Earth Observation based Monitoring of Urbanization and ...

Doctoral Thesis in Geoinformatics

Earth Observation based Monitoring of Urbanization and Environmental Impact in Kigali, RwandaTHEODOMIR MUGIRANEZA

Stockholm, Sweden 2021

kth royal institute of technology

Earth Observation based Monitoring of Urbanization and Environmental Impact in Kigali, RwandaTHEODOMIR MUGIRANEZA

Doctoral Thesis in GeoinformaticsKTH Royal Institute of TechnologyStockholm, Sweden 2021

Academic Dissertation which, with due permission of the KTH Royal Institute of Technology, is submitted for public defence for the Degree of Doctor of Philosophy on Wednesday the 15th of December 2021, at 1:00 p.m. in Kollegiesalen 110, Brinellvägen 8, Stockholm.

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Abstract

Urbanization is one of the great challenges in the 21st century. Despitebeing an engine for the global economy, urban areas consume 78% of World,senergy and emit more than 60% of greenhouse gas emission. Sub-SaharanAfrican cities, e.g. Kigali, are characterized by rapid population growth andaccelerated land use/land cover change. Yet, the implementation of policiesand regulations catalyzing sustainable urbanization is constrained by scarceand fragmented data related to land use/land cover spatial patterns andchanges in population. Collected statistics are most of the time outdated,or geographically aggregated to large heterogeneous administrative entities,which is judged meaningless for informed decision making. Therefore, there isa need for timely and reliable data, and tools to monitor the spatio-temporalpatterns of urbanization and its environmental impact for informed and sus-tainable decision making. The objectives of this thesis are i)to investigate theuse of multi-temporal and multi-resolution Earth observation data for map-ping and monitoring urbanization patterns and trends in Kigali, Rwanda, acomplex urban area characterized by a subtropical highland climate; and ii)toanalyze the environmental impacts of urbanization using the integration ofland cover information classified from Earth observation data with landscapemetrics and ecosystem services. Using satellite imagery from 1984 to 2021,spatial patterns and temporal trends of urbanization in Kigali were investi-gated and analyzed. Specifically, optical satellite imagery at medium to veryhigh resolution, i.e. Landsat TM/ETM+/OLI at 30m resolution, Sentinel-2 MSI at 10-20m resolution and WorldView-2 at 2m spatial resolution wereused for land use/land cover mapping and change analysis. Diverse imageprocessing techniques, including texture feature analysis using Gray level Co-occurrence matrix, pan- sharpening and derivation of various biophysical in-dices, were applied to enhance land use/land cover classification and analy-sis. Various land use/land cover classification methods were used, includingpixel- and object-based support vector machine classification, Google EarthEngine-LandTrendr cloud computing, and a hybrid framework combining in-termediate classification results derived from both random forest classifica-tion, and U-Net deep neural networks. The land use/land cover classes werethen used not only to derive indices characterizing spatio-temporal changesin urban landscape composition and configuration, but also to analyze theimpacts of land use/land cover change on ecosystem services. Areas whichprovide ecosystem services were evaluated in terms of changes in spatial at-tributes and structure of landscape patches. The most prominent ecosystemservices in the study area divided into three groups - provisioning, regulat-ing and supporting services - were further analyzed using a matrix spatiallylinking landscape units with service supply and demand budgets. In one ofthe studies, a monetary based valuation approach was performed for assessingspatio-temporal change in value of selected ecosystem services.

Using multi-temporal, multi-resolution Earth observation data, five totwelve land use/land cover classes were derived with an overall accuracy ex-ceeding 83% and with Kappa coefficients above 0.8. The most prominentchange was the conversion of croplands into built-up areas. As a result, the

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built-up areas increased from 2.13 km2 to 100.17 km2 between 1984 and 2016.The results revealed that the urbanization between 1987 to 1998 was char-acterized by slow development, with an annual growth rate less than 2%.The post-conflict period (1995 on-wards) was characterized by acceleratedurbanization, with a 4.5% annual growth rate. From 2004, urbanization waspromoted due to migration pressure and investment promotion in the con-struction sector. The five-year interval analysis from 1990 to 2019 revealedthat impervious surfaces increased from 4233.5 to 12116 hectares, with a 3.7%average annual growth rate. In order to map urban land use/land cover atfine scale, very high resolution WorldView-2 imagery was acquired and an-alyzed using object- and rule-based classification. Urban land cover at finescale could be mapped with an overall accuracy exceeding 85% (kappa above0.8). Multi-temporal Sentinel-2 MSI data were found advantageous for mon-itoring spatio-temporal trends of urban development, and producing reliablebaseline data for the analysis of urban landscape changes at entire city scalewith sufficient details. During the 37 years study period, landscape fragmen-tation could be observed, in particular in forest and cropland. The landscapeconfiguration indices demonstrate that, in general, the land cover pattern re-mained stable for cropland, but that it was highly changed for built-up areas.Estimated changes in ecosystem services amount to a loss of 69 million USdollars because of cropland degradation in favour of urban areas and in again of 52.5 million within urban systems between 1984 and 2016. Most ofthe ecosystem services bundles show that built-up areas have a high demandon ecosystem services, whereas green and blue space are strong contributorsin supplying bundles of ecosystem services. The study demonstrated thatmulti-temporal multi-resolution Earth observation data and advanced imageprocessing offer great opportunities for quantifying urbanization, and analyz-ing its environmental impacts using landscape metrics and ecosystem servicesvariables. Medium resolution data, Landsat and Sentinel-2 MSI, were founduseful for global annual urban growth and environmental impact analysis atentire city scale. Very- high-resolution satellite data are still only available athigh cost. Therefore, land use/land cover mapping based on very high reso-lution data should be produced only at special occasion based on cost-benefitanalysis. Meanwhile, open data policy and free access to cloud computing sys-tems such as Google Earth Engine were also found cost-effective and useful forcontinuous monitoring of the complex dynamics of urban land use/land cover,especially in areas where the cost of Earth observation data is restricting dueto budget reasons, and in data-scarce regions.

The thesis contributes to the development of approaches for mappingand monitoring urban development and associated environmental impact inSub-Saharan through the exploration of potential and limitations of multi-resolution remote sensing data. Methodological frameworks for urban landcover production based on state-of-the-art machine learning, deep learning,and Earth observation big data analytics were implemented and tested. Theresearch output compiled in this thesis demonstrated that the open-accessEarth observation data are cost-effective data source for monitoring urban-ization and for investigating the impact of spatial structure changes on the

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distributions and patterns of ecosystem service bundles. The frameworksdeveloped in this research can easily be transferred to other Sub-SaharanAfrican cities. Future research will explore the integration of multiple-sourcedata, i.e., Earth observation data, population statistics and other types ofdata to detect and map urban deprivation and environmentally sensitive ar-eas. Finally, the combination of optical and radar remote sensing data, theuse of machine learning and deep learning methods in a cloud computing en-vironment will be further investigated to develop a dynamic framework forcontinuous urban land use/land cover change monitoring.

Keywords: Earth observation, Landsat, Sentinel-2, WorldView-2, Ur-banization, land cover classification, Support vector machines, Random forest,U-Net, LandTrendr, Landscape metrics, Ecosystem services, Environmentalimpact analysis, Kigali, Rwanda.

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Sammanfattning

Urbaniseringen är en av de stora utmaningarna på 2000-talet. Trots attstäderna är en motor för den globala ekonomin förbrukar de 78% av världensenergi och släpper ut mer än 60% av utsläppen av växthusgaser. Städer i Af-rika söder om Sahara, t.ex. Kigali, kännetecknas av snabb befolkningstillväxtoch accelererande förändringar i markanvändning och marktäcke. Genomfö-randet av strategier och bestämmelser som katalysator för en hållbar urbani-sering hindras dock av bristfälliga och fragmenterade uppgifter om rumsligamönster för markanvändning och marktäcke samt befolkningsförändringar.Den insamlade statistiken är oftast föråldrad eller geografiskt aggregerad tillstora heterogena administrativa enheter, vilket bedöms vara meningslöst förett välgrundat beslutsfattande. Det finns därför ett behov av aktuella och till-förlitliga uppgifter och verktyg för att övervaka urbaniseringens rumsliga ochtidsmässiga mönster och dess miljöpåverkan för ett välgrundat och hållbartbeslutsfattande. Målen för denna avhandling är i) att undersöka användningenav multi-temporala och multiupplösta jordobservationsdata för att kartläggaoch övervaka urbaniseringsmönster och trender i Kigali, Rwanda, ett kom-plext stadsområde som kännetecknas av ett subtropiskt höglandskli- mått,och ii) att analysera urbaniseringens miljöpåverkan med hjälp av integre-ring av information om marktäcke som klassificerats från jordobservationsda-ta med landskapsmetriker och ekosystemtjänster. Med hjälp av satellitbilderfrån 1984 till 2021 undersöktes och analyserades rumsliga mönster och tidst-render för urbaniseringen i Kigali. Optiska satellitbilder med medelhög tillmycket hög upplösning, dvs. Landsat TM/ETM+/OLI med 30 m upplåsning,Sentinel-2 MSI med 10-20m upplösning och WorldView-2 med 2 m rumsligupplösning, användes för kartläggning av markanvändning och marktäcke ochanalys av förändringar. Olika bildbehandlingstekniker, inklusive texturanalysmed hjälp av Gray level Co-occurrence matrix, pan-skärpning och framställ-ning av olika biofysiska index, användes för att förbättra klassificering ochanalys av markanvändning och marktäcke. Olika metoder för klassificering avmarkanvändning och marktäcke användes, bland annat pixel- och objektbase-rad super- portvektormaskinklassificering, Google Earth Engine-LandTrendrcloud computing och en hybridram som kombinerar mellanliggande klassifice-ringsresultat från både random forest-klassificering och U-Net djupa neuralanätverk. Klasserna för markanvändning/markbeläggning användes sedan intebara för att ta fram index som karakteriserar rums- och tidsrelaterade föränd-ringar i stadslandskapets sammansättning och konfiguration, utan också föratt analysera effekterna av förändringar i markanvändning/markbeläggningpå ekosystemtjänster. Områden som tillhandahåller ekosystemtjänster utvär-derades med avseende på förändringar i landskapsfläckarnas rumsliga attributoch struktur. De mest framträdande ekosystemtjänsterna i undersökningsom-rådet, uppdelade i tre grupper - försörjande, reglerande och stödjande tjänster- analyserades vidare med hjälp av en matris som rumsligt kopplar sammanlandskapsenheter med budgetar för utbud och efterfrågan av tjänster. I en avstudierna tillämpades en monetär värderingsmetod för att bedöma den tids-och rumsmässiga förändringen i värdet av utvalda ekosystemtjänster. Medhjälp av multitemporala jordobservationsdata med flera upplösningar har sju

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till tolv klasser för markanvändning/markbeläggning tagits fram med en totalnoggrannhet som överstiger 83% och med Kappa-koefficienter över 0.8. Denmest framträdande förändringen var omvandlingen av åkermark till bebygg-da områden. Som ett resultat av detta ökade de bebyggda områdena från2.13km2 till 100.17 km2 mellan 1984 och 2016. Resultaten visade att urba-niseringen mellan 1987 och 1998 kännetecknades av en långsam utveckling,med en årlig tillväxttakt på mindre än 2%. Perioden efter konflikten (1995 ochframåt) kännetecknades av en accelererande urbanisering, med en årlig till-växttakt på 4.5%. Från och med 2004 främjades urbaniseringen på grund avmigrationstryck och investeringsfrämjande åtgärder inom byggsektorn. Ana-lysen av femårsintervallet från 1990 till 2019 visade att de ogenomträngligaytorna ökade från 4233.5 till 12116 hektar, med en genomsnittlig årlig till-växttakt på 3.7%. För att kartlägga detaljerad markbeläggning i städerna,i synnerhet områden med stadsbrist, t.ex. informella bosättningar, förvärva-des mycket högupplösta bilder från WorldView-2 och analyserades med hjälpav objekt- och regelbaserad klassificering. Urban markbeläggning i fin skalakunde kartläggas med en total noggrannhet på över 85% (kappa över 0.8).MSI-data från Sentinel-2 med flera tidpunkter visade sig vara fördelaktiga föratt övervaka rumsliga och tidsmässiga trender i stadsutvecklingen och för attproducera tillförlitliga grunddata för analysen av förändringar i stadsland-skapet på hela stadens skala med tillräcklig detaljrikedom. Under den 37-åriga studieperioden kunde landskapsfragmentering observeras, särskilt närdet gäller skog och åkermark. Indexen för landskapskonfiguration visar attlandskapsbilden i allmänhet förblev stabil för åkermark, men att den för-ändrades kraftigt för bebyggda områden. De uppskattade förändringarna iekosystemtjänsterna uppgår till en förlust på 69 miljoner US-dollar på grundav att åkermark försämras till förmån för stadsområden och till en vinst på52,5 miljoner US-dollar inom stadsområden mellan 1984 och 2016. De flestaav ekosystemtjänstpaketen visar att bebyggda områden har ett stort behov avekosystemtjänster, medan grönområden och blåa områden bidrar starkt tillatt tillhandahålla ekosystemtjänster. Studien visade att multitemporala jor-dobservationsdata med flera upplösningar och avancerad bildbehandling gerstora möjligheter att kvantifiera urbaniseringen och analysera dess miljöpå-verkan med hjälp av landskapsmetriker och variabler för ekosystemtjänster.Data med medelhög upplösning, Landsat och Sentinel-2 MSI, visade sig varaanvändbara för global årlig urban tillväxt och miljökonsekvensanalys på helastadsskalan. Satellitdata med mycket hög upplösning är fortfarande endasttillgängliga till en hög kostnad. Därför bör kartläggning av markanvändningoch marktäcke baserad på data med mycket hög upplösning endast göras vidsärskilda tillfällen på grundval av en kostnads-nyttoanalys. Samtidigt konsta-terades det att en politik för öppna data och fri tillgång till molndatasystemsom Google Earth Engine också är kostnadseffektiva och användbara för kon-tinuerlig övervakning av den komplexa dynamiken hos markanvändning ochmarktäcke i städer, särskilt i områden där kostnaden för jordobservationsdataär begränsande av budgetskäl och i regioner där det är ont om data. Avhand-lingen bidrar till utvecklingen av metoder för kartläggning och övervakningav stadsutveckling och tillhörande miljöpåverkan i länderna söder om Saha-

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ra genom att utforska potentialen och begränsningarna hos fjärranalysdatamed flera upplösningar. Metodiska ramar för produktion av markbeläggningi städerna baserade på avancerad maskininlärning, djupinlärning och ana-lys av stora datamängder från jordobservationer har genomförts och testats.Denna avhandlingsforskning visade att jordobservationsdata med öppen till-gång är en kostnadseffektiv datakälla för övervakning av urbanisering ochför att undersöka effekterna av förändringar i den rumsliga strukturen påfördelningen och mönstren av ekosystemtjänstpaket. De ramar som utveck-lats i denna forskning kan lätt överföras till andra städer söder om Sahara.Framtida forskning kommer att utforska integrationen av data från flera käl-lor, dvs. jordobservationsdata, befolkningsstatistik och andra typer av dataför att upptäcka och kartlägga stadsbrist och miljökänsliga områden. Slutli-gen kommer kombinationen av optiska och radarbaserade fjärranalysdata ochanvändningen av metoder för maskininlärning och djupinlärning i en moln-datormiljö att undersökas ytterligare för att utveckla en dynamisk ram förkontinuerlig övervakning av förändringar av markanvändning och marktäckei städer.

Nyckelord: Jordobservation, Landsat, Sentinel-2 MSI, WorldView-2, Ur-banisering, Klassificering av marktäcke, Stödvektormaskiner, Slumpmässigskog, U-Net, LandTrendr, Landskapsmetriker, Ekosystemtjänster, Miljökon-sekvensanalys, Kigali, Rwanda.

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Acknowledgements

This thesis is the result of combined efforts from various people and organiza-tions. First and foremost, I would like to express my gratitude to my SupervisorProf. Yifang Ban for her scientific guidance, valuable comments and suggestionsfor achieving this milestone. Special thanks are also addressed to my assistant su-pervisor, Associate Prof. Jan Haas at Karlstad University for his technical adviceon environmental impact assessment. I also present my gratitude to Dr AndreaNascetti for technical advice on data processing and quality assessment. Cordialthanks is further addressed to Prof. Emmanuel Twarabamenye from University ofRwanda for worthy advice and guidance especially at the early stage of my PhDjourney. I also acknowledge the fruitful cooperation and exchange of ideas with Se-bastian Hafner while processing Sentinel-2 data. I am indebted to Docent Dr HansHauska and Dr Stefanos Georganos for proofreading carefully the final draft of mythesis and guiding me to fix the English language shortcomings and writing errors.I am grateful to Copernicus Progamme of the European Space Agency (ESA), andto the United State Geological Survey (USGS) for freely availing Sentinel-2 MSIand Landsat satellite data that were used in the present study. Your transforma-tive wave of technologies is really bring a positive impact in analyzing our changingenvironment and in academic career progression.

This research was possible because of the financial support provided by theSwedish International Development Cooperation Agency (SIDA),s University ofRwanda (UR)-Sweden Programme for Research, Higher Education and Institu-tional Advancement. I am highly indebted to UR for this support and for providingwith me a study leave. My gratitude is particularly addressed to the administra-tive staff from UR-Sweden Coordination Office both in Rwanda and in Sweden.Thanks to Team Leaders of GIS Sub-programme namely Associate Prof. Dr Gas-pard Rwanyiziri from UR Side, and Prof. Petter Pilesjö from Lund University side.Many thanks to you Raymond Ndikumana, Charles Gakomeye, Alexis Karara, DrSylvie Mucyo and Claudine Mukalinguyeneza, for arranging all needed logistics. Iam also highly indebted to Mr Ernest Ntakobangize for collecting some fieldworkand population data. Many thanks to Kigali City Authority for welcoming me dur-ing my visit in One Stop Center and for availing the population data to me. Mygratitude is further addressed to my colleagues and workmates in GeoinformaticsDivision at KTH, and friends with whom we shared the ups and downs during mystay in Stockholm. The administrative support from Susan Hellström and ThereseGellerstedt is also highly appreciated. Big thanks to my extended family for moralsupport and encouragement. Last, but not least, my special thanks are addressedto my wife Clementine Kagirimpundu, and to our children, Ineza Muhoze Lorenaand Iganze Tega Gabriella, for encouragement and prayers. Much love!

Theodomir MugiranezaDecember, 2021Stockholm, Sweden

Contents

Contents x

List of Figures xii

List of Tables xv

List of Acronyms xvii

1 Introduction 11.1 Background and rationale . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Research objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.3 Thesis organization structure . . . . . . . . . . . . . . . . . . . . . . 61.4 Declaration of Contributions . . . . . . . . . . . . . . . . . . . . . . 7

2 Optical remote sensing for urbanization monitoring: Sensors,methods and applications 92.1 Optical satellite sensors for urban applications . . . . . . . . . . . . 9

2.1.1 Medium-resolution sensors . . . . . . . . . . . . . . . . . . . . 102.1.2 HR and VHR sensors . . . . . . . . . . . . . . . . . . . . . . 11

2.2 Big data analytics and EO . . . . . . . . . . . . . . . . . . . . . . . . 132.3 Remote sensing based methods for urban LULC classification . . . . 14

2.3.1 Pixel versus object-based classification . . . . . . . . . . . . . 152.3.2 Machine learning and deep learning based methods . . . . . . 152.3.3 EO time series based analysis . . . . . . . . . . . . . . . . . . 17

2.4 Spatio-temporal urban LULC change through the lens of EO . . . . 192.5 Remotely sensed data for urbanization environmental impact analysis 23

2.5.1 Landscape structure change analysis . . . . . . . . . . . . . . 232.5.2 LULC change impact on urban ecosystem services . . . . . . 25

3 Study Area and Data description 273.1 Study area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273.2 Data description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.2.1 Satellite imagery . . . . . . . . . . . . . . . . . . . . . . . . . 29

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CONTENTS xi

3.2.2 Ancillary data . . . . . . . . . . . . . . . . . . . . . . . . . . 313.2.3 LULC classification scheme . . . . . . . . . . . . . . . . . . . 31

4 Methodology 334.1 Data pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . 344.2 Data processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

4.2.1 Texture analysis with GLCM . . . . . . . . . . . . . . . . . . 354.2.2 Spectral indices derivation . . . . . . . . . . . . . . . . . . . . 364.2.3 Image segmentation . . . . . . . . . . . . . . . . . . . . . . . 37

4.3 LULC extraction and classification . . . . . . . . . . . . . . . . . . . 384.3.1 Support Vector Machine classification . . . . . . . . . . . . . 384.3.2 LandTrendr-Google Earth Engine based prediction . . . . . . 394.3.3 OBIA rule-based classification . . . . . . . . . . . . . . . . . 414.3.4 Hybrid classification with random forest and U-Net . . . . . . 43

4.4 Post-classification processing and validation . . . . . . . . . . . . . . 464.5 Landscape structure change analysis . . . . . . . . . . . . . . . . . . 484.6 Ecosystems services analysis . . . . . . . . . . . . . . . . . . . . . . . 50

5 Results and Discussion 535.1 LULC classification results and urbanization analysis . . . . . . . . . 53

5.1.1 Pixel-based classification based on Landsat data . . . . . . . 545.1.2 GEE-LT based prediction of LULC change . . . . . . . . . . 575.1.3 Hierarchical and OBIA rule-based classification . . . . . . . . 605.1.4 Hybrid LULC classification based on Sentinel-2 MSI . . . . . 62

5.2 Landscape structure change with landscape metrics . . . . . . . . . . 675.3 Ecosystem services . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

5.4.1 Remote sensing based framework for urban LULC mapping . 725.4.2 Data selection criteria and mapping scale . . . . . . . . . . . 735.4.3 Spatial environmental monitoring indicators . . . . . . . . . . 735.4.4 Linking landscape metrics with ecosystem services . . . . . . 74

5.5 Research contributions . . . . . . . . . . . . . . . . . . . . . . . . . . 76

6 Conclusions and Future Research 786.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 786.2 Limitations, recommendations and outlook . . . . . . . . . . . . . . 80

Bibliography 81

A Appended Papers 108

List of Figures

1.1 Urbanization prospects at Earth planetary scale in 2030 horizon. Source:United Nations, Department of Economic and Social Affairs . . . . . . . 2

1.2 Relationships among the four papers included in the study . . . . . . . 6

2.1 Distinction between ML and DL processing chain for classification and/orsegmented map production. . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.2 Conceptual model of LandTrendr fitting spectral index (e.g., NDVI)values to spectral-temporal segments for spatio-temporal dynamics of apixel undergoing disturbance, recovery, and stability in 21 years. Thefirst temporal segment starting from the first vertex to the second ver-tex illustrates the original model with a sequential and slight change.The model is fitted to a no change event. From the second to the thirdvertices, the pixel underwent a great disturbance, translating to an im-portant land cover change, followed by a recovery period (from third tofourth vertices). The last land cover change processes in the same pixelwere characterized by stability in inter-annual variations (conceptualmodel adapted from Kennedy et al. (2010)). . . . . . . . . . . . . . . . . 18

