Project Returns for Wetland Inventory Pilots

The Wetland Inventory Pilot project is part of ongoing efforts to develop innovative wetland mapping methodologies across Alberta, Canada with the objective to refine and establish wetland inventory mapping methodologies using advanced Earth Observation (EO) imagery and machine learning (ML) techniques. Wetland Inventory was developed for four pilot study areas within Alberta for the Boreal 1: South Hay-Zama Lake (7,613 km2), Boreal 2: North Slave Lake (21,215 km2), Grassland located east of Brooks (5,065 km2) and Parkland located east of Red Deer (5,170 km2). These areas were selected to represent the broad diversity of wetlands and land-uses in the province. The Boreal study areas utilized EO sources from Sentinel-1, and Sentinel-2 satellite imagery, Advanced Land Observing Satellite (ALOS) Digital Elevation Model (DEM) and high-resolution airborne Light Detection and Ranging (LiDAR. The Grassland and Parkland study areas contained a comprehensive collection of EO satellite (Sentinel-1, Sentinel-2 and PlanetScope) imagery, aerial imagery, and high-resolution airborne LiDAR. Training and validation data included photo-interpreted plots, and field validated survey sites and helicopter survey sites. The project developed wetland inventory for each pilot area that identifies and delineates both the wetland class and form of a given wetland, according to the Alberta Wetland Classification System (AWCS) (ESRD 2015) and the Wetland Inventory Mapping Standards (AEP 2020). The four pilot areas were completed in collaboration with Government of Alberta (GOA), Ducks Unlimited Canada (DUC), and Alberta Biodiversity Monitoring Institute (ABMI) The wetland inventory pilots dataset is not intended to replace site specific or local information to describe wetland type, area and location.

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Last Updated October 20, 2025, 00:37 (UTC)
Created October 20, 2025, 00:37 (UTC)
Domain / Topic
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Environment
Title
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Project Returns for Wetland Inventory Pilots
Description
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The Wetland Inventory Pilot project is part of ongoing efforts to develop innovative wetland mapping methodologies across Alberta, Canada with the objective to refine and establish wetland inventory mapping methodologies using advanced Earth Observation (EO) imagery and machine learning (ML) techniques. Wetland Inventory was developed for four pilot study areas within Alberta for the Boreal 1: South Hay-Zama Lake (7,613 km2), Boreal 2: North Slave Lake (21,215 km2), Grassland located east of Brooks (5,065 km2) and Parkland located east of Red Deer (5,170 km2). These areas were selected to represent the broad diversity of wetlands and land-uses in the province. The Boreal study areas utilized EO sources from Sentinel-1, and Sentinel-2 satellite imagery, Advanced Land Observing Satellite (ALOS) Digital Elevation Model (DEM) and high-resolution airborne Light Detection and Ranging (LiDAR. The Grassland and Parkland study areas contained a comprehensive collection of EO satellite (Sentinel-1, Sentinel-2 and PlanetScope) imagery, aerial imagery, and high-resolution airborne LiDAR. Training and validation data included photo-interpreted plots, and field validated survey sites and helicopter survey sites. The project developed wetland inventory for each pilot area that identifies and delineates both the wetland class and form of a given wetland, according to the Alberta Wetland Classification System (AWCS) (ESRD 2015) and the Wetland Inventory Mapping Standards (AEP 2020). The four pilot areas were completed in collaboration with Government of Alberta (GOA), Ducks Unlimited Canada (DUC), and Alberta Biodiversity Monitoring Institute (ABMI) The wetland inventory pilots dataset is not intended to replace site specific or local information to describe wetland type, area and location.

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unrestricted
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Open Government Licence - Alberta
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https://open.alberta.ca/opendata/gda-6b83b31a-fa66-4fba-8469-d29a5ef952b4
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Alberta Environment and Protected Areas, Government of Alberta
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[email protected]
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None
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https://geodiscover.alberta.ca/geoportal/rest/metadata/item/d0445dab0b074fe9bd3a7292f8426b1c
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