Automatic procedures for mapping glacier velocity from repeat-pass SAR – example of southern Svalbard
Kosovar Malaj
Master Thesis in Geosciences
Discipline: Geomorphology and Geomatics
Automatic procedures for mapping glacier velocity from repeat-pass SAR
– example of southern Svalbard
Master Thesis in Geosciences - UIO
Student: Kosovar Malaj
Supervisor: Andreas Max Kääb
©Kosovar Malaj 2019
Automatic procedures for mapping glacier velocity from repeat-pass SAR – example of southern Svalbard
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Abstract
Interconnections between glacier´s behaviour and climate changes are profound. Being able to predict climate changes and mitigate their effects requires an extensive understanding of both glacial and sub-glacial activities, amongst other things. In the other hand, as climate changes intensify, glaciers behaviour become more and more unpredictable, diverting from their historical patterns and cycles, fact that make glacier monitoring almost a necessity.
Changes and destabilization in glacier dynamics can profoundly affect communities located around them, but not only them, the whole socio-economic structure can be seriously challenged if glacier degradation continues with the same rhythm. Because of that whether glaciers are surging, retreating or accumulating urges not only academic interest anymore.
Glaciers surface velocity indicates whether glaciers are surging, advancing or retreating.
Using remote sensing tools, one is able to compute glacier surface velocity with high accuracy, using high temporal resolution data with no geographic restriction.
In this project automated procedures are developed to compute glacier velocities using Synthetic Aperture Radar (SAR) images from Sentinel-1. Being weather independent, able to acquire data day and night combined with high temporal resolution, makes Sentinel-1 very efficient for tasks such as glacier monitoring. Sentinel-1 revisits the same scene every 6 days, enabling a monitoring ability that has not been possible before.
South Spitsbergen located in southern Svalbard is chosen as a testing site. Glacier velocities from southern Svalbard are computed with Gamma and Sentinel Application Platform (SNAP) algorithms using level-1 single-look complex (SLC) and Grand Range Detection (GRD) data. Velocity images computed with Gamma and SNAP are compared to each other and their quality assessed individually and with respect to each other.
About the author
I am Kosovar Malaj, born in Kosovo in 1988 where I finished elementary and high school.
As freshmen at University of Prishtina I was transferred to NTNU Gjøvik (then “Gjøvik University College”) where in 2012 I completed bachelor degree studies in Geomatics. While studying in Gjøvik, I was an exchange student in British Columbia Institute of Technology in
Acknowledgments
Andreas Max Kääb has originally developed shell script used to download Sntilnel-1 data as well as Gamma scripts. Versions used in this project are slightly modified.
Andreas has supervised and advised me throughout the whole project and I want to thank him for that.
In addition to Andreas I want to thank the hole Geoscience department at University of Oslo for the constructive advices and feedbacks on this project specifically but also throughout my time at university of Oslo.
Table of Contempt
1 Introduction ... 2
1.1 Aim of the project ... 3
1.2 Structure ... 4
2 Study Area ... 5
2.1 Climate ... 5
2.2 Previous glacier monitoring ... 6
2.3 Surge-type glaciers ... 9
3 Theory and Data ... 10
3.1 Remote Sensing Radar ... 10
3.1.1 Synthetic Aperture Radar (SAR) ... 12
3.1.2 Interferometric SAR (InSAR) ... 15
3.2 Changing in terrain geometry ... 18
3.3 Spectral characteristics of terrain ... 21
3.4 Data ... 22
3.4.1 SENTINEL – 1 ... 22
3.4.1.1 Lavel – 0 Sentinel-1 Data ... 25
3.4.1.2 Lavel – 1 Sentinel-1 Data ... 25
3.4.1.3 Level – 2 Sentinel-1 data ... 28
3.4.2 Digital Elevation Model ... 29
4 Methods ... 33
4.1 Background ... 33
4.1.1 Sentinel-1 data distribution ... 33
4.1.2 Shell script ... 34
4.2 Processing with SNAP ... 35
4.2.1 Open and view data ... 36
4.2.2 Apply Orbit File ... 36
4.2.3 Coregistration ... 37
4.2.4 Offset tracking ... 38
4.2.5 Terrain correction ... 40
4.2.6 Creating a GPF Graph ... 42
4.3 Processing with Gamma ... 43
4.3.1 From SLC raw data to Gamma format data ... 44
4.3.2 Initial offsets ... 45
4.3.3 Offset algorithm ... 45
4.3.4 Offset tracking ... 47
4.3.5 Geocoding ... 48
4.4 Analysing ... 50
5 Results ... 51
5.2.2 Winter results ... 69
6 Discussion ... 73
6.1 Parameters ... 73
6.1.1 Raw data temporal resolution ... 73
6.1.2 Processing parameters ... 75
6.1.3 SNAP ... 78
6.1.4 Gamma ... 78
6.2 Summer vs winter ... 79
6.2.1 SNAP ... 79
6.2.2 Gamma ... 80
6.3 SNAP vs Gamma ... 80
6.3.1 Summer velocities ... 81
6.3.2 Winter velocities ... 83
6.4 Surging glacier ... 88
6.4.1 Strongbreen ... 88
6.4.2 Recherchebreen surge ... 88
6.5 General discussion ... 91
7 Conclusion ... 92
Bibliography ... 94
Appendixs ... 100
Appendix A - SNAP Scripts ... 100
Offset tracking ... 100
Parameter file ... 105
Appendix B - Gamma scripts ... 106
Generating S1 Gamma files ... 106
Offset tracking ... 107
Appendix C - Shell script ... 111
Table of Figures
Figure 2 1 Map of the southern Svalbard: 1 Nathorstbreen, 2 Strongbreen, 3
Recherchebreen, 4 Penckbreen ... 8
Figure 3 1 Real-aperture SLAR imaging technique. From (Ulaby et al., 2014) ... 10
Figure 3 2 Microvave remote sensor main classes. From: (Ulaby et al., 2014) ... 11
Figure 3 3 Left: Phase compensation in focused SAR: L=phase delay, R=range, Lf = maximum range for focused SAR, Linf=maximum range for unfocused SAR; Right: SAR signal processing after applying signal delayes and real time signal procesing ... 12
Figure 3 4 SAR image resolution From: (Ulaby et al., 2014) ... 14
Figure 3 5 Raw data (a), after range comprestion (b) and after azimuth comprestion (c) from two point targets, showing real part of SAR signal. From: (Ulaby et al., 2014) ... 14
Figure 3 6 SAR processing steps. From: (Ulaby et al., 2014) ... 15
Figure 3 7 Along-track interferometric configuration (a) cross-track configuration in (b) and along-track - two independent acquisitions (c) ... 16
Figure 3 8 Parallel-ray approximation for InSAR geometry from A1 to P where R1 = R and R2 = R – δ. From: (Ulaby et al., 2014) ... 16
Figure 3 9 Observing moving objects with along-track conficuration. From: (Ulaby, Long et al., 2014) ... 16
Figure 3 10 Kinematic boundry conditions, their relation to terrain changes and their mesurment. Exempel of glacier surface. From (Kääb, 2005) ... 19
Figure 3 11 Velocity image obteind from cross-corelation method ... 20
Figure 3 12 Atmosferic transmission, optical, microwave spectrum and spectral range of different sensors among others Sntinel-1. From: (Kääb, 2005) ... 21
Figure 3 13 Sentinel-1 instrument and its components. From: (EuropianSpaceAgency, (b)) ... 22