2.3 Landscape structure in four different periods. The urban area patchesare increasing in Time 2, whilst the size of green space patches is re-ducing. In Time 4 urban patches are highly aggregated and coherent,whereas green space patches are highly divided and fragmented (adaptedfrom McGarigal et al. (2002)) . . . . . . . . . . . . . . . . . . . . . . . . 24

2.4 Most occurring urban ecosystem services. Source: Gómez-Baggethunet al. (2013) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.1 Geographic location of Kigali in Rwanda. The left side map showsRwanda with its bordering countries and location of Kigali and Rwan-dan provinces. In the right side, Sentinel-2 MSI with false color compos-ite display (Near-Infrared Red, Red and Green) is used for illustratingKigali with its three districts . . . . . . . . . . . . . . . . . . . . . . . . 28

3.2 Spatial and demographic evolution of Kigali City. Source: (NISR, 2012)and (Michelon, 2012) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

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LIST OF FIGURES xiii

4.1 Categorization of the papers, analytical components in multi-temporaland multi-resolution framework . . . . . . . . . . . . . . . . . . . . . . . 33

4.2 Overview of the methodology used for LULC classification. . . . . . . . 344.3 Illustration of GLCM features computation. The diagram A illustrates

the image central pixel (pixel of interest) that will receive new valueafter GLCM computation and the angle direction during computationprocess. The diagram B represents various image gray value for eachpixels, whilst the diagram C portrays the calculated GLCM using 00

direction angle and distance equal to 1. Original image is having 8gray levels. Pixels with 1,1 pair combination in 00 direction angle areoccurring once, whilst pixels with 1,2 pair combination in the same an-gle direction are occurring twice. There is no pair combination with1,3. In diagrams D and E, a 3x3 moving window (also called kernel) isapplied for calculating the new value in the pixel of interest using pre-defined GLCM measures from any of the 14 statistical measurementse.g. Mean, Homogeneity, Entropy, Correlation, Variance, Standard De-viation, Contrast, etc. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

4.4 Illustration of SVM with linearly separable data. Adapted from Sheykhmousaet al. (2020) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

4.5 Processing chain for progressive LULC prediction, and area estimate andaccuracy assessment. The year of detection (YOD), the change dura-tion (DUR), and change magnitude (MAG) are combined with a changemap derived from two baseline classifications for continuous LULC re-construction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

4.6 Multi-stage and hierarchical object based extraction and classificationframework. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

4.7 Workflow for urban density index computation: (A) SVM refined classi-fication; (B) road network; (C) blocks segments generated based on roadnetwork; (D) urban density index map. Urban density index and green-ness density index maps. The two indices, value is ranging from 0 to 1.The built-up area is characterized by low greenness density index andhigh urban density index. Conversely, green structures are characterizedby high greenness density index and low urban density index. . . . . . . 43

4.8 Proposed U-Net architecture adaptation. White boxes correspond tomulti-channel feature maps. Number of channels and x-y-size are de-noted on top of the box and on its left side, respectively. Operations arevisualized as color-coded arrows connecting the feature maps (see legend) 44

5.1 LULC classification results in 1984, 2001, 2009 and 2016 based on 30 mLandsat data and seven classes . . . . . . . . . . . . . . . . . . . . . . . 55

5.2 Small overview maps of LULC classification results in core urban areasin 1984, 2001, 2009 and 2016 based on 30 m Landsat data and sevenclasses. Left column: Landsat images, False Color Composite; Rightcolumn: LULC classification. . . . . . . . . . . . . . . . . . . . . . . . . 56

LIST OF FIGURES xiv

5.3 Dense annual LULC change from 1988 to 2019 based on GEE-LT pre-diction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

5.4 Five years progressive LULC change from 1990 to 2019 based on GEE-LandTrendr prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

5.5 VHR classified LULC with 12 classes based on OBIA rule-based classi-fication and multi-stage refinement. . . . . . . . . . . . . . . . . . . . . . 61

5.6 Details from the classification (Fig. 5.5) and their respective areas innormal colour display of WV-2 image. In row (A), the selected WV-2 multispectral images areas are presented. In row (B), correspondingextracted LULC are illustrated. . . . . . . . . . . . . . . . . . . . . . . . 62

5.7 Performance of U-Net on training and validation data sets while pre-dicting the percentage of impervious surface. . . . . . . . . . . . . . . . 63

5.8 Cross-comparison between RF based urban classification results (imper-vious surface) and U-Net based prediction of impervious surface. Col-umn (A) shows VHR WV-2 images, (B) Input Sentinel-2 MSI imagesin the U-Net model; (c) Percentage impervious surface training labelsderived from a WV-2 based LULC map; (d) RF merged high and lowdensity built-up area, and (e) Predicted Percentage Impervious Surface. 64

5.9 Maps illustrating the Sentinel-2 MSI based LULC in 2016 and 2021.The left image represents the 2016 classification, whilst the right imagerepresents the 2021 classification. . . . . . . . . . . . . . . . . . . . . . 66

5.10 Detailed classification excerpts illustrating the LULC change from 2016to 2021 around designated Kigali Economic Zone (1st and 2nd columns)and in Southern zone around Gahanga sector (3rd and 4th columns).The top row represents the false color composite (NIR, Red, and Green)of Sentinel-2 MSI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

5.11 Multi-temporal change in landscape composition indices from 1984 to2016. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

5.12 Multi-temporal change in landscape configuration indices from 1984 to2016. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

5.13 CA change from 2016 to 2021. . . . . . . . . . . . . . . . . . . . . . . . 695.14 Spatio-temporal change of supply and demand of ES bundles from 2016

to 2021 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

List of Tables

3.1 Overview and specifications of used multispectral data . . . . . . . . . . 303.2 Scheme of proposed LULC classes . . . . . . . . . . . . . . . . . . . . . 32

4.1 Proposed spectral indices derived from multispectral imagery. . . . . . . 374.2 Proposed landscape metrics for landscape composition and pattern anal-

ysis based on (McGarigal et al., 2002). The table captures the level ofanalysis at which the LM indices were applied and investigated, themeasurement unit and range . . . . . . . . . . . . . . . . . . . . . . . . 50

4.3 Proposed eight urban ecosystem services bundles adapted from typologyof urban ecosystem services proposed by Gómez-Baggethun et al. (2013)with their corresponding LULC and influencing landscape metrics . . . 51

5.1 Cross-comparison of overall classification accuracies, Kappa coefficients,number of LULC classes, classifier and spatial resolutions distributedamong four studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

5.2 Producer,s and user,s accuracies for Landsat based classification . . . . 545.3 Predicted accuracies at class level with class weight, standard error, pro-

ducer’s and user’s accuracy and overall accuracy with a 95% ConfidenceInterval (CI). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

5.4 OBIA rule based classification accuracies . . . . . . . . . . . . . . . . . 605.5 Comparison of hybrid (RF+U-Net) and RF classification accuracies for

combined HDB and LDB. The assessment was performed using F1 score,recall and precision metrics based on combined HDB and LDB validationsamples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

5.6 Accuracies of the 2016 and 2021 classified LULC based on hybrid clas-sification (RF+U-net) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

5.7 Landscape CA composition with net change from 1984 to 2016 based onmulti-temporal Landsat images. . . . . . . . . . . . . . . . . . . . . . . . 67

5.8 Supply and demand of ES based on the framework adapted from Burkhardet al. (2012). Supply and demand is ranging between -5 (high demand)and +5 (highly supply). Zero score (0) is considered as neutral balance.The sum of ES score per LULC and the net change of ES budgets arelisted in the bottom rows . . . . . . . . . . . . . . . . . . . . . . . . . . 70

xv

LIST OF TABLES xvi

5.9 Estimated value of selected ES in Kigali between 1984 and 2016 . . . . 715.10 Change of landscape metrics across ES providing LULC and correspond-

ing ES bundles from 2016 to 2021. The "n.a" means that the LM indexis not influencing the considered ES bundle . . . . . . . . . . . . . . . . 75

List of Acronyms

DBSI Dry Bare Soil IndexDEM Digital Elevation ModelDL Deep LearningDNNs Deep Neural NetworksEO Earth ObservationEOS Earth Observing SystemES Ecosystem ServicesESA European Space AgencyESV Ecosytem Services ValueGEE Google Earth EngineETM+ Enhanced Thematic Mapper PlusGEE-LT Google Earth Engine-LandTrendrGLCM Gray-Level-Co-Occurrence MatrixLM Landscape metricsHR High-resolutionLULC Land Use/Land CoverML Machine LearningMNDWI Modified Normalized Difference Water IndexMSI Multispecral InstrumentMSS Mutispectral ScannerNASA National Aeronautics and Space AdministrationNDBI Normalized Difference Built-up IndexNDMI Normalized Difference Moisture IndexNDVI Normalized Difference Vegetation IndexNIR Near InfraredNISR National Institute of Statistics of RwandaOBIA Object-Based Image AnalysisOLI-TIRS Operational Land Imager and Thermal Infrared SensorRF Random ForestSAR Synthetic Aperture RadarSPOT Système Probatoire pour l,Observation de la TerreSRTM Shuttle Radar Topographic MissionSSA Sub-Saharan AfricaSVM Support Vector MachineSWIR Short-Wave InfraredTCG Tasseled Cap GreennessTCW Tasseled Cap WetnessTM Thematic MapperVHR Very-High-ResolutionWV-2 WorldView-2

xvii

Chapter 1

Introduction

“If you can,t measure it, you probably can,t manage it. Things youmeasure tend to improve.” — Edward Arthur Seykota

1.1 Background and rationale

The Earth is continuously facing large-scale and local environmental changes asevidenced by accelerated urbanization, deforestation, repetitive flooding, loss ofbiodiversity, rising sea levels, wildfires, melting glaciers and climate change, to namea few. It is believed that the above-mentioned changes are mainly linked to human-induced processes, biophysical attributes, natural hazards, and their interactions(Briassoulis, 2009; Garg et al., 2019; Veldkamp and Lambin, 2001). The 2020 livingPlanet Report (Almond et al., 2020) revealed that 75% of the Earth,s ice-free landsurface has already been significantly altered. Globally, more than 5.87 millionkm2 of land are expected to be converted into urban areas by 2030 (Seto et al.,2012), and approximately 475,000 ha/year of arable land, in developing countries,were projected to be converted into urban space from 1990 to 2020 (USAID, 1988).Projections of future urban growth are further illustrating that the World urbanpopulation will increase to nearly 5.2 billion by 2030 (Cohen, 2004; Seto et al., 2012;United Nations, 2015). By 2050, population growth and urbanization are projectedto add 2.5 billion people to the World,s urban population, where nearly 90% willbe concentrated in China, Indian sub-continent, Southern East of Asia, and SubSaharan African (SSA)(Cohen, 2004; Seto et al., 2012; United Nations, 2015). Morethan 5.87 million km2 of land are expected to be converted into urban areas by2030 to accommodate this growth. 662 cities will have at least 1 million residentsand megacities (i.e. cities with a population of more than 10 million people) willincrease from 34 in 2020 up to 43 by 2030 (Seto et al., 2012; United Nations,2015). Figure 1.1 illustrates the location of the main World urbanization hotspotsespecially megacities in 2030.

1

CHAPTER 1. INTRODUCTION 2

Figure 1.1: Urbanization prospects at Earth planetary scale in 2030 horizon.Source: United Nations, Department of Economic and Social Affairs

Cities and metropolitan regions are considered as important areas for economicopportunities and an engine for development (Forte et al., 2019; Kleniewski andThomas, 2019). The 2011 report on economic power of cities highlighted that morethan half of the global gross domestic product (GDP) equivalent to 30 trillion USDollars was concentrated in the top 600 cities, and it is expected that nearly 60 %equivalent of 64 trillion US Dollars of global GDP will be generated in the same citiesby 2025 (Dobbs et al., 2011). Nonetheless, urbanization may also lead to negativeoutcomes, such as deterioration of the quality of life and environmental degradation(De Souza, 2001; Hardoy et al., 1992; Gál et al., 2019; Nathaniel, 2021; Rahmanet al., 2011). Cropland conversion, land use competition and wetlands alteration aresome of the aftermaths linked to rapid urbanization (van Vliet et al., 2017). Despitetiny land fractions being occupied by urbanized areas (2% of the Earth,s surface),cities account for 60% to 80% of global energy consumption (Schneider et al., 2010;UN, 2018a). Previous studies illustrated that there is a strong correlation betweenexcessive urbanization and an increase of greenhouse gas emissions (Dhakal, 2010;Didenko et al., 2017; McGee and York, 2018), and the latter is contributing toglobal warming and climate change and variability.

In SSA, urban population growth has almost been on average 5% per year overthe last two decades, and future urban growth is expected to add 300 million inhab-


itants to urban areas between 2000 and 2030 (Kessides, 2006). However, the rate ofurbanization in the sub-region is not at the same pace with the availability of basicprovisions such as water, electricity, public space, and sewage systems. Ventilationspace and recreation zones are threatened or even non-existent, given that the spaceoccupation is congested. In most cases, the urban expansion in the sub-region isfollowing the "tenure-occupancy-servicing" trajectory leading to the proliferation ofinformal settlements with poor sanitation infrastructure, and deteriorated qualityof life. Land market speculation, squatting and informal settlements developmentare among the key characteristics in many cities. Due to unexpected urbanizationintensity and patterns, the existing urban land related data, tools and policies arenot coping with proper land use monitoring and sustainable urban land manage-ment. Furthermore, suppliers of technical infrastructures are operating withoutreliable information about the distribution of future demand (Hill et al., 2014).Therefore, monitoring spatio-temporal urban land use/land cover (LULC) changedynamics is essential for sustainable urban land administration and management,and environmental impact analysis. Up-to-date data and cost-effective methodsare needed for tangible and detailed urban LULC information extraction for trac-ing the continuous dynamic change in complex urban environments. Reliable dataand information on trajectories of LULC patterns, and on extension of deprivationareas, such as slums, informal settlements and environmentally sensitive zones, areparamount for responding to the pressing urban land administration and manage-ment questions. The availability of such kind of data and information could beconsidered as a multipurpose public good. In one way, such kind of informationcan help in inventorying who own which land/property, how big it is, where it islocated and which spatio-temporal change has affected it. In another way, sameinformation can also be used for predicting future urban development scenarios andassociated environmental impact.

The East Africa sub-region stretching across Rwanda, Burundi, Uganda, Kenya,Tanzania and South Sudan is registering projected high records in future urbaniza-tion prospects (United Nations, 2015). There is no doubt that rapid and uncon-trolled urbanization in the sub-region will be coupled with proliferation of informalsettlements, alteration of existing LULC systems through land conversion, reduc-tion of agricultural land and various small-scale urban redevelopments for the sakeof smart urban built environment. Yet, traditional methods for LULC informationproduction relying on ground-based surveying, house-per-house census, and spo-radic small-scale airborne survey missions cannot cope with the new trends andpace in supplying near-real time and cost-effective geospatial data, and informa-tion about LULC change dynamics. Furthermore, authorities in charge of urbanplanning are facing hindering factors such as scarce and fragmented data (Wardropet al., 2018). Collected statistics are most of the time outdated or geographicallyaggregated to large heterogeneous administrative entities, which are judged lesshelpful in informed decision making pertaining to urban land management (Kufferet al., 2016) and environmental impacts analysis.

In Rwanda, the average annual rate of change of proportion urban population


(3.7%) was the highest in Eastern Africa between 2010 and 2015 (United Nations,2015). Kigali, which is the largest city, is characterized by a high degree of urbanprimacy (UN, 2018b) i.e. cities with largest urban population share, main eco-nomic activity, and political power that are several times greater than the othercities (Güneralp et al., 2017). The widespread informal economy and informal hu-man settlements are among the key challenges faced by planning authorities inKigali (Baffoe et al., 2020). On the other hand, huge investments were made forcreating and operationalizing a multipurpose cadastre that is serving various as-pects, including land transactions management, land-based tax collection, land usezoning, environment auditing and impact assessment, to name a few. The recentlyendorsed city master plan for the 2050 horizon, intends to turn Kigali into a vibranteconomic hub, smart and eco-friendly city (City of Kigali, 2020). However, there isno cost-effective mechanism for continuous monitoring of LULC change processes.The city authorities are mainly relying on field visits for monitoring illegal con-structions and enforcing environmental sustainability. Therefore, a near-real time,cost-effective data collection and production framework is a needed starting pointfor fostering sustainable urban dynamics and continuous monitoring of SustainableDevelopment Goals implementation.

Remotely sensed data can play a vital role in producing cost-effective and near-real time data that are suitable not only, for LULC change analysis in general,but also for urban change detection and environmental impact analysis in par-ticular. While conventional methods, such as field measurements and on-demandairborne survey missions are considered as labour-intensive and time consuming; re-mote sensing methods offer competitive benefits including synoptic view of events,large area coverage at regular revisits, near-real time data acquisition, quantita-tive measurement of ground features using radiometrically calibrated sensors, semi-automated computerized processing and analysis, and relatively low cost per unitarea of coverage (Chin, 2001; Chuvieco, 2016). The large number of satellite con-stellations and different sensors have positively influenced the capacity of acquiringmulti-source Earth observation (EO) data with diverse spatial, spectral and tem-poral resolutions (Camara et al., 2016; Nativi et al., 2015; Sidhu et al., 2018).Increased computing power and cloud computing systems have dramatically rev-olutionized the ability for LULC mapping and spatio-temporal landscape changeanalysis (Gorelick et al., 2017; Amani et al., 2020; Tamiminia et al., 2020; Yaoet al., 2020). In 1998, the former Vice President of the United Stated of America,Al Gore, emphasized how the advances in technological innovation are catalyzingthe acquisition of georeferenced data, and illustrated the need for such data, andhow they are worthwhile for environmental change monitoring, and for informeddecision making:

"A new wave of technological innovation is allowing us to capture,store, process and display an unprecedented amount of information aboutour planet and a wide variety of environmental and cultural phenomena.[· · ·] I believe we need a Digital Earth. [· · ·] We have the opportunity to


turn a flood of raw data into understandable information about our so-ciety and our planet. This data will include not only high-resolutionsatellite imagery of the planet, digital maps, and economic, social, anddemographic information. If we are successful, it will have broad so-cietal and commercial benefits in the areas such as education, decisionmaking for sustainable future, land-use planning, agricultural and crisismanagement." — Gore (1999)

Furthermore, a growing number of literature illustrated the added-value of re-mote sensing and geographic information systems in mapping, monitoring and mod-elling urban development and associated environmental impact (e.g. Furberg et al.,2020; Gamba and Herold, 2009; Seto et al., 2012; Haas and Ban, 2017; Loret et al.,2017; Weng, 2014; Singh et al., 2017; Zhou et al., 2012; Mojaddadi Rizeei et al.,2019; Kamusoko, 2017). However, the utility of remote sensing applications in urbanlandscape fragmentation and spatial pattern analysis based on measurable environ-mental indicators is not yet fully explored, especially in the forecasted urbanizingenvironment of SSA. EO data applications seem to be the point of departure im-plementing and operationalizing the fit-for-purpose and cost-effective methods forproduction of urban information to be used in particular applications at a particu-lar scale. The proposed study aims at retrospective and perspective urban growthanalysis and associated environmental impact, based on multi-temporal and multi-resolution satellite data, derived landscape metrics (LM) indices, and ecosystemservices (ES) concepts. The proposed analytical framework is deemed an addi-tional effort for speeding up the production of urban LULC information, which ishighly needed for informed decision making pertaining to urban planning and sus-tainable land management. The research is mainly using open and free EO datathat are cost-effective for continuous monitoring of complex urban LULC dynam-ics, especially in environments with EO data affordability issues, and in data-scarceregions. Meanwhile, I explored the potential of using very-high- resolution (VHR)data for urban mapping at fine scale. The research is further exploring and as-sessing the scope and utility of EO data to fill the gaps resulting from scarce andfragmented data, and outdated statistics observed in most of the cities in globalsouth nations including Kigali, Rwanda.

1.2 Research objectives

The present research aims at investigating the utility of medium, high and VHRoptical multi-resolution remotely sensing data, LM and ES concepts in urbanizationmonitoring, and environmental impact analysis in Kigali, Rwanda. Specifically, thestudy aims at:

• Exploring state-of-the-art remote sensing methods for urban LULC informa-tion extraction and classification


• Monitoring the spatio-temporal patterns of urbanization in Kigali, Rwandain the last 37 years since 1984

• Analyzing the evolution of spatio-temporal patterns of landscape configura-tion and composition in Kigali, Rwanda and associated environmental impactbetween 1984 and 2021

• Investigating the importance of coupling the LULC information derived fromremotely sensed classified imagery with LM and ES supply and demand formapping urbanization trends and the respective environmental impact

1.3 Thesis organization structure

The thesis is structured as follows: Chapter 1 introduces the research context, therationale and the study objectives. In Chapter 2, an overview of remote sensingsensors and methods for urban LULC information extraction is presented. In ad-dition, remote sensing based methods for analyzing urban landscape and ES arereviewed. The data used and study area description are introduced in Chapter 3,whilst the methodology used for data processing and analysis is discussed in Chap-ter 4. Chapter 5 summarizes the results and discusses the research findings. Theconclusions are made and future research directions indicated in Chapter 6. Thelink and relationships among the four papers included in the thesis are presentedin Figure 1.2.

Figure 1.2: Relationships among the four papers included in the study


The above-mentioned relationships are mainly based on the spatial resolutionof the data, the temporal analysis aspect and the thematic application. The firsttwo papers are based on 30 m medium resolution Landsat data. In Paper I, multi-temporal time series data collected separately were used with single pass LULCclassification and a subsequent environmental impact analysis was performed usingLM and ES variables. By shifting from static to continuous multi-temporal analysis,the Paper II presents a dynamic time series based analysis for tracking LULCchange trajectories with cross-sensors, and spectral normalization implemented inLandTrendr. In Paper III, the analysis is based on single date VHR WorldView-2data. Meanwhile, the analysis shifted from uni-temporal (used in Paper III) tobi-temporal based analysis using Sentinel-2 MSI data in Paper IV. The thesis isbased on the aggregate of four articles referred to by their Roman numerals aslisted below.

[I]. Mugiraneza, T., Ban, Y. and Haas, J. (2019). Urban land cover dynamicsandtheir impact on ecosystem services in Kigali, Rwanda using multi-temporalLand-sat data. Remote Sensing Applications: Society and Environment 13, 234-246; https://doi.org/10.1016/j.rsase.2018.11.001

II. Mugiraneza, T., Nascetti, A., Ban, Y., (2020). Continuous Monitoring of UrbanLand Cover Change Trajectories with Landsat Time Series and LandTrendr-GoogleEarth Engine Cloud Computing. Remote Sensing, 12(18), 2883;https://doi.org/10.3390/rs12182883

[III]. Mugiraneza, T., Nascetti, A., Ban, Y., (2019). WorldView-2 Data forHierar-chical Object-Based Urban Land Cover Classification in Kigali: Integrating Rule-Based Approach with Urban Density and Greenness Indices.Remote Sensing 11(18),2128; https://doi.org/10.3390/rs11182128

[IV]. Mugiraneza, T., Hafner S., Haas, J., Ban, Y., (2021). Monitoring urbanizationand environmental impact based on Sentinel-2 MSI data and ecosystem service bun-dles. Manuscript submitted to International Journal of Applied Earth Observationand Geoinformation.

