Approximately 1000 Canadian Hydrographic Service (CHS) charts cover Canada’s oceans and navigable waters. Many charts use information collected with techniques that predate the more advanced technologies available to Hydrographic Offices (HOs) today. Furthermore, gaps in survey data, particularly in the Canadian Arctic where only 6% of waters are surveyed to modern standards, are also problematic. Through a Canadian Space Agency (CSA) Government Related Initiatives Program (GRIP) project, CHS is exploring remote sensing techniques to assist with the improvement of Canadian navigational charts. Projects exploring optical/Synthetic Aperture Radar (SAR) shoreline extraction and change detection, as well as optical Satellite-Derived Bathymetry (SDB), are currently underway. This paper focuses on SDB extracted from high-resolution optical imagery, highlighting current results as well as the challenges and opportunities CHS will encounter when implementing SDB within its operational chart production process. SDB is of particular interest to CHS due to its ability to supplement depths derived from traditional hydrographic surveys. This is of great importance in shallow and/or remote Canadian waters where achieving wide-area depth coverage through traditional surveys is costly, time-consuming and a safety risk to survey operators. With an accuracy of around 1 m, SDB could be used by CHS to fill gaps in survey data and to provide valuable information in dynamic areas.

The Canadian Hydrographic Service (CHS) publishes nautical charts covering all Canadian waters. Through projects with the Canadian Space Agency, CHS has been investigating remote sensing techniques to support hydrographic applications. One challenge CHS has encountered relates to quantifying its confidence in remote sensing products. This is particularly challenging with Satellite-Derived Bathymetry (SDB) where minimal in situ data may be present for validation. This paper proposes a level of confidence approach where a minimum number of SDB techniques are required to agree within a defined level to allow SDB estimates to be retained. The approach was applied to a Canadian Arctic site, incorporating four techniques: empirical, classification and photogrammetric (automatic and manual). Based on International Hydrographic Organization (IHO) guidelines, each individual approach provided results meeting the CATegory of Zones Of Confidence (CATZOC) level C requirement. By applying the level of confidence approach, where technique combinations agreed within 1 m (e.g., all agree, three agree, two agree) large portions of the extracted bathymetry could now meet the CATZOC A2/B requirement. Areas where at least three approaches agreed have an accuracy of 1.2 m and represent 81% of the total surface. The proposed technique not only increases overall accuracy but also removes some of the uncertainty associated with SDB, particularly for locations where in situ validation data is not available. This approach could provide an option for hydrographic offices to increase their confidence in SDB, potentially allowing for increased SDB use within hydrographic products.

Synthetic Aperture Radar (SAR) has been used in characterizing intertidal zones along northern Canadian coastlines. RADARSAT-2, with its full polarimetric information, has been considered for monitoring these vulnerable ecosystems and helping enhance the navigational safety of these waters. The RADARSAT Constellation Mission (RCM) will ensure data continuity with three identical SAR satellites orbiting together, providing superior revisit capabilities. The three satellites are equipped with multiple configurations, including single-polarization (HH, HV, VV), conventional (HH-HV, VV-VH, and HH-VV), hybrid (i.e., compact) dual polarization, and fully polarimetric (FP) modes. This study investigates the potential of the compact polarimetric (CP) mode for mapping an intertidal zone located at Tasiujaq village on the southwest shore of Ungava Bay, Quebec. Simulated RCM data were generated using FP RADARSAT-2 images collected over the study site in 2016. Commonly used tools for CP analysis include Raney m-delta classification and the hybrid dual polarizations RH-RV (where the transmitter is right-circular and the receivers are horizontal and vertical linear polarizations) and RR-RL (where the transmitter is right circular and the receivers are right-circular and left-circular polarizations). The potential of CP is compared with single, conventional dual-pol, and FP. The Freeman–Durden and Touzi discriminators are used for FP analysis. The random forest classifier is used as a classification approach due to its well-documented performance compared to other classifiers. The results suggest that the hybrid compact (RR-RL and RH-RV) dual polarizations provide encouraging separability capacities with overall accuracies of 61% and 60.7%, respectively, although they do not perform as well as conventional dual-pol HH-HV (64.4%). On the other hand, the CP polarimetric m-delta decomposition generated slightly less accurate classification results with an overall accuracy of approximately 62% compared to the FP Freeman–Durden (67.08%) and Touzi discriminators (71.1%).

