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Project Returns for Identification and Characterization of Abandoned Padded Wellsites Using Remote Sensing

Description

The objective of this project was to map and characterize abandoned padded wellsites (i.e., constructed with imported mineral soil materials between 1940 and 2020) in peatlands within the Boreal... More ≫

Description

The objective of this project was to map and characterize abandoned padded wellsites (i.e., constructed with imported mineral soil materials between 1940 and 2020) in peatlands within the Boreal and Foothills Regions of the Green area in Alberta. This Project used high-resolution LiDAR data combined with open-access optical data from Sentinel-2 to build a supervised machine learning model that detected padded sites. The classification procedure achieved 81.2% accuracy when predicting no pad and 73.9% accuracy when predicting a pad, for an overall classification accuracy of 78%. As a result, a total of 7,077 padded sites and 8,006 unpadded sites in peatland areas were obtained from the supervised classification. This dataset contains the locations of those 7,077 padded sites. These sites will be used by the Project team to select sites for a field-based research program and will be provided to EPA to enable improved regulatory efficiency in the Province of Alberta.

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Alberta Satellite Land Cover

Description

Alberta Satellite Land Cover (ASLC) raster is produced for Government of Alberta and is available to the general public. The ASLC Mosaic is a land cover dataset for the province of Alberta. It is... More ≫

Description

Alberta Satellite Land Cover (ASLC) raster is produced for Government of Alberta and is available to the general public. The ASLC Mosaic is a land cover dataset for the province of Alberta. It is a set of individual projects completed in the last decade for wildfire historical burns and various areas. As the remote sensing technology and related information extraction approaches have made significant advancements during last 20 years the process naturally evolved from classification schema targeting Landsat 5 to one integrating various sources of remote sensing data, information extraction procedures, different open and commercial toolboxes for processing and more automation. Integration of multiple data types includes various optical and radar data free of charge as well as GOA's purchased LiDAR data swaths. Since raster values in products from individual projects are identical with values of initial Alberta Ground Cover Classification product, it was possible to merge all those...

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