1.4 Declaration of Contributions

The contributions to the papers that support this thesis are declared as follows:

Paper ITheodomir Mugiraneza, the 1st author, performed the analysis, methodology im-plementation and drafted the manuscript. Yifang Ban, the 2nd author, initiatedthe idea of using medium resolution images (i.e. Landsat) for urbanization moni-toring and environmental impact analysis in Kigali, Rwanda. She also contributedto methodology development and analysis of the results. Jan Haas, the 3rd author,


helped in computing LM indices and advised on ES valuation. Yifang Ban and JanHaas revised the manuscript.

Paper IITheodomir Mugiraneza, the 1st author, tested and evaluated the time line for imagesacquisition and indices selection the Google Earth Engine-LandTrendr (GEE-LT)environment, analyzed the data and drafted the paper. Andrea Nascetti, the 2nd

author, designed the experiment for continuous pixel-based land cover reconstruc-tion and revised the paper. Yifang Ban, the 3rd author conceived the idea of usingLandsat time series for the analysis of urban land cover change trajectories. Shealso contributed to analysis of the results and the writing of part of the manuscript,and thoroughly edited the final manuscript.

Paper IIITheodomir Mugiraneza, the 1st conducted the experiment, analyzed the data anddrafted the manuscript. Andrea Nascetti, the 2nd, conceived and designed theexperiment on urban density index computation and revised the paper. YifangBan, the 3rd author, conceived the idea of improving urban land cover classificationusing high resolution data with an object-based approach. She also contributed toevaluation of the parameters for segmentation process, analysis of the results, co-wrote part of the manuscript, and thoroughly edited the manuscript.

Paper IVTheodomir Mugiraneza, 1st author, conducted the experiment, analyzed the dataand drafted the paper. Sebastian Hafner, the 2nd author, designed the experimenton imper- viousness density computation and in implementing the U-Net model andregression analysis and wrote part of the methodology. Jan Haas, 3rd author, con-tributed in designing the framework for linking LM to ES bundles and manuscriptediting. Yifang Ban, the 4th author, conceived the idea of using Sentinel-2 MSI forurbanization monitoring and environmental impact analysis, and using deep learn-ing (DL) for improving LULC classification, and assessing environmental impactusing the bundles of ES and LM. She also supervised the whole data processingand result analysis, and thoroughly revised the manuscript.

Chapter 2

Optical remote sensing forurbanization monitoring: Sensors,methods and applications

Large number of satellite constellations and different sensors have positively im-pacted the capacity of acquiring multi-source EO data. The improved computingcapabilities, and accelerated development of cloud computing systems have dra-matically revolutionized the ability for land cover mapping and spatio-temporallandscape change analysis. Furthermore, the analysis ready EO data and the ac-cess to big data analytics capabilities has opened the opportunities for continuouslymonitoring of our changing environment such as tracking urbanization developmentand its associated environmental impact. The geo-information market is continu-ously being flooded by different kinds of satellite imagery and Earth resources,

exploration is feasible even in remote and inaccessible areas. Various EO sensorsranging from high to low temporal, spectral, radiometric and spatial resolutionshave emerged for several applications. In this chapter, the most popular opticalsatellite sensors for urban applications are presented. In addition, methods andtechniques used for urban information extraction and remote sensing applicationsin urbanization monitoring and environmental impact analysis are discussed. Theemphasis is put on optical multispectral remote sensing. Furthermore, the chapterdiscusses LM which is perceived as a tool for performing spatio-temporal analysisof landscape configuration and composition using measurable indices derived fromclassified satellite data. Finally, the concepts of ES and their estimated values areexplored.

2.1 Optical satellite sensors for urban applications

A variety of space-borne EO sensors are used for urban LULC classification andmapping, and environmental impact analysis. A comprehensive review of global

9

CHAPTER 2. OPTICAL REMOTE SENSING FOR URBANIZATIONMONITORING: SENSORS, METHODS AND APPLICATIONS 10

land cover observation capacity from civilian EO satellites can be read in Belwardand Skøien (2015). According to Weng (2014) space-borne EO sensors for urbanapplications are grouped into four broad categories including optical, SyntheticAperture Radar (SAR), thermal infrared and Nighttime Lights sensors. Opticalsensors depend on a secondary source of electromagnetic energy (mainly the sun) forilluminating the Earth. While using optical sensors, information on the illuminatedEarth surface is captured in the visible, near, shortwave and thermal infrared partof the electromagnetic spectrum (Jensen et al., 1996). Based on spectral resolution(see Kerle et al., 2004), optical sensors can be categorized into four main sub-groupsincluding i) monospectral or panchromatic sensors collecting the single spectral orgrey-scale images, ii) multispectral sensors collecting several multispectral bandsimagery, iii) superspectral sensors acquiring images with tens of spectral bands,and iv) hyperspectral sensors capturing images with hundreds of spectral bands.Optical sensors provide data at coarse, medium, high-resolution (HR) and VHR.According to EOS (2019) and ESA (2018), spatial resolutions of satellite imageryare broadly ranged into three main categories including:

• Low-resolution: over 60m/pixel

• Medium-resolution: >10m-30m/pixel

• HR to VHR: 30cm-10m/pixel

For each of the spatial resolutions, there are pros and cons (EOS, 2019), and thescale of mapping including global, regional and local depends on number of factorssuch as data affordability and accessibility, performance of software and hardwareinfrastructure, a priori cost-benefit analysis for data acquisition, and others. In theframework of the present research, only medium, and VHR multispectral data wereused.

2.1.1 Medium-resolution sensorsThe 30 m moderate spatial resolution Landsat data are commonly used for global,regional and local urban mapping and urbanization monitoring. In almost fivedecades, the Landsat programme with multispectral optical sensors ranging fromthe Mutispectral Scanner (MSS), Thematic Mapper/Enhanced Thematic MapperPlus (TM/ETM+) to the Landsat 8 Operational Land Imager (OLI) and ThermalInfrared Sensor (TIRS) has become the major data source for spatio-temporal ur-ban land cover change monitoring. The Landsat programme has the longest recorduseful for spatial temporal analysis of landscape dynamics with spatial resolutionsranging from 15 m, 60m to 120 m depending on the spectral band (Walawenderet al., 2014). The free and open access to all archived Landsat images since 2008has further opened opportunities for developing novel change detection algorithms,data calibration and pre-processing enhancement based on time series data (Zhu,2017; Wulder et al., 2012), and numerous studies on urbanization mapping and


monitoring were carried out. Visible, infrared and panchromantic Landsat bandsare widely used for urban applications including LULC mapping and change de-tection analysis (e.g. Haas and Ban, 2014; Li et al., 2015a; Masek et al., 2000;Ottinger et al., 2013; Li et al., 2015b, 2018a; Zhu and Woodcock, 2014; Liu et al.,2018), global human settlement extent extraction (Pesaresi et al., 2016), land sur-face biophysical indices derivation such as vegetation, soil, water and built-up areaspectral indices (e.g. Estoque and Murayama, 2015; Zha et al., 2003; Sinha et al.,2016; Zhang and Weng, 2016), urban impervious surface extraction (e.g. Dengand Zhu, 2020; Zhang and Weng, 2016; Xu et al., 2018; De Colstoun et al., 2017;Zhang et al., 2020) to name a few. Landsat thermal bands record the emittedthermal radiation from the Earth’s surface which is then converted into radianttemperature that is often used for deriving information on urban heat islands andanalysing land surface temperatures (e.g. Estoque and Murayama, 2015; Singhet al., 2014; Aniello et al., 1995; Walawender et al., 2014; Rasul et al., 2015). Apartfrom Landsat data, the Advanced Spaceborne Thermal Emission and ReflectionRadiometer (ASTER) systems are also providing long-term data at 15 m to 30m spatial resolutions. Furthermore,the first three generations of Système Proba-toire pour l,Observation de la Terre (SPOT 1,2,3) with three multi-spectral bands(Green, Red, Near Infrared) at 20 m resolution and 60 km swath width, and a 10mpanchromatic band, are resourceful medium resolution data for mapping and andquantifying urban dynamics.

2.1.2 HR and VHR sensorsIn their book on high spatial resolution remote sensing, He and Weng (2018) stressedthat the emergence of HR and VHR data opened the opportunities for addressingenvironmental questions through the analysis at very fine spatial scales. Accordingto Gamba et al. (2011) urban related applications are among the front-runnersin using HR and VHR data. In this category, we can enumerate SPOT 4,5,6and 7 that are providing multi-spectral data with resolution ranging from 10 to6 m and panchromatic data at 5 to 1.5 m (https://earth.esa.int/eogateway/missions/spot). The IKONOS imagery is considered the first commercial VHRsatellite sensor launched in 1999 with 0.82 m and 4 m spatial resolutions imagery.QuickBird with 0.6 m resolution, 4 multi-spectral bands and 1 panchromanitc bandfollowed the new shift towards VHR imagery after it was placed to the orbit in 2001.During 2006 and 2007, many new commercially available VHR satellites, such asEROS B1, Resurs DK-1, KOMPSAT-2, IRS Cartosat 2, and WorldView-1 (WV-1)were successfully launched, and they are offering VHR imagery of the Earth witha very short revisit time (Aguilar et al., 2012). Later on, GeoEye-1 with 0.5 mpanchromatic and 2 m multispectral resolution and the new WV generations 2, 3and 4 emerged on the remote sensing market as more sophisticated commercial VHRsatellite sensors. This move has gradually impacted the information generationfor urban mapping applications at very fine scale. With their high spectral andgeometric resolutions, VHR data are providing the possibilities of mapping detailed


urban structures such as detecting road network hierarchy and building footprints(Bouziani et al., 2010; Jin and Davis, 2005; Li et al., 2016; Nobrega et al., 2006;Valero et al., 2010; Yan and Zhao, 2003), production of detailed urban land use plans(Fraser et al., 2002; Mathieu et al., 2007; Taubenböck et al., 2010; Meinel et al.,2001), cadastral index mapping for boundary survey and land right adjudication(Lemmen and Zevenbergen, 2010), informal settlements and slum mapping (Kohliet al., 2016; Kuffer et al., 2016; Rhinane et al., 2011; Aminipouri et al., 2009; Kitand Lüdeke, 2013; Rhinane et al., 2011; Owen and Wong, 2013; Fallatah et al.,2020), socio-economic urban segregation (Duque et al., 2015; Hemerijckx et al.,2020; Kuffer et al., 2020), impervious surface detection and estimation (Mohapatraand Wu, 2010; Yang and He, 2017; Chaudhuri et al., 2017; Zhang et al., 2020).VHR satellite data are also suitable for 3D city model reconstruction and highrise building extraction (e.g. Bachofer and Hochschild, 2015; Tao and Yasuoka,2002; Vanhuysse et al., 2017). Nevertheless, HR and VHR imagery can suffer fromhigh spectral variation within the same LULC class. Factors that increase intra-class variability are related to topographic and shadow effects, different types ofvegetation and vegetation density, among other things (see Asner and Warner,2003; Lu et al., 2010; Lu and Weng, 2009). Computing requirements for processinghighly dimensional data are also a point to be taken into account when handlingHR data. Indeed, computers with powerful processors are beneficial for reducingprocessing time when processing HR and VHR data. From a budget perspective,the cost of commercialized HR imagery is still high. Considerable financial costswould be required to map large areas and the incurred expenditure can hardly becovered by most of the countries with a budget deficit.

With the launch of the twin optical satellites, Sentinel-2A MSI and Sentinel-2BMSI, by the Copernicus Program of the European Space Agency (ESA), globalcoverage of visible and infrared data with a wide swath are freely available everyfive days at 10 m, 20 m, 60 m spatial resolution. The 10 m and 20 m bands ofSentinel-2A MSI and Sentinel-2B are preferably used for urban extent extractionand classification, while the 20 m bands are in most cases resampled to 10 mfor harmonizing the spatial resolution across visible, near-infrared and shortwavemulti-spectral bands. It could be noted that Sentinel-2 MSI is considered bothHR and medium resolution data given that it is offering data at both 10m and20m spatial resolution. Sentinel-2 MSI data has unlocked a great potential fordeveloping new tools and methods for various LULC applications (Phiri et al.,2020), and particularly for urbanization monitoring. For instance, a global humansettlements product was recently released based on composite Sentinel-2 imagery(Corbane et al., 2021), and a growing number of regional and local studies relatedto Sentinel-2 MSI based urbanization monitoring and environment impacts do exist(e.g. Lefebvre et al., 2016; Qiu et al., 2020; Zhang et al., 2021; Cai et al., 2020;Haas and Ban, 2018; Furberg et al., 2019).

Apart from the above-mentioned optical sensors, manned aircraft and unmannedaerial vehicles equipped with multispectral, hyperspectral, and thermal sensors aswell as laser scanners at centimeter resolution are emerging as suitable VHR tools


in various urban applications (e.g. He and Weng, 2018; Yao et al., 2019; Lahotiet al., 2020; Noor et al., 2018).

2.2 Big data analytics and EO

The concept of EO big data refers to the collection of voluminous amounts of multi-temporal and multi-resolution satellite data that may be scaled up to petabytes insize. Such data are originally characterized by 4Vs, i.e. volume, variety, veracityand velocity (Douglas, 2012), and later extended to 5V with the value concept.The first V refers to the volume of data which is growing explosively and extendsbeyond our capability of handling large data sets; volume is the most commondescriptor of big data (Hsu et al., 2015). Velocity refers to the fast generation andtransmission of data across the Internet as exemplified by data collection from socialnetworks, massive array of sensors from the micro (atomic) to the macro (global)level and data transmission from sensors to supercomputers and decision-makers.Variety refers to the diverse data forms and in which model and structural data arearchived. Veracity refers to the diversity of quality, accuracy and trustworthinessof the data. All four Vs are important for reaching the 5th V, which focuses onspecific research and decision-support applications that improve our lives, work andprosperity (Mayer-Schonberger and Cukier, 2013).

EO big data analytics through cloud computing services are nowadays seen asnovel approaches for handling the large amount of remote sensing data for urbanmapping and modelling. Various frameworks have been developed and research ef-forts have been deployed for handling the challenges brought by retrieving, storing,processing analyzing, and disseminating EO big data. A recent review of EO bigdata analytics and processing framework can be read in Gomes et al. (2020). One ofthe highlighted popular platforms for handling EO big data is Google Earth Engine(GEE); a cloud-based geospatial processing platform, offering a large set of user-friendly API for analyzing freely available satellite images, producing statistics andmaps, and graphical representation of the investigated phenomena through parallelcomputing (Gorelick et al., 2017). GEE is mainly composed of two componentsworking in sync with each other, namely Google Earth Engine Playground (EEP)and Google Engine Explorer (EE). Other well-known and trending EO platformsfor big data analytics include, but not limited to, the Open data cubes (ODC)(Lewis et al., 2017), and the European,s Space Agency SentinelHub that can beaccessed via https://www.sentinel-hub.com. The main bottleneck in using big datais that they are both structured and unstructured, as their size is beyond the abil-ity of commonly used software tools to capture, manage and process them withina tolerable elapsed time (Taylor-Sakyi, 2016). The widely used solution againstabove-mentioned bottleneck is the MapReduce architecture for parallel cloud com-puting (Pavlo et al., 2009).

The availability of analysis ready EO data and the access to big data analyticshave opened up the opportunities for a variety of applications such as the contin-


uous monitoring of urban LULC change trajectories (e.g. Mugiraneza et al., 2020;Dong et al., 2020; Liu et al., 2020; Qiu et al., 2020), analysing urbanization environ-mental side-effects such as urban heat islands (Ravanelli et al., 2018; Shandas et al.,2019; Fashae et al., 2020), LULC conversion (Hassan and Southworth, 2018; Sidhuet al., 2018; Schneider, 2012; Pandey et al., 2018), and urban deprivation modelling(Leonita et al., 2018; Wang et al., 2019b). Recently, many approaches for mappingand monitoring the extent of human settlements and urban development at theglobal scale using EO big data platforms have been produced. For instance, theGlobal Human Settlement Layer commissioned by the European Commission (Pe-saresi et al., 2016) consists of global settlements extents extracted from Landsat,Sentinel-1 SAR and Sentinel-2 MSI data modelled through Symbolic Machine learn-ing methods. Other worthwhile databases generated using EO big data analyticsinclude the i)Global Urban Footprint derived using German TerraSAR-X combinedwith TanDEM-X SAR images (Esch et al., 2013), and the ii)Global Human Built-up And Settlement Extent dataset based on 30m resolution Landsat images (Wanget al., 2017).

2.3 Remote sensing based methods for urban LULCclassification

According to Di Gregorio (2005), land cover consists of (bio)physical cover on theEarth,s surface such as vegetation, man-made features and water, whereas land useencompasses the arrangements, activities and inputs people undertake in a certainland cover to produce, change or maintain it. As the land use and land cover areclosely related, the synthesis term "LULC" is generally used in classification (Fisheret al., 2005), except for some specific studies that are solely focusing on one term.

LULC classification is among the front line tasks of remote sensing and can beextracted and classified using both visual and digital image classification. Visualimage interpretation is carried out by using image interpretation techniques (Kerleet al., 2004), whilst digital image interpretation is conducted using computer sys-tems with specific processing and classification algorithms. Trained image analystsutilize the tone, colour, texture, shape, size, orientation, pattern, shadow silhou-ette, site, and situation of objects in the urban landscape to discriminate them(Jensen, 2005; Kerle et al., 2004). According to Jensen (2005), it is more importantto have high spatial resolution (often < 5m) than high spectral resolution (i.e., alarge number of multispectral bands) when extracting urban/suburban informa-tion from remotely sensed data. Geometric elements are especially useful in imageinterpretation when high spatial resolution imagery of urban environments is avail-able. Digital image classification is either performed by training individual pixelsor based on spectral grouping using object-based approaches. The processing chainof urban LULC extraction and classification involve a number steps such as pre-processing and image enhancement and various methods for training and validatingthe results. A comprehensive review on data, algorithms, systems considerations,


methods and applications of remote sensing of urban areas is captured in Wengand Quattrochi (2018) and Yang (2011). In the following sub-sections, I discussmost methods and techniques used for LULC extraction and classification includingpixel-based versus object-based image analysis (OBIA), machine learning and deeplearning methods, and time series based analysis.

2.3.1 Pixel versus object-based classificationTraditional methods for information extraction from remotely sensed images useper-pixel image analysis either through supervised or unsupervised classifications.According to Lillesand et al. (2008), supervised and unsupervised classifications areapplied in two separate steps. Supervised classification is preceded by a pixel cat-egorization process by specifying the numerical descriptors of various LULC typespresent in the scene at selected sample sites of known cover called training areas.Unsupervised classification consists of training the classifier and categorizing thespectral signatures, into the predicted LULC classes (Jensen, 2005). Since around2000, OBIA-based classification has emerged as a new approach for overcomingthe shortcomings of pixel-based methods such as negligence of spatial concepts.(Blaschke and Hay, 2001). Instead of classifying an image based on pixel units, LCcategories which are more or less homogeneous are spectrally clustered into regions(known as segments or objects) (Benz et al., 2004; Blaschke, 2010; Jensen, 2005).A recent review on OBIA LULC classification is found in Ma et al. (2017). Thenext step consists of training the sample objects corresponding to the predefinedLULC classes and the application of a specific classifier or an ensemble of classifiers(Blaschke, 2010; Flanders et al., 2003; Qin et al., 2013). Object-based approacheswere found effective for urban LULC extraction especially when processing HR orVHR data. They allow the integration of context based rules, human knowledge andspectral information content. This is helpful in avoiding confusion among diverseLULC classes with similar spectral reflectance (Blaschke et al., 2014). Furthermore,object-based classification has the potential of reducing the “salt and pepper” chal-lenge which is commonly experienced in pixel based image classification (Lu andWeng, 2009). However, the drawback in the object-based approach is mainly therisk of over/under segmentation, given that there are no pre-defined optimum pa-rameters for generating objects through image segmentation algorithms (Liu andXia, 2010). Therefore, trial and error in the segmentation process is usually needed,and segmentation parameters often have to be empirically determined.

2.3.2 Machine learning and deep learning based methodsTraditional classifiers such as linear regression, maximum likelihood, K-nearestneighbor, are characterized by shallow architectures, and in most cases, they failto extract and classify features in complex landscapes such as urban environments(Huang et al., 2018). Meanwhile, state-of-the-art machine learning (ML) classifiersincluding random forest (RF), support vector machine (SVM), and deep neural


networks (DNNs) are frequently used for LULC extraction and classification. MLand DL methods are reputable for handling data of high dimensionality, and foradapting well in mapping classes with very complex characteristics (Maxwell et al.,2018). They are outperforming classic parametric algorithms, especially when itcomes to modelling and predicting non-linear and complex LULC such as the onesin urban environments (Rodriguez-Galiano et al., 2012; Zhong et al., 2019).

The use of of conventional ML classifiers such as SVM and RF usually in-volve a number of steps in the LULC processing chain including (i)pre-processing,(ii)feature engineering, (iii) classifier training and application, and (iv) post-processing.On the other hand, DL requires only input of multi modal data for class and/orsegmented map production through alternating spatial convolutions followed byactivation units and pooling layers (Zhang et al., 2016; Zhu et al., 2017). The pro-cessing chain distinction between ML and DL for classification or segmented mapproduction is illustrated in Figure 2.1.

Figure 2.1: Distinction between ML and DL processing chain for classificationand/or segmented map production.

Over the past two decades, SVM and RF have drawn attention to image clas-sification (Sheykhmousa et al., 2020), for their remarkable performance in severalremote sensing applications including LULC classification (e.g. Rodriguez-Galianoet al., 2012; Mountrakis et al., 2011; Thanh Noi and Kappas, 2018). Meanwhile, DLbeing significantly successful in dealing with big data, seems to be a great candi-date for exploiting the potentials of complex and massive datasets (Zhu et al., 2017;Wu et al., 2019; Qiu et al., 2020). A comprehensive review for DL applications inremote sensing is found in (Zhu et al., 2017; Camps-Valls et al., 2021).

DL and state-of-the-art ML methods such as RF and SVM have been success-fully applied in urban LULC extraction and classification such as built-up areadetection (Tan et al., 2021), large-scale human settlement extraction (Qiu et al.,2020), land consumption rate (Hagenauer et al., 2019), urban village mapping (Panet al., 2020), slum mapping (Leonita et al., 2018), object-based urban green infras-tructure change monitoring (Furberg et al., 2020), to name a few.


