{"title":"Applicability of the longitudinal profile–based cirque classification—A test with large cirque datasets in the southeastern Tibetan Plateau","authors":"Tian Jia, Ping Fu, Cheng-Zhi Qin","doi":"10.1002/esp.5991","DOIUrl":null,"url":null,"abstract":"<p>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 (<i>c</i>-value) and cirque height, or a threshold approach based on the power coefficient (<i>b</i>-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 <i>c</i>-value and <i>b</i>-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 <i>c</i>-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 <i>b</i>-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, <i>c</i>-value based non-linear classifiers and <i>b</i>-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.</p>","PeriodicalId":11408,"journal":{"name":"Earth Surface Processes and Landforms","volume":"49 14","pages":"4709-4723"},"PeriodicalIF":2.8000,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth Surface Processes and Landforms","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/esp.5991","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOGRAPHY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
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.
准确的海蚀圈分类对于了解海蚀圈的形成和古气候影响至关重要。与专家分类法和基于参数的方法相比,基于纵向剖面的峡谷分类法更具优势。这种分类方法将指数函数或幂函数拟合到岩圈剖面上,采用基于指数系数(c 值)和岩圈高度的线性分类器,或基于幂系数(b 值)的阈值方法来划分岩圈和非岩圈。不过,以前的研究仅限于小样本集。我们的研究将这一方法扩展到了青藏高原东南部更广泛的数据集上,在两个更大的样本集上评估了其有效性。基于 c 值和 b 值的方法用两种分类器进行了测试:一种是以前研究中的原始分类器,另一种是本研究数据集训练的参数修正分类器。结果表明,基于 c 值的方法能有效地对典型和非典型椎间盘突出进行分类,与原始分类器相比,基于改良分类器的方法性能明显提高。与原始分类器相比,基于 b 值的方法和改良分类器在识别典型海蚀圈方面表现良好,但对非海蚀圈的识别效果较差。对于所有类型的盘旋和非盘旋,两种方法在非盘旋分类方面的性能都有所提高,但在盘旋分类方面略有折衷。此外,还开发了基于 c 值的非线性分类器和 b 值最佳阈值,用于对圆形和非圆形进行分类,并讨论了它们在该分类中的改进性能。总体而言,基于纵向剖面的分类方法对典型的堰塞湖和非堰塞湖更为有效,通过考虑堰塞湖的其他空间结构信息,还有进一步改进的潜力。
期刊介绍:
Earth Surface Processes and Landforms is an interdisciplinary international journal concerned with:
the interactions between surface processes and landforms and landscapes;
that lead to physical, chemical and biological changes; and which in turn create;
current landscapes and the geological record of past landscapes.
Its focus is core to both physical geographical and geological communities, and also the wider geosciences