Flood mapping from radar remote sensing using automated image classification techniques

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Promotors

prof. dr. ir. Niko E.C. Verhoest

Hydro-Climate Extremes Lab (H-CEL) Department of Environment

Faculty of Bioscience Engineering – Ghent University prof. dr. ir. Frieke M.B. Van Coillie

Remote Sensing|Spatial Analysis Lab (REMOSA) Department of Environment

Faculty of Bioscience Engineering – Ghent University Committee members

prof. dr. ir. Kathy Steppe (Chairman)

prof. dr. ir. Katrien Van Eerdenbrugh (Secretary) dr. ir. Marco Chini

dr. Guy J.-P. Schumann prof. dr. Tim Van de Voorde Dean

prof. dr. ir. Marc Van Meirvenne Rector

prof. dr. ir. Rik Van de Walle

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Lisa Landuyt

Flood mapping from radar remote sensing using automated image

classification techniques

Dissertation submitted in fulfillment of the requirements for

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Dutch translation of title

Radargebaseerde overstromingskartering aan de hand van geautomatiseerde beeldclassificatietechnieken

Reference

L. Landuyt. Flood mapping from radar remote sensing using automated image classification techniques. PhD thesis, Ghent University, June 2021.

ISBN 978-94-6357-415-0

This work was supported by the Research Foundation Flanders (FWO; fellow- ship G017916N).

The author and the promotors give the authorisation to consult and to copy parts of this work for personal use only. Every other use is subject to the copyright laws. Permission to reproduce any material contained in this work should be obtained from the author.

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Acknowledgements

This PhD manuscript is the result of four years of reading, programming, writing, trying and retrying, leaning back and thinking,... generally summarized as scientific research. While research is often considered individualist, and I definitely felt like sitting on my PhD island alone from time to time, I wouldn’t have been able to accomplish this journey without the people surrounding me.

First of all I would like to sincerely thank my promotors, professors Niko Verhoest and Frieke Van Coillie. Thank you for inspiring me during my Master’s studies, for giving me the opportunity to start this PhD and connecting me to the right people, and for sticking with me until the end with your listening ear and valuable feedback at crucial moments.

I feel fortunate to have been surrounded by a bunch of great colleagues. Katrien, Alexandra en Brecht, my big sisters and brother at the lab, thank you for instantly making me feel at home and being such inspiring examples and friends. Brianna, Dominik, Jorn and the other office 10 people, thanks for the good vibes, chit chats and plant revival adventures. Matthias, Hans and Jessica, thanks for sharing your experience and taking me around at my first conferences, you guys rock and roll science. Thank you Bos, for being my companion during the last months, Jeroen and Marie-Leen, for the always vivid lunch discussions, and Jasper, for being such a committed teacher. I could go on a little longer but keeping things short, I would like to thank the entire LHWM/H-CEL and REMOSA families for all the good – not always scientific – conversations, technical support and fun moments. Thank you also to Rudi, Anabelle and Inge, for always being available when I needed IT or administrative support.

This PhD introduced me to academia, where a network is so very important.

Guy, thank you for getting me up to speed with the (in the meantime epic, at least for me) Dee imagery and making time to meet and discuss during

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my first year, that motivated me a lot. Your passion and commitment to the scientific community are truly inspirational. To the LIST team, Renaud, Patrick, Ramona and Marco, thank you for your openness, for welcoming me and for the great work you are doing for our community. Also a big thanks to the Cloud to Street team, true bridging-the-gap pioneers, I gained valuable skills and vision during our collaboration. At last, I would like to sincerely thank Bram and Joost from VMM as well as Jurgen from VITO.

The TerraFlood project was not only a pleasant collaboration but also a big motivator during the final months of my PhD.

As far as they were not mentioned yet, I would also like to thank the members of the jury committee for their highly-appreciated feedback and suggestions, which allowed to further improve this manuscript.

Tot slot wil ik ook graag alle fijne mensen bedanken die ervoor zorgden dat mijn leven de afgelopen jaren veel meer was dan een doctoraat alleen. Mijn vrienden, bio-ingenieurs in alle geuren en kleuren, ge¨emancipeerde architectes, spring-in-’t-veld juristes, aimabele cineastes, Pluk-ers, globetrotters,... bedankt voor jullie interesse en steun, voor het groter maken van mijn wereld en vooral voor alle plezierige momenten! Mijn fantastische (schone) familie, ouders, grootouders, tantes en nonkels, het liefste metekind ter wereld, bedankt om er altijd te zijn en me door dik en dun te steunen. Papa en mama, bedankt om me te tonen dat doorzetten altijd rendeert, maar nog meer dat liefde altijd alles overwint. Jullie onvoorwaardelijkheid is onbetaalbaar. Suzusje, merci voor je complementariteit, kracht en eeuwige vrolijkheid. JoKa, JoMa en Sander, bedankt om me zo een fijne en warme tweede thuis te geven. En last but not least, Dries, bedankt om me het proces te leren appreci¨eren, om je passie, Afrikaanse ritmes en geniale concepten met mij te delen, maar bovenal om zo een entertainend en avontuurlijk fundament van vertrouwen te zijn. Ik kan niet wachten om te zien wat de toekomst brengt met jou aan mijn zijde.

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List of Figures

1.1 Global occurrences of flood disasters in 2000-2020. . . 3

1.2 Overview of past, present and announced SAR missions. . . 6

2.1 Electromagnetic spectrum. . . 10

2.2 Observation geometry of side-looking radar. . . 12

2.3 Concept of synthetic aperture. . . 13

2.4 Geometric effects due to terrain distortion. . . 18

2.5 Radar scattering mechanisms. . . 23

2.6 Influence of local height variations, wavelength and incidence angle on backscatter. . . 25

2.7 Scattering contributions in a vegetation canopy. . . 27

3.1 Example of histogram thresholding. . . 36

4.1 Overview of full SAR scenes, subsets and corresponding his- tograms for all study areas. . . 61

4.2 Accuracy overview of global thresholding based on the Otsu, KI, Li and Yen algorithm. . . 67

4.3 Accuracy overview of thresholding approaches and active contour models. . . 70

4.4 Contingency maps for tiled thresholding on the Fergus and Tay image. . . 71

4.5 Contingency maps for active contour modeling on the Trent and Dee 2006 image. . . 75

4.6 Accuracy overview of change detection approaches. . . 78 4.7 Contingency maps for change thresholding and HSBA-Flood on