2.3.3 EO time series based analysisThe wave of geo-ICT innovation and open access to voluminous EO data haveopened the possibilities for analyzing LULC dynamics using time series algorithms.Seasonal changes, such as seasonal snow cover (e.g. Immerzeel et al., 2009), seasonalwetland inundations or hydrological regime variations (e.g. Dronova et al., 2015;Bian et al., 2017) are analyzed using time series algorithms suitable for short-termperiod trends. Long-term changes, such as trends in vegetation greenness, arefrequently analyzed using annual time series. EO time series algorithms are furtherutilized for abrupt change monitoring, such as wildfire (e.g. Franks et al., 2013;Goetz et al., 2006) and deforestation detection (e.g. Kennedy et al., 2010; Margonoet al., 2012). Gradual changes, such as land degradation (e.g. Eckert et al., 2015;Yengoh et al., 2015), forest disturbance and recovery (e.g. Cohen et al., 2010; Zhaoet al., 2016), and urban development (e.g. Li et al., 2018b), are also among the typeof trends that can be analyzed using satellite time series algorithms.

Time series algorithms such as LandTrendr implemented in GEE are efficientfor pre-processing the input images by using the image seasonal or annual compo-sition, or filtering them based on temporal statistics such as the seasonal mean,standard deviation, median, quantile values and various percentiles of the selectedtime series (Gorelick et al., 2017). LandTrendr comprises of a set of trajectoriesand spectral-temporal segmentation algorithms that are useful for annual land dis-turbance and recovery detection. It takes into account the intensity of the LULCdisturbance known as the magnitude of change, the duration of the event expressedin the number of years, and the year when the land disturbance or recovery occurs(Kennedy et al., 2010). Recently, the implementation of the LandTrendtr algo-rithm in the GEE platform opened the opportunities for online collection of allavailable Landsat data and processing them in a cloud-based environment with auser-friendly application programming interface (API) (Kennedy et al., 2018). InFigure 2.2, I illustrate the conceptual LandTrendr model that is hypothetically usedfor tracking the sequence of LULC disturbance and recovery in 21 years time-spanusing normalized difference vegetation index (NDVI) index.

The LandTrendr algorithm was initially tested in forest disturbance and recov-ery detection in the Pacific Northwest of the United States of America (U.S.A),western Oregon, and Washington by Kennedy et al. (2010). The findings of theirstudy confirmed that the model outperformed the bi-temporal change detectionpreviously carried out by Cohen et al. (1998) in tracking trends related to forestdisturbance and regrowth in the same study area. A growing number of studieshave subsequently proven the effectiveness of LandTrendr algorithms in investigat-ing the patterns of forest disturbance and recovery (e.g. Fragal et al., 2016; Cohenet al., 2018). Furthermore, trends in mining-induced LC change based on GEE-LTwere successfully tracked in Richards Bay Minerals Site, South Africa (Dlamini andXulu, 2019), and in Central East Queensland, Australia (Yang et al., 2018). Thecombination of LandTrendr-based indices and Landsat time-series data allowed forthe analysis of cropland conversion patterns in a 10-year time span around Dongt-


Figure 2.2: Conceptual model of LandTrendr fitting spectral index (e.g., NDVI)values to spectral-temporal segments for spatio-temporal dynamics of a pixel un-dergoing disturbance, recovery, and stability in 21 years. The first temporal segmentstarting from the first vertex to the second vertex illustrates the original model witha sequential and slight change. The model is fitted to a no change event. From thesecond to the third vertices, the pixel underwent a great disturbance, translatingto an important land cover change, followed by a recovery period (from third tofourth vertices). The last land cover change processes in the same pixel were char-acterized by stability in inter-annual variations (conceptual model adapted fromKennedy et al. (2010)).

ing Lake, China (Zhu et al., 2019), and the detection of impervious surfaces intwo urban areas of Jiangsu Province, China, including Nanjing (Xu et al., 2019)and Xinbei District (Wang et al., 2019a). Nonetheless, with respect to the use ofGEE-LT framework for analyzing urban LULC dynamics and change trajectories,available research is scanty and limited. As such, the successful application of theGEE-LT framework in data scarce areas as in SSA, may prove of immense value foridentifying urban LULC change, especially when taking under consideration thatVHR satellite images are expensive and less affordable by local urban planninginstitutions. Additionally, it is hard to acquire continuously and annually fit-for-purpose optical images for regular monitoring of land cover dynamics, given thatatmospheric attenuations sometimes affect the quality of data acquired from optical


sensors.Other widely used EO-based time series algorithms for land change processes

include, but are not limited to:

• (i) Breaks For Additive Season and Trend (BFAST) (Verbesselt et al., 2010)implemented for seasonal trends analysis;

• (ii) Continuous Change Detection and Classification (CCDC) (Zhu and Wood-cock, 2014), which was found to be worthwhile for long-term and gradualchange detection;

• (iii) Time-Weighted Dynamic Time Warping (TWDTW) (Maus et al., 2016),which consists of comparing temporal similarities of known seasonality ofa land cover event with unknown time series, and finding optimal alignmentbetween them through dynamic space-time classification; (Huang et al., 2010);

• (iv) TimeSat designed for seasonal trends monitoring of land surface processestaking into account the seasonal parameters (Eklundh and Jönsson, 2016).

Time-series change detection algorithms may not produce similar results, evenif using the same input data. Some algorithms are hard to implement and more de-manding in terms of computation requirements. Therefore, users should be cautiousabout the choice, and the selection should be based on the application domain, sup-porting computational platform and study objectives. The common characteristicsfor all times series for land change are large number of missing data (Kandasamyet al., 2013) which each algorithm tackles differently.

The performance of EO time series algorithms heavily depends on their accom-modating computational platform and parameters to be taken into account. Tradi-tional standalone architectures and personal computers may not efficiently handleadvanced EO big data processing tasks (Camara et al., 2016). Recent technolog-ical advances have enhanced the computation performance through server-clientapplication platforms or through the shift to cloud computing. The cloud-basedprocessing platforms allow users the possibility to interact with EO data without in-teraction with back-end computing and data management infrastructures (Giulianiet al., 2020), and it is also the case for GEE-LT architecture. While consideringthe standpoint of ease of use and the development maturity, GEE was identified asone of the best options for big EO data management and analysis (Gomes et al.,2020).

2.4 Spatio-temporal urban LULC change through the lensof EO

Urban extent classification and LULC mapping is one of the forefront applicationdomains of remote sensing. Mapping and extracting information related to urbanextent and urban LULC change analysis are quite challenging tasks, given the


complexity of the urban landscape. This complexity is pertinent to a mixture ofurban structures and complex spatial patterns of LULC. Therefore, appropriate androbust methods are highly needed for continuously monitoring the spatio-temporalurban LULC change as well as for making informed decisions related to sustainableurban development (Kabisch et al., 2019; Acuto et al., 2018).

Urban LULC change analysis can be performed as uni-temporal, bi-temporalor multi-temporal image analysis. With the current software and algorithms de-velopment, and the availability of remotely sensed time series data from EO satel-lites, remote sensing and Geographic Information Systems approaches, coupled withstatistical methods are used extensively in various LULC change detection appli-cations. Seen through the lens of remote sensing, change detection is defined asthe process of identifying differences in the state of an object or phenomenon byanalysing a pair of images acquired on the same geographical area at different times(Macleod and Congalton, 1998; Singh, 1989). According to Lu et al. (2004), thereare two categories of LULC change detection analysis: one is pre-classification, alsoknown as “binary” change detection, which only detects change and non-change,and the other is post-classification change detection known as “from-to” changedetection, which detects the trajectories of LULC changes, usually based on de-tailed classification results. The second change detection approach is based on therectification of more than one classified images independently, and the generationof thematic maps followed by a comparison of the corresponding labels to iden-tify areas where change has occurred (Al-doski et al., 2013; Lu et al., 2005, 2004;Asokan and Anitha, 2019). In all circumstances, it is emphasized that good prac-tices in change detection research should provide the information not only on thechanged area and change rate, but also the spatial distribution of changed types,change trajectories of LULC types as well as the accuracy assessment of the changedetection results.

A growing body of literature related to spatio-temporal urban mapping, LULCclassification, and change detection analysis was inventoried. E.g. Fichera et al.(2012) carried out a change detection analysis in Avellino, Italy from 1954 to 2004using multi-source imagery including aerial photographs, ancillary data and Land-sat images. Local landscape patterns were investigated by evaluating landscapeindices measured along the two transects (W-E and SW-NE directions) convergingin the core urban area of the Avellino City. According to the authors, signifi-cant LULC conversions were detected and it was revealed that urban LULC hadquintupled from 1954 to 2004. A comparative study on urbanization impact wassuccessfully conducted in South Asia in the rapidly urbanizing cities of Mumbai(India), Colombo (Sri Lanka), Karachi (Pakistan), and Dhaka (Bangladesh) basedon Sentinel-2 data, LM, urban-rural gradients and grid-based methods (Ranagalageet al., 2021). Using Stockholm, Sweden as a case study, Furberg and Ban (2021)investigated the utility of a multiscale based approach to monitor urban LULCchange and associated environmental impact. Their findings illustrated that theuse of multiresolution satellite data is worthwhile for identifying multiple sociallyand ecologically relevant scales of urban change analysis. Haas and Ban (2014) eval-


uated urbanization and associated environmental impacts in China,s three largestand most important urban agglomerations: Jing-Jin-Ji, the Yangtze River Deltaand the Pearl River Delta. They determined urban indices for quantifying urbanexpansion based on classified Landsat and HJ-1A/B images. The classified re-motely sensed images were further transformed into ES and a monetary approachwas implemented to determine changes in ES values. Similarly, a comparative studyon satellite monitoring of urbanization between Stockholm, Sweden and Shanghai,China was further conducted by Haas et al. (2015) using Landsat images from1989 to 2010. The impact of urbanization on ES supply was investigated and amonetary approach was used for quantifying the value of ES in the study areas.Furberg and Ban (2012) studied urban LULC change detection using pixel-basedSVM classification in the Greater Toronto Area, Canada between 1985 and 2005.A LM analysis was performed based on classified Landsat images for characterizingthe landscape patterns in the area which indicated highly dominant fragmenta-tion in both agricultural and low density built-up areas. Herold et al. (2003b)applied OBIA on VHR images coupled with texture analysis for LULC assessmentin Santa Barbara, California, USA. Similarly, the spatial and temporal dynamicsof urban sprawl in Guangzhou, China was characterized by Yu and Ng (2007) bycombining Landsat TM images, landscape metrics and urban-rural gradient analysisalong two selected transects. Post-classification land cover change detection anal-ysis using multi-temporal Landsat images was carried out by Yuan et al. (2005)in seven-county Twin Cities Metropolitan Area in Minnesota. Güler et al. (2007)studied land cover change in Samsun, Turkey between 1980 and 1999 using hy-brid classification and post-classification on Landsat images. While analyzing theurban expansion in Isfahan, Iran between 1956 and 2006, Soffianian et al. (2010)used Landsat images for intermediate change detection analysis in residential ar-eas between 1975 and 2001. Both Normalized Difference Built-up Index (NDBI)and NDVI were introduced in their analysis for separating residential and agricul-tural areas. Zhang and Ban (2010) coupled Vegetation-Impervious-Surface modelwith Tasseled Cap Transformations (which highlights the characteristics of features,

brightness, vegetation and greenness) for monitoring impervious surface sprawl inthe greater Shanghai Area, China. They specified two classes in the classified im-ages including impervious surface sprawl and non-impervious surface sprawl. Intheir results, at least 90% producer,s and user,s accuracies were achieved with akappa of 0.84 after Iterative Self-Organizing Data Analysis unsupervised classifi-cation of Landsat images. The high producer,s and user,s accuracies of classifiedimages are dependent on both input images and classification methodology includ-ing image processing and classification methods. Furberg and Ban (2013) assessedthe spatio-temporal urban LULC changes in Stockholm, Sweden between 1986 and2006 using an object-based and rule-based approach with multi-resolution SPOTimages and successfully performed change detection with seven land cover classes.Haas and Ban (2017) fused HR multispectral Sentinel-2A MSI (10m) with HRactive Sentinel-1A SAR (5x20 m resolution) for objected-based urban LULC classi-fication in Zurich, Switzerland and high quality classification results were achieved


with about 80% accuracies.In East Africa, studies using Landsat data as input for urban LULC mapping

are limited, despite free access to raw data. However, successful examples of satel-lite based urbanization monitoring do exist. Typical ones include LULC monitoringusing multi-sensor satellite data in Nakuru, Kenya by Mubea and Menz (2012), andurban growth pattern development in Kampala, Uganda between 1989 and 2010(Vermeiren et al., 2012). Furthermore, Abebe (2013) quantified urban growth pat-terns in Kampala, Uganda using Landsat imagery and LM. Remote sensing effortsusing medium-resolution data include the analysis of LULC change dynamics inKigali, Rwanda utilizing the temporal stationarity of changes among LULC cate-gories(Akinyemi et al., 2017), as well as measuring continuous urban developmenttracking and urbanization effects on landscape change and ES (Mugiraneza et al.,2019a, 2020). Moreover, Basnet and Vodacek (2015) carried out a LULC track-ing study in the cloud prone Lake Kivu region by combining Landsat imagery andancillary data. Change detection based on Landsat and LM techniques was alsoperformed by Kayiranga et al. (2016) targeting the fragmentation and spatial pat-tern change around and in Nyungwe-Kibira Park located in south-west of Rwanda.Finally, LULC and environmental change analysis in a cloud prone area of the cen-tral Albertine Rift in the north-western Rwanda was performed by Akinyemi et al.(2017).

With the emergence of HR data, numerous researchers in urban remote sens-ing focused on mapping and detecting urban features at fine scale. E.g. Pu et al.(2011) tested the performance of object-based and pixel-based methods applied onIKONOS data for detailed urban LULC classification in Tampa Bay, Florida, USA.Their findings revealed that an objected-based approach outperformed a pixel-basedmethod especially when applying advanced classifiers such as Artificial Neural Net-works. Kuffer et al. (2014) combined spectral information extracted from Quick-bird and IKONOS imagery with LM indices for quantifying the morphological dif-ferences in urban planned and unplanned areas in New Delhi, India and Dar esSalaam, Tanzania. Based on the homogeneous urban patches derived from seg-mented HR images, LM indices and multi-criteria evaluation, the success of theirdeveloped urban settlement index was confirmed with more than 70% accuracy.Kohli et al. (2016) developed an objected oriented and rule-based method for slumdetection using VHR QuickBird imagery over Pune, India. Their method consistsof integrating expert knowledge with hierarchical classification and resulted in slumdetection with 60% agreement comparing to reference ancillary data. Building foot-prints extraction based on Pléiades multispectral data and elevation information,were successfully detected and mapped in Kigali, Rwanda using an SVM classifier(Bachofer and Hochschild, 2015). Kuffer et al. (2016) tested the utility of Gray-Level-Co-occurence Matrix (GLCM) variance to classify slums and planned areasusing a VHR image in Mumbai and Ahmedabad, India and Kigali, Rwanda, re-ceptively. By applying a pixel-based RF classifier, more than 85% of the slumareas were correctly detected. Kit and Lüdeke (2013) illustrated the usefulness ofcombining Canny and Line-Segments-Detection algorithms for slum detection in


Hyderabad megacity in India. Furthermore, mapping and analyzing informal andplanned settlements in the urban environment using HR data and object basedapproaches was well established in different areas of SSA, such as Voi Township,Kenya (Hurskainen and Pellikka, 2004) Kibera ward in Nairobi, Kenya (Veljanovskiet al., 2012), Kisumu in Western Kenya (Mathenge, 2011), and Cape Town, SouthAfrica (Hofmann et al., 2001), to name a few. Even though VHR remote sensingbased methods for urban LULC mapping and urban feature detection are gettingpopular among the scientific community, there is still the challenge of data afford-ability, given that most of the HR image products are commercial and the priceper unit is still high.

2.5 Remotely sensed data for urbanization environmentalimpact analysis

Measurable environmental variables can be a promising baseline for establishing aframework for enhancing environmental services through restoration of deterioratedecosystems. Previous studies illustrated the potential of using LM and ES in eval-uating the urbanization effects on environmental impact in both core urban andsurrounding metropolitan regions. In sections 2.5.1 and 2.5.2, LM and ES based onclassified satellite images are respectively discussed as promising and cost-effectiveindicators for urbanization monitoring.

2.5.1 Landscape structure change analysisThe structure and spatial pattern of landscape dynamics are easily quantified andevaluated using measurable variables. According to McGarigal et al. (2002), spatialstructure and patterns can be quantified in a variety of ways. Broadly considered,their analysis involves four basic types of spatial data corresponding to differentrepresentations of spatial heterogeneity including (1) spatial point patterns rep-resenting collections of entities where the geographic locations of the entities areof primary interest; (2) linear network patterns indicating collections of intersectedand networked linear landscape elements; (3) surface patterns illustrating quantita-tive measurements that vary continuously across the landscape and (4) categorical(or thematic) map patterns portraying data in which the system property of inter-est is represented as a mosaic of discrete patches. One of the popular methodologiesfor analyzing the spatial structure, and urban landscape fragmentation is the use ofLM. LM are originally rooted in the field of landscape ecology and were designed toquantify and characterize landscape configuration and composition (Gardner andO,Neill, 1991; McGarigal et al., 2002). The LM methodology consists of derivingindices on LULC maps using one (or more) of the six metrics methods namelyedge-area, shape, core area, contrast, aggregation and diversity metrics (McGarigalet al., 2002). Metrics can be derived at patch, class (patch mosaic) and landscape


levels. Figure 2.3 illustrates the hypothetical landscape change in four time periods,where spatio-temporal urban inclusion is contributing to green space fragmentation.

Figure 2.3: Landscape structure in four different periods. The urban area patchesare increasing in Time 2, whilst the size of green space patches is reducing. In Time4 urban patches are highly aggregated and coherent, whereas green space patchesare highly divided and fragmented (adapted from McGarigal et al. (2002))

Note that a patch is understood as a single entity, characterized by the sameLULC type and homogeneous environmental conditions, whilst the patch mosaic isdefined as a group of patches disaggregated in different categories (classes). At thelandscape level, the indices are calculated taking into account the landscape as awhole without making a distinction between patch mosaic and patch itself.

Hundred of indices for characterizing the landscape evolution can be derivedusing LM. An overview on their use is presented in Uuemaa et al. (2009). Theabove-mentioned overview reiterated that some LM indices are correlated to eachother, while others are used for a goal oriented analysis. Hence, the choice of LMvariables is motivated by their importance in characterizing the landscape in theselected case study. A number of LM indices were reported useful for analyzingspatial pattern of urban landscape evolution such as patch diversity and richness,landscape fragmentation, and connectivity analysis (Seto and Fragkias, 2005; Weng,2007; Jain et al., 2011). Furthermore, several researchers found that various LMhave potential for analyzing spatio-temporal urban landscape change (e.g. Abebe,2013; Herold et al., 2002; Akın and Erdoğan, 2020; Dadashpoor et al., 2019). Indeed,the analysis of spatio-temporal change in the landscape is a visually depicting thelandscape fragmentation and structure change. For instance the expansion of urbansprawl as a function of landscape configuration is illustrated in Figure 2.3.

Several studies integrating the LM in urban environmental monitoring have beencarried out. For instance, Furberg et al. (2020) established the link between LMindices with ES for evaluating the impact of urban growth on green infrastructureand urban ES provision, in Stockholm, Sweden, between 2005 and 2015. Haasand Ban (2014) used a core set of five LM indices namely, Mean Patch Area,Largest Patch Index, Percentage of Landscape, Landscape Shape Index, Numberof Patches for characterizing landscape pattern change and fragmentation in China,sthree largest urban agglomerations: Jing-Jin-Ji, the Yangtze River Delta and thePearl River Delta from 1990 to 2010. In a similar fashion, five metrics including


the percentage of landscape, aggregation index , Shannon,s diversity index, largepatch index, and patch density were used by Madanian et al. (2018) for analyzingthe urbanization effects on land surface temperature variation in Isfahan city, Iranfrom 1985 to 2015. Shukla and Jain (2019) developed an interlink between LM,gradient analysis and density index for modelling urban sprawl in Lucknow city,India. Their findings emphasized the utility of combining LM indices with gradientanalysis in modelling urban growth trajectories.

2.5.2 LULC change impact on urban ecosystem servicesThe Millennium Ecosystem Assessment and The Economics of Ecosystems and Bio-diversity Foundations defined ES as the benefits provided by ecosystems, includingprovisioning, regulating, cultural, and supporting services (Reid et al., 2005; Millen-nium Ecosystem Assessment, 2005; Kumar, 2012). According to (De Groot et al.,2012; Sukhdev and Kumar, 2008), provisioning services include goods obtainedfrom ecosystems amongst others products such as food, fuel and timber, whereasregulating services imply benefits obtained from ecosystem processes such as cli-mate regulation and disease control, air quality purification, carbon sequestrationand storage as well as moderation of extreme events, among others. Habitat sup-porting services imply ecological functions underlying the production of ES suchas nutrient cycling, genetic diversity maintenance, whilst cultural services denoteintangible benefits from the nature interaction such as aesthetic appreciation andinspiration for culture, recreation and ecotourism. According to Gómez-Baggethunet al. (2013), urban environments are the concert hub of both ES and disservicesareas. The most occurring ES in urban areas are summarized in Figure 2.4.

Figure 2.4: Most occurring urban ecosystem services. Source: Gómez-Baggethunet al. (2013)

Satellite based ES analysis consists of classifying LULC and deriving the EScorresponding to each of the proposed LULC categories. The analysis of urban


ES includes their inventory and valuation. Meanwhile, satellite-based ecosystemvaluation consists of quantifying the areas occupied by each LULC category andestimating the ES value associated to that category. The valuation of ES consistsof determining either absolute monetary or relative value of the services offered byecosystems (Costanza et al., 1997; Sukhdev et al., 2010). Various approaches areused for ecosystem services valuation (ESV), and the most cited methods includesocio-cultural based valuation, economic cost-benefits analysis, and ecological re-silience based valuation (Kumar, 2012; Millennium Ecosystem Assessment, 2005;Costanza et al., 1997). Previous studies illustrated the potential of remotely senseddata, Geographic Information Systems and spatial mapping in evaluation of ES.For instance, Feng et al. (2010) revealed that classified remote sensing data areworthwhile in quantitative assessment of LULC, biodiversity, and carbon, waterand soil related ES. As such, spatially explicit and quantitative based estimatesfor ES evaluation with classified LULC maps can be derived from remote sensedimagery as illustrated by Andrew et al. (2014) Burkhard et al. (2009), Burkhardet al. (2012), and Haas and Ban (2017).

Chapter 3

Study Area and Data description

3.1 Study area

Kigali the capital of Rwanda in the Central-East African region. Geographically,it is located slightly South of the Equator at 1°56′38′′S and 30°03′34′′E. Kigaliis Rwanda,s capital and largest city, being the country,s most important businesscentre and main port of entry. Its population is estimated at 1.135 million residingin an area covering 730Km2 (NISR, 2012). The city is composed of three districtsnamely Gasabo, Nyarugenge and Kicukiro. The geographic location of the studyarea in East African region is shown in Figure 3.1.

