Detection and characterization of icebergs in Kongsfjorden (Svalbard) based on ground-based radar images and additional remote sensing data

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Insinööritieteiden korkeakoulu | Master's thesis
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Nordic Master's Programme in Cold Climate Engineering
60 + 5
This thesis focuses on the exploitation of ground-based radar images to detect icebergs. Additional remote sensing data from space-borne Synthetic Aperture Radar (SAR), Unmanned Aerial Vehicle (UAV) and in-situ boat tracks has been used to compare and validate the results. The investigation site is located in Kongsfjorden (Svalbard) and the combined data acquisition took place during a two-week campaign in April 2018. Five tidewater glaciers terminate in Kongsfjorden and produce a large number of icebergs of different sizes and shapes. The ground-based radar had an elevated position in Ny-Ålesund to overview a several kilometer-wide section of the fjord. The ground-based radar used during the campaign is the GAMMA Portable Radar Interferometer (GPRI). The 5 min temporal resolution of the dataset allows one to make comparisons with the above mentioned auxiliary remote sensing data. The software Python was used to process the GPRI data. Firstly, the GPRI images were pre-processed to account for the decreasing performance in range resolution. Secondly, an area of interest located between Ny-Ålesund and Blomstrandhalvøya was chosen. Hereby, it is important to focus only on the sea region and to leave out lagoons and other coastal lines. The area of interest covers approximately 2 km long region with only water and icebergs passing by while leaving Kongsfjorden. Thirdly, a threshold was applied to the GPRI images in order to separate potential icebergs from the sea background. Analysing histograms of both iceberg and sea background is important to find the appropriate threshold. This makes sure to include as many true positive as possible. In general, we can choose between two threshold modes, namely the automated and the manual threshold methods. The automated threshold method relies on the 99.93th percentile and shows the best compromise between all GPRI images. The automated threshold method is efficient and preferably used for big amounts of data and small time slots, because one loses small icebergs or detect false alarms. Therefore, it is more effective to decide on the manual threshold method. It is time-consuming, but one can more easily distinguish between iceberg and sea background by adjusting the threshold manually. Fourthly, in order to document important parameters based on GPRI images, we extract the count, size and position of every detected iceberg. Finally, the resulting GPRI images can be georeferenced and compared with auxiliary data. The software QGIS is a useful tool to compare the GPRI image products with satellite SAR images, drone images and boat tracks. After evaluating the GPRI images with auxiliary data, it turned out that the number of detected icebergs can be increased by choosing the manual threshold method, since the positive alarms are the majority in comparison to false alarms. For the future, the automated version could be improved by applying an advanced target detection, which is already used in synthetic aperture radar imagery. The developed algorithm for iceberg detection could be further developed to track. The GPRI’s temporal resolution of 5 min is predestined for such a tracking system, because it is easier to separate different icebergs within a shorter time frame. The potential value of the results can not be overseen in terms of climate research. In the future, scientists can build upon the findings to determine the mass balance of tidewater glaciers by observing how much calved ice is leaving the fjord system. In addition, the ground-based radar is showing a high potential in detecting icebergs, even if those are rather small. This could provide new insights on the distribution, volumetry and motion of icebergs, valuable for documenting oceanic currents.
Rautiainen, Miina
Thesis advisor
Lauknes, Tom Rune
Rouyet, Line
GPRI, detection, iceberg, remote sensing, satellite SAR, UAV