Master Thesis Proposals
Presented here are thesis proposals for students, supervised by staff at the Earth Observation group.
The scope of these proposals can be adjusted and are equally suitable for students from the 5-year integrated masters (30sp) and the 2-year master’s program in Physics (60sp)
We welcome suggestions from prospective students on specific topics and can together with the student help setting up a master's project. You are also welcome to pick one of the already suggested projects below.
The Earth Observation group currently have the following project proposals:
- Multi-frequency SAR study of thinner sea ice and ridged areas
- Can cold snow interefere with radar signal?
- Influence of height, distance, shape and orientation of pressure ridges on the X-band ice ship radar
- Understanding sea ice roughness and surface state from a combination of CryoSat-2 radar altimetry, ICESat-2 laser altimetry and SAR
- Machine Learning for Sea Ice Classification
- Sea Ice Samples for Earth Observation Applications
- Characterization of low backscatter areas in SAR imagery using Passive Microwave and thermal sensors
Other environmental applicationas and technology development
- Counting seals from space - using data from seal count cruises to the east Greenland coast
- New approach to SAR Terrain correction
- Naturally occurring oil seeps in the Barents Sea
- Machine Learning in Synthetic Aperture Radar Imagery
Sea ice related projects
Multi-frequency SAR study of thinner sea ice and ridged areas
The Arctic sea ice landscape is rapidly transforming and this includes a thinning of the sea ice and a reduction of the sea ice extent. The dark and often cloudy environment means that satellite borne radar sensors, with multi-polarization capabilities, play a key role in the sea ice monitoring. The marginal ice zone in particular is subjected to cloud cover and fog and is also an area of specific interest for guiding ship traffic and for the maritime industry. Ice regimes differ in composition and areal fractions of ice types, ice concentration, and degree of deformation.
C-band SAR are used operationally to map presence of sea ice as well as classify different sea ice types. However, L-band SAR has been shown to be sensitive to deformed sea ice areas and in addition does the good signal-to-noise ratio at L-band enable good separation of young ice types from surrounding thicker sea ice and the open water. The larger penetration depth in L-band SAR is of general benefit for characterizing and classifying sea ice, as it improves discrimination between first (FYI) and multiyear ice (MYI), and is of specific benefits for detection and characterization of leads and thin ice areas. The low noise floor for ALOS-2 enables reliable separation of thin ice areas from thick sea ice also in the presence of frost flowers.
This project will build upon overlapping fully-polarimetric L- and C-band data collected during the MOSAiC campaign in 2019-2020 and during the N-ICE2015 campaign in 2015. The aim is to investigate the importance of the pixel resolution for the detection of the thin ice and ridges and how this varies with the two different frequencies. This can be done using both spatially and temporally overlapping medium-resolution images such as those from the Sentinel-1 or by simulating images with a lower resolution. The outcome is expected to provide answers to the size of thin ice areas and ridges can be captured in the high-resolution fully polarimetric images and how does this translate to the daily medium-resolution Sentinel-1 images.
Contact: Malin Johansson (email@example.com)
Can cold snow interefere with radar signal?
It is normally assumed that the cold snow layer is transparent for the light in microwave part of the spectrum. The actively sent pulse of light should scatter predominantly on the sea ice surface or shortly after penetrating it. Recent research shows that that does not hold for salty and wet snow (Nandan et al, 2017). This knowledge is essential for correct retrieval of sea ice thickness and snow from radar altimeters mounted on satellites (e.g. CryoSat-2, Sentinel-6 and future ESA missions).At MOSAiC several thousand snow depth measurements were collected each week along several-kilometer-long transects. At the same location sea ice thickness was recorded. The X-band ice radar mounted on the research ship (Oikkonen et al, 2017) was recording continuously over large area surrounding those measurements. The first look at the imagery suggests that large artificial snow accumulations can change X-band radar intensities. While the Arctic snow cover is typically thin (10-30 cm), large amounts of snow can accumulate also in naturally-occurring features of pack ice -in the pressure ridges (Liston et al, 2018). When the ridges are first formed the snow cover there is same thin as everywhere else and even blocks of bare ice are exposed. Over the time, ridges as rough features in windy environment accumulate deep snow drifts, in places over 1m deep. For this master thesis topic, the candidate will explore the development of ship radar back-scatter intensities and compare them to the snow depth observed in-situ on the ice. The candidate should be interested in sea ice geophysical processes and statistical analysis of the data. The analysis could be potentially extended to satellite radar data and radar signal modeling.