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5.1 Overview of situation and SAR image pair for all study areas. . 87 5.2 Overview of the OBIAflood methodology. . . 91 5.3 Violin plots comparing the class distributions of DL and FV

across the different SAR features for the Sava and Volta 3 ROI. 96 5.4 Accuracy overview of CC and CC+PP for different FS/k com-

binations. . . 98 5.5 Three-class F1 scores throughout the processing steps for clus-

tering based on the SAR and SARwCopt features withk=10. . 100 5.6 Contingency maps of the final classification for all study areas. 102 6.1 Situation of the ROI and the flood events analyzed. . . 110 6.2 Abundance of the DL, PW, OF and PFF classes across the

considered flood cases. . . 113 6.3 Overview of the TerraFlood methodology. . . 115 6.4 Drone imagery, Sentinel-1 composite and TerraFlood map for

floods in Boortmeerbeek and along the Poekebeek. . . 121 6.5 Accuracy overview of the OBIAflood method. . . 122 6.6 Accuracy overview of the TerraFlood method across a range of

parameter values. . . 124 6.7 Relative occurrence of true classes in the pixel-based, object-

based and combined TerraFlood PF class. . . 126 6.8 Comparison of class-averaged and class specific (DL, PW and

OF) F1 scores for the pixel-based, object-based and combined TerraFlood maps. . . 127 6.9 Accuracy overview of pixel- and object-based thresholding, the

OBIAflood method and the TerraFlood method. . . 129 6.10 Sentinel-1 composite, ground truth map and TerraFlood map

for floods along the Barebeek, in Herk-de-Stad and in Wellen. . 130 6.11 Relative occurrence of true classes in the pixel-based, object-

based and combined TerraFlood LTF class. . . 132 6.12 Number of flood detections across the Sentinel-1 archive in 2015–

2020 for Lo-Reninge, Herk-de-Stad and Oostkamp. . . 136 6.13 Detected flooded area throughout 2020 for Lo-Reninge, Herk-

de-Stad and Oostkamp. . . 137 B.1 Similar to Figure 6.10 but for floods in Aartselaar, Alken, Geet-

bets and Halen. . . 168 B.2 Similar to Figure 6.10 but for floods in Laakdal, Lummen,

Maaseik and Meerhout. . . 169 B.3 Similar to Figure 6.10 but for floods in Mol, Oudsbergen, Pelt

and Zoutleeuw. . . 170 B.4 Similar to Figure 6.10 but for floods along the Hanzamevaart

and in Putte-Bonheiden. . . 171 viii

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List of Tables

2.1 Designation of radar bands. . . 10

2.2 Overview of a selection of past, current and upcoming spaceborne SAR sensors with their properties. . . 20

2.3 Main characteristics of Sentinel-1 acquisition modes. . . 21

4.1 Overview of the assessed flood mapping approaches. . . 51

4.2 SAR image properties for each of the study areas. . . 60

4.3 Contingency table . . . 64

4.4 Accuracy measures for different thresholding approaches on the Trent S5 subset. . . 68

5.1 Overview of dates/date ranges of the used Sentinel-1 and Sentinel-2 imagery for each of the study areas. . . 88

5.2 Overview of feature subspaces considered for object clustering. 92 5.3 RG parameters for the PW, OF, and FV classes. . . 94

5.4 Accuracy of CC+PP for SARwCopt-10 expressed in terms of different metrics for each of the study areas. . . 101

6.1 Acquisition times of the drone, Sentinel-1 and Sentinel-2 imagery used for quantitative validation. . . 112

6.2 Look-up table specifying in which class a combination of classes results. . . 118

A.1 Three-class F1 scores for K-means clustering and clustering classification, applied on the Fergus case. . . 154 A.2 Three-class F1 scores for K-means clustering and clustering

classification complemented by a post-processing refinement,

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A.3 Three-class F1 scores for K-means clustering and clustering classification, applied on the Shannon case. . . 156 A.4 Three-class F1 scores for K-means clustering and clustering

classification complemented by a post-processing refinement, applied on the Shannon case. . . 157 A.5 Three-class F1 scores for K-means clustering and clustering

classification, applied on the Sava case. . . 158 A.6 Three-class F1 scores for K-means clustering and clustering

classification complemented by a post-processing refinement, applied on the Sava case. . . 159 A.7 Three-class F1 scores for K-means clustering and clustering

classification, applied on the Volta case. . . 160 A.8 Three-class F1 scores for K-means clustering and clustering

classification complemented by a post-processing refinement, applied on the Volta case. . . 161 B.1 Qualitative assessment of the flood visibility and TerraFlood

maps for all 140 considered SAR images. . . 164

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List of Acronyms

ACM Active Contour Model

BOA Bottom Of Atmosphere

CC Cluster Classification

CFC Cloud-Free Composite

CGLS Copernicus Global Land Service CRF Conditional Random Field CSI Critical Success Index DEM Digital Elevation Model

DL Dry Land

DTM Digital Terrain Model

EM Electromagnetic

EMS Emergency Mapping Service

EO Earth Observation

ESA European Space Agency

EW Extra-Wide swath

FV Flooded Vegetation

FN False Negatives

FP False Positives

FS Feature Space

GFP Global Flood Partnership

GG Generalized Gaussian

GRD Ground-Range Detected

H Horizontally polarized

HAND Height Above Nearest Drainage HSBA Hierarchical Split-Based Approach

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IF Invisible Forested area InSAR Interferometric SAR

IW Interferometric Wide swath KI Kittler and Illingworth

LC Land Cover

MMU Minimal Mapping Unit

MRF Markov Random Field

NASA National Aeronautics and Space Administration

OA Overall Accuracy

OBIA Object-Based Image Analysis

OF Open Flooding

PA Producer’s Accuracy

PC Proportion Correct

PDF Probability Density Function

PF Probable Flooding

PFF Probably Flooded Forest

PP Post-Processing

PW Permanent Water

RCS Radar Cross-Section

RG Region Growing

ROI Region Of Interest

SAR Synthetic Aperture Radar

SEPA Scottish Environment Protection Agency SFS Structural Feature Set

SLC Single Look Complex

SM Stripmap Mode

SNAP Sentinel Application Platform SRTM Shuttle Radar Topography Mission

TN True Negatives

TP True Positives

UA User’s Accuracy

UN-SPIDER United Nations Platform for Space based Information for Disaster Management and Emergency Response

V Vertically polarized

VITO Vlaams Instituut voor Technologisch Onderzoek (Flemish Institute for Technological Research)

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VMM Vlaamse Milieumaatschappij (Flanders Environment Agency)

WFC Within-Flood Confusion

WV Wave mode

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List of Symbols

a positive constant (–)

A area (m²)

A_D Ashman D coefficient (–)

α smoothing factor (–)

b positive constant (–)

β antenna beamwidth (rad)

c speed of light (m/s)

C contour (–)

d distance between line segment centers (px)

D Euclidean distance (m)

δ_p penetration depth (m)

E energy function (–)

⁰_r dielectric constant (–) ⁰⁰_r dielectric loss factor (–)

η cross entropy (–)

ζ weighing parameter (–)

F goodness function (–)

G antenna gain (dB)

γ curvature weight (–)

