A characteristic periglacial landform: Automated recognition and delineation of cryoplanation terraces in eastern Beringia

• Published in: Permafrost and Periglacial Processes
• Main Authors: Queen, Clayton W., Nelson, Frederick E., Gunn, Grant E., Nyland, Kelsey E.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2020
• Subjects: Permafrost and Periglacial Processes; Seward Peninsula; Alaska; Beringia
• Online Access: http://dx.doi.org/10.1002/ppp.2083; https://onlinelibrary.wiley.com/doi/pdf/10.1002/ppp.2083; https://onlinelibrary.wiley.com/doi/full-xml/10.1002/ppp.2083

Abstract Automated recognition and delineation of specific landforms and their constituent elements ranks among the most active areas of contemporary geomorphological research. This study contributes to that literature by applying semi‐ and fully automated recognition procedures to upland periglacial geomorphic landscapes. The Cryoplanation Terrace semi‐Automated Recognition (CTAR) algorithm utilizes basic terrain parameters to identify locations of cryoplanation terraces (CTs) from the high‐resolution ArcticDEM. Using a multistep process, candidate areas are identified based on morphometric characteristics. CTAR uses terrain derivatives to search ridges, hills, and mountains for flat areas bounded by abrupt breaks in slope. Because CTs are found exclusively in upland periglacial environments, some locations require that low‐lying areas be filtered out. To assess accuracy, CTAR was tested at five local study sites distributed across eastern Beringia, each containing multiple CTs delimited manually in a previous study. CTAR performed well, with an overall accuracy of 90%. A strong linear relationship exists between the size of CTAR‐delimited terraces and those identified in a previous study through air‐photo interpretation. In addition to identifying nearly all of the CTs in the five study areas, a fully automated version of the algorithm (GEE‐CTAR), implemented in Google Earth Engine, identified nearly 8,000 previously unmapped potential CTs in the Seward Peninsula region of western Alaska. The ability to identify CTs from digital elevation models provides a useful tool for recognizing and delineating upland periglacial topography. Objective recognition of large erosional landform elements created by periglacial processes is a critical step in developing the field of periglacial geomorphometry.