classification methods such as these into five different categories, namely per-pixel, subpixel, per-field, contextual and knowledge- based approaches. Among them, pixel-based (per-pixel) and object-based (per-field) are commonly used and therefore reviewed below.
2.1.1 Pixel-based classification
Pixel-based unsupervised classification techniques are often best suited to large-area land-cover classifications if the study area is not well-known, due to the tremendous amount of training data otherwise required (Cihlar 2000). Supervised classification presents a better alternative when there is a defined area to be studied and when medium resolution satellite data and prior knowledge of the site are available. Landsat data has historically been important for many different types of land-cover assessments including monitoring of urban areas, but its usage recently increased significantly since
23 the United States Geological Survey Landsat archive was made freely available in 2009 (Wulder et al. 2012; Weng et al. 2014). Herold (2009) notes the usefulness of Landsat data for detection of land-cover configuration within urban environments.
Many supervised algorithms have been developed to perform pixel-based classification. Li et al. (2014) tested and compared 13 of these, including MLC, K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Random Forests (RF), Support Vector Machines (SVM) and other machine learning algorithms, by classifying Landsat data over an urban area in China. They found that most of the algorithms performed well given sufficiently representative training samples. MLC proved to be the most robust algorithm in that it required the least amount of training data in order to achieve one of the highest accuracies. Several studies have demonstrated the basic utility of the MLC classifier when used on Landsat TM imagery (e.g. Wakelyn 1990; Lo 1998; Weng 2002; Kamusoko and Aniya 2007). However, more studies have shown that the results obtained from MLC can be significantly improved when combined with other techniques and data inputs (e.g. Hansen et al. 2001; Liu et al. 2002). Herold et al. (2007) have noted that additional information, such as textural, spatial or contextual, is often required to aid in successfully discriminating spectral signals for mapping urban land- use types. Lu and Weng (2005) found that the addition of higher resolution fused data and texture images improved classification of Landsat ETM+ data over urban areas. Incorporation of texture features in general have been found to improve image classification accuracy, especially through the use of gray- level co-occurrence matrices (GLCM) to derive them (Haralick et al. 1973), for Landsat and SPOT data alike (Franklin and Peddle 1990; Gong et al. 1992; Butusov 2003; Rodriguez-Galiano et al. 2012). This also holds true for classifications over urban areas (Shaban and Dikshit 2001; De Martinao et al. 2003; Herold et al. 2003; Su et al. 2008).
A group of supervised learning algorithms that perform classification well with Landsat data are support SVM, originally introduced by Vapnik (1995) and Cortes and Vapnik (1995). Unlike MLC, SVM are non-parametric classifiers which construct a hyperplane in high-dimensional space with the largest possible distance to the nearest training data point of any class. Since mapping in high-dimensional space can be computationally heavy, a kernel function is defined to suit the problem at hand. Mountrakis et al. (2011) reviewed remote sensing applications of SVM and highlighted the advantages of their ability to generalize well with limited training data and the lack of requirement on underlying data distribution. SVM have been employed to map land cover (e.g. Huang et al. 2002; Dixon and Candade 2008; Mathur and Foody 2008) and have been particularly successful in mapping urban land cover (e.g. Huang et al. 2009; Hu and Ban 2012; Niu and Ban 2013).
24 Srivastava et al. (2012) tested various kernel functions and found in all cases that SVM yielded consistently higher accuracy classifications of Landsat imagery for land-cover change investigation than did MLC. Dixon and Candade (2008) compared classification of Landsat TM data using MLC, SVM and Artificial Neural Network (ANN) and found that both ANN and SVM outperformed MLC. SVM and ANN showed similar results in terms of accuracy but the training time required by SVM was much less than for ANN. Jin et al. (2005) tested SVM and MLC on textural features including GLCM and found that SVM provided higher classification accuracy and better generalization than MLC no matter which texture features were used. Yet Li et al. (2014) found no advantage to using SVM over MLC in either a pixel- based or object-based classification of Landsat data over an urban area. They concluded that the quality and quantity of the training samples play a bigger role in achieving high accuracy than the algorithm itself. Shao and Lunetta (2012) found that SVM outperformed both the neural networks and CART algorithms based on MODIS times-series data in tests with regard to training sample size, sample variability and landscape homogeneity.
2.1.2 Object-based image analysis and classification
A number of studies have demonstrated the advantages of using object-based image analysis (OBIA) over traditional pixel-based classification in urban environments with medium- to high-resolution satellite imagery (Stefanov et al. 2001; Wang et al. 2004; Cleve et al. 2008; Jacquin et al. 2008; Blaschke 2010; Ban and Jacob 2013; Jebur et al. 2013). Sometimes referred to as image segmentation, object-based image analysis can be defined as the division of an image into spatially continuous, disjoint and homogeneous regions, also known as objects, based on color, shape and scale parameters. A main advantage of using spatial information to create objects is that the objects will more likely correspond to actual structures or on-site features than individual pixels do (Ban et al. 2010). The decision rules used tend to be dominated by the knowledge of the human analyst rather than computer algorithms (Franklin and Wulder 2002) and there is the possibility of incorporating diverse types of data to improve accuracy (Stefanov et al. 2001).
Thus, in comparison to other pixel-based classification methods, OBIA has consistently returned superior results in land-cover classification accuracy thanks to its use of contextual information such as shape and neighborhood in addition to spectral data (Wentz et al. 2009; Li et al. 2014; Phiri and Morgenroth 2017). Tehrany et al. 2014 found that both of the object-based classification approaches they tested (KNN and SVM) performed better than the pixel-based method (decision tree classification) for land-cover mapping.
25 Powers et al. (2012) tested the performance of OBIA classification by resampling images to different spatial resolutions (5, 10, 15, 20, 25 and 30m) and found that 10 m yielded the highest classification accuracy. Indeed, 10 m resolution SPOT satellite imagery has been used successfully to obtain high accuracy urban land-cover maps in combination with OBIA techniques (Su et al. 2009; Dimitrakopoulos et al. 2010; Jebur et al. 2014; Tehrany et al. 2014). Chen et al. (2009) made clear the advantages of employing an object-oriented knowledge-based classification method with SPOT 5 imagery in an urban environment over a pixel-based approach. The former yielded the highest accuracy of several compared methods and could create and distinguish between meaningful objects such as roads and buildings, while providing a convenient way of incorporating ancillary data such as a digital elevation model (DEM) and textural information for the classification. Jacquin et al. (2008) revealed OBIA’s improved capacity to delineate urban extent at regional scales with SPOT data. Newman et al. (2011) demonstrated the advantages of an object-based approach over pixel-based with regard to forest fragmentation assessment. Ruiz Hernandez and Shi (2018) combined OBIA, spatial metrics and texture analysis with the RF algorithm to generate a high- accuracy urban land-cover classification. In their review of supervised object- based land-cover image classification, Ma et al. (2017) found that SPOT data provided the highest accuracy of all sensor types tested with the exception of unmanned aerial vehicles and that the RF and SVM supervised classifiers yielded the highest mean classification accuracies. They note that some uncertainties still exist with regard to feature selection in the process of SVM classification but that previous research suggests that higher accuracy occurs when the number of features is less than 30 (Guan et al. 2013, Ghosh and Joshi 2014). Non-parametric classifiers like SVM often provide better results in complex landscapes and are more suitable when using non-spectral data such as contextual information in classification since there is no assumption of normal data distribution (Lu and Weng 2007). Statistics Sweden (2008) has used rule-based OBIA with SPOT 5 data for classification of urban green areas and the Swedish Environmental Protection Agency has employed an object- based MLC classification approach with SPOT data to classify nature types particularly in protected areas (Metria GeoAnalys 2009). The more recent CadasterENV Sweden project involves continually updated land-cover mapping for all of Sweden originally based on SPOT 5 data, which is now being replaced with Sentinel-2 data (Metria AB 2015).
ESA’s Sentinel-2 mission is providing new opportunities for land-cover mapping and environmental monitoring thanks to its data’s cost-free availability and high temporal and spatial resolutions of up to 10 m (Drusch et al. 2012). Recent research highlights the advantages of the use of Sentinel- 2 imagery for urban land-cover classification. The findings of Momeni et al. (2016) suggest that, where high-resolution data is not available, the advanced
26 spectral capabilities of recent sensors like Sentinel-2 combined with OBIA and an SVM classifier hold potential for high accuracy mapping of complex urban land cover. The research of Thanh Noi and Kappas (2018) confirms that the SVM classifier performs better (higher accuracy and less sensitivity to training sample sizes) than either RF or KNN when applied to Sentinel-2 data. Employing KTH-SEG, an edge-aware region growing and merging algorithm, Haas and Ban (2018) demonstrated that the object-based SVM classification of Sentinel-2 and Landsat data could produce reliable land-cover maps for analysis of ecosystem changes.
High-resolution data, such as IKONOS, QuickBird or WorldView, has proven useful in the classification of urban land cover types (e.g. Shackelford and Davis 2003; Myint et al. 2011; Haas and Ban 2017; Mugiraneza et al. 2019) but has also been used to map various kinds of fauna habitat (e.g. Mutuku et al. 2009; Recio et al. 2013) and green and blue structures (e.g. Sawaya et al. 2003; Mathieu et al. 2007; Lang et al. 2018) and ecosystem services (e.g. Lakes and Kim 2012; Haas and Ban 2017). Momeni et al. (2016) found that the classification of high-resolution data over complex urban environments through use of OBIA and the SVM classifier is an optimal combination to achieve high land-cover classification accuracy. Other research confirms the utility of this combination of tools for effective land-cover classification with high-resolution data (Kavzoglu et al. 2015; Qian et al. 2015; Pande-Chhetri et al. 2017).
2.2 Indicator-based assessment of environmental impact of landscape changes with remote sensing and GIS
Indicators are very useful as measurable criteria that point to the current conditions of more complex phenomena. Aspinall and Pearson (2000) define them as “simple measures that represent key components of the system and have meaning beyond the attributes that are directly measured.” Environmental indicators supply information on environmental problems and can help identify key factors that cause pressure on the environment (Smeets and Weterings 1999). They can thus help raise awareness of the condition of ecosystems and assist policy makers in their planning decisions (Revenga 2005). The DPSIR (Driving forces, Pressure, State, Impact, Response) framework for reporting on environmental issues (Smeets and Weterings 1999) shown in Figure 1 can be helpful in understanding the processes that result from human influences on the surrounding natural environment and vice versa and in classifying types of indicators as measures of different stages of this interaction.
27
Figure 1 The DPSIR framework for Reporting on Environmental Issues (adapted from Smeets and Weterings 1999)
This framework shows the relationships between “Driving forces and the resulting environmental Pressures on the State of the Environment and Impacts resulting from changes in environmental quality and on the societal Response to these changes in the environment” (Smeets and Weterings 1999). As will be seen, the indicators used in this research primarily measure pressures on and the state of the natural and semi-natural environment in and around the study area sites. Figure 2 shows environmental issues examined in this thesis in terms of the DPSIR framework. Here, we see that population growth is a main driver of urbanization which places pressure on green areas, resulting in alteration of their state via fragmentation, leading to impacts such as loss of biodiversity and/or impaired ecosystem function/services (Alberti 2005; Haddad et al. 2015). These impacts in turn negatively affect human well-being through reduced benefits from ecosystem services (MA 2005).
28 Population •Driver growth
Urbanization •Pressure
Landscape Fragmentation: •State degradation/loss of habitat
Loss of biodiversity/ Impaired •Impact ecosystem services
Decline in human well- being
Figure 2 Specific concepts addressed in the thesis and their role within the DPSIR framework
There are many kinds of indicators that have been developed to measure pressure and state conditions of the environment. Examples within the categories of biological, physical and chemical environmental indicators include measures of air quality such as pollutant emissions and water pollution such as eutrophication, changes in water temperature or incidence of fish diseases. Socio-economic environmental indicators could include measures of human-generated waste and human health measures such as mortality and morbidity in relation to environmental quality (UNEP/RIVM 1994). Indicators must be carefully selected to fit the purpose of an investigation or program and often a set of indicators is required to assess the state of the environment (Niemeijer and de Groot 2008; Van Oudenhoven et al. 2012).
While many environmental indicators and indices have emerged over the past several decades, their derivation from remote sensing and GIS data is a more recent field of research. Klemas (2001) notes that remote sensors can monitor landscape level environmental indicators and that these “become particularly important as we shift to larger temporal, spatial and organizational scales in order to study and compare the cumulative effects of… ecosystem degradation over entire landscapes and regions” (Klemas 2001; Haines-Young et al. 1993).
29 The body of research on environmental indicators and indices derived from remote sensing data and GIS techniques has and continues to grow. Klemas (2001) showed how remote sensing data can be used to detect landscape-level coastal environmental indicators, specifically changes in land cover, riparian buffers and wetland condition. Revenga (2005) highlighted useful examples of indicators for gauging ecosystem conditions derived from GIS and remote sensing data. Nichol and Wong (2007) assessed urban environmental quality using six different parameters or indicators derived from a variety of satellite data; the parameters were air temperature and quality, vegetation density, building density and height, and noise. Krishnaswamy et al. (2009) developed a multi-date NDVI (normalized difference vegetation index) distance measure as a surrogate for forest type to measure its variability on a single, continuous quantitative scale. Imhoff et al. (2010) used impervious surface area from a Landsat-based dataset and land surface temperature from MODIS to measure the urban heat island effect across a range of biomes in the United States. Liang and Weng (2011) extracted physical environmental variables from Landsat data and combined these with socio-economic data to construct urban environmental quality indices for a decadal comparison. Lakes and Kim (2012) evaluated the use of an aggregated urban environmental indicator known as “Biotope Area Ratio” together with classified remote sensing data for assessing and managing urban ecosystem services. Haas and Ban (2014) used urbanization indices and landscape metrics to investigate the magnitude and environmental impact of urban growth in three major urban agglomerations in China. Michaud et al. (2014) used remotely sensed environmental indicators such as the dynamic habitat index and snow cover to extrapolate moose habitat in southern and central Ontario. De Sherbinin et al. (2014) developed indicators derived from satellite data in three categories: ambient air pollution, coastal eutrophication and biomass burning. Behling et al. (2015) developed an automated GIS system that can derive urban ecological indicators from hyperspectral remote sensing data and height information. Lawley et al. (2016) identified vegetation condition indicators readily detectable with remote sensing methods and Lopez and Frohn (2017) have highlighted research examples that use remote sensing and landscape ecology metrics to assess ecosystems in various watersheds and coastal areas. Mariano et al. (2018) used remote sensing indicators such as leaf area index and evapotranspiration from MODIS data to assess the effects of drought and land degradation on ecosystems in Northeastern Brazil.
2.2.1 Landscape ecology metrics as indicators of landscape change and environmental impact
30 An important concept established in the field of landscape ecology is that a landscape’s pattern strongly influences its ecological processes and characteristics (Forman and Godron 1986; Turner 1989; McGarigal and Marks 1995). Landscape fragmentation often negatively affects native ecosystem function, and habitat fragmentation has been identified as one of the greatest threats to biodiversity worldwide (Botequilha Leitão and Ahern 2002; Leitão et al. 2006; Lindenmayer and Fischer 2006; Haddad et al. 2015) Landscape fragmentation is often caused by conversion to human land cover such as urban centers and transportation networks. Landscape ecology metrics are well-established tools to measure change in landscape pattern and landscape fragmentation in particular (O’Neill et al. 1988; Turner 1990; Haines-Young et al. 1993; Hargis et al. 1998; Botequilha Leitão and Ahern 2002; McGarigal 2002; Uuemaa et al. 2009). It is worth noting that landscape metrics are also known as spatial metrics when taken out of the landscape ecology context according to Herold et al. (2005) who define them as “measurements derived from the digital analysis of thematic-categorical maps exhibiting spatial heterogeneity at a specific scale and resolution.” When applied to multi-temporal datasets, they can be used to describe and analyze change in degree of spatial heterogeneity over time (Dunn et al. 1991) and constitute an indicator useful for monitoring environmental impact from urbanization since changes in spatial heterogeneity affect how a landscape functions as an ecological unit (Turner 1989).
A number of different sets of metrics, based on the work of O’Neill et al. (1988), have been developed, tested and revised (McGarigal and Marks 1995; Riitters et al. 1995; Hargis et al. 1998). However, many researchers have urged caution in the use of landscape metrics, because they are often strongly correlated and can be confounded (Li and Reynolds 1994; McGarigal and Marks 1995; Botequilha Leitao and Ahern 2002; Leitao et al. 2006). Li and Wu (2004) point out the variable responses of certain landscape indices to changes in classification scheme as well as the difficulty in interpreting them since they often represent more than one aspect of spatial pattern. They stress that simple metrics such as patch size, edge, inter-patch distance and proportion are more likely to generate meaningful inferences. Proportion of natural areas is one of the main indicators of the City Biodiversity Index (Secretariat of the Convention on Biological Diversity 2013), a self- assessment tool that allows cities to monitor progress in their biodiversity conservation efforts.
Landscape metrics derived from remote sensing data have been used to assess the impact of disturbances and land-use changes on the environment (Uuemaa et al. 2013; and e.g. Narumalani et al. 2004; Li et al. 2005; Kamusoko and Aniya 2007; Long et al. 2010) and to characterize patterns of urban growth (Reis et al. 2014; and e.g. Herold et al. 2003; Herold et al. 2005; Wu et al.
31 2011). Lausch et al. (2015) have found that high hemeroby (low naturalness and high human pressure on landscapes) reduces heterogeneity in space and time within patterns and that the patch matrix model, which constitutes the framework for calculation of landscape metrics, is particularly suited for analysis of these kinds of, most notably urban, landscapes. A substantial body of research continues to explore the dynamics between changes in the environment in response to urban expansion with the help of landscape metrics. One project that undertook this task is the Urban Environmental Monitoring or 100 Cities Project (Wentz et al. 2009). Research efforts in this specific area include Zhang et al. (2011) who used landscape metrics to analyze and evaluate scenario-based simulation results of urban expansion in Shanghai, Gao et al. (2012) who used landscape indices to assess change in ecological security in nine cities in the Pearl River Delta and Su et al. (2012 and 2014) who employed landscape metrics to gauge the impacts of urbanization on an eco-regional scale. Gbanie et al. (2018) employed them in pre- and post-war periods to examine fragmentation due to urbanization driven by internally displaced persons in Sierra Leone. Hernández-Moreno et al. (2018) utilized landscape metrics to assess the impact of urban expansion on green infrastructure in seven Chilean cities over three decades. Dobbs et al. (2018) used them along with other indicators to study the spatio-temporal patterns of ecosystem services and changes in their provision due to urbanization in two major Latin American cities, also over a 30-year period.
2.2.2 Remote sensing and the use of ecosystem service indicators
Anthropogenic influence and impact on the condition and quality of natural and semi-natural environments has increasingly been assessed and presented in terms of ecosystem services, which can be defined as the benefits that humans derive from ecosystems (MA 2005), mainly in order to make them more “visible” and more easily integrated into and accounted for in policy decision making processes. Studies examining their definition, categorization and measurement have been underway since the early 1990s (De Groot 1992; Costanza et al. 1997; Daily 1997). Various efforts to map, model and assign measurable values to ecosystem services at the landscape level were the subject of later research (De Groot et al. 2002; Troy and Wilson 2006; Willemen et al. 2008; Burkhard et al. 2009; Nelson et al. 2009; Tallis and Polasky 2009). Development of a standardized ecosystem service classification system and valuation approach for decision-making became a priority. The Millennium Ecosystem Assessment (MA 2005) proposed a widely used classification system of ecosystem services under the headings of supporting, regulating, provisioning and cultural services. The Economics of
32 Ecosystems and Biodiversity (TEEB) initiative was launched in 2007 to offer a structured approach to valuation of ecosystem services and biodiversity for policy makers (European Communities 2008). The Common International Classification of Ecosystem Services (CICES) system was published in 2013 and has recently been revised (Haines-Young and Potschin 2018). Using CICES for typology, Maes et al. (2016) presented an analytical framework and proposed indicators for assessing the state of ecosystem services in the European Union.
Urban areas rely heavily on ecosystem services to meet the needs of the populations they contain and yet the expansion of urban regions can have serious detrimental effects on ecosystem services provided by the surrounding natural landscape (Alberti 2005; Seto et al. 2012; Newbold et al. 2015). Bolund and Hunhammar (1999) were among the first to apply the concept of ecosystem services specifically to urban areas and Haase et al. (2014) and Luederitz et al. (2015) have performed quantitative reviews of urban ecosystem service assessment research. Urban ecosystem services such as air filtration, microclimate regulation, water regulation, waste treatment and recreational/cultural values are important because of their direct effects on human health and security (Bolund and Hunhammar 1999; Gómez-Baggethun et al. 2013).
Remote sensing data has increasingly been used to gauge the extent and condition of ecosystem services. Feng et al. (2010) outlined the use of remote sensing to monitor land cover, biodiversity and carbon-, water- and soil- related ecosystem services and noted that monitoring can be done directly, indirectly or in combination with ecosystem models. Alcaraz-Segura et al. (2013) reviewed the use of remote sensing to quantify and monitor ecosystem services, particularly in relation to carbon and water cycles, biodiversity and energy balance. De Araujo Barbosa et al. (2015) summarized remote sensing variables used in the assessment and valuation of ecosystem services and Andrew et al. (2015) reviewed and identified information needs for spatial assessments of ecosystem services. Recently, there has been more research interest and specific efforts to map and/or monitor urban ecosystem services based on or in connection to remote sensing data (e.g. Lakes and Kim 2012; Raciti et al. 2014; Haas and Ban 2017; Dobbs et al. 2018).
Land cover figures as one of the most oft used remotely sensed ecosystem service indicators (de Araujo Barbosa et al. 2015). The use of land cover as a proxy measure of ecosystem services is possible thanks to its multiple linkages to a range of services such as watershed protection and carbon storage (Konarska et al. 2002). Several approaches for analyzing change in ecosystem services from land cover have emerged, including monetary valuation and bundling. With the former, the services provided by the ecosystem present in
33 each land-cover type are identified and assigned an areal monetary value based on calculations and previous research, and these are summed based on the extent of the particular land cover (Ayanu et al. 2012, e.g. Costanza et al. 1997; Wu et al. 2013). In this way, land-cover change can function as an indicator of change in ecosystem value and to some extent condition. But the question of how to assign value to ecosystem services is challenging and the focus of much research and debate (Costanza and Folke 1997; Ludwig 2000; Turner et al. 2003; Chee 2004; De Groot et al. 2010; Turner et al. 2010; Davidson 2013; Costanza et al. 2017). Ecosystem service bundling most often focuses on the spatial coincidence of delivery of a range of services (Berry et al. 2016). Mapping these provides a basis for assessment (e.g. Nelson et al. 2009), the analysis of which can be enhanced with additional datasets such as biophysical properties (Van der Biest et al. 2014) or level of regulation or accessibility of areas (Itkonen et al. 2015). A relatively new development in remote sensing based assessments of urban ecosystem services in connection to land cover is the mapping and importance of urban green spaces and green infrastructure (Derkzen et al. 2015; Calderón-Contreras and Quiroz-Rosas 2017; Kopecká et al. 2017; Banzhaf et al. 2019). Green infrastructure can be defined as a network of natural and semi-natural areas managed and designed to deliver a wide variety of ecosystem services (European Commission 2013). Changes in the spatial attributes of ecosystem service- providing land cover, such as green infrastructure, in urban areas have not often been examined and there is opportunity for further research in this area (Frank et al. 2012; Haas and Ban 2018). Multi-temporal remote sensing-based urban land-cover classifications provide a basis for assessment of these environmental spatial changes and estimation of their influence on ecosystem service provision.
