Sea ice and icebergs - Cirfa

WP2: Monitoring Sea Ice and Icebergs

The decrease of sea ice extent and thickness in the Arctic over the last decades, especially during summer months, is closely linked to climate change. The expectation of an easier access to Arctic regions has triggered considerations concerning the expansion of marine transport and the possibilities to exploit natural resources in the Arctic Ocean. Hence, satellite monitoring of the Arctic sea ice and icebergs is inevitable for evaluations of chances and risks.

At CIRFA we develop methods for information retrieval from satellite images which are tested by the Norwegian Ice Service to complement manual analysis of the images. Together, we are working towards a 24/7 automatic sea ice forecasting for the ocean and the Norwegian fjords.

Safe navigation in polar regions requires timely and accurate information about the presence and characteristics of sea ice and the occurrence of icebergs.
Image credit: Alex Perz / Unsplash

Field photography of icebergs for improving and validating methods for iceberg detection in satellite images. Image credit: William Copeland.

Researchers at CIRFA and MET are developing new ways to classify and map sea ice, and make their results available to ship captains and crews for navigation through challenging sea ice conditions. Image credit: CIRFA.

We develop and improve remote sensing methodologies and algorithms to automatically map Arctic sea-ice conditions, and provide enhanced methods for detecting icebergs. Besides synthetic aperture radar (SAR) as key data source, we also use optical and thermal satellite images, passive microwave and altimeter data.

Our studies focus on semi- and fully automated sea ice classification, retrieval of sea ice drift and deformation, observing temporal and spatial variations of sea ice conditions, and on finding optimal algorithms and radar configurations for monitoring icebergs.

The methods used include multivariate statistical analysis, anomaly detection, constant false alarm rate (CFAR) approaches, and machine learning/deep learning techniques. For validation, we use data from field measurements and generate a data base of SAR and optical images acquired over sea ice and icebergs at different seasons and meteorological conditions.

Research tasks

Development and improvement of (semi-)automated algorithms for sea ice type classification suitable for operational applications

Integration of data from new field measurement techniques (e.g. drones) into algorithm development

Retrieval and modelling of ice drift and deformation for short-term forecasting of ice conditions

Comparison and improvement of algorithms for detecting icebergs in open water and in sea ice

Team members

Wolfgang.Dierking@awi.de

Professor II and WP 2 leader

anthony.p.doulgeris@uit.no

Employer page at UiT

WP 2 co-leader and leader of UiT`s Earth Observation group

torbjorn.eltoft@uit.no

Remote Sensing, Signal Processing, Image Processing, Neural Networks

jack.c.landy@uit.no

Satellite laser and radar altimetry to study the physical properties of polar sea ice and oceans

truls.t.karlsen@uit.no

Multi-frequency (C- and L-band) SAR sea ice classification

Sea ice classification with AI methods

debanshu.ratha@uit.no

Radar polarimetry for sea ice applications

eduard.khachatrian@uit.no

Multimodal Integrated Remote Sensing for Arctic Sea Ice monitoring

johannes.p.lohse@uit.no

Automated Classification of Sea Ice Types in SAR Imagery

polona.itkin@uit.no

Sea ice deformation and its impact on the sea ice mass balance, SiDRIFT project

anca.cristea@npolar.no

Sea ice classification from multimodal remote sensing data

wenkai.guo@uit.no

Cross-platform application of a sea ice classification method for detecting deformed ice, SiDRIFT project

anna.telegina@uit.no

Multi-temporal remote sensing of dynamic Arctic phenomena

laust.farch@uit.no

Mapping and Modeling of Iceberg Occurrences in the Barents Sea

andrea.marinoni@uit.no

Posts about this research topic

A warm welcome to our new PhD candidate Anna Telegina

A warm welcome to our new PhD candidate Laust Færch

Announcement of PhD position on iceberg remote sensing and modelling

Other research topics

WP1: Measuring ocean surface currents and wind from space.

WP3: Studying oil slicks and look-a-likes at the ocean surface.

WP4: Using drones in an extreme weather environment.

WP5: Improving forecasts by combining observations with models.

WP6: Comparing remote observations with reality, and calibrating datasets.

WP7: Developing new products to support safe maritime activities in the Arctic.