Current Organization: PCI Geomatics
Year began using PCI SW: 1996
Picture: (Animation of incidence angles over Vancouver)
Having just started my career with RADARSAT International (RSI) in Vancouver, I took a keen interest in working with imagery collected from RADARSAT-1, which had only recently been launched (1995). At the time, not many people knew about Radar imagery, which was very different from satellite optical images such as Landsat. I started to work in the company image processing lab where PCI software was available in order to generate some illustrations that were used in teaching and promotional material.
At the time the RADARSAT-1 images were stored on Exabyte tapes, which I learned how to load and display in Image Works. Using the same location (Vancouver) I created illustrations showing the effects of incidence angles on mountainous features. I spent many long evenings learning all about SAR imagery with several sets of RADARSAT-1 images, using PCI Software. The material I created lived on for many years and was used to train people around the world on how to best acquire SAR imagery, taking into account local terrain.
Current Organization: PCI Geomatics
Year began using PCI SW: 2000
My career is deeply entwined with the use of PCI Geomatica and can be described as a love affair with this technology.
I got exposed to PCI Geomatica in 2001 while working as a research intern at Infoterra GmbH now Airbus DS. The goal was to automate image processing chains. After returning back to South Africa, I made my involvement at the Centre for Geographical Analysis at the University of Stellenbosch dependent on the availability of PCI Geomatica for my future work. In 2005 I was one of the beta testers of Geomatica 10, which for the first time allowed the automation of an end to end workflow for the radiometric and geometric correction of optical satellite imagery. First workflows supported Spot and Landsat imagery.
The Satellite Applications Centre of the Council for Scientific and Industrial Research, South Africa's satellite ground receiving station, negotiated open access to Spot 2,4,&5 satellite imagery covering South Africa in 2006. I won one of the first contracts to process 400 Spot 2 /4 scenes for the South African Police with these PCI based processing chains. It took a bit more than one day to process all imagery. This drew the attention of the Satellite Applications Centre which at the time could process only 4 Spot scenes a day using manual workflows. After comparing my PCI based workflows with that of Pixel Factory, the Satellite Application Centre bought my workflows and employed me as Technology Manager for their Earth Observation unit. I left the institution in 2011 after becoming a civil servant as part of the newly formed South African National Space Agency. I created my own company co-operating in an ecosystem of small space companies in South Africa.
We continued to develop technology around PCI Geomatica, of which a mining service is worth mentioning. A processing chain extracting mine dump volume changes and delivering this information to a mine, on the same day imagery was acquired, raised considerable interest in the mining industry.
In mid 2016 I was privileged to join the PCI Geomatics team as Senior Scientist, where I intend to develop and implement technologies for the improved radiometric and geometric correction and processing of earth observation data.
Current Organization: Nova Scotia Community College
Year began using PCI SW: 2016
Picture: (Receiving PCI Geomatics Excellence in Remote Sensing Award, April 2017)
As a recent graduate of the Advanced Diploma in Geographic Sciences – Remote Sensing Concentration at the Nova Scotia Community College, I had many opportunities to work with Geomatica. For one of my projects, I was interested to understand how remote sensing imagery can be used to study climate change. One of my projects, I chose to work on the Glacier Bay National Park and Preserve, located on the South Eastern Alaska coastline, a UNESCO World Heritage Site.
This National Park contains 11 glaciers, and studying their evolution over time using Remote Sensing is an interesting climate change monitoring approach, especially given the rich multi-temporal archives available through the Lansdat series of satellites. Through the use of Geomatica, Landsat imagery was processed in order to perform quantitative analyses. Atmospheric correction and image normalization algorithms were applied to derive at surface reflectance for all multispectral bands.
The Image Channel Algorithm (ARI) was used for computing new band ratios to be applied to the RGB image. Three new band ratios were created using the ARI algorithm; Band 7 (SWIR2) / Band 4 (Red), Band 2 (Blue) / Band 5 (NIR), Band 4 (Red) / Band 6 (SWIR 1). A value of 1 was also applied to all denominators in the ratios to eliminate any possibilities of dividing by zero. In order to create the RGB composite a new raster layer was created and the ratios were assigned as follows Red: Band 7 (SWIR2) / Band 4 (Red), Green: Band 4 (Red) / Band 6 (SWIR 1), Blue: Band 2 (Blue) / Band 5 (NIR). In order to improve the visualization of the composite an Equalization enhancement was applied to the image. In order to highlight snow/ice within the scene a Normalized Difference Snow Index (NDSI), where NSDI = (Green-SWIR 1) / (Green+SWIR1), was applied and added as a pseudo-color layer.
Using band ratios and an NDSI can both aid in the analysis of Snow and Ice within Glacier Bay. The RGB image is better suited for defining clear boundaries between snow/ice and the surrounding vegetation, rocks, and open water. The index image is better suited for analyzing transition zones between glacier ice flows, glacial melt, and the open water. Both of these images could be compared temporally to other images in order to recognize the patterns of glacier retreat or advancement within Glacier Bay National Park.