2.2.3 Connectivity analysis to assess environmental impact
Certain landscape metrics provide the possibility to evaluate landscape connectivity, and connectivity measures comprise a central City Biodiversity Index indicator (Secretariat of the Convention on Biological Diversity 2013). Habitat connectivity plays a significant role in biodiversity conservation due to its effect on meta-population dynamics (Hanski et al. 2008). Network analysis of landscape connectivity offers the possibility to examine the configuration and interconnections of habitat patches and their relative importance for maintenance of biodiversity. Graph-theoretic approaches to modeling landscape connectivity, which respresent the landscape as a set of nodes (habitat patches) and links (connecting elements), have been developed (e.g. Urban and Keitt 2001; Estrada and Bodin 2008; Bodin and Zetterberg 2011) and increasingly used in conservation planning and ecological
34 assessments (e.g. Zetterberg et al. 2010; Saunders et al. 2016; Huang et al. 2018). Of the many graph-theoretic indices available, the probability of connectivity index (PC) has characteristics that make it well-suited to assessment of functional connectivity across a range of scales. PC is based on a probabilistic connection model and its value is defined as the probability that two randomly selected habitat patches are connected based on the defined dispersal behavior and the structure of the landscape in question. Saura and Pascual-Hortal (2007) developed the index and compared it to other landscape indices and their respective detection capabilities, where it performed well. Saura and Rubio (2010) further conceptually developed this index and demonstrated its flexibility in analysis by showing how it can be separated into three component fractions representing different ways in which a node can contribute to habitat availability in the network. The PC index has since been increasingly used in landscape ecological assessments (e.g. Perez- Hernandez et al. 2015; Herrera et al. 2017; Diniz et al. 2018). It is applied in Paper V to assess changes in habitat availability and connectivity for a representative species dependent on coniferous forest in Stockholm City.
2.3 Stockholm green infrastructure monitoring
Four out of five of the articles in this thesis relate directly to the Stockholm area and consultation with local and regional planning authorities in Stockholm has influenced research choices in some of the work. This section provides context for the Stockholm related research and an overview of management approaches for and monitoring of green infrastructure in this metropolitan region.
Regional green structure assessments by planning authorities in Stockholm began appearing in the 1980s and 90s (e.g., Stockholms Läns Landsting Regionplanekontoret 1985, RTK 1992b and 1996). Since then and with the advent of more readily available remote sensing data and advances in mapping and GIS techniques, several spatial urban environmental and ecological studies in Stockholm have been undertaken at the municipal level (e.g. Löfvenhaft et al. 2002 and Lundberg et al. 2008) and at the regional metropolitan level (e.g. Mörtberg and Wallentinus 2000 and Colding et al. 2003). Fewer studies have been performed at the county level and beyond (e.g., Karlson and Mörtberg 2015; Ekologigruppen 2017).
In 2003, the Stockholm Urban Assessment (SUA) was selected as a sub-global assessment example within the global Millennium Ecosystem Assessment. A main objective of more recent SUA research is to raise awareness among planners and policy makers of the role that Stockholm green structure plays in
35 generating ecosystem services. Both the SUA and its revisitation in 2013 were based on a study area that encompasses the urban metropolitan core of Stockholm but that represents only 15% of the total land area of Stockholm County (Colding 2013). While there is considerable political will to preserve green structure in the Stockholm region, both studies state that it continues to be steadily reduced and fragmented by urban expansion (Colding et al. 2003; Colding 2013). Colding (2013) acknowledges that there is a lack of data on how much green structure has been lost in the most recent decades (i.e. since the 1990s) and Colding et al. 2003 and Borgström 2003 emphasize the importance of shifting the management/research perspective from the local to the regional landscape scale. Hence, there is a need for recent and reliable information on urban expansion-induced loss of green structure in Stockholm, particularly at the county level.
In response to the Swedish government’s recently adopted requirements regarding planning for green infrastructure, the Stockholm County Administrative Board (SCAB) drafted a green infrastructure action plan. One of the key considerations when it comes to evaluating and planning for Stockholm’s green infrastructure is change in relation to forests, specifically the loss of valuable core areas or important links due to clear cuts or exploitation (SCAB 2018). Analysis of core areas and ecological corridors for both coniferous/mixed and broadleaved/deciduous forests was performed as a basis for the draft action plan’s conclusions and recommendations pertaining to forested areas by Ekologigruppen AB (2017). In 2018, the Stockholm County Growth and Regional Planning Administration (TRF) produced a new regional development plan, known as RUFS 2050; the previous plan being valid for 2010. In the regional development plans, a network of 10 green wedges, which follow the urban structure from the outskirts of the metropolitan area in towards the center of Stockholm, represent the region’s green infrastructure. These large green areas provide flora and fauna with habitat and the possibility of dispersal as well as many of the region’s ecosystem services (TRF 2018). The first regional plan for Stockholm appeared in 1958, but the concept of green wedges was not introduced until the mid-1980s (Stockholms Läns Landsting Regionplanekontoret 1985) and began to receive more planning attention and evaluation in the early 1990s (Regionplane- och Trafikkontoret 1992a and 1992b). They continue as a central environmental planning and conservation component in each new regional plan.
The draft regional green infrastructure action plan, the SUA and other Stockholm planning actors via meetings and conferences (e.g., IALE 2017) have expressed the need to “see beyond” administrative boundaries which most often do not coincide with ecoregion boundaries. Much of the planning process in the Stockholm region happens on the municipal level but
36 municipalities often have difficulty in obtaining a clear, wholistic picture of factors that cross city boundaries. The regional action plan points out specifically that green connections and valuable areas that cross municipal boundaries need to receive more planning attention so that the functions and value of the green wedges in particular do not disappear (SCAB 2018). Comparison of classified remotely sensed urban land-cover data with administrative boundaries and significant green infrastructure can reveal these transboundary “hotspots.”
On a local level, the City of Stockholm has invested in the development of geographic information about the significance of its green infrastructure in order to facilitate the integration of landscape ecological aspects of biodiversity into planning and environmental assessment. The environmental management department of Stockholm City commissioned local researchers (Mörtberg et al. 2006 and 2007) to develop landscape ecological assessment tools and materials, and the results include habitat network geographic datasets for representative focal species in the Stockholm region developed based on available environmental geographic data and expert knowledge- based ecological profiles. Likely impacts on the habitat networks were estimated based on a then current city planning scenario. Following creation of the habitat network datasets, Stockholms City’s environmental management department developed a geographic dataset representing ecologically significant areas in the municipality (Miljöförvaltningen 2014). This dataset was created based on the different representative habitat networks and on the city’s other geographic environmental information sources such as the oak database, the city biotope map and the sitings database for red listed species (ArtArken). Ecologically significant areas in the dataset are divided into three categories, referred to as ecologically significant 1) core areas, 2) habitat for species of conservation concern and 3) dispersal zones. The ecological qualities of core areas mean that they are often home to a wide variety of plant and animal life and play an important role in maintaining ecosystem function. Habitat for species worth protecting is based on location information from the sitings database for red listed species and their habitat requirements. Dispersal zones are areas important for the movement of species between habitat patches or core areas.
An important resource in planning for future urban development is understanding how regional land cover has changed and how urban areas have developed in the past. This presupposes the ability to compare urban areas historically in order to identify and understand the trends of urban growth in a metropolitan region. However, more often than not, available information and historical and current datasets pertaining to urbanization are incomparable due to the differing methods and data sources used to produce them. For example, Statistics Sweden, the government body responsible for official
37 statistics, cautions that its areal statistics for built-up areas and land use per county for the year 2010 “are not comparable with statistics published in earlier editions of the statistical yearbook due to changes in methods and data” (Statistics Sweden 2014). Consistent methodology and data sources are fundamental to long-term studies of urban development but are often in reality not feasible due to lack of available data or changes and advances in technology which render new and improved results incomparable to earlier ones. Change analysis based on remotely sensed urban land-cover classifications possesses the advantage of uniform methods and like data sources that ensure spatio-temporal comparability.
3 Study areas and data description
Environmental impact from urbanization was examined at three different spatial scales in this research. A regional extent (roughly 6 000 to 7 000 km2) characterized the study areas of the Greater Toronto Area, Canada, Stockholm County, Sweden and Shanghai, China. The metropolitan area was also examined in Stockholm (roughly 2 000 km2) and the final study involved more detailed analysis over the City of Stockholm (municipal, approximately 200 km2).
Table 1 provides summarized information on the multispectral remote sensing data used and Table 2 provides an overview of geographic datasets used for each study in the thesis.
38 Table 1 Overview and characteristics of the multispectral data used in each study Paper Study Area Mission Bands used Scenes Date/Year nr (res.) I Greater Toronto Landsat 5 TM R, NIR, 6 1985, 1995, Area SWIR (30m) 2005 II Stockholm SPOT-1/-5 G, R, NIR 4 1986, metropolitan (20/10m) 2006/2008 area III Stockholm Landsat 5/7 R, NIR, 12 1989/1990, County and TM/ETM+ SWIR (30m) 2000/2002, Shanghai 2009/2010 IV Stockholm Sentinel-2A 2-8A, 11, 12 1 August 23, County (10m) 2015 IV Stockholm SPOT-5 G, R, NIR, 10 2005-2008 County SWIR (10m) V Stockholm City WorldView-2 B, G, R, NIR 2 July 4, 2018 (0.5m) V Stockholm City QuikBird-2 B, G, R, NIR 2 2003/2005 (0.6m)
Table 2 Overview of the geographic datasets used in each study Paper Study Area Geographic dataset Source/Provided by nr I Greater Toronto Oak Ridges Moraine Geological Survey of Area boundary Canada I Greater Toronto Regional watershed Toronto Region and Area boundaries; Conservation Authority Environmentally significant areas II Stockholm Outdoor noise levels in Stockholm Regional and metropolitan Stockholm County Traffic Planning Authority area IV Stockholm Nature reserves; Stockholm County County Deciduous ecocorridors Administrative Board (Länsstyrelsen) IV Stockholm RUFS green wedges, core Stockholm Growth and County areas and green weak links Regional Planning Administration (TRF) V Stockholm City Ecologically significant Stockholm City green infrastructure; Environmental Crested tit habitat Management Department (Miljöförvaltningen)
39 3.1 Greater Toronto Area
The Greater Toronto Area (GTA) is the most populous metropolitan area in Canada and is one of the fastest growing urban areas in North America. The GTA’s population grew by roughly 1.75 million between 1985 and 2005 to reach a total of 5.5 million. Located on the northwest shore of Lake Ontario with an area of 7 125 km2, it includes Metropolitan Toronto and four other regional municipalities (Durham, Halton, Peel and York) with a combined population of about six million. The Oak Ridges Moraine (ORM), an environmentally significant and sensitive area that lies north of Toronto, covers an area of 1 900 km2, is the largest glacial remnant in Ontario and acts as a groundwater recharge/discharge area for approximately 65 watercourses. In addition to its importance for water quality in the region, the ORM contains most remaining natural areas in the GTA bio-region including forests, wetlands, and various plant and animal species, and provides for most of the recreational opportunities for the GTA’s significant population (Government of Ontario 2019; Richmond Hill 2019). Yet the increase of built-up areas on the ORM has raised concern pertaining to water quality and quantity. Increased water withdrawal and contamination could impact those living in the GTA, fisheries, wildlife and conservation. Future urban growth within the surrounding municipalities could have significant impact on the important resources that the ORM provides and on the overall environmental quality of the region.
Landsat 5 TM imagery with 30 m spatial resolution was acquired from three different years for the GTA study: 18 July and 12 August 1985, 30 July and 24 August 1995, and 2 and 25 July 2005. Two scenes (adjacent orbit paths) were necessary for each year in order to obtain coverage of the whole GTA. Images from summers acquired on near anniversary dates/months were selected to provide the maximum differentiation of major land-cover classes and comparable classification results from various years. The major land- cover classes in the area are: water, forest, golf courses, agriculture, low- density built-up (residential areas), high-density built-up (including roads and industrial areas), construction sites and parks/grassy fields. The study area over the GTA is approximately 7 600 km2 and is outlined in yellow in Figure 3.
40
Figure 3 2005 Landsat TM images and study extent of the Greater Toronto Area (Red: TM4, Green: TM5, Blue: TM3) shown with regional municipal boundaries in yellow, the Oak Ridges Moraine outlined in white and TRCA watersheds (within which environmentally significant areas were designated) outlined in dark gray.
3.2 Stockholm County, metropolitan area and City
Sweden’s capital, Stockholm, is located on the country’s east coast and includes a large archipelago extending into the Baltic Sea. Stockholm municipality covers land area of around 188 km², while Stockholm County covers 6 519 km² (Stockholms stad, 2018). The Stockholm County landscape is characterized by its green wedges, which make up the majority of the region’s green infrastructure which in turn plays a crucial role in providing the region’s ecosystem services. The first to receive the Green Capital Award from the European Union in 2010, Stockholm is often cited as a city that leads in sustainable urban development and in preserving the balance between its
41 green and built-up spaces (Metzger and Olsson 2013; Nelson 2006). Over the past few decades, there has been substantial urban growth in Stockholm, now the largest city in Scandinavia. Growth in the area is due mainly to the increasing population, which rose by 22% between 2000 and 2015 in Stockholm County. Urbanization due to population increase and the demand for new housing is placing pressure on the natural environment such as the green wedges in and around Stockholm. The regional planning authorities presently have a goal of creating 22 000 new dwellings each year up to 2030 (TRF 2018).
Four separate investigations of the Stockholm area have been performed. They are based on different types of remote sensing imagery and examined different study area extents, which will hereafter be referred to as the Stockholm metropolitan area (study area in Paper II), Stockholm County (one of two study areas in Paper III and the study area in Paper IV) and Stockholm City (study area in Paper V). Paper II presents the results from the first SPOT image-based Stockholm investigation. Two scenes of SPOT-1 and two scenes of SPOT-5 imagery with green, red and near-infrared bands over the Stockholm area were acquired for this study: two on 13 June 1986, one on 5 August 2006 and one on 4 June 2008. The images were selected from the peak of the vegetation growth season to maximize the spectral differences between built-up areas and vegetation and to avoid detection of unreal changes caused by seasonal differences between years. Two scenes from 2006 were sought but an appropriate 2006 growing season image over the northern parts of Stockholm County was not available. The scene from 2008 is therefore used as a substitute. The SPOT imagery from 1986 was from SPOT 1 with a resolution of 20 m, while that of 2006 and 2008 was from SPOT 5 with a resolution of 10 m.
3.2.1 Stockholm metropolitan area
The study area in Paper II is based on the extent of the satellite images and, excluding an edge buffer, includes all or part of the territory of 18 municipalities in Stockholm County (see left-hand image in Figure 4). The total land area covered by the satellite images is approximately 2 220 km2. For this study, the major land-cover classes are low-density residential areas (LDB), high-density residential built-up areas (HDB), industrial/commercial areas, forest, open land, forest and open land mixed, and water. The HDB class is unique since much of the high-density built-up areas in the city center of Stockholm are composed of buildings that have businesses on the ground floor but residences on the upper floors. Therefore the HDB areas classified in the center of Stockholm do include some commercial areas, which could at the
42 same time be classified as residential. In contrast, the industrial/commercial class includes, for example, industrial park areas as well as shopping centers.
Figure 4 Study area extents from Papers II, III, IV and V: To the left, the Stockholm County municipalities included in the study in Paper II are outlined in white and labeled in yellow with 2006 and 2008 SPOT-5 imagery as backdrop. To the right, the boundary of Stockholm County is outlined in yellow with 2009/2010 Landsat ETM+ imagery as backdrop. The County line is the extent of the study area in Papers III and IV. Stockholm City, the study area in Paper V, is outlined in white in the right-hand image.
A number of geographic datasets were collected from several national and regional Swedish sources for this study, namely Lantmäteriet (The Swedish National Land Survey), Regionplanekontoret Stockholms Läns Landsting (RTK) and Storstockholms Lokaltrafik (SL). These included data on land cover and transportation networks (Lantmäteriet), public transport stops/stations (Lantmäteriet and SL), and large parks and noise-disturbed areas (RTK). This data was used in the construction of environmental indicators for the greater Stockholm area.
43 3.2.2 Stockholm County
Stockholm County, which covers nearly 7 000 km2 (excluding water), is the basis for the study area of the second and third Stockholm investigations described in Papers III and IV (see the right-hand image in Figure 4). In Paper III, six scenes of Landsat 5 and 7 imagery, two for each decade to cover the entire Stockholm County area, were acquired: two on 7 July 1989, and one each on 24 September 2000, 4 August 2002, 28 June 2009 and 24 June 2010. In some cases, the difference in image dates is not exactly 10 years but can deviate up to two years due to the fact that there are no images available at the same anniversary or that images that lie closer to the decennial anniversary suffer from high cloud cover. The images are acquired in the same vegetation period from May to September and are considered the most suitable Landsat images for the purpose of the study. The major land-cover classes in the area, given the type of imagery and classification technique employed in this study, are low-density residential areas (LDB), high density built-up areas (HDB) including industrial/commercial areas, forest, agricultural/open land, parks/urban green areas, and water.
The study area in Paper IV is Stockholm County, which contains 26 municipalities including Stockholm city (see Figure 5). The total analysis area is roughly 13 790 km2, of which land area is approximately 6 690 km2. The major land-cover classes for this study are similar to those of the others but expanded thanks to the higher spatial and spectral resolutions of the data used: high-density residential/commercial built-up areas (HDB), roads/railway, low-density built-up areas (LDB), urban green space, golf courses, forest, agriculture, bare rock/clear cut areas, wetlands and water. Some of the above land-cover classes can benefit from further explanation. HDB in this study over Stockholm County also includes industrial, commercial or construction areas such as industrial parks or shopping centers more likely to be found on the outskirts of urban areas. The roads/railway class also includes airport runways. The LDB land-cover class for Stockholm is characterized by suburban residential areas with vegetation around or between buildings. Prime examples of urban green space are city parks or sports fields, but the class also includes any otherwise undesignated green area in or around urban areas. The eastern part of the Stockholm County region is characterized by the archipelago with its more than 30 000 islands. Stockholm City itself is partially built on a group of islands. Thus, part of the natural geomorphology of this region includes frequent occurrence of bare rock outcroppings especially along the coast. The bare rock/clear cut class takes into account these outcroppings as well as clear cut formerly forested areas which often expose a mix of the bare rock/soil land surface underneath.
44
Figure 5 Study area from Paper IV: Stockholm County and municipality boundaries are outlined in yellow and white respectively. Municipalities are labeled in yellow. Green wedges and nature reserves located within the county are also shown with Sentinel-2A imagery from August 23, 2015 as backdrop.
45 The remotely sensed data used in Paper IV comes from the Sentinel and SPOT satellite systems. A single Sentinel-2A MSI image at product level 1C captured on August 23, 2015 and covering the majority of the geographic area of Stockholm County was acquired and used in classification. It was necessary to acquire ten scenes of SPOT 5 imagery at product level 1A in order to cover the whole of Stockholm County. For some sections of the county over which no suitable imagery from 2005 could be found, good quality SPOT imagery from the summer months of the closest year possible were used. The dates of the ten images are as follows: 16 June 2005 (2 images), 2 July 2005 (1 image), 11 July 2005 (1 image), 2 September 2005 (1 image), 5 August 2006 (1 image), 23 May 2007 (1 image), 4 June 2008 (2 images) and 19 September 2008 (1 image). The 2005 imagery covers approximately 50% of the study area, 2006 and 2007 imagery cover about 10% each and the imagery from 2008 covers roughly 30%. The images were selected from the full vegetation growth season to maximize the spectral differences between built-up areas and vegetation and to thereby reduce detection of unreal changes caused by seasonal differences between years. The G, R, NIR and SWIR bands of the SPOT 5 satellite imagery, which have 10m spatial resolution, were used for segmentation and classification. The B, G, R and NIR bands of Sentinel-2A have 10 meter spatial resolution while the four red edge bands (5, 6, 7 and 8a) and the two SWIR bands (11 and 12) have 20 meter spatial resolution.
3.2.3 Stockholm City
Stockholm City, which contains 14 city districts (see Figure 6), is the study area in Paper V. The total area included in the analysis of this study is approximately 216 km2, of which land area is approximately 189 km2. The major land-cover classes in the region are buildings, transport/construction, urban green structure, grass/open fields, coniferous forest, deciduous forest, rock outcrop, barren land/soil, agricultural land, wetlands and water. The transport/construction class includes roads, railway, airport runway and pavement in general, as well as construction and industrial extraction sites. Prime examples of urban green structure (UGS) are city parks, allotment areas or gardens, but the class also includes any otherwise undesignated green area composed of a mix of trees, shrubs and grass in or around urban areas. Part of the natural geomorphology of the Stockholm region includes frequent occurrences of bedrock outcroppings especially along the coastline. The rock outcrop class takes into account this geomorphological trait.
46
Figure 6 The Stockholm City study area used in Paper V. District boundaries are outlined in white and labeled in yellow. The three types of ecologically significant green infrastructure are shown in different shades of blue with WorldView-2 imagery from July 4, 2018 as backdrop. (Satellite image courtesy of the DigitalGlobe Foundation)
Two WorldView-2 (WV-2) images at product level Standard 2A captured on July 4, 2018 and covering slightly more than the geographic area of Stockholm City were acquired for classification. Two scenes of QuickBird-2 (QB-2) images at product level Standard 2A captured on June 14, 2003 (covering approx. 90% of Stockholm City) and September 6, 2005 (covering approx. 10% of Stockholm City) were also acquired. Efforts were made to obtain images from the full vegetation growth season to maximize the spectral differences between built-up areas and vegetation and in order to avoid detection of non-permanent changes caused by seasonal differences. The same four multispectral bands from both sets of imagery were used for segmentation and classification, namely the blue, green, red and near infrared bands. Only four of the eight multispectral bands available in the WV-2 data were used in
47 order to reduce processing time and to not exceed computer memory constraints as well as to match the spectral input for the segmentation and classification of the 2003 QB-2 imagery.
3.3 Shanghai
Shanghai is located on the eastern seaboard of China and is a major financial center for the country. Greater Shanghai is also a productive agricultural area (Zhang et al. 2011). Population growth in the region has been tremendous over the past few decades, with an increase of 72% between 1990 and 2010. From 23.03 million in 2010, its total population is expected to reach 28.4 million by 2025 (United Nations 2012). The rapid urbanization that has accompanied this impressive growth has put pressure, specifically in the form of pollution, on the surrounding ecosystem (Ren et al. 2003).
One of the two study areas in Paper IV is the total area of Shanghai, which measures about 6 340 km2. Land cover types in this region include HDB areas, commercial and industrial areas, ports, airports and residential areas. Agriculture tends to border the urban areas, with strips of rural areas including villages and farms. There are varying forms of water such as sea, lakes, rivers, aquacultures and wetlands. Trees and forests tend to be scarce and connected stands are found almost exclusively in managed urban parks.
Landsat was chosen as image data for the joint study over Stockholm and Shanghai given its standardized high quality and immediate availability. Here again six scenes of Landsat 5 and 7 imagery, two for each decade to cover the entire Shanghai area, were acquired on the following dates: 18 May 1987, 11 August 1989, 14 June 2000, 3 July 2001, and two on 17 July 2009.
4 Methodology
Each study presented here was composed of several methodological steps ranging from pre-processing of satellite imagery to calculation of environmental indicators based on classification results. The general approach in each research was to process the imagery, classify it, assess classification accuracy, calculate environmental indicators and indices and evaluate the results. Figure 7 illustrates how the image processing and classification methodologies compare for each study area. The flowchart in Figure 8 compares environmental indicator calculation for each study area.
48
Figure 7 Flowchart comparing image processing and classification methodologies for each study area
4.1 Image processing
4.1.1 Image pre-processing
Satellite images for the Greater Toronto Area and Stockholm metropolitan area studies (Papers I and II) were geometrically corrected with the help of regional topographic maps based on at least eight GCPs for each image and with a root-mean-square error of one third of a pixel or lower. The Stockholm/Shanghai Landsat imagery (Paper III) did not require geometric correction since it was issued by the Global Land Survey in the most appropriate coordinate system for the study. Each pair of scenes was then mosaicked using neighborhood color balancing. With regard to imagery used in Paper IV, Sentinel-2 Level 1C images are radiometrically and geometrically correct when delivered and the only pre-processing step necessary was pan- sharpening of the 20 m resolution spectral bands (5-7, 8a, 11, 12) to 10 m. Since level 1A processing of SPOT imagery includes radiometric but not geometric correction, it was necessary to orthorectify each of the 10 SPOT images using GSD-Elevation data, grid 50+ nh (Lantmäteriet 2016) and the rational polynomial coefficients (RPC) provided within the image metadata. We performed RPC bundle adjustment using at least eight manually collected tie points for each SPOT image with reference to the Sentinel-2A image. As
49 to the imagery used in Paper V, processing standard 2A provides WV-2 and QB-2 images that are radiometrically corrected and orthorectified. Thus only two pre-processing steps were necessary. First, the WV-2 and QB-2 multispectral bands (spatial resolutions: 2 m and 2.4 m respectively) were pansharpened to 0.5 m and 0.6 m respectively based on their panchromatic bands (Zhang 2002). Secondly, since two scenes of both types of imagery were required to cover the geographic area of Stockholm City, both pairs of images were mosaicked. Once these steps were completed, however, a slight shift (around 5-10 meters) due to a residual geolocation error was discovered between the QB-2 and WV-2 images. Given the higher geolocation accuracy of the WV-2 sensor (CE90 <3.5m) compared to the QB-2’s (CE90 23m) (DigitalGlobe 2014 and 2016), the WV-2 image was used as the reference image for co-registration which was completed with sub-pixel residual error and corrected the problem.
