Monitoring landslide disturbances using distributed acoustic sensing under extreme weather conditions

Abstract Extreme weather significantly challenges the effective and timely monitoring of landslide disasters. Distributed acoustic sensing (DAS) offers unique capabilities for monitoring slope failures during extreme weather events such as typhoons by transforming pre-deployed optical fiber cables into high-resolution vibration-acoustic sensor arrays. This study documents sudden shifts in landslide disturbance signals during a super typhoon’s passage using DAS with 1 Hz downsampled modulated signals. By leveraging multi-domain analysis (time-frequency-space), we identify landslide disturbance micro-deformation signatures, revealing interconnected spatial responses and dynamic patterns. We introduce a spatiotemporal indicator evaluation framework to monitor landslide occurrence and evolution under extreme weather conditions. The monitoring of landslide occurrence correlates well with post-disaster incident records and meteorological data. These results demonstrate that DAS systems can enhance early detection and high-resolution monitoring of landslide disasters under extreme weather conditions, highlighting the potential for comprehensive natural disaster management.

Authors

Institutions

Publication Details

Journal
npj natural hazards.
Published
2026-02-27
DOI
https://doi.org/10.1038/s44304-026-00182-y
Primary Topic
Landslides and related hazards
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Monitoring landslide disturbances using distributed acoustic sensing under extreme weather conditions

Kai Yang, Yiyuan Yang, Qinmin Yang, Weiqiang Zhu et al.
npj natural hazards.
Landslides and related hazards
article

Monitoring landslide disturbances using distributed acoustic sensing under extreme weather conditions

Kai Yang, Yiyuan Yang, Qinmin Yang, Weiqiang Zhu, Chengyuan Zhu
article en

Abstract

Abstract Extreme weather significantly challenges the effective and timely monitoring of landslide disasters. Distributed acoustic sensing (DAS) offers unique capabilities for monitoring slope failures during extreme weather events such as typhoons by transforming pre-deployed optical fiber cables into high-resolution vibration-acoustic sensor arrays. This study documents sudden shifts in landslide disturbance signals during a super typhoon’s passage using DAS with 1 Hz downsampled modulated signals. By leveraging multi-domain analysis (time-frequency-space), we identify landslide disturbance micro-deformation signatures, revealing interconnected spatial responses and dynamic patterns. We introduce a spatiotemporal indicator evaluation framework to monitor landslide occurrence and evolution under extreme weather conditions. The monitoring of landslide occurrence correlates well with post-disaster incident records and meteorological data. These results demonstrate that DAS systems can enhance early detection and high-resolution monitoring of landslide disasters under extreme weather conditions, highlighting the potential for comprehensive natural disaster management.

npj natural hazards.Vol. 3(1)
Planetary Science Institute (US), University of Oxford (GB), State Key Laboratory of Industrial Control Technology (CN), Zhejiang University (CN), South China University of Technology (CN), University of California, Berkeley (US)
National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province
Climate action
Openalex Percentile: Top 47%
Landslides and related hazards
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.