Figure 3 14 Key parameters of Sentinel-1 C-SAR instrument. From: (EuropianSpaceAgency, (b)) ... 23
Figure 3 15 Four image acquiring modes of Sentinel-1. From: (EuropianSpaceAgency, (b)) ... 25
Figure 3 16 GRD data product ... 26
Figure 3 17 SLC data product ... 28
Figure 3 18 Principal of DTM generation from Photogrammetry. From: (Kääb, 2005) .. 30
Figure 3 19 Laser scaning principal. From: (Malaj & Sandvoll, 2012) ... 31
Figure 3 20 Representation of along-track stereo geometry of the ASTER backward and nadir system From: (Kääb, 2005) ... 31
Figure 3 21 Principal of SAR interferometry and terrain effect on it. From: (Kääb, 2005) ... 32
Figure 4 1 Data procesing work flow ... 33
Figure 4 2 Image maching principel, position deviation and displasment ... 40
Figure 4 3 Terreing correction geometry. From: (EuropianSpaceAgency, (d)) ... 41
Figure 5 2 Winter velocities for southern Svalbard. Left: SNAP; Right: Gamma. ... 52
Figure 5 3 Strongbreen´s summer velocity computed with SNAP ... 54
Figure 5 4 Nathorstbreen´s summer velocity computed with SNAP ... 55
Figure 5 5 Recherchebreen´s summer velocity computed with SNAP ... 56
Figure 5 6 Penckbreen´s summer velocity computed with SNAP ... 57
Figure 5 7 Strongbreen´s winter velocity computed with SNAP ... 59
Figure 5 8 Nathorstbreen´s winter velocity computed with SNAP ... 60
Figure 5 9 Recherchebreen´s winter velocity computed with SNAP ... 61
Figure 5 10 Penckbreen´s winter velocity computed with SNAP ... 62
Figure 5 11 Detecting sea-ice melting and glacial calving presses using different temporal resolution. ... 63
Figure 5 12 Strongbreen´s summer velocity computed with Gamma ... 65
Figure 5 13 Nathorstbreen´s summer velocity computed with Gamma ... 66
Figure 5 14 Recherchebreen´s summer velocity computed with Gamma ... 67
Figure 5 15 Penckbreen´s summer velocity computed with Gamma ... 68
Figure 5 16 Strongbreen´s winter velocity computed with Gamma ... 69
Figure 5 17 Nathorstbreen´s winter velocity computed with Gamma ... 70
Figure 5 18 Recherchebreen´s winter velocity computed with Gamma ... 71
Figure 5 19 Penckbreen´s winter velocity computed with Gamma ... 72
Figure 6 1 Summer velocity image; Left: summer; Right: winter ... 74
Figure 6 2 Different registration windows comparations ... 76
Figure 6 3 Velocity images computed with different output gird size, using SNAP. ... 77
Figure 6 4 Differences of summer and winter velocities. Left: SNAP. Right: Gamma. ... 80
Figure 6 5 Differences between Strongbreen´s summer velocity computed with SNAP and Gamma ... 82
Figure 6 6 Differences between Penckbreen´s summer velocity computed with SNAP and Gamma ... 83
Figure 6 7 Differences between Strongbreen´s winter velocity computed with SNAP and Gamma ... 84
Figure 6 8 Differences between Nathorstbreen´s winter velocity computed with SNAP and Gamma ... 85
Figure 6 9 Differences between Recherchebreen´s winter velocity computed with SNAP and Gamma ... 85
Figure 6 10 Differences between Penckbreen´s winter velocity computed with SNAP and Gamma ... 86
Figure 6 11 Velocity differences computed with SNAP and Gamma. Left: summer. Right: Winter ... 87
Figure 6 12 SNAP interpolation methodes. Left: Nearest Neighbour. Right: Bicubic ... 87
Figure 6 13 Recherchebreen´s velocity under surging. Monitored from August 2018 (surging start) until April 2019 (last month monitored) ... 90
List of Tables Table 1 Deviation of the summer velocities ... 83
Table 2 Deviation of the winter velocities ... 86
1 Introduction
Glaciers and ice caps are the biggest contributors to sea level rise, and as the climate continues to warm up, computing, monitoring and understanding glacier movement is becoming more and more important (Førland, Benestad et al., 2011; B Lefauconnier &
Hagen, 1990; Bernard Lefauconnier, Hagen et al., 1999; van der Bilt, Bakke et al., 2015).
Satellite Synthetic Aperture Radar (SAR) data collected from different satellites is a crucial tool for studying glaciers and ice caps. SAR is an active radar method used to determine distance from sensor (ex: radar antenna in the satellite) to the terrain by measuring the travel- time of microwave signals using synthetically extended antenna (Altena, Scambos et al., 2019; Haemmig, Huss et al., 2014; Joughin, Smith et al., 2010; Kääb, Leinss et al., 2018;
Schellenberger, Dunse et al., 2014; Schellenberger, Van Wychen et al., 2016;
Schneevoigt, Sund et al., 2012; Adrian Schubert, Faes et al., 2013; T. Strozzi, Kääb et al., 2017; T. Strozzi, Luckman et al., 2002; Tazio Strozzi, Paul et al., 2017; M. Sund, Lauknes et al., 2014). SAR images can be acquired under all weather conditions, do not need visible light and have the potential of calculating and mapping terrain displacement in 3D at
centimetre level accuracy and better. SAR generates 2D image from radar reflectivity of the illuminated objects from a scene, containing phase and intensity information of the
backscattered signal. In order to be able to apply interferometric processes, two images of the same scene from slightly different orbit configuration and time are needed. Using the
backscattered signal from two or more SAR images, one may be able to calculate
displacement using cross-correlation method. Different methods can be used to calculate displacement from two or more SAR images (Kääb, 2005; Rosen, Hensley et al., 1998;
Ulaby et al., 2014). Advantages and disadvantages of a method over the others depend on terrain conditions and raw data types among other things. Compared with interferometry, offset tracking method requires less coherence between images and since Svalbard is known for very bad coherence and varied condition, offset tracking may be the most suitable method for velocity computation of the testing area. Even though offset tracking method (used in this project) is less depended on coherence than interferometry, still choosing the right size
is considered to be a big advantage, but when it comes to terrain displacement measurements one may need to divide the scenes into subset images in order to reduce variations that occur over time.
Compared with SAR, one may reach better accuracy acquiring data with optics stereo and laser scanning methods (Nichol, Shaker et al., 2006; Toutin, 2001, 2004) but for the purpose of this project, the accuracy achieved with SAR is adequate and the ability to acquire data in all weather conditions with high temporal resolution is significantly more important for the purpose of this project.
Monitoring glacier movement is a demanding task, and methods used to compute and quantify movement are complicated and require well-trained personnel, powerful computers and large digital storage space. In Svalbard few large glaciers are being continuously
monitored 24 hours per day using global positioning system (GPS) technology. Smaller glaciers like Stongbreen, which are very active, could be interesting to be monitored as well.