4.1.2 Texture analysis
Texture, or the spatial distribution of tonal variations in an image, can aid in the identification of objects or regions of interest in an image (Haralick et al. 1973). Gray level co-occurrence matrix (GLCM)-generated texture features have been particularly useful in urban areas (Shaban and Dikshit 2001; Herold et al. 2003; Ban et al. 2015; Gamba and Aldrighi 2012). Haralick textural analysis was performed in all studies to improve classification accuracy. Based on trials and findings from past research (Baraldi and Parmiggiani 1995; Clausi 2002), GLCM mean, standard deviation (variance) and correlation textures of the infrared band for the GTA (Paper I) and the red band for the Stockholm metropolitan area study (Paper II) were selected. For the Stockholm-Shanghai study (Paper III), variance was calculated on Landsat bands 4 and 5. For the Stockholm County classifications involving Sentinel- 2A and SPOT-5 data (Paper IV), trials revealed that a gray-level difference vector (GLDV) contrast feature calculated across all bands yielded better results than use of GLCM mean, contrast or standard deviation features calculated based on the red or near-infrared bands. GLDV was used for similar reasons as a feature in OBIA classification of the high-resolution data over Stockholm City (Paper V).
4.2 Image classification
There were four classification methods used in this research: 1) supervised pixel-based classification using the maximum likelihood classifier (MLC) algorithm with textural and spectral data inputs (parametric); 2) object- and
50 knowledge-based classification with textural and spectral data inputs (non- parametric); 3) pixel-based support vector machine (SVM) classification (non-parametric) with textural and spectral data inputs; and 4) object-based SVM classification with shape, textural and spectral input features. These were selected progressively based on appropriateness for the study area, degree of prior knowledge and remote sensing data available, as well as the experience and degree of success of other authors in using them to conduct studies of land-cover change over metropolitan regions, as outlined in the literature review above.
4.2.1 Maximum likelihood classification
The maximum likelihood algorithm (MLC) was used to classify Landsat imagery based TM bands 3, 4 and 5 and the above-mentioned texture features for the GTA (Paper I). Eight land-cover classes were classified: water, forest, parks, golf courses, agriculture, HDB, LDB and construction sites. Eight different types of agriculture were initially classified and later aggregated to form the agriculture class. When issues of separability between certain urban vs. natural land-cover types arose, it was decided to create two broad land- cover masks for rural and for urban areas and to perform two separate MLC classifications under these.
4.2.2 Object-based image analysis and rule-based classification
For the Stockholm study utilizing only SPOT (Paper II), the images were first segmented using eCognition based on several input data: spectral data, GLCM texture measures and a preliminary MLC of the SPOT data. The scale parameter was 20 and the homogeneity criteria were set at 0.1 for shape and 0.5 for compactness. These parameters were selected based on trials and provided the most appropriate objects for classification in that they most closely represented discrete areas of the different land-cover types found in the region, namely water, LDB, HDB, industrial/commercial areas, forest, forest and open land mixed, and open land. The resulting objects were then classified sequentially into the major land cover types through the construction of rules based on the object’s mean value of various input data.
51 4.2.3 Support vector machine classification
For the Stockholm-Shanghai study utilizing Landsat data (Paper III), an SVM classifier was used on bands 3, 4 and 5 plus the texture features mentioned above to identify the following land-cover classes in Stockholm: LDB, HDB (including industrial/commercial areas), forest, agricultural/open land, parks/urban green areas, and water. Urban and rural masks were subsequently used to correct the more consistent misclassifications, most often caused, in the case of Stockholm, by confusion between HDB and bare agricultural fields and between forest and LDB areas.
4.2.4 OBIA SVM classification
An object-based analysis approach combined with the SVM classifier was used to classify the Sentinel-2A and SPOT-5 imagery over Stockholm County (Paper IV) and the WV-2 and QB-2 scenes over Stockholm City (Paper V). Bands 1-8a, 11 and 12 from Sentinel-2A and all four SPOT 5 bands were used in segmentation and classification. For segmentation, scale parameters of 15 for 2005 and 50 for 2015 were used and homogeneity criteria were set at 0.1 for shape and 0.5 for compactness for 2005 and 0.7 for both criteria for 2015. Training samples were gathered for 19 different classes initially: 4 for HDB, 1 for LDB, 3 for roads, rail and runway, 3 for UGS, 1 for golf courses, 2 for agriculture, 2 for forest, 1 for water, 1 for bare rock/clear cuts and 1 for wetlands. The classes were aggregated following classification. Various object features and classification algorithms were tested. The best results were obtained from the SVM classification algorithm trained with statistics from one textural, three shape (asymmetry, roundness and rectangular fit) and two spectral data features (mean and standard deviation of the above-mentioned spectral bands). Following classification, proximity and length thresholds, ancillary data and a set of rural masks were used to correct misclassification errors.
Segmentation, feature extraction and classification of the high-resolution imagery (Paper V) was performed based on the B, G, R and NIR bands. For segmentation, scale parameters of 100 for 2003 and 125 for 2018 were used and homogeneity criteria were set at 0.1 for shape and 0.5 for compactness for both years. Training samples were gathered for 17 different classes initially: 6 for buildings, 2 for transport/construction, 1 for UGS, 2 for grass/open fields, 1 for coniferous forest, 1 for deciduous forest, 1 for bare rock, 1 for barren land/soil, 1 for water and 1 for shadow. Given their limited extent, wetlands and agricultural areas were masked in after classification. Spatial relationships between classes such as relative border thresholding were used to re-assign
52 shadow, and low-density urban and forest masks were used to correct misclassifications.
To remove noise such as very small classification artifacts, all classifications were filtered using the sieve tool in PCI Geomatica with a filter size of a few pixels as a final processing step.
4.3 Accuracy assessment
Once the classifications were completed, accuracy assessments were performed using random sample vector points for verification of each land- cover category. Overall accuracy, the Kappa statistic, and user’s and producer’s accuracies were calculated and used as accuracy measures.
Figure 8 Flowchart comparing environmental indicator calculations for each study area
4.4 Landscape metrics
Based on the issues outlined in section 2.2.1 related to the use of landscape metrics, a core set of landscape metrics was selected for analyzing changes in each of the study locations. Table 2 provides a brief description of each of the metrics calculated in this research. Table 3 shows in which study area each metric was applied.
53
Table 3 Compiled set of landscape metrics used in the current research Landscape Composition Description Metrics The percentage or proportion of each class in the Class Area Percentage landscape. The Class Area (CA) calculated by (PLAND) Fragstats is divided by the total landscape area to obtain this percentage. Patch Density (PD) Number of patches per square kilometer Mean Patch Size (MPS) or (Area-weighted) average patch size of a class of Area-weighted Mean Patch patches Size (AMPS) A simple measure of dominance; quantifies at the Largest Patch Index (LPI) class level the percent of landscape area comprised by the largest patch.
Landscape Configuration Description Metrics Mean Patch Shape Index (Area-weighted) average of the ratio of perimeter (PSI_MN) or Area-weighted to minimum possible perimeter given the number Mean Shape Index of cells in the patch; measure of shape (AWMSI) complexity. Area-weighted mean The sum of the ratios of patch perimeter to area perimeter to area ratio multiplied by proportional patch abundance (AWMPAR) Quantifies edge contrast (degree of contrast Total Edge Contrast Index between a patch and its neighbors) as a (TECI) percentage of maximum possible Measured in meters per hectare: CWED = 0 when there is no class edge in the landscape. CWED Contrast-weighted Edge increases as the amount of class edge in the Density (CWED) landscape increases and/or as the contrast along the class edges increases. Core area represents the area in the patch greater than the specified depth-of-edge distance from the Total Core Area (TCA) perimeter. TCA is an aggregation of core areas over all patches of the corresponding patch type. Measures relative area-weighted distance between Area-weighted Mean patches of the same class – measures degree of Proximity Index (AMPI) isolation of corresponding patch type Also measures area-weighted distance but Area-weighted Mean considers size, proximity and similarity of all Similarity Index (AMSI) patches within a specified search radius of the focal patch Measures the physical connectedness of the Cohesion corresponding patch type; cohesion increases as
54 the class becomes more clumped or aggregated in its distribution. Measures connectivity of the land-cover type and is defined based on the number of functional joinings between patches of the corresponding Connectance index patch type, where each pair of patches is either (CONNECT) connected or not based on a user-specified distance criterion. Reported as percentage of the maximum possible connectance given the number of patches in the land-cover class A measure of the relative aggregation of different patch types at the landscape level: Contagion approaches 0 when the patch types are maximally Contagion (CONTAG) disaggregated and interspersed. CONTAG = 100 when all patch types are maximally aggregated; i.e., when the landscape consists of a single patch. A measure of diversity over the whole landscape. Shannon’s Diversity Index SHDI increases as the proportional distribution of (SHDI) area among patch types becomes more equitable.
For the selection rationale for the metrics used with the GTA landscape, please refer to the Methodology section of Paper I.
For the Stockholm research utilizing SPOT data, the difference in resolution of the images could compromise the comparability of metrics generated from them. While the creation of objects helps to reduce this risk, the seven original land-cover classes were further aggregated to three larger classes: urban, natural and water classes for generation of the metrics. The area-weighted shape metrics are used since they give more weight to larger patches. The influence of the original difference in resolution of the classifications might thereby be further minimized since larger patches are more likely to be agglomerations of objects rather than individuals.
In regard to the metrics used in the Stockholm/Shanghai study (Paper III), the area-weighted mean metrics are used rather than their basic mean equivalents since they provide a landscape-centric perspective of landscape structure (they reflect the average conditions of a pixel chosen at random). This landscape- centric perspective is best suited to this research since two different landscapes are being studied and compared. The contrast-weighted edge density index (CWED) is used for similar reasons (as opposed to using the total edge contrast index): the CWED standardizes edge to a per unit area basis that facilitates comparison between landscapes of different size. Edge is quantified from the perspective of its functional significance and thus landscapes with the same CWED would be presumed to have the same total magnitude of edge effects.
55
For the selection rationale for the metrics used in construction of urban ecosystem service bundles for the Stockholm County landscape, please refer to section 4.3.1 of Paper IV.
Table 4 Table indicating in which studies each landscape metric was used Landscape Metric Used in Class Area (CA) or All studies Percentage of Landscape (PLAND) Patch Density (PD) Papers I-III Mean Patch Size (MPS) or Papers I and III Area-weighted Mean Patch Size (AMPS) Largest Patch Index (LPI) Paper III Mean Patch Shape Index (PSI_MN) or Area- Papers I-III weighted mean shape index (AWMSI) Area-weighted mean perimeter to area ratio Paper II (AWMPAR) Total Edge Contrast Index (TECI) Paper I Contrast-weighted Edge Density (CWED) Papers III and IV Total Core Area (TCA) Paper IV Area-weighted Mean Proximity Index (AMPI) Paper I Area-weighted Mean Similarity Index (AMSI) Paper I Cohesion Papers III and IV Connectance index (CONNECT) Paper II Contagion (CONTAG) Papers I and III Shannon’s Diversity Index (SHDI) Paper IV
4.5 Urban and environmental indicators and indices
4.5.1 Greater Toronto Area
In addition to landscape metrics, other indicators were also explored as a measure of spatial patterns of urban growth. In particular, indicators measuring urban compactness that are not dependent on patch shapes were considered useful. Three of the indicators used were developed by Kasanko et al. (2007a; 2007b), namely average weighted distance, co-central ring density and degree of settlements dispersion. All five of the indicators calculated are presented in Table 4. For details of how these indicators were selected, used and calculated, please refer to the Methodology section of Paper I.
56 Table 5 Urban indicators used in the Greater Toronto Area study (Paper I) Urban Indicators Description Measures the mean weighted distance Average Weighted Distance Indicator of residential area patches from the (AWDI) city center Proportions of urban versus non-urban Co-central Ring Density Analysis areas in 5 km rings spreading out from (CRDA) the city center are measured Border length of built-up areas in each Degree of Settlements Dispersion municipality is divided by the (DSD) minimum possible length given the surface area Urban Overlap with Environmentally Degree of overlap of expanding urban Significant Areas (ESAs) areas with ESAs is calculated Urban Perimeter of Environmentally Increase in urban perimeter of ESAs Significant Areas between 1985 and 2005 is calculated
Environmentally significant areas (ESAs) were established by the Metropolitan Toronto and Region Conservation Authority. As discussed in the Methodology section of Paper I, the change in composition of the surrounding land-cover of an ESA determines if it is subjected to either beneficial or adverse ecological effects.
4.5.2 Stockholm metropolitan area
Bearing in mind Stockholm’s regional environmental goals, a series of environmental indicators were developed and calculated to see how forested areas in particular have changed as a result of urban growth. Rankings for each indicator were later compiled to construct an environmental impact (EI) index. The indicators are described in Table 5 below. For details on the selection process for these indicators and further explanations, please see section 3.3.2 of Paper II.
57 Table 6 Environmental impact indicator specifications used in Paper II (the unit for comparison is the large forested area or LFA) Environ- mental Definition/ indicator description Calculation method Unit For larger forested areas (min 3 km2), Amount of minimum width should green areas be 500m (RTK 2008). Square Use Zonal Thickness and important for All meet the criteria of kilo- Calculate area in ArcMap. ecosystem min. 500m width. meters services Ranked according to their area (larger is better). Use perimeter as indication of perforation in green areas (calculated with both external and Condition of Degree of intactness of internal boundaries). No unit green areas forested areas (how Compare this to min. poss. (index - important for much have the forests length given area. P or lower is ecosystem been disturbed? Clear C = 2Pi*sqrt(A/Pi) better) services cut areas, roads, etc.) min Iperf = Preal/Pmin.poss (C = circumference, P = perimeter, A = area, Pi = 3.14...) Area Weighted Distance between green areas defined above and all HDB and No unit industrial/ commerical (index - I = A * ∑ (D2/A ) areas, with size of the prox LFA DU higher is Promixity of (A = area, D= distance, LFA and urban areas better, green areas to DU = dense urban patch) taken into account. The represent intense urban Use centroids of LFAs and nearness of dense s an LFA land use HDB/IC areas. Use "Point urban and industrial which is distance" in ArcMap. areas places pressure not as on green areas. This stressed) index is intended as a rough measure of that pressure. Urban areas in direct Create layer of urban, Degree of proximity to forested water, open and vegetation Percent urban areas place pressure on LC categories. Combine urban perimeter these, through pollution this layer with LFA line peri- surrounding and cutting off LFAs file using "Identity" to find meter LFAs from other natural degree of edge with urban areas, for example. areas.
58 Use RTKs Stockholm county 40-65 dB dataset (buffered road/rail network) created 2004. Use "Identity" on LFA Area of To what extent are layer with noise layer and LFA Noise level LFAs affected by noise export the over 55 dB affected pollution? overlap areas. Area in by >55 meters of LFA affected by dB (km) highest noise levels (55- 65). 55 dB is WHO's outdoor noise limit (WHO 1999).
With the indicator results as input, the compilation of the Environmental Impact Index is intended to assess broader change in the natural environment and the ecosystem services it provides to the region. The method for compiling the index is simple with no weighting system in order to ensure as much transparency as possible. Once all indicators were calculated, the analysis units were ranked according to each indicator. An average of the indicator rankings was taken to provide an overall index score. The major advantage with this index and these indicators is that they are comparative (the same methods and standards are used to derive them from each classification for each analysis unit). They can therefore be used as a measure of the differences between 1986 and 2006.
4.5.3 Stockholm and Shanghai comparison
In order to compare the speed and magnitude of urbanization, two indices are calculated for all three decades in both locations in the Stockholm/Shanghai study. The Urban Land Index is the ratio of urban land to total land and the Urban Expansion Index is the difference in urban land over a period of time as calculated by Hu et al. (2009). An Urban Green Index was also devised to quantify the development of urban green spaces (UGS) in comparison to simultaneous urban development. The index is calculated as the ratio of UGS increase divided by the sum of increases in HDB and LDB. For details on the calculation of these indices, please refer to section 3.5 of Paper III.
4.5.4 Stockholm County
The environmental impact indicators used in the Stockholm County investigation between 2005 and 2015 (Paper IV) were selected with the
59 region’s green infrastructure in mind. The urban vs. non-urban composition of a 200 m buffer zone around the Swedish EPA’s nature reserves was calculated for both decades and the change quantified. Given the importance of and pressure on oak forests in the Stockholm region, the deciduous forest ecological corridor network is compared with change in urban areas (HDB, LDB, roads/railway) and the change in percentage of overlap is calculated. The green wedge network is also evaluated in terms of overlap with growing urban areas. Transboundary “hotspots” of urban impact where these types of green infrastructure converge and span administrative boundaries are also identified. For details on the background, selection rationale and calculation of these indicators, please refer to section 4.3.2 of Paper IV.
4.5.5 Stockholm City
The environmental impact indicators used for analysis in Stockholm City between 2003 and 2018 (Paper V) were selected with regard to the green infrastructure geographic information available. Urban land-cover change in relation to the three types of ecologically significant green infrastructure identified by the city is investigated. The probability of connectivity metric (PC) is used to assess changes in habitat availability and connectivity for a representative species dependent on coniferous forest, one of the land-cover types most affected by urban growth during the study period. The equivalent connected area (ECA) index, which can be derived directly from PC, is also utilized to compare temporal changes in habitat connectivity since it represents the size of one habitat patch that would provide the same connectivity probability value as the habitat pattern in the landscape examined (Saura et al. 2011). The change in ECA and the change in habitat area can and are compared directly to see which was more impacted by urban expansion. For more information about the methodological steps and calculation of these indicators, please see section 3.3.2 of Paper V.
4.6 Monitoring of ecosystem services
4.6.1 Stockholm and Shanghai comparison
Ecosystem service value for each decade in the Stockholm County/Shanghai study was calculated based on the valuation scheme of Costanza et al. (1997). The scheme is reported to account for the following ecosystem services: gas regulation, climate regulation, disturbance regulation, water regulation, water supply, erosion control, soil formation, nutrient cycling, waste treatment, pollination, biological control, habitat/refugia, food production, raw materials,
60 genetic resources and recreational and cultural services. Table 2 under Section 3.7 of Paper III lists the land-cover classes identified in the study area, their corresponding biomes and ecosystem service values per hectare and year based on Costanza et al. (1997).
It should be noted that in Costanza’s original valuation there was neither a value attributed to urban areas nor for UGS. Since UGS such as parks or golf courses are predominately composed of trees, water bodies and lawns, a value has been defined in this study by averaging the values of forests, grasslands and lakes/rivers and assigned to UGS.
4.6.2 Stockholm County
Haas and Ban (2018) recently incorporated landscape metrics in the construction of urban ecosystem service provision bundles and used these to measure the impact of urbanization on regional ecosystem service provision in Beijing, China. Their methodology is adapted and applied to the Stockholm County landscape in Paper IV in order to monitor change in ecosystem service provision, as well as to test their methodology at the full resolution provided by Sentinel-2 data and its effectiveness in a moderately urbanizing context. Relevant land-cover classes are used to calculate the landscape metrics both in terms of proportion and spatial configuration, which are then bundled according to the land-cover classes’ similar ecosystem service provision capacities. In addition, a proximity metric was calculated by generating a 200 m buffer around HDB and LDB classified areas and identifying areal amounts of the different land-cover classes within the buffer zone. Green and blue areas within the buffer zone are considered more valuable for provision of ecosystem services to nearby inhabitants than green/blue areas farther away. A ratio between the areal amount of each class and the amount of urban areas (HDB+LDB) was calculated in order to relate the proximity criterion to urban growth. The resulting metric values were summed and normalized for each ecosystem service bundle. Bundle values from 2015 were compared to those from 2005 as baseline and the percent changes in service provision were quantified.
In order to more clearly illustrate the gains and losses in urban ecosystem service provision, benefit transfer and the valuation scheme published by Liu et al. (2010) was adapted and utilized. Since one of the spatial attribute principles adopted in the study indicates that green and blue structure in proximity to urban areas is more valuable for ecosystem service provision than those located farther away, a proximate green/blue structure class is added to the scheme. This class is comprised of all green and blue structures within the
61 200 m buffer zone of built-up areas used to calculate the proximity metric. For details on the construction of the urban ecosystem service provision bundles and method of ecosystem service valuation, please refer to section 4.3.1 of Paper IV.
5 Results and discussion
5.1 Image classifications
The accuracy results for each of the classifications performed in this research are reported in Table 6. High overall accuracies (>85%) were obtained in all cases. MLC classification under urban and rural masks yielded the highest accuracies, followed by OBIA rule-based and OBIA SVM classifications which had similar accuracies and finally pixel-based SVM classification. It may be surprising that the MLC approach provided the best reported accuracy results given that OBIA and SVM are newer approaches and have often been cited as better alternatives to MLC in the literature. Quality of the input imagery is a factor since noise-free and normally distributed images are easier to classify. The quantity and quality of the training data are also important for achieving high accuracy classification results, even more so than the choice of algorithm (Li et al. 2014). The resolutions of the satellite data differ in some instances and this affects the level of classification: high accuracy will be more challenging to obtain with a higher number of classes. Therefore the OBIA SVM classification in fact provided the best results given its higher resolution (<1 m/10 m vs. 30 m) and number of thematic classes (9/10 vs. 8) in comparison to the GTA classification. Yet the MLC algorithm applied under urban/rural masks returned higher accuracy results with slightly more land- cover classes than were achieved with the pixel-based SVM classifications. This confirms that MLC can still under certain circumstances deliver better results with pixel-based classifications than SVM. For details on the respective classification accuracy results, see the Classification section under Results and Discussion in Paper I, sections 4.2 of Papers II and III, section 3.1 of Paper IV and section 4.1 of Paper V.
62 Table 7 Comparison of overall classification accuracies and kappa statistics Classification Number of Number of Overall type, location classes validation Kappa accuracy (%) and year points/class Pixel-based MLC with urban/rural masks: GTA 1985 8 500 94.9 0.94 GTA 1995 8 500 92.9 0.92 GTA 2005 8 500 94.3 0.94 OBIA rule-based classification: Stockholm 7 600 92.4 0.91 metro 1986 Stockholm 7 600 92.1 0.91 metro 2006 Pixel-based SVM: Stockholm 6 600 90.0 0.88 County 1989 Stockholm 6 600 89.0 0.87 County 2000 Stockholm 6 600 88.2 0.86 County 2010 Shanghai 1990 8 500 88.1 0.86 Shanghai 2000 8 500 87.8 0.86 Shanghai 2010 8 500 89.4 0.88 OBIA SVM classification: Stockholm 9 1000 90.5 0.89 County 2005 Stockholm 9 1000 92.4 0.91 County 2015 Stockholm City 9 500 93.0 0.92 2003 Stockholm City 9 500 91.5 0.90 2018
For the GTA classification, the use of the agricultural and urban masks significantly improved the classification of agriculture and parks for all three decades, as well as construction sites vs. bare soil. Please see the Results and Discussion section of Paper I for comparison with the original classifications (without urban/agricultural masks). The land-cover classification results for the GTA in 1985 and 2005 are shown in Figure 9.
63
Figure 9 Land-cover classification results for the Greater Toronto Area in 1985 and 2005
64
The land-cover classification result for the Stockholm metropolitan area in 2006 is shown in Figure 10. In regard to the lower resolution of the 1986 SPOT imagery over the greater Stockholm area, the objects generated by the segmentation for 1986 were for the most part of a similar size as those for 2006, but the 2006 segmentation did have a greater amount of smaller objects. This led to a slightly more detailed classification, especially when it came to correctly isolating mixed forest and open land, and to identifying very small green areas (trees, grass, parks, etc.) within LDB areas.
Figure 10 Land-cover classification result for Stockholm metropolitan area in 2006
65 The land-cover classification results for Stockholm County and Shanghai are shown in Figure 11. For Stockholm County, there was a slight over-detection of LDB and UGS in the 2000 classification as well as an under-detection of HDB areas. This was mainly attributable to noise in the 2000 image, which dampened the spectral differences between these areas. Agricultural areas were slightly under-detected in 1989 and 2010 and forested areas slightly over-detected for these years due to a certain type of bright vegetation that was confused with forest. UGS was also consistently difficult to classify because of its spectral similarity to agricultural and LDB areas. The use of rural and urban masks helped to improve the classification of UGS somewhat.
