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.

中文翻译：

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独特的冰川环面地貌：自动识别和划定贝林吉亚东部的冻土阶地

自动识别和划定特定地貌及其构成要素属于当代地貌研究最活跃的领域。这项研究通过将半自动和全自动识别程序应用于陆缘冰缘地貌景观，为该文献做出了贡献。低温台面半自动识别（CTAR）算法利用基本地形参数从高分辨率ArcticDEM中识别低温台面（CT）的位置。使用多步骤过程，可以根据形态特征识别候选区域。CTAR使用地形导数搜索山脊，丘陵和山脉，寻找由坡度突然中断所界定的平坦区域。由于CT仅在高陆缘冰川环境中发现，因此某些位置要求将低洼地区过滤掉。为了评估准确性，对CTAR在分布于Beringia东部的五个本地研究站点进行了测试，每个站点都包含在先前研究中手动定界的多个CT。CTAR表现良好，总体准确度达90％。CTAR分隔梯田的大小与先前研究中通过航空照片解释确定的梯田之间存在很强的线性关系。除了识别这五个研究区域中几乎所有的CT，在Google Earth Engine中实施的算法的完全自动化版本（GEE-CTAR），还识别了阿拉斯加西部苏厄德半岛地区近8,000个以前未映射的潜在CT。从数字高程模型识别CT的能力提供了一种有用的工具，用于识别和描绘高地冰缘周围的地形。