Kigali was originally composed of a small neighborhood around Nyarugengeplateau and Nyamirambo extending over just 0.08 Km2 with 357 inhabitants. Itwas declared the capital city of Rwanda in 1960 with an urbanized area of 2.5Km2

and an estimated population of approximately 6000 (Dalmasso, 1987). Since 1960,and until the administrative restructuring of 1991, Kigali was part of the thenKigali-Ngali Province while the administrative entity under the urban regime wasonly concerning Nyarugenge municipality. The build-up area was extended withina limited number of sectors while the largest part of the city remained rural. From1991 until 2002, the city expanded, composed of three municipalities, namely Nyaru-genge, Kacyiru and Kicukiro. The 2002 administrative restructure extended the cityboundary by incorporating the entire former Butamwa municipality and a big partof the former Rubungo and Gikomero municipalities. Since the last restructure in2006, the city extents over 730 km2, shared among three districts, Gasabo, Kicukiroand Nyarugenge with a population of 1.3 million (NISR, 2012). In Figure 3.2, Iillustrate the spatial and demographic evolution of Kigali since its creation in 1907until the last General Population and Demographic Census in 2012.

Kigali,s LULC is composed of built-up areas fulfilling different urban functions;green space composed of forest, open vegetated land, agriculture, and wetlands.The proportion of the population living in unplanned settlements is estimated at79% (Hitayezu et al., 2018). However, these areas present diversity in terms of

27

CHAPTER 3. STUDY AREA AND DATA DESCRIPTION 28

Figure 3.1: Geographic location of Kigali in Rwanda. The left side map showsRwanda with its bordering countries and location of Kigali and Rwandan provinces.In the right side, Sentinel-2 MSI with false color composite display (Near-InfraredRed, Red and Green) is used for illustrating Kigali with its three districts

basic infrastructures and service provision. Bare lands are scattered throughoutdifferent corners of the city especially in the urban fringe and rural areas of the cityand consist of land under construction and uncovered soil. Two large wetlands ofNyabugogo and Nyabarongo are localized respectively along the western and south-ern city boundary and small valleys are separating hills across the city. From thesouth-west to N-W, Kigali is mainly covered by forest plantation composed of Euca-lyptus on the highlands of Mont Kigali and Jali plateau. More than 60% of the citylandscape is still rural, where agriculture dominates the livelihood activities. As arapidly growing city, constructions and various urban (re)development activities arecontinuously increasing. There is an increasing demand for land in peri-urban areasfor housing and industrial development and other non-agricultural activities. Parcelsubdivisions, expropriation, peri-urban LULC conversion and land tenure changeare continuously taking place though selling, buying, and urban (re)development.The demolition of existing constructions, skyscrapers edification, densification andrenewal of road networks are among prominent urban redevelopment measures.


Figure 3.2: Spatial and demographic evolution of Kigali City. Source: (NISR, 2012)and (Michelon, 2012)

3.2 Data description

3.2.1 Satellite imageryOptical multispectral and multiresolution satellite images were used for investigat-ing the spatio-temporal urban LULC changes and associated environmental impactin Kigali, Rwanda for a period of 37 years between 1984 and 2021. Landsat im-ages were used for pixel-based classification, whilst Sentinel-2 MSI served for LULCextraction and classification using hybrid ML methods involving the combinationof DL and pixel based random forest classification. VHR WorldView-2 data werefurther used for object-based LULC classification and analysis. A Digital Eleva-tion Model (DEM) with 30m resolution was further retrieved and included in theprocessing chain in all four papers. The DEM was produced by the Shuttle RadarTopographic Mission (SRTM) through a joint effort of NASA, the National Geospa-tial Intelligence Agency, and the German and Italian Space Agencies (Farr et al.,2007). Since 2018, this elevation dataset can be accessed via Google Earth Enginecatalog. All acquired data were projected in Universal Transverse Mercator (UTM)with the WGS-84 datum. Table 3.1 illustrates the acquisition date, sensor name,and spectral and spatial resolution of EO data used in the study.


Table 3.1: Overview and specifications of used multispectral dataPaper EO mission BWL (in nm) Spatial resolution Acquisition date

I

Landsat-5 TM

Blue: 450-520

30 m

1984-06-20Green: 520-600 2009-06-25Red: 630-690NIR: 760-900SWIR1: 1550-1750SWIR1: 2080 - 2350

Landsat-7 ETM+

Blue: 0.450-0.520RGB, NIR and SWIR: 30 m;

2001-08-030

Green: 520-600Red: 630-690

PAN: 15mNIR: 760-900SWIR1: 1550-1750SWIR1: 2080 - 2350PAN: 520-900

Landsat-8 OLI

CA: 433-453RGB, NIR and SWIR: 30 m;

2016-06-28

Blue: 450-515Green: 525-600

PAN : 15 m

Red: 630-680NIR: 845-885SWIR 1: 1560-1660SWIR 2: 2100-2300PAN: 0.50-0.680

II Annual composites of Landsat time series

RGB, NIR and

30 m 1987-2019SWIR with cross-sensors, harmonizedspectral wavelengths

III WV-2

Blue: 450-510 RGB, NIR, Yellow andRed-edge: 2 m;

2016-05-17

Green: 510-580 PAN : 0.5 mYellow: 585-625Red: 655-690Red-edge: 705-745NIR-1: 780-920NIR-2: 860-1040PAN: 400-900

IV Composites of Sentinel-2 MSI

Blue: 458-523RGB and NIR: 10 m; 2015-10-01-2016-04-30Green: 543-578

Red: 650-680

Red-edge and SWIR: 20m 2020-10-01-2021-04-07

NIR: 785-900Red-edge 1: 698-713Red-edge 2: 734-748Red-edge 3: 765-785Red-edge 4: 855-875SWIR-1: 1565-1655SWIR-2: 2100-2280

BWL: Proposed input bands and wavelengths specifications; RGB: Red Green, Blue; NIR: NearInfrared; SWIR: Shortwave-infrared; PAN: Panchromatic band

In Paper I, the LULC data was derived from four Landsat images with 30mresolution downloaded from the United States Geological Survey (USGS) resourcerepository (https://earthexplorer.usgs.gov/). The first image was acquired on20th June 1984 by the Thematic Mapper (TM) sensor on board on Landsat-4. Thesecond image was taken by the Enhanced Thematic Mapper Plus (ETM+) mountedon Landsat-7, on 30th August 2001, and the third image was acquired using theTM of Landsat-5 on 25th June 2009. The Operational Land Imager (OLI) on boardLandsat-8 was used to acquire the fourth image on 28th June 2016. The selectedbands for LULC classification included parts of the visible (red, green and blue)


and infrared (one near-infrared and two short-wave infrared bands) spectrum. Allimages were taken on satellite track path/row 172/061. Dense Landsat time seriesused in Paper II were acquired from the GEE data catalog.

In Paper III, the LULC was extracted from VHR WV-2 data. The WV-2imagery was acquired on 17th May 2016. The WV-2 satellite is a VHR space-bornesensor launched in 2009 with eight multispectral bands ranging from blue to thenear infrared parts of the electromagnetic spectrum and one panchromatic band(450-800 nm), The spatial resolution of WV-2 multispectral bands is 2m, whereaspanchromatic band is 0.5m spatial resolution. The sub-set of WV-2 used image inpaper III covers an important part of Kigali City in the central South-West andEast. Its extent approximates to 281.38 km2 and all LULC categories observed inKigali are represented.

In Paper IV, Sentinel-2 MSI time series were acquired for extracting and classify-ing urban LULC and impervious density for 2016 and 2021 respectively. Sentinel-2MSI is a European Space Agency satellite mission. It is a wide-swath, multi-spectralimaging mission supporting the Copernicus Programme for land monitoring stud-ies (European Space Agency, 2015). The images were acquired and pre-processedin Google Earth Engine cloud computing environment. Median composite imagerywas pre-processed in GEE to reduce atmospheric attenuations and retain high qual-ity information. Among the 13 bands of Sentinel-2A MSI, only ten bands at 10mand 20m spatial resolution were considered and three bands at 60 m spatial resolu-tion were excluded. The 2016 median composites were acquire between 1st October2015 and 30th April 2016, whereas the 2021 images were acquired between 1st Oc-tober 2020 and 7th April 2021.

3.2.2 Ancillary dataApart from the above-mentioned multispectral and multi-temporal images, someancillary data were also used for validation and cross-checking of the results. GoogleEarth imagery covering the same study area was used as a reference dataset tocollect validation samples and perform a visual inspection of the temporal changeof LULC. Additionally, some field data such as Global Positioning System (GPS)points were collected for verification and cross-checking with data extracted fromGoogle Earth imagery.

3.2.3 LULC classification schemeThe proposed LULC classes are illustrated in Table 3.2. As HR data were usedin Paper III, LULC was classified at a more detailed spatial scale compared tothe specified classes in other papers. Thus, the number of classes were increasedfrom seven to eleven by subdividing wetlands into flooded and drained wetlandsand introducing the road network categorized into paved and unpaved roads, andurban green space (UGS).


Table 3.2: Scheme of proposed LULC classes

LULC class Description

1. High density built-up area (HDB)Built-up area with congested buildings, multistorybuildings and warehouses

2. Low density built-up area (LDB)Built-up area with ventilation space especiallyhigh standing zones, zones and in rural areas

3. Informal settlementsCongested small buildings with irregular shape,absence of both ventilation space andgreen structures

4. Paved roadLinear features corresponding to tarmac road,paved parking areas and airport land line

5. Unpaved roadLinear features corresponding to road withunpaved surface

6. Urban green space (UGS) Gardens and golf courses with trimmed lawn7. Bare land Uncovered, permissive land/soil

8. CroplandCultivated zones occupied with post-harvestfields, pasture lands either perennial orseasonal crops, post-harvest fields, pasture lands

9. Upland agriculture

Semi-humid and dry low lands occupied byperennial crops (especially sugar cane) andseasonal crops (especially irrigated rice,vegetables and fruits)

10. Lowland agriculture

Semi-humid and dry lowlands localized invalleys and occupied by perennial crops(especially sugar cane) and seasonal crops(especially irrigated rice) and vegetables

11. ForestForest plantation with dense canopy structureand forest with low canopy structure

12. WetlandPermanent flooded low lands with aquaticflora mainly papyrus

13. WaterLakes and water bodies naturally created orman-made water bodies including fish pondsand swimming pools

Chapter 4

Methodology

The proposed methodology followed a number of steps including data preparationthrough pre-processing, feature crafting such as GLCM features computation andspectral indices derivation. The results were presented using statistics and LULCmaps, impeviousness density maps, and urban and green density indices maps. Theenvironmental impact analysis was performed using LM analysis and the evalua-tion of ES. Figure 4.1 illustrates the categorization of the papers, and analyticalcomponents in a multi-temporal and multi-resolution framework.

Figure 4.1: Categorization of the papers, analytical components in multi-temporaland multi-resolution framework

Paper II and Paper III were exclusively dedicated to the analysis of urbanLULC patterns and composition, whereas the analysis of environmental impactusing LM and ES were developed in Paper I and Paper IV. In Paper I, LM andES were independently analyzed, whilst in the Paper IV, the linkage between LMand bundles of ES was established for measuring the urbanization environmentalside-effects. Figure 4.2 illustrated the processing chain for LULC extraction and

33

CHAPTER 4. METHODOLOGY 34

classification.

Figure 4.2: Overview of the methodology used for LULC classification.

4.1 Data pre-processing

In Paper I, each of the four multi-temporal Landsat images, originally downloadedin separate files from the United States Geological Survey (USGS) resource repos-itory, were first stacked and bands were assembled into a single TIFF file. Each ofthe four images was spatially and spectrally subset using the study area boundingbox. Linear histogram stretching and different band combinations were performedallowing on-screen visual inspection of spatial LULC distribution. The two Landsatimages that served as a baseline classification in Paper II were co-registered usingautomatic registration performed in ENVI 5.3 software. L8-OLI was consideredas a base image for co-registering the 1987 L5-TM. A cross-correlation matching


method and 0.2 minimum matching score were proposed for automatic tie pointsgeneration. The image transformation was set as a geometric model, whereas thefirst-order polynomial transform was chosen as the transformation model. In total,100 tie points were proposed, and the ones with more than XY pixels, reprojec-tion errors were eliminated until we retained 24 high accurate points with a rootmean square error (RMSE) > = 0.8. In Paper III, WorlView-2 images were in tilesand were assembled using sticking and then a seamless mosaicking was performedthrough PCI Geomatica software. In Paper IV, the 20 m spatial resolution bands ofSentinel-2A MSI were pan-sharpened and resampled to 10m resolution to enhanceconsistency with the initial 10 m bands using the Gram-Schmidt pan-sharpeningmethod (Laben and Brower, 2000) implemented in ENVI 5.3 Software. The 10m NIR was considered as the reference pan-sharpening band for simulating andmerging the 20m lower resolution bands to a higher spatial resolution.

4.2 Data processing

4.2.1 Texture analysis with GLCMTexture analysis with GLCM features was performed and later integrated in theclassification in all four papers. GLCM is a texture measurement based on second-order statistics for image texture analysis. It quantitatively describes the probabil-ity of relationships between the brightness values of neighboring pixels at a distanceand is orientation invariant within the image (Haralick et al., 1973). GLCM allowsthe computation of the joint probability of two pixels which have particular (co-occurring) gray-level values, with a distance ( pixels in a dimension) along a givendirection (00, 450, 900, and 1350). Fourteen parameters can be derived from eachGLCM, which collectively reflect the homogeneity, local contrast, correlation, vari-ance and complexity of an image (Zhang and Ban, 2010). The output consists ofa single two-dimension raster layer containing derived measurements for all pixelsand may be input into further analysis (Hall-Beyer, 2017). Among the 14 GLCMtexture measures proposed by Haralick et al. (1973), only mean, variance, contrast,entropy, standard deviation,and angular second moment were computed on Green,Red and NIR bands of Landsat (in Paper I and Paper II) and Sentinel-2 images (inPaper IV). With regard to co-occurrence pixel joint probabilities calculation, thechosen direction was invariant (00). The kernel size was chosen based on successfulstudies on GLCM computation in urban areas and data resolution. For Landsatbased analysis an 11 by 11 Kernel size was empirically determined as most suitable,whilst 5 by 5 kernel size was used for Sentinel-2 data in Paper IV. The proposedfeatures were identified as the best performing GLCM texture measures for gen-erally improving the LULC classification while combined with the spectral bands(e.g. Lu et al., 2014; Shaban and Dikshit, 2001; Wentao et al., 2014). In Figure 4.3,I illustrate the processing chain for GLCM computation.


Figure 4.3: Illustration of GLCM features computation. The diagram A illustratesthe image central pixel (pixel of interest) that will receive new value after GLCMcomputation and the angle direction during computation process. The diagram Brepresents various image gray value for each pixels, whilst the diagram C portraysthe calculated GLCM using 00 direction angle and distance equal to 1. Originalimage is having 8 gray levels. Pixels with 1,1 pair combination in 00 directionangle are occurring once, whilst pixels with 1,2 pair combination in the same angledirection are occurring twice. There is no pair combination with 1,3. In diagrams Dand E, a 3x3 moving window (also called kernel) is applied for calculating the newvalue in the pixel of interest using predefined GLCM measures from any of the 14statistical measurements e.g. Mean, Homogeneity, Entropy, Correlation, Variance,Standard Deviation, Contrast, etc.

4.2.2 Spectral indices derivationA number of spectral indices were computed and stacked with original bands asinput to the classification. The proposed spectral indices are captured in Table 4.1.Spectral indices derived from remote sensed data are reported to improve LULCclassification, given that they reflect the biophysical properties in the area underinvestigation (e.g. Polykretis et al., 2020; Chen et al., 2019; Bannari et al., 1995).

Additionally, they are qualified as unbiased, scalable, rapid, and quantitative ininformation extraction (Javed et al., 2021). The Normalized Difference Vegetation


Table 4.1: Proposed spectral indices derived from multispectral imagery.

Index name Equation based on bands References

Normalized Difference Vegetation Index (NIR-R)/NIR+R) Rouse et al. (1974)(NDVI)Modified Normalized Difference Water Index (SWIR-G)/(SWIR+G) Han-Qiu (2005)(MNDWI)Normalized Built-up Index (SWIR-NIR)/(SWIR+NIR) Zha et al. (2003)(NDBI)Dry Bare Soil Index ((SWIR-G)/SWIR+G))-NDVI Rasul et al. (2018)(DBSI)

Index (Rouse et al., 1974) has been the most widely used indices for characterizingvegetation cover. Modified Normalized Difference Water Index (MNDWI) proposedby Han-Qiu (2005), was proved efficient in enhancing water features extraction,while efficiently suppressing noises from interfering land cover such as built-uparea,vegetation and soil cover (Xu, 2006). Furthermore, the Normalized differenceBuilt-up Index (NDBI) is used to extract built environments condition throughthe normalized ratio of SWIR and NIR bands, and numerous studies applied thisindex for improving built-up area extraction especially in urban environments (e.g.Zha et al., 2003; He et al., 2010; Bhatti and Tripathi, 2014; Ettehadi Osgouei et al.,2019). The other used index was the Dry Bare Soil Index (DBSI) that was proposedby Rasul et al. (2018) for extracting the bare land especially in dry land areas. Thethree first indices were combined with Landsat and Sentinel-2 bands for pixel-basedLULC classification in Paper I, Paper II and Paper IV, respectively. DBSI was onlyused in Paper IV. Spectral indices were further used as object feature properties inPaper III, while performing object-based image classification. The indices valueswere computed at segment/object level.

4.2.3 Image segmentationIn Paper III, a multi-stage object-based feature extraction and classification wasperformed in a hierarchical way for extracting urban LULC at a fine scale. I,first, performed the image segmentation using the multi-resolution segmentationalgorithm implemented in Definiens eCognition software Version 9.1.2. Accordingto Baatz (2000), multi-resolution segmentation uses a region growing and mergingalgorithm based on similar spectral grouping. Previous studies illustrated thatmulti-resolution segmentation is a suitable algorithm to easily generate meaningfulsegments that are adapted to the spatial pattern of land cover distribution (e.g.Hofmann et al., 2001; Rahman and Saha, 2008). The segmentation was performedon the WV-2 visible (blue, green, red and yellow), near-infrared-1 (NIR1), near-infrared-2 (NIR2) and panchromatic bands. All bands were given the same weight(1) except NIR1 and NIR2 that were considered twice as important for maximizingthe distinction among different green structures such as forest, cropland and UGS.The segmentation parameters were empirically tested and deemed satisfactory inproducing meaningful segments. A scale parameter (SP) of 60 was empirically found


suitable for generating the segments corresponding to the spatial configuration ofland cover spectral grouping. With regard to the composition of a homogeneitycriterion, both the shape and compactness were fixed to 0.5.

4.3 LULC extraction and classification

LULC classification and extraction was carried out using various approaches includ-ing SVM and RF classification, spectral temporal segmentation using LandTrendralgorithm, U-Net DL model, regression analysis, and various GEE-based processingsteps.

4.3.1 Support Vector Machine classificationSVM models were trained and applied in Paper I, Paper II and Paper III duringthe LULC classification step. SVM is a non-parametric supervised classificationalgorithm based on statistical learning theory (Kavzoglu and Colkesen, 2009). It isused for supervised classification where it constructs a hyperplane or a set of hy-perplanes in high or infinite dimensional feature space for separating the predefinedclasses. Good class separation is achieved with the hyperplane that has the largestdistance (called margin) to the nearest training data point (called support vectors)of any class. In Paper I and Paper II, the pixel-based SVM classification was per-formed in ENVI 5.3.1, whereas object-based SVM was implemented in eCognition9.3.1 in Paper III. Note that the SVM classification in Paper II was performedfor generating baseline classification maps that were used for building the GEE-LTframework. When dealing with multiclass data that cannot be linearly separated,SVM have the possibility to use the kernel function which allows the separation ofconcerned classes in highly dimensional feature space. To do so, the Radian BasisFunction (RBF) SVM kernel type was used. The RBF kernel on two samples x andx’, represented as feature vectors in some input space, is defined as:

K(x, x′) = exp(−γ ∥ x − x

′∥

2), γ > 0 (4.1)

where:

γ = the gamma term in the kernel function for all kernel types except linearγ = 1

σ2 where σ is a free parameter

The Gamma, penalty parameter together with pyramid levels and classificationprobability threshold in RBF kernel function were left with default values of 1, 100,0, and 0, respectively. Figure 4.4 illustrates the linear SVM plotted for classifyingthe two classes based on predefined samples.

The SVM classifier was selected given that it is considered a high-quality clas-sification algorithm yielding excellent results in LC classification applications, asseveral studies have shown (Foody and Mathur, 2004; Huang et al., 2002; Niu andBan, 2013; Shao and Lunetta, 2012).


Figure 4.4: Illustration of SVM with linearly separable data. Adapted fromSheykhmousa et al. (2020)

4.3.2 LandTrendr-Google Earth Engine based predictionThe GEE-LT framework was used for predicting continuous and dynamic LULCchange trajectories in Paper II. Prior to GEE-LT implementation, baseline andkeyframe LULC maps for the predefined starting and ending time periods weregenerated. The RBF was chosen while generating LULC maps using pixel-basedSVM. Five classes were considered including an urban class composed of high andlow-density built-up areas, impervious surfaces, paved road networks, airports, andparking lots; open land constituted by areas occupied by cultivated lands, grassland, urban green spaces, and bare land; forest corresponding to native montaneforests, secondary (derived) forests, and forest plantations; wetlands correspondingto low land dominated either by permanent flooded zones with vegetated cover orseasonally flooded low land occupied by cropland. The fifth proposed LULC classwas water described as permanent water bodies such as lakes, fish ponds, and waterchannels.


Landsat images were seamlessly stacked in GEE and the LandTrendr algorithmwas used for spectral-temporal segmentation. Spectral-temporal metrics were con-structed based on Landsat time series acquired from 1987 to 2019 using L5-TM,L7-ETM+, and L8-OLI sensors. Landsat annual collections were constrained tothe date range from 1 September to 30 June of the following year. This time spancorresponds to the two main rainy seasons, and the short-term dry season in thestudy area. The optimal LULC representation during the proposed time rangecould be effectively inventoried. To trace the LULC changes, we proposed the useof a combination of six indices/bands, including red and shortwave infrared (SWIR)bands, NDVI, NDMI, Tasseled Cap Greenness (TCG), and Tasseled Cap Wetness(TCW).

Figure 4.5: Processing chain for progressive LULC prediction, and area estimateand accuracy assessment. The year of detection (YOD), the change duration(DUR), and change magnitude (MAG) are combined with a change map derivedfrom two baseline classifications for continuous LULC reconstruction.

