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

Abstract 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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Publication Details

Journal
Earth Surface Processes and Landforms
Published
2024-09-23
DOI
https://doi.org/10.1002/esp.5991
Citations
5
Primary Topic
Cryospheric studies and observations
Type
article
Field-Weighted Citation Impact
1.88

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article

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

Ping Fu, Tian Jia, Cheng‐Zhi Qin
5 citations
Earth Surface Processes and Landforms
Cryospheric studies and observations
1.88
article

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

Ping Fu, Tian Jia, Cheng‐Zhi Qin
article en
5 citations

Abstract

Abstract 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.

Earth Surface Processes and LandformsVol. 49(14)
University of Nottingham Ningbo China (CN), Chinese Academy of Sciences (CN), Institute of Geographic Sciences and Natural Resources Research (CN), University of Chinese Academy of Sciences (CN), Shaanxi Normal University (CN)
National Natural Science Foundation of China, Shaanxi Normal University, State Key Laboratory of Resources and Environmental Information System
Openalex Percentile: Top 15%
Cryospheric studies and observations
1.88
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