γ⁰ radar-cross section normalized to plane perpendicular to line of sight from sensor to elliptical model of ground surface (–

or dB)

Γ gamma function (–)

I image array (–)

J cost function (–)

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k weight of local statistics deviation (–) k_s kernel size (–)

κ weighing parameter (–)

λ wavelength (m)

LA antenna side length (m)

m₁ first moment (unit depending on sample) µ mean (unit depending on sample)

n sample size (–)

p probability (–)

P cumulative probability (–)

P_r received power (W)

P_t transmitted power (W)

P_dark percentage of darkest pixels (%)

φ mean of sample variance distribution (unit depending on sample)

ψ gamma distribution shape parameter (–)

r resolution (m)

r_dil radius dilation operator (px)

R slant-range distance between sensor and target (m)

ρ tension weight (–)

S entropy (–)

σ radar cross-section (Chapter 2, m²)

standard deviation (Chapter 4–5, unit depending on sample) σ⁰ radar cross-section normalized to ground area (– or dB) σ_B between-class variance (unit depending on sample)

T threshold value (dB)

τ length transmitted pulse (s) θ_i incidence angle (rad)

ν variance (unit depending on sample)

ν unit vector (–)

ω class fraction (–)

δ· local change of a variable (unit depending on variable)

·⁺ next variable (–)

·⁻ previous variable (–)

·_b background class (–)

·_f foreground class (–) xvi

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Summary

Floods are a hazard of major concern, causing substantial fatalities and economic losses. These losses are expected to further accumulate in the future, as both the frequency and magnitude of flood events are projected to increase due to climate change. Insights into the occurrence and dynamics of these disas- trous events are thus of paramount importance for the protection of livelihoods across the world, both in the near and far future.

Synthetic Aperture Radar (SAR) satellite imagery is particularly suited to observe floods due to the synoptic view, low cost and timely availability of satellite imagery and the all-weather imaging capabilities of SAR sensors. The resulting observations are crucial for various purposes, including emergency relief, post-disaster damage assessment, the calibration and validation of flood prediction models, and risk assessment.

Despite the clear advantages of SAR imagery, several factors complicate the flood extent retrieval from this imagery type. These include surfaces or land dynamics characterized by a SAR backscatter similar to that of water/flooding, as well as the presence of urban features and vegetation. Moreover, existing approaches often lack the robustness and automation necessary for operational purposes. This thesis aims to contribute to the accuracy and automation of SAR-based flood mapping approaches, by elaborating on several of the remaining challenges. More specifically, the objectives of this thesis are:

1. to investigate the state of the art in SAR-based flood mapping and identify the strengths and limitations of existing methods, as well as possible trends;

2. to assess the potential of C-band SAR for the delineation of flooded

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3. to identify the main obstacles with respect to automated flood monitoring, and develop an approach that allows putting science into practice.

In the process of pursuing these objectives, special attention is given to automation, as this is key for objective and timely observations, and to optimally employing available data, as additional data can substantially improve flood observations but not handling these critically may be have adverse effects.

Additionally, the potential of object-based image analysis (OBIA) techniques is investigated, as they have proven their added value using optical imagery but SAR-based applications remain limited. Sentinel-1 imagery is the main data source considered in this thesis, as this medium-resolution C-band imagery is freely available and provides consistent global coverage.

First, the state of the art in SAR-based flood mapping is investigated. Distin- guishing between approaches for the retrieval of open water, flooded vegetation and urban flooding, deployed input data and classification techniques are discussed. As it is difficult to draw conclusions regarding the strengths and limitations of these classification techniques based on their scientific publica- tions, an in-depth assessment and comparison of a selection of these is carried out. This selection includes thresholding, active contour modeling and the HSBA-Flood method, and both single scene and change detection-based maps are generated.

To tackle the second objective of this thesis, the detectability of both woody and herbaceous vegetation using Sentinel-1 is investigated. Moreover, an automated, object-based clustering approach, making use of globally and freely available data only, is presented and applied on four study areas with varying characteristics. The resulting flood maps discriminate between dry land, permanent water, open flooding and flooded vegetation. Forests are indicated too, in order to underline the uncertainty related to these areas where flooding cannot or only to a limited extent be detected.

In the last part of this thesis, an approach for operational flood monitoring in Flanders is presented. This approach was developed for and with input of the local water manager, i.e.the Flanders Environment Agency, and makes use of high-resolution ancillary data available for the region of interest. By combining a pixel-based and an object-based approach, a discrimination is made between dry land, permanent water, open flooding, probable flooding, flooded vegetation and probably flooded forests. The approach is extensively tested on flood events of different sizes that occurred between 2016 and 2020.

Both the detectability of these flood events and the accuracy of the developed algorithm, in the presence and absence of flooding, are assessed and discussed.

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Samenvatting

Overstromingen zijn een natuurlijke fenomeen, maar zorgen jaarlijks voor veel dodelijke slachtoffers en aanzienlijke economische schade. In de toekomst zal de schade veroorzaakt door overstromingen enkel toenemen, aangezien verwacht wordt dat zowel de frequentie als de magnitude van deze gebeurtenissen zal toenemen ten gevolge van de klimaatverandering. Het is dus van uiterst groot belang inzicht te krijgen in het voorkomen en de dynamieken van overstromingen om samenlevingen wereldwijd te beschermen, zowel in de nabije als verdere toekomst.

Radarsatellietbeelden zijn uiterst geschikt om overstromingen in kaart te bren- gen dankzij hun synoptisch bovenaanzicht en snelle beschikbaarheid. Bovendien worden radarsensoren niet gehinderd door bewolking. De resulterende overstro- mingskaarten zijn uiterst nuttig voor verschillende doeleinden, waaronder nood- hulp en schadevaststelling in de nasleep van een overstroming. Daarenboven kunnen ze bijdragen aan de paraatheid in de aanloop naar een overstroming, via het kalibreren en valideren van overstromingsmodellen, en aan de preventie van overstromingen, via risicobeoordelingen.

Ondanks de grote voordelen van radarbeelden, zijn er ook verschillende factoren die de aflijning van overstromingen op basis van deze beelden bemoeilijken.

Wateroppervlakken worden gekenmerkt door een lagebackscatter, waardoor ze te herkennen zijn als donkere zones op radarbeelden. Er zijn echter verschillende landschapselementen, zoals asfalt oppervlakken en droge braak- liggende terreinen, die een gelijkaardig voorkomen hebben. Ook kunnen land- bouwactiviteiten of bodemvocht- en vegetatiedynamieken leiden tot variaties in backscatter zoals die typisch verwacht worden bij overstromingen. Daarnaast ontbreekt bij bestaande karteringsmethoden vaak nog de automatisering en robuustheid die nodig is voor operationele doeleinden. Deze doctoraatsthesis

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stromingskarteringsmethoden door in te zetten op verschillende uitdagingen.

