Application of UAV and GB-SAR in Mechanism Research and Monitoring of Zhonghaicun Landslide in Southwest China

Liu, Bo; He, Kun; Mei, Han; Hu, Xiewen; Ma, Guotao; Wu, Mingyang

doi:10.3390/rs13091653

Cited by 19 publications

(9 citation statements)

References 37 publications

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“…Photogrammetry is a cost-efficient and very commonly used method for acquiring surface data, such as orthophotos and DEMs (Brückl et al 2006;Stumpf et al 2015;Peternel et al 2017;Devoto et al 2020;Liu et al 2021). It is based on the basic principles of image matching and stereo-vision using passive optical sensors (camera).…”

Section: Original Papermentioning

confidence: 99%

Landslide monitoring techniques in the Geological Surveys of Europe

et al. 2023

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Landslide monitoring is a mandatory step in landslide risk assessment. It requires collecting data on landslide conditions (e.g., areal extent, landslide kinematics, surface topography, hydrogeometeorological parameters, and failure surfaces) from different time periods and at different scales, from site-specific to local, regional, and national, to assess landslide activity. In this analysis, we collected information on landslide monitoring techniques from 17 members of the Earth Observation and Geohazards Expert Group (from EuroGeoSurveys) deployed between 2005 and 2021. We examined the types of the 75 recorded landslides, the landslide techniques, spatial resolution, temporal resolution, status of the technique (operational, non-operational), time of using (before the event, during the event, after the event), and the applicability of the technique in early warning systems. The research does not indicate the accuracy of each technique but, rather, the extent to which Geological Surveys conduct landslide monitoring and the predominant techniques used. Among the types of landslides, earth slides predominate and are mostly monitored by geological and engineering geological mapping. The results showed that Geological Surveys mostly utilized more traditional monitoring techniques since they have a broad mandate to collect geological data. In addition, this paper provides new insights into the role of the Geological Surveys on landslide monitoring in Europe and contributes to landslide risk reduction initiatives and commitments (e.g., the Kyoto Landslide Commitment 2020).

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Section: Original Papermentioning

confidence: 99%

Landslide monitoring techniques in the Geological Surveys of Europe

et al. 2023

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“…Although UAV-photogrammetry-derived data cannot provide a highly accurate or real-time state of the instability, they can cover a wider landslide area rather than just specific observation points, thereby contributing to a considerably better understanding of the landslide kinematics. Recently, UAV photogrammetry has been successfully applied to landslide mapping and monitoring [21,[43][44][45][46][47][48]. However, its applicability is limited to non-vegetated areas [49].…”

Section: Uav Photogrammetrymentioning

confidence: 99%

Recognition of Landslide Triggering Mechanisms and Dynamics Using GNSS, UAV Photogrammetry and In Situ Monitoring Data

et al. 2022

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Detecting the mechanism of landslides and evaluating their dynamics is challenging, especially concerning composite landslides. For this purpose, several investigation and monitoring techniques should be implemented to obtain reliable information on landslide characteristics (e.g., geological and hydrogeological conditions and type of landslide processes), kinematics (displacement rate), and potential triggering mechanisms (e.g., change in groundwater table and precipitation). The Urbas landslide in northwest Slovenia has been studied for decades through geological, geotechnical, geodetic, and remote sensing investigations. However, due to the complexity of the landslide and the short duration of continuous monitoring, no assessment of its dynamics has been made. To meet this need, this study analysed continuous and periodic monitoring of landslide displacements using data from the global navigation satellite system (GNSS), a wire extensometer, unmanned aerial vehicle (UAV) photogrammetry, and hydrometeorological sensing (groundwater table, precipitation). The results of this study show that the dynamics of the Urbas landslide differ along the landslide area, depending on local geological and hydrogeological conditions. Consequently, certain parts of the landslide are at different evolutionary states and respond differently to the same external triggers.

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“…Differential and multi-temporal Synthetic Aperture Radar (SAR) Interferometry can provide ground displacement radar satellite images. Landslide detection and mapping of past and present mass movements can be detected by Object-Based Image Analysis (OBIA) of optical satellite images [17]. An unmanned aerial vehicle (UAV) was used for rainfall data analysis , causative factor , post landslide behaviour and mechanism of landslide.…”

Section: Slidementioning

confidence: 99%

A prototype model for detection and classification of landslides using satellite data

Sharma

Sapate³

2022

J. Phys.: Conf. Ser.

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Landslides are natural and manmade disasters that cause threat to human life and lead to huge economic loss. Last few decade number of approaches have been developed for early detection of landslide for protecting life and saving properties. This paper proposes a prototype for an artificial intelligent model to detect and predict different types of landslides in hilly area with remote sensing techniques. All developing countries are following a steep increase in development of infrastructure like buildings, roads tunnels bridges railway tracks. Demand of connecting remote area is very high but on other side of environment it is also true that high demand of construction in morpho material area is causing many disasters like landslide. Landslide causes the loss of property and life so an early alarming will be help full for disaster management. Remotely sensed data pre-processed with artificial intelligent technologies will be helpful for landslide detection, creating landslide susceptibility map and inventory. Focus of this study is on enhancing the accuracy to detect landslide, list out the different features for extraction from satellite images and pre processing steps. This research also focuses on application of robust early prediction of type of landslide. This research will help in detection of landslide early to protect economical losses and human lives.

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Application of UAV and GB-SAR in Mechanism Research and Monitoring of Zhonghaicun Landslide in Southwest China

Cited by 19 publications

References 37 publications

Landslide monitoring techniques in the Geological Surveys of Europe

Landslide monitoring techniques in the Geological Surveys of Europe

Recognition of Landslide Triggering Mechanisms and Dynamics Using GNSS, UAV Photogrammetry and In Situ Monitoring Data

A prototype model for detection and classification of landslides using satellite data

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