Automating the monitoring processes could help reduce manpower and time consummation massively.
1.1 Aim of the project
The main goal of the project is to automate the procedure for terrain movement computation suitable for deriving glacier velocities, using Sentinel Application Platform (SNAP) and Gamma platform, as well as an automated Sentinel-1 data downloading procedure using Shell script. Scripts are tasted in Southern Svalbard. Computed velocities for Nathornsbreen, Strongbreen, Recherchebreen and Penckbreen are going to be presented in details. The reasons why parameters vary as the terrain condition change will be discussed also.
Shall script can be used to download data from the entire Sentinel-1 coverage of all Sentinel- 1 data types available, by just defining the corner coordinates of the interested area. SNAP and Gamma script by definition should work for all level-1 data, unconstrained by location.
Both SNAP and Gamma are developed for data acquired on Interferometric Wide swath (IW) mode. In this exercises with SNAP Ground Range Detection (GRD) data type of IW mode are used, while with Gamma Single Look Complex (SLC) data type. Details about the data can be found in chapter 3. All three scripts should be easy to use for everybody with
Geosciences background, without the need of being experts in Gamma nor SNAP and Shell.
1.2 Structure
This project is divided into seven main parts: (1) introduction, (2) study area, (3) theory and data, (4) methods, (5) results, (6) discussion and (7) conclusions. In part 2 one will find a short intro of the testing area. Information on Sentinel-1 in general and more specifically its products and the structure of data that has been used in this project can be found in part 3 together with the Radar measurement theory and SAR technology. Method, steps and parameters used in terrain movement calculation with SNAP and Gamma algorithm are explained in part 4. Shell script is used to automate image-downloading process. In part 5, terrain movement result driven from SNAP and Gamma automated procedure of the same area and using same parameters are represented. In part 6 the results are discussed in detail and in part 7 a short conclusion can be found. ArcGIS applications and SNAP Desktop are used for map generation, result analysing and visualisation. Shell script, SNAP script, and Gamma script can be found in attachments in their entirety.
2 Study Area
The focus area for this study is south Svalbard. With slight changes, the procedure should be applicable in other areas as well. Parameters in the procedure are adjusted to fit as god as possible glacier´s velocities and sizes from Southern Svalbard, specifically Nathorsbreen, Strongbreen, Recherchebreen and Penckbreen. All of those glaciers are located in South- Spitsbergen national park, which covers the whole Sothern part of Spitsbergen Island.
Svalbard is composed of many islands; Spitsbergen is the largest with just under 40 000 km2, followed by Nordauslandet with over 14 500 km2, Edgeøya with around 5 000 km2, and a number of smaller islands such as Barentsøya, Wilhelmøya, Kongsøya, Svenskøya, Lågøya etc. South Spitsbergen costal area consists of glaciers and moraines, while the inland areas are dominated from glacier systems and nunataks. Glaciers of different type cover almost 60% of Svalbard’s total territory at ≈ 62 300 km2 (Hagen, Liestøl et al., 1993; LIESTØL, 1993).
Most of the morphologically classified glacier types are represented in Svalbard. The most common glacier types in Svalbard are valley and cirque glaciers as well as complex glacier systems and ice caps. Most of the valley glaciers are terminated at sea, otherwise know as tidewater glaciers, such as Stongbreen and Recherchebreen Nathorstbreen, and some are land-terminated glaciers such as Penckbreen. Cirque glaciers are mostly located in high mountain ranges of the islands. Ice caps coves relatively flat areas such as Edgeøya, Barentsøya etc (Hagen et al., 1993). When geophysical classified, most of the glaciers in Svalbard (all four glaciers part of this study – figure 2 1) are subpolar glaciers according to Hagen et al. (1993). Etzelmüller and Hagen (2005) classify subpolar glaciers as polythermal glaciers in respect to their interaction with permafrost. A subpolar or polythermal glacier consists of temperate ice (at the pressure melting point), cold ice (below pressure malting point) and some part of the glacier body is frozen to the ground.
2.1 Climate
Glaciers are very much affected by the climate, temperature changes, yearly number of positive/negative degree-days, precipitation and the length of ablation season, among others.
Mean annual air temperature (MAAT) in Svalbard is -5°C. -5°C is considered high mean temperature considering the northern position of Svalbard. Svalbard is considered to have relatively high winter temperatures and stable summer temperatures. On the west cost of Spitsbergen the mean temperature in July is around 5°C, but temperatures above 10°C are rare. While for February – March, which is the coldest period of the year, the mean temperature varies between negative (8 – 16)°C. The reason for the “high” winter
temperatures is the transport of warmer air from the lower altitudes, often in connection to passage of low-pressure frontal systems. In addition to warmer air, the North Atlantic current transports warmer seawater to the shore of west Spitsbergen contributing greatly to the ice- free sea even during the coldest months of the year. Alternate passage of warm and cold fronts at any given time of the year can cause snowfall during the summer months and temperatures above freezing point during the winter months (Hagen et al., 1993).
In the inner parts of Spitsbergen temperatures are a few degrees lower during the winter and higher during the summer and may be consider more continental than on the cost. The temperature is slightly lower at the east coast than on the west coast (Hagen et al., 1993).
Interaction between glaciers and permafrost in Svalbard is present, especially obvious at lend terminated glaciers. As the result of high freezing degree-days (FDD) of about 2000-3000 days per year, Svalbard permafrost area is continues. The depth of the permafrost area in Svalbard reaches over 450-m (Etzelmüller & Hagen, 2005). All land-terminated glaciers have their front on the permafrost environment, crating ice core and push moraines among others landforms as the result of glacier advances and retreatment.
Every disturbance of the glacier basal (from thaw to frozen or visa-versa) anywhere along the glacier´s body can effect the dynamic of glacial and sub glacial processes, even leading to catastrophic collapses (Nuth, Gilbert et al., 2019).
Svalbard climate and its effects on the glaciers and ice caps are out of the scope of this study, among other studies that assess those issue are: (Førland et al., 2011; B Lefauconnier &
Hagen, 1990; Bernard Lefauconnier et al., 1999; van der Bilt et al., 2015)
2.2 Previous glacier monitoring
show that the valley occupied from Recherchebreen in 1838 (observed by scientific
expedition of the same year) (LIESTØL, 1993) was ice-free in 1600, fact that indicates that sometime between those dates Recherchebreen had surged. For that reason and the fact that scientist form the 1838 expedition explain Recherchebreen´s ice as vastly crevassed, LIESTØL (1993) choose to note 1838 as the first know surge of Recherchebreen and consequently became the first known glacier surge in Svalbard. Knowing now the normal surge frequencies of Recherchebreen and other glaciers in Svalbard, it wouldn’t have been exceptional for Racherchebreen or any other glacier in the region to have surged earlier than that. Suring is the characteristic of majority of the valley glaciers in Svalbard. Scientist believes that surging events were common in the past for many valley glaciers in Svalbard, just as they are now.