The selection of these bands and indices for spectral-temporal segmentationwas first based on visual inspection of the LandTrendr results. The best candidateindices/bands were those that were able to highlight more changing patterns andthose that were believed to capture valuable information related to the land surfaceproperties in the study area. The NDVI was selected given that it is the most


widely used index for LULC assessment (e.g. Tucker et al., 1985; Gamon et al.,1995). NDVI was also previously proved worthwhile in greenness proxy monitoringin urban and peri-urban environments (e.g. Bhandari et al., 2012; Li et al., 2017).The NDMI index is a normalized ratio between NIR and SWIR (NDMI = (NIR-SWIR)/(NIR+SWIR), and it was found to be useful for tracking changes in plantbiomass (Hardisky et al., 1983). We assumed that cropland conversion to imper-vious surfaces is associated with a decrease in the NDMI value. Furthermore, thetwo features of the tasseled cap transformation (i.e., TCG and TCW) were foundto be valuable for dimensionality reduction with minimal information loss (Kauthand Thomas, 1976), and they were previously applied for enhancing urban LCchange analysis (e.g. Haas and Ban, 2014; Seto et al., 2002; Torbick and Corbiere,2015). For TCG and TCW computation, I adopted cap transformation coefficientsdefined for reflectance data, as proposed by Crist (1985). The proposed transforma-tion coefficients were successfully adopted in previous LandTrendr-related studies(e.g. Cohen et al., 2018; Kennedy et al., 2010) involving the use of annual collec-tion of stacked Landsat time series. Figure 4.5 illustrates the proposed workflowfor GEE-LT for reconstructing LULC change trajectories and applying the Area2

methodology to estimate the area and accuracy assessment.A novel methodology to integrate spectral bands and multiple indices derived

from a collection of Landsat time-series with two keyframes maps was proposed forlabeling the LT-detected changes from 1987 to 2019 as illustrated in the Figure 4.5.

4.3.3 OBIA rule-based classificationA multistage and hierarchical object extraction and classification framework wasdesigned and implemented for enhancing class separability and refining the OBIAbased SVM classification as shown in Figure 4.6. The framework involves a num-ber of processing steps including i)OBIA SVM based classification, ii)filtering theclassification using geometric and spectral thresholding, iii)integration of computedurban density and greenness density indices in the classification using logical andarithmetic operators and iv)feature based extraction. The rules were constructedbased on the attributes of the objects and features. The selected features includedthe statistics of the WV-2 bands, object,s geometry and shape, such as the asym-metry, rectangular fit, and compactness. The lowland agricultural class was delin-eated based on the slope and elevation criteria of the DEM (i.e., slope<= 5% andelevation <=1500 m). The proposed criteria for delineating lowland agriculturewere empirically tested and adapted to the local topography. The step involvingthe computation of urban density and greenness indices (urban density index andgreenness density index respectively) were used for further refining HDB and LDBareas.

The road networks (considering both paved and unpaved roads) were extractedfrom first generated classification and 8bits binary raster layer was generated. Thenewly created raster layer was segmented using the multiresolution algorithm im-plemented in eCognition. The urban density index was computed based on the


Figure 4.6: Multi-stage and hierarchical object based extraction and classificationframework.

LULC proportional occurrence in the newly generated segments. The road net-work was considered appropriate for delineating urban blocks, such as built-up ar-eas and paved surfaces. With values ranging from 0 (absence of urban structures)to 1 (presence of 100% urban structures), the urban density index raster layer wasproduced to represent the amount of the built-up structures in each segment. Tocompute greenness density index, the same level 2 segments were used to determinethe NDVI mean value for each of the segment. The proposed greenness density in-dex values are ranging from 0, i.e., the absence of vegetation to 0.7 for vegetationclasses, such as the forest, UGS, agriculture and wetland. Figure 4.7 illustrates theproposed workflow for computing urban and greenness density indices .


Figure 4.7: Workflow for urban density index computation: (A) SVM refined clas-sification; (B) road network; (C) blocks segments generated based on road network;(D) urban density index map. Urban density index and greenness density indexmaps. The two indices, value is ranging from 0 to 1. The built-up area is charac-terized by low greenness density index and high urban density index. Conversely,green structures are characterized by high greenness density index and low urbandensity index.

4.3.4 Hybrid classification with random forest and U-NetIn Paper IV, LULC predictions based on Sentinel-2 MSI imagery was performedusing a hybrid approach. The proposed method consisted of combining RF classi-fication results with urban impervious surface extracted using U-Net deep convo-lutional model. RF is a non-parametric ML classifier based on the idea of growingmultiple decision trees on random subsets of the training data and related variables(Breiman, 2001). Its implementation involves the partition of grown and split trees


until a majority voting in classification trees is reached.On the other hand, DNNs such such as U-Net are able to learn rich features

from the training data automatically and they have achieved state-of-the-art per-formance in many image classification and semantic segmentation tasks, includingurban structure extraction and classification (e.g. Zhang et al., 2018; Guo et al.,2020, 2018; Corbane et al., 2021). U-Net is a fully convolutional network architec-ture originally developed for biomedical image segmentation (Ronneberger et al.,2015). U-Net architecture consists of encoder and decoder sequences with a seriesof deep convolutional operations alternating with max-pooling layers. The networkis convoluted and pooled a number of times, then up-convoluted to fit the outputsegmentation map.

Figure 4.8: Proposed U-Net architecture adaptation. White boxes correspond tomulti-channel feature maps. Number of channels and x-y-size are denoted on top ofthe box and on its left side, respectively. Operations are visualized as color-codedarrows connecting the feature maps (see legend)

The processing chain for implementing the proposed hybrid LULC predictionconsisted of a number of steps including RF LULC classification, imperviousnessdensity extraction using U-Net, and the combination of RF and U-Net based resultsusing arithmetic and logical operators. RF based classification was implemented inScikit-learn which is a ML library implemented in Python (Pedregosa et al., 2011).Prior to class prediction, LULC categories were conceptualized and proposed inthe study area. Seven LULC classes were considered in the classification schemeincluding built-up areas, cropland, forest, bare land, wetland and water. Built-upareas consist of constructed zones and sealed surfaces by paved roads, parking lots,airport and runways. The classification was based on 55 stacked bands including 10


pan-sharpened Sentinel-2 MSI spectral bands, 40 GLCM derived texture features,three spectral indices presented in Table 1, plus elevation and slope data. In total,500 trees were proposed for training the RF model, whilst other parameters wereset as default. The training samples consisted of pixels randomly selected in rowimages, and 2,000 to 10,000 pixels were chosen for each class.

Like the original architecture, the proposed U-Net adaptation consists of a con-tracting path (left side) capturing context and an expansive path (right side) en-abling precise localization. The former path repeatedly applies downsampling steps.The later step in the proposed network consists of applying twice the operationtriplet 3x3 convolution (Conv) with padding, batch normalization (BatchNorm)and rectified linear unit activation function (ReLu), followed by a 2x2 max pooling(MaxPool) operation. With each downsampling step, the number of feature chan-nels is doubled, starting at 64 channels, while the x-y-size is halved. In additionto increasing the number of features and, consequently, the number of parameters,adding more downsampling steps also increases the receptive field. The receptivefield describes the size of a pixel’s adjacency in the input image considered for itsclassification. Our U-Net adaptation consists of two downsampling steps in thecontracting path and two upsampling steps in the extensive path. Upsamplingsteps inverse the operations in the downsampling path by doubling the x-y-size andhalving the number of channels in the feature maps. This is done via a 2x2 up-convolution (UpConv) operation followed by the applying two times the operationtriplet also used in downsampling steps. Furthermore, skip connections are used todirectly convey feature maps from the contracting path to the corresponding stepsin the extensive path. In the end, the feature map has the originally x-y-size ofthe input image and 64 channels. The final operation converts this feature mapinto a single channel output layer scaled between 0 and 1 via a 1x1 convolution fol-lowed by the sigmoid activation function. In total, the network has approximately930,000 trainable parameters. The main difference between the proposed U-Netarchitecture adaptation and the original architecture is that the depth of the net-work, i.e., the number of downsampling and upsampling paths, was reduced from 4to 2. This results in a lighter network with fewer parameters and a smaller receptivefield. Recent studies mapping human settlements from Sentinel-2 MSI images usingDNNs showed that good results can be achieved using light network architectures(Qiu et al., 2020; Corbane et al., 2021). According to Qiu et al. (2020), the lightarchitecture even compared favorably to larger architectures. Since our applicationis closely related to human settlement mapping, and since we used same input data(Sentinel-2 MSI), we assume that two downsampling steps are sufficient.

The network was trained using Sentinel-2 MSI patches of size 64x64 pixels asinput. Labels for supervised training were leveraged from a VHR LULC map. AllLULC classes corresponding to impervious surfaces (i.e. paved roads and buildings)were remapped to impervious and all other classes to non-impervious. Thereafter,the percentage of surface covered by impervious surfaces for each Sentinel-2 MSIpixel was computed, resulting in the percentage of impervious surface product. Weformulate predicting percentage of impervious surface from Sentinel-2 MSI data


as a model optimization task seeking to minimize the Mean Square Error (MSE)loss function shown in Eq. 4.2. The initial learning rate was set to 10−4 whilethe AdamW function was used to optimize it Loshchilov and Hutter (2017). Thenetwork was then trained for 200 epochs with a batch size of 16 on a Nvidia TitanXp graphics card. Finally, the results from U-Net and RF were merged togetherusing logical and geometric operators for refining the final LULC classification.

LMSE = 1N

N∑i=0

(yi − yi)2 (4.2)

where y and y denote label and network prediction, respectively.

4.4 Post-classification processing and validation

The first draft of classification output was in most cases suffering from small noisyand erroneously classified pixels/objects (Lillesand et al., 2015). Therefore, post-classification refinement is an important step for enhancing the quality of the finalmap product. On the other hand, validation or accuracy assessment is an integraland significant component of most mapping and classification tasks involving re-motely sensed data (Congalton, 2001). A classified LULC map without a rigorousvalidation component is useless, as it does not reflect the degree of correctness be-tween predicted results and independent ground truth data. In the present study,a series of post-classification clean-up operations were performed. A sieve classmethod was used for filtering the classified images that were suffering from the saltand pepper problem, i.e. small erroneously classified pixels. The filtering processallowed a threshold to be specified for the smallest polygon to be merged into aneighbor. After filtering the classified image and assigning new value using major-ity analysis technique, LULC classes were aggregated for producing smoothed andmaps with meaningful spatial pattern.

The validation samples were mainly collected from independent VHR GoogleEarth images for assessing the post 2000 classified images. Classified Landsat dataacquired before 2000 were validated using randomly collected samples in the rawimages, given that the Google Earth imagery with high quality were not available.For each of the considered LULC classes, 300 to 2000 sample points were collectedfor accuracy assessment. In Paper I, Paper III and Paper IV, I adopted the classicaccuracy assessment approach (Congalton, 1991) using the confusion matrix, Over-all Accuracy, producers,s Accuracy, User,s Accuracy and Kappa Coefficient. InPaper II, the area square and accuracy assessment (Area2) was adopted as qualityassessment methodology. The Area2 was implemented in GEE clouding computingenvironment.

n =(∑

h WhSDh

SE(y)

)2(4.3)


where:

n = the number of samplesWh = the stratum weight corresponding to the weight of each of LULC classSDh = the stratum standard deviationsSE(y) = the target standard error of the stratum area estimate

By adapting Equation (4), 1481 random samples were proposed for 1987 and1500 samples in 2019 for area estimation and accuracy assessment. The areal weightfor each stratum (i.e. each of our proposed LULC classes) was determined and usedfor allocating the number of samples. The more the stratum weight is significant,the more the number of samples that were allocated (Olofsson et al., 2013). HRGoogle Earth imagery and background Landsat images were used for collectingand labeling validation samples. A stratified estimator was adopted for computingthe strata proportion area estimate, standard error quantification, and accuracyassessment. The 95% confidence interval with a 0.005 margin error was adoptedfor accuracy assessment. The stratified estimator is expressed as the sum of themeans of the simple random samples within strata weighted by stratum weightscalculated as relative proportions of the population within strata (Olofsson et al.,2013; Stehman, 2013). As proposed by Olofsson et al. (2013), the producer,s anduser,s accuracies, and overall accuracy are expressed as follows:

Ui = pii

pi.(4.4)

Pj = pjj

p.j(4.5)

O =q∑

j=1pjj. (4.6)

where:

i = Mapped category represented in rowj = Reference category represented in columnj = number of considered categoriesUi = User’s Accuracy in class i

Pj = Producer’s Accuracy in category j

O = Overall Accuracy

In Paper IV, the validation of the percentage of impervious surface map wascarried out by comparing the trained and validation labels as expressed in Equation4.7. Labels for supervised training and validation were extracted from a VHR LULCmap generated based on WV-2 data (Mugiraneza et al., 2019b).


RMSE =

√√√√ 1n

n∑i=1

(yi − yi)2 (4.7)

where yi and yi corresponds to label and prediction, respectively.

4.5 Landscape structure change analysis

In the present thesis, the change in landscape composition and pattern was investi-gated in Paper I and Paper II through LM indices. Table 4.2 summarizes selectedLM indices. The indices were derived based on classified LULC in Paper I and Pa-per IV. FRAGSTATS Version 4.2.1 which is a spatial pattern analysis program forquantifying landscape structure (McGarigal et al., 2002), was used for LM computa-tion. The choice of LM indices was motivated by their importance in characterizingthe landscape composition and spatial pattern in the study area. In paper IV, theselected LM indices were spatially related to LULC classes which are providingES. The linkage between LM and ES allowed the evaluation of landscape structurechange impact on spatial distribution and pattern of ES. The landscape patternswere computed and analyzed at class and landscape levels. In total, twelve indiceswere computed and analyzed for characterizing the dynamics of LULC change inthe study area from 1984 to 2016 (Paper I), and from 2016 to 2021 (Paper IV). Thedescription of each of the selected metrics, how they are useful in characterizinglandscape and their influence on the change of ES are explained below.

• Class Area (CA): CA refers to the sum of the areas of all patches of thecorresponding patch type. The measurement unit in hectares and the rangemeasurement is from one to positive infinite. It is assumed that larger areasprovide more ES.

• Number of Patches (NP): It corresponds to the number of patches inthe landscape per patch category. This index is useful for measuring thedominance and the diversity of patch categories in the landscape and, inconjunction with CA evaluates class fragmentation for two points in time.

• Patch Density (PD): Number of patches per 100 ha. High density valuetranslates more fragmentation in the landscape. It is likely that the den-sity variation of patch mosaic in a particular patch class could portray theoccurrence and distribution pattern of particular ecosystem services in thelandscape.

• Largest Patch Index (LPI): Area of the largest patch of the correspondingpatch type divided by total landscape area (in square meter), multiplied by100.


• Edge Density (ED): It is the total length of edge of the patch category inthe landscape. Edge alteration through landscape change can affect biodiver-sity service provision through decrease of species movement along the linearpatches. The more edges are shared with detrimental classes, e.g. roads,buildings, industry or agriculture (fertilization), the lower the ecological qual-ity and integrity of service providing patch.

• Contrast Weighted Edge Density (CWED): CWED expresses the sumof the lengths of all edge segments of a particular patch type multiplied bytheir corresponding contrast weights divided by the area for a particular class.

• Landscape Shape Index (LSI): LSI equals 0.25 (adjustment for raster for-mat) times the sum of the entire landscape boundary and all edge segmentswithin the landscape boundary involving the corresponding patch type, di-vided by the square root of the total landscape area.

• Total Edge Contrast Index (TECI): Sum of the lengths of each edge seg-ment involving the corresponding patch type multiplied by the correspondingcontrast weight, divided by the sum of the lengths (m) of all edge segmentsinvolving the same type

• Aggregation Index (AI): AI equals the number of like adjacencies involvingthe corresponding class, divided by the maximum possible number of likeadjacencies involving the corresponding class

• TCA:The sum of the total interior area of patch category after a user-specified edge buffer is eliminated. As some species need to be in a givensize of TCA, this index is a suitable for measuring the habitat biodiversityabundance. In the present study, a 50m edge depth around each of the patchwas was specified for deducting the TCA.

• Cohesion Index (COHESION): Patch cohesion index measures the phys-ical connectedness of the corresponding patch type. As the COHESION isdecreasing toward zero, the proportion of the landscape comprised of the fo-cal class decreases, and becomes increasingly subdivided and less physicallyconnected. COHESION decrease is detrimental for regulating and support-ing ES that require connected patches, e.g. erosion control, habitat formationor pollination. Cultural services can be effected too when formerly cohesivelandscape elements become fragmented.

• Shannon Diversity Index (SHDI): SHDI refers to the proportional abun-dance of patch mosaic in the landscape. It is a useful index to measure patchdiversity in the landscape. Therefore, SHDI could be helpful to assess theabundant occurrence of ES in the landscape under consideration.


Table 4.2: Proposed landscape metrics for landscape composition and pattern anal-ysis based on (McGarigal et al., 2002). The table captures the level of analysis atwhich the LM indices were applied and investigated, the measurement unit andrange

Paper # LM index Analysis level Unit Range

I, IV CA Class Hectare CA >0, without limitI, IV NP Class Number NP =1, without limitI, IV PD Class Number per

100 haPD >0, constrained bycell size

I LPI Class Percentage 0< LPI <= 100I TECI Class Percentage 0< = TECI >= 100I,IV ED Class Meter per ha ED >= 0, without limitI,IV CWED Class Meter per

hectareCWED>=0, withoutlimit

I LSI Class number LSI>=0, without limitI AI Class Percentage 0 <=AI>= 100IV TCA Class Hectare TCA >0, without limit

I,IV COHESION Class Percentage 0< COHESION<= 100IV SHDI Landscape Number SHDI>=0, without

limit

The proposed indices were subdivided into four main categories. The first cate-gory includes four landscape composition and fragmentation indices namely CA,NP, PD and LPI and COHESION. The second LM category is composed of fourlandscape configuration indices namely TECI, CWED, LSI, AI, and ED. The thirdcategory consists of TCA used for characterizing the core area which is an impor-tant variable for habitat abundance. Finally, the fourth category include SHDIused for landscape diversity characteristics. SHDI was analyzed at landscape level.

4.6 Ecosystems services analysis

In Paper I, the inventory and valuation of ES concerned the pixel-based classifiedLandsat time series image. The classified LULC classes were first converted intoecosystems/biomes based on classification elaborated by Millennium EcosystemAssessment and TEEB Foundations (Millennium Ecosystem Assessment, 2005). InPaper IV, ES bundles were analyzed based on classified LULC and linked withLM indices for evaluating the influence of LM on ES. The eight most occurring ESbundles in the study area were inventoried, and eight proposed LM indices werelinked with these bundles as illustrated in Table 4.3, for assessing their influence interm of occurrence, distribution and pattern.


Table 4.3: Proposed eight urban ecosystem services bundles adapted from typologyof urban ecosystem services proposed by Gómez-Baggethun et al. (2013) with theircorresponding LULC and influencing landscape metrics

# Ecosystem services Provided through Influencing LM

1 Food provision

Cropland CA, NP, PD, COHESION, SHDIUGS CA, NP, PD, COHESION, SHDIWetland CA, NP, PD, COHESION, SHDIWater CA, NP, PD, COHESION, SHDI

2 Flood protection

Cropland CA, NP, PD, COHESION, SHDIForest CA, NP, PD, COHESION, SHDIWetland CA, NP, PD, COHESION, SHDIWater CA, NP, PD, ED, CWED, COHESION, SHDI

3 Local climate regulation

Cropland CA, NP, COHESION, SHDIForest CA, NP, COHESION, SHDIUGS CA, NP, COHESION, SHDIWetland CA, NP, COHESION, SHDIWater CA, NP, COHESION, SHDI

4 Water provisionWetland CA, NP, PD, COHESION, SHDIWater CA, NP, PD, ED, CWED, COHESION, SHDI

5 Air quality purificationForest CA, NP, PD, COHESION, SHDIWetland CA, NP, PD, COHESION, SHDI

6 Surface runoff and erosion control

Forest CA, NP, PD, COHESION, SHDICropland CA, NP, PD, COHESION, SHDIUGS CA, NP, PD, COHESION, SHDIWetland CA, NP, PD, COHESION, SHDI

7 Recreation and aesthetic valueUGS CA, NP, PD, COHESION, SHDIForest CA, NP, PD, COHESION, SHDIWetland CA, NP, PD, COHESION, SHDI

8 Habitat for biodiversity

Wetland CA, NP, PD, ED, TCA, CWED, COHESION, SHDIForest CA, NP, PD, ED, TCA, CWED, COHESION, SHDIWater CA, NP, PD, ED, TCA, CWED COHESION, SHDICropland CA, NP, PD, ED, TCA, CWED, COHESION, SHDIUGS CA, NP, PD, ED, TCA, CWED, COHESION, SHDI

The Monetary based ESV carried out in Paper I concerned five ecosystemsnamely: wetlands, forest, open land (cropland), water and urban systems equiv-alent to HDB and LDB. The services and benefits gained from proposed ecosys-tems/biomes were fist inventoried. Then after, the valuation consisted of estimatingthe approximate monetary values in US Dollars using the valuation scheme pro-posed by Costanza et al. (1997). According to this scheme, the total value of ESis equal to the products of the areas covered by corresponding service and theestimated value in US dollar as expressed in equation 4.8.

ESV =N∑

i=1(CAiUV ) (4.8)

where:

ESV = Ecosystem Service ValueCAi = class area in patch i expressed in haUV = Ecosystem service unit value expressed in US Dollar


Furthermore, the supply and demand and budgeting of bundles of ES was carriedout using a non-monetary approach adapted from the supply and demand matrixproposed by Burkhard et al. (2012). According to the aforementioned matrix, eachof the considered service bundle is assigned to a score. Proposed scores are rangingfrom -5 (maximum demand) to +5 (maximum supply) depending on which extent agiven LULC class is influencing the corresponding ES bundle. The total ES budgetper LULC class was calculated by multiplying cumulative sum of ES bundle scorewith the area covered by each LULC class. The negative balance score translatethe demand of ES, whilst the positive balance illustrates the service supply. Zerobalance means that the service is neither contributing to supply nor to demanding.

Chapter 5

Results and Discussion

This chapter presents the results from the four compiled papers that make up thedissertation and discusses the main findings. First, the LULC classification resultsand associated accuracies are demonstrated. Second, the spatiotemporal change ofthe landscape patterns, structure and composition, as well as changes in ES in thestudy area are presented. In the second section of the chapter, I reflect on the i)remote sensing framework for LULC classification, ii) data selection criteria andmapping scale, iii) spatially explicit environmental monitoring variables, and iv)linkage between LM and ES, and my contribution to the research on the subject.

5.1 LULC classification results and urbanization analysis

In all four studies, the overall classification exceeded 83% and Kappa coefficientswere always greater than 0.79. The GEE-LT based prediction (in Paper II) generallyyielded superior accuracies comparing to others. It is assumed that this is mainlydue to the fact that the prediction was not so much influenced by human-inducederror propagation. Details on producer,s and user,s accuracies, and small overviewmaps from each of the four papers are reported from 5.1.1 to 5.1.4 subsections. Theoverall accuracies and kappa coefficients are presented in Table 5.1.