De objectieven van deze doctoraatsthesis zijn:

1. het onderzoeken van destate of the art en het identificeren van de sterktes en zwaktes van bestaande methodes, alsook mogelijke trends;

2. het onderzoeken van het potentieel van C-band radar voor het aflijnen van overstroomde vegetatie, en het ontwikkelen van een methode die toelaat overstroomde vegetatie op een geautomatiseerde manier te karteren;

3. het identificeren van de grootste obstakels voor geautomatiseerde over- stromingsmonitoring, en het ontwikkelen van een monitoringsmethode die toelaat de brug te slaan naar de praktijk.

Bij het nastreven van deze objectieven wordt extra aandacht geschonken aan automatisering en optimaal datagebruik. Daarenboven wordt ook het potentieel van objectgebaseerde beeldanalysetechnieken onderzocht. Binnen deze doctoraatsthesis wordt voornamelijk gebruik gemaakt van Sentinel-1 radarbeelden, aangezien deze globaal en gratis beschikbaar zijn.

In een eerste deel van deze thesis wordt de state of the art beschreven. Meer bepaald worden beschouwde data en gebruikte karteringstechnieken uitgelicht, en dit voor de aflijning van zowel open overstromingen als overstroomde vegetatie en urbane overstromingen. Aangezien het vergelijken van technieken op basis van enkel de bijhorende publicaties moeilijk is, wordt een selectie ook grondig geanalyseerd en vergeleken op basis van beelden van zes overstromingen.

Om aan het tweede objectief te voldoen wordt in de eerste plaats de detecteerbaarheid van zowel lage overstroomde vegetatie als overstroomde bossen met behulp van Sentinel-1 onderzocht. Daarnaast wordt een objectgebaseerde methode voorgesteld die enkel gebruik maakt van gratis en globaal beschik- bare data. In de resulterende kaarten wordt onderscheid gemaakt tussen niet-overstroomd land, permanent water, open overstroming en overstroomde vegetatie. Ook bossen, waar de overstromingsstatus ongekend is, worden aangeduid.

In het laatste deel van deze thesis wordt een methode voor operationele overstro- mingsmonitoring in Vlaanderen voorgesteld. Deze methode werd ontwikkeld voor en met input van de lokale waterbeheerder, namelijk de Vlaamse Milieu- maatschappij, en maakt gebruik van gegevens die beschikbaar zijn specifiek voor Vlaanderen. Naast overstroomde gebieden en overstroomde vegetatie worden ook mogelijks overstroomde gebieden en waarschijnlijk overstroomde bossen aangeduid. Deze methode is uitvoerig getest aan de hand van verschillende types overstromingen die plaats vonden tussen 2016 en 2020. Zowel de detecteerbaarheid van deze overstromingen als de accuraatheid van het karteringsmethode worden uitgebreid besproken.

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1

CHAPTER 1 Introduction

This chapter provides the motivation of this thesis, by elaborating on the occurrence and impact of floods as well as the importance of accurate and timely observations for decisive risk and disaster management. Subsequently, the objectives and structure of this thesis are presented.

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1 1.1 Motivation

Societies around the world are increasingly threatened by floods. The IPCC (2018) defines this natural hazard as the overflowing of the normal confines of a stream or other body of water, or the accumulation of water over areas that are not normally submerged. Several types of floods can be discriminated, including river (fluvial) floods, flash floods, urban floods, pluvial floods, sewer floods, coastal floods, and glacial lake outburst floods (IPCC, 2018). Among all disasters, floods were the most frequent and affected most people in the past two decades. As visualized in Figure 1.1, China, the USA, India, the Philippines and Indonesia are the countries that were most frequently hit by flooding in these two decades. Throughout this period, about 1.65 billion people were affected by flooding, while economic losses are estimated at 651 billion USD. The latter is a significant underestimation, as economic losses were reported for only 35% of the recorded disaster events (all types) (CRED and UNDRR, 2020). In 2019 alone, 194 flood events caused 5110 deaths and 36.8 billion USD of economic damages. Most of these losses are caused by a limited number of major events. For example, flooding due to high monsoon rains in India caused 2000 deaths and 10 billion USD of economic losses (CRED, 2020).

Climate change is causing the hydrological cycle to intensify (Huntington, 2006).

Among others, observations point out that globally more areas have experienced increases rather than decreases in the frequency, intensity and/or amount of heavy precipitation since 1950 (Hartmann et al., 2013; Schleussner et al., 2017). These trends are expected to continue in the future, although differing regionally. For Europe for example, a robust increase in mean precipitation over central and northern Europe in winter but only over northern Europe in summer, and decreases in mean precipitation in central/southern Europe in summer are expected under the 2°C warming level (Vautard et al., 2014;

Jacob et al., 2018; Kjellstr¨om et al., 2018). As a consequence, both the frequency and magnitude of flood events are expected to increase in the future (Hoegh-Guldberg et al., 2018). Among others, the expected probability of occurrence of flood events with a current occurrence interval longer than the return period of present-day flood protections is expected to increase in all continents, leading to a widespread increase of flood hazard (Alfieriet al., 2017).

With the exception of potential decreases in northern latitudes due to less snowmelt, expected increases in frequency and/or magnitude are also reported on the continental and regional scales (Mallakpour and Villarini, 2015; Roudier et al., 2016; Donnellyet al., 2017; Thoberet al., 2018; Mohammedet al., 2017).

Insights into flood occurrence and dynamics are thus of paramount importance for the protection of livelihoods across the world, both in the near and far future.

2

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1

Figure 1.1: Global occurrences of flood disasters in 2000-2020 (CRED / UCLouvain, 2021).

Observations of flooding, and disasters in general, are crucial for both risk and crisis management. Wisneret al. (2002) identified four major phases in the disaster management cycle, i.e. prevention and preparedness before a disaster hits, and response and recovery afterwards. Observations can contribute to each of these phases. Information on risk, which is the result of hazard, mostly assessed based on observations, vulnerability and exposure, is crucial for prevention and preparedness (Kron, 2005; Ka´zmierczak and Cavan, 2011).

Examples here include risk-aware spatial planning, the location and design of hazard mitigation structures such as retention basins and dykes for flood prevention, and risk awareness building (Ran and Nedovic-Budic, 2016; Bubeck et al., 2012; Burningham et al., 2008). Also model predictions are invaluable in this context, as they increase preparedness in the run-up to a disaster (Smithet al., 2016). Observations are of prime importance for the calibration and validation of these models (Schumannet al., 2009a; Gobeyn et al., 2017).