Figure 11 Land-cover classification results for Shanghai (top row) and Stockholm County (bottom row) in (from left to right) 1989/90, 2000 and 2010
The land-cover classification results for Stockholm County in 2015 are shown in Figure 12. The only significant problem when it came to classification comparability between 2005 and 2015 was in relation to the accuracies of the road/railway and HDB classes, which were those that were most confused with one another. The HDB and road/railway classes were therefore combined
66 for the analysis of landscape change and generation of statistics since they are both characterized by impervious surfaces and often have similar environmental impact. Overestimation of roads and lower accuracy for that class with Sentinel-2 data than with SPOT-5 data could be related to the larger segment size used which may have combined small HDB areas with what were mainly road segments. The same reason could account for the slightly lower accuracy for the urban green area class with Sentinel-2 data than with SPOT-5, although the general trend of some urban green area confusion with LDB, golf courses, agriculture and forest was the same for both types of data. The agriculture, forest and wetland classes, on the other hand, were more accurately classified with Sentinel-2 data than with SPOT-5 data mostly likely due to the variety of spectral bands included with Sentinel-2, especially red edge bands that provide more nuanced spectral information helpful in separating vegetation classes.
The land-cover classification result for Stockholm City in 2018 is shown in Figure 13. For both the 2003 and 2018 classifications, there was some confusion between the building and transport/construction classes and between barren land and transport/construction, which can be expected given how spectrally similar these classes usually are. The coniferous forest class in the 2018 classifications is slightly overestimated at the expense of deciduous forest and urban green structure. The overestimation is due in part to longer shadows in the 2018 imagery, which were sometimes classified as coniferous forest within deciduous forest and urban green structure areas.
67
Figure 12 Land-cover classification result for Stockholm County in 2015
68
Figure 13 Land-cover classification result for Stockholm City in 2018
In all studies, the texture features were instrumental in achieving sufficiently high accuracy in the classification results. In the GTA, this was especially true in helping the maximum likelihood classifier to distinguish land-cover types such as golf courses from parks/pasture and agricultural areas. For Stockholm, texture was crucial when it came to isolating built-up areas such as LDB from other types of mixed but natural landscape and HDB from industrial areas. The shape features incorporated into the OBIA SVM classification over Stockholm County and City were particularly useful in distinguishing man- made structures and areas from more naturally-occurring land cover.
5.2 Landscape change and potential environmental impact analysis using landscape metrics
5.2.1 Urban growth and landscape change trends
69 Figure 14 illustrates the growth in urban areas and decrease in natural and semi-natural land cover in each of the study areas during the years examined. Urban land cover in this instance includes HDB, LDB, the transport network and industrial or construction sites. All other land-cover classes are included in the non-urban category with the exception of water, which is excluded entirely. One may note the difference in values between the Landsat and S2/SPOT-5 Stockholm County classifications in the years 2005 and 2010. This is attributable in general to the difference in resolution but also specifically as regards non-urban land cover to the fact that a significant portion of the outer Stockholm archipelago was missing from the Landsat images and therefore not accounted for in the land-cover statistics.
The figure indicates relative rates of growth/loss and it becomes clear that Shanghai urbanized and lost non-urban areas most rapidly, followed by the GTA at a much more moderate rate and finally Stockholm with the lowest rate. In addition, the growth and loss rates themselves in Shanghai are increasing while those for the GTA and Stockholm are relatively steady. What also emerges clearly from this chart is the fact that the GTA and Stockholm are landscapes still clearly dominated by more natural land-cover types. For Shanghai, however, it appears that a shift in dominance from more natural to urban land-cover types is imminent and could perhaps have happened already given the trajectory displayed. There is still a large difference in relative amounts of urban vs. non-urban land-cover types in the GTA and Stockholm, which indicates that a shift in landscape dominance is not imminent. It appears that these landscapes will remain dominated by more natural land-cover types for some time to come. More specifically, according to the largest patch index results at the class level, the GTA is dominated by agricultural land and Stockholm County by forest. For Shanghai, a transition occurred from dominance by agricultural land before the year 2000 to HDB afterwards.
70
Figure 14 Growth trends for urban vs. non-urban land cover in the GTA, Shanghai, Stockholm County and region during the period 1985-2015
Figure 15 shows the trend of contagion values for the GTA, Stockholm County and Shanghai study areas based on their Landsat classification results, as well as the contagion trends for the Stockholm County 10 m and Stockholm City classifications. All decreased signifying that each landscape was becoming more disaggregated and patch types were more interspersed. In comparing the 30 m classifications, the different rates at which this was occurring are of interest. The GTA landscape shows the fastest, steadiest and most dramatic shift towards disaggregation. Shanghai’s trend picks up speed after the year 2000 while the rate for Stockholm County slows down following that year. The rates for these two landscapes appeared to be very similar to begin with but later clearly diverged, with Shanghai’s rate nearing that of the GTA in the second decade. While one cannot compare the actual contagion values for the different Stockholm classifications given their different resolutions or extents which affect calculation of the metric, the contagion trends for each of them appear very similar, if slightly steeper for the most recent classifications. The information contained in this chart points generally to how quickly these landscapes are becoming fragmented. More specific information about landscape change results in each of the study areas is reported in the following sections.
71
Figure 15 Contagion trends for the GTA, Shanghai, Stockholm County and City during the period 1985-2018
5.2.2 Greater Toronto Area
Urban areas in the GTA grew by about 39% between 1985 and 2005. With regard to the landscape composition metrics, the most significant proportional results in the GTA as a whole are in regard to loss of agricultural lands and parks in favor of low-density built-up areas and golf courses. Forested areas remained relatively unchanged, comprising around one-fifth of the landscape over the twenty year period. Only agricultural areas decreased, while nearly all other categories increased over the same time period, most significantly LDB areas and golf courses. The most relevant landscape metric results are presented graphically in the Landscape Metrics section under Results and Discussion in Paper I.
It is positive that the amount and size of forested areas remained fairly constant over the two decades since forest constitutes the most natural habitat available in this landscape and is crucial to its ecological function. But the trend of smaller and fewer patches of agriculture and parks/grassy fields (the two semi-natural land-cover types in the landscape) may have a negative effect on species diversity and persistence. Moreover the trend of low-density built-up areas replacing agricultural areas can directly impact species populations in relation to connectivity. So while it is positive that forest areas remain relatively unchanged, isolation of this important habitat increased due to the replacement of agricultural patches with low-density built-up areas.
72 This occurrence may hinder how natural processes operate across the whole landscape.
The landscape configuration metric results confirm increased fragmentation of the landscape due to urban development and generally indicated increased vulnerability for natural and semi-natural land-cover types and the biodiversity that depends on them through, for example, greater edge, contrast and lowered connectivity. For more detailed discussion of the landscape metrics results and what they mean for the GTA landscape and the ORM in particular, please see the Landscape Metrics section under Results and Discussion in Paper I.
5.2.3 Stockholm metropolitan area
Looking at the change in class area percentage between 1986 and 2006 in the Stockholm metropolitan area, natural land cover dropped and urban areas increased by approximately 1.5% of the whole landscape or just over 33 km2. Urban areas increased by just under 10% while natural areas decreased by 2%. Calculated landscape metric results indicate that both became more fragmented: natural areas became more isolated or shrank whereas new small urban patches came into being. Sections 4.3.1 and 4.3.2 of Paper II provide more information on land-cover changes that occurred during this period in the Stockholm metropolitan area.
The landscape metric results reveal that urban growth in the Stockholm metropolitan area occurred mainly at the expense of natural land-cover types while this was not entirely the case for the city of Stockholm during the same period (where there was some conversion of LDB to HDB). This circumstance is not necessarily surprising, since the natural areas within the city are limited and often protected, while those outside are more plentiful and frequently carry fewer restrictions when it comes to development.
Of note is that the difference in resolution in the Stockholm SPOT imagery limited the use of landscape metrics for comparison purposes since it gave rise to an inconsistent delineation of the forest and open land mixed class between classifications. This by extension caused problems for comparability of landscape metrics results for the forest, open land, and forest and open land mixed land-cover classes. The use of consistent resolutions and definition of a separate urban green structure class has helped to avoid this dilemma in the more recent research.
73 5.2.4 Stockholm and Shanghai Comparison
Between 1989 and 2010, the speed and magnitude of urban growth in Shanghai exceeded that of Stockholm County by far. In total, urban areas in Shanghai increased by 120% or 1 768 km2. In Stockholm, a 12% increase in urban areas could be observed, which corresponds to an area of 100 km2. This increase in both HDB and LDB areas came predominately at the expense of agricultural land in both study areas. From the landscape metrics results, we can deduce that the growth of HDB areas was mainly responsible for increased fragmentation in both of the city regions as new patches were added in previously more natural areas, while LDB areas tended to expand through consolidation with pre-existing patches.
Based on the statistics generated from the land-cover classifications of Stockholm County, urban areas grew by about 7% or 57 km2 between 1989 and 2000 and by about 5% or 43 km2 between 2000 and 2010 in Stockholm County. The percent of urban growth between 1989 and 2010 was over 12% or 100 km2. This is relatively similar to the 10% increase found at the regional level. Looking at the change in class area percentage between 1989 and 2010, natural land cover dropped and urban areas increased by approximately 1.4% of the whole landscape (just over 100 km2), which is also very similar to the change for regional Stockholm (1.5%). With regard to the landscape composition metrics, the most significant proportional changes are in regard to loss of agricultural/open land in favor of low-density and high density built- up areas. Forest remained relatively unchanged during the two decades.
The landscape metric results indicate both positive and negative potential impacts for the regional ecosystem in Stockholm. On the one hand, it is certainly positive that the amount and size of forested areas has remained constant over the two decades and that forest dominates the landscape through by far the largest patch; forest constitutes the most natural habitat available in this landscape and is crucial to its ecological function - for air filtration, rainwater drainage, microclimate regulation and noise reduction (Bolund and Hunhammar 1999). On the other hand, the trend of smaller and fewer patches of agriculture (a semi-natural land-cover type in the landscape) may have a negative effect on species diversity, movement and persistence. Both the patches and the species that depend on them become more vulnerable. The configuration metrics revealed a greater amount of edge interface between forest and urban areas in Stockholm. The increased interface between natural land-cover types and urban areas tends to have a negative impact on the flora and fauna found in the vicinity as it increases their vulnerability to adverse external effects. In addition, the agricultural class lost some physical
74 connectedness and this could mean loss of more natural land-cover conduits for fauna moving between forested areas.
In terms of natural land cover important for the regional ecosystem in Shanghai, there was some loss of the already relatively small amount of forest left in the region as well as wetlands. As seen in Figure 15, the contagion metric for Shanghai continuously decreased over the two decades indicating a transition towards a more fragmented landscape. From an ecological perspective, a higher degree of fragmentation, especially through built-up areas and a lower degree of patch connectivity, has negative effects upon flora and fauna. The amount of natural habitat is reduced and the ecological quality of habitat edges decreases through often human-induced external effects, such as carbon dioxide emissions along roads or contaminant loads from industry or noise and light pollution. Dispersal capacities of species may be limited due to fragmented green spaces and the genetic pool is decreased through the inhibition of genetic interchange due to infrastructural boundaries. For more detailed discussion and graphic representation of the landscape metric results in both Shanghai and Stockholm County, please refer to section 4.4 of Paper III.
5.2.5 Stockholm County
The investigation of Stockholm County between 2005 and 2015 revealed that non-urban land cover dropped and urban areas increased by just over 2% of the County land area or approximately 116 km2. Urban areas increased by 15% while non-urban areas decreased by just under 4%. Figure 3 in section 5.2.1 of Paper IV shows graphically the change in land area percentages for the different land-cover classes. Looking at the 2005 percentages, it is clear that forest dominates the Stockholm County landscape at just over 57%, followed by agriculture at 16% and then urban areas (LDB plus HDB/roads) at 11.5%. By 2015, urban growth had shifted the percentages such that the gap between agriculture and urban areas had narrowed significantly with agriculture at 15.2% and urban areas at 13.3%. Given the urban growth trajectory, this would suggest that urban areas may soon replace agriculture as the second largest land-cover category in the Stockholm County landscape.
In relation to the 2005 class areas, forest decreased by approximately 2% or just over 80 km2 but retains its dominant position in the landscape. Agricultural areas decreased by nearly 4% or 39 km2. Golf courses and wetlands remained relatively stable during this period with no significant change detected for wetlands and a 4 km2 or 14% increase for golf courses. LDB areas increased by 15% or 79 km2 and HDB/road areas increased by 14%
75 or nearly 37 km2. Section 5.2.1 of Paper IV provides more information on land-cover changes that occurred during this period in Stockholm County. Most of the landscape metrics calculated for this region were integrated into urban ecosystem service provision bundles, the results and analysis of which are described in section 5.3.4 below.
5.2.6 Stockholm City
Quantification of land-cover change in Stockholm City between 2003 and 2018 showed that non-urban land cover dropped and urban areas increased by 1.7% of the city land area or approximately 327 hectares. Urban areas increased by approximately 4% while non-urban areas decreased by 2%. Figure 2 in section 4.2 of Paper V displays graphically the change in land area percentages for the different land-cover classes.
In relation to their 2003 class areas, buildings increased by 2.3% or approximately 125 ha and transport/construction areas increased by 12% or roughly 202 ha. The classes most impacted by this expansion were coniferous forest, which decreased by approximately 3% or 101 ha, and grass/open fields, which were reduced by 10% or 134 ha. Deciduous forest decreased by about 1% or 34 ha. The largest proportional decrease was for barren land that underwent a 19% decrease. This was largely due to conversion of dirt sports fields to artificial grass, a type of land cover that was classified as rock outcrop in the 2018 classification and accounts for a small percentage increase in the rock outcrop class. Wetlands and agricultural fields remained relatively stable with a slight increase for wetlands due to the later summer vegetation conditions in the 2018 imagery and a slight decrease for agricultural areas. Urban green space registered a very slight increase of less than 1%. Information on land-cover changes that occurred according to administrative boundaries during this period in Stockholm City is provided in section 4.2.1 of Paper V.
5.3 Assessing impact with urban form, ecosystem service and green infrastructure indicators
5.3.1 Greater Toronto Area
Results from the urban form indicators showed how urban areas changed geographically in the GTA. The average weighted distance dropped for both
76 decades, revealing that much of the urban growth occurred relatively close to the urban center of the region, that additional urban areas were being added close to or directly amongst previously existing urban areas. The co-central ring density analysis indicated that urban proportions increased in nearly all areas outside of the metropolitan center over the two decades, including on portions of the ORM. Dispersion of settlements increased on the ORM between 1985 and 1995, while for the GTA and on a municipal average the more significant increase occurred between 1995 and 2005. The majority of ESAs were increasingly surrounded by urban areas between 1985 and 2005, furthering their isolation from other natural areas and exposure to negative impacts from adjacent development. For more details and graphic representations of these results, see the Urban Compactness Indicator and ESA sub-sections under Results and Discussion in Paper I.
5.3.2 Stockholm metropolitan area
For the study over the Stockholm metropolitan area, the environmental impact indicator results consist of a series of tables that can be found in the Appendix. Positive changes in area and perforation signified regrowth of forest in that particular LFA. Changes in rank for the proximity to intense urban land-use indicator depended mainly on the location of new industrial/commercial or HDB areas. Negative changes in the percent of urban perimeter indicator were caused by construction of new LDB areas or roads in direct proximity to LFAs. Positive changes in the area affected by noise were most often due to loss of part of the area in the LFA affected by noise. Each LFA is ranked according to each indicator and a subsequent average ranking across the indicators provides its overall EI (environmental impact) index score. These overall index scores for both years are shown in Table 5 and Figure 3 in section 4.3.3 of Paper II.
The most noticeable changes in terms of environmental impact and urban expansion were in the east and north of the study area. Although a good deal of the urban expansion between 1986 and 2006 occurred in the northwest, the LFAs in that area were relatively small. There are larger and more far-reaching LFAs in the northeast and these registered more of the effects of the urban development.
The results from the environmental impact index showed that the best average ranking scores occur in the northern and eastern portions of the study area. The worst tended to be closer to the center of the Stockholm metropolitan area with a few exceptions. When it came to negative changes in rankings, areas in the northeast dropped the most. This shift in the northeast was mainly due to
77 construction of new LDB areas. Generally, in terms of changes in rankings between 1986 and 2006, the most improved analysis units are those that are closest to the central Stockholm area, while the least improved or worsened units tend to be on the outskirts of the study area due to the fact that the urban expansion occurred mostly in the suburban areas. For graphic depictions of the index, more specific information about the changes and discussion of their causes, see section 4.3.3 of Paper II.
5.3.3 Stockholm and Shanghai comparison
Table 5 under section 4.3 of Paper III shows the urban land, urban expansion and urban green index results for Shanghai and Stockholm County between 1989 and 2010. Urban green spaces in Stockholm grew by about 11% and in Shanghai by 425% over two decades. The total increase in built-up areas in Shanghai was about 120% from 1989 to 2009. Urban expansion proceeded slightly faster in the second decade than in the first. Urban growth in Stockholm is also apparent but at a much slower pace, especially from 2000 to 2009. Urban expansion both in terms of speed and spatial extent occurred predominately from 1989 to 2000. A modest increase of urban areas of 1.5% can be observed over two decades in Stockholm, which corresponds to an expansion of about 12% of their original extent.
Tables 7-9 under section 4.5 of Paper III provide information on how ecosystem service values changed over the two decades in both Shanghai and Stockholm. While ecosystem service values remained rather steady and increased slightly in Stockholm, land-cover changes in Shanghai’s metropolitan area resulted in drastic ES value losses. The modest expansion in urban areas that occurred in Stockholm was accompanied by an increase in urban green spaces (mainly golf courses) that may enhance quality of life for Stockholm residents and the region’s attractiveness to visitors. The growth in urban green spaces led to an increase in ecosystem services due to the higher ecosystem service values attributed to urban green spaces (compared to those assigned the agricultural land that was replaced). In 1989, ecosystem service values were almost equally high in both study areas. While the value in Stockholm slightly increased, a substantial loss in Shanghai was observed, reducing the value of ecosystem services to 63% of their initial value.
The ecosystem service valuation results showed a large loss of ecosystem service clearly for Shanghai, but the results for Stockholm were less clear. This tool may work relatively well for cities like Shanghai where urban growth and landscape changes are dramatic. It is more problematic, however, when applied to regions like Stockholm whose urbanization and landscape changes
78 over decades are more subtle and when ecologically important urban features for the city such as allotment gardens (Barthel and Elmqvist 2007) are not accounted for in the valuation approach. The valuation approach used here is thus not refined enough to capture smaller yet important and perhaps cumulative losses in terms of ecosystem service function in more moderately urbanizing areas.
5.3.4 Stockholm County
Table 5 and Figure 4 in section 5.2.1 of Paper IV report and show graphically the changes in urban ecosystem service provision in Stockholm County between 2005 and 2015. Loss of forested areas led to a notable decrease in global climate regulation, but decrease in the proximity of forest and increase in proximity of low-density built-up areas were the main causes of decreases in the provision of temperature regulation, air purification, noise reduction. These proximity changes also slightly negatively affected recreation, place values and social cohesion. Overall, there was a decadal ecosystem service loss of 4.6 million USD (2015 exchange rate).
Municipal land area percentage changes of urban areas and of green structure are reported and shown under section 5.2.2 of Paper IV. Seven municipalities experienced more than 5% urban growth and four municipalities lost more than 5% of their green structure, all of which are located to the north of Stockholm City.
Urban areas within a 200 m buffer zone around the Swedish EPA’s nature reserves in Stockholm County increased by 16% over the decade, with several examples of new urban areas constructed along the boundary of nature reserves. Urban expansion also overlapped the deciduous ecological corridor network and green wedge and core areas to a relatively small but increasing degree, often in close proximity to weak green links in the landscape. Section 5.2.2 of Paper IV provides more detailed information regarding these changes and includes a number of specific urban proximity and overlap examples with the various kinds of Stockholm’s green infrastructure.
5.3.5 Stockholm City
Overall, there was a 9% increase in urban areas within the green infrastructure of Stockholm City. Expansion of the transport network negatively affected ecologically significant core areas, while construction of buildings had a greater impact on habitat for species of conservation concern. Dispersal zones
79 were affected by both types of urban growth and had the largest area converted (28 ha). Investigation of change in habitat connectivity for a species dependent on coniferous forest revealed a slight decrease in probable connectivity due to shrinkage and fragmentation of habitat patches mainly as a result of expansion of the transport network near the outskirts of the city. More detailed information on and graphic representations of these changes can be found in sections 4.2.1 and 4.2.2 of Paper V.
5.4 Comparison of the GTA, Stockholm and Shanghai investigations
Each study in the thesis has certain advantages and drawbacks. Observation at three different points in time, instead of only two, is valuable since this third point helps to establish and evaluate a trend. It is also better to use the same data resolution for the points in time being compared; it makes use of landscape metrics more reliable, simplifies the classification process and makes the classifications within a given study more directly comparable. The Stockholm metropolitan area classifications were not as robust in light of these factors.
Is it possible to compare the urban growth rates and patterns in these three very different urbanizing regions given the large difference in their population sizes (see Figure 14)? The land areas of each of the study extents are relatively close in size. The number of people living in the GTA is about three times the population of Stockholm, while Shanghai’s population is two and a half times the population of Sweden. Given this context, it seems that urban growth rates reflected to some extent the relative population sizes. Shanghai’s urban growth of 120% was tremendous, the GTA’s between 1985 and 2005 was impressive at nearly 40%, while the various rates for Stockholm, at just under 10% (Stockholm metropolitan area), 12% (Stockholm County, 30 m) and 15% (Stockholm County, 10 m), could be termed modest in comparison. It seems population sizes could to some extent act as predictors of urban area increase at this landscape scale: just over 100 km2 in Stockholm County (30 m), nearly 600 km2 in the GTA and over 1700 km2 in Shanghai.
The layout and geography of the landscapes themselves may play a significant role in shaping urban growth in these areas. For example, Stockholm’s geomorphology is unique and the growth of urban areas is constrained by the many surrounding bodies of water. The city center itself is essentially a group of islands and the possibility for urban expansion there, with its protected green spaces, is very limited. Growth is thereby often diverted to the rural- urban fringe, either to the north or south of the city center, given the inlet from
80 the Baltic Sea to the east of Stockholm which opens up into Mälaren on its west side. This configuration may be pushing urban growth in the Stockholm area towards more suburbanization, which is dominated by single-family residential development (Seto et al. 2010), than otherwise might have occurred. This is also why the urban compactness indicators applied to the GTA would not have produced as meaningful results if applied to the Stockholm metropolitan or County landscapes.
The GTA has its own constraints. Toronto is built on the northern shore of Lake Ontario and, probably given the desirability of living/working in proximity to water, the city has spread east-west along the shoreline and then north. This geographical layout also allowed the GTA to have developed a more compact urban area at its core. The rural-urban fringe in the GTA is most often characterized by an interface between urban and agricultural areas, while in Stockholm this interface is between urban areas and forested or open/agricultural land. Shanghai shares several of the GTA’s geographical and physical characteristics, such as proximity to water, a compact urban core and an urban-agricultural interface at its rural/urban fringe. Given these conditions, the GTA and Shanghai may be more prone to peri-urbanization (Seto et al. 2010). The danger and possible solution for this condition are noted by Lambin et al. (2001): “In the developed world, large-scale urban agglomerations and extended peri-urban settlements fragment the landscapes of such large areas that various ecosystem processes are threatened. Ecosystem fragmentation, however, in peri-urban areas may be offset by urban-led demands for conservation and recreational land uses.” The TRCA itself appears to be evidence of such an attempt to offset fragmentation in the GTA. Urban planning authorities in Shanghai have adopted “greening” policies in recent years, which have resulted in more green areas in the city center (Haas and Ban 2017; Wu et al. 2019).
