Identifying Alpine Lakes with Shoreline Features

Hu, Zhimin; Feng, Min; Sui, Yijie; Yan, Dezhao; Zhang, Kuo; Xu, Jinhao; Liu, Rui; Sthapit, Earina

doi:10.3390/w16223287

Open AccessArticle

Identifying Alpine Lakes with Shoreline Features

by

Zhimin Hu

¹

,

Min Feng

^2,3,*

,

Yijie Sui

²,

Dezhao Yan

^2,4

,

Kuo Zhang

^2,4

,

Jinhao Xu

²

,

Rui Liu

¹ and

Earina Sthapit

⁵

¹

College of Geography and Tourism, Chongqing Normal University, Chongqing 401331, China

²

National Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China

³

Academy of Plateau Science and Sustainability, Qinghai Normal University, Xining 810016, China

⁴

University of Chinese Academy of Sciences, Beijing 100049, China

⁵

Central Department of Hydrology and Meteorology, Tribhuvan University, Kathmandu 44600, Nepal

^*

Author to whom correspondence should be addressed.

Water 2024, 16(22), 3287; https://doi.org/10.3390/w16223287

Submission received: 23 October 2024 / Revised: 5 November 2024 / Accepted: 8 November 2024 / Published: 15 November 2024

(This article belongs to the Topic Advances in Hydrological Remote Sensing)

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Abstract

Alpine lakes located in high-altitude mountainous regions act as vital sentinels of environmental change. Remote-sensing-based identification of these lakes is crucial for understanding their response to climate variations and for assessing associated disaster risks. However, the complex terrain and weather conditions in these areas pose significant challenges to accurate detection. This paper proposes a method that leverages the high precision of deep learning for small lake and lake boundary extraction combined with deep learning to eliminate noise and errors in the identification results. Using Sentinel-2 data, we accurately identified and delineated alpine lakes in the eastern Himalayas. A total of 2123 lakes were detected, with an average lake area of 0.035 km². Notably, 76% of these lakes had areas smaller than 0.01 km². The slope data is crucial for the lake classification model in eliminating shadow noise. The accuracy of the proposed lake classification model reached 97.7%. In the identification of small alpine lakes, the recognition rate of this method was 96.4%, significantly surpassing that of traditional deep learning approaches. Additionally, this method effectively eliminated most shadow noise present in water body detection results obtained through machine learning techniques.

Keywords: alpine lake; Sentinel-2 Imagary; deep learning; ConvNeXt; shoreling feature

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MDPI and ACS Style

Hu, Z.; Feng, M.; Sui, Y.; Yan, D.; Zhang, K.; Xu, J.; Liu, R.; Sthapit, E. Identifying Alpine Lakes with Shoreline Features. Water 2024, 16, 3287. https://doi.org/10.3390/w16223287

AMA Style

Hu Z, Feng M, Sui Y, Yan D, Zhang K, Xu J, Liu R, Sthapit E. Identifying Alpine Lakes with Shoreline Features. Water. 2024; 16(22):3287. https://doi.org/10.3390/w16223287

Chicago/Turabian Style

Hu, Zhimin, Min Feng, Yijie Sui, Dezhao Yan, Kuo Zhang, Jinhao Xu, Rui Liu, and Earina Sthapit. 2024. "Identifying Alpine Lakes with Shoreline Features" Water 16, no. 22: 3287. https://doi.org/10.3390/w16223287

APA Style

Hu, Z., Feng, M., Sui, Y., Yan, D., Zhang, K., Xu, J., Liu, R., & Sthapit, E. (2024). Identifying Alpine Lakes with Shoreline Features. Water, 16(22), 3287. https://doi.org/10.3390/w16223287

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