Multi-platform, multi-sensor technologies for Earth observation, surveillance, information retrieval in Northern and Arctic regions
The project is a long-term project funded by the Norwegian Research Council under the Research Initiative for Northern Norway, focusing on the development of new advanced technologies for Earth observation from both satellites and Unmanned Aerial Systems (UAS). The objectives are to improve existing and develop new services and products by adapting and implementing state-of-the-art methods in EO sciences, and to develop and implement EO sensors for UAS platforms.
The project will be carried out in collaboration between the University of Tromsø, Northern Research Institute, Narvik University College, and the major research institutes and industries with competence and expertise in Earth observation in Northern Norway, and should deliver output of significant benefit to these industries, as well as institutes organizations and management authorities.
The research activities aim to expand the existing competence and experience owned by the collaborating partners to areas of significance for industry and important society branches (e.g. environmental management authorities).
The project will comprise three interrelated research and development areas:
- Methods and Algorithms: Development of basic methods and algorithms for information retrieval from data from Earth observation sensors, quality assurance and modification of existing satellite algorithms for arctic conditions. This research includes information retrieval from both satellite and UAV borne instruments.
- Models: Development of mathematical and numeric models for prediction of the impact of emissions from polluting sources.
- Platforms and sensors: Development of sensors and UAS platforms dedicated to surveillance tasks and reliable routine operation in the Arctic under all kinds of conditions.
Participants:Torbjørn Eltoft, Camilla Brekke, Anthony Doulgeris and Ding Tao
The project shall build capacity for enhancing the measuring, reporting and verification (MRV) of forest in Tanzania through the application of advanced remote sensing techniques. The project is funded through the Norwegian embassy in Dar es Salaam, as part of a bilateral agreement between the governments of Norway and Tanzania.
The project is performed in support of and in collaboration with the United Nation's Collaborative Programme on Reducing Emissions from Deforestation and Forest Degradation in Developing Countries. It is also contributing to the establishment of a national demonstrator under the Group of Earth Observations Forest Carbon Tracking (GEO-FCT) project.
Tanzanian project partners are Sokoine University of Agriculture and the University of Dar es Salaam. Norwegian project partners are the University of Tromsø, the Norwegian University of Life Sciences, the Norwegian Forest and Lanscape Institute, Northern Research Institute (NORUT), Norwegian Computing Centre, and Kongsberg Satellite Services.
Within the project, the University of Tromsø will perform research and development activities related to change detection and biomass estimation using SAR, and also data fusion of change detection with optical and SAR data.
Participants:Stian Normann Anfinsen, Gökhan Kasapoğlu,Torbjørn Eltoft
This project is funded by Total E&P Norge AS.
Oil spills, both deliberate discharges and accidents, are an important environmental problem, posing a threat to the marine and coastal environment. Remote sensing, particularly with SAR, has become an important tool for oil spill detection and monitoring. SAR is especially useful in Arctic areas due to the day and night, all weather capabilities. One shortcoming of SAR is the fact that several natural phenomena, called look-alikes, have the same appearance as oil spills in SAR imagery. This includes natural films, grease ice, low wind areas etc. Recent advances in sensor technology, e.g. multi-polarization options, are likely to improve the possibilities for detection, characterization and for classification between oil spills and look-alikes, and between different types of oils.
The overall aim of this project is to develop new methodologies based on multi-spectral radiometric data and multi-polarization and multi-frequency SAR data together with other auxiliary information to enable improved detection and characterization of oil slicks and their look-alikes in Arctic oceans.
Participants:Stine Skrunes, Camilla Brekke, Torbjørn Eltoft
The project is funded by the Research Council of Norway.
Surface cover classification and information about change are important for updating land cover maps and the management of natural resources. Multi-temporal analysis of SAR data allows monitoring changes in land cover using properties of the backscatter intensity.
This project intends to develop advanced signal processing and information retrieval approaches for temporal analysis of SAR and POLSAR data, which can result in robust change detection and trend analysis algorithms. The idea is to extract texture features, which together with features derived from target decomposition methods, will enhance the classification capabilities, and enable improved change detection. Polarimetric SAR (PolSAR) data contains both phase and amplitude information from radar returns transmitted in two different polarizations.
The project will provide reliable detection of changes using state-of-the art developments in signal processing, statistical and physical modeling, and pattern recognition based on time series of SAR and polarimetric SAR data. Particularly, we will focus on the use Markov Random Filed (MRF) models to exploit statistical spatial correlation of intensity levels among neighboring pixels, and on the combination of non-Gaussian statistics and contextual information within the framework of the MRF theory. The application areas will be related to surveillance of polar areas, with main focus on detecting changes Arctic glaciers.
Participants:Vahid Akbari, Anthony Doulgeris, Stian Anfinsen, Torbjørn Eltoft, Gabriele Moser, and Sebastiano Serpico
The project is funded by Research Council of Norway (Arctic EO project), Fram Centre, RDA, and University of Tromsø.
In this project, we shall develop and validate remote sensing algorithms for characterizing and mapping of Arctic sea ice properties.
Multi-channel synthetic aperture radar (SAR) observations over ice infested areas north of Svalbard and in the Fram Straight will be acquired, collocated in time and space with measurements on the ice, and from air-borne instruments. Based on satellite data, we will generate high-resolution sea ice type maps, and retrieve geophysical parameters such as thickness and ice concentration. Ground truth data will be collected during field work at point stations on the ice, from helicopter flights carrying instruments like the EM-bird, and from UAV flights carrying optical and multispectral radiometers. These data will subsequently be used for validation of the satellite products.
Through a detailed analysis of the simultaneously collected SAR and in-situ measurements, we aim to get improved understanding of the relationship between radiometric -, polarimetric -, and statistical signal properties and the physical characteristics of sea ice.
Participants:Mari-Ann Moen, Ane Fors, Anthony Doulgeris, Stian Anfinsen, Torbjørn Eltoft
>Detection and characterization of anthropogenic oil pollution in the Barents Sea by synthetic aperture radar
The project is funded by Norwegian Reserach Council, through the NORRUSS program. The project is a joint effort between University of Tromsø (UIT) and Russian Academy of Sciences, and their respective partners.
Sea ice is one of the major challenges for the marine traffic and with increased Arctic marine traffic the risk for accidents is increasing. Oil spills, biogenic slicks and newly formed sea ice have the same dark appearance in satellite images. We therefore aim to investigate what differentiates newly formed ice from oil spills and biogenic slicks. Different types of Synthetic Aperture Radar (SAR) images will be used for this identification and monitoring. Real green LIDAR will be used to study how an oil spills evolves with time.
The aim is to develop an algorithm that is suitable for operational classification of oily substances in the Arctic. This will aid the operational oil spill preparedness and clean up services. The primary focus area is the Barents Sea sea ice edge.
Participants:Camilla Brekke, Torbjørn Eltoft, Malin Johansson
Andrei Ivanov, Dmitry Ivonin, Gunnar Spreen, Rune Storvold, Kongsberg Satellite Services AS
CIRFA is is a Centre for Research-based Innovation (SFI) and is funded by the Research Council of Norway (grant number 237906) together with 6 research partners and 12 industry partners. CIRFA shall do research on methods and technologies that can reliably detect, monitor, integrate and interpret multi-sensor data describing the physical environment of the Arctic, and efficiently assimilate this information into models to perform predictions of sea ice state, meteorological and oceanographic conditions, on both short and long timescales.