Marie Hoekstra


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Lake Ice-Water Classification of RADARSAT-2 Images by Integrating IRGS Segmentation with Pixel-Based Random Forest Labeling
Marie Hoekstra, Mengshi Jiang, David A. Clausi, Claude R. Duguay
Remote Sensing, Volume 12, Issue 9

Changes to ice cover on lakes throughout the northern landscape has been established as an indicator of climate change and variability, expected to have implications for both human and environmental systems. Monitoring lake ice cover is also required to enable more reliable weather forecasting across lake-rich northern latitudes. Currently, the Canadian Ice Service (CIS) monitors lakes using synthetic aperture radar (SAR) and optical imagery through visual interpretation, with total lake ice cover reported weekly as a fraction out of ten. An automated method of classification would allow for more detailed records to be delivered operationally. In this research, we present an automatic ice-mapping approach which integrates unsupervised segmentation from the Iterative Region Growing using Semantics (IRGS) algorithm with supervised random forest (RF) labeling. IRGS first locally segments homogeneous regions in an image, then merges similar regions into classes across the entire scene. Recently, these output regions were manually labeled by the user to generate ice maps, or were labeled using a Support Vector Machine (SVM) classifier. Here, three labeling methods (Manual, SVM, and RF) are applied after IRGS segmentation to perform ice-water classification on 36 RADARSAT-2 scenes of Great Bear Lake (Canada). SVM and RF classifiers are also tested without integration with IRGS. An accuracy assessment has been performed on the results, comparing outcomes with author-generated reference data, as well as the reported ice fraction from CIS. The IRGS-RF average classification accuracy for this dataset is 95.8%, demonstrating the potential of this automated method to provide detailed and reliable lake ice cover information operationally.


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Operating Procedures and Calibration of a Hyperspectral Sensor Onboard a Remotely Piloted Aircraft System For Water and Agriculture Monitoring
Kevin Kyung-Kuk Kang, Marie Hoekstra, M. Foroutan, Amir M. Chegoonian, Kiana Zolfaghari, Claude R. Duguay
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

This work describes a pilot study in southern Ontario, Canada evaluating the use of the ‘Headwall Nano-Hyperspec’ hyperspectral imager onboard a Remotely Piloted Aircraft System (RPAS). Hyperspectral imagers are extremely useful for monitoring vegetation health and water quality, among other environmental parameters. However, guidelines on the use of this specific instrument for these applications are not yet available. As such, recommended operational settings and calibration procedures are presented here, based on nearly 50 flight campaigns over water bodies and vineyards. Using these procedures, spectral reflectance was successfully captured using an RPAS.

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Canadian Water Microsatellite Mission - Concept Design
Kiana Zolfaghari, Marie Hoekstra, Claude R. Duguay, David Rudolph, Ian D’Souza
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

Canada has vast water resources that span an enormous range in geography, climate, and ecosystems [1]. Water supply and water quality are the two critical issues relevant to water resources, not only in Canada but globally in a warming climate. The water microsatellite mission described here aims to better prepare end users to respond to the emerging spectrum of water futures issues by revolutionizing remote sensing of water quality and quantity parameters, and permitting unprecedented interconnection and data gathering from Canadian environmental monitoring networks.