Water bodies, forest, and cropland are easily separated with high accuracy onboth medium and VHR data. Built-up area at fine scale, including HDB, LDB andinformal settlements, could be easily discriminated only in the VHR WV-2 data.Nonetheless, Landsat and Sentinel-2 MSI are found useful for mapping LULC atthe entire city scale. Wetlands were found difficult to distinguish given that theirspectral responses are confused with cropland or shallow water, whereas bare land isinterfering with LDB. The OBIA rule-based approach was found useful to optimizeand discriminate urban LULC at fine scales, but the classification coverage of theentire city would imply a high financial and computational cost. The results fromthe spatio-temporal analysis, as reported in Paper I, Paper II and Paper IV, illus-trated that Kigali experienced rapid growth of both HDB and LDB, and a decrease

53

CHAPTER 5. RESULTS AND DISCUSSION 54

Table 5.1: Cross-comparison of overall classification accuracies, Kappa coefficients,number of LULC classes, classifier and spatial resolutions distributed among fourstudies

Data resolution Covered area Classifier Year # Classes OA (%) Kappa Paper

30 m 730Km2 Pixel-based SVM

1984

7

87.84 0.830

I2001 91.68 0.8902009 90.01 0.8702016 92.87 0.910

30 m 609.57Km2 GEE-LT prediction

1987

5

91.33 0.886

II

1990 96.05 0.9251995 96.08 0.9252000 96.26 0.9402005 92.96 0.8872010 92.74 0.8842015 92.65 0.8842019 89.87 0.845

2 m 205.1Km2Object-based SVM

2016 12 85.36 0.823 IIIand rule-based

10 m 730Km2Hybrid classification 2016

783.73 0.794

IV(RF+U-Net) 2021 83.75 0.800

in cropland and forest in the last four decades since 1984. The spatio-temporalpattern of urban development shows that three urban development scenarios wereexperienced including i) infill consisting built-up areas and impervious surface de-velopment in small green patches located in the core urban zones, ii) extension ofHBD and LDB in urban fringe zones and iii) construction development at distantlocations from the core urban and fringe zones. A high degree of densification ofpaved roads was also noticeable in Kigali during the study period.

5.1.1 Pixel-based classification based on Landsat dataThe Producers and users accuracies for Landsat based classifications are presentedin Table 5.2. The most difficult classes to distinguish were LDB with only 52.1%and bare land with 53.8% producer’s accuracy in 2001.

Table 5.2: Producer,s and user,s accuracies for Landsat based classification

Producer,s Accuracy (in %) User,s accuracy (in %)

# LULC 1984 2001 2009 2016 1984 2001 2009 2016

1 Open land 94 98 96.7 96.5 79.6 83 60.9 80.92 Forest 99.4 94.6 88.6 97.3 86.6 93.9 97.9 97.13 HDB 71.5 99.3 98.3 99.7 98.1 86.5 94.7 92.44 LDB 67.5 52.1 85.6 86.6 76.8 100 95 98.25 Bare land 78.1 53.8 93.5 74.8 54.6 96.7 90.8 94.96 Water 97.1 94 96 98.4 83.8 92.4 98.4 96.37 wetlands 86.5 90.7 85.2 90.1 99.3 98.5 98.5 96.4


LDB areas are confused with HDB, whereas spectral overlapping between bareand open lands is reported. Meanwhile, forest predictions were remarkably goodin all four periods with more than 88% producer,s accuracies. In all four classifiedimages, the omission error is high in the bare land class with 45% in 1984, 46% in2001 and 25% in 2016 respectively. Underestimation of LDB is also found in 2001with a 47% omission error where most of validation samples were counted as HDB.The generated LULC maps illustrate the progressive increase in built-up areas from1984 to 2016 in Figure 5.1, and the excerpts showing detailed classification resultsare depicted in Figure 5.2.

Figure 5.1: LULC classification results in 1984, 2001, 2009 and 2016 based on 30m Landsat data and seven classes


Figure 5.2: Small overview maps of LULC classification results in core urban areasin 1984, 2001, 2009 and 2016 based on 30 m Landsat data and seven classes. Leftcolumn: Landsat images, False Color Composite; Right column: LULC classifica-tion.


From an urbanization point of view, the major urban LULC change occurred inKigali, Rwanda from 1984 to 2016. The most prominent changes occurred in crop-lands, that were considerably reduced in favour of built-up areas, which increasedfrom 2.13 Km2 to 100.17 Km2 between 1984 and 2016.

5.1.2 GEE-LT based prediction of LULC changeThe synergy between the baseline change maps and GEE-LT derived indices/bandsallowed the reconstruction of the dense annual LULC change maps from 1988 to2019 as illustrated in Figure 5.3.

Figure 5.3: Dense annual LULC change from 1988 to 2019 based on GEE-LTprediction

The study results showed extensive urban growth between 1987 and 2019, witha 226.4% growth rate. During that time span, the built-up area extended from 3976to 4405 hectares. The five-year interval period analysis from 1990 to 2015 illustratedthat impervious surfaces increased from 4233.5 to 11648.29 hectares with 3.7 % av-erage annual urban growth rate. The period from 1991 to 1995 was characterized bya decreasing urban growth, with an annual average rate ranging between 1.8% and2%. The slow urbanization during this period could be explained by socio-economicand political instability and economic recession in Rwanda, which characterized theabove-mentioned period. Indeed, the 1991-1995 period coincided with the five-yearwar and armored conflict that culminated with the 1994 genocide against Tutsi inRwanda. The post-genocide period was characterized by an urbanization take off.From 2004 onwards, continuous urban expansion was observed, with an increase


in annual urban growth averaged to 4%. The post-genocide reconstruction period,especially from 2004, was characterized by servicing new construction sites andbuilt-up area expansion, such as the construction of the Kigali Special EconomicZone, expansion of Kigali International Airport, densification of tarmac roads, andestate development in various zones of the city. This period also coincided withan important migration flux to Kigali of newly repatriated Rwandan refugees whowere mainly living in neighboring countries.

Figure 5.4: Five years progressive LULC change from 1990 to 2019 based on GEE-LandTrendr prediction

Since 2014, a continuous and gradual urban growth is observed with an incre-mental increase (see Figure 5.3). A probable cause would be linked to the urbanpolicy enforcement translated by the new urban planning regulation and construc-tion standards imposed after endorsing the 2013 revised Kigali City Land UseMaster Plan, and the integration of the city growth perspective in Rwanda Vision2020. Endorsed planning standards and regulations are more or less limiting thespread of illegal construction and expansion of informal settlements. The overallclassification accuracies in all considered time spans exceed 92% with a margin errorbetween 1% and 2%. In general, the standard error (SE) is less than 0.01, exceptin the open land class where SE increased to that threshold, respectively, in 2005,2010, and 2015.


Table 5.3: Predicted accuracies at class level with class weight, standard error,producer’s and user’s accuracy and overall accuracy with a 95% ConfidenceInterval (CI).

2015

Wi SE PA UA OA

Accuracies

Ur 0.217 0.008 0.82 ± 0.069 0.94 ± 0.028

0.92 ± 0.02OL 0.632 0.010 0.96 ± 0.030 0.92 ± 0.024For 0.047 0.003 0.88 ± 0.140 0.90 ± 0.079Wet 0.1 0.005 0.91 ± 0.097 0.90 ± 0.069WT 0.005 0.000 1 ± 0.049 0.98 ± 0.048Total 1

2010

Wi SE PA UA OA

Accuracies

Ur 0.205 0.008 0.81 ± 0.074 0.95 ± 0.028

0.92 ± 0.02

OL 0.643 0.010 0.97 ± 0.029 0.92 ± 0.024For 0.048 0.003 0.88 ± 0.140 0.90 ± 0.079Wet 0.1 0.005 0.91 ± 0.098 0.90 ± 0.069WT 0.005 0.000 1 ± 0.053 0.97 ± 0.052Total 1

2005

Wi SE PA UA OA

Accuracies

Ur 0.199 0.008 0.79 ± 0.077 0.97 ± 0.024

0.92 ± 0.02


2000

Wi SE PA UA OA

Accuracies

Ur 0.104 0.006 0.81 ± 0.109 0.99 ± 0.009

0.95 ± 0.02OL 0.735 0.009 0.98 ± 0.023 0.95 ± 0.020For 0.054 0.004 0.88 ± 0.146 0.89 ± 0.083Wet 0.103 0.005 0.90 ± 0.097 0.93 ± 0.061WT 0.004 0.000 1 ± 0.085 0.96 ± 0.082Total 1

1995

Wi SE PA UA OA

Accuracies

Ur 0.072 0.002 0.98 ± 0.064 0.96 ± 0.041

0.96 ± 0.01



1990

Wi SE PA UA OA

Accuracies

Ur 0.069 0.002 0.97 ± 0.066 0.96 ± 0.042

0.96 ± 0.01


Ur = Urban; OL = Open land; For = Forest; Wet = Wetland; WT = Water; Wi = ClassWeight; SE = Standard Error; PA = Producer,s Accuracy; UA = User,s Accuracy; OA = OverallAccuracy.

5.1.3 Hierarchical and OBIA rule-based classificationThe integrated object-based and rule-based approach resulted in 12 land coverclasses with an overall accuracy at 85.36% and a kappa coefficient at 0.8228. Table5.4 shows producer,s and user,s Accuracies for each of the 12 generated classes.

Table 5.4: OBIA rule based classification accuraciesLULC class HDB LDB IS PR UPR UGS UPA LLA Forest Bare land Wetland WaterPA (in %) 72.2 81.5 77 97.3 90.3 82.6 92.8 94.1 91.4 70.1 76.7 99.1UA (in %) 90.6 55.1 90.2 79.6 52.8 60.7 70.7 94.1 98.3 93.8 100 97.8

HDB = High Density Built-up; LDB = Low Density Built-up; IS = Informal Settlement; PR =Pave road; UPR = Unpaved road; UGS = Urban Green Space; UPA = Upland agriculture; LLA

= Lowland agriculture; PA = Producer,s Accuracy; UA = User,s Accuracy.

Figure 5.5 illustrates the LULC classification at fine scale based on WV-2 im-age. With a thematic layer representing valleys derived from DEM, the lowlandand highland agriculture classes were separated. As a result, the LULC classesincreased from ten to eleven. The geometric rules and the computed urban densityand green density indices helped in the classification refinement for several classes.For example, the confusion between HDB and LDB was reduced, and the producer,saccuracies reached 72.2% and 81.2% respectively. The informal settlements weresuccessfully mapped with producer,s and user,s accuracies at 77% and 90.2% re-spectively. The integrated approach involving the combination of a rule-set, densityindices and texture features allowed the informal settlements extraction. Spectrally,it is challenging to separate informal settlements from HDB. The texture featuresusing GLCM were helpful in detecting informal settlements with 77% producer,sand 90% user,s accuracies, respectively. The creation of a HDB mask was helpfulto speed up the computation process while applying the feature extraction usingSVM. Nevertheless, small HDB patches were also found intercepting the informalsettlements objects due to the presence of houses surrounded by isolated and small


vegetation patches. Figure 5.6 depicts the performance of OBIA rule based classi-fication using VHR data.

Figure 5.5: VHR classified LULC with 12 classes based on OBIA rule-based classi-fication and multi-stage refinement.


Figure 5.6: Details from the classification (Fig. 5.5) and their respective areas innormal colour display of WV-2 image. In row (A), the selected WV-2 multispectralimages areas are presented. In row (B), corresponding extracted LULC are illus-trated.

5.1.4 Hybrid LULC classification based on Sentinel-2 MSIThe hybrid approach consisted of combining the intermediate classification resultsfrom U-Net based impervious surface extraction and RF based LULC classifica-tion. Prior to combining RF and U-Net intermediate classification results, urbanimperviousness density for both 2016 and 2021 was successfully extracted usingthe U-Net convolutional networks model based on Sentinel-2A MSI. The modelvalidation for percentage of impervious surface illustrates that initially, the RMSEdecreases rapidly for both data sets and reached approximately 0.2 after 50 epochs.At the end of training phase, the model achieved RMSEs of 0.177 (training) and0.176 (validation) as presented in Figure 5.7. The hybrid classification combiningthe outcomes of a conventional RF classification and the U-Net-based percentage ofimpervious surface product and delineated city boundary, allowed to derive LULCmaps for 2016 and 2021. Table 5.5 illustrates the comparison between RF andhybrid classifications performance in built-up area extraction.


Figure 5.7: Performance of U-Net on training and validation data sets while pre-dicting the percentage of impervious surface.

The classification results demonstrated an increase on the F1 score and Precisionvalues, by 0.07 and 0.161, respectively. The Recall values in both classification caseswas similar with a slight improvement in the RF classification. The quantitativeevaluation illustrated that the hybrid classification outperformed the one using RF(only classifying HDB and LDB) as illustrated in Table 5.5. Figure 5.8 shows theperformance of U-Net over RF in detecting impervious surface.

Table 5.5: Comparison of hybrid (RF+U-Net) and RF classification accuracies forcombined HDB and LDB. The assessment was performed using F1 score, recall andprecision metrics based on combined HDB and LDB validation samples.

Classifier F1 score Precision Recall

RF 0.749 0.651 0.881Hybrid (RF+U-Net) 0.819 0.812 0.826


Figure 5.8: Cross-comparison between RF based urban classification results (im-pervious surface) and U-Net based prediction of impervious surface. Column (A)shows VHR WV-2 images, (B) Input Sentinel-2 MSI images in the U-Net model;(c) Percentage impervious surface training labels derived from a WV-2 based LULCmap; (d) RF merged high and low density built-up area, and (e) Predicted Per-centage Impervious Surface.


User,s and producer,s accuracies at the class level indicated that five out of sevenclasses exhibited more than 80% with wetland and water classes going beyond 90%.Bare land was the least accurate class with producer,s accuracy of 63% in the 2016classification. Bare land is mainly confused either with LDB area or with croplandwhile some wetland patches are also confused with cropland, negatively impactingproducer,s accuracy, particularly in 2016. Table 5.6 present the producer,s anduser,s accuracies across seven proposed LULC. Table 5.6 present the producer,sand user,s accuracies across seven proposed LULC.

Table 5.6: Accuracies of the 2016 and 2021 classified LULC based on hybrid clas-sification (RF+U-net)

2016 2021

User,s Accuracy(%) Producer,s Accuracy (%) User,s Accuracy (%) Producer,s Accuracy (%)

1.HDB 87.2 83.6 87.8 83.32.LDB 75.9 80.3 76.0 82.43.Cropland 83.1 87.8 83.8 85.84.Forest 80.7 78.0 81.2 85.05.Bare land 65.0 63.4 79.8 66.86.Wetland 92.6 76.7 89.8 82.27.Water 95.8 97.3 94.9 92.3

Using the generated boundary delineating urbanized area, LULC classes wereincreased from seven to eight as presented in Figure 5.9. From 2016 to 2021, allLULC classes were affected by urbanization through extension, infill and leapfrog-ging. In terms of area, cropland was the class that decreased the most, whereasUGS got increased following the rapid urban development pace. Figure 5.10 illus-trates details on remarkable urbanized hotspots in the Eastern and Southern partsof Kigali between 2016 and 2021.


Figure 5.9: Maps illustrating the Sentinel-2 MSI based LULC in 2016 and 2021.The left image represents the 2016 classification, whilst the right image representsthe 2021 classification.

Figure 5.10: Detailed classification excerpts illustrating the LULC change from2016 to 2021 around designated Kigali Economic Zone (1st and 2nd columns) andin Southern zone around Gahanga sector (3rd and 4th columns). The top rowrepresents the false color composite (NIR, Red, and Green) of Sentinel-2 MSI


5.2 Landscape structure change with landscape metrics

The change in landscape spatial pattern and composition are remarked in all LULCcategories. A large increase in CA is found in HDB and LDB; the area is graduallydecreasing in open land, mainly cropland and in forest as illustrated in Table 5.7.

Table 5.7: Landscape CA composition with net change from 1984 to 2016 based onmulti-temporal Landsat images.

LULC nameCA (in ha) Net change (in %)

1984 2001 2009 2016 1984-2001 2001-2009 2009-2016 1984-2016Open land 52704.5 48357.9 47155.9 40337.7 -8.2 -2.5 -14.5 -23.5Forest 7669.8 8266.3 4914.5 7304.8 7.8 -40.5 48.6 -4.8HDB 563.1 4358.6 4741.7 5563.2 674 8.8 17.3 887.9LDB 1572.5 937.2 3684.2 4453.9 -40.4 293.1 20.9 183.2Bare land 1763.2 1015.5 3922.3 4157.6 -42.4 286.3 6 135.8Water 1421.8 1004.8 838.4 1256.6 -29.3 -16.6 49.9 -11.6Wetland 8049.8 9679.5 8230 10488.6 20.2 -15 27.4 30.3

At the landscape level, the NP has increased from 5,119 in 1984 to 10,882 patchesin 2016. Intensive landscape fragmentation was experienced especially after 2009due to the diversification of LDB pockets in the urban fringe and neighboring ruralareas. PD almost doubled from 1984 and 2016 with 7 to 14.7 patches per 100 hain 1984 and 2016, respectively. The LPI is found in croplands that decreased byapproximately 50% between the first and last dates (69.9% in 1984 against 36.9%in 2016). Figure 5.11 and Figure 5.12 present the change in landscape compositionand configuration experienced in Kigali from 1984 to 2016.

The major landscape changes in Kigali were located in the Eastern and South-ern edges while three urbanization scenarios emerged. Firstly, new buildings andpaved roads contributed to infilling green and vacant land in the core urban area.Secondly, peri-urban cropland adjacent to core urban zones was converted intobuilt-up areas contributing to core urban area extension. The third urban devel-opment scenario is characterized by new concentrated built-up patches occurringat some distance from the existing urban area, bypassing vacant cropland zones.Built-up areas expansion through the urban sprawl and infill resulted in CA in-crease in HDB, LDB and UGS, whilst cropland and forest areas decreased by 8.6%and 3.6% respectively. Since 2016, changes in landscape spatial pattern and compo-sition was characterized by the extension of built-up area mainly in the southern,eastern zones, and in north-west. The existing croplands were importantly con-verted into built-up areas in Gahanga, Ndera, Rusoro and Kigali Sectors. Figure5.13 illustrates the change in CA in respective LULC classes.


Figure 5.11: Multi-temporal change in landscape composition indices from 1984 to2016.

Figure 5.12: Multi-temporal change in landscape configuration indices from 1984to 2016.


Figure 5.13: CA change from 2016 to 2021.

5.3 Ecosystem services

The most often occurring components of ES that are inventoried in Kigali includeProvisioning, Regulating, Supporting and Cultural services. Locally occurringLULC categories are not only regulating the local climate, but also provide foodfor the local communities. The majority of the local LULC are even attributedaesthetic and educational value, and they support nutrient cycling and soil stabil-ity. Flood regulating services are provided through forest and cropland by reducingthe effects of rain drop intensity on soil stability and in intercepting part of surfacerunoff for infiltration. Forest cover on Mount Jali and Mount Kigali is a flood limit-ing factor in the downstream catchments of Nyabugogo and Gatsata where variousbusinesses are concentrated. Forest cover is also contributing to soil stability andsurface runoff mitigation in various corners of Kigali, e.g. Gikondo and Gatengaresidential areas and the Kimisagara-Cyahafi-Nyabugogo lowland corridor. Forestsalso play an important role for charcoal and construction material production andfor carbon sequestration. Wetlands are mainly contributing to food provision, nu-trient cycling and water balance regulation. Large wetlands such as Nyabugogoand Nyabarongo are occupied by sugar cane that is used for sugar production.Irrigated rice and vegetable patches are also extended to wetlands and the pro-duction is contributing to food provision for the city. It is reported that 60% ofthe land is occupied by cropland in peri-urban and rural areas, and this is an im-portant zone for food production and farming activities. According to MillenniumEcosystem Assessment (2005), urban classes composed of built-up areas and vari-ous infrastructural classes play a vital role for city dwellers, well-being. This is alsothe case in Kigali, Rwanda. Constructed infrastructures such as sewage system,drainage changes, evacuation of rainfall gauges across the roads are contributing to


flash flood regulation and to city sanitation.Water provision services are mainly generated by rivers, lakes and fishponds.

The water used in Kigali comes from the Yanze River intake located 5 km NW ofthe city and treated at the Kimisagara Water Treatment Plant. The second watertreatement plant supplying water in Kigali is located in Kanyinya Sector (in placecalled Nzove), where water is abstracted from Nyabarongo River in the N-W of thecity. According to Electrogaz (2007a), the Yanze River intake has a discharge ofabout 2,500 m3 per hour in the rainy season and 800 m3 per hour in the dry season.The capacity of Nzove Water Treatment Plant is evaluated at 40,000 m3 per day(Electrogaz, 2007b). UGS and forest are mainly contributing to recreation andaesthetic services. UGS are gathering places for entertainment, resting, weddingcelebrations, etc. UGS also contribute to the city scenery and aesthetics. UnlikeHDB and LDB, all other LULC classes are contributing to the biodiversity servicebundle. Based on the framework for qualitative ecosystem services adapted fromBurkhard et al. (2012), the ecosystem balance of the eight proposed ES bundles ispresented in Table 5.8.

Table 5.8: Supply and demand of ES based on the framework adapted fromBurkhard et al. (2012). Supply and demand is ranging between -5 (high demand)and +5 (highly supply). Zero score (0) is considered as neutral balance. The sumof ES score per LULC and the net change of ES budgets are listed in the bottomrows

Ecosystem services HDB LDB Cropland Forest UGS Wetland Water1. Food provision -5 -4 +5 0 +1 +4 +22. Flood protection -4 -5 +1 +3 0 +3 +23. Local climate regulation -5 -4 +1 +5 +1 +2 +24. Water provision -5 -5 -1 0 -3 -4 +55. Air quality purification -5 -5 0 +5 0 +1 06. Surface runoff and erosion control -1 -1 +2 +5 +2 0 07. Recreation and aesthetic value -4 -4 +2 +5 +3 +1 +58. Habitat for biodiversity -2 -2 +3 +5 +2 +4 +4Cumulative sum of ES budgets -31 -30 13 28 6 11 20Total ES budgets per LULC area in 2016 -93598.92 -199826.10 555858.03 253468.32 18784.5 71377.02 19226.00Total ES budgets per LULC area in 2021 -101395.73 -262549.20 508311.57 244349.00 25954.5 71658.51 21691.80Net change of ES budgets -7796.81 (8.3%) -62723.1 (31.4%) -47546.46 (-8.6%) -9119.32 (-3.6%) 7170 (38.2%) 486.21 (0.4%) 2465.8 (12.8%)

One finding of this study is that the increase in urbanized areas has a negativeimpact on ES. Regulating services provided by forests and wetlands are threatened(Kasangaki, 2013). Even if drainage and sanitation infrastructures as part of urbanES are playing a role in regulation of surface water runoff and waste treatment, theyare not proportional to built-up area extension. Urban development in Kigali is to-day not progressing at the same pace as sustainable urbanization coping strategies.Repetitive floods and landslide events are occurring in low lands, contributing tohuman and property loss (Nsengiyumva, 2012). These events are largely triggeredby inappropriate urban water drainage infrastructure and cleared land on hillsidessurrounding the core urbanized areas. A large number of endangered householdsare in informal settlements; they are located in high risk areas such as steep slopesand flood prone areas, and result from uncontrolled urban development (Bizimana


and Schilling, 2009). Looking at the spatio-temporal impact of urbanization on ES,I find that an increase in urban growth results in an increase in the demand of ES,and a decrease in the supply capacity of ES, as illustrated in Figure 5.14.