Scandinavian scientist studied Svalbard for the first time early in 19th century. Since 1950 Norwegian Polar Research Institute (NPRI) constantly monitors many of the glaciers in Svalbard. During the 20th century research groups from many different countries, including Norwegian, Swedish, German and Russians scientists studied Svalbard glaciers (LIESTØL, 1993). After airborne technology breakthrough, scientists are able to monitor larger areas, but still monitoring the entire Svalbard archipelago was not possible until the spaceborne
technology came along and was fully applicable. Using Landsat missions at first and now with excellent temporal resolution of Sentinel missions, one is able to detect all types of terrain changes, with great accuracy. NPRI have even mounted GPS antenna receivers on the surface of the Svalbard glaciers. GPS technology enables NPRI to monitor changes at glacier surfaces in real-time. Some argues that SAR is “the best” tool when it comes to monitoring glaciers surface velocities in Svalbard because of its independence from the illuminations source and weather conditions.
Using different commercials platforms one have taken advantage of high temporal and spatial resolution for spaceborne data to developed automated and semi-automated procedures for computing glacier surface velocities and mass balance changes. There are a number of studies that assess glacier displacement and destabilization by using spaceborne data. (Altena et al., 2019; Haemmig et al., 2014; Joughin et al., 2010; Kääb et al., 2018; Schellenberger et al., 2014; Schellenberger et al., 2016; Schneevoigt et al., 2012; Adrian Schubert et al., 2013;
T. Strozzi et al., 2017; T. Strozzi et al., 2002; Tazio Strozzi et al., 2017; M. Sund et al., 2014).
From previous studies it is known that the largest glacier or glacier system in south Spitsbergen is Nathorstbreen (Schellenberger et al., 2016), which has been surging lately (Nuth et al., 2019; M. Sund et al., 2014).
In this project Nathorstbreen, Stongbreen, Recherchebreen and Penckbreen were monitored between august 2018 and April 2019. All of them are Surging glacier, three of them tidewater glacier and one land terminated glacier. All located in south Spitsbergen (Sothern Svalbard).
2.3 Surge-type glaciers
Surge type glaciers are glaciers that experience cyclic instabilities resulting in surges not forced by external factors but rather intern factors such as: glaciers size, mass balance variations, and flow dynamics (Julian A Dowdeswell, Hamilton et al., 1991; LIESTØL, 1993). Thought, historically, external factors like climate changes, seawater temperature rise are not thought to be the main drivers of the glacier surges, lately there are studies that suggest that external forces like climate can trigger surges as well (Nuth et al., 2019).
Relatively short and quick advance of the glaciers tongue is know as active phase or surging phase. It is followed by long retreating period known as quiescent phase. During the
quiescent phase masse builds up, mostly at accumulation area. When mass build-up reaches a certain level glacier will start surging. It is thought that for the surge to happen, in addition to sufficient mass in accumulation area, water build-up at the glacier basal is required as well.
That will re-organizes the basal drainage channels, which will make possible for the surge to take place (Julian A Dowdeswell et al., 1991). Based on Nuth et al. (2019) suggestions and the unexpected surge of Recherchebreen (explained in chapter 5 and 6), what was known to be surging signs and requirements may not always be the case now.
Surge-type glaciers in Svalbard behave differently from other places. In Svalbard surge-type glacier´s active phase may last between 3 – 10 years, which is significantly longer than on other places while quiescent phase lasts between 50-500 years (Julian A Dowdeswell et al., 1991). Ice velocities during the active phase are also usually slower for glaciers in Svalbard when compared to glaciers in Greenland and Island for instance.
The largest surges registered in Svalbard are the surge of Bråsvellbreen which advanced about 10km in one year with a average velocity of about 30 m/day, and the surge of
Negribreen (1935) which advanced 12 km in one year reaching even higher velocity than 30 m/day (LIESTØL, 1993). The collapse of Nathorstbreen in 2009 can also be counted as one of the biggest surge in Svalbards glaciers history with 40-m/day velocities at the peak, advancing well over 12 km between 2009 and 2016 (Nuth et al., 2019).
Previously documented surges of the glaciers subject to this project are:
• Recherchebreen 1838 and 1945 (LIESTØL, 1993),
• Strongbreen between 1970-76 (LIESTØL, 1993),
• Nathorstbreen 1936 and 2009 (Nuth et al., 2019; M. Sund et al., 2014),
3 Theory and Data
3.1 Remote Sensing Radar
Historically radar stands for radio detection and ranging, but radar technology is also used for earth imaging. Radar images contain amplitude and phase information. That information can be used to generate Digital elevation models (DEM), orthoimages and determine terrain changes, using different methods. Basically, a remote sensing radar system is made from antenna, transmitter and receiver components, as illustrated in figure 3 1. Signal is
transmitted via an antenna, the backscattered signal is then received also via an antenna and then processed. Some systems use reciprocal antenna device, which can be used for both transmitting and receiving other use separate antennas. Radar antennas can be found in many different shapes and lengths, based on the shape and length but not only, they properties may vary. Some of the antennas properties are: radiation pattern, gain, radiation efficiency, polarization, purity etc. (entire paragraph: (Ulaby et al., 2014)).
Sentinel-1 antennas are dual polarized which means they are able to send and receive signal in both horizontal (H) and vertical (V) polarization as explained later in this chapter. Dual polarization accusations are collected over land and some costal areas. Fact that makes dual polarized accusations suitable tool for many applications like: agriculture, forestry,
hydrology, geomorphology etc (Dunse, Schellenberger et al., 2015; Kellndorfer, Pierce et al., 1998; Schellenberger et al., 2014; Schneevoigt et al., 2012).
Radar technology was first used to detect ships in early 1900´s and a couple of decades later the radar technology was developed further and used as an tool to detect aircrafts as well. For remote sensing purpose, pulsated radar was firstly used to measure ionosphere layer. In order to be able to measure the distance from the earth surface to the ionosphere layer, radar pulses were sent from one location and reflected signals were recorded in another location. By the end of 1930s radar technology was further developed and used in many more countries, primarily for aircraft and ship detections but also the early ground based radars were used for weather condition observations (entire paragraph: (Ulaby et al., 2014)).
Side Looking Airborne Radar (SLAR) was developed later around 1950s. SLAR technology made possible to achieve better resolution images compared to rotating antennas used before, due to it´s larger antenna size. In SLAR, aircrafts were fitted with as long as 15m antennas.
Even when long antennas are used, resolution degrades dramatically with increased angle, which is the main reason why SLAR is not very much used to collect data from space.
Theoretically the best resolution expect from SLAR, if such a system would have been used in Sentinel-1 - is around 2 km in azimuth direction, and image resolution would have not be square in almost all the cases (entire paragraph: (Ulaby et al., 2014)).
Figure 3 2 Microvave remote sensor main classes. From: (Ulaby et al., 2014)
Even though Synthetic-aperture Radar (SAR) technology was developed early, the fist open
data resolution especially from space. In the figure 3 2 from (Ulaby et al., 2014) are presented the main classes of microwave sensor , this project is focuses on spaceborne SAR technology (Ulaby et al., 2014).
3.1.1 Synthetic Aperture Radar (SAR)
In traditional sense remote sensing radars are used either for monitoring or imaging, where the main difference between the two is: signals Doppler spectrum processing (Ulaby et al., 2014). With present satellite temporal resolution and software automation possibilities, imaging radar signals can and are being used for monitoring terrain changes, wild fires and other hazardous events (Schellenberger et al., 2014; Adrian Schubert et al., 2013; Toutin &
Gray, 2000).
