AutoCirque: An automated method to delineate glacial cirque outlines from digital elevation models

Li, Yingkui; Zhao, Zhibin

doi:10.1016/j.geomorph.2021.108059

Cited by 10 publications

(3 citation statements)

References 48 publications

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“…Evans and Cox, 1995;Federici and Spagnolo, 2004;Seif and Ebrahimi, 2014). In recent years, object-based image classification, deep learning, and automated approaches have been developed to help identify and map cirque outlines (Eisank et al, 2010;Anders et al, 2015;Li and Zhao, 2022;Scuderi and Nagle-McNaughton, 2022).…”

Section: Input Datasetsmentioning

confidence: 99%

“…Advances in Geographic Information Systems (GIS) and remote sensing (RS) techniques and the availability of Digital Elevation Models (DEMs) of global coverage (Anders et al, 2010;Principato and Lee, 2014;Li and Zhao, 2022) have allowed for the morphological analysis of large cirque datasets to reconstruct palaeoclimate and environmental conditions and to test the buzzsaw hypothesis (Barr and Spagnolo, 2015;Mitchell and Humphries, 2015;Evans and Cox, 2017;Zhang et al, 2020;Li et al, 2023). An ArcGIS toolbox, ACME (Automated Cirque Metric Extraction) was developed by Spagnolo et al (2017) to derive 16 morphological metrics, including length, width, circularity, planar and three-dimensional (3D) area, elevation, slope, aspect, plan closure, and hypsometry.…”

Section: Introductionmentioning

confidence: 99%

“…Both these features are traditionally based on manual digitization. Li and Zhao (2022) developed an ArcGIS toolbox, AutoCirque, to automatically delineate cirque outlines from DEMs, partially resolving the need to automate digitization. However, cirque threshold midpoints are still based on manual digitization, which could be perceived as somewhat subjective and, being time-consuming, might hinder the morphometric analysis of large cirque datasets for regional comparisons.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

ACME2: An extended toolbox for automated cirque metrics extraction

Li,

Evans,

Spagnolo

et al. 2024

Geomorphology

Self Cite

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Section: Input Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ACME2: An extended toolbox for automated cirque metrics extraction

Li,

Evans,

Spagnolo

et al. 2024

Geomorphology

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Applicability of the longitudinal profile–based cirque classification—A test with large cirque datasets in the southeastern Tibetan Plateau

Jia,

Fu,

Qin

2024

Earth Surf Processes Landf

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Accurate cirque classification is essential for understanding their formation and palaeoclimatic implications. Longitudinal‐profile‐based cirque classification offers advantages over expert classification and parameter‐based methods. This classification fits exponential or power functions to cirque profiles, employing linear classifiers based on the exponential coefficient (c‐value) and cirque height, or a threshold approach based on the power coefficient (b‐value) to classify cirques and non‐cirques. However, previous studies were limited to small sample sets. Our study extends this methodology to more extensive datasets on the southeastern Tibetan Plateau, evaluating its effectiveness across two larger sample sets. Both c‐value and b‐value based methods are tested with two classifiers: the original classifier from previous studies and the parameter‐refitted classifier trained by datasets of this study. The results show that the c‐value‐based method effectively classifies typical cirques and non‐cirques, with notable enhancements in performance based on the refitted classifier compared to the original one. The b‐value‐based method with the refitted classifier performs well in typical cirque identification but is less effective for non‐cirques compared to the original classifier. For all‐type cirques and non‐cirques, both methods demonstrated improved performance in non‐cirque classification although there was a slight trade‐off of cirque classification. Additionally, c‐value based non‐linear classifiers and b‐value optimal threshold for classifying cirque and non‐cirque have been developed, and their improved performance in this classification is discussed. Overall, the longitudinal‐profile‐based classification is more effective for typical cirques and non‐cirques, with potentials for further improvement by considering additional spatial structure information of cirques.

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Major scale forms

Evans¹

2025

Encyclopedia of Quaternary Science

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AutoCirque: An automated method to delineate glacial cirque outlines from digital elevation models

Cited by 10 publications

References 48 publications

ACME2: An extended toolbox for automated cirque metrics extraction

ACME2: An extended toolbox for automated cirque metrics extraction

Applicability of the longitudinal profile–based cirque classification—A test with large cirque datasets in the southeastern Tibetan Plateau

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