- Nandan, V., Geldsetzer, T., Yackel, J., Mahmud, M., Scharien, R., Howell, S., ... Else, B. (2017). Effect of snow salinity on CryoSat‐2 Arctic first‐year sea ice freeboard measurements. Geophysical Research Letters, 44, 10,419–10,426. https://doi.org/10.1002/2017GL074506
- Oikkonen, A., Haapala, J., Lensu, M., Karvonen, J., andItkin, P. (2017), Small‐scale sea ice deformation during N‐ICE2015: From compact pack ice to marginal ice zone, J. Geophys. Res. Oceans, 122, 5105–5120, doi:10.1002/2016JC012387.
- Liston, G. E., Polashenski, C., Rösel, A., Itkin, P., King, J., Merkouriadi, I., & Haapala, J. (2018). A distributed snow‐evolution model for sea‐ice applications (SnowModel). Journal of Geophysical Research: Oceans, 123, 3786‐ 3810. https://doi.org/10.1002/2017JC013706
Contact: Polona (firstname.lastname@example.org), Anthony(email@example.com)
Influence of height, distance, shape and orientation of pressure ridges on the X-band ice ship radar
The images from navigational sea ice ship radar could be used to develop sea ice classification (e.g. see pioneering work with ship radar by Kaspersen (2017), and sea ice classification examples in Lohse et al, 2020). The images with radar intensities can be comparedto airborne data: Airborne Laser Scanner (ALS) from helicopter on MOSAiC expedition (for example data from previous campaign see: Itkin et al, 2018). Some data is available as ‘raw’ radar back-scatter, but majority of the data is already converted to signal intensity. How can we best use such limited data? Additional information that can be used is in-situ data from ice cores and snow pits inside the radar footprint, ice observations from the ship, and back trajectory analysis of SAR images and passive microwave products. The candidate should be interested in image analysis, sea ice geophysical processes and statistical analysis of the data. The topic has a potential todevelop into a PhD thesis.
- Kaspersen K. M., 2017, Master Thesis at UiT
- Lohse J, Doulgeris AP,Dierking W (2020). Mapping sea-ice types fromSentinel-1 considering the surface-typedependent effect of incidence angle. Annals of Glaciology1–11. https://doi.org/10.1017/aog.2020.45
- Itkin, P., Spreen, G., Hvidegaard, S. M., Skourup, H., Wilkinson, J., Gerland, S., & Granskog, M. A. (2018). Contribution of deformation to sea ice mass balance: A case study from an N‐ICE2015 storm. Geophysical Research Letters, 45, 789–796. https://doi.org/10.1002/2017GL076056
- Cruise reports from MOSAiC expedition (in preparation, drafts available as internal material on request from firstname.lastname@example.org)
- Cruise report from Nansen Legacy cruise SSQ2 –April/May 2021 (draft available in June 2021)
Contact: Polona (email@example.com) and Anthony (firstname.lastname@example.org)
Understanding sea ice roughness and surface state from a combination of CryoSat-2 radar altimetry, ICESat-2 laser altimetry and SAR
Satellite altimeters like ESA’s CryoSat-2 and NASA’s ICESat-2 offer new perspectives for understanding sea ice properties in the Arctic. Over the past two decades sea ice in the Arctic basin has transitioned from mainly thick, old, and rough multi-year ice to thinner, smoother, and younger first-year ice types, as the northern polar climate has warmed at a rate 2-3 timesfaster than the global average.