SAR is achieved by moving an antenna over a static target (technique used by all spaceborne satellites) or by moving a target while the antenna is stationary. SAR generates an image in two-dimensions. Along axis represents flight direction and range axis across the flight direction, where location of the target is determined (Kääb, 2005; Ulaby et al., 2014).
The real antenna aperture on a moving satellite (Sentinel-1 in this case) is synthetically starched in order to produce a longer antenna aperture. The longer the aperture, the higher image spatial resolutions can be achieved. Signals are recorded for each antenna position at slightly different time and then are processed as they were recorded simultaneously at each position. The proses is illustrated in figure 3 3 (Right) (Ulaby et al., 2014).
R1 R2 R3 R4 R5
ϕ1 ϕ2 ϕ3 ϕ4 ϕ5
Real time sum SAR
R5
R4
R3
R2
R1
L2
L1 L4 L5
Unfocusted SAR (Luf) Focused SAR (Lf)
When antenna aperture is of certain length it needs to be focused on a target. In that case phase delay needs to be taken into account since the antenna aperture is long and phase delay plays a significant role. This is called focused SAR. Unfocused solution (unfocused SAR) shorten the synthetic aperture to that point that the phase delay for the arriving signal can be neglected, see figure 3 3 (Left). The distance from target to the summing point at each antenna position must be the same in both solutions (Ulaby et al., 2014).
SAR resolution primarily depends on the beamwidth of the real aperture length for the along- flight direction (azimuth direction) and on the side looking antenna aperture angle for across- track direction (range direction) when the effective pulse wavelength (λ) and the distance of the aperture to the target are given, figure 3 4. The smaller the real aperture length, the finer azimuth resolution can be achieved, opposite to SLAR where the longer antenna gives better resolution. In range direction the smaller the side looking angle the finer the resolution, at any given effective pule length. In the other hand, the aperture length also decides the amount of signal that is send and received. Small antenna means less signal send and received. The width of the swath is depended on the side looking angel, the larger the angel the wider the swath. Different missions have different goals, for some it is more important to cover large scenes, which means large side looking angel, while for others fine resolution may be the main focus (Ulaby et al., 2014). For some missions is very important to have square pixel resolution like in GRD format has, while for others to use the data format with best resolution is more important, like in SLC data format. SLC data have a better resolution in azimuth direction than in range. Depending on the mission’s purpose one is forced to trade between different parameters, acquiring mode, data formats, data resolutions etc.
With Sentinel-1 data (used in this exercise) is possible to get a combination of 250km range coverage, and a approximately 5 m by 20 m resolution for SLC data and 20m by 22m for GRD data, (details in section 3 4), those parameters are satisfactory for this exercise.
The maximum length of the unfocused synthetic aperture is:
Luf = √R0λ ⁄ 2 (Ulaby et al., 2014) [1]
When neglecting the phase delay the optimal unfocused resolution is:
ruf = R0λ ⁄ 2Luf (Ulaby et al., 2014) [2]
While the best possible resolution in focused SAR is:
SAR is a complicated system, which can be explained in few different ways, equivalent to each other. In different times and different countries have approached SAR differently. In (Ulaby et al., 2014) are explained in more details the following approaches:
• Synthesized antenna aperture
• Doppler beam sharpening
Figure 3 4 SAR image resolution.
From: (Ulaby et al., 2014)
• Correlation or matched filtering with referenced point-target response
• Dechirping of Doppler frequency shift
• Optical-focusing equivalent
Radiometric resolution of the data is identified by the noise equivalent. Image quality depends on radiometric resolution and spatial
resolution. Depending on data and the application those data are used in, one might be forced to trade between the two. In (Lauknes, Johnsen et al., 1998;
Ulaby et al., 2014) one can find more information on the synthetized looks, scatter pattern, noise equivalent calculations, impulse response function, speckles and other parameters that plays a
significant role in SAR image quality.
SAR processing consists of complicated mathematical calculations for which many algorithms are available, most of them are digital now. Most commonly the first step in SAR processing is range compression before pulse compression and azimuth compression. Under pulse compression step, Doppler centroid information
(Neo, Wong et al., 2008; A. Schubert, Small et al., 2014; T. Strozzi et al., 2002; Ulaby et al., 2014).
Figure 3 6 SAR processing steps. From: (Ulaby et al., 2014)
To be able to apply Interferometric SAR (InSAR) technics, two images of the same scene from two different angels or at different time are needed. InSAR can be achieved by having two antennas in the same aircraft (Sentinel-1) or flying tandem aircraft one after another (ERS-1, and ERS-2). Most of the InSAR systems record both the amplitude and the phase information for each scene in both antennas.
3.1.2 Interferometric SAR (InSAR)
As mentioned above radar pulses/echo contains amplitude and phase information, fact that make them suitable for interferometric processes. Interferometry is applicable on data that contains phase information. Interferometry is an imaging technique, which exploits phase differences between two radar echoes in order to be able to measure topography, train movement over time or other surface changes. This technique is able to measure phase differences between two radar echoes. It is able to measure distance/range in centimetre level and even millimetre level accuracy, applicable both from space and terrestrially (Kääb, 2005;
Rosen et al., 1998; Ulaby et al., 2014).
Three receiver configurations mostly used in radar interferometry are:
1. Cross-track with two independent radars
2. Cross-track with common transmitted antennas - same aircraft with two receiving antennas (single pass)
3. Along-track - two independent acquisitions at a different time from different radars or repeat track
Figure 3 7 Along-track interferometric configuration (a) cross-track configuration in (b) and along-track - two independent acquisitions (c)
Stereoscopy is reached using cross-track configurations, which make it possible for DEM generations, while along-track configuration is used for terrain movement detection. In order to be able to generate DEM from radar echoes, phase changes due to changes in range, known as flat earth have to be removed using a DEM approximation. The reaming phase differences represent the terrain elevation (Kääb, 2005; Ulaby et al., 2014).
If the distance to the point is measured from two different antenna positions separated by an known baseline (B), the interferometric phase difference (Δ) depends on the slant range difference represented in figure 3 8 (Ulaby et al., 2014). Mathematically it can be represented as:
𝜑 =
!!"!
Δ
[4] From where we get:Δ =
!!"!
𝜑
[5]Where:
λ – is the radar wavelength
φ – is interferometric phase at a target
Δ – is range differences measured from 2 different positions (R1 – R2)
n – is 2 if the same antenna is used for both transmitting/receiving, 1 if separate antennas
Figure 3 8 Parallel-ray approximation for InSAR geometry from A1 to P where R1 = R and R2 = R – δ. From: (Ulaby et al., 2014)
𝑧=𝑅 𝑐𝑜𝑠𝜃 [6]
𝛥= 𝐵𝑠𝑖𝑛(𝜃−𝛼) [7]
Where:
B – Baseline distance between 2 antennas (in this case the time difference between acquisitions)
α – is the angel between 2 antennas perpendicular to flying direction.