Figure 5.14: Spatio-temporal change of supply and demand of ES bundles from2016 to 2021

The economic valuation of ES was based on the framework proposed by Costanzaet al. (1997). Table 5.9 represents the change in ESV per LULC area providing theES. The estimated ESV was 573.4 million US Dollars per year in 1984. This valueincreased slightly to 610.1 million US Dollars per year in 2001 and decreased in2009. The ESV increased again in 2016 due to the increase of both HDB and LDB.From 1984 to 2016, 23.5% of the initial ESV of land that was supposed to be usedfor cropland was lost and forest values decreased by 4.8%. Urban systems yieldprogressively high ESV. The ESV changes for wetland and water need cautious in-terpretation due to misclassifications and spectral confusion between wetlands andcropland classes.

Table 5.9: Estimated value of selected ES in Kigali between 1984 and 2016

Global terrestrial biome Equivalent ecosystem ES unit value (in USD/ha)Estimated ESV (in millions USD) Net change of ESV (in %)

1984 2001 2009 2016 1984-2016Swamps/Floodplains Wetland 25 681 206.7 248.6 211.4 269.4 30.3Tropical forest Forest 5 382 41.3 44.5 26.4 39.3 -4.8Cropland Cropland 5 567 293.4 269.2 262.5 224.4 -23.5Lakes/Rivers Water 12 512 17.8 12.6 10.5 15.7 -11.6Urban systems HDB and LDB 6 661 14.2 35.3 56.1 66.7 369.1Total Value 573.4 610.1 566.9 615.7 7.4


It should be noted that while the monetary-based valuation of ES in the presentstudy is informative, the ESV presented in the study do not reflect open marketvalues. ES values should be interpreted in the sense of willingness to maintainecosystems and can be used in preparing a baseline valuation framework for thepayment of environmental service. The results are, however, good inputs for guidingthe preparation of cost effective methods for ES assessment. As such, the data,resolution and universal valuation methods need to be cautiously interpreted. Thevalues of ES provided by urban systems and urban green spaces needs to be based onmore information and appropriate models in order to be well implemented in assetand property valuation. As shown in previous studies (e.g. Anderson et al., 2017;Costanza et al., 1997), the value of ES in the present study is a global estimate. Anational assessment would give a more holistic picture on the values of natural andman-made capital in the study area.

5.4 Discussion

5.4.1 Remote sensing based framework for urban LULCmapping

The quality of urban LULC maps largely depends on a number of factors, includ-ing the quality of the input data, the approaches used for data pre-processing andthe robustness of the classifier/model for predicting informational classes. Prior toclassifier/model training, pre-processing was found as an important step to enhancethe quality of the input data. The quality of a LULC classification in complex envi-ronments is depending on spectral features, feature selection and feature extractionprocesses and data fusion. The use of GLCM texture features and the integrationof spectral based biophysical indices such as NDVI, MNDWI, NDBI, DBSI in theclassification feature space were found useful in enhancing class separability, andin avoiding spectral overlapping. In Paper II, the use of GEE-LT framework in thecloud computing environment allowed me to track LULC change trajectories withless human-induced error propagation. I found that the GEE-LT framework allowsmulti-functional data processing with a user-friendly API and interactive data visu-alization at the intermediate and final processing stages. In paper III it was found,that the use of spectral features only, for object-based classification is not sufficientfor producing high quality maps depicting detailed urban LULC at fine scale. Amultistage and hierarchical urban extraction and classification framework is rec-ommended when dealing with object-based image analysis. Using OBIA SVM anda rule-based approach, 12 LULC classes were successfully derived based on eightWV-2 spectral bands, spectral indices, GLCM texture measures and a DEM. Thisnew object- and hierarchical-based classification procedure resulted in an overallclassification accuracy of 85.36% with a kappa coefficient at 0.82. The classifica-tion procedures used in all four papers exceeded 83%. All procedures used -theproposed SVM classifier, the GEE-LT based prediction, and the combination of RF


and U-Net DNNs performed well in extracting and classifying LULC in complexurban environments. I believe that the proposed methods for urban LULC classi-fication and extraction could be transferred and successfully adapted in differenturban environments.

5.4.2 Data selection criteria and mapping scaleThe selection of suitable data is an important step for efficient subsequent analysis.Given that the landscape can be affected by seasonal variations, such as a temporarychock of dry season or an abrupt flooding, the acquisition of single date imagerycould erroneously affect the classification quality. It was found that seasonal orannual composite imagery is helping in minimizing the effects related to temporarylandscape change that can affect the quality of the image scene. Filtering theimagery using temporal statistics was found useful for enhancing the data input tothe classification algorithm.

According to Kuffer et al. (2018), the scale of mapping is a forefront decision,when employing remotely sensed data for urban LULC monitoring. Furthermore, acost-benefit analysis should be carried out before data acquisition. VHR imagery isstill expensive to acquire, but it is useful in urban mapping at a fine scale. Coverageof the city with such imagery would involve a huge budget that is still a challenge inmost SSA countries. Therefore, a rational decision related to scale of mapping needsto be made in advance. Some aspects of urbanization monitoring, such as annualtrends of urbanization or environmental change, can be performed at large scalesusing freely available imagery such as Landsat or Sentinel-2 MSI. However, otherurban growth indicators such as slum mapping and urban deprivation indicatorsneed to be investigated using VHR data (Leonita et al., 2018; Hofmann et al., 2001).In cases where deprived areas such as informal settlements are occupying continuousand homogeneous big patches, freely available medium-resolution imagery such asSentinel-2 MSI can be used instead of VHR data.

5.4.3 Spatial environmental monitoring indicatorsThe environmental consequences of urbanization could be better understood byusing measurable indicators expressing spatial patterns and the composition oflandscape structure change. In the present research, analysis of environmental im-pacts resulting from urbanization in Kigali, Rwanda, was investigated using mea-surable indices derived from LM and ES. Using fragmentation indicators such asthe dynamic change in NP, CA, PD, LPI, the urban LULC change intensity couldbe successfully investigated. The change in landscape configuration was analysedthrough edge and connectivity indices. Edge effect indices which are materialized bythe change in physical condition at an ecosystem boundary or within the adjacentecosystem (Fischer and Lindenmayer, 2007) are useful indicators for investigating


the patterns of landscape change trajectory and associated ES changes. Neverthe-less, the spatial resolution of the image could affect the final classified LULC mapand subsequent LM and derived ES. For instance, a 30m resolution pixel can con-tain more than one land cover class. Built-up areas mixed with forest in the samepixel are not easily segregated, given that the dominating spectral signatures de-termine the assignment of spectral value and this can affect the computed indices.Anderson et al. (2017) have discussed the effect spatial resolution of EO data onthe estimated value of ES. The increase of the number of LDB patches, especiallyat the urban fringe zones, impacted the provision of services offered by croplandecosystems negatively. The decrease of the forest CA affected the estimated valueof forest ES as well. A high AI in the cropland class indicates the amount of classconnectivity compared to other patch mosaics in the study area.

5.4.4 Linking landscape metrics with ecosystem servicesThe provision of ES in urban areas is affected by landscape fragmentation and spa-tial pattern change resulting from urbanization processes. It has been shown thatthe fragmentation of natural landscapes during urbanization processes is linked toloss of biodiversity and change in ecological and ecosystem function (e.g. Zambranoet al., 2019; Burkhard et al., 2012; Qi et al., 2014; Tang et al., 2020). The size, shapeand distribution of LULC classes influence ES provision and quality, e.g. larger for-est patches provide a higher timber supply. They also contribute to a larger extentto climate, temperature and air pollution regulation and carbon sequestration thansmaller areas of the same class. Regarding habitat functions, disrupted corridorswithin ecological networks can hinder species dispersal, and contaminated edgesmay limit the habitat function of certain species (Haas and Ban, 2017).

LM provide the means to describe the spatial composition and configuration ofa landscape, and to derive detailed information on specific aspects of land coverclasses. In this study, a subset of the most commonly used LM that can quantifythe influence of spatial characteristics on ES provision as illustrated in Table 5.10is chosen. Indeed, I attempted a systematic account on the influence of landscapecharacteristics on ES.One of the findings of this study is that reliable links between LM and ES in termsof LULC changes and implications are yet to be achieved. The main challengelies in the fact that the vast variation of landscape composition and configuration,scale dependency and spatial distribution of ecosystem provisioning LULC types,and benefiting human population can not yet be connected to each other. Support-ing services may be considered less location dependent when they are not directlyenjoyed by humans in the vicinity. For instance, pollination of crops can occuranywhere, but they can also be consumed anywhere on the globe. Cultural andrecreational services are normally enjoyed and perceived by humans that are atthe site of ES provisions. Furthermore, the socio-economic context defines whichservices and how they are used. In Kigali, most of the crops are consumed closethe origin.


Table 5.10: Change of landscape metrics across ES providing LULC and corre-sponding ES bundles from 2016 to 2021. The "n.a" means that the LM index is notinfluencing the considered ES bundle

1. Food provision

Class CA NP PD COHESION

Cropland -3657.42 (-8.6%) -246 (-8.9%) -0.22 (-8.84%) -0.01%Wetland 25.59 (+0.39%) -36 (-0.98%) -0.03 (-0.9%) -0.06%2. Flood protection

Class CA NP PD COHESION ED

Cropland -3657.42 (-8.6%) -246 (-8.9%) -0.22 (-8.84%) -0.01% n.aForest -325.69 (-3.6%) -1983 (-21.03%) -1.78 (-20.97%) 0% n.aUGS 1195 (+38.2%) 944 (+18.4%) 0.85 (+18.4%) 0.53% n.aWetland 25.59 (+0.39%) -36 (-0.98%) -0.03 (-0.9%) -0.06% n.aWater 123.29 (+12.8%) 149 (+167.4%) 0.13 (+162.5%) -0.14% 0.43 (+16.1%)Class CWED SHDI

Cropland n.a 0.0718 (+4.6%)Forest n.a 0.0718 (+4.6%)UGS n.a 0.0718 (+4.6%)Wetland n.a 0.0718 (+4.6%)Water 0.4 (+16.13%) 0.0718 (+4.6%)3. Local climate regulation

Class CA NP PD COHESION SHDI

Forest -325.69 (-3.6%) -1983 (-21.03%) -1.78 (-20.97%) 0% 0.0718 (+4.6%)UGS 1195 (+38.2%) 944 (+18.4%) 0.85 (+18.4%) 0.53% 0.0718 (+4.6%)Wetland 25.59 (+0.39%) -36 (-0.98%) -0.03 (-0.9%) -0.06% 0.0718 (+4.6%)4. Water provision

Class CA NP PD COHESION ED

Wetland 25.59 (+0.39%) -36 (-0.98%) -0.03 (-0.9%) -0.06% n.aWater 123.29 (+12.8%) 149 (+167.4%) 0.13 (+162.5%) -0.14% 0.43 (+16.1%)Class CWED SHDI

Wetland n.a 0.0718(+4.6%)Water 0.4 (+16.13%) 0.0718 (+4.6%)5. Air quality purification


Forest -325.69 (-3.6%) -1983 (-21.03%) -1.78 (-20.97%) 0% 0.0718 (+4.6%) %Wetland 25.59 (+0.39%) -36 (-0.98%) -0.03 (-0.9%) -0.06% 0.0718 (+4.6%)6. Surface runoff and erosion control


Cropland -3657.42 (-8.6%) -246 (-8.9%) -0.22 (-8.84%) -0.01% 0.0718 (+4.6%)Forest -325.69 (-3.6%) -1983 (-21.03%) -1.78 (-20.97%) 0% 0.0718 (+4.6%)UGS 1195 (+38.2%) 944 (+18.4%) 0.85 (+18.4%) 0.53% 0.0718 (+4.6%)Wetland 25.59 (+0.39%) -36 (-0.98%) -0.03 (-0.9%) -0.06% 0.0718 (+4.6%)7. Recreation and aesthetic value


Forest -325.69 (-3.6%) -1983 (-21.03%) -1.78 (-20.97%) 0% 0.0718 (+4.6%)UGS 1195 (+38.2%) 944 (+18.4%) 0.85 (+18.4%) 0.53% 0.0718 (+4.6%)Wetland 25.59 (+0.39%) -36 (-0.98%) -0.03 (-0.9%) -0.06% 0.0718 (+4.6%)


8. Habitat for biodiversity

Class CA NP PD TCA COHESION

Cropland -3657.42 (-8.6%) -246 (-8.9%) -0.22 (-8.84%) -2269.59 (-13.6%) -0.01%Forest -325.69 (-3.6%) -1983 (-21.03%) -1.78 (-20.97%) 31.08 (+1.9%) 0%UGS 1195 (+38.2%) 944 (+18.4%) 0.85 (+18.4%) -0.04 (-0.02%) 0.53Wetland 25.59 (+0.39%) -36 (-0.98%) -0.03 (-0.9%) -186.57 (-5.3%) -0.06%Water 123.29 (+12.8%) 149 (+167.4%) 0.13 (+162.5%) 8.04 (+3.8%) -0.14%Class ED CWED SHDI

Cropland -2.73 (-4.02%) -1.59 (-3.54%) 0.0718 (+4.6%)Forest -3.77 (-10.7%) -2.55 (-10.02%) 0.0718 (+4.6%)UGS 6.41 (+34%) 6.41 (+34%) 0.0718 (+4.6%)Wetland 0.49 (+3.6%) 0.42 (+5.24%) 0.0718 (+4.6%)Water 0.43 (+16.1%) 0.4 (+16.13%) 0.0718 (+4.6%)

Food products for instance from the Americas like dairy products, meat and cornare exported, negatively affecting many other ES values. From these considera-tions, it can be summarized that scale and transferability of services are highlylocation-dependent. ES in Kigali are mostly experienced locally or in the sur-rounding hinterland as might be in similar capitals in SSA emerging economies.Supporting services are universal and exceed the boundary of the local study area;several goods produced in the study area are also transported outwards.

5.5 Research contributions

Extracting and classifying LULC in complex environments such as urban areasfrom remotely sensed data is still challenging, given the high spatial and spectralheterogeneity and overlap among contiguous and intersected LULC classes (Hashimand Hamid, 2021; Moreira and Galvão, 2010; Herold et al., 2003a; Momeni et al.,2016; Schneider, 2012). The nature of land transformation to urban and the re-sulting environmental side effects need to be documented to the best of our ability(Wentz et al., 2009) to enable us to make informed decisions pertaining to sustain-able urban development. In this context, this thesis contributes to the developmentof approaches for mapping and monitoring urban development and environmentalimpact associated with it. This is done exploring the potential and limitations ofmultiresolution remotely sensed data, LM and explicit spatial urban ES. Doing so,the results contribute in aspects of methodology and applications per se.

From a methodological aspect, the present research illustrates the potential ofwell-established methods in remote sensing for mapping and monitoring the spatio-temporal dynamics of urban areas. Through this research, I could suggest multi-resolution data selection criteria, and spectral and spatial resolution requirementsfor urban mapping applications. Combining spectral features with texture features,and/or with derived biophysical spectral indices was found to be valuable. Further-more, I could show that the combination of spectral features with feature selection,feature extraction, data fusion and dimensionality reduction resulted in a much bet-


ter transferability for mapping complex urban areas, as reported by previous studies(e.g. Kabir et al., 2010; Kuffer et al., 2016; Stromann et al., 2020). A novel cloudbased framework for dynamic urban LULC change trajectories based on Landsattime series and spectral temporal segmentation algorithm, i.e. LandTrendr, wasfurther developed and implemented. The use of an EO big data analytics platform,such as GEE cloud computing, and advanced ML algorithms were found a robustapproach for processing image time series.

With regard to the applications aspect, this research focused on deriving envi-ronmental variables using LM for spatio- temporal analysis of urban environmentalchange monitoring. The actual data in this thesis are from SSA that are undergoingrapid urbanization, but I believe that methods and approaches used are universallyapplicable. The methods and techniques recommended and used throughout thepresent research can be used to produce cost-effective geospatial data and informa-tion about urban LULC patterns, and urban environmental change, especially inregions where data are fragmented and scarce.

Chapter 6

Conclusions and Future Research

6.1 Conclusions

The present research demonstrated the potential of multi-resolution and multi-temporal satellite data in investigating the spatio-temporal urban LULC changedynamics, and in analyzing urbanization environmental side-effects using Kigali,Rwanda as case study. State-of-the-art methods and techniques for classifyingLULC in complex urban environments, and characterizing urbanization dynamicsusing multi-resolution remote sensed data were explored and developed. Further-more, the spatio-temporal change of urban landscape composition and configura-tion, and impacts of LULC change on the supply and demand of ES were inves-tigated based on classified LULC using LM and ES indicators. Subsequently, theinfluence of the LULC patch mosaic on the spatial distribution and pattern of ESwas investigated.

Medium-resolution data (20-30m) were found useful for generally tracking ur-ban LULC change trajectories, whereas HR and VHR data (at>10m) are suitablefor detailed urbanization mapping, particularly for disaggregating built-up areas,and green and blue spaces at a fine scale. It was found that non-parametric MLclassifiers such as SVM and RF performed well in LULC classification in complexurban environments. The use of GEE-LT framework was further found robust fordynamic tracking of urban LULC change trajectory using Landsat time series in acloud computing environment. This novel framework could further help in avoidingpotential error propagation while estimating LULC changes between two generatedmaps, or when performing post-classification refinement. The present study demon-strated that the high quality of the produced LULC information based on satellitedata depends on the combination of spectral bands, spectral indices and texturalfeatures such as the ones derived from GLCM. The confusion among LULC classesdue to high spectral variability occurring while classifying VHR data (at<=5m)could be avoided by integrating spectral bands with rule-based approaches. Fur-thermore, object- and rule-based analysis, and DL models were found useful for

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characterizing the spatial variation of urban density and impervious surfaces.Classification results illustrated that the combination of spectral bands with bio-

physical indices and GLCM texture features and the application of non-parametricML classifiers yielded good classification results with overall accuracies ranging from83.7% to 96%, and kappa coefficients between 0.794 and 0.940, respectively. VHRWorldView-2 data and object-based SVM classification, refined by geometric rulesand the integration of the urban density index resulted in thematically rich urbanLULC mapping with 12 LULC classes and an overall accuracy of 85.36%, and akappa coefficient of 0.823. Multi-resolution and multi-temporal classified imageryillustrated that Kigali underwent a rapid urbanization in the last 37 years from1984 to 2021. The urbanized area increased approximately from 2013 ha in 1984to 12,023 ha in 2021. The estimated of annual average of urbanization growth ratewas evaluated at 4.5%. During the study period, three urban development scenariosare observed including including i) infill through housing and infrastructures devel-opment in core urban areas, ii) urban sprawl in fringe zones and iii) developmentof urban patches at distant locations intercepted by cropland. The last scenario iscommonly known as leapfrogging urban development.

It was found that the quantification of urbanization environmental impactsbased on classified satellite data, and using both LM indices and ES is a promis-ing methodology for monitoring environmental side-effects of urbanization. It wasrevealed that the study area underwent landscape fragmentation, with changes inlandscape composition and configuration as a result of high paced urban develop-ment at 469% growth rate in the last 37 years since 1984. Forest and croplandwere the most fragmented classes, whilst built-up areas (both HDB and LDB) weremostly the aggregated and expanded classes due to accelerated urbanization duringalmost four decades. ES in the study area included regulating services such as floodand climate extreme weather events regulation and erosion control, provisioning ser-vices such as food production, water and construction material provision, supportservices such as nutrient and water recycling, aesthetic benefits, and recreationalareas, and habitat for biodiversity. Based on multi-temporal classified Landsat timeseries, an estimated value of 42.3 million US Dollars per year was lost between 1984and 2016 due to alterations of ES mainly resulting from urbanization and clear-cutting and increasing the area occupied by buildings and road networks. The ESVderived in the study area should be regarded as a value for restoring and main-taining the ecosystems through willingness to pay. Moving from a monetary basedapproach of ES valuation to a more qualitative change in ES bundles, the presentstudy explored the utility of using a matrix, spatially linking landscape units withES supply and demand budgets.

This thesis contributes to the development of approaches for mapping and mon-itoring urban development and associated environmental impact in SSA throughthe exploration of potential and limitations of multi-resolution remote sensing data.Methodological frameworks for urban LULC production based on state-of-the-artML, DL, and EO big data analytics were implemented and tested. Furthermore,the thesis demonstrated that the open access EO data are cost-effective data source

CHAPTER 6. CONCLUSIONS AND FUTURE RESEARCH 80

for monitoring urbanization and for investigating the impact of spatial structurechanges on the distributions and patterns of ecosystem service bundles. Through re-search findings, it was found that the urban development leads to the fragmentationand change pattern of existing LULC. It was found that the change in landscapeconfiguration and composition in urbanizing environment is correlated with changein supply and demand of ES. The increase in built-up areas as a result of urbaniza-tion, induce high demand of ES bundles for urban well-being. A reduction of areacovered by cropland, forest and UGS, alteration of wetlands, and contamination ofwater lead to a decrease of ES supply capacity. The frameworks developed in thisresearch can easily be transferred to other SSA cities.

6.2 Limitations, recommendations and outlook

Optical satellite data is affected by the presence of cloud that are persistent intropical regions. Therefore, data acquisition for continuous urbanization monitor-ing could be a challenge. Data acquisition on anniversary dates was not possibleduring this study. In order to avoid dependence on cloud-free skies, the combina-tion of free SAR data such as Sentinel-1 and available optical Sentinel-2 MSI datathrough data fusion is recommended as an extra data source. The interpretationof the information derived from medium-resolution images such as Landsat couldbe complemented by field visits and validation of results with existing data whichcan reduce misclassifications and thus, resulting in a more realistic classificationoutcome. The use of ancillary data could contribute in data calibration and thenimprove the quality of final LULC products. HR data is believed to further increasethe quality of classified images which could be useful input for remote sensing basedmethods for valuation of ES. Geo-ICT infrastructures are critical for EO data pro-cessing and dissemination. The goal of present research was beyond the assessmentof hardware and software performance for handling big EO data including advancedML and DL models. On the basis of limitations and potential pending issues tobe addressed, a number of points are proposed for future research. It is useful tokeep exploring and developing novel and cost-effective methodologies for detect-ing and mapping dynamic change of deprived and environmentally sensitive urbanareas such as slums and informal settlements. Remote sensing data are continu-ously becoming more voluminous and treating them requires super- computing andadvanced skills. Given that the present thesis was dedicated to the use of opticalremotely sensed data, future research will investigate the use of SAR and the fusionon SAR and optical data to overcome optical data availability gaps and improvemapping accuracy. Furthermore, future research will explore the use of advancedmachine learning and deep learning methods in a cloud computing environment tofurther develop a dynamic framework for continuous urban LULC change moni-toring. In addition, future research is also planned to evaluate the integration ofmultiple-source data, i.e., EO data, population statistics and other types of datato detect and map urban deprivation and environmentally sensitive areas.

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