This GIS dataset depicts the surficial geology of the Ipiatik River area (NTS 73M/SE) (GIS data, polygon features). The data were created in geodatabase feature class format and output for public distribution in shapefile format.
These data comprise the polygon features of Alberta Geological Survey Map 649, Surficial Geology of the Ipiatik River Area (NTS 73M/SE).
Our processes included:
1. making a LiDAR imagery mosaic for NTS 73M/SE;
2. relief shading the LiDAR digital elevation model (DEM);
3. making a Landsat-8 best pixel composite for the region using multiple Landsat-8 scenes from 2017–2019;
4. determining the distribution of organic deposits based on a subset of wetland polygons from the Alberta Biodiversity Monitoring Institute (ABMI) Wetland Inventory (DeLancey et al., 2020; ABMI, 2021), retaining only the bog and fen wetland classes. These polygons were generalized by filtering small polygons (<5 ha) and/or merging small polygons with larger neighbouring polygons of a different wetland class. Wetland boundaries were also smoothed to remove jagged pixel-pattern edges, and simplified to match the map scale of other surficial unit polygons;
5. delineating and digitizing non-peatland polygons and line features across the LiDAR image of NTS 73M/SE using ArcMap 10.8;
6. labelling spatial features;
7. checking the quality of the digital file; and
8. proofreading the text.
References:
ABMI Wetland Inventory; available online: https://abmi.ca/home/data-analytics/da-top/da-product-overview/Advanced-Landcover-Prediction-and-Habitat-Assessment--ALPHA--Products/ABMI-Wetland-Inventory.html
ABMI (2021): ABMI Wetland Inventory - metadata; Alberta Biodiversity Monitoring Institute, Edmonton, Alberta, Canada.
DeLancey, E.R., Simms, J.F., Mahdianpari, M., Brisco, B., Mahoney, C. and Kariyeva, J. (2020): Comparing deep learning and shallow learning for large-scale wetland classification in Alberta, Canada; Remote Sensing 2020, v. 12, 2, https://doi.org/10.3390/rs12010002.