Evidence map›Paper›PMID 39460239›Full record

ArticleSensors (Basel, Switzerland)2024

Deformation Monitoring and Analysis of Baige Landslide (China) Based on the Fusion Monitoring of Multi-Orbit Time-Series InSAR Technology.

Kai Ye, Zhe Wang, Ting Wang, Ying Luo, Yiming Chen, Jiaqian Zhang, Jialun Cai

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Kai YeCollege of Environment and Resources, Southwest University of Science & Technology, Mianyang 621010, China.
Zhe WangCollege of Environment and Resources, Southwest University of Science & Technology, Mianyang 621010, China.
Ting WangSchool of Life Science and Engineering, Southwest University of Science & Technology, Mianyang 621010, China.
Ying LuoCollege of Environment and Resources, Southwest University of Science & Technology, Mianyang 621010, China.
Yiming ChenCollege of Environment and Resources, Southwest University of Science & Technology, Mianyang 621010, China.
Jiaqian ZhangCollege of Environment and Resources, Southwest University of Science & Technology, Mianyang 621010, China.
Jialun CaiCollege of Environment and Resources, Southwest University of Science & Technology, Mianyang 621010, China.ORCID 0000-0002-8349-2711

Funding

Ministry of Science and Technology of the People's Republic of China 2019YFC1803500Ministry of Science and Technology of the People's Republic of China 2019YFC1803504Southwest University of Science and Technology Doctoral Program 22zx7171Southwest University of Science and Technology Doctoral Program 23zx7175the Bureau of Science and Technology Aba Qiang Tibetan Autonomous Prefecture R22YYJSYJ0004the Bureau of Science and Technology Aba Qiang Tibetan Autonomous Prefecture R23YYJSYJ0010the Key R&D Projects of Sichuan Provincial Science and Technology Department 2018SZ0298the Key R&D Projects of Sichuan Provincial Science and Technology Department 2023YFS0390the National Natural Science Foundation of China No. 41402248
6 · The paper itself

Abstract

Due to the limitations inherent in SAR satellite imaging modes, utilizing time-series InSAR technology to process single-orbit satellite image data typically only yields one-dimensional deformation information along the LOS direction. This constraint impedes a comprehensive representation of the true surface deformation of landslides. Consequently, in this paper, after the SBAS-InSAR and PS-InSAR processing of the 30-view ascending and 30-view descending orbit images of the Sentinel-1A satellite, based on the imaging geometric relationship of the SAR satellite, we propose a novel computational method of fusing ascending and descending orbital LOS-direction time-series deformation to extract the landslide's downslope direction deformation of landslides. By applying this method to Baige landslide monitoring and integrating it with an improved tangential angle warning criterion, we classified the landslide's trailing edge into a high-speed, a uniform-speed, and a low-speed deformation region, with deformation magnitudes of 7~8 cm, 5~7 cm, and 3~4 cm, respectively. A comparative analysis with measured data for landslide deformation monitoring revealed that the average root mean square error between the fused landslide's downslope direction deformation and the measured data was a mere 3.62 mm. This represents a reduction of 56.9% and 57.5% in the average root mean square error compared to the single ascending and descending orbit LOS-direction time-series deformations, respectively, indicating higher monitoring accuracy. Finally, based on the analysis of landslide deformation and its inducing factors derived from the calculated time-series deformation results, it was determined that the precipitation, lithology of the strata, and ongoing geological activity are significant contributors to the sliding of the Baige land-slide. This method offers more comprehensive and accurate surface deformation information for dynamic landslide monitoring, aiding relevant departments in landslide surveillance and management, and providing technical recommendations for the fusion of multi-orbital satellite LOS-direction deformations to accurately reconstruct the true surface deformation of landslides.

Indexed as

Baige landslidedeformation analysismulti-orbit fusionSentinel-1Atime-series InSAR

Identifiers

PMID39460239
PMCPMC11510798

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.