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Dear Colleagues,

The improvement of UAV technology and the deployment of cube satellites have reduced the waiting time for acquiring remote sensing images from weeks to hours. More detailed pre-event imagery and near real-time post-event data are now available that can detect the co-event phenomena globally. A minimized data tasking time across the event should capture only the co-event displacement, which is important in carrying out validation with the numerical model output. Earthquakes also induce multiple types of hazards, such as liquefication, that could be detected by remote sensing. Reginal landslide ratio after a major seismic event decays as time elapses, but the decay rate is controlled by many yet-to-be-studied factors. Ground-based InSAR offers an immediate measurement that responds to the target area and prior-defined threshold that served as the automatic early warning detection system. Examples or treatments to improve the GBInSAR signal coherence in plantation regions or to apply GBInSAR in detecting the ground subsidence are also suitable for this issue. The capability of InSAR varies with bandwidth and polarity of signals, multi-reflection from rugged terrain and plantation; any effort to conquer those obstacles should vastly improve the usage of it.

Papers on all of the remote-sensing-related techniques applied for earthquake reconnaissance or damage assessment, or triggered landslide displacement monitoring/early warning, or displacement field measurement/monitoring are welcome. Research on either the spatial or temporal changes regarding those topics detected by any type of remote sensing platforms is also wanted. Any proof of concept/technology articles regarding this topic are also welcome, as well as case studies with significant impact or unique phenomena illustrations.

Dr. Teng-To Yu
Guest Editor

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Keywords

Earthquake damage assessment and geotechnical earthquake reconnaissance
Earthquake-triggered landslides
Remote sensing of earthquake pre/co/post-seismic displacement monitoring
Optical, radar, laser image
LiDAR, InSAR, GBInSAR, DRONE, UAV, multispectral, shoebox satellite

Published Papers (2 papers)

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Research

20 pages, 8063 KiB

Open AccessArticle

Different-Classification-Scheme-Based Machine Learning Model of Building Seismic Resilience Assessment in a Mountainous Region

by Haijia Wen, Xinzhi Zhou, Chi Zhang, Mingyong Liao and Jiafeng Xiao

Remote Sens. 2023, 15(9), 2226; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15092226 - 22 Apr 2023

Cited by 4 | Viewed by 1558

Abstract

This study aims to develop different-classification-scheme-based building-seismic-resilience (BSR)-mapping models using random forest (RF) and a support vector machine (SVM). Based on a field survey of earthquake-damaged buildings in Shuanghe Town, the epicenter of the Changning M 5.8 earthquake that occurred on 17 June 2019, we selected 19 influencing factors for BSR assessment to establish a database. Based on three classification schemes for the description of BSR, we developed six machine learning assessment models for BSR mapping using RF and an SVM after optimizing the hyper-parameters. The validation indicators of model performance include precision, recall, accuracy, and F1-score as determined from the test sub-dataset. The results indicate that the RF- and SVM-based BSR models achieved prediction accuracies of approximately 0.64–0.94 for different classification schemes applied to the test sub-dataset. Additionally, the precision, recall, and F1-score indicators showed satisfactory values with respect to the BSR levels with relatively large sample sizes. The RF-based models had a lower tendency for overfitting compared to the SVM-based models. The performance of the BSR models was influenced by the quantity of total datasets, the classification schemes, and imbalanced data. Overall, the RF- and SVM-based BSR models can improve the evaluation efficiency of earthquake-damaged buildings in mountainous areas. Full article

(This article belongs to the Special Issue Remote Sensing of Earthquake Engineering and Earthquake-Triggered Landslides and Displacement Monitoring)

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18 pages, 8187 KiB

Open AccessArticle

Seasonal Surface Fluctuation of a Slow-Moving Landslide Detected by Multitemporal Interferometry (MTI) on the Huafan University Campus, Northern Taiwan

by Chiao-Yin Lu, Yu-Chang Chan, Jyr-Ching Hu, Chia-Han Tseng, Che-Hsin Liu and Chih-Hsin Chang

Remote Sens. 2021, 13(19), 4006; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13194006 - 06 Oct 2021

Cited by 6 | Viewed by 2262

Abstract

A slow-moving landslide on the Huafan University campus, which is located on a dip slope in northern Taiwan, has been observed since 1990. However, reliable monitoring data are difficult to acquire after 2018 due to the lack of continuous maintenance of the field measurement equipment. In this study, the multitemporal interferometry (MTI) technique is applied with Sentinel-1 SAR images to monitor the slow-moving landslide from 2014–2019. The slow-moving areas detected by persistent scatterer (PS) pixels are consistent with the range of previous studies, which are based on in situ monitoring data and field surveys. According to the time series of the PS pixels, a long period gravity-induced deformation of the slow-moving landslide can be clearly observed. Moreover, a short period seasonal surface fluctuation of the slow-moving landslide, which has seldom been discussed before, can also be detected in this study. The seasonal surface fluctuation is in-phase with precipitation, which is inferred to be related to the geological and hydrological conditions of the study area. The MTI technique can compensate for the lack of surface displacement data, in this case, the Huafan University campus, and provide information for evaluating and monitoring slow-moving landslides for possible landslide early warning in the future. Full article

(This article belongs to the Special Issue Remote Sensing of Earthquake Engineering and Earthquake-Triggered Landslides and Displacement Monitoring)

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Remote Sensing of Earthquake Engineering and Earthquake-Triggered Landslides and Displacement Monitoring

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