Moreover, they can contribute to improving predictions by adjusting model states through a data assimilation framework (Garc´ıa-Pintado et al., 2015;

Hostache et al., 2018). Disaster response strongly depends on timely and accurate observations too. They provide insights on e.g. where and up to which extent housing, transport, energy and water supply are affected, which are crucial for decisive emergency relief (Voigtet al., 2016; Schumannet al., 2018). Lastly, observations are also important for recovery, more specifically in the context of damage assessment for insurance claims and reconstruction (Schumannet al., 2018).

Disaster observations come in many forms. For flooding, these include news reports, social media content, pictures taken by local residents, information on

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1

_(Fohringer et al., 2015; Vlaamse Milieumaatschappij, 2016; Van Wesemael et al., 2019; Schumannet al., 2016). Satellite imagery is particularly valuable, especially for large scale events or in data scarce regions, due to its low cost, synoptic view and timely availability (Voigtet al., 2016; Schumannet al., 2018).

Of course, timeliness is relative and depends on the location, scale and duration of the disaster.

Recognizing the importance of satellite-based disaster observations, several initiatives have been established that aim to contribute to the creation and dissemination of these products as well as to capacity building. The United Nations Platform for Space based Information for Disaster Management and Emergency Response (UN-SPIDER) was established in 2006. The program’s mission is to “ensure that all countries and international and regional organi- zations have access to and develop the capacity to use all types of space-based information to support the full disaster management cycle”, which is achieved by connecting the disaster management, risk management and space commu- nities, by capacity building and by institutional strengthening (UN General Assembly, 2007). The International Charter on Space and Major Disasters (https://www.disasterscharter.org) brings together space agencies and space system operators from around the world, in the aim of coordinating resources and expertise for rapid response to major disaster situations. Recog- nized users, mostly disaster relief organisations and civil protection agencies, can activate the charter at any time and at no cost. Also the Copernicus Emergency Management Service (EMS,https://emergency.copernicus.eu) aims to provide crisis information to stakeholders. It provides on-demand mapping as well as early warning and monitoring services for different types of disasters. The SERVIR program (https://www.servirglobal.net), a joint initiative of National Aeronautics and Space Administration (NASA) and United States Agency for International Development, focuses on Earth observation (EO) capacity building in developing countries. It aspires to empower decision makers with tools, products, and services to act locally on climate- sensitive issues such as disasters, agriculture, water, and ecosystems and land use. While scientific research is crucial to continuously improve methodologies and products, it is only relevant if these products reach end users and provide them with the information they need. The Global Flood Partnership (GFP, https://gfp.jrc.ec.europa.eu/) is a multi-disciplinary group of scientists, operational agencies and flood risk managers focused on developing efficient and effective global tools that can address flood-related challenges, focusing on forecasting, monitoring and impact assessment. Through among others the dissemination of products and yearly meetings where findings and experiences are shared, this partnership contributes to bridging the gap between science and practice. Several other initiatives and services exist, as discussed in more detail by Schumannet al. (2018).

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Flood observations can provide information on both flood depth and flood

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extent. Flood depth can be retrieved directly, by means of altimetry techniques, or indirectly, by combining flood extent observations with topography information (Durandet al., 2008; Cohenet al., 2018; Cianet al., 2018b). This thesis focuses on the retrieval of flood extent, which is typically done using optical or Synthetic Aperture Radar (SAR) imagery (Nottiet al., 2018). Due to its ease of interpretation and the distinct spectral signature of water, optical imagery has long been the main source of EO-derived flood observations.

Moreover, long-term data sets exist, such as the Landsat archive dating back to the 1970s (Wulder et al., 2012). However, optical sensors are hampered by cloud cover, which is often persistent during flood events. As such, both consistent monitoring and emergency mapping can be hampered. Moreover, flooding beneath vegetation canopies is difficult to detect (Schumann et al., 2018). SAR sensors are active sensors and thus independent of an external illumination source. They send out signals and measure the signals reflected back towards the sensor, with the backscatter intensity referring to the amount of transmitted signal that is sent back to the sensor. Moreover, SAR signals can penetrate clouds and vegetation, albeit to varying extents depending on the wavelength. SAR imagery is a relatively new EO data source, as the first operational missions were launched only in the 1990s. As can be seen in Figure 1.2, the number of SAR missions has increased sharply throughout the past 15 years. This has lead to considerable advances in SAR applications, including flood mapping (Shenet al., 2019b; Kellndorfer, 2019; McNairn and Brisco, 2004).

SAR imagery provides great opportunities for flood mapping and monitoring, but imposes some additional challenges too. SAR imagery is less intuitive to interpret, as image brightness is not linked to spectral reflectance but to the roughness and dielectric properties of the imaged surfaces. Water surfaces are typically smooth and, as will be explained in more detail in Chapter 2, reflect incoming SAR signals in a specular way. As a result, they typically appear darker on SAR imagery than their rougher surroundings (Meyer, 2019).

However, several factors complicate the retrieval of water extents from SAR imagery. First, SAR imagery is prone to speckle, causing random brightness variations across neighboring pixels, even for homogeneous surfaces. Second, factors like wind and protruding vegetation may cause the water surface to roughen and thereby decrease the contrast with the surrounding land. Third, smooth surfaces like paved surfaces and bare fields are easily confused with the targeted water surfaces. Additionally, backscatter decreases, typically associated with flooding, can also be caused by agricultural activities or soil moisture/vegetation dynamics. An adequate SAR-based flood mapping should consider these elements in order to provide accurate observations. Moreover, automation is key to ensure objective and timely products.

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Figure 1.2: Overview of past, present and announced SAR missions. X-, C- and L-band refer to SAR wavelengths of 2.5–3.75, 3.75–7.5 and 15–30 cm respectively (cfr. Table 2.1). Adjusted from UNAVCO (2021).

1.2 Objectives of this thesis

While the added value of SAR imagery for flood mapping and monitoring is beyond question, several challenges remain. In addition to the aforementioned sources of confusion, also the presence of vegetation and urban features complicate flood extent retrieval. In vegetated areas, the detectability of flooding strongly depends on the degree to which the SAR signal can penetrate the vegetation canopy. If sufficient penetration occurs, flooding is expected to enhance double bounce backscattering, leading to increased SAR intensities (Pierdiccaet al., 2018). However, the resulting SAR intensity is the result of a complex combination of backscattering mechanisms, and the detectability of the double bounce enhancement strongly depends on the relative contributions of the different mechanisms, which in turn depend on the polarization of the SAR signal (Martinis and Rieke, 2015). Especially for shorter SAR wavelengths, both the detectability of flooded vegetation and the separability from other classes remain unclear. In urban areas, the presence of buildings causes double 6

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bounce scattering to be the main backscattering mechanism. The presence of

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water is expected to enhance this mechanism, although the detectability of this enhancement by means of backscatter intensity alone is questioned (Pierdicca et al., 2018). Additionally, buildings provoke geometric effects like layover and radar shadow, complicating information retrieval even more. Lastly, high spatial and temporal resolutions are necessary to capture small scale structures and highly dynamic changes, both characteristic for the urban environment (Chini et al., 2016). Despite its relevance, urban flood mapping falls outside

the scope of this work.