The nature of the environmental impact varied in the different study areas. There was similarity in the location of new urban areas along the outskirts of existing urban areas, since the center of each city did not leave much room for urban expansion. Urban expansion in the GTA tended to repossess solely agricultural land, while open/agricultural land and forest was converted in Stockholm. Far more land was converted to urban use in the GTA and Shanghai, which also have much larger urban areas and populations to begin with. The environmental impact that occurred in the GTA was characterized by urban expansion onto the ORM and by increased urban surroundings for nearly all ecologically significant areas, in essence furthering their isolation from the larger ecosystem. Loss of wetlands and already sparse forest comprised the bulk of negative environmental impact in Shanghai whose ecosystem service values dropped dramatically. Environmental impact in Stockholm included relatively small negative changes in ecosystem service
81 value and provision, increased urban area within and around important green infrastructure and was characterized by fragmentation and loss of forested and agricultural areas due to construction of roads and expansion of LDB areas and industrial sites. The choice of environmental indicators for impact assessment was tailored to the very different characteristics of these urban landscapes. Their selection was heavily influenced by what local planning authorities regarded as important in terms of environmental quality. The environmental impact indicators for the Stockholm region were designed based on local environmental information and planning priorities, which render their results site-specific and ideally more useful for urban planning. To complement these place-specific analyses, landscape metrics were used to gain an overview of the spatial-temporal changes in all three areas and are slightly more comparable, although there were limitations for the Stockholm metropolitan area study.
Landscape metrics were most successfully used to evaluate potential environmental impact in the GTA study due to the establishment of their connection with regional environmental conditions and ecological principles as laid out by the TRCA (see the Methodology and Results sections of Paper I). Estimation of probable environmental impact in the Stockholm/Shanghai study was weaker due to the lack of specific regional ecological information and principles to which the landscape metrics results could be tied. Landscape metrics can be applied to any landscape with a high quality classification but credible inferences about potential environmental impact are more difficult to make from their results unless relevant information about region-specific environmental conditions and ecosystem function has been obtained. A widely applicable remote sensing-based environmental impact analysis approach appears to be possible up to a point - selection of the indicators used within it must be adapted to the site in question in order to obtain meaningful and useful results. Spatio-temporal remote sensing data-based studies often involve a top-down approach but bottom-up complementary environmental data is necessary for estimation and interpretation of change results when assessing regional environmental impact.
Obtaining information about local ecosystem functions and services at work in the specific regions is crucial to selecting appropriate indicators and making relevant connections between indicator results and potential environmental impact. But this can also be challenging as reliable information is not always available – ecological information appeared to be much more difficult to obtain for Shanghai than for Stockholm, for example. There is also the question of the type and usefulness of information that is available. The TRCA provides general environmental information for the portion of the GTA under its jurisdiction but investigation has been undertaken more recently and is ongoing into specific ecosystem services that the ORM provides, most notably
82 tied to groundwater (NRC 2013). In Stockholm, there is a plethora of environmental information available on the municipal level (for example, biotope maps, habitat maps and species citings and monitoring databases), but there is relatively little information available about green structure and ecosystem function at the county level, although newer datasets are emerging (e.g. Ekologigruppen 2017). Such conditions can limit an investigation by changing its focus or extent.
Papers I, II, IV and V have a dual-level analysis approach for assessing environmental impact. The various methods used on each level are listed per study area in Table 7. Paper III is of interest because of its comparison of two completely different urbanizing regions but would have been strengthened by looking at specific and more detailed examples of environmental impact from each region. A dual level of analysis carries the advantage of identifying environmental impact trends for the landscape while at the same time highlighting possible areas for mitigation/conservation efforts and may thereby be more directly useful in urban planning and decision-making.
Table 8 Outline of dual levels of analysis in each study (LC: land cover) Landscape level Regional/local level regional LC change statistics, urban Greater LC change/ growth impact on the ORM and Toronto Area fragmentation statistics around environmentally significant areas Stockholm LC change/ metropolitan spatially-explicit urban growth fragmentation statistics area impacts on large forested areas LC change/ Stockholm fragmentation statistics -- County (30 m) and ESV change LC change/ municipal LC change statistics, urban Stockholm ESV and ES bundle growth/overlap in ecologically County (10 m) provision changes significant green infrastructure district LC change statistics, urban Stockholm growth within ecologically significant LC change statistics City green infrastructure and impact on habitat network
5.5 Comparison of the Stockholm investigations
One advantage with performing multiple Stockholm studies is the information that emerges from the use of different spatial scales. Many researchers have emphasized the importance of scale when it comes to assessing landscape pattern and environmental change (Bissonette 1997; Gibson et al. 2000; Wu
83 2004). In this research, there are at least two sets of scale comparisons that could be made: 1) comparison of the county level classifications which have (nearly) the same spatial extent but different resolutions and 2) the 10 m resolution Stockholm County classification and the roughly 1 m Stockholm City classification which have nearly the same temporal scale.
With regard to the Stockholm County classifications, the higher resolution provided by the Sentinel-2 and SPOT-5 data enabled the classification of a more detailed land-cover class scheme and the possibility to more consistently detect wetlands. The location of where most environmental impact registered was the on the whole the same in the Stockholm metropolitan area and both County studies, that is, to the north of Stockholm City over the whole time period examined. Environmental impact in the Stockholm metropolitan area study was mainly characterized by fragmentation and loss of portions of large forested areas due to construction of roads and expansion of LDB areas. The study on the county level examining changes between 2005 and 2015 (10 m resolution) also found that forests were more impacted than agricultural areas by expansion of LDB areas and industrial sites. In the 30 m resolution county classifications, forests appeared to remain more or less intact while mainly open land was converted (as was the case in the GTA) between 1990 and 2010. The difference in resolution between the county classifications may have impeded detection of the reduction of forest edges in particular.
The change in pace of urbanization, which was slightly faster from 2005-2015 (15% urban growth) than from 1990-2010 (12%), could partially be due to higher resolution providing more accurate distinction of urban and non-urban classes. But an increase in the pace of urban expansion in Stockholm in recent years is likely, if one compares past and current regional development plans. Urban vs. non-urban land-cover percentages for the Stockholm County classifications between the years 2000 and 2015 are shown in Table 8. These percentages correspond remarkably well given that the 2000 and 2010 classifications are based on lower resolution data. The closeness of the percentages in 2005 and 2010 can also be partially explained by the fact that about half the SPOT-5 images used were from years later than 2005, including two 2008 images over more central parts of the Stockholm region.
Table 9 Comparison of urban vs. non-urban land-cover percentages for the Stockholm County classifications between 2000 and 2015 based on 30 m and 10 m resolution data 2000 (30 m) 2005 (10 m) 2010 (30 m) 2015 (10 m) Urban land cover 9% 11% 11% 13% Non-urban land cover 91% 89% 89% 87%
84
To facilitate comparison of the 10 m Stockholm County and Stockholm City classifications, the green structure land area percentages in Stockholm city districts from each classification between 2003 and 2018 are listed in Table 9. While the classifications are not from exactly the same years, the percentage differences between the years compared are strikingly similar, as are the averages of these cross-year comparisons. Let us take districts with less than 50% green structure in the high-resolution classifications as representative of HDB (three fall into this category: Norrmalm, Södermalm and Kungsholmen) and those with greater than 50% green structure as representative of LDB. The average percentage differences then indicate that HDB areas in the 10 m classifications include roughly 20% green structure while the LDB class contains approximately 33%. Thus there is a significant difference in the green structure that is taken into account between the county and city level investigations. The increased representativeness of green structure in the high- resolution classifications enables more detailed analyses of environmental impact, such as assessment of habitat connectivity, albeit at a smaller spatial scale. The larger the spatial scale and the lower the resolution of the data, the more necessary broader estimations become in order to be able to carry out analysis – this is the classic trade-off between extent and resolution. The level of detail in a classification scheme is dependent on these and the scheme adopted plays a significant role in how a landscape is represented and how it can be analyzed. Ideally, analyses at multiple spatial scales over the same landscape could be performed in order to provide the most detailed information possible over the widest relevant area possible. The more information available, the more well-informed and environmentally appropriate urban planning decisions can be made.
85 Table 10 Comparison of Stockholm City green structure land area percentages per district as estimated from the 2005-2015 county classifications and 2003-2018 city classifications County Difference in classifications City classifications green structure (SPOT-5/S2) (QB-2/WV-2) estimation 2003- 2018- City district 2005 2015 2003 2018 2005 2015 Bromma 33 29 68 66 35 37 Enskede-Årsta- Väntör 30 27 62 61 31 34 Farsta 42 37 71 70 29 32 Hägersten- Liljeholmen 14 12 55 53 41 41 Hässelby- Vällingby 29 27 64 63 35 37 Kista-Rinkeby 56 49 71 67 16 18 Kungsholmen 9 8 41 38 31 30 Norrmalm 7 6 19 18 12 12 Skarpnäck 54 51 79 77 24 27 Skärholmen 34 32 70 69 35 37 Spånga-Tensta 34 31 64 61 30 30 Södermalm 13 12 34 33 21 22 Älvsjö 21 19 64 62 43 42 Östermalm 40 37 65 64 26 26 Average: 29 30
6 Conclusions and future research
6.1 Conclusions
This research demonstrates the utility of indicators derived from remote sensing in assessing both the spatio-temporal dynamics of urban growth and its environmental impact in several different metropolitan regions and at different scales. Differing classification techniques were employed for the unique study areas given their individual characteristics and the remote sensing data and tools available for analysis. Landscape metrics calculated based on the classifications were useful in quantifying the urban growth trends for each study area, facilitating their comparison, and drawing conclusions
86 about potential environmental impact when linked to region-specific environmental information. Environmental indicators were selected based on the specific environmental characteristics and issues emphasized by local planning authorities in each region when possible. The indicator results highlighted spatio-temporal challenges in terms of sustainable urban growth unique to each landscape.
The research concerning the GTA demonstrates that MLC classification of multitemporal Landsat TM imagery and their texture measures can be used to map spatio-temporal patterns of urban expansion with high accuracy with the help of urban/rural masks. The results show that urban areas in the GTA grew by about 20% between 1985 and 1995 and by about 15% between 1995 and 2005. Results from the landscape metrics and urban compactness indicators show that low-density built-up areas increased significantly in the GTA between 1985 and 2005, mainly at the expense of agricultural areas. The contagion index values for both the GTA and the ORM decreased over this period of time, indicating increased fragmentation of these two landscapes. Yet the AWDI also dropped for both decades, revealing that much of the urban growth occurred relatively close to the urban center of the region, that additional urban areas were being added close to or directly amongst previously existing urban areas. Nevertheless, as shown by the CRDA, urban proportions increased in nearly all areas outside of the metropolitan center over the two decades, including on portions of the ORM. Dispersion of settlements increased on the ORM between 1985 and 1995, while for the GTA and on a municipal average the more significant increase occurred between 1995 and 2005. The majority of ESAs were increasingly surrounded by urban areas between 1985 and 2005, furthering their isolation from other natural areas. These findings may encourage concerned organizations and authorities in Ontario to review policy regarding the ORM and watershed protection in the GTA.
The research involving the Stockholm metropolitan area reveals the effectiveness of a knowledge- and rule-based OBIA approach to classifying multitemporal SPOT imagery and texture features to map spatio-temporal patterns of urban growth with high accuracy. Urban areas in the Stockholm metropolitan area increased and rural/natural land-cover types decreased by about 1.5% of the study area or 33 km2. The landscape change metrics indicated that both became more fragmented: natural areas became more isolated or shrank whereas new small urban patches came into being. The most noticeable changes in terms of environmental impact and urban expansion were in the east and north of the study area. Although a good deal of the urban expansion between 1986 and 2006 occurred in the northwest, larger and more far reaching LFAs in the northeast registered more of the effects of the urban development. The results from the EI index showed that the best average
87 ranking scores occur in the northern and eastern portions of the study area, while the worst tended to be closer to the center of the Stockholm metropolitan area with a few exceptions. However, areas in the northeast decreased most in EI ranking between 1986 and 2006, while the most improved analysis units were close to the central Stockholm area. The shift in the northeast was mainly due to construction of new LDB areas. While these changes are subtler than those in the GTA, information about them may help inform future policy choices by Stockholm’s regional planning authorities.
The Stockholm County/Shanghai comparative study demonstrated that SVM classified multi-temporal Landsat imagery can be used to map spatio-temporal patterns of urban expansion with high accuracy with the aid of texture measures and urban/rural mask refinements. Urban areas increased ten times as much in Shanghai as they did in Stockholm, at 120% and 12% respectively. Between 1989 and 2010, both HDB and LDB areas in Stockholm County grew mainly at the expense of agricultural areas. The landscape metrics results show that fragmentation in both study regions occurred mainly due to the growth of high-density built-up areas in previously more natural environments, while the expansion of low-density built-up areas was for the most part in conjunction with pre-existing patches. The growth in urban areas resulted in ecosystem service value losses of approximately 445 million USD in Shanghai, mostly due to the decrease in natural coastal wetlands, while in Stockholm the value of ecosystem services changed very little. The Shanghai landscape has undergone swifter urban growth and a resulting heavier environmental impact on the regional ecosystem than has Stockholm. Evaluation of change in ecosystem service value as applied in this study seems to work well for dramatically urbanizing landscapes but is not specific enough for application to areas undergoing modest urban expansion.
The Stockholm County research at 10 m resolution revealed the advantage of OBIA SVM classification through an increased number of land-cover classes and improved accuracy. This in turn widened the analysis alternatives. Urban areas increased by 15%, while non-urban land cover decreased by 4% between 2005 and 2015. In terms of ecosystem services, changes in proximity of forest and low-density built-up areas were the main cause of lowered provision of temperature regulation, air purification and noise reduction. There was a decadal ecosystem service loss of 4.6 million USD (2015 exchange rate). Urban areas within a 200 m buffer zone around the Swedish environmental protection agency’s nature reserves increased 16%, with examples of urban areas constructed along nature reserve boundaries. Urban expansion overlapped the deciduous ecological corridor network and green wedge/core areas to a small but increasing degree, often in close proximity to weak but important green links in the landscape.
88 Finally, the Stockholm City research based on high-resolution data demonstrated the utility of an OBIA SVM classification approach at this smaller but more detailed scale. Between 2003 and 2018, non-urban land cover dropped and urban areas increased by 1.7% of the city land area or approximately 327 hectares. Urban areas increased by approximately 4% while non-urban areas decreased by 2%. The most significant urban expansion occurred through the expansion of the transport network, paved surfaces and construction areas, which increased by 12%, mainly at the expense of grass fields and coniferous forest. Examination of urban growth within ecologically significant areas indicated that the most land area was lost in ecological dispersal zones (28 ha) while the highest percent change was within habitat for species in need of protection (14%). Connectivity analysis of the habitat network for the European crested tit, a sensitive representative of small coniferous forest-dependent bird species in Stockholm, in both years revealed that overall connectivity decreased slightly through patch fragmentation and areal loss mainly caused by road expansion at the outskirts of the city.
Although the study areas, data and techniques differed substantially, some conclusions can be drawn from analysis of the results. Pixel-based classification algorithms such as MLC and SVM can work well at a regional landscape scale (thousands of kilometers) and are applicable to different landscapes when used in conjunction with texture measures and urban/rural masks. These methods work well with 30 m resolution data such as Landsat TM/ETM+. However, object-based image analysis combined with the SVM algorithm provides better results at this scale with the use of higher resolution data (10 m such as SPOT/Sentinel-2) by yielding high accuracy with an increased number of classes. High-resolution data, such as WorldView-2 or QuickBird, of around 1 m combined with object-based image analysis is necessary for detailed studies of urban structures, green areas and/or habitat analysis.
Landscape metrics work well for regional environmental impact and ecosystem service analysis in different locations and at different scales as long as they can be tied to region-specific environmental information and principles. Proximity/buffer and habitat network analysis can effectively represent localized environmental impacts from urban development. A dual level (landscape and local) in environmental impact analysis from urban growth is recommended in order to increase utility of the results in planning for sustainable urban development. While it is important to be aware of the regional environmental trends, information about localized impacts provides policy makers with proposed areas for specific restorative/conservation intervention. In some cases, collaboration of several planning authorities may be necessary if the impact crosses administrative boundaries. In terms of temporal interval for analysis, this would depend on how rapidly the study site
89 is urbanizing. For fast-growing cites, an interval of 1-2 years may be suitable while for more moderately paced urbanization 5-10 years would likely be sufficient.
Uncertainty is an intrinsic part of remote sensing of the environment in that the data is collected from a distance. Given the study area sizes and geophysical variation, the resolutions of the satellite imagery, and high- performing although imperfect classifier algorithms, the classification results from each of the studies are limited by a certain amount of error, and this error has a tendency to propagate as indicators are calculated. Evaluating the accuracy allows one to decide if the classification results are accurate enough for analysis and indicator calculation. Accuracy was evaluated at the landscape levels and thus conclusions about general trends in the landscape were most reliable, while results on more localized levels were less certain. This motivated the choice in several studies to aggregate the urban vs. green structure classes prior to calculating indicators on local levels, in order to increase their reliability and lower the effects of error propagation. In addition, the employed methods of evaluation of ecosystem service change have relied on the composition and spatial configuration of land cover. They do not measure specific biophysical changes in ecosystem function or service provision. Therefore, the results from process model studies based on empirical data in the relevant region could be useful as a validation source and complement in monitoring ecosystem function or service change. Finally, the land-cover classes used here are often specific to the landscape under examination and this makes their comparability or integration with other land- cover change studies and datasets difficult.
6.2 Future research
It could therefore be important to look into the possibility of a more standardized, flexible, transferable and comparable classification scheme to enable/facilitate the comparison of different land-cover assessments between methods and years. The EAGLE concept, for example, an object-oriented data model for land monitoring (Arnold et al. 2018), may offer this possibility. It might also important to explore options for more standardized, transferable and comparable indicators for environmental impact assessment so as to be able to contribute to the monitoring of UN Urban SDG indicators such as 11.7.1, publicly accessible open and green space as a proportion of urban area.
A means of expanding the high-resolution research for Stockholm County may be possible through introducing change detection into the classification
90 process. This would highlight changed areas relevant for closer examination often found in the urban-rural fringe. Classification of these only would allow for more detailed environmental impact analysis and cross-comparison of the dual-level analysis while avoiding the computational burden and errors associated with a county-wide classification. Connectivity analysis of the green infrastructure at the county level might also be of interest if relevant ecosystem properties of the landscape can be established.
The habitat connectivity analysis undertaken in Paper V was in a sense a first trial of the method in connection with the classifications. The next phase of the high-resolution data research would therefore be to assess the other two representative species habitats available for Stockholm City: that of oak- dependent invertebrates and amphibians. The planned amphibian connectivity assessment is of particular interest because it will be based on connection probabilities rather than distances. The probabilities will be extracted inversely from a cost-surface model of land cover in Stockholm City and thereby more directly incorporate the influence of the matrix into the habitat connectivity assessment. In addition, the City of Stockholm has expressed an interest in assessment of urban growth impact within water catchment areas in the city. It may also be possible to look into impact at the watershed level with use of the 10 m Stockholm County classifications.
91 References:
Alberti, M., 2005. The effects of urban patterns on ecosystem function. International Regional Science Review 28(2):168–192.
Alcaraz-Segura, D., Di Bella, C.M. and Straschnoy, J.V. (eds), 2013. Earth Observation of Ecosystem Services. CRC Press: Boca Raton.
Andrew, M.E., Wulder, M.A., Nelson, T.A. and Coops, N.C., 2015. Spatial data, analysis approaches, and information needs for spatial ecosystem service assessments: a review. GIScience and Remote Sensing, 52(3), 344- 373.
Aplin, P., 2005. Remote sensing: ecology. Progress in Physical Geography 29(1): 104-113.
Arnold, S., Smith, G., Hazeu, G., Kosztra, B., Perger, C., Banko, G., Soukup, T., Strand, G.H., Sanz, N.V. and Bock, M., 2018. The EAGLE Concept: A Paradigm Shift in Land Monitoring. In Ahlqvist, O., Varanka, D., Fritz, S. and Janowicz, K. (eds), Land Use and Land Cover Semantics. CRC Press: Boca Raton.
Aspinall, R. and Pearson, D., 2000. Integrated geographical assessment of environmental condition in water catchments: Linking landscape ecology, environmental modelling and GIS. Journal of Environmental Management 59: 299-319.
Ayanu, Y.Z., Conrad, C., Nauss, T., Wegmann, M. and Koellner, T., 2012. Quantifying and mapping ecosystem services supplies and demands: a review of remote sensing applications. Environmental science and technology 46(16): 8529-8541.
Ban, Y., Hu, H. and Rangel, I. 2010. Fusion of QuickBird MS and RADARSAT-1 SAR Data for Land-Cover Mapping: Object-based and Knowledge-Based Approach. International Journal of Remote Sensing 31(6): 1391-1410.
Ban, Y. and Jacob, A., 2013. Object-based fusion of multitemporal multiangle ENVISAT ASAR and HJ-1B multispectral data for urban land- cover mapping. IEEE Transactions on Geoscience and Remote Sensing 51(4): 1998-2006.
92 Ban, Y., Jacob, A. and Gamba, P., 2015. Spaceborne SAR data for global urban mapping at 30 m resolution using a robust urban extractor. ISPRS Journal of Photogrammetry and Remote Sensing 103: 28-37.
Ban, Y. and Wu, Q., 2005. RADARSAT SAR data for landuse/land-cover classification in the rural-urban fringe of the Greater Toronto Area. In Proceedings of 8th AGILE Conference on Geographic Information Science: 26-28.
Ban, Y., Yousif, O. and Hu, H., 2014. Fusion of SAR and Optical Data for Urban Land Cover Mapping and Change Detection. In Weng, Q. (ed), Global Urban Monitoring and Assessment through Earth Observation. CRC Press: Boca Raton.
Banzhaf, E., de la Barrera, F. and Reyes-Paecke, S., 2019. Urban Green Infrastructure in Support of Ecosystem Services in a Highly Dynamic South American City: A Multi-Scale Assessment of Santiago de Chile. In Atlas of Ecosystem Services (pp. 157-165). Springer, Cham.
Baraldi, A. and Parmiggiani, F., 1995. An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters. IEEE Transactions on Geoscience and Remote Sensing, 33(2): 293-304.
Barnsley, M.J. and Barr, S.L., 2000. Monitoring urban land use by earth observation. Surveys in Geophysics 21(2): 269–289.
Barthel, S. and Elmqvist, J.N., 2007. The history of allotment areas in 20th century Sweden and their role in maintaining ecosystem services in urban areas. Presented at Seventh International Conference on Urban History: European City in Comparative Perspective, Panteion University, Athens - Piraeus, Greece, 27-30 October 2004, Session: Green spaces in cities since 1918: politics, ideologies, and perceptions.
Behling, R., Bochow, M., Foerster, S., Roessner, S. and Kaufmann, H., 2015. Automated GIS-based derivation of urban ecological indicators using hyperspectral remote sensing and height information. Ecological Indicators 48: 218-234.
Belward, A.S. and Skøien, J.O., 2015. Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites. ISPRS Journal of Photogrammetry and Remote Sensing 103: 115-128.
93 Berry, P., Turkelboom, F., Verheyden, W. and Martín-López, B., 2016. Ecosystem services bundles. In Potschin, M. and Jax, K. (eds): OpenNESS: Operationalisation of Natural Capital and Ecosystem Services. Ecosystem Services Reference Book. EC FP7 Grant Agreement no. 308428. Available at: www.openness-project.eu/library/reference-book (accessed 2019-11-07).
Bissonette, J.A. (ed), 1997. Wildlife and landscape ecology: effects of pattern and scale. Springer Science & Business Media: New York.
Blaschke, T., 2010. Object based image analysis for remote sensing. ISPRS journal of photogrammetry and remote sensing 65(1): 2-16.
Bodin, Ö. and Zetterberg, A., 2011. MatrixGreen: Landscape Ecological Network Analysis Tool–User manual. Paper V, Doctoral Thesis. Connecting the dots. KTH Architecture and the Built Environment. Stockholm.
Bolund, P. and Hunhammar, S., 1999. Ecosystem services in urban areas. Ecological Economics 29(2): 293-301.
Botequilha Leitão, A. and Ahern, J., 2002. Applying landscape ecological concepts and metrics in sustainable landscape planning. Landscape and Urban Planning 59: 65-93.
Boyle, S.A., Kennedy, C.M., Torres, J., Colman, K., Pérez-Estigarribia, P. E. and Noé, U., 2014. High-resolution satellite imagery is an important yet underutilized resource in conservation biology. PLoS One 9(1): e86908.
Bruzzone, L. and Demir, B., 2014. A review of modern approaches to classification of remote sensing data. In Manakos, I. and Braun, M. (eds), Land Use and Land Cover Mapping in Europe. Springer: Dordrecht.
Burkhard, B., Kroll, F., Müller, F. and Windhorst, W., 2009. Landscapes’ capacities to provide ecosystem services–a concept for land-cover based assessments. Landscape online 15(1): 22.
