Abstract
Anthropogenic footprints are required by land use managers and policy makers as it provides information on the human impact. The current method of mapping these features requires extensive manual interpretation and requires assumptions based on typical or average areas, consequently impacting the accuracy of current products. The objective of this study is to create a method to efficiently and accurately map the well pad and gas plant footprint in Alberta and to integrate this method into a semi-automated software solution for the production of anthropogenic footprint map layers. The proposed methodology uses a unique combination of geographic object-based image analysis (GEOBIA) and geographic information system (GIS) algorithms in an intelligent mapping system. The system has two components: Feature Extraction System and Automated Quality Control System. The Feature Extraction System is designed specifically for SPOT 2.5 m panchromatic image data. The automated quality control system checks the resultant objects using predefined rules and certain criteria to find the best possible footprint for the well sites. The results show that the produced well pads have more than 80% accuracy. This study addresses current issues in mapping accuracy and developed a processing framework that allows timely automated production of well site and gas plant footprint.
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Acknowledgments
The author is thankful to The National Research Council-Industrial Research Assistance Program (NRC-IRAP) of Canada and Alberta Innovates Technology Futures for the financial support of this study. Alberta Department of Energy has provided some of the spatial data used in this study. The author gratefully acknowledges the assistance of Tom Churchill for his expertise on energy activities and well pads in Alberta.
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Kocabas, V. (2018). A Semi-Automated Software Framework Using GEOBIA and GIS for Delineating Oil and Well Pad Footprints in Alberta, Canada. In: Thill, JC., Dragicevic, S. (eds) GeoComputational Analysis and Modeling of Regional Systems. Advances in Geographic Information Science. Springer, Cham. https://doi.org/10.1007/978-3-319-59511-5_13
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DOI: https://doi.org/10.1007/978-3-319-59511-5_13
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