It is critical we develop a strong understanding of the physical changes in the sea ice accompanying this decadal shift, so that we can better simulate sea ice floes in numerical models of Arctic climate. Principal challenges of numerical sea ice modelling are to predict how ice floes react to winds and ocean currents, and how quickly the ice melts from above in summer months, both of which depend closely on the sea ice surfaceroughness. However, there is not yet an established pan-Arctic sea ice roughness data product available from satellite remote sensing.
Both altimetry and SAR imaging sensors are sensitive to the sea ice roughness. The plot above shows roughness height observations from the CryoSat-2 radar altimeter overlaid on a Sentinel-1 SAR scene from the Canadian Arctic Archipelago in spring 2018. Sea ice roughness is evidently higher over older multi-year sea ice floes. This project will integrate roughness observations from a new CryoSat-2 data product https://data.bas.ac.uk/full-record.php?id=GB/NERC/BAS/PDC/01257 with those from the ICESat-2 laser altimeter ATL10 product https://nsidc.org/data/atl10. The aim will be to test the sensitivity of radar and laser altimeters to the sea ice type, surface state and roughness over a set of coinciding SAR scenes. For example, the EO group will be acquiring fully polarimetric RADARSAT-2 images coinciding with both altimeters at Cryo2Ice tracks https://cryo2ice.org/. This work will contribute towards establishing a baseline sea ice roughness data product for the Arctic Ocean.
Contact: Jack Landy (email@example.com)
Sea Ice Samples for Earth Observation Applications
Unlike freshwater ice, sea ice is saline and it freezes unevenly. The brine concentrates in pockets and channels. If sea ice survives the summer melt its structure is further complicated by brine drainage and melt-freeze cycles of snow that covers it. These features influence the light reflection from snow and sea ice surfaces. Data from space-borne radars and laser altimeters are widely used for understanding climate processes in remote areas like the Arctic Ocean. They are also valuable climate model validation data resources. Still, the ground data is scarce and radar signals are not completely understood.
Studying in situ samples of sea ice is a key piece of the jigsaw for better understanding remote satellite measurements of the ice cover.
This project will make use of in situ snow and sea ice data collected from several recent research expeditions to the Arctic (for example on the recent cruise of UiT to the Belgica Bank in the East Greenland Sea and the large Norwegian collaboration the Nansen Legacy Project). There may be opportunities to learn about snow and sea ice sampling during short training days on fjord ice near Tromsø, regularly run in the spring.
The candidate will work with in situ observations to understand the density, salinity, and microstructure of snow and sea ice collected from different locations and in time periods in the Arctic. Training will be provided for these analytical methods.
Depending on the preference of the candidate several options are possible to connect the in situ data with satellite remote sensing observations:
- Photography of sea ice core sections can be analyzed by automatic detection of sea ice crystal size, orientation, brine channels and bubble content. The core of the master thesis project will be the development and testing of feature detection software.
- Sea ice crystal size, orientation, brine channels and bubble content and other ground data already collected (sea ice and snow salinity, snow grain properties) can be applied in a simple radiative transfer model to simulate the satellite radar signal. The core of the master thesis project will be sensitivity studies with the numerical model and comparison of its output to the earth-observing satellite products collected by our research group over the same locations in situ snow and sea ice samples were taken.
- Sea ice density measured from ice core samples can be combined with other data collected in the field (sea ice thickness and snow depth, snow density) and related to the satellite products from radar and laser altimeters on earth-observing satellites. The core of the master thesis project will be to build a sea ice density database from several Arctic expeditions and compare it to satellite altimetry data of the sea ice thickness and snow depth in the European Arctic available from our research group.
The master student candidate will become an integrated part of the Earth Observation System group at UiT, taking part in research seminars, training sessions, and group social outings. The student will also benefit from collaboration with researchers at the Norwegian Polar Institute, who ked or took an active part in the collection of in situ datasets.