Butusov, O.B., 2003. Textural classification of forest types from Landsat 7 imagery, Mapping Sciences and Remote Sensing 40: 91–104.
Calderón-Contreras, R. and Quiroz-Rosas, L.E., 2017. Analysing scale, quality and diversity of green infrastructure and the provision of Urban Ecosystem Services: A case from Mexico City. Ecosystem services 23: 127- 137.
94 Chee, Y.E., 2004. An ecological perspective on the valuation of ecosystem services. Biological conservation 120(4): 549-565.
Chen, M., Su, W., Li, L., Zhang, C., Yue, A. and Li, H., 2009. Comparison of Pixel-based and Object-oriented knowledge-based Classification Methods using SPOT5 Imagery. WSEAS Transactions on Information Science and Applications 3(6): 477-489.
Cihlar, J., 2000. Land cover mapping of large areas from satellites: status and research priorities. International Journal of Remote Sensing 21: 1093- 1114.
Clausi, D.A., 2002. An analysis of co-occurrence texture statistics as a function of grey level quantization. Canadian Journal of remote sensing 28(1): 45-62.
Cleve, C., Kelly, M., Kearns, F.R. and Moritz, M., 2008. Classification of the wildland–urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography. Computers, Environment and Urban Systems 32: 317–326.
Colding, J., 2013. Local Assessment of Stockholm: Revisiting the Stockholm Urban Assessment. In Elmqvist, T., Fragkias, M., Goodness, J., Güneralp, B., Marcotullio, P.J., McDonald, R.I., Parnell, S., Schewenius, M., Sendstad, M., Seto, K.C. and Wilkinson, C. (eds); Urbanization, Biodiversity and Ecosystem Services: Challenges and Opportunities: A Global Assessment. Springer: Dordrecht, Germany: 313–335.
Colding, J., Elmqvist, T., Lundberg, J., Ahrné, K., Andersson, E., Barthel, S., Borgström, S., Duit, A., Ernstsson, H. and Tengö, M., 2003. The Stockholm Urban Assessment (SUA-Sweden). In Millennium Ecosystem Assessment Sub-Global Summary Report; Beijer Discussion Paper Series No. 182; The Beijer Institute of Ecological Economics, Royal Academy of Sciences: Stockholm, Sweden.
Cortes, C. and Vapnik, V.N., 1995. Support-Vector Networks. Machine Learning 20(3): 273-297.
Costanza, R., d’Arge, R., de Groot, R., Farber, S., Grasso, M., Hannon, B., Limburg, K. Naeem, S., O’Neill, R.V., Paruelo, J., Raskin, R.G., Sutton, P. and van den Belt, M., 1997. The value of the world’s ecosystem services and natural capital. Nature 387(15): 253–260.
95 Costanza, R., De Groot, R., Braat, L., Kubiszewski, I., Fioramonti, L., Sutton, P., Farber, S. and Grasso, M., 2017. Twenty years of ecosystem services: how far have we come and how far do we still need to go? Ecosystem services 28: 1-16.
Costanza, R. and Folke, C., 1997. Valuing ecosystem services with efficiency, fairness, and sustainability as goals. In Daily, G. (ed), Nature’s services: societal dependence on natural ecosystems. Island Press: Washington, D.C.: 49-70.
Daily, G.C., 1997. Nature’s Services: Societal Dependence on Natural Ecosystems. Island Press: Washington, D.C.
Davidson, M.D., 2013. On the relation between ecosystem services, intrinsic value, existence value and economic valuation. Ecological Economics 95: 171-177.
Dawson, T.P., Cutler, M.E.J. and Brown, C., 2016. The role of remote sensing in the development of SMART indicators for ecosystem services assessment. Biodiversity 17(4): 136-148. de Araujo Barbosa, C.C., Atkinson, P.M. and Dearing, J.A., 2015. Remote sensing of ecosystem services: a systematic review. Ecological Indicators 52: 430-443.
De Groot, R.S., 1992. Functions of nature: evaluation of nature in environmental planning, management and decision making. Wolters- Noordhoff BV: Groningen, Netherlands.
De Groot, R.S., Alkemade, R., Braat, L., Hein, L. and Willemen, L., 2010. Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecological Complexity 7(3): 260-272.
De Groot, R.S., Wilson, M.A. and Boumans, R.M.J., 2002. A typology for classification, description and valuation of ecosystem functions, goods and services. Ecological Economics 41(3): 393–408.
De Martinao, M., Causa, F. and Serpico, S.B., 2003. Classification of optical high resolution images in urban environment using spectral and textural information. 2003 IEEE International Geoscience and Remote Sensing Symposium, Proceedings 1: 467–469.
96 De Sherbinin, A., Levy, M.A., Zell, E., Weber, S. and Jaiteh, M., 2014. Using satellite data to develop environmental indicators. Environmental Research Letters 9(8): 084013.
Derkzen, M.L., van Teeffelen, A.J. and Verburg, P.H., 2015. Quantifying urban ecosystem services based on high‐resolution data of urban green space: an assessment for Rotterdam, the Netherlands. Journal of Applied Ecology 52(4): 1020-1032.
DigitalGlobe, 2016. WorldView-2 Datasheet. Available at: https://dgv4-cms- production.s3.amazonaws.com/uploads/document/file/130/WorldView2-DS- WV2-rev2.pdf (accessed 2019-11-05)
DigitalGlobe, 2014. QuickBird Datasheet. Available at: https://dgv4-cms- production.s3.amazonaws.com/uploads/document/file/131/QuickBird-DS- QB-Prod.pdf (accessed 2019-11-05)
Dimitrakopoulos, K., Gitas, I.Z., Polychronaki, A., Katagis, T. and Minakou, C., 2010. Land Cover/Use Mapping Using Object Based Classification of SPOT Imagery. Remote Sensing for Science, Education, and Natural and Cultural Heritage, Proceedings of EARSeL: 263-272.
Diniz, M.F., Machado, R.B., Bispo, A.A. and Brito, D., 2018. Identifying key sites for connecting jaguar populations in the Brazilian Atlantic Forest. Animal conservation 21(3): 201-210.
Dixon, B. and Candade, N., 2008. Multispectral landuse classification using neural networks and support vector machines: one or the other, or both? International Journal of Remote Sensing 29(4): 1185-1206.
Dobbs, C., Hernández-Moreno, Á., Reyes-Paecke, S. and Miranda, M.D., 2018. Exploring temporal dynamics of urban ecosystem services in Latin America: The case of Bogota (Colombia) and Santiago (Chile). Ecological Indicators 85: 1068-1080.
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A, Spoto, F., Sy, O., Marchese, F. and Bargellini, P., 2012. Sentinel-2: ESA's optical high- resolution mission for GMES operational services. Remote Sensing of Environment 120: 25-36.
97 Duarte, G.T., Santos, P.M., Cornelissen, T.G., Ribeiro, M.C. and Paglia, A.P., 2018. The effects of landscape patterns on ecosystem services: meta- analyses of landscape services. Landscape Ecology 33(8): 1247-1257.
Dunn, C.P., Sharpe, D.M., Guntenspergen, G.R., Stearns, F. and Yang, Z., 1991. Methods for analyzing temporal changes in landscape pattern. In Turner, M.G. and Gardner, R.H. (eds), Quantitative methods in landscape ecology: the analysis and interpretation of landscape heterogeneity. Springer-Verlag: New York: 173-198.
Ekologigruppen AB, 2017. Regional Grön Infrastruktur i Stockholms Län: Bakgrund för Analyser av Värdekärnor och Spridningszoner [Regional Green Infrastructure in Stockholm County: Background for the Analysis of Core Areas and Ecological Zones]. The report can be obtained from the Stockholm County Administrative Board. Information about the project available at: https://www.ekologigruppen.se/projekt/gron-infrastruktur- stockholms-lan/ (accessed 2019-04-21). (In Swedish)
Estrada, E. and Bodin, Ö., 2008. Using network centrality measures to manage landscape connectivity. Ecological Applications 18(7): 1810-1825.
European Commission, 2013. Green Infrastructure (GI) — Enhancing Europe’s Natural Capital. Communication COM 249. Available at: https://eur-lex.europa.eu/legal- content/EN/TXT/?uri=CELEX:52013DC0249 (accessed 2019-11-14).
European Communities, 2008. The Economics of Ecosystems and Biodiversity (TEEB). Available at: http://doc.teebweb.org/wp- content/uploads/Study%20and%20Reports/Additional%20Reports/Interim% 20report/TEEB%20Interim%20Report_English.pdf (accessed 2019-04-27).
Feng, X., Fu, B., Yang, X. and Lü, Y., 2010. Remote sensing of ecosystem services: An opportunity for spatially explicit assessment. Chinese Geographical Science 20(6), 522-535.
Forman, R. and Godron, M., 1986. Landscape Ecology. Wiley: New York.
Frank, S., Fürst, C., Koschke, L. and Makeschin, F., 2012. A contribution towards a transfer of the ecosystem service concept to landscape planning using landscape metrics. Ecological Indicators 21: 30-38.
Franklin, S.E. and Peddle, D.R., 1990. Classification of SPOT HRV imagery and texture features, International Journal of Remote Sensing 11: 551–556.
98
Franklin, S.E. and Wulder, M.A., 2002. Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas. Progress in Physical Geography 26(2): 173-205.
Frohn, R.C. and Lopez, R.D., 2017. Remote sensing for landscape ecology: new metric indicators: monitoring, modeling, and assessment of ecosystems. CRC Press: Boca Raton.
Gamba, P. and Aldrighi, M., 2012. SAR Data Classification of Urban Areas by Means of Segmentation Techniques and Ancillary Optical Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5(4): 1140–1148.
Gamba, P. and Herold, M., 2009. Global mapping of human settlement: experiences, datasets, and prospects. CRC Press: Boca Raton.
Gao, Y., Wu, Z., Lou, Q., Huang, H., Cheng, J. and Chen, Z., 2012. Landscape ecological security assessment based on projection pursuit in Pearl River Delta. Environmental monitoring and assessment 184(4): 2307- 2319.
Gerard, F., Petit, S., Smith, G., Thomson, A., Brown, N., Manchester, S., Wadsworth, R., Bugar, G., Halada, L., Bezak, P., Boltiziar, M., De badts, E., Halabuk, A., Mojse, M., Petrovic, F., Gregor, M., Hazeu, G., Mucher, C.A., Wachowicz, M., Huitu, H., Tuominen, S., Kohler, R., Olschofsky, K.; Ziese, H., Kolar, J., Sustera, J., Luque, S., Pino, J., Pons, X., Roda, F., Roscher, M. and Feranec, J., 2005. Linking pan-European land cover change to pressures on biodiversity. Biopress final report, 1st January 2003-31st December 2005, sections 5 and 6. Progress in Physical Geography 34(2): 183-205.
Gbanie, S., Griffin, A. and Thornton, A., 2018. Impacts on the urban environment: land cover change trajectories and landscape fragmentation in Post-War Western Area, Sierra Leone. Remote Sensing 10(1): 129.
Ghosh, A. and Joshi, P.K., 2014. A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery. International Journal of Applied Earth Observation and Geoinformation 26: 298-311.
Gibson, C.C., Ostrom, E. and Ahn, T.K., 2000. The concept of scale and the human dimensions of global change: a survey. Ecological Economics 32(2): 217-239.
99 Gómez-Baggethun, E., Gren, Å., Barton, D.N., Langemeyer, J., McPhearson, T., O’Farrell, P., Andersson, E., Hamstead, Z. and Kremer, P., 2013. Urban ecosystem services. In Elmqvist, T., Fragkias, M., Goodness, J., Güneralp, B., Marcotullio, P.J., McDonald, R.I., Parnell, S., Schewenius, M., Sendstad, M., Seto, K.C. and Wilkinson, C. (eds); Urbanization, biodiversity and ecosystem services: Challenges and opportunities. Springer: Dordrecht, Germany: 175-251.
Government of Ontario, 2019. “Oak Ridges Moraine”. Ministry of Municipal Affairs and Housing. Available at: https://www.ontario.ca/page/oak-ridges-moraine (accessed 2019-11-05).
Gómez, C., White, J.C. and Wulder, M.A., 2016. Optical remotely sensed time series data for land cover classification: A review. ISPRS Journal of Photogrammetry and Remote Sensing 116: 55-72.
Gong, P., Marceau, D.J. and Howarth, P.J., 1992. A comparison of spatial feature extraction algorithms for land-use classification with SPOT HRV data. Remote Sensing of Environment 40: 137–151.
Guan, H., Li, J., Chapman, M., Deng, F., Ji, Z. and Yang, X., 2013. Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests. International Journal of Remote Sensing 34(14): 5166-5186.
Haas, J., 2016. Remote Sensing of Urbanization and Environmental Impacts. Doctoral dissertation, KTH Royal Institute of Technology. Stockholm.
Haas, J. and Ban, Y., 2018. Urban Land Cover and Ecosystem Service Changes based on Sentinel-2A MSI and Landsat TM Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11: 485– 497.
Haas, J. and Ban, Y., 2017. Mapping and monitoring urban ecosystem services using multitemporal high-resolution satellite data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(2): 669-680.
Haas, J. and Ban, Y., 2014. Urban growth and environmental impacts in Jing-Jin-Ji, the Yangtze, River Delta and the Pearl River Delta. International Journal of Applied Earth Observation and Geoinformation 30: 42-55.
100 Haase, D., Larondelle, N., Andersson, E., Artmann, M., Borgström, S., Breuste, J., Gomez-Baggethun, E., Gren, Å., Hamstead, Z., Hansen, R., Kabisch, N., Kremer, P., Langemeyer, J., Rall, E.L., McPhearson, T., Pauleit, S., Qureshi, S., Schwarz, N., Voigt, A., Wurster, D. and Elmqvist, T., 2014. A quantitative review of urban ecosystem service assessments: Concepts, models, and implementation. Ambio 43(4): 413-433.
Haddad, N.M., Brudvig, L.A., Clobert, J., Davies, K.F., Gonzalez, A., Holt, R.D., Lovejoy, T.E., Sexton, J.O., Austin, M.P., Collins, C.D. Cook, W.M., Damschen, E.I., Ewers, R.M., Foster, B.L., Jenkins, C.N., King, A.J., Laurance, W.F., Levey, D.J., Margules, C.R., Melbourne, B.A., Nicholls, A.O., Orrock, J.L., Song, D. and Townshend, J.R., 2015. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Science Advances 1(2): e1500052.
Haines-Young, R., Green, D.R. and Cousins, S.H., 1993. Landscape ecology and GIS. Taylor and Francis: London.
Haines-Young, R. and Potschin, M.B., 2018. Common International Classification of Ecosystem Services (CICES) V5.1 and Guidance on the Application of the Revised Structure. Available at: https://cices.eu/content/uploads/sites/8/2018/01/Guidance-V51- 01012018.pdf (accessed 2019-04-27).
Halada, L., Jongman, R.G.H., Gerard, F., Whittaker, L., Bunce, R.G.H., Bauch, B. and Schmeller, D.S., 2009. The European Biodiversity Observation Network-EBONE. In Hřebíček, J., Hradec, J., Pelikán, E., Mírovský, O., Pillmann, W., Holoubek, I. and Bandholtz, T. (eds), Proceedings of European conference of the Czech Presidency of the Council of the EU TOWARDS eENVIRONMENT. Challenges of SEIS and SISE: Integrating Environmental Knowledge in Europe. Masaryk University: 177- 188.
Hansen, M.J., Franklin, S.E., Woudsma, C.G. and Peterson, M., 2001. Caribou habitat mapping and fragmentation analysis using Landsat MSS, TM, and GIS data in North Columbia Mountains, British Columbia, Canada. Remote Sensing of Environment 77(1), 50-65.
Hanski, I., Sven Erik, J. and Brian, F., 2008. Metapopulation Models. Encyclopedia of Ecology. Academic Press: Oxford: 2318–2325.
Haralick, R.M., Shanmugam, K. and Dinstein, I.H., 1973. Textural features for image classification. IEEE Transactions on systems, man and cybernetics 3(6): 610–621.
101
Hargis, C.D., Bissonette, J.A. and David, J.L., 1998. The behavior of landscape metrics commonly used in the study of habitat fragmentation. Landscape Ecology 13(3): 167–186.
Hartfield, K.A., Landau, K.I. and Van Leeuwen, W.J., 2011. Fusion of high resolution aerial multispectral and LiDAR data: Land cover in the context of urban mosquito habitat. Remote Sensing 3(11): 2364-2383.
Hernández-Moreno, Á. and Reyes-Paecke, S., 2018. The effects of urban expansion on green infrastructure along an extended latitudinal gradient (23°S–45°S) in Chile over the last thirty years. Land Use Policy 79: 725–733.
Herold, M., 2009. Some Recommendations for Global Efforts in Urban Monitoring and Assessments from Remote Sensing. In Gamba, P. and Herold, M. (eds), Global Mapping of Human Settlement: Experiences, Datasets, and Prospects. CRC Press: Boca Raton.
Herold, M., Couclelis, H. and Clarke, K.C., 2005. The role of spatial metrics in the analysis and modeling of urban land use change. Computers, Environment and Urban Systems 29(4): 369–399.
Herold, M., Liu, X. and Clarke, K.C., 2003. Spatial metrics and image texture for mapping urban land use. Photogrammetric Engineering and Remote Sensing 69(9): 991–1002.
Herold, M., Schiefer, S., Hostert, P. and Roberts, D.A., 2007. Applying Imaging Spectrometry in Urban Areas. In Weng, Q. and Quattrochi, D.A. (eds), Urban Remote Sensing. CRC Press: Boca Raton.
Herrera, L.P., Sabatino, M.C., Jaimes, F.R. and Saura, S., 2017. Landscape connectivity and the role of small habitat patches as stepping stones: an assessment of the grassland biome in South America. Biodiversity and conservation 26(14): 3465-3479.
Hu, H. and Ban, Y., 2012. Multitemporal RADARSAT-2 ultra-fine beam SAR data for urban land cover classification. Canadian Journal of Remote Sensing 38(1): 1-11.
Hu, Y., Ban, Y., Zhang, Q. and Liu, J., 2009. The Trajectory of Urbanization Process in the Yangtze River Delta During 1990 to 2005. 2009 Joint Urban Remote Sensing Event 1–8.
102 Huang, C., Davis, L.S. and Townshend, J.R.G., 2002. An assessment of support vector machines for land cover classification. International Journal of Remote Sensing 23(4): 725-749.
Huang, Y., Huang, J.L., Liao, T.J., Liang, X. and Tian, H., 2018. Simulating urban expansion and its impact on functional connectivity in the Three Gorges Reservoir Area. Science of the total environment 643: 1553-1561.
Huang, Y.C., Li, P., Zhang, L. and Zhong, Y., 2009. Urban land cover mapping by spatial-spectral feature analysis of high resolution hyperspectral data with decision directed acyclic graph SVM. In 2009 Joint Urban Remote Sensing Event. IEEE.
International Association for Landscape Ecology: the Swedish chapter (IALE), 2017. IALE Conference 2017: Green Infrastructure. Conference output documents (in Swedish). Available at: http://iale.se/sw/iale-konferens- 2017-gron-infrastruktur (accessed 2019-04-21).
Imhoff, M.L., Zhang, P., Wolfe, R.E. and Bounoua, L., 2010. Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sensing of Environment 114(3): 504-513.
Jacquin, A., Misakova, L. and Gay, M., 2008. A hybrid object-based classification approach for mapping urban sprawl in periurban environment. Landscape and Urban Planning 84: 152–165.
Jebur, M.N., Mohd Shafri, H.Z., Pradhan, B. and Tehrany, M.S., 2013. Per- pixel and object-oriented classification methods for mapping urban land cover extraction using SPOT 5 imagery. Geocarto International 29(7): 792- 806.
Jensen, J.R., 2005. Introductory Digital Image Processing. Prentice Hall: Upper Saddle River, NJ.
Jin, S., Li, D. and Gong, J., 2005. A comparison of SVMs with MLC algorithms on texture features. In MIPPR 2005 Image Analysis Techniques Proc. of SPIE, 6044: 60442B-1.
Kamusoko, C. and Aniya, M., 2007. Land Use/Cover Change and Landscape Fragmentation Analysis in the Bindura District, Zimbabwe. Land Degradation and Development 18: 221-233.
103 Karlson, M. and Mörtberg, U., 2015. A spatial ecological assessment of fragmentation and disturbance effects of the Swedish road network. Landscape and Urban Planning 134: 53-65.
Kasanko, M., Sagris, V. and Lavalle, C., 2007(a). Analysing the compactness of urban areas by using indicators derived from data acquired by remote sensing. In 2007 Urban Remote Sensing Joint Event, pp. 1-7. IEEE.
Kasanko, M., Sagris, V., Lavalle, C., Barredo, J.I., Petrov, L., Steinnocher, K., Loibl, W. and Hoffmann, C., 2007(b). GEOLAND spatial planning observatory: How remote sensing data can serve the needs of urban and regional planning. In 2007 Urban Remote Sensing Joint Event: 1-10. IEEE.
Kavzoglu, T., Colkesen, I. and Yomralioglu, T., 2015. Object-based classification with rotation forest ensemble learning algorithm using very- high-resolution WorldView-2 image. Remote Sensing Letters, 6(11): 834- 843.
Klemas, V.V., 2001. Remote Sensing of Landscape-Level Coastal Environmental Indicators. Environmental Management 27(1): 47-57.
Konarska, K.M., Sutton, P.C. and Castellon, M., 2002. Evaluating scale dependence of ecosystem service valuation: a comparison of NOAA- AVHRR and Landsat TM datasets. Ecological Economics 41(3): 491–507.
Kopecká, M., Szatmári, D. and Rosina, K., 2017. Analysis of urban green spaces based on Sentinel-2A: Case studies from Slovakia. Land 6(2): 25.
Kotharkar, R. and Bagade, A., 2018. Evaluating urban heat island in the critical local climate zones of an Indian city. Landscape and Urban Planning 169: 92-104.
Itkonen, P., Kopperoinen, L., Viinikka, A., Olazabal, E. and Heikinheimo, V., 2015. International experiences and mapping approaches in ecosystem service valuation. In Jäppinen, J.-P. and Heliölä, J. (eds), Towards a sustainable and genuinely green economy. The value and social significance of ecosystem services in Finland (TEEB for Finland). Synthesis and roadmap. The Finnish Environment 1en/2015. The Finnish Ministry of Environment, Helsinki: 46- 55.
Krishnaswamy, J., Bawa, K.S., Ganeshaiah, K.N. and Kiran, M.C., 2009. Quantifying and mapping biodiversity and ecosystem services: Utility of a
104 multi-season NDVI based Mahalanobis distance surrogate. Remote Sensing of Environment 113(4): 857-867.
Lakes, T. and Kim, H.O., 2012. The urban environmental indicator “Biotope Area Ratio”—An enhanced approach to assess and manage the urban ecosystem services using high resolution remote-sensing. Ecological Indicators 13(1): 93-103.
Lambin, E.F., Turner, B.L., Geist, H.J., Agbola, S.B., Angelsen, A., Bruce, J.W., Coomes, O.T., Dirzo, R., Fischer, G., Folke, C., George, P.S., Homewood, K., Imbernon, J., Leemans, R., Li, X., Moran, E.F., Mortimore, M., Ramakrishnan, P.S., Richards, J.F., Skånes, H., Steffen, W., Stone, G.D., Svedin, U., Veldkamp, T.A., Vogel, C. and Xu, J., 2001. The causes of land- use and land-cover change: moving beyond the myths. Global Environmental Change 11: 261–269.
Lang, S., Blaschke, T., Kothencz, G. and Hölbling, D., 2018. Urban Green Mapping and Valuation. In Weng, Q., Quattrochi, D. and Gamba, P.E. (eds), Urban remote sensing. CRC press: Boca Raton.
Lausch, A., Blaschke, T., Haase, D., Herzog, F., Syrbe, R.U., Tischendorf, L. and Walz, U., 2015. Understanding and quantifying landscape structure—A review on relevant process characteristics, data models and landscape metrics. Ecological Modelling 295: 31–41.
Lawley, V., Lewis, M., Clarke, K. and Ostendorf, B., 2016. Site-based and remote sensing methods for monitoring indicators of vegetation condition: An Australian review. Ecological Indicators 60: 1273-1283.
Leitão, A.B., Miller, J., Ahern, J. and McGarigal, K., 2006. Measuring landscapes: A planner's handbook. Island press: Washington DC.