The digital revolution has, among others, led to an exponential increase in data availability, bringing us to the so-called big data era (Cai and Zhu, 2015). This trend extends to EO data, and is reinforced by the open data policy of large space agencies (Wulder et al., 2012; Showstack, 2014). The rapidly growing data availability challenges the existing methods for SAR-based flood mapping, which typically make use of one or two SAR images only (Tweleet al., 2016;

Clementet al., 2018). However, data quality and computational requirements need to be considered too. Moreover, the relevance of additional data should be questioned at any time. As such, the added value of extending the data input for SAR-based flood mapping remains a topic under investigation.

Despite considerable efforts, several methodological challenges remain. Among the approaches that were developed throughout the past years, the majority is presented based on a single case study only. Additionally, scene sizes are mostly chosen in a subjective way, full scene processing is rare, and scene specific parameter optimization frequently occurs. As such, the robustness and transferability of these methods are questionable. Moreover, most approaches are developed based on SAR imagery of flood events only. Although this might seem logic, these images do not reveal potential weaknesses linked to confusion with low backscatter or backscatter decreases not induced by flooding, which is of major importance for automated flood detection and monitoring.

This thesis aims to contribute to the accuracy and automation of SAR-based flood mapping approaches, by elaborating on several of the aforementioned challenges. More specifically, the objectives of this thesis are:

1. to investigate the state of the art in SAR-based flood mapping and identify the strengths and limitations of existing methods, as well as possible trends;

2. to assess the potential of C-band SAR for the delineation of flooded vegetation, and suggested an approach for doing so in an automated way;

3. to identify the main obstacles with respect to automated flood monitoring, and develop an approach that allows putting science into practice.

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In the process of pursuing these objectives, special attention is given to:

• automation, as this is key for objective and timely observations;

• optimally employing available data, as additional data can substantially improve flood observations but not handling these critically may have adverse effects;

• object-based image analysis (OBIA) techniques and how they compare to traditional pixel-based approaches, as they have proven their added value using optical imagery but SAR-based applications remain limited.

Sentinel-1 imagery is the main data source considered in this thesis, as this medium-resolution C-band imagery is freely available and provides consistent global coverage.

1.3 Structure of this thesis

In the following chapters, the aforementioned objectives are tackled. To start, Chapter 2introduces the main concepts discussed in the remainder of this thesis, and elaborates on their physical fundamentals. The next two chapters target the first objective defined in Section 1.2. More specifically,Chapter 3 provides an extensive but non-exhaustive overview of existing SAR-based flood mapping approaches and elaborates on differences and similarities. In Chapter 4, a selection of these approaches is critically assessed and compared, considering both their accuracy and robustness. Chapter 5tackles the second objective, as the potential of Sentinel-1 imagery for flood mapping in vegetated areas is investigated. Both herbaceous and woody vegetation are considered, and an automated object-based classification approach is presented. The latter is applied on several flood events, of which some comprise flooded vegetation and others do not. InChapter 6, the insights from the previous chapters are combined in order to tackle the third objective. More specifically, an automated flood monitoring approach for the region of Flanders is presented, which makes use of Sentinel-1 imagery and locally available data to discriminate several flood classes. Finally, Chapter 7 summarizes the main conclusions of this thesis and suggests potential future research topics that could contribute to increasing the impact of SAR-based flood observations even further.

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CHAPTER 2 Synthetic Aperture Radar: theoretical background

This chapter provides the theoretical background and physical fundamentals of the concepts covered in the remainder of this thesis. The principles of radar and Synthetic Aperture Radar are introduced, and radar specific image properties are discussed. Next, an overview of possible interactions between SAR signals and the Earth’s surface is given, and factors influencing this interaction are discussed. Finally, the behavior of SAR signals upon interaction with water surfaces is discussed. To this matter, a discrimination is made between open water or flood surfaces, flooded vegetation and urban flooding.

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2 2.1 Electromagnetic radiation

Electromagnetic (EM) radiation refers to all energy consisting of time varying electric and magnetic fields, propagated through space as waves. Key properties of waves include the phase, amplitude and wavelength, which is inversely proportional to the wave’s energy. As can be seen in Figure 2.1, depicting the electromagnetic spectrum, different categories of EM radiation can be distinguished based on wavelength. It is important to note that the boundaries between these different categories of EM radiation are not sharp. For example, wavelengths in the visible portion of the EM spectrum, which correspond to the sensitivity of the human eye, range from approximately 0.4µm to 0.7µm. On the other hand, the microwave portion of the EM spectrum has wavelengths that range from about 1 mm to 1 m. The latter is the main topic of this thesis, and is further divided in so-called radar bands, as listed in Table 2.1.

Figure 2.1: Electromagnetic spectrum. Boundaries between the different types of radiation are not sharp. After Lillesandet al.(2008).

Table 2.1: Designation of radar bands. Adjusted from Woodhouse (2006).

Band Frequency (GHz) Wavelength (cm)

Ka 27–40 0.75–1.11

K 18–27 1.11–1.67

Ku 12–18 1.67–2.50

X 8–12 2.50–3.75

C 4–8 3.75–7.50

S 2–4 7.50–15

L 1–2 15–30

P 0.3–1 30–100

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2 2.2 Synthetic Aperture Radar

2.2.1 Principle of imaging radar

Radar, originating from the acronymradio detection and ranging, makes use of the microwave portion of the EM spectrum. It is a technique based on the principle of echolocation, allowing to estimate the distance of an object by transmitting a signal and measuring the time needed for an echo to return.

Originally developed to detect the presence and distance of objects with mainly military purposes, it is now also frequently used as a sensor on satellite platforms. Whereas most satellite sensors are passive, i.e. measuring the amount of radiation emitted by the Earth’s surface in a specific portion of the EM spectrum, radar sensors actively send out pulses of microwaves and measure the returned response. Radiometers are their passive counterpart operating in the microwave portion of the EM spectrum (Lillesandet al., 2008;

Woodhouse, 2006).

Radar sensors on satellite platforms can be both imaging and non-imaging.

Non-imaging sensors include altimeters, determining the elevation of the Earth’s surface by measuring the return time from a pulse emitted perpendicular to the surface (Woodhouse, 2006). However, the remainder of this thesis will focus on imaging radar sensors, which typically have a side-looking geometry and provide 2D images describing the Earth’s surface.

The typical observation geometry of a side-looking radar sensor is shown in Figure 2.2. The radar sensor, mounted on a platform moving in the azimuth or along-track direction, transmits short microwave pulses towards a target.

Based on the time between the pulse transmission and response reception, the distance between the sensor and the target or slant rangeR can be calculated.