θ0 – is the extended incidence along the centre of the swath and is related to θ by:
θ = dθ+θ
R – is the range from the sensor to the target
When slant range (R), baseline (B), wavelength (λ) and the absolute value of the angle φint
are know or calculated, it should be possible to create a topographic map by phase
unwrapping. The measured phase (known as wrapped phase) is limited to a range of (0 – 2π), the process of converting it into an absolute phase is called phase unwrapping (Ulaby et al., 2014). Unwrapped phase is not “limited”, at least not at the same extend as wrapped phase but the process is very complicated. More about the process can be found in (Ulaby et al., 2014).
Interferometry cannot differentiate between vertical and horizontal motion. When interested in horizontal motion, a DEM is used as a “true” topography. The DEM will remove phase differences coursed by elevation changes, the accuracy of which will affect the final horizontal motion results. When interested on vertical motion, possible horizontal changes will be considered as distortions, and if significant they will affect the quality of the final product together with other distortions. For more details in parallel and perpendicular
components and distortions assessment see (Ulaby et al., 2014). By along-track configuration (Sentinel-1 configuration) surface motion can be measured, where baseline B can be
described as the time difference between 2 acquisitions, figure 3 9 (Ulaby et al., 2014).
If:
𝜑!"# = 𝜑!−𝜑! =−!!
!" 𝑅!−𝑅! [8] and
𝑅! = 𝑅!+𝑣!!sinθ [9] than:
Figure 3 9 Observing moving objects with along-track conficuration. From: (Ulaby, Long et al., 2014)
𝜑!"# =!!!"B𝑣!𝑠𝑖𝑛𝜃 [10]
where:
B – Baseline distance between 2 antennas (in this case the time difference between acquisitions)
u- is aircrafts/satellites velocity
vr –is the radial velocity of the object and is related to the true object velocity (v) by:
𝑣! =𝑣 𝑐𝑜𝑠𝜙 Figure 3 9.
To quantify how well the objects radial velocity is determined, we can substitute phase and radian velocity measurement in equation with phase standard deviation (sφint ) and radian velocity standard deviation svr and we get (Ulaby et al., 2014):
𝑠!" = !!!!"#$!" 𝑠!"#$ [11]
Interferometry measure phase differences, those measurements are fundamentally depended on good coherence between two images. In areas with pure coherence can be challenging to implement this method, if possible at all. The quantification of temporal, spatial, thermal, geometrical decorrelation and other decorrelation effects can be found in (Ulaby et al., 2014).
3.2 Changing in terrain geometry
Terrain displacement can be caused from many different factors, happens under certain conditions and affects certain landscape. Some types of landscape are more exposed to rapid mass displacement while others are in constant movement. Monitoring all kinds of terrain movements is important, some because of their hazardous nature and the potential risk such movement may impose on the society, others to understand their behaviour and analyse long- term effects. Terrain movement can be horizontal or vertical but most often a combination of the two. Terrain movement can be characterised by their scale, velocity, movement type, time of occurrence, time on set etc. Based on their characteristics different remote sensing
technologies can be used to quantify different terrain movements (Kääb, 2005).
Kinematic boundary condition at the surface can be used to understand and measure
𝑏= !!!"!+ 𝑣!!+!!!"!+𝑣!!+!!!"!−𝑣!! [12] (Kääb, 2005) Where
b - The mass balance at the surface.
!!!
!" - The mass balance change over the time
𝑣!! - Horizontal surface velocity in x direction 𝑣!! - Horizontal surface velocity in y direction 𝑣!! - Vertical surface velocity
!!!
!" 𝑎𝑛𝑑 !!!"! - Terrain slope
From this formula one can drive the horizontal and vertical velocities, after assuming or measuring the medium compressibility and the strain rate (Kääb, 2005; Kääb & Funk, 1999).
Figure 3 10 shows the velocity (vx) as a function of surface and direction.
𝑣!!+!!!"! [13] (Kääb, 2005) Velocity at a surface point as a function of surface (s).
𝑣!!+!!!"! [14] (Kääb, 2005) Velocity at a surface point as a function in a direction (b).
Figure 3 10 Kinematic boundry conditions, their relation to terrain changes and their mesurment. Exempel of glacier surface. From (Kääb, 2005)
To determine terrain displacement different methods can be used:
• Differences between two DEMs generated at two different times
• Quantitative movement analyses
• Terrestrial SAR
• Digital matching of optical imagery from satellite or terrestrial
• Photogrammetric methods
• Matching etc.
Some of those methods are explained in (Kääb, 2005). In this project offset tracking method is used, both with Gamma and SNAP algorithm, Explained in details in chapter 4.
Offset tracking method is based on image matching method, where terrain movement are estimated by matching two images of the same scene acquired at two different times.
Image matching method can be used for terrain displacement measurement from repeat images. It can be used both for optical and radar images. Applied on coregistered SAR images (coregistered SAR is equivalent to orthoimage generated from stereo optical images (Toutin, 2008)), this method returns horizontal displacement directly (𝑣!! and 𝑣!! ) (Debella- Gilo & Kääb, 2011, 2012a, 2012b; Heid & Kääb, 2012; Kääb, 2005). There are two main techniques for image matching:
• Block matching technique and
• Feature matching technique
Block matching is an area based matching technique, which compares grey-values in
optical/radar images. It calculates terrain movement between two images on a selected blocks or section at a time. Choosing the right block size (her referred to as registration window on chapter 4) can be crucial. Block image matching can further be divided into spatial domain method and frequency domain method.
Some of the most used techniques are:
• Cross-correlation and least-square matching in the spatial domain
• Furrier and wavelet functions from frequency domain (Debella-Gilo & Kääb, 2011, 2012a, 2012b; Heid & Kääb, 2012).
3.3 Spectral characteristics of terrain
In remote sensing, sensors are divided into two main categories: active sensors and passive sensors. Passive sensors (Sentinel-2) need a radiation source like the sun or radiation from none black body objects like the earth’s surface (Kääb, 2005), while active sensors (Sentinel- 1) emits their own signal and are fully independent of other sources.
Sntinel-1 operates in C band (see figure 3 12) with a wavelength ≈ 5,546cm.
Figure 3 12 Atmosferic transmission, optical, microwave spectrum and spectral range of different sensors among others Sntinel-1. From: (Kääb, 2005)
The backscattered signal in microwave is a primarily a function of the complex dielectric ground properties, water contempt of the illuminated area, roughness of the surface, applied wavelength, polarisation and incidence angel in relation to surface orientation. Radar signals can penetrate surfaces depending on the signals wavelength and the surfaces properties, fact that can be advantages or disadvantages depending on the purpose of the user, even though the penetration depth might be complicated to calculate (Ulaby et al., 2014). One may be able to define surface properties by the amount of the radar signal that get absorbs or backscatters by a particular surface.
An object is considered to be a black body if it has the ability to absorb 100% of the radiation exposed to and emits 0%. The biggest advantage of the microwaves when compared with passive visible (VIS), Infrared (IR) and thermal infrared (TIR) is that microwaves in addition to being independent from other illumination sources, also penetrates clouds and are
independent of the weather. Weather independence is really important in areas like Svalbard where long periods (months) with no cloud free days are not unusual (Entire paragraph:
The spectral domain of the radar signal is characterized by its wavelength, intensity (amplitude), phase and polarization (Kääb, 2005).
