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Article

The Dynamic Monitoring of River-Ice Thickness on the Qinghai–Tibet Plateau: Four-Dimensional Structure-from-Motion Photogrammetry

1
School of Civil and Hydraulic Engineering, Lanzhou University of Technology, Lanzhou 730050, China
2
State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(16), 2887; https://doi.org/10.3390/rs17162887
Submission received: 8 July 2025 / Revised: 4 August 2025 / Accepted: 18 August 2025 / Published: 19 August 2025
(This article belongs to the Special Issue Application of Remote Sensing in Snow and Ice Monitoring)

Abstract

River-ice, a significant element of the cryosphere, plays a crucial role in hydrological processes. However, the effectiveness of current river-ice monitoring techniques on the Qinghai–Tibet Plateau is limited due to the complex interplay of environmental and topographical factors in this extensively ice-covered region. To overcome the inadequacies of traditional monitoring approaches in plateau settings, this research introduces a 4D-SfM photogrammetry method for river-ice monitoring. Experimental measurements of river-ice thickness were conducted on the upper reaches of the Heihe River in the Qilian Mountains during the freezing period of 2023–2024. The study evaluated accuracy variations across three different shooting distances: close-range (0.5 m–1.5 m), mid-range (3 m–10 m), and long-range (25 m–60 m). In this study, 4D-SfM photogrammetry not only accurately represents the nonlinear processes of river-ice formation and melting but also sensitively detects abrupt changes in thickness. Between 6 February and 4 April 2024, river-ice underwent a cumulative melt of 77.8 cm, followed by a cumulative growth of 72.2 cm between 26 November and 26 December 2024. Notably, between 24 and 25 December 2024, 4D-SfM photogrammetry successfully captured an extreme event in which river-ice thickness surged by approximately 30 cm. Measurement accuracy decreased with increasing shooting distance, as indicated by an increase in RMSE from 0.43 cm to 3.97 cm. Additionally, factors such as image brightness and ice surface irregularities significantly impact measurement precision. Moreover, the measurement area expanded from 11.38 m2 to 2642 m2 with increased shooting distances. Therefore, achieving a balance between shooting distance and measurement accuracy is essential when employing 4D-SfM photogrammetry for river-ice monitoring. This study provides a valuable resource for utilizing 4D-SfM photogrammetry to monitor river-ice thickness on the Qinghai–Tibet Plateau.
Keywords: 4D-SfM photogrammetry; river-ice thickness; shooting distance; measurement accuracy; Qinghai–Tibet Plateau 4D-SfM photogrammetry; river-ice thickness; shooting distance; measurement accuracy; Qinghai–Tibet Plateau

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MDPI and ACS Style

Fan, Y.; Zhang, Y.; Liu, J.; Chen, R.; Lyu, Z.; Wang, L.; Ao, X. The Dynamic Monitoring of River-Ice Thickness on the Qinghai–Tibet Plateau: Four-Dimensional Structure-from-Motion Photogrammetry. Remote Sens. 2025, 17, 2887. https://doi.org/10.3390/rs17162887

AMA Style

Fan Y, Zhang Y, Liu J, Chen R, Lyu Z, Wang L, Ao X. The Dynamic Monitoring of River-Ice Thickness on the Qinghai–Tibet Plateau: Four-Dimensional Structure-from-Motion Photogrammetry. Remote Sensing. 2025; 17(16):2887. https://doi.org/10.3390/rs17162887

Chicago/Turabian Style

Fan, Yanwei, Yao Zhang, Junfeng Liu, Rensheng Chen, Zijie Lyu, Lei Wang, and Xinmao Ao. 2025. "The Dynamic Monitoring of River-Ice Thickness on the Qinghai–Tibet Plateau: Four-Dimensional Structure-from-Motion Photogrammetry" Remote Sensing 17, no. 16: 2887. https://doi.org/10.3390/rs17162887

APA Style

Fan, Y., Zhang, Y., Liu, J., Chen, R., Lyu, Z., Wang, L., & Ao, X. (2025). The Dynamic Monitoring of River-Ice Thickness on the Qinghai–Tibet Plateau: Four-Dimensional Structure-from-Motion Photogrammetry. Remote Sensing, 17(16), 2887. https://doi.org/10.3390/rs17162887

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