Skip to main content

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





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.
https://www.facebook.com/Applied.Geography
http://geogisgeo.blogspot.com
🌏🌎
🌐🌍

Comments

Popular posts from this blog

Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification:   - You have prior knowledge about the c...

Isodata clustering

Iso Cluster Classification in Unsupervised Image Classification Iso Cluster Classification is a common unsupervised classification technique used in remote sensing. The "Iso Cluster" algorithm groups pixels with similar spectral characteristics into clusters, or spectral classes, based solely on the data's statistical properties. Unlike supervised classification, Iso Cluster classification doesn't require the analyst to predefine classes or training areas; instead, the algorithm analyzes the image data to find natural groupings of pixels. The analyst interprets these groups afterward to label them with meaningful information classes (e.g., water, forest, urban). How Iso Cluster Classification Works The Iso Cluster algorithm follows several steps to group pixels: Initial Data Analysis : The algorithm examines the entire dataset to understand the spectral distribution of the pixels across the spectral bands. Clustering Process :    - The algorithm starts by divid...

Spectral Signature vs. Spectral Reflectance Curve

Spectral Signature  A spectral signature is the unique pattern in which an object: absorbs energy reflects energy emits energy across different wavelengths of the electromagnetic spectrum. ✔ Key Points Every natural and man-made object on Earth interacts with sunlight differently. These interactions produce a distinct pattern , just like a "fingerprint". Sensors on satellites record these patterns as digital numbers (DN values) . These patterns help to identify and differentiate objects such as vegetation, soil, water, snow, buildings, minerals, etc. ✔ Examples of Spectral Signatures Healthy vegetation → High reflectance in NIR , strong absorption in red Water → Strong absorption in NIR and SWIR , low reflectance Dry soil → Gradual increase in reflectance from visible to NIR Snow → High reflectance in visible , low in SWIR ✔ Why Spectral Signature Matters It allows: Land cover classification Chan...

Applications of Remote Sensing

Remote sensing is the science of obtaining information about the Earth's surface without direct contact, using sensors mounted on satellites, aircraft, or drones. It is widely applied in environmental monitoring, agriculture, urban studies, and disaster management due to its synoptic coverage, temporal continuity, and objective data acquisition.  Vegetation, Soil, and Water Studies 1.1 Vegetation Studies Vegetation analysis using remote sensing is based on the spectral reflectance behavior of plants . Healthy vegetation absorbs most of the red light for photosynthesis and reflects strongly in the near-infrared (NIR) region due to internal leaf structure. Key Concepts and Terminologies Spectral Signature : Unique reflectance pattern of vegetation across wavelengths. Chlorophyll Absorption : Strong absorption in blue and red wavelengths. Canopy Reflectance : Combined reflectance of leaves, branches, and background soil. Important Vegetation Indices NDVI (Normalized Difference Vegetat...

Classification of Mixed Pixels

In remote sensing, a mixed pixel (also called a mixed cell or mixel ) is a pixel that contains more than one land-cover type inside its area. This is very common when: spatial resolution is coarse (e.g., 30 m, 250 m) land cover is patchy or complex boundaries between features exist (forest–agriculture, land–water edges) Traditional hard classification assigns each pixel to one single class only → either forest or soil or water. But mixed pixels contain fractions of several classes simultaneously, so hard classification produces errors. Two major solutions are: Spectral Mixture Analysis (SMA) Fuzzy Classification 1. Spectral Mixture Analysis (SMA) (Also called Linear Spectral Unmixing ) ✔ Concept SMA assumes that the reflectance recorded by a pixel is a linear combination of the reflectance of pure materials within that pixel. These pure materials are called endmembers . ✔ Endmembers Endmembers are spectrally pure classes found...