PhD STUDENT – SAR Remote Sensing of Sea Ice to Detect Ice Break-up Events in the Canadian Arctic University of Manitoba

We are seeking a motivated student for a Ph.D. thesis project starting in spring/summer or fall 2021 to develop satellite-derived methods to detect sea ice break-up events in the Canadian Arctic.

Extreme weather events such as storms in the Arctic Ocean during the winter season and accelerated sea ice thinning during spring/summer season impacts sea ice stability and strength, leading to break up events. Winter break up events critically impact the migration activities and subsistence livelihoods of Canadian Arctic communities reliant on sea ice for navigation in coastal zones. Break up events also increase open water areas by amplifying the ice-albedo feedback, facilitating faster marine navigability through ice infested waters. As coastal ice conditions continue to change, web platforms and mobile apps sharing information about dangerous conditions resulting from sea ice break up events are becoming increasingly needed. SIKU, the Indigenous Knowledge social network, currently allows users to share information about dangerous conditions caused by the formation of sea ice cracks and ridges. The addition of break-up events to SIKU's alert system would increase user access to information pertinent to their safety and well-being.

Presently operational SAR satellite missions such as Canada's Radarsat Constellation Mission (RCM) and the European Space Agency's Sentinel-1 offers high spatial and temporal baseline imagery, delivering high-resolution Interferometric SAR products capable of detecting sea ice movements and break-up events, which can be then be integrated into the SIKU app, as user-friendly risk and hazard avoidance maps, impactful towards the safety and livelihood of indigenous communities.

The proposed Ph.D. project will focus on developing InSAR techniques and machine learning methods to automatically detect sea ice break up events in various Canadian Arctic communities, from SAR data (e.g. RCM, Sentinel-1, RADASAT-2), and validated using cloud-free optical satellite imagery (e.g. Sentinel-2, Worldview etc) and crowdsourcing. The final SAR-derived sea ice break-up product will be further integrated into the SIKU app, and delivered as user-friendly sea ice hazard maps.

The Ph.D. student will be supervised by Prof. Julienne Stroeve and mentored by Drs. Vishnu Nandan and David Jensen. The student will also work with the SIKU app research and technical team. The student's research will be conducted within the Centre for Earth Observation Science (umanitoba.ca/ceos), Department of Environment & Geography at the University of Manitoba, Winnipeg.

The successful candidate will have an M.Sc. (or equivalent) degree in remote sensing, or related field, with demonstrated experience in working with SAR data, Geographic Information Systems, and strong python/R programming skills. Knowledge/Experience in InSAR techniques and machine learning methods will be considered an asset. The studentship is fully funded over a 4-year period as part of Prof. Stroeve's Canada 150 Chair program.

Initial applications should be sent directly to Prof. Julienne Stroeve (Julienne.Stroeve@umanitoba.ca) and include: two letters of academic reference; a copy of your University transcripts; a letter of intent (1-2 pages) briefly describing your previous research or experience and a short research proposal fitting the above thesis topic, touching on objectives/hypotheses, preferred methods, and scientific significance; and an English Language test score, such as TOEFL or IELTS, if you are an international student with English as a second language. For further information, please contact Dr. Stroeve.

Application deadline: Open until filled

....

Vineesh V
Assistant Professor of Geography,
Directorate of Education,
Government of Kerala.
http://geogisgeo.blogspot.com
🌏🌎
🌐🌍

Comments

Photogrammetry – Types of Photographs

In photogrammetry, aerial photographs are categorized based on camera orientation , coverage , and spectral sensitivity . Below is a breakdown of the major types: 1️⃣ Based on Camera Axis Orientation Type Description Key Feature Vertical Photo Taken with the camera axis pointing directly downward (within 3° of vertical). Used for maps and measurements Oblique Photo Taken with the camera axis tilted away from vertical. Covers more area but with distortions Low Oblique: Horizon not visible High Oblique: Horizon visible 2️⃣ Based on Number of Photos Taken Type Description Single Photo One image taken of an area Stereoscopic Pair Two overlapping photos for 3D viewing and depth analysis Strip or Mosaic Series of overlapping photos covering a long area, useful in mapping large regions 3️⃣ Based on Spectral Sensitivity Type Description Application Panchromatic Captures images in black and white General mapping Infrared (IR) Sensitive to infrared radiation Veget...

Photogrammetry – Geometry of a Vertical Photograph

Photogrammetry is the science of making measurements from photographs, especially for mapping and surveying. When the camera axis is perpendicular (vertical) to the ground, the photo is called a vertical photograph , and its geometry is central to accurate mapping. Elements of Vertical Photo Geometry In a vertical aerial photograph , the geometry is governed by the central projection principle. Here's how it works: 1. Principal Point (P) The point on the photo where the optical axis of the camera intersects the photo plane. It's the geometric center of the photo. 2. Nadir Point (N) The point on the ground directly below the camera at the time of exposure. Ideally, in a perfect vertical photo, the nadir and principal point coincide. 3. Photo Center (C) Usually coincides with the principal point in a vertical photo. 4. Ground Coordinates (X, Y, Z) Real-world (map) coordinates of objects photographed. 5. Flying Height (H) He...

Raster Data Structure

Raster Data Raster data is like a digital photo made up of small squares called cells or pixels . Each cell shows something about that spot — like how high it is (elevation), how hot it is (temperature), or what kind of land it is (forest, water, etc.). Think of it like a graph paper where each box is colored to show what's there. Key Points What's in the cell? Each cell stores information — for example, "water" or "forest." Where is the cell? The cell's location comes from its place in the grid (like row 3, column 5). We don't need to store its exact coordinates. How Do We Decide a Cell's Value? Sometimes, one cell covers more than one thing (like part forest and part water). To choose one value , we can: Center Point: Use whatever feature is in the middle. Most Area: Use the feature that takes up the most space in the cell. Most Important: Use the most important feature (like a road or well), even if it...

Photogrammetry

Photogrammetry is the science of taking measurements from photographs —especially to create maps, models, or 3D images of objects, land, or buildings. Imagine you take two pictures of a mountain from slightly different angles. Photogrammetry uses those photos to figure out the shape, size, and position of the mountain—just like our eyes do when we see in 3D! Concepts and Terminologies 1. Photograph A picture captured by a camera , either from the ground (terrestrial) or from above (aerial or drone). 2. Stereo Pair Two overlapping photos taken from different angles. When seen together, they help create a 3D effect —just like how two human eyes work. 3. Overlap To get a 3D model, photos must overlap each other: Forward overlap : Between two photos in a flight line (usually 60–70%) Side overlap : Between adjacent flight lines (usually 30–40%) 4. Scale The ratio of the photo size to real-world size. Example: A 1:10,000 scale photo means 1 cm on the photo...

Logical Data Model in GIS

In GIS, a logical data model defines how data is structured and interrelated—independent of how it is physically stored or implemented. It serves as a blueprint for designing databases, focusing on the organization of entities, their attributes, and relationships, without tying them to a specific database technology. Key Features Abstraction : The logical model operates at an abstract level, emphasizing the conceptual structure of data rather than the technical details of storage or implementation. Entity-Attribute Relationships : It identifies key entities (objects or concepts) and their attributes (properties), as well as the logical relationships between them. Business Rules : Business logic is embedded in the model to enforce rules, constraints, and conditions that ensure data consistency and accuracy. Technology Independence : The logical model is platform-agnostic—it is not tied to any specific database system or storage format. Visual Representat...

Vineesh V, Geography

Search This Blog