3.4 Data
3.4.1 SENTINEL – 1
Sentinel - 1 is a European Radar Observatory mission for the Copernicus
(EuropianSpaceAgency, (a)), launched by a joint initiative from European Space Agency (ESA) and European Commission (EC) (EuropianSpaceAgency, (b)).
Synthetic Aperture Radar (SAR) operates in wavelengths not interfered by clouds or weather and is not dependent on other illumination souses for example the Sun, which means that SAR images can be acquired day and night independent of weather conditions and cloud coverage. This is a big advantage, especially in areas like Svalbard that can be covered by clouds for months consequently (Førland et al., 2011; Ulaby et al., 2014).
Sentinel -1 is composed of a consolation of two satellites: Sentinel – 1A and Sentinel – 1B sharing the same near-polar, sun-synchronous orbit with 12 days (d) repeat cycle and 180°
orbital phasing difference, both operates in C-band (centre frequency 5.405 GHz), continuing the heritage of ESA´s ERS-1, ERS-2, ENVISAT and Canadian RADARSAT-1, and
RADARSAT-2 (EuropianSpaceAgency, (b)). It is designed to work in pre-defined operation mode, with a temporal resolution of 6 days.
Its high resolution makes it a perfect tool for continues radar mapping of the Earth including services related to:
• Sea-ice mapping
• Land/ice movement
• Surveillance of the marine environment
• Monitoring land surface
• Hazard mapping such as wild fires
• Ship detection and marine security
• Forecasting ice condition at sea
• Mapping to support crises situation etc. (EuropianSpaceAgency, (b))
Figure 3 14 Key parameters of Sentinel-1 C-SAR instrument. From: (EuropianSpaceAgency, (b))
”The spacecraft is a three-axis, stabilised satellite, characterised by sun, star, gyro and magnetic field sensors, a set of four reaction wheels dedicated to orbit and attitude control and three torque rods as actuators to provide steering capabilities on each axis. The satellite is
equipped with two solar array wings capable of producing 5 900 W (at end of life) to be stored in a modular battery” (EuropianSpaceAgency, (b)).
It is based on the PRIMA (Piattaforma Italiana Multi Applicativa) bus, continuation from previous satellites: RADARSAT-2 and COSMO-SKYMED, which used the same bus. The bus delivers very accurate pointing knowledge ( 0.004° or better ) on all three axis, high pointing accuracy ( ≈ 0.01° on all three axis), real-time orbit determination and a dedicated propulsion system for precise orbit determination.
Throughout normal operation, the reference orbit is maintained within an Earth-fixed orbital tube with 100 m in diameter.
Data handling, management of the attitude, orbit control systems, propulsion, power, thermal control, spacecraft autonomy, failure detection identification and recovery, and
communication with the ground are some of the information that are supplied by satellite platform.
Sentinel-1´s spacecraft weighed approximately 2 300 kg when launched.
Ground Segment consists of Core Ground Segment, Collaborative Ground Segment and Copernicus contributing missions (entire paragraph: (EuropianSpaceAgency, (b))).
Sentinel-1 is capable of acquiring data in four different modes:
Stripmap (SM) – mode provides 80 km swath with a 5 m by 5 m resolution, where one of the six different swaths can be chosen by adjusting the predefined elevation beam. Each elevation beam operates in different incidence angel. This imaging mode is provided as a continuation of ERS and Envisat missions (EuropianSpaceAgency, (b)).
Interferometric Wide swath (IW) – imaging mode provides a combination between high- resolution data (5 m by 20 m) and large swath coverage of 250 km. IW is the default and the main acquisition mode for land coverage.
IW imaging mode divides each scene into 3 sub-swaths using Terrain Observation with Progressive Scans SAR (TOPSAR). This technique allows the beam to be steered both in range and azimuth direction for each burst avoiding scalloping and achieving higher resolution products. Overlapping of the Doppler spectrum in the azimuth domain and the wave number spectrum in elevation makes it possible to reach interferometry. TOPSAR technique delivers homogenous image quality throughout the swath (EuropianSpaceAgency,
marine, polar zones and other areas where wide coverage is more important than data resolution (EuropianSpaceAgency).
Wave (WV) – is used for detecting the direction, height and wavelength of ocean waves (EuropianSpaceAgency).
Figure 3 15 Four image acquiring modes of Sentinel-1. From: (EuropianSpaceAgency, (b))
3.4.1.1 Lavel – 0 Sentinel-1 Data
Level – 0 (raw data) data cannot be used without processing them first with SAR processor.
They consist of the sequence of Flexible Dynamic Block Adaptive Quantization (EuropianSpaceAgency). Level – 0 data are not subject to this project.
3.4.1.2 Lavel – 1 Sentinel-1 Data
Level – 1 are the data used by most users. Those data are transformed Level – 0 data (raw data) by Instrument Processing Facility (IPF). Level – 1 data are pre-processed, Doppler centroid estimated, single look complex focused and post-processed into either Single Look Complex (SLC) or Ground Range Detected (GRD). (EuropianSpaceAgency).
GRD are focused SAR data products that have been detected, multi-looked and
16). The ellipsoid projection corrections in GRD products are performed using the terrain height specified in the product general annotation. The terrain height in annotation file varies in azimuth but not in range. Terrain heights can be different for each IW/EW sub-swath in an image.
Slant range coordinates are projected onto the ellipsoid of the Earth (WGS84) and
transformed into ground range coordinates. Each pixel value represents detected amplitude, while phase information is lost in GRD data. After all the processing steps GRD data ends up with approximately square resolution pixels (20 m by 22 m for full resolution) and square pixel spacing (10 m by 10 m for full resolution). Speckle reduction in GRD data reduces spatial resolution. In each burst multi-looking process is performed separately before merging all bursts and all sub-swaths into a single, contiguous, ground range, detected image per polarisation. Same steps and in same order are performed in both IW and EW GRD products (entire paragraph: (EuropianSpaceAgency)).
SLC data format (figure 3 17) are focused SAR data just like GRD, but SLC data are geo- referenced using orbit and attitude information from the satellite platform, resulting in slant- range geometry images. Slant range coordinates are defined as the line-of-sight from the radar to each observed object in the ground (EuropianSpaceAgency, (b)). “The products are in zero-Doppler orientation where each row of pixels represents points along a line
perpendicular to the sub-satellite track. The products include a single look in each dimension using the full available signal bandwidth and complex samples (real and imaginary)
preserving the phase information. The products have been geo-referenced using the orbit and attitude data from the satellite and have been corrected for azimuth bi-static delay, elevation antenna pattern and range spreading loss” (EuropianSpaceAgency, (b)).
SLC are acquired in 3 different modes: SM IW and EW. Dependent from the mode data are arranged differently. SLC data in SM mode contain one image per polarisation. Data in SM mode consists of only one strip. Data in IW mode consists of three sub-swaths. IW SLCs consists of three images for each sub-swath in single polarisation and six images in dual polarisation data. EW consists of five sub-swaths, meaning that EW SLC data consist of five images for each sub-swath for single polarisation and ten images for dual polarisation
products.
Each sub-swath of IW and EW SLCs consists of a sett of bursts in azimuth. Each complex burst data are focused separately and presented in azimuth-time order as single sub-swath image. Corners of each burst are filled black.
