AI-Based Tracking of Fast-Moving Alpine Landforms Using High Frequency Monoscopic Time-Lapse Imagery
Active rock glaciers and landslides are critical indicators of permafrost dynamics in high mountain environments, reflecting the thermal state of permafrost and responding sensitively to climate change. Traditional monitoring methods, such as Global Navigation Satellite System (GNSS) measurements an...
| Main Authors: | , , , , |
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| Format: | Text |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/egusphere-2024-2570 https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2570/ |
| Summary: | Active rock glaciers and landslides are critical indicators of permafrost dynamics in high mountain environments, reflecting the thermal state of permafrost and responding sensitively to climate change. Traditional monitoring methods, such as Global Navigation Satellite System (GNSS) measurements and permanent installations, face challenges in measuring the rapid movements of these landforms due to environmental constraints and limited spatial coverage. Remote sensing techniques offer improved spatial resolution but often lack the necessary temporal resolution to capture sub-seasonal variations. In this study, we introduce a novel approach utilising monoscopic time-lapse imagery and Artificial Intelligence (AI) for high-temporal-resolution velocity estimation, applied to two subsets of time-lapse datasets capturing a fast-moving landslide and rock glacier at the Grabengufer site (Swiss Alps). Specifically, we employed the Persistent Independent Particle tracking (PIPs++) model for tracking and the AI-based LightGlue matching algorithm to transfer 2D image data into 3D object space and further into 4D velocity data. This methodology was validated against GNSS surveys, demonstrating its capability to provide spatially and temporally detailed velocity information. Our findings highlight the potential of image-driven methodologies to enhance the understanding of dynamic landform processes, revealing spatio-temporal patterns previously unattainable with conventional monitoring techniques. By leveraging existing time-lapse data, our method offers a cost-effective solution for monitoring various geohazards, from rock glaciers to landslides, with implications for enhancing alpine safety and informing climate change impacts on permafrost dynamics. This study marks the pioneering application of AI-based methodologies in environmental monitoring using time-lapse image data, promising advancements in both research and practical applications within geomorphic studies. |
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