The look angle θ_l refers to the angle between the nadir and the sensor’s line of sight, while the angle between the SAR signal and normal to the Earth’s surface at the point of incidence is called the incidence angleθi. These angles differ, as the Earth’s curvature needs to be taken into account. Moreover, the incidence angle refers to the Earth’s surface, which is assumed to have no topography. Taking into account terrain topography, the local incidence angle refers to the angle between the incident SAR signal and the ground surface at the point of incidence (Lillesandet al., 2008).

The resolution of an image refers the smallest distance required between two objects in order to be detected separately by the sensor. For radar sensors this value differs in the azimuth and range direction, i.e. the direction along and perpendicular to the flight line (cfr. Figure 2.2). In the slant-range direction, i.e.in the direction along the sensor’s line of sight, this value depends on the

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et al., 2008):

r_SR= cτ

2 (2.1)

where c corresponds to the speed of light. This value can be converted into the ground-range resolution via the incidence angleθ_i (Lillesandet al., 2008):

r_GR = r_SR

sinθ_i = cτ

2 sinθ_i (2.2)

In the azimuth direction, the resolution depends on the antenna’s beamwidth β, which is defined as the ratio between the wavelengthλof the transmitted pulses and the side length of the antennaL_A:

r_A=R·β =R· λ

L_A (2.3)

where R corresponds to the slant-range distance between the sensor and the target (cfr. Figure 2.2).

As Equation 2.2 and 2.3 indicate, the ground resolution in both the range and azimuth direction vary across the scene. The (ground) range resolution depends on the incidence angle and improves (or decreases) with increasingθ_i or increasing distance from nadir. On the other hand, the azimuth resolution degrades (or increases) with increasing distance from the sensor and thus with increasing distance from nadir. Furthermore, the dependence of the azimuth resolution on the slant-range distance from the sensor has important

Figure 2.2: Observation geometry of side-looking radar. The radar system moves along its track in the azimuth direction and illuminates a continuous swath on the ground. The incidence angleθ_i determines the ground-range resolution while the beamwidthβ determines the azimuth resolution. After Meyer (2019).

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implications for spaceborne radar sensors. For example, in order to obtain a ground resolution of 100 m with a C-band radar (λ≈5 cm) at a height of 800 km height under an incidence angle of 40^◦, an antenna length of over 400 m would be required. In order to overcome this issue, the synthetic aperture principle was developed.

2.2.2 Principle of SAR and SAR backscatter

Synthetic Aperture Radar sensors are capable of providing reasonable resolutions while employing a short physical antenna, by exploiting the sensor’s movement along the track. This principle can be explained by the Doppler effect associated to this movement. More precisely, echoes from objects located in the front part of the beam will be shifted to higher frequencies, while echoes from objects in the back part of the beam will be shifted to lower frequencies. As Figure 2.3 shows, each target on the ground is detected several times when a significantly wide beam is used. By processing the return signals at the different sensor locations according to their Doppler shift, a sequence of acquisitions made by a moving, short antenna sensor can be “synthesized” into the acquisition of a sensor with a much longer antenna. This principle is also known as Doppler beam sharpening (Meyer, 2019; Moreiraet al., 2013).

Figure 2.3: Concept of synthetic aperture. Different acquisitions of an antenna with lengthL, moving along its flight line, capture the same point P. By making use of the Doppler shift, this sequence of acquisitions can be synthesized into an acquisition made by a much longer antenna. As the shades of grey indicate, the number of times a target is seen increases with increasing distance from nadir.

After Meyer (2019).

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Due to the conical shape of the beam, targets in the near range, i.e. close to nadir, will be viewed less frequently than targets in the far range. As a consequence, the effective antenna length will increase with increasing distance from the nadir. The total possible length of the synthetic aperture now determines the finest obtainable resolution. Based on the two-way phase shift associated with a given target, the following relationship between the azimuth resolution and real antenna length of a SAR sensorL_A can be deduced (Ulaby and Long, 2014):

r_A,SAR= L_A

2 (2.4)

Moreover, SAR systems make use of chirping in order to allow both a good range resolution and a sufficiently high signal strength. As Equation 2.1 and 2.2 indicate, the range resolution improves when the pulse length τ decreases.

On the other hand, a sufficiently high signal power is needed for the two-way distance to the Earth surface and back, and generating short pulses with a high peak power is challenging. To overcome this issue, SAR pulses are frequency modulated or chirped. By sweeping the signal over a range of frequencies, a longer pulse length and thus higher signal power are obtained, while the different frequencies allow to distinguish the different fractions of the pulse and the return signals, allowing for a high range resolution. When the frequency of a pulse is varied over a bandwidthβ_p, the range resolution is (Woodhouse, 2006):

r_GR,SAR= c

2βpsinθi

(2.5) Just as all radar sensors, SAR sensors transmit microwave signals and measure the returned responses. Whereas the timing of the returned response is used to locate the target, the strength of the response depends on the properties of the target and is used to construct the 2D SAR intensity image. As the amount of transmitted energy is known and the amount of received energy can be measured, the proportion of the transmitted energy returned or scattered back to the sensor can be calculated. This proportion can be used to describe the radar cross-section (RCS orσ), which denotes the area of a target assuming it would redirect the incident energy isotropically (Woodhouse, 2006):

σ= Pr

P_t4πR² (2.6)

where Pt andPr refer to transmitted and received power respectively, andR refers to the slant-range distance between sensor and target.

Equation 2.6 holds for discrete, isotropic scatterers, while most targets, including the Earth’s surface, are distributed targets. In contrast to discrete targets, 14

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the returned or backscattered power from a distributed target increases when the area of measurement increases. As such, a more generalized way to quantify both discrete and distributed targets is convenient. This can be achieved by dividing the RCS by the geometrical area of the ground surfaceA, resulting in the (normalized) backscatter coefficientσ⁰ (Woodhouse, 2006):

σ⁰= σ

A (2.7)

Furthermore it is important to note that the actual power received back from the sensor does not only depend on the RCS of the observed target but also on sensor-related parameters,i.e.the slant-range distance between the target and the sensorR and the directional sensitivity or gain of the antennaG. The latter refers to the combined effect of the loss of power within the antenna and the fact this power is not transmitted isotropically. Considering the two-way power drop off, from sensor to target and back to sensor, the radar equation describes the power received by the sensor P_r (Ulaby and Long, 2014):

P_r=P_t G²λ²σ

(4π)³R⁴ (2.8)

Combining Equation 2.7 and 2.8, the backscatter coefficient can be calculated as follows:

σ⁰ =P_r (4π)³R⁴

AP_tG²λ² (2.9)

As such, a 2D intensity image can be constructed in which each pixel gives an indication of the reflectivity of the corresponding ground surface area. This reflectivity depends on both sensor and surface parameters, as will be further elaborated on in Section 2.3.