In each mode, focused bursts have different time duration and overlapping duration, in IW each of them has a duration of ≈ 2.75 overlap of around ≈ 0.4 seconds.
Sub-swaths images (composed of a series of burst) in all modes (IW/EW SLC) are re- sampled to a common pixel spacing grid in both directions. In order to be able to generate repeat data, brust synchronisation is performed in both IW and EW mode.
In order to be able to merge bursts and swaths together, dimensions, timing and location information for each burst are recorded into swath timing data set (entire paragraph:
(EuropianSpaceAgency, (b))). SLC data pixel resolution is between 2.7 m by 22 m and 3.5 m by 22 and pixel spacing 2.3 m by 14.1 m.
In this project we have used Level – 1 Single Look Complex and Grand Range Detected data.
SLC data have been processed with Gamma algorithm while GRD data using SNAP. The fact that those two data formats have different resolutions and pixel spacing is taken into account under processing steps.
Figure 3 17 SLC data product
3.4.1.3 Level – 2 Sentinel-1 data
3.4.2 Digital Elevation Model
Digital elevation model (DEM) describes a 2.5D grid based model of a reference surface where each grid cell contains easting, northing and elevation information, projected on a local or global georeferenced coordinate system. Digital terrain model (DTM) is the representation of the bare ground surface of the earth excluding vegetation, building and other man made objects. In many cases DTM and DEM expressions do mean the same thing but different authors might define them differently. Digital surface model (DSM) is a representation of top surface of the earth where top of the buildings and vegetation are included.
In this project the digital elevation model is downloaded from Norwegian Polar Institute (NPI), which is Norway’s central governmental institution for mapping, scientific research and monitoring in the Arctic and Antarctic. DEM products produced from NPI are free for use and can be found in different accuracy and resolutions. DEM used in this project covers the whole Svalbard but the resolution varies. The all-covering model used here is at 20 m by 20 m resolution. It covers the area from North: 80,82906°, South: 74,335505°, East:
33,51505°, West: 10,45257°, projected in EUREF89 UTM sone 33-35, 2d (NorwegianPolarInstitute, 2015).
The downloaded DEM was then converted to WGS 1984 UTM 33N before being used in SNAP and Gamma, even though the difference between EUREF89 and WGS 1984 (around 40cm difference) are negligible for the accuracy of the final products expected in this project (Kumar, 1988; Langley, 1998; StatensKartverke, 2002).
The DEM used here represents heights over ellipsoid with a reference from 1954, projected on UTM map projection.
The DEM used here is mainly generated by stereo models using areal photos, but in some areas elevation contours, lakes and coastlines are also used. The areas where stereo models are used, the DEM have an accuracy of approx. 2-5 m, in areas where old elevation contours, lake and coastlines are used the accuracy is approx. 25 m. In the glaciated areas the accuracy can be a bit worst than 2-5m accuracy even if generated from stereo models
(NorwegianPolarInstitute, 2015).
DEM models acquired with airborne methods have a better resolution and accuracy than those acquired with space borne methods nevertheless airborne acquisition take more time, are more expensive and are depended on flying access over the area of interest. (Fujisada,
1998; Hasegawa, Matsuo et al., 2000; Jacobsen, 2003; Kääb, 2005; LANG, 1999; Toutin, 2001, 2004, 2006, 2008; Toutin & Gray, 2000; Welch, Jordan et al., 1998).
Airborne methods for DEM acquisition includes:
• Photogrammetry – using this method DEM can be derived from overlapping images.
Different models can be used depending on weather images are digitally acquired or analogue. Figure 3 19 from (Kääb, 2005) shows the principle of DEM generation for photogrammetry.
Figure 3 18 Principal of DTM generation from Photogrammetry. From: (Kääb, 2005)
• Laser profiling and scanning – the distance between the sensor and the surface can be estimated from the travel time the pulse takes from the sensor to the surface, reflected at the surface and than being detected at the receiver (Malaj & Sandvoll, 2012).
Figure 3 19 Laser scaning principal. From: (Malaj & Sandvoll, 2012)
• Interferometry synthetic aperture radar (InSAR) – same principles as spaceborne InSAR method.
Spaceborne acquired DEMs have coarser resolution and poorer accuracy in general but much better temporal resolution, cheaper and broader coverage.
Main spaceborn methods for DEM generation includes:
• Optical stereo method – the very basic principle of optical stereo is like
photogrammetry. Two optical images of the same area taken from two different angels at different times. Different satellites have different consolation of cameras in different angels. Figure 3 21 from (Kääb, 2005) represents along track acquisition method using ASTER satellite.
Figure 3 20 Representation of along-track stereo geometry of the ASTER backward and nadir system From:
(Kääb, 2005)
• Interferometrical SAR (InSAR) – The distance from the sensor (radar antenna) to the surface is determined by measuring the run time of the microwave signal.
Backscattered signals in cross track direction (range) and along track direction (azimuth) gives a radar image. The antenna can be synthetically extended by continuously combining received signals from different antenna positions as the satellite travels along the orbit. By using the phase difference of each terrain point measured from two different antennas or antenna positions, one can measure the distance very precisely (Figure 3 21). More information about InSAR method can be found in chapter 3 and (Baselice, Ferraioli et al., 2009; Rosen et al., 1998; Toutin &
Gray, 2000) among others
• LIDAR altimetry – same general principle as laserscanning, where the height of the terrain surface is determined by measurement of the travel time of the nadir looking laser pulse (Kääb, 2005; Malaj & Sandvoll, 2012).
Figure 3 21 Principal of SAR interferometry and terrain effect on it. From: (Kääb, 2005)
If a DEM with more uniform accuracy is needed, stereo/radar satellite images can be used to generate a DEM of the desisted areas. Using optic stereo images from for example ASTER an accuracy of better than 15m may be reached with a resolution of 30 m by 30 m or maybe even better. In this project the accuracy and the resolution provide by INP is good enough.
DEMs provided by INP and Norwegian Mapping Agency (in Norwegina “Kartverket”) are usually a mix of almost every method mentioned above, and the accuracy within a relatively small area can very significantly.
4 Methods
4.1 Background
Shell script is used for downloading raw radar images from Sentinel open hub web page (EuropianSpaceAgency, (a)). Raw and proceeded data are stored in “Lagringshotell”- storage server owned by Oslo University (UIO), so that they are accessible from the geo-ice-hiperf desktop. Geo-ice-hiperf is a windows server owned by UIO as well.
Glacier velocities are computed once using Gamma platform and the second time using SNAP platform. Results from both methods are presented and compared in detail in chapter 5 and 6 and the processes are discussed (which one is faster, easier, more reliable etc.).
Velocity images generated with SNAP and Gamma have been visualised and analysed using ArcGIS and SNAP Desktop applications. See the flow diagram in figure 4 1.
Figure 4 1 Data procesing work flow
4.1.1 Sentinel-1 data distribution
Sentinel-1 data are available online, free of charge. They can be accessed on ESA open hub web page (EuropianSpaceAgency, (a)) based on those principals:
• “Anyone can access acquired SENTINEL data. In particular, no distinction is made between public, commercial and scientific uses, or between European and non-
ArcGIS/SNAP Desktop Shell script
Gamma SNAP
Downloading
Processing
Analysing