Suitability Assessment of X-Band Satellite SAR Data for Geotechnical Monitoring of Site Scale Slow Moving Landslides

Brú, Guadalupe; Escayo, Joaquín; Fernández, José; Mallorquí, J.J.; Iglesias, Rubén; Sansosti, E.; Abajo, Tamara; Morales, Antonio

doi:10.3390/rs10060936

Cited by 13 publications

(9 citation statements)

References 41 publications

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“…This software allows us to improve on previous results in several ways: by studying two geometries (ascending and descending) and including a deformation time-series for each coherent pixel within our study area and an error estimation of the velocity results [52]. The CPT algorithm and Subsidence software have been successfully used to perform multiple studies of different scenarios [8,[53][54][55]. We also increased the time period considered, from 2004 to 2010, and the number of radar images.…”

Section: A-dinsarmentioning

confidence: 99%

Geodetic Study of the 2006–2010 Ground Deformation in La Palma (Canary Islands): Observational Results

et al. 2020

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La Palma is one of the youngest of the Canary Islands, and historically the most active. The recent activity and unrest in the archipelago, the moderate seismicity observed in 2017 and 2018 and the possibility of catastrophic landslides related to the Cumbre Vieja volcano have made it strongly advisable to ensure a realistic knowledge of the background surface deformation on the island. This will then allow any anomalous deformation related to potential volcanic unrest on the island to be detected by monitoring the surface deformation. We describe here the observation results obtained during the 2006–2010 period using geodetic techniques such as Global Navigation Satellite System (GNSS), Advanced Differential Synthetic Aperture Radar Interferometry (A-DInSAR) and microgravimetry. These results show that, although there are no significant associated variations in gravity, there is a clear surface deformation that is spatially and temporally variable. Our results are discussed from the point of view of the unrest and its implications for the definition of an operational geodetic monitoring system for the island.

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Section: A-dinsarmentioning

confidence: 99%

Geodetic Study of the 2006–2010 Ground Deformation in La Palma (Canary Islands): Observational Results

et al. 2020

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“…In this paper, we analyse only the use of the accelerometer as a tilt measuring device. MEMSs are commonly produced using silicon components, which possesses significant electrical and mechanical advantages over other materials [2,3]. Unfortunately, a variety of systematic errors affects the performance characteristics of these accelerometers.…”

Section: Principles Of Acceleration Measurement and Application To Timentioning

confidence: 99%

“…The measurement and monitoring of landslide or infrastructure tilt, together with position variation monitoring, is an important feature of deformation control [1] that is usually detected with GNSS or total station instruments. Displacements are also usually monitored with total stations and GNSS receivers [2], but other techniques, such as SAR, can be efficiently applied [3]. It may be useful to measure the variations in the inclination of a structure or rock mass or the support of a GNSS antenna with independent instruments.…”

Section: Introductionmentioning

confidence: 99%

Improving GNSS Landslide Monitoring with the Use of Low-Cost MEMS Accelerometers

2019

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Observation and monitoring of landslides and infrastructure is a very important basis for land planning, human activities, and safety. Geomatic techniques for deformation monitoring have usually involved GNSS and total station measurements or, more generally, expensive geodetic instruments, but other techniques, such as SAR (Synthetic Aperture Radar), can be efficiently applied. Using low-cost sensors could be an interesting alternative solution if the accuracy requirements can be satisfied. This paper shows the results obtained for tilt measurements using MEMS accelerometers, which were combined with mass-market GNSS sensors for monitoring five sites located on landslides. The use of a MEMS-like inclinometer requires an important calibration process to remove bias and improve the solution’s accuracy. In this paper, we explain the MEMS calibration procedure employed, with a simple and cheap solution. The results indicate that with a simple calibration, it is possible to improve measurement accuracy by one order of magnitude, reaching an angular accuracy of a few hundredths of a degree, verified by an independent technique.

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“…Based on the slope, aspect, incidence angle, and orbit direction, [46][47][48][49] estimated an R-index representing the reliability of each pixel position of the radar image: the larger the R-index value, the higher the visibility. Additionally, land use was further considered in combination with the R-index to reveal reliable areas in SAR images.…”

Section: Introductionmentioning

confidence: 99%

Processing Framework for Landslide Detection Based on Synthetic Aperture Radar (SAR) Intensity-Image Analysis

Lin

Lin²,

Gasselt

2021

Remote Sensing

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We present an object-based image analysis (OBIA) approach to identify temporal changes in radar-intensity images and to locate land-cover changes caused by mass-wasting processes at small to large scales, such as landslides. Our approach is based upon change detection in SAR intensity images that remain in their original imaging coordinate system rather than being georeferenced and map-projected, in order to reduce accumulation of filtering artifacts and other unwanted effects that would deteriorate the detection efficiency. Intensity images in their native slant-range coordinate frame allow for a consistent level of detection of land-cover changes. By analyzing intensity images, a much faster response can be achieved and images can be processed as soon as they are made publicly available. In this study, OBIA was introduced to systematically and semiautomatically detect landslides in image pairs with an overall accuracy of at least 60% when compared to in-situ landslide inventory data. In this process, the OBIA feature extraction component was supported by derived data from a polarimetric decomposition as well as by texture indices derived from the original image data. The results shown here indicate that most of the landslide events could be detected when compared to a closer visual inspection and to established inventories, and that the method could therefore be considered as a robust detection tool. Significant deviations are caused by the limited geometric resolution when compared to field data and by an additional detection of stream-related sediment redeposition in our approach. This overdetection, however, turns out to be potentially beneficial for assessing the risk situation after landslide events.

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Suitability Assessment of X-Band Satellite SAR Data for Geotechnical Monitoring of Site Scale Slow Moving Landslides

Cited by 13 publications

References 41 publications

Geodetic Study of the 2006–2010 Ground Deformation in La Palma (Canary Islands): Observational Results

Geodetic Study of the 2006–2010 Ground Deformation in La Palma (Canary Islands): Observational Results

Improving GNSS Landslide Monitoring with the Use of Low-Cost MEMS Accelerometers

Processing Framework for Landslide Detection Based on Synthetic Aperture Radar (SAR) Intensity-Image Analysis

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