Sea ice coring sites (purple circles) at the landfast ice of the Belgica Bank (East Greenland Sea) between 27 and 29 April 2022. The satellite image in the background coves an area of approximately 30 by 20 km. Photo on the right shows how sea ice sample is extracted by ice coring.
For additional information about this master thesis opportunity contact firstname.lastname@example.org or email@example.com.
Machine Learning for Sea Ice Classification
Machine learning methods have been frequently used for remote sensing applications. Lately, a new approach has been successfully introduced for estimating geophysical sea ice information from dual-polarimetric SAR data, information which in general require quad-polarimetric observations. The dual-polarimetric SAR data is from the freely available wide swath data from the Sentinel-1 satellites, which are often preferred for sea ice monitoring. This project will further study machine learning models for sea ice monitoring by using the established methodology together with machine learning classifiers to address sea ice classification. The study will in particular address the problem of distinguishing between sea ice and open water under various weather conditions, and potentially develop a tool/methodology for robust large-scale localization of the ice edge.
You will learn to process SAR data, develop and apply machine learning methods and understand sea ice remote sensing.
Contact: Katalin Blix (firstname.lastname@example.org)
Characterization of low backscatter areas in SAR imagery using Passive Microwave and thermal sensors
Synthetic aperture radar (SAR) images are operationally used for sea ice type characterization and ice edge monitoring in the Arctic. The marginal ice zone (MIZ) is challenging for sea ice classification and is experiencing highly varying wind conditions. The varying wind conditions results in a wide range of SAR backscatter values. High wind speeds make the backscatter values similar to the thicker sea ice and the low wind reduce the backscatter values. At the same time is the MIZ an area with a high degree of new ice formation, also resulting in low backscatter values. Due to the low SAR signal within these areas it may not be possible to correctly classify these areas using the SAR information alone. However, inclusion of passive microwave and thermal band from optical sensors can aid this.
The goal within this project is to detect the low backscatter areas and then accurately classify them. The project will use a low backscatter segmentation algorithm, developed within the group, to identify the low backscatter areas using Sentinel-1 images. Sentinel-1 provides daily images over the European Arctic and is therefore ideal to use here as changes detection between two images can further aid the classification procedure.
The Passive microwave and optical sensors will be used to assess if they can allow for characterization of these low backscatter areas. It is expected that areas with too high surface temperatures could be identified using the thermal information and the sensitivity to thinner sea ice types within the passive microwave can identify the thinner sea ice areas. The different resolution for the different satellite sensors needs to be addressed, e.g. the passive microwave sensors has comparatively large pixel resolution compared to Sentinel-1 and will therefore not be able to aid if the dark features are too narrow or too close to the land. The Barents Sea has a high degree of new ice formation and will be the study area for this project.
Contact: Malin Johansson (email@example.com)
Other environmental applications and technology development
Counting seals from space - using data from seal count cruises to the east Greenland coast
Every 5 years a cruise lead by Institute of Marine Research counts the number of adult harp and hooded seals and their newborn seal cubs on the sea ice east of Greenland. The seal count takes place in late March when the cubs are just born. Optical images acquired from airplanes are used for the count. The images have a high enough resolution to determine the seal type. The campaign normally takes place for one day and is flow in a lawnmower pattern to ensure that each seal is only counted once. However, during the 2018 campaign the flight campaign was split over 2 days, and this means that some seals might have been counted twice. Using pattern recognition techniques this project aims to identify sea ice floes from one day to the next to ensure that seals located on the same floe are not counted twice. The images are orthorectified and georeferenced, but the sea ice drift between the two days is difficult to account for.