Li, C., Wang, J., Wang, L., Hu, L. and Gong, P., 2014. Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery. Remote Sensing 6(2): 964-983.
Li, H. and Reynolds, J.F., 1994. A simulation experiment to quantify spatial heterogeneity in categorical maps. Ecology 75: 2446-2455.
Li, H. and Wu, J., 2004. Use and misuse of landscape indices. Landscape Ecology 19: 389-399.
105
Li, S., Sui, Y., Sun, Z., Wang, F. and Li, Y., 2005. Landscape pattern and fragmentation of natural secondary forests in the eastern mountainous region, northeast China: A case study of Mao'ershan forests in Heilongjiang Province. Journal of Forestry Research 16(1): 35-38.
Li, Y., Zhu, X., Sun, X. and Wang, F., 2010. Landscape effects of environmental impact on bay-area wetlands under rapid urban expansion and development policy: a case study of Lianyungang, China. Landscape and urban planning 94(3): 218-227.
Liang, B. and Weng, Q., 2011. Assessing urban environmental quality change of Indianapolis, United States, by the remote sensing and GIS integration. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 4(1): 43-55.
Lindenmayer, D.B. and Fischer, J., 2006. Habitat Fragmentation and Landscape Change. Island Press: Washington.
Liu, S., Costanza, R., Troy, A., D’Aagostino, J. and Mates, W., 2010. Valuing New Jersey’s Ecosystem Services and Natural Capital: A Spatially Explicit Benefit Transfer Approach. Environmental Management 45: 1271–1285.
Liu, T. and Yang, X., 2015. Monitoring land changes in an urban area using satellite imagery, GIS and landscape metrics. Applied Geography 56: 42-54.
Liu, X.H., Skidmore, A.K. and Van Oosten, H., 2002. Integration of classification methods for improvement of land-cover map accuracy. ISPRS Journal of Photogrammetry and Remote Sensing 56(4): 257–268.
Lo, C.P., 1998. Application of Landsat TM Data for Quality of Life Assessment in an Urban Environment. Computers, Environment and Urban Systems 21(3/4): 259-276.
Long, J.A., Nelson, T.A. and Wulder, M.A., 2010. Characterizing forest fragmentation: Distinguishing change in composition from configuration. Applied Geography 30(3): 426-435.
Löfvenhaft, K., Björn, C. and Ihse, M., 2002. Biotope patterns in urban areas: a conceptual model integrating biodiversity issues in spatial planning. Landscape and Urban Planning 58(2-4): 223-240.
Löfvenhaft, K., Runborg, S. and Sjögren-Gulve, P., 2004. Biotope patterns and amphibian distribution as assessment tools in urban landscape
106 planning. Landscape and urban planning 68(4): 403-427.
Lopez, R.D. and Frohn, R.C., 2017. Remote Sensing for Landscape Ecology: New Metric Indicators. CRC Press: Boca Raton.
Lu, D. and Weng, Q., 2007. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing 28(5): 823-870.
Lu, D. and Weng, Q., 2005. Urban classification using full spectral information of Landsat ETM+ imagery in Marion County, Indiana. Photogrammetric engineering and remote sensing 71(11): 1275–1284.
Ludwig, D., 2000. Limitations of economic valuation of ecosystems. Ecosystems 3(1): 31-35.
Luederitz, C., Brink, E., Gralla, F., Hermelingmeier, V., Meyer, M., Niven, L., Panzer, L., Partelow, S., Rau, A.-L., Sasaki, R., Abson, D. J., Lang, D.J., Wamsler, C. and von Wehrden, H., 2015. A review of urban ecosystem services: Six key challenges for future research. Ecosystem Services 14: 98- 112.
Lundberg, J., Andersson, E., Cleary, G. and Elmqvist, T., 2008. Linkages beyond borders: targeting spatial processes in fragmented urban landscapes. Landscape Ecology 23(6): 717.
Ma, L., Li, M., Ma, X., Cheng, L., Du, P. and Liu, Y., 2017. A review of supervised object-based land-cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing 130: 277-293.
Maes, J., Barbosa, A., Baranzelli, C., Zulian, G., e Silva, F.B., Vandecasteele, I., Hiederer, R., Liquete, C., Paracchini, M.L., Mubareka, S., Jacobs-Crisioni, C., Castillo, C.P. and Lavalle, C., 2015. More green infrastructure is required to maintain ecosystem services under current trends in land-use change in Europe. Landscape ecology 30(3): 517-534.
Maes, J., Liquete, C., Teller, A., Erhard, M., Paracchini, M.L., Barredo, J.I., Grizzetti, B., Cardoso, A., Somma, F., Petersen, J.E., Meiner, A., Gelabert, E.R., Zal, N., Kristensen, P., Bastrup-Birk, A., Biala, K., Piroddi, C., Egoh, B., Degeorges, P., Fiorina, C., Santos-Martín, F., Naruševičius, V., Verboven, J., Pereira, H.M., Bengtsson, J., Gocheva, K., Marta-Pedroso, C., Snäll, T., Estreguil, C., San-Miguel-Ayanz, J., Pérez-Soba, M., Grêt- Regamey, A., Lillebø, A.I., Malak, D.A., Condé, S., Moen, J., Czúcz, B., Drakou, E.G., Zulian, G. and Lavalle, C., 2016. An indicator framework for
107 assessing ecosystem services in support of the EU Biodiversity Strategy to 2020. Ecosystem services 17: 14-23.
Mairota, P., Cafarelli, B., Didham, R.K., Lovergine, F.P., Lucas, R.M., Nagendra, H., Rocchini, D. and Tarantino, C., 2015. Challenges and opportunities in harnessing satellite remote-sensing for biodiversity monitoring. Ecological Informatics 30: 207-214.
Mariano, D.A., dos Santos, C.A., Wardlow, B.D., Anderson, M.C., Schiltmeyer, A.V., Tadesse, T. and Svoboda, M.D., 2018. Use of remote sensing indicators to assess effects of drought and human-induced land degradation on ecosystem health in Northeastern Brazil. Remote Sensing of Environment 213: 129-143.
Mathieu, R., Freeman, C. and Aryal, J., 2007. Mapping private gardens in urban areas using object-oriented techniques and very high-resolution satellite imagery. Landscape and Urban Planning 81(3): 179-192.
Millennium Ecosystem Assessment (MA), 2005. Ecosystems and Human Well-being: Synthesis. Island Press: Washington, DC.
Mathur, A. and Foody, G.M., 2008. Multiclass and binary SVM classification: Implications for training and classification users. Geoscience and Remote Sensing Letters, IEEE 5(2): 241-245.
McGarigal, K., 2002. Landscape pattern metrics. In El-Shaarawi, A.H. and Piegorsch, W.W. (eds), Encyclopedia of environmetrics, 2. John Wiley and Sons: Chichester, England: 1135-1142.
McGarigal, K. and Marks, B.J., 1995. FRAGSTATS: Spatial pattern analysis program for quantifying landscape structure. Gen. Tech. Rep. PNW-GTR- 351. US Department of Agriculture, Forest Service, Pacific Northwest Research Station.
McGarigal, K. and McComb, W.C., 1995. Relationships between landscape structure and breeding birds in the Oregon Coast Range. Ecological monographs 65(3): 235-260.
Metria AB, 2015. CadasterENV Sweden: a multi-scale and multi-purpose land cover monitoring system. Version 3.0. Available online: https://www.naturvardsverket.se/upload/sa-mar-miljon/kartor/cadenv- executive-summary-english-version.pdf (accessed 2019-04-26)
108 Metria GeoAnalys, 2009. Ny metod för kontinuerlig naturtypskartering av skyddade områden (KNAS). Rapport för Naturvårdsverket. [Landscape ecological analysis i Stockholm city: method development with amphibians as an example] (in Swedish). Miljöförvaltningen, Stockholm City.
Metzger, J. and Olsson, A.R., 2013. Sustainable Stockholm: Exploring Urban Sustainability in Europe’s Greenest City. Routledge: New York.
Michaud, J.S., Coops, N.C., Andrew, M.E., Wulder, M.A., Brown, G.S. and Rickbeil, G.J., 2014. Estimating moose (Alces alces) occurrence and abundance from remotely derived environmental indicators. Remote Sensing of Environment 152: 190-201.
Miljöförvaltningen, 2014. Stockholms ekologiska infrastruktur – Bakgrund och beskrivning av databas och karta [Stockholms ecological infrastructure - Background and description of the database and map]. Version 2014-02-14. Available at: http://miljobarometern.stockholm.se/content/docs/mp15/4/ESBO_Bed%C3 %B6mningsgrunder.pdf (accessed 2019-10-24) (in Swedish)
Momeni, R., Aplin, P., and Boyd, D., 2016. Mapping complex urban land cover from spaceborne imagery: the influence of spatial resolution, spectral band set and classification approach. Remote Sensing 8(2): 88.
Moreno-Martínez, Á., Camps-Valls, G., Kattge, J., Robinson, N., Reichstein, M., van Bodegom, P., Kramer, K., Cornelissen, J.H.C., Reich, P., Bahn, M., Niinemets, Ü., Penuelas, J., Craine, J.M., Cerabolini, B.E.L., Minden, V., Laughlin, D.C., Sack, L., Allred, B., Baraloto, C., Byun, C., Soudzilovskaia, N.A. and Running, S.W., 2018. A methodology to derive global maps of leaf traits using remote sensing and climate data. Remote sensing of environment 218: 69-88.
Mörtberg, U., Zetterberg, A. and Gontier, M., 2007. Landskapsekologisk analys i Stockholms stad: Habitatnätverk för eklevande arter och barrskogsarter [Landscape ecological analysis i Stockholm city: habitat networks for oak-dependent species and coniferous forest species]. Miljöförvaltningen, Stockholm City. (in Swedish)
Mörtberg, U., Zetterberg, A. and Gontier, M., 2006. Landskapsekologisk analys i Stockholms stad: Metodutveckling med groddjur som exempel [Landscape ecological analysis i Stockholm city: method development with amphibians as an example]. Miljöförvaltningen, Stockholm City. (in Swedish)
109 Mörtberg, U. and Wallentinus, H.G., 2000. Red-listed forest bird species in an urban environment—assessment of green space corridors. Landscape and Urban planning 50(4): 215-226.
Mountrakis, G., Im, J. and Ogole, C., 2011. Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing 66(3): 247-259.
Mugiraneza, T., Nascetti, A. and Ban, Y., 2019. WorldView-2 Data for Hierarchical Object-Based Urban Land Cover Classification in Kigali: Integrating Rule-Based Approach with Urban Density and Greenness Indices. Remote Sensing 11(18): 2128.
Mutuku, F.M., Bayoh, M.N., Hightower, A.W., Vulule, J.M., Gimnig, J.E., Mueke, J.M., Amimo, F.A. and Walker, E.D., 2009. A supervised land cover classification of a western Kenya lowland endemic for human malaria: associations of land cover with larval Anopheles habitats. International Journal of Health Geographics 8(1): 19.
Myint, S.W., Gober, P., Brazel, A., Grossman-Clarke, S. and Weng, Q., 2011. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment 115: 1145–1161.
Nagendra, H., Lucas, R., Honrado, J.P., Jongman, R.H., Tarantino, C., Adamo, M. and Mairota, P., 2013. Remote sensing for conservation monitoring: Assessing protected areas, habitat extent, habitat condition, species diversity, and threats. Ecological Indicators 33: 45-59.
Narumalani, S., Hlady, J.T. and Jensen, J.R., 2002. Information extraction from remotely sensed data. Manual of geospatial science and technology: 288-324.
Narumalani, S., Mishra, D.R. and Rothwell, R.G., 2004. Change detection and landscape metrics for inferring anthropogenic processes in the greater EFMO area. Remote Sensing of Environment 91(3-4): 478-489.
Nelson, A., 2006. Stockholm Case Study: City of Water. In Open Space Systems Report; University of Washington: Seattle, USA. Available online: https://depts.washington.edu/open2100/Resources/1_OpenSpaceSystems/Op en_Space_Systems/Stockholm_Case_Study.pdf (accessed 2019-04-21).
110 Nelson, E., Mendoza, G., Regetz, J., Polasky, S., Tallis, H., Cameron, D.R., Chan, K.M., Daily, G.C., Goldstein, J., Kareiva, P.M., Lonsdorf, E., Naidoo, R., Ricketts, T.H. and Shaw, M.R., 2009. Modeling multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at landscape scales. Frontiers in Ecology and the Environment 7(1): 4-11.
Newbold, T., Hudson, L.N., Hill, S.L., Contu, S., Lysenko, I., Senior, R.A., Börger, L., Bennett, D.J., Choimes, A., Collen, B., Day, J., De Palma, A., Diaz, S., Echeverria-Londono, S., Edgar M.J., Felmand, A., Garon, M., Harrison, M.L.K., Alhusseini, T., Ingram, D.J., Itescu, Y., Kattge, J., Kemp, V., Kirkpatrick, L., Kleyer, M., Lginha Pinto Correia, D., Martin, C.D., Meiri, S., Novosolov, M., Pan, Y., Phillips, H.R.P., Purves, D.W., Robinson, A., Simpson, J., Tuck, S.L., Wiher, E., White, H.J., Ewers, R.M., Mace, G.M., Scharlemann, J.P.W. and Purvis, A., 2015. Global effects of land use on local terrestrial biodiversity. Nature 520(7545): 45-50.
Newman, M.E., McLaren, K.P. and Wilson, B.S., 2011. Comparing the effects of classification techniques on landscape-level assessments: pixel- based versus object-based classification. International Journal of Remote Sensing 32(14): 4055-4073.
Newton, A.C., Hill, R.A., Echeverría, C., Golicher, D., Benayas, J.M.R., Cayuela, L. and Hinsley, S.A., 2009. Remote sensing and the future of landscape ecology. Progress in Physical Geography 33(4): 528-546.
Nichol, J.E. and Wong, M.S., 2007. Assessing Urban Environmental Quality with Multiple Parameters. In Weng, Q. and Quattrochi, D.A. (eds), Urban Remote Sensing. CRC Press: Boca Raton.
Niemeijer, D. and de Groot, R.S., 2008. A conceptual framework for selecting environmental indicator sets. Ecological Indicators 8(1): 14-25.
Niu, X. and Ban, Y., 2013. Multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using an object-based support vector machine and a rule-based approach. International Journal of Remote Sensing 34(1): 1-26.
Natural Resources Canada (NRC), 2013. "Evaluation of the Groundwater Geoscience Program", Evaluation Report. Available at: https://www.nrcan.gc.ca/evaluation/reports/2013/11142 (accessed 2019-11- 04)
Olff, H. and Ritchie, M.E., 2002. Fragmented nature: consequences for biodiversity. Landscape and Urban Planning 58(2): 83-92.
111
O'Neill, R.V., Krummel, J.R., Gardner, R.H., Sugihara, G., Jackson, B., DeAngelis, D.L., Milne, B.T., Turner, M.G., Zygmunt, B., Christensen, S.W., Dale, V.H. and Graham, R.L., 1988. Indices of landscape pattern. Landscape Ecology 1(3): 153–162.
Padilla, M., Stehman, S.V., Ramo, R., Corti, D., Hantson, S., Oliva, P., Alonso-Canas, I., Bradley, A.V., Tansey, K., Mota, B., Pereira, J.M. and Chuvieco, E., 2015. Comparing the accuracies of remote sensing global burned area products using stratified random sampling and estimation. Remote Sensing of Environment 160: 114-121.
Pande-Chhetri, R., Abd-Elrahman, A., Liu, T., Morton, J. and Wilhelm, V.L., 2017. Object-based classification of wetland vegetation using very high-resolution unmanned air system imagery. European Journal of Remote Sensing 50(1): 564-576.
Pérez-Hernández, C.G., Vergara, P.M., Saura, S. and Hernández, J., 2015. Do corridors promote connectivity for bird-dispersed trees? The case of Persea lingue in Chilean fragmented landscapes. Landscape ecology 30(1): 77-90.
Pesaresi, M., Corbane, C., Julea, A., Florczyk, A., Syrris, V. and Soille, P., 2016. Assessment of the added-value of Sentinel-2 for detecting built-up areas. Remote Sensing 8(4): 299.
Petit, S., Firbank, L., Wyatt, B. and Howard, D., 2001. MIRABEL: models for integrated review and assessment of biodiversity in European landscapes. AMBIO: A Journal of the Human Environment 30(2): 81-88.
Pettorelli, N., Laurance, W.F., O'Brien, T.G., Wegmann, M., Nagendra, H. and Turner, W., 2014(a). Satellite remote sensing for applied ecologists: opportunities and challenges. Journal of Applied Ecology 51(4): 839-848.
Pettorelli, N., Safi, K. and Turner, W., 2014(b). Satellite remote sensing, biodiversity research and conservation of the future. Philosophical Transactions of the Royal Sociecty B 369: 20130190.
Phiri, D. and Morgenroth, J., 2017. Developments in Landsat land cover classification methods: A review. Remote Sensing 9(9): 967.
Powers, R.P., Hay, G.J. and Chen, G., 2012. How wetland type and area differ through scale: A GEOBIA case study in Alberta's Boreal Plains. Remote Sensing of Environment 117: 135-145.
112
Qian, Y., Zhou, W., Yan, J., Li, W. and Han, L., 2015. Comparing machine learning classifiers for object-based land cover classification using very high resolution imagery. Remote Sensing 7(1): 153-168.
Qin, Y., Niu, Z., Chen, F., Li, B. and Ban, Y., 2013. Object-based land cover change detection for cross-sensor images. International Journal of Remote Sensing 34(19): 6723-6737.
Raciti, S.M., Hutyra, L.R. and Newell, J.D., 2014. Mapping carbon storage in urban trees with multi-source remote sensing data: Relationships between biomass, land use, and demographics in Boston neighborhoods. Science of The Total Environment 500: 72-83.
Recio, M.R., Mathieu, R., Hall, G.B., Moore, A.B. and Seddon, P.J., 2013. Landscape resource mapping for wildlife research using very high resolution satellite imagery. Methods in Ecology and Evolution 4(10): 982-992.
Regionplane- och Trafikkontoret (RTK), 2008. Grönstruktur och landskap i regional utvecklingsplanering. [Green structure and landscape i regional development planning]. Rapport 9:2008. Available at: http://www.rufs.se/globalassets/h.-publikationer/2008- 9_r_gronstruktur_landskap.pdf (accessed 2019-11-14). (in Swedish)
Regionplane- och Trafikkontoret (RTK), 1996. Grönstrukturen i Stockholmsregionen [Green structure in the Stockholm region]. Rapport 1996:2. Stockholm. (in Swedish)
Regionplane- och Trafikkontoret (RTK), 1992a. Regionplan 1991 för Stockholms län 1990 – 2020. [Regional plan 1991 for Stockholm County 1990 - 2020] (in Swedish).
Regionplane- och Trafikkontoret (RTK), 1992b. Stockholms Gröna Kilar [Stockholms Green Wedges] (in Swedish). Rapport 1992: Stockholm.
Reis, J.P., Silva, E.A. and Pinho, P., 2014. A review of spatial metrics for urban growth and shrinkage. In De Silva, E., Healey, P., Harris, N. and Van den Broeck, P. (eds), Research Methods in Spatial and Regional Planning, Routledge: 279-292.
Ren, W., Zhong, Y., Meligrana, J., Anderson, B., Watt, W.E., Chen, J. and Leung, H.L., 2003. Urbanization, land use, and water quality in Shanghai: 1947–1996. Environment International 29(5): 649-659.
113 Revenga, C., 2005. Developing indicators of ecosystem condition using geographic information systems and remote sensing. Regional Environmental Change 5(4): 205-214.
Richmond Hill, 2019. "Oak Ridges Moraine". Available at: https://www.richmondhill.ca/en/things-to-do/Oak-Ridges- Moraine.aspx (accessed 2019-11-04).
Riitters, K.H., O'Neill, R.V., Hunsaker, C.T., Wickham, J.D., Yankee, D.H., Timmins, S.P., Jones, K.B. and Jackson, B.L., 1995. A factor analysis of landscape pattern and structure metrics. Landscape Ecology 10(1): 23–39.
Rodriguez-Galiano, V.F., Chica-Olmo, M., Abarca-Hernandez, F., Atkinson, P. M. and Jeganathan, C., 2012. Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture. Remote Sensing of Environment 121: 93-107.
Rose, R.A., Byler, D., Eastman, J.R., Fleishman, E., Geller, G., Goetz, S., Guild, L., Hamilton, H., Hansen, M., Headley, R., Hewson, J., Horning, N., Kaplin, B.A., Laporte, N., Leidner, A., Leimgruber, P., Morisette, J., Musinsky, J., Pintea, L., Prados, A., Radeloff, V.C., Rowen, M., Saatchi, S., Schill, S., Tabor, K., Turner, W., Vodacek, A., Vogelmann, J., Wegmann, D.W. and Wilson, C., 2015. Ten ways remote sensing can contribute to conservation. Conservation Biology 29(2): 350-359.
Ruiz Hernandez, I.E. and Shi, W., 2018. A Random Forests classification method for urban land-use mapping integrating spatial metrics and texture analysis. International Journal of Remote Sensing 39(4): 1175-1198.
Saunders, M.I., Brown, C.J., Foley, M.M., Febria, C.M., Albright, R., Mehling, M.G., Kavanaugh, M.T. and Burfeind, D.D., 2016. Human impacts on connectivity in marine and freshwater ecosystems assessed using graph theory: a review. Marine and Freshwater Research 67(3): 277-290.
Saura, S., Estreguil, C., Mouton, C. and Rodríguez-Freire, M., 2011. Network analysis to assess landscape connectivity trends: application to European forests (1990–2000). Ecological Indicators 11(2): 407-416.
Saura, S. and Pascual-Hortal, L., 2007. A new habitat availability index to integrate connectivity in landscape conservation planning: comparison with existing indices and application to a case study. Landscape and Urban Planning 83:91–103.
114 Saura, S. and Rubio, L., 2010. A common currency for the different ways in which patches and links can contribute to habitat availability and connectivity in the landscape. Ecography 33(3): 523-537.
Sawaya, K.E., Olmanson, L.G., Heinert, N.J., Brezonik, P.L. and Bauer, M.E., 2003. Extending satellite remote sensing to local scales: land and water resource monitoring using high-resolution imagery. Remote Sensing of Environment 88(1-2): 144-156.
Stockholm County Administrative Board (SCAB) [Länsstyrelsen Stockholm], 2018. Förslag Till Grön Infrastruktur Regional Handlingsplan för Stockholms Län [Draft—Green Infrastructure: Regional Action Plan for Stockholm County] Report 2018:1. Version 2018-02-15. Available at: https://www.lansstyrelsen.se/download/18.276e13411636c95dd933a55/1526 903019168/Rapport%202018- 1%20F%C3%B6rslag%20till%20gr%C3%B6n%20infrastruktur%20regional %20handlingsplan%20f%C3%B6r%20Stockholms%20l%C3%A4n.pdf (accessed 2019-04-21). (In Swedish)
Secretariat of the Convention on Biological Diversity, 2013. “City Biodiversity Index (or Singapore Index)”. Available at https://www.cbd.int/subnational/partners-and-initiatives/city-biodiversity- index (accessed 2019-10-27).
Seto, K.C., Güneralp, B. and Hutyra, L.R., 2012. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences 109(40): 16083-16088.
Seto, K.C., Sanchez-Rodriguez, R. and Fragkias, M., 2010. The New Geography of Contemporary Urbanization and the Environment. Annual Review of Environment and Resources 35: 167–194.
Shaban, M.A. and Dikshit, O., 2001. Improvement of classification in urban areas by the use of textural features: the case study of Lucknow city, Uttar Pradesh. International Journal of Remote Sensing 22: 565–593.
Shackelford, A.K. and Davis, C.H., 2003. A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas. IEEE Transactions on GeoScience and Remote Sensing 41(10): 2354-2363.
Shao, Y. and Lunetta, R.S., 2012. Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using
115 limited training data points. ISPRS Journal of Photogrammetry and Remote Sensing 70: 78-87.
Smeets, E. and Weterings, R., 1999. Environmental Indicators: Typology and Overview. EEA Technical Report no. 25. European Environment Agency: Copenhagen.
Srivastava, P.K., Han, D., Ramirez, M.R., Bray, M. and Islam, T., 2012. Selection of classification techniques for land use/land cover change investigation. Advances in Space Research 50: 1250-1265.
