Ground surface deformations induced by tunneling under deep-seated landslides in the Northern Apennines of Italy imaged using advanced InSAR techniques

2016 ◽  
pp. 363-370
Author(s):  
B Bayer ◽  
L Bertello ◽  
A Simoni ◽  
M Berti ◽  
D Schmidt ◽  
...  
Keyword(s):  
Ground Surface ◽  
Northern Apennines ◽  
Surface Deformations

scholarly journals INTEGRATION OF GIS AND ADVANCED REMOTE SENSING TECHNIQUES FOR LANDSLIDE HAZARD ASSESSMENT: A CASE STUDY OF NORTHWEST SYRIA

2020 ◽  
Vol VI-3/W1-2020 ◽  
pp. 27-34
Author(s):  
M. Hammad ◽  
B. V. Leeuwen ◽  
L. Mucsi
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Landslide Susceptibility ◽  
Ground Surface ◽  
Landslide Hazard ◽  
Slope Aspect ◽  
Hazard Mapping ◽  
Surface Deformations ◽  
Time Period ◽  
Total Study Area

Abstract. Landslide susceptibility and hazard mapping has been developed providing remarkable results through the integration of geographic information system (GIS) and remote sensing. In this regard, some approaches have considered the use of Sentinel-1 data and time-series interferometric synthetic aperture radar (InSAR) techniques, such as differential InSAR (D-InSAR) and persistent scatterers interferometric (PSI), for providing precise information about total amount and velocity of ground-surface deformations and landslides within a specific area during a specific time period which is important for disaster management’s planning process.In this paper, artificial neural network (ANN) was used as a statistical analysis method for landslide susceptibility mapping in Northwest Syria using multi-layer perceptron (MLP) neural network on a training dataset of one dependent variable (landslide or non-landslide) and nine independent variables (slope, aspect, curvature, land cover, NDVI, lithology, distance from faults, distance from road, distance from stream networks). The resulting map of landslide susceptibility was validated using area under curve (AUC) analysis using a testing dataset which showed 90.28% of AUC value. Then, landslide susceptibility map was reclassified into high-moderate-low classes and integrated with intensity map of mean velocity of ground-surface deformations during the time period form (16 October 2018) until (21 March 2019) by using a landslide hazard matrix in a GIS environment in order to get landslide hazard map of the study area for that time period. The result shows that around 44.4%, 52.9% and 2.5% of total study area was classified as a high, moderate and low hazard zone of landslide, respectively.

scholarly journals LANDSLIDE INVESTIGATION USING DIFFERENTIAL SYNTHETIC APERTURE RADAR INTERFEROMETRY: A CASE STUDY OF BALLORAN DAM AREA IN SYRIA

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 133-138
Author(s):  
M. Hammad ◽  
L. Mucsi ◽  
B. V. Leeuwen
Keyword(s):  
Synthetic Aperture Radar ◽  
Surface Deformation ◽  
Ground Surface ◽  
Radar Interferometry ◽  
Synthetic Aperture ◽  
Main Road ◽  
North West ◽  
Synthetic Aperture Radar Interferometry ◽  
Surface Deformations ◽  
Aperture Radar

<p><strong>Abstract.</strong> Landslides are one of the main geological hazards that can cause critical damage to the infrastructure in an area and can result in serious risks to the people’s safety there. Landslides can be investigated and monitored using field survey, aerial mapping and high resolution optical satellite data analysis. However, these methods are relatively time-consuming. Interferometric synthetic aperture radar (InSAR) can investigate and monitor landslides and provide sub-centimetre accuracy for ground-surface deformation when time series analysis techniques are employed. In this research, differential synthetic aperture radar interferometry was applied on Sentinel-1 data of two Single Look Complex (SLC) images from 16 October 2018 and 21 March 2019 in the Interferometric Wide (IW) swath mode using the Sentinel application platform (SNAP) to determine the extreme ground-surface deformations, as a prelude to landslides occurrence in Balloran dam area in the north-west of Syria, where the ophiolite complex deposits of the Maastrichtian are exposed causing, due to the heavy rains, several landslides affecting the road network in this area every year. The results reveal ground-surface deformations during the study period along the satellite line of sight near to the main road in Balloran dam area with a maximum value reaches to around 20 cm. The D-InSAR results were compared to the D-GPS results of 10 validation points along the main road in the study area, where the RMS difference value was 20 cm.</p>

A Technique for Surveying of Ground Surface Deformations in Mine Field

2018 ◽  
Vol 54 (5) ◽  
pp. 874-882
Author(s):  
D. V. Dorokhov ◽  
F. K. Nizametdinov ◽  
S. G. Ozhigin ◽  
S. B. Ozhigina
Keyword(s):  
Ground Surface ◽  
Mine Field ◽  
Surface Deformations
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