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

Accuracy Assessment

Accuracy assessment is the process of checking how correct your classified satellite image is . 👉 After supervised classification, the satellite image is divided into classes like: Water Forest Agriculture Built-up land Barren land But classification is done using computer algorithms, so some areas may be wrongly classified . 👉 Accuracy assessment helps to answer this question: ✔ "How much of my classified map is correct compared to real ground conditions?" Goal The main goal is to: Measure reliability of classified maps Identify classification errors Improve classification results Provide scientific validity to research 👉 Without accuracy assessment, a classified map is not considered scientifically reliable . Reference Data (Ground Truth Data) Reference data is real-world information used to check classification accuracy. It can be collected from: ✔ Field survey using GPS ✔ High-resolution satellite images (Google Earth etc.) ✔ Existing maps or survey reports 🧭 Exampl...

Change Detection

Change detection is the process of finding differences on the Earth's surface over time by comparing satellite images of the same area taken on different dates . After supervised classification , two classified maps (e.g., Year-1 and Year-2) are compared to identify land use / land cover changes . Goal To detect where , what , and how much change has occurred To monitor urban growth, deforestation, floods, agriculture, etc. Basic Concept Forest → Forest = No change Forest → Urban = Change detected Key Terminologies Multi-temporal images : Images of the same area at different times Post-classification comparison : Comparing two classified maps Change matrix : Table showing class-to-class change Change / No-change : Whether land cover remains same or different Main Methods Post-classification comparison – Most common and easy Image differencing – Subtract pixel values Image ratioing – Divide pixel values Deep learning methods – Advanced AI-based detection Examples Agricult...

Landsat 8 Band designation and Band Combination.

Landsat 8 Band designation and Band Combination. Landsat 8-9 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) Bands Wavelength (micrometers) Resolution (meters) Band 1 - Coastal aerosol 0.43-0.45 30 Band 2 - Blue 0.45-0.51 30 Band 3 - Green 0.53-0.59 30 Band 4 - Red 0.64-0.67 30 Band 5 - Near Infrared (NIR) 0.85-0.88 30 Band 6 - SWIR 1 1.57-1.65 30 Band 7 - SWIR 2 2.11-2.29 30 Band 8 - Panchromatic 0.50-0.68 15 Band 9 - Cirrus 1.36-1.38 30 Band 10 - Thermal Infrared (TIRS) 1 10.6-11.19 100 Band 11 - Thermal Infrared (TIRS) 2 11.50-12.51 100 Vineesh V Assistant Professor of Geography, Directorate of Education, Government of Kerala. https://www.facebook.com/Applied.Geography http://geogisgeo.blogspot.com

Landsat band composition

Short-Wave Infrared (7, 6 4) The short-wave infrared band combination uses SWIR-2 (7), SWIR-1 (6), and red (4). This composite displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation has lighter shades. Urban areas are blue and soils have various shades of brown. Agriculture (6, 5, 2) This band combination uses SWIR-1 (6), near-infrared (5), and blue (2). It's commonly used for crop monitoring because of the use of short-wave and near-infrared. Healthy vegetation appears dark green. But bare earth has a magenta hue. Geology (7, 6, 2) The geology band combination uses SWIR-2 (7), SWIR-1 (6), and blue (2). This band combination is particularly useful for identifying geological formations, lithology features, and faults. Bathymetric (4, 3, 1) The bathymetric band combination (4,3,1) uses the red (4), green (3), and coastal bands to peak into water. The coastal band is useful in coastal, bathymetric, and aerosol studies because...

Geographic phenomena fields objects boundaries.

In geography, geographic phenomena refer to features or processes that can be observed and studied on Earth's surface. These phenomena can be classified into three main categories: fields , objects , and boundaries . Each category has distinct characteristics, representations, and applications in Geographic Information Systems (GIS). 1. Fields A field represents continuous, spatially varying data where a value is present at every location within the study area. It describes conditions that exist across a geographic area. Characteristics : Continuity : Fields have no discrete boundaries; the data is continuous. Gradual Variability : The values of a field change gradually across space. Representation : Typically modeled using raster data in GIS, where a grid structure assigns a value (e.g., temperature or elevation) to each cell. Examples : Temperature Map : Shows temperature variation across a region. Rainfall Distribution : Displays rainfall levels over a large g...

Vineesh V, Geography

Search This Blog