For the initial stage of the seal counting an AI based algorithm called Sealhunt is used. The algorithm identifies images that doesn’t contain any seals at all (main purpose) and identifies seals where possible. The main initial purpose is to remove images lacking seals and this has so far had priority in the algorithm and a higher number of false positives have been accepted. The development would be to work on reducing the number of false positives in the algorithm. High resolution optical images from the airplane (and possible satellite images) from both 2018 and 2022 will be used for this purpose. Information about the 2022 cruise can be found here: https://imr.brage.unit.no/imr-xmlui/handle/11250/3012654
The project/s will be supervised by Malin Johansson/Anthony Doulgeris at UiT and Martin Biuw from IMR.
Contact: Malin Johansson (firstname.lastname@example.org), Anthony (email@example.com)
New approach to SAR Terrain correction
Synthetic aperture radar (SAR) back-scattered signals are dependent on both the surface prop-erties (making them useful for remote sensing) and the viewing geometry (which complicatesthe interpretation). The signal strength varies systematically with the incidence angle withan approximately exponential relation, or linear in the log-domain (in decibels, dB). This istraditionally corrected with a single rate for the entire image as a pre-processing step known asincidence angle correction. However, many research papers observe different rates of decreasefor different surface classes, mostly related to surface roughness properties, and so this one ratecorrection is not ideal.
We have recently had great success by modelling thisincidence angle effectas a class prop-erty (rather than an image property), thus allowing different rates per class. This has beendemonstrated for maritime monitoring where the satellite viewing angle is changing smoothlyacross the imaging swath on the flat ocean surface. We believe that the per-class rates mayalso help land-based classification where terrain slopes vary the signals, but this has not yetbeen demonstrated.
The project shall explore how to introduce per class land-based terrain correction into segmentation or classification algorithms. The first stage may search for large flat terrains, suchas large scale agriculture, to confirm its benefits in a similar case to the flat ocean case, andsubsequent cases will seek to introduce the local terrain angles (derived from Digital ElevationModels, DEMs) with different degrees of rough surface topography (from hills and glaciers tomountains) for evaluation of the approach.
Contact: Anthony (firstname.lastname@example.org)
Naturally occurring oil seeps in the Barents Sea
In Barents Sea hydrocarbons (oil and natural gas) are stored in the seabed, and methane gas and oil is naturally seeping out and rising to the ocean surface. The oil is then observed in synthetic aperture radar (SAR) images. They represent possibly the biggest hydrocarbon seepage hotspots at the Arctic shelf and may act as a significant and unquantified source of methane (green house gas), ethane and propane (gases negatively affecting air quality), and oil (potent pollutant).
In the summers of 2021 and 2022 dedicated Radarsat-2 satellite data campaigns to collect consistent data over Hopendjupet were set up and also overlapped with cruises conducted by CAGE. Since the first of ESA’s Sentinel-1 satellites was launched in 2014 there has been regular images taken over Hopendjupet, this data will complement the existing dataset for time series analysis. Another area of great interest is Kong Karlsforland where extensive seepages have been observed and where satelite images are provided near daily.
Within this project will you be working with developing existing methods to detect and classify these naturally occurring oil seeps in SAR images, connect the observations to in-situ information such as wind speed and direction and confirmed observation of oil spills.
Contact: Malin Johansson (email@example.com)
Machine Learning in Synthetic Aperture Radar Imagery
Synthetic Aperture Radar (SAR) is the preferred imagery for several applications, since it can monitor the surface independently on the light and weather condition. The freely available wide swath data collected by SAR sensors onboard the Sentinel-1 satellites are often preferred for monitoring purposes. However, these sensors operate in dual-polarimetric (dual-pol) mode, which limits the information retrieval capacity. The project would attempt to overcome this limitation by introducing machine learning approaches, that will learn the relationship between different polarizations. This would increase the information that can be retrieved from dual-pol systems, and hence allow a broader application area. You will learn how machine learning techniques can be used to increase information retrieval capacity, process SAR data and use it in Earth Observation, and understand sea ice remote sensing.
Contact: Katalin Blix (firstname.lastname@example.org)