This thesis focuses on the usage of SAR intensity imagery for flood mapping.

However, other wave properties of the returned SAR signal such as the polarization and phase can be analyzed too. In SAR polarimetry, the sensitivity of different polarizations to specific scattering mechanisms (cfr. Section 2.3.1) is exploited. Fully polarimetric SAR sensors can transmit and receive signals in both the horizontal and vertical polarization. Based on the amplitude and phase of the different polarizations, a scattering matrix can be constructed.

This matrix can in turn be used for the calculation of derived parameters, e.g. by means of the Freeman-Durden decomposition, which can be linked to

the different scattering mechanisms (Woodhouse, 2006). SAR interferometry or InSAR makes use of the signal’s phase information. If two SAR images acquired under the same viewing geometry are available, the coherence, defined as the complex correlation between the phases of the returned signals, can be

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calculated. As the interferometric phase is highly linked to the arrangement of scatterers in a resolution cell, the coherence can expose subtle changes which are not apparent in the intensity of the signal (Pierdiccaet al., 2018).

2.2.3 Radiometric effects

SAR images are subject to different radiometric effects, influencing the resulting backscatter irrespective of system and surface properties. First, a systematic brightness gradient is often apparent in SAR imagery. In case of a flat topography, near range regions typically appear brighter than far range regions. This effect is caused by the improving (or decreasing) ground-range resolution with increasing incidence angle (cfr. Equation 2.2) on the one hand, and the inverse relation between the local incident angle and the backscatter on the other hand. The latter will be discussed further in Section 2.3.

Secondly, SAR images are characterized by a grainy or “salt-and-pepper”

appearance, caused by the so-called speckle effect. It is inherent to all coherent imaging systems and the result of interference among the coherent echoes of the individual scatterers within the distributed target,i.e.a single resolution cell (Woodhouse, 2006). The resulting backscatter from a resolution cell is the coherent sum of the, often countless, scatterers within the corresponding ground surface area. Even if these scatterers are all equally strong, the phase of their response will vary randomly due to their different positions. As such, differences in the arrangement of scatterers will cause the summed response signal to vary randomly from pixel to pixel, even for homogeneous surfaces (Lillesandet al., 2008; Meyer, 2019). Speckle is often referred to as noise, as it complicates the interpretation of SAR imagery. However, it is not noise in the strict sense of the word, as it is a deterministic and repeatable phenomenon (Woodhouse, 2006). If an area is viewed twice from the same position under the same ground conditions, the speckle effect of the two observations is generally highly correlated. Moreover, it cannot be reduced by increasing the transmitted signal power due to its multiplicative character (Moreiraet al., 2013).

In order to reduce the speckle effect and increase the interpretability of SAR imagery, two image processing techniques are commonly used: multiple-look processing and speckle filtering. Multiple-look processing or multilooking is a technique used to improve the radiometric resolution, and reduce the speckle effect, at the expense of the spatial resolution of a SAR image. This is done by block-averaging independent neighboring pixels. An image generated by replacing N pixels with a new pixel bearing their average value is referred to as an N-look image. The higher N, the lower the speckle effect but the more the spatial resolution is reduced (Ulaby and Long, 2014). In the post-processing stage, speckle can be further reduced by applying a speckle filter. Throughout 16

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the past decades, a large variety of speckle filters has been developed. They generally aim at averaging out homogeneous areas while preserving borders, and typically make use of a moving window to update pixel values based on their neighbors. Whereas non-adaptive filters use the same weighting function across the image, adaptive filters use a weighting function depending on the local image statistics. Commonly used speckle filters include the Lee filter (Lee, 1980), the Frost filter (Frost et al., 1982), and the Enhanced Lee and

Frost filters (Lopeset al., 1990).

2.2.4 Geometric effects

In SAR imagery, several geometric distortions caused by the oblique viewing geometry of the system occur. In this section, the effects caused by terrain distortion are discussed. An in-depth discussion of other effects, due to for example range and motion distortion, is provided by Ulaby and Long (2014).

As SAR systems geolocate targets based on their slant range to the sensor, local height variations cause relief displacement. More specifically, as the slant-range difference between the bottom and top of a slope facing the sensor is smaller than the ground-range difference, it is projected as narrower than an identical slope facing away from the sensor. This phenomenon is called foreshortening and is illustrated in Figure 2.4. As the distance|BT_G|is shorter than|BT_R|, the slope facing the sensor is visualized shorter than it is. Figure 2.4 also illustrates how this effect becomes more pronounced as the incidence angle decreases or the slope of the feature increases. In the extreme case, foreshortening becomes layover. Layover occurs when the slant-range distance to the top of the target is shorter than the slant-range distance to the bottom of the target. In this case, the top of the feature will be mapped closer to the nadir than the bottom and the feature appears to “lay over” the lower part of the feature or even other features closer to the nadir.

Local height variations also impact the appearance of slopes facing away from the sensor or regions behind vertical targets. If the slope is steeper than the complement incidence angle at the top of the slope, no information can be retrieved from the region behind the slope. It will thus appear as dark in the image, a phenomenon called radar shadow. Shadow effects become more pronounced as the local slope increases or the incidence angle increases, i.e.as the distance from the nadir increases, as is illustrated in Figure 2.4.

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Figure 2.4: Geometric effects due to terrain distortion. Foreshortening occurs if the distance from the bottom of a feature to its top in the ground range is smaller than in the slant range (|BT_G|<|BT_R|). Foreshortening becomes layover if T_G is closer to nadir than B. Radar shadow occurs if the slopeαexceeds the complement of the incidence angleθ_i. After Ulaby and Long (2014).

2.2.5 Spaceborne SAR sensors & Sentinel-1

SAR remote sensing is a relatively young field of research. After some ex- perimental missions like Seasat and the SIR missions in the late 1970’s to early 1990’s, the first operational missions were launched only in the 1990’s.

A selection of past, present and upcoming sensors, along with their characteristics, is listed in Table 2.2. Through time, the number of SAR sensors, as well as the spatial and temporal resolution they provide, has increased considerably. Varying spatial resolutions are the result of different imaging modes, often with a trade-off between swath width and resolution. The im- proved temporal resolution is mainly the result of a transition from single satellite missions to constellations of multiple, identical satellites. For example, the Sentinel-1, RCM and COSMO-Skymed constellations comprise 2, 3 and 4 satellites respectively.

Not included in the provided list but of increasing abundance and importance are microsatellites carrying SAR sensors. These sensors are smaller, lighter and - most importantly - a lot less expensive than the satellites listed in Table 2.2.

By launching them in large constellations, both high temporal and spatial resolutions as well as a wide coverage can be obtained. Whereas the traditional SAR missions are typically lead by space agencies, a fast growing number of small start-up companies are developing and launching these microsatellites for commercial purposes. The Finnish start-up ICEYE was the first to launch

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