scholarly journals GNSS Multipath Errors Mitigation Algorithm Based on Observation Domain for Landslide Deformation Monitoring

2020 ◽  
Vol 570 ◽  
pp. 062046
Author(s):  
He Haibo ◽  
Wang Li ◽  
Shu Bao ◽  
Zhang Yaohui ◽  
Li Long
Keyword(s):  
Deformation Monitoring ◽  
Landslide Deformation ◽  
Multipath Errors

scholarly journals Evaluation of Long-Range Mobile Mapping System (MMS) and Close-Range Photogrammetry for Deformation Monitoring. A Case Study of Cortes de Pallás in Valencia (Spain)

2020 ◽  
Vol 10 (19) ◽  
pp. 6831
Author(s):  
Francesco Di Stefano ◽  
Miriam Cabrelles ◽  
Luis García-Asenjo ◽  
José Luis Lerma ◽  
Eva Savina Malinverni ◽  
...  
Keyword(s):  
Long Range ◽  
New Technologies ◽  
3D Models ◽  
Deformation Monitoring ◽  
Mobile Mapping ◽  
Close Range Photogrammetry ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Close Range ◽  
Landslide Deformation

This contribution describes the methodology applied to evaluate the suitability of a Long-Range Mobile Mapping System to be integrated with other techniques that are currently used in a large and complex landslide deformation monitoring project carried out in Cortes de Pallás, in Valencia (Spain). Periodical geodetic surveys provide a reference frame realized by 10 pillars and 15 additional check points placed in specific points of interest, all with millimetric accuracy. The combined use of Close-Range Photogrammetry provides a well-controlled 3D model with 1–3 cm accuracy, making the area ideal for testing new technologies. Since some zones of interest are usually obstructed by construction, trees, or lamp posts, a possible solution might be the supplementary use of dynamic scanning instruments with the mobile mapping solution Kaarta Stencil 2 to collect the missing data. However, the reliability of this technology has to be assessed and validated before being integrated into the existing 3D models in the well-controlled area of Cortes de Pallás. The results of the experiment show that the accuracy achieved are compatible with those obtained from Close-Range Photogrammetry and can also be safely used to supplement image-based information for monitoring with 3–8 cm overall accuracy.

scholarly journals Landslide Deformation Monitoring by Adaptive Distributed Scatterer Interferometric Synthetic Aperture Radar

2019 ◽  
Vol 11 (19) ◽  
pp. 2273 ◽  
Author(s):  
Hongguo Jia ◽  
Hao Zhang ◽  
Luyao Liu ◽  
Guoxiang Liu
Keyword(s):  
Time Series ◽  
Synthetic Aperture Radar ◽  
Deformation Monitoring ◽  
Synthetic Aperture ◽  
Economic Losses ◽  
Interferometric Synthetic Aperture Radar ◽  
Mountainous Areas ◽  
Landslide Area ◽  
Landslide Deformation ◽  
Aperture Radar

Landslide is the second most frequent geological disaster after earthquake, which causes a large number of casualties and economic losses every year. China frequently experiences devastating landslides in mountainous areas. Interferometric Synthetic Aperture Radar (InSAR) technology has great potential for detecting potentially unstable landslides across wide areas and can monitor surface displacement of a single landslide. However traditional time series InSAR technology such as persistent scatterer interferometry (PSI) and small-baseline subset (SBAS) cannot identify enough points in mountainous areas because of dense vegetation and steep terrain. In order to improve the accuracy of landslide hazard detection and the reliability of landslide deformation monitoring in areas lacking high coherence stability point targets, this study proposes an adaptive distributed scatterer interferometric synthetic aperture radar (ADS-InSAR) method based on the spatiotemporal coherence of the distributed scatterer (DS), which automatically adjusts its detection threshold to improve the spatial distribution density and reliability of DS detection in the landslide area. After time series network modeling and deformation calculation of the ADS target, the displacement deformation of the landslide area can be accurately extracted. Shuibuya Town in Enshi Prefecture, Hubei Province, China, was used as a case study, along with 18 Sentinal-1A images acquired from March 2016 to April 2017. The ADS-InSAR method was used to obtain regional deformation data. The deformation time series was combined with hydrometeorological and related data to analyze landslide deformation. The results show that the ADS-InSAR method can effectively improve the density of DS distribution, successfully detect existing ancient landslide groups and determine multiple potential landslide areas, enabling early warning for landslide hazards. This study verifies the reliability and accuracy of ADS-InSAR for landslide disaster prevention and mitigation.