The Canadian Hydrographic Service (CHS) supports safe navigation within Canadian waters through approximately 1000 navigational charts as well as hundreds of publications. One of the greatest challenges faced by the CHS is removing gaps in bathymetric survey data, particularly in the Canadian Arctic where only 6% of navigational water is surveyed to modern standards. Therefore, the CHS has initiated a research project to explore remote sensing methods to improve Canadian navigational charts. The major components of this project explore satellite derived bathymetry (SDB), coastline change detection and coastline extraction. This paper focuses on the potential of two stereo satellite techniques for deriving SDB: (i) automatic digital elevation model (DEM) extraction using a semi-global matching method, and (ii) 3D manual delineation of depth contours using visual stereoscopic interpretation. Analysis focused on quantitative assessment which compared estimated depths from both automatic and 3D manual photogrammetric approaches against available in situ survey depths. The results indicate that the 3D manual approach provides an accuracy of <2 m up to a depth of 15 m. Comparable results were obtained from the automatic approach to a depth of 12 m. For almost all investigated depth ranges for both techniques, uncertainties were found to be within the required vertical accuracies for the International Hydrographic Organization category zone of confidence (CATZOC) level C classification for hydrographic surveys. This indicates that both techniques can be used to derive navigational quality bathymetric information within the investigated study site. While encouraging, neither technique was found to offer a single solution for the complete estimation of depth within the study area. As a result of these findings, the CHS envisions a hybrid approach where stereo- and reflectance-based bathymetry estimation techniques are implemented to provide the greatest understanding of depth possible from satellite imagery. Overall, stereo photogrammetry techniques will likely allow for new potential for supporting the improvement of CHS charts in areas where modern surveys have not yet been obtained.

This paper presents a geographic information system (GIS) model that the Canadian Hydrographic Service (CHS) developed to prioritize hydrographic survey and charting at a national scale. Canada has the largest coastline in the world; determining its survey and charting priorities at a national scale is a challenging task, requiring sufficient data to provide national coverage. In order to achieve this task and manage the geospatial layers, CHS has developed a GIS-based model, the CHS Priority Planning Tool (CPPT). Geospatial information of navigational significance (e.g., traffic patterns, water depth, and infrastructure) have been compiled into a GIS model to identify where CHS’s hydrographic survey and charting priorities exist. Probability risk modelling, such as a risk of grounding and collision model, as well as a drift model, are included in the CPPT to ensure that CHS has proper mitigation measures in “high-risk” areas. Other environmental factors such as ice and wind speed are also included to help define national priorities for CHS. The CPPT is operational and is currently being used to define and prioritize CHS’s survey and charting requirements nationally for multiple years. A GIS web tool has been developed to facilitate accessibility for all Department of Fisheries and Oceans employees and to aid in decision making regarding CHS’s national priorities.

In 2014, through the World-Class Tanker Safety System (WCTSS) initiative, the Government of Canada launched the Northern Marine Transportation Corridors (NMTC) concept. The corridors were created as a strategic framework to guide Federal investments in marine transportation in the Arctic. With new government investment, under the Oceans Protection Plan (OPP), the corridors initiative, known as the Northern Low-Impact Shipping Corridors, will continue to be developed. Since 2016, the Canadian Hydrographic Service (CHS) has been using the corridors as a key layer in a geographic information system (GIS) model known as the CHS Priority Planning Tool (CPPT). The CPPT helps CHS prioritize its survey and charting efforts in Canada’s key traffic areas. Even with these latest efforts, important gaps in the surveys still need to be filled in order to cover the Canadian waterways. To help further develop the safety to navigation and improve survey mission planning, CHS has also been exploring new technologies within remote sensing. Under the Government Related Initiatives Program (GRIP) of the Canadian Space Agency (CSA), CHS has been investigating the potential use of Earth observation (EO) data to identify potential hazards to navigation that are not currently charted on CHS products. Through visual interpretation of satellite imagery, and automatic detection using artificial intelligence (AI), CHS identified several potential hazards to navigation that had previously gone uncharted. As a result, five notices to mariners (NTMs) were issued and the corresponding updates were applied to the charts. In this study, two AI approaches are explored using deep learning and machine learning techniques: the convolution neural network (CNN) and random forest (RF) classification. The study investigates the effectiveness of the two models in identifying shoals in Sentinel-2 and WorldView-2 satellite imagery. The results show that both CNN and RF models can detect shoals with accuracies ranging between 79 and 94% over two study sites; however, WorldView-2 images deliver results with higher accuracy and lower omission errors. The high processing times of using high-resolution imagery and training a deep learning model may not be necessary in order to quickly scan images for shoals; but training a CNN model with a large training set may lead to faster processing times without the need to train individual images.