The Hakai Institute has expertise in all facets of remote sensing, and particularly Light Detection and Ranging (LiDAR) and image data acquisition, processing, and applications. The Airborne Coastal Observatory (ACO) and other complementary tools have put us at the forefront of mapping British Columbia’s dynamic landscape, from glaciers to coastal oceans.
Geospatial data of this sort is vital for strategically addressing economic, social, and environmental priorities, particularly in the context of pervasive and rapid climate change and associated hazards.
The vast reservoirs of geospatial data amassed by Hakai Institute and by other organizations across Canada, however, face a bottleneck, in which our ability to gather this data has outstripped the ability to use it—and thereby realize the potential benefits to provincial agencies, municipalities, First Nations, and industry.
In order to address the considerable challenges in mobilizing LiDAR and other geospatial data, Natural Resources Canada (NRCan) established the GeoConnections funding program with a mandate to lead the evolution of geospatial data infrastructure in Canada.
In 2024 the Hakai Institute received GeoConnections funding to develop and test a scalable cloud-optimized Application Programming Interface (API) that is web-based and easily accessible by multiple user groups. The project will use LiDAR data from three case study regions in BC that have experienced recent landscape-altering events.
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Partners
Natural Resources Canada (NRCan) is a department of the Government of Canada. GeoConnections is an ongoing program with the mandate to lead the evolution of Canadian Geospatial Data Infrastructure (CGDI) with standards-based technologies and operational policies for data sharing and integration.
GeoBC is an agency of the British Columbia provincial government that, among its other responsibilities, maintains a large repository of (LiDAR) data captured via ongoing comprehensive aerial surveys.
Timeframe
This project began in mid-2024 and will conclude March 31, 2025.
Context
LiDAR has, in many ways, revolutionized land surveying. Considering the vastness of the Canadian landmass, it is no surprise that Canada is a recognized leader in the collection and analysis of Earth observation datasets, including LiDAR.
Given the tremendous volume and spatial complexity of these datasets, however, we require state-of-the art methods to visualize and analyze LiDAR data. Data streaming of LiDAR provides one solution to reduce the bottleneck that often arises between acquisition and analysis.
A key component of our cloud-based LiDAR web-portal will be to present time-series spatial data that shows elevation change through time in dynamic environments such as glacierized or hazardous terrain. To develop and test our application, we selected regions where climate change impacts indigenous communities, freshwater availability and tourism. These regions include Elliot Creek, Mount Robson Provincial Park, and Place Glacier.
As part of Hakai’s established principles of open data and data mobilization, we have data available for a broad suite of users to understand its importance for decision-making, ethical use and future potential. This project unlocks the potential of LiDAR and makes its analysis easily accessible to a diversity of users, including First Nations and government policy and decision-makers. It implants Hakai’s data mobilization strategy and the British Columbia government’s investment in free and open LiDAR.
When the successful pilot study is completed, it is hoped that the results are scalable to support province-wide geospatial data as part of GeoBC’s ongoing LiDAR program
Outcomes
The team has assembled the LiDAR data into cloud-based formats with associated metadata for viewing online. Meetings with key user groups, including Homalco First Nation representatives, have provided important feedback as the web portal is refined to ensure maximum utility for a broad user base. Geospatial team members continue to explore various integrations that will help users view and manipulate big spatial data.
