PhD Position in Transformative Technologies and Smart Watersheds Project University of Waterloo

-


PhD Position in Transformative Technologies and Smart Watersheds Project University of Waterloo

Title of Opportunity: Application of novel airborne Ku and L-band SAR observations for watershed-scale seasonal snow mapping

Start date: 1 September 2020 or negotiable

A fully funded four-year PhD position is available in the 'Transformative Sensor Technologies and Smart Watersheds for Canadian Water Futures' project (TTSW) at the University of Waterloo. The position is part of Global Water Futures: Solutions to Water Threats in an Era of Global Change, a large collaborative initiative involving multiple Canadian universities and partner organizations. TTSW aims to develop, test, and employ advanced terrestrial, sub-orbital, and satellite remote sensing tools targeted to support research regarding the emerging spectrum of water related issues throughout cold regions.

More Information

The CryoSAR airborne radar system is a new and unique CFI-funded synthetic aperture radar (SAR) system specifically designed to make fully polarimetric and InSAR-capable observations of cold season environments at Ku and L-band frequencies. The successful PhD candidate will explore ways that CryoSAR observations of snow can be used to estimate the distributions of snow water equivalent (SWE) at watershed-scales. To achieve this, the candidate will be expected to develop remote sensing modelling approaches that focus on SWE retrievals from SAR backscatter and InSAR observations. The successful candidate will be encouraged to be an active participant in winter field campaigns in prairie and alpine environments to characterize SWE and snowpack microstructure properties. They will also have access to a dedicated high-performance GPU-based processing system capable of conducting end-to-end SAR processing and SWE retrieval modelling.

The successful candidate will work under the supervision of Dr. Richard Kelly, and will collaborate with researchers at partner organizations involved with the CryoSAR project.

Eligibility

Ideally, you will have a strong background in quantitative remote sensing science, preferably with an understanding of Earth system science processes, especially hydrological science. Ideally, you should hold a degree in geographical science, geophysics, Earth science or engineering. The candidate should have strong analytical capabilities with a high degree of comfort across coding environments such as C, Python, R, IDL, Matlab or other programming languages commonly used in remote sensing. Strong communication skills are essential and the candidate should be able to work both independently and within a group setting both in field environments and in the lab.

Full funding is available for four years, pending satisfactory progress through the PhD program.

Application Instructions

Interested applicants should submit a cover letter stating their motivation and experience. In addition, a curriculum vitae, unofficial transcripts, and contact information for three references should be included in a single .pdf file and sent to Dr. Richard Kelly (rejkelly@uwaterloo.ca) with [PhD-TTSW-RichardKelly-2020] in the subject line.

We thank all applicants for their interest. However, only selected candidates will be contacted.



....

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

Comments

Post a Comment

Popular posts from this blog

Spatial feature manipulation in remote sensing

-
Image
Spatial feature manipulation in remote sensing refers to the process of altering or modifying the spatial characteristics of a particular feature in an image or data set. This can be done for a variety of reasons, such as to improve the accuracy or clarity of the image, to enhance the interpretability of the data, or to extract specific information from the image. One common method of spatial feature manipulation is resampling, which involves changing the resolution or spatial extent of an image. This can be done to match the resolution of other data sets, to reduce the size of the image for faster processing, or to increase the resolution for greater detail. Another technique is spatial filtering, which involves applying mathematical operations to the image data to remove noise or highlight specific features. This can be done using convolution filters, which apply a pre-defined mathematical function to the image data, or using image enhancement techniques, such as contrast stretching
Read more

Grey level thresholding. Level slicing. Contrast stretching lo p

-
Image
Grey level thresholding. Level slicing. Contrast stretching. Image enhancement Lillesand and Kiefer (1994) explained the goal of image enhancement procedures is to improve the visual interpretability of any image by increasing the apparent distinction between the features in the scene. This objective is to create "new" image from the original image in order to increase the amount of information that can be visually interpreted from the data. Enhancement operations are normally applied to image data after the appropriate restoration procedures have been performed. Noise removal, in particular, is an important precursor to most enhancements. In this study, typical image enhancement techniques are as follows: Grey level thresholding Grey level thresholding is a simple lookup table, which partitions the gray levels in an image into one or two categories - those below a user-selected threshold and those above. Thresholding is one of many methods for creating a binary mask for an i
Read more

Spatial filtering, Remote Sensing. Low-pass filter. High-pass filter

-
Spatial filtering encompasses another set of digital processing functions which are used to enhance the appearance of an image. Spatial filters are designed to highlight or suppress specific features in an image based on their spatial frequency. Spatial frequency is related to the concept of image texture. It refers to the frequency of the variations in tone that appear in an image. "Rough" textured areas of an image, where the changes in tone are abrupt over a small area, have high spatial frequencies, while "smooth" areas with little variation in tone over several pixels, have low spatial frequencies. A common filtering procedure involves moving a 'window' of a few pixels in dimension (e.g. 3x3, 5x5, etc.) over each pixel in the image, applying a mathematical calculation using the pixel values under that window, and replacing the central pixel with the new value. The window is moved along in both the row and column dimensions one pixel at a time and the ca
Read more