Water saturation in the bedrock or colluvium is highly related to most landslide hazards, and rainfall is likely a crucial factor. The dynamic processes of onsite rock/soil mechanics could be revealed via monitoring using the electrical resistivity tomography (ERT) technique and Archie’s law. This study aims to investigate water saturation changes over time using time-lapse ERT images, providing a powerful method for monitoring landslide events. A fully automatic remote resistivity monitoring system was deployed to acquire hourly electrical resistivity data using a nontraditional hybrid array in the Lantai area of Yilan Taiping Mountain in Northeast Taiwan from 2019 to 2021. Six subzones in borehole ERT images were examined for the temporal and spatial resistivity variations, as well as possible pathways of the groundwater. Two representative cases of inverted electrical resistivity images varying with precipitation may be correlated with water saturation changes in the studied hillslope, implying the process of rainfall infiltration. Layers with decreased and increased electrical resistivity are also observed before sliding events. Accordingly, we suggest that high-frequency time-lapse ERT monitoring could play a crucial role in landslide early warning.

No abstract

<p>The deep-seated landslides often caused severe hazard due to the large area and landslide mass associated with the landslide movement. Thus, monitoring the landslide movement is an important task for landslide hazard management. The Microelectromechanical Systems (MEMS) technique developed rapidly in recent years provides the ability of low-cost sensors and easy installation for monitoring of the landslide movement in field. Typically, the landslide movement monitoring using MEMS is based on the tilt angle determined from the measured ground acceleration variations in three directions, and being subjected to the signal noise. We adopt Moving Window Fast Fourier Transform and other seismic wave analysis in this study to improve resolution of the seismic signals and achieve a sound detection of deep-seated landslide movement. The MEMS was installed at the Lantai deep-seated landslide study area, which measured the ground accelerations mid-slope of the landslide. The seismic signals recorded for eleven earthquake events and three heavy rainfall events are selected for analysis. It was found that the signal frequency can be separated from the system responses and related to the landslide movement. Validations were conducted by comparing the analysis results to the field monitoring data of in-place inclinometer and borehole extensometer while available. It is suggested that the landslide movement can be identified with seismic signal at approximately 17 Hz, and the results are consistent for both earthquake-induced and rainfall-induced events. </p>

<p>In remote sensing of landslide investigation, the interpretation of optical image is the main method at present. However, when a disaster occurs, it is very difficult to obtain images without cloud coverage. For example, Typhoon Lubi in August 2021 and Typhoon Nissa in 2022 caused many landslides and road interruptions. However, due to the cover of clouds and fog, it was impossible to obtain satellite images in time to judge the scale of the disaster. Unmanned vehicles are also affected by weather factors, which greatly increases the risk of flight. Therefore, it is extremely necessary to develop disaster identification methods that are not affected by weather.</p> <p>In this study, the long electromagnetic waves of synthetic aperture radar (SAR) are not affected by cloud and fog to develop a landslide detection model for radar images. The reference range of the location and scale of the landslide can be obtained under bad weather conditions to make up for the weather limitations when evaluating the scope of the disaster with optical images.</p> <p>In this study, the NDSI&RVID method is used as the index for the identification and interpretation of the landslide area, and the analysis and discussion of the landslide area is carried out in combination with multi-time series and different orbital data. The effect of landslide identification is improved by three methods: single-sequence identification and interpretation stacking, multi-time-series index stacking, and multi-time-series image stacking. Among them, better interpretation results can be achieved by stacking multiple time-series images. It is recommended to use the number of 4 images before the disaster and 1 image after the disaster for data interpretation. Although the image pixel classification effect still needs to be improved, the identification rate for landslides of more than 10 hectares can reach more than 90%. In the absence of optical images, it has considerable reference value.</p>

No abstract