Deformation monitoring of typical loess landslide case studies through combining InSAR and UAV

2021 ◽  
Author(s):  
Qingkai Meng ◽  
Federico Raspini ◽  
Pierluigi Confuorto ◽  
Ying Peng ◽  
Haocheng Liu
Keyword(s):  
High Speed ◽  
Slope Failure ◽  
Deformation Gradient ◽  
Regional Scale ◽  
Deformation Characteristic ◽  
Deformation Monitoring ◽  
Gansu Province ◽  
Landslide Monitoring ◽  
Loess Landslide ◽  
Landslide Deformation

<p>InSAR is an advanced earth observation (EO) technique for retrieving past, subtle (millimetre-level) and continuous surface movements over a long period, which has been widely applied in landslide deformation monitoring and detecting precursory signals of deformation. However, limited by the maximum detected deformation gradient from two consecutive scenarios, singular InSAR has hampered the recognition application for high-speed slides or earth flows, leading to a misleading understanding of slope evolution. Being a high-resolution photogrammetry technology, UAV represents a suitable tool to detect meter-level displacement rates and estimate ground detachment. Thus, InSAR and UAV's synergic analysis can detect the kinematic variation of geographical and geomorphological features, corresponding surface displacements to cross-validation. In the present work, two representative cases illustrated how the combination of InSAR and UAV could be applied in loess landslide deformation monitoring. One case, located in Hongheyan, Gansu Province, China, was selected to reconstruct landslide morphology, identify deformation evolution behaviour and produce dynamic deformation zonation maps using 85 Sentinel-1A SAR images and three UAV fight surveys from pre-sliding to post-sliding. The integrated deformation results illustrate the slide of theHongheyan slope was triggered by heavy rainfall, became suspended owing to the topography effect after the occurrence, and reactivated recently. Another case, located in Qinghai-Gansu province, calculated two-dimensional displacements (vertical-horizontal) by decomposing the ascending and descending Sentinel-1 images to reclassify the regional slope failure type into the translational slide, rotational slide and loess flow based on deformation characteristic. Overall, multi-source information fusion is a new approach for landslide monitoring from regional-scale failure classification to specific-scale slope deformation evolution, giving the comprehensive understanding for local government or Civil Protection to take sufficient precautions for risk mitigation.</p>

Global quantification of InSAR sensitivity for landslide deformation tracking

2021 ◽  
Author(s):  
Adriaan van Natijne ◽  
Roderik Lindenbergh ◽  
Thom Bogaard
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Radar Data ◽  
Google Earth ◽  
Project Planning ◽  
Deformation Monitoring ◽  
Radar Signal ◽  
Sensitivity Index ◽  
Potential Indicator ◽  
Landslide Deformation