Stefanov, W.L., Ramsey, M.S. and Christensen, P.R., 2001. Monitoring urban land cover change: An expert system approach to land cover classification of semiarid to arid urban centers. Remote Sensing of Environment 77: 173-185.
Ståhl, G., Allard, A., Esseen, P.-A., Glimskär, A., Ringvall, A., Svensson, J., Sundquist, S., Christensen, P., Gallegos Torell, Å., Högström, M., Lagerqvist, K., Marklund, L., Nilsson, B. and Inghe, O., 2011. National Inventory of Landscapes in Sweden (NILS) — scope, design, and experiences from establishing a multiscale biodiversity monitoring system. Environmental Monitoring and Assessment 173(1–4): 579–595.
Statistics Sweden, 2014. Statistical Yearbook of Sweden 2014. Communications Department. Available at http://share.scb.se/ov9993/data/historisk%20statistik//SOS%201911- /Statistisk%20%C3%A5rsbok%20(SOS)%201914-2014/Statistisk-arsbok- for-Sverige-2014.pdf (accessed 2019-11-05).
Statistics Sweden, 2008. Grönytor i tätort. Satellitdata som stöd vid kartering av grönytor i och omkring tätorter. [Green areas in urban agglomerations. Satellite data in support of green area mapping in and around urban centers]. DNR (Rymdstyrelsen): 180/06. (in Swedish)
Stockholms Läns Landsting Regionplanekontoret, 1985. En skogsbacke i handen är bättre än tio i skogen. [A forested hill in the hand is better than ten in the forest]. Rapport 1985:1. (in Swedish)
Stockholm City, 2018. Statististical Year-book of Stockholm 2018. Available at: http://statistik.stockholm.se/attachments/article/38/Statistisk%20%C3%83% C2%A5rsbok%20f%C3%83%C2%B6r%20Stockholm%202018.pdf (accessed 2018-10-11).
116 Su, S., Ma, X. and Xiao, R., 2014. Agricultural landscape pattern changes in response to urbanization at eco-regional scale. Ecological Indicators 40: 10- 18.
Su, S., Xiao, R., Jiang, Z. and Zhang, Y., 2012. Characterizing landscape pattern and ecosystem service value changes for urbanization impacts at an eco-regional scale. Applied Geography 34: 295-305.
Su, W., Zhang, C., Zhu, X. and Li, D., 2009. A hierarchical object oriented method for land cover classification of SPOT 5 imagery. WSEAS Transactions on Information Science and Applications 6(3): 437-446.
Su, W., Li, J., Chen, Y., Liu, Z., Zhang, J., Low, T.M., Suppiah, I. and Hashim, S.A.M., 2008. Textural and local spatial statistics for the object‐ oriented classification of urban areas using high resolution imagery. International Journal of Remote Sensing 29(11): 3105-3117.
Tallis, H. and Polasky, S., 2009. Mapping and valuing ecosystem services as an approach for conservation and natural‐resource management. Annals of the New York Academy of Sciences 1162(1): 265-283.
Tehrany, M.S., Pradhan, B. and Jebu, M.N., 2014. A comparative assessment between object and pixel-based classification approaches for land use/land cover mapping using SPOT 5 imagery. Geocarto International 29(4): 351-369.
Thanh Noi, P. and Kappas, M., 2018. Comparison of random forest, k- nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery. Sensors 18(1): 18.
Tillväxt- och Regionplaneförvaltningen (TRF), 2018. Regional Utvecklingsplan för Stockholmsregionen: RUFS 2050 [Regional Development Plan for the Stockholm Region 2050]. Report 2018:10. TRN 2015-0015. Available online: http://rufs.se/globalassets/e.-rufs- 2050/rufs_regional_utvecklingsplan_for_stockholmsregionen_2050_tillgang lig.pdf (accessed 2019-04-21). (In Swedish)
Troy, A. and Wilson, M.A., 2006. Mapping ecosystem services: practical challenges and opportunities in linking GIS and value transfer. Ecological economics 60(2): 435-449.
Turner, M.G., 1989. Landscape Ecology: The Effect of Pattern on Process. Annual Review of Ecology and Systematics 20: 171-197.
117
Turner, M.G., 1990. Spatial and temporal analysis of landscape patterns. Landscape Ecology 4(1): 21–30.
Turner, R.K., Morse‐Jones, S. and Fisher, B., 2010. Ecosystem valuation. Annals of the New York Academy of Sciences 1185(1): 79-101.
Turner, R.K., Paavola, J., Cooper, P., Farber, S., Jessamy, V. and Georgiou, S., 2003. Valuing nature: lessons learned and future research directions. Ecological economics 46(3): 493-510.
Turner, W., Rondinini, C., Pettorelli, N., Mora, B., Leidner, A.K., Szantoi, Z., Buchanan, G., Dech, S., Dwyer, J., Herold, M., Koh, L.P., Leimgruber, P., Taubenboeck, H., Wegmann, M., Wikelski, M. and Woodcock, C., 2015. Free and open-access satellite data are key to biodiversity conservation. Biological Conservation 182: 173-176.
Uuemaa, E., Antrop, M., Roosaare, J., Marja, R. and Mander, Ü., 2009. Landscape metrics and indices: an overview of their use in landscape research. Living Reviews in Landscape Research 3(1): 1-28.
Uuemaa, E., Mander, Ü. and Marja, R., 2013. Trends in the use of landscape spatial metrics as landscape indicators: a review. Ecological Indicators 28: 100-106.
UNEP/RIVM, 1994. An Overview of Environmental Indicators: State of the art and perspectives. UNEP/EATR.94-01; RIVM/402001001. Environmental Assessment Sub-Programme, UNEP: Nairobi.
United Nations, 2018. World Urbanization Prospects: The 2018 Revision, Key Facts. Department of Economic and Social Affairs, Population Division. Available at: https://population.un.org/wup/Publications/Files/ WUP2018-KeyFacts.pdf (accessed 2019-04-21).
United Nations, 2012. World Urbanization Prospects: The 2011 Revision, United Nations Department of Economic and Social Affairs/Population Division.
Urban, D. and Keitt, T., 2001. Landscape connectivity: A graph-theoretic perspective. Ecology 82:1205–1218.
Van der Biest, K., D’Hondt, R., Jacobs, S., Landuyt, D., Staes, J., Goethals, P. and Meire, P., 2014. EBI: an index for delivery of ecosystem service bundles. Ecological Indicators 37: 252-265.
118
Van Oudenhoven, A.P., Petz, K., Alkemade, R., Hein, L. and de Groot, R.S., 2012. Framework for systematic indicator selection to assess effects of land management on ecosystem services. Ecological Indicators 21: 110-122.
Vapnik, V.N., 1995. The Nature of Statistical Learning Theory. Springer- Verlag: New York.
Wakelyn, L.A., 1990. Wetland inventory and mapping in the Northwest Territories using digital Landsat data. Yellowknife: Government of the Northwest Territories Department of Renewable Resources, File Report No. 96.
Wang, Z., Wei, W., Zhao, S. and Chen, X., 2004. Object-oriented Classification and Application in Land Use Classification Using SPOT-5 PAN Imagery. 2004 IEEE International Geoscience and Remote Sensing Symposium Proceedings 5: 3158-3160.
Weng, Q., 2014. Global urban monitoring and assessment through earth observation. CRC Press: Boca Raton.
Weng, Q., 2002. Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. Journal of Environmental Management 64: 273–284.
Weng, Q, Gamba, P., Mountrakis, G., Pesaresi, M., Lu, L., Kemper, T., Heinzel, J., Xian, G., Jin, H., Miyazaki, H., Xu, B., Quresh, S., Keramitsoglou, I., Ban, Y., Esch, T., Roth, A. and Elvidge, C.D., 2014. Urban Observing Sensors. In Weng, Q. (ed), Global Urban Monitoring and Assessment through Earth Observation. CRC Press: Boca Raton.
Wentz, E.A., Anderson, S., Fragkias, M., Netzband, M., Mesev, V., Myint, S.W., Quattrochi, D., Rahman, A. and Seto, K.C., 2014. Supporting global environmental change research: A review of trends and knowledge gaps in urban remote sensing. Remote Sensing 6(5): 3879-3905.
Wentz, E.A., Stefanov, W.L., Netzband, M., Möller, M.S. and Brazel, A.J., 2009. The Urban Environmental Monitoring/100 Cities Project: Legacy of the First Phase and Next Steps. In Gamba, P. and Herold, M. (eds), Global Mapping of Human Settlement: Experiences, Datasets, and Prospects. Taylor and Francis Press:
119 World Health Organization (WHO), 1999. Berglund, B., Lindvall, T. and Schwela, D.H. (eds). Guidelines for Community Noise. Accessed on 2014- 11-19 from http://www.who.int/docstore/peh/noise/Comnoise-1.pdf
Willemen, L., Verburg, P.H., Hein, L. and van Mensvoort, M.E., 2008. Spatial characterization of landscape functions. Landscape and Urban Planning 88(1): 34-43.
Wu, J., 2004. Effects of changing scale on landscape pattern analysis: scaling relations. Landscape Ecology 19(2): 125-138.
Wu, J., Jenerette, G.D., Buyantuyev, A. and Redman, C.L., 2011. Quantifying spatiotemporal patterns of urbanization: The case of the two fastest growing metropolitan regions in the United States. Ecological Complexity 8(1): 1-8.
Wu, K.Y., Ye, X.Y., Qi, Z.F. and Zhang, H., 2013. Impacts of land use/land cover change and socioeconomic development on regional ecosystem services: The case of fast-growing Hangzhou metropolitan area, China. Cities 31: 276-284.
Wu, Z., Chen, R., Meadows, M. E., Sengupta, D. and Xu, D., 2019. Changing urban green spaces in Shanghai: trends, drivers and policy implications. Land Use Policy 87: 104080.
Wulder, M.A., Masek, J.G., Cohen, W.B., Loveland, T.R. and Woodcock, C. E., 2012. Opening the archive: How free data has enabled the science and monitoring promise of Landsat. Remote Sensing of Environment 122: 2-10.
Yang, X. (ed), 2011. Urban Remote Sensing: Monitoring, Synthesis and Modeling in the Urban Environment. CRC Press?
Zetterberg, A., Mörtberg, U.M. and Balfors, B., 2010. Making graph theory operational for landscape ecological assessments, planning, and design. Landscape and urban planning 95(4): 181-191.
Zhang, Y., 2002. Problems in the fusion of commercial high-resolution satellite as well as Landsat 7 images and initial solutions. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 34: 587–592.
Zhang, Q., Ban, Y., Liu, J. and Hu, Y., 2011. Simulation and analysis of urban growth scenarios for the Greater Shanghai Area, China. Computers, Environment and Urban Systems 35(2): 126-139.
120 Zhou, W., Troy, A. and Grove, M., 2008. Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data. Sensors 8(3): 1613-1636.
121 122 Appendix: Environmental Impact Indicator Results (Paper II)
Table 1. Environmental Impact Index Results in 1986 (Note that LFA 1 and 6 have greater areas than those given here. They extend beyond the classification boundary and thus their full areas are not calculable. Due to this circumstance, they are given the same highest rank for area.) Prox. to Intense Noise (area ID parish/ Area - Thickness 500m width Perforation Urban land use % Urban affected - ID municipality km2 (m - radius) (250m radius)? Indicator Indicator Perimeter km2) Huddinge_ 1 Botkyrka >189.4 883 yes 20.45 615.6 0.069 20.34 2 Haninge 128.4 1269 yes 15.53 425.6 0.20 1.55 3 Boo 11.5 390 yes 7.97 19.5 0.185 1.18 4 West Österåker 17.0 976 yes 5.38 33.6 0.053 0.68 West 5 Norrsunda 6.2 444 yes 3.76 24.1 0.070 3.92 North 6 Österåker >146.0 1159 yes 15.40 703.8 0.074 4.28 7 Trångsund 10.5 467 yes 5.29 22.0 0.138 0.65 8 Skarpnäck 3.2 371 yes 4.76 5.3 0.12 0.24 9 East Nacka 14.7 793 yes 5.97 24.0 0.1801 1.55 10 Lovö 4.4 420 yes 4.46 6.3 0.012 0.00 11 Vaxholm 20.8 615 yes 9.43 31.1 0.016 0.26 Sollentuna_ 12 Järfälla 12.9 529 yes 6.92 19.6 0.008 2.81 Danderyd_ 13 Täby 9.0 382 yes 7.07 11.2 0.19 0.43 Central 14 Österåker 10.3 603 yes 5.29 30.0 0.063 0.002 15 Ed_Kallhäll 9.6 745 yes 4.55 21.3 0.03 3.56 123 16 Ed 6.5 515 yes 4.06 17.1 0.011 0.75 Norrsunda_ 17 Vallentuna 3.9 364 yes 4.76 13.0 0.00 2.55 Central 18 Vallentuna 10.6 497 yes 6.38 33.3 0.056 0.05 19 Sankt Mikael 10.2 720 yes 4.15 15.6 0.39 0.80 20 East Österåker 12.2 607 yes 4.40 44.8 0.10 1.80 21 Flemingsberg 4.0 357 yes 4.47 7.2 0.15 0.07 22 Huddinge 3.7 445 yes 4.61 6.8 0.04 0.09 23 West Nacka 7.0 534 yes 4.72 9.3 0.36 0.16 Sollentuna_ 24 Fresta 6.0 504 yes 4.44 9.4 0.11 1.06 North 25 Norrsunda 18.0 598 yes 7.81 76.1 0.023 15.14 North 26 Vallentuna 14.3 657 yes 6.97 64.6 0.00 0.53 27 Viksjö 4.4 447 yes 3.88 7.8 0.142 0.18 Jakobsberg_ 28 Kallhäll 3.2 668 yes 2.24 5.8 0.1796 0.52 Täby_ 29 Vallentuna 20.5 707 yes 7.48 42.7 0.13 1.54 30 East Vallentuna 24.8 717 yes 8.68 81.0 0.050 0.34
124
Table 2. Environmental Impact Ranking per Indicator and Average Rank for 1986 Prox. to Intense Perforation Urban land use Urban Noise (area ID ID parish/municipality Area Indicator Indicator Perimeter affected) Average Rank 1 Huddinge_Botkyrka 1 30 2 14 30 15.4 2 Haninge 3 29 3 28 22 17.0 3 Boo 13 25 18 26 19 20.2 4 West Österåker 8 17 9 11 15 12.0 5 West Norrsunda 22 2 13 15 27 15.8 6 North Österåker 1 28 1 16 28 14.8 7 Trångsund 15 16 15 21 14 16.2 8 Skarpnäck 30 14 30 19 8 20.2 9 East Nacka 9 18 14 25 21 17.4 10 Lovö 24 8 28 5 1 13.2 11 Vaxholm 5 27 11 6 9 11.6 12 Sollentuna_Järfälla 11 20 17 3 25 15.2 13 Danderyd_Täby 19 22 22 27 11 20.2 14 Central Österåker 16 15 12 13 2 11.6 15 Ed_Kallhäll 18 10 16 8 26 15.6 16 Ed 21 4 19 4 16 12.8 17 Norrsunda_Vallentuna 27 13 21 1 24 17.2 18 Central Vallentuna 14 19 10 12 3 11.6 19 Sankt Mikael 17 5 20 30 17 17.8 20 East Österåker 12 6 7 17 23 13.0 21 Flemingsberg 26 9 26 23 4 17.6 125 22 Huddinge 28 11 27 9 5 16.0 23 West Nacka 20 12 24 29 6 18.2 24 Sollentuna_Fresta 23 7 23 18 18 17.8 25 North Norrsunda 7 24 5 7 29 14.4 26 North Vallentuna 10 21 6 1 13 10.2 27 Viksjö 25 3 25 22 7 16.4 28 Jakobsberg_Kallhäll 29 1 29 24 12 19.0 29 Täby_Vallentuna 6 23 8 20 20 15.4 30 East Vallentuna 4 26 4 10 10 10.8
126 Table 3. Environmental Impact Index Results in 2006 (Note that LFA 1.1 and 6 have greater areas than those given here. They extend beyond the classification boundary and thus their full areas are not calculable. Due to this circumstance, they are given the same highest rank for area.) Prox. to Intense Noise (area ID parish/ Area - Thickness 500m width Perforation Urban land use % Urban affected - ID municipality km2 (m - radius) (250m radius)? Indicator Indicator Perimeter km2) Huddinde_ 1.1 Botkyrka >178.4 922 yes 22.41 3087 0.130 16.11 1.2 West Haninge 6.1 552 yes 6.51 105 0.303 0.31 2 Haninge 126.9 996 yes 18.38 1081 0.298 1.62 3 Boo 9.9 372 yes 10.80 76 0.475 0.87 4 West Österåker 16.9 676 yes 6.16 131 0.248 0.61 5 West Norrsunda 7.6 551 yes 3.89 79 0.084 4.98 6 North Österåker >135.6 1127 yes 20.10 1807 0.127 3.97 7 Trångsund 12.2 1044 yes 5.28 160 0.125 0.53 8 Skarpnäck 3.7 412 yes 5.06 52 0.118 0.45 9 East Nacka 15.2 656 yes 7.27 232 0.282 1.64 10 Lovö 5.7 624 yes 5.60 96 0.056 0.00 11 Vaxholm 24.7 760 yes 8.22 350 0.058 0.49 Sollentuna_ 12 Järfälla 13.1 542 yes 6.81 156 0.052 2.97 13 Danderyd_Täby 10.5 417 yes 7.67 113 0.232 0.53 14 Central Österåker 6.7 593 yes 4.69 61 0.410 0.00 15.1 Ed_Kallhäll 7.1 602 yes 4.15 69 0.207 3.63 15.2 South Ed 3.3 364 yes 4.804 30 0.032 0.30 16 Ed 7.4 609 yes 4.468 56 0.018 0.67 Norrsunda_ 17 Vallentuna 3.6 371 yes 4.801 28 0.016 2.67
127 18 Central Vallentuna 9.2 534 yes 6.492 68 0.029 0.05 19 Sankt Mikael 9.1 794 yes 4.373 62 0.437 0.65 20 East Österåker 11.4 504 yes 7.376 73 0.239 2.06 21 Flemingsberg 3.5 420 yes 4.633 19 0.182 0.13 22 Huddinge 4.3 442 yes 4.630 36 0.038 0.10 23 West Nacka 6.9 496 yes 6.37 58 0.306 0.22 24 Sollentuna_Fresta 6.7 650 yes 3.96 93 0.075 1.21 25 North Norrsunda 15.5 602 yes 7.54 148 0.068 12.67 26 North Vallentuna 17.8 784 yes 8.13 188 0.001 0.22 27 Viksjö 3.5 447 yes 5.04 40 0.107 0.11 Jakobsberg_ 28 Kallhäll 3.1 607 yes 3.00 36 0.208 0.47 29 Täby_Vallentuna 10.2 535 yes 5.69 107 0.059 0.25 30.1 East Vallentuna 12.4 467 yes 9.04 98 0.119 0.02 30.2 NE Vallentuna 7.7 462 yes 6.65 48 0.012 0.30
For comparability with 1986: (these values are used when comparing rankings between 1986 and 2006, instead of the true values for LFA 1.1, 1.2, 15.1, 15.2, 30.1 and 30.2. These LFAs are penalized for becoming divided or fragmented by dropping one ranking spot in the overall ranking, see below.) (avr) (avr) Prox. to (sum) Noise (area ID (sum) Perforation Intense Urban (avr) % Urban affected by >55dB) ID parish/municipality Area - km2 Indicator land use Indicator Perimeter km2 1 Huddinge_Botkyrka 184.5 14.46 1595.8 0.22 16.43 15 Ed_Kallhäll 10.4 4.48 49.5 0.12 3.93 30 East Vallentuna 20.1 7.85 73.0 0.07 0.33
128 Table 4. Environmental Impact Ranking per Indicator and Average Rank for 2006 Prox. to Intense Perforation Urban land use Urban Noise (area ID ID parish/municipality Area Indicator Indicator Perimeter affected) Average Rank 1.1 Huddinde_Botkyrka 1 33 1 20 33 17.60 1.2 West Haninge 25 20 13 29 13 20.00 2 Haninge 3 31 3 28 24 17.80 3 Boo 15 30 18 33 22 23.60 4 West Österåker 6 17 10 26 19 15.60 5 West Norrsunda 19 2 17 14 31 16.60 6 North Österåker 1 32 2 19 30 16.80 7 Trångsund 11 14 7 18 17 13.40 8 Skarpnäck 28 13 26 16 14 19.40 9 East Nacka 8 23 5 27 25 17.60 10 Lovö 26 15 15 9 1 13.20 11 Vaxholm 4 28 4 10 16 12.40 12 Sollentuna_Järfälla 9 22 8 8 28 15.00 13 Danderyd_Täby 13 26 11 24 18 18.40 14 Central Österåker 23 9 23 31 2 17.60 15.1 Ed_Kallhäll 21 4 20 22 29 19.20 15.2 South Ed 32 11 31 6 11 18.20 16 Ed 20 6 25 4 21 15.20 17 Norrsunda_Vallentuna 29 10 32 3 27 20.20 18 Central Vallentuna 16 19 21 5 4 13.00 19 Sankt Mikael 17 5 22 32 20 19.20 20 East Österåker 12 24 19 25 26 21.20 129 21 Flemingsberg 30 8 33 21 7 19.80 22 Huddinge 27 7 29 7 5 15.00 23 West Nacka 22 18 24 30 9 20.60 24 Sollentuna_Fresta 24 3 16 13 23 15.80 25 North Norrsunda 7 25 9 12 32 17.00 26 North Vallentuna 5 27 6 1 8 9.40 27 Viksjö 31 12 28 15 6 18.40 28 Jakobsberg_Kallhäll 33 1 30 23 15 20.40 29 Täby_Vallentuna 14 16 12 11 10 12.60 30.1 East Vallentuna 10 29 14 17 3 14.60 30.2 NE Vallentuna 18 21 27 2 12 16.00
For comparability with 1986: Prox. to Adjusted Average ID parish/ Intense Urban Urban Noise (area Average Rank (division ID municipality Area Perforation land use Perimeter affected) Rank penalty) Huddinge_ 1 Botkyrka 1 28 2 21 30 16.4 17.4 15 Ed_Kallhäll 14 6 25 16 26 17.4 18.4 30 East Vallentuna 5 24 18 10 10 13.4 14.4
130 Table 5. Change in indicator rankings and average ranking for Environmental Impact between 1986 and 2006 Prox. to Intense Average Average ranking ID parish/ Area - Perforation Urban land use % Urban Noise (area ranking change with ID municipality km2 Indicator Indicator Perimeter affected - km2) change deduction for division 1 Huddinge_Botkyrka 0 2 0 -7 0 -1 -2 2 Haninge 0 0 0 2 1 1 1 3 Boo -3 -2 2 -4 0 -1 -1 4 West Österåker 1 1 -1 -13 -1 -3 -3 5 West Norrsunda 3 0 -2 2 -1 0 0 6 North Österåker 0 -2 0 -2 1 -1 -1 7 Trångsund 4 3 8 4 0 4 4 8 Skarpnäck 4 2 6 4 -3 3 3 9 East Nacka 0 -2 9 0 -1 1 1 10 Lovö 0 -6 15 -2 0 1 1 11 Vaxholm 1 1 7 -2 -4 1 1 12 Sollentuna_Järfälla 1 1 9 -3 0 2 2 13 Danderyd_Täby 6 -1 11 5 -4 3 3 14 Central Österåker -6 6 -9 -15 0 -5 -5 15 Ed_Kallhäll 4 4 -9 -8 0 -2 -3 16 Ed 1 -1 -4 1 -2 -1 -1 Norrsunda 17 Vallentuna 0 3 -8 -1 0 -1 -1 18 Central Vallentuna -3 1 -9 8 0 -1 -1 19 Sankt Mikael -1 1 0 1 0 0 0 20 East Österåker 0 -15 -10 -6 0 -6 -6 21 Flemingsberg -2 1 -4 4 -2 -1 -1 22 Huddinge 3 4 0 4 1 2 2 131 23 West Nacka -1 -5 2 2 -2 -1 -1 24 Sollentuna_Fresta 0 4 9 6 -2 3 3 25 North Norrsunda -1 2 -4 -4 0 -1 -1 26 North Vallentuna 4 -4 0 0 6 1 1 27 Viksjö -4 -8 -1 8 2 -1 -1 Jakobsberg_ 28 Kallhäll -1 0 1 4 0 1 1 29 Täby_Vallentuna -9 8 -4 11 11 3 3 30 East Vallentuna -1 2 -14 0 0 -3 -4
132
133