<p>Landslides are lurking hazards, that often remains unnoticed. Fortunately, unstable slopes frequently show precursory deformation preceding more destructive accelerations. Thanks to satellite remote sensing, regional deformation monitoring is now available in near real-time.</p><p>Deformation time series are required for both training and validation of models for landslide nowcasting and forecasting. Various studies have shown that satellite Interferometric Synthetic Aperture Radar (InSAR) is capable of delivering the desired deformation time series. Although satellite radar data, such as from the Copernicus Sentinel-1 program, is freely available, application is not (yet) straightforward: InSAR processing is complex, computational intensive and requires specialist knowledge. Moreover, assessment of the potential of the technique on specific slopes requires experience.</p><p>Therefore, we present two concepts to a-priori assess the potential of InSAR landslide deformation tracking. First, the sensitivity index, available globally, indicates the minimum visibility of deformation in the radar signal on any slope. Second, the detection potential indicator, provided as Google Earth Engine application, performs a preliminary analysis of the Sentinel-1 data available at any specific location. Our analysis shows that on 89% of the world's slopes deformation is likely to be detectable with InSAR.</p><p>The detection potential indicator is a valuable tool in the project planning phase, while exploring the site specific possibilities for InSAR deformation monitoring. Furthermore, the sensitivity index provides overview of the slopes where large scale, machine learning driven, landslide nowcasting and forecasting are likely to succeed. We will present an analysis of the global sensitivity index, as well as demonstrate how to apply our detection potential application on a case study.</p>

Landslide deformation monitoring with ALOS/PALSAR imagery: A D-InSAR geomorphological interpretation method

Geomorphology ◽  
2015 ◽  
Vol 231 ◽  
pp. 314-330 ◽  
Author(s):  
Romy Schlögel ◽  
Cécile Doubre ◽  
Jean-Philippe Malet ◽  
Frédéric Masson
Keyword(s):  
Deformation Monitoring ◽  
Alos Palsar ◽  
Landslide Deformation ◽  
Interpretation Method

Precision Evaluation of Georobot Automatic Continuous Deformation Monitoring

2012 ◽  
Vol 256-259 ◽  
pp. 2338-2342
Author(s):  
Jun Jiang ◽  
Huai En Zeng ◽  
Xuan Li ◽  
Li Ming Yu ◽  
Shi Gui Li ◽  
...  
Keyword(s):  
Deformation Monitoring ◽  
Target Point ◽  
Precision Engineering ◽  
Stationary Target ◽  
Positioning Precision ◽  
Continuous Deformation ◽  
Precision Evaluation ◽  
Landslide Deformation ◽  
In Virtue Of ◽  
Tracking Measurement

Georobot is widely used to precision engineering measurement, especially in automatic continuous deformation monitoring, e.g. dam and landslide deformation monitoring, in virtue of the unique ATR and tracking technology. However it is hard to evaluate the actual tracking measurement precision because of the complex circumstance in specific application. The paper designs reasonable experiments solution, including test of tracking measurement of the stationary target point for the positioning precision and test of tracking measurement of the regular shape for the measuring deformation precision. The experiment result shows the positioning precision and measuring deformation precision both can reach the sub-millimeter level if centering error is eliminated, otherwise maybe in about 1.5mm level. It validates the reliability and efficiency of Georobot in automatic continuous deformation monitoring.

scholarly journals TWO-DIMENSIONAL LOESS LANDSLIDE DEFORMATION MONITORING WITH MULTIDIMENSIONAL SMALL BASELINE SUBSET (MSBAS) – A CASE STUDY OF XINYUAN No.2 LANDSLIDE, GANSU, CHINA

2018 ◽  
Vol XLII-3 ◽  
pp. 2341-2345
Author(s):  
C. Y. Zhao ◽  
X. J. Liu ◽  
W. Zhu ◽  
W. F. Zhu
Keyword(s):  
Deformation Monitoring ◽  
Vertical Deformation ◽  
Two Dimensional ◽  
Loess Landslide ◽  
Landslide Deformation ◽  
Small Baseline Subset ◽  
Small Baseline ◽  
First Time ◽  
East West

The deformation monitoring of active landslides is of great importance for the safety of human lives and properties. And twodimensional deformation result can give us more thoughts on the landslide type and deformation process. In this study, multidimensional small baseline subsets (MSBAS) technique is introduced and tested to compute two-dimensional deformation rate and time series for both east-west and vertical deformation of Xinyuan No.2 landslide by simultaneously processing ascending and descending TerraSAR-X data acquired from January 2016 to November 2016. Results show not only the spatiotemporal characteristics of this landslides, but the retrogressive loess landslide failure mode is revealed for the first time with the two-dimensional deformation.

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