Monitoring Three Gorge Area Landslide currently movement by Mutilplatform SAR Interferometry

Three Gorge area landslide hazards developed very fast after the Dam started to impound the water since 2007. There were lots of research literatures concentrated on the Badong Huangtupo Landslide area for the whole city center had to change its position in 2009. Several literatures used Envisat SAR images time series to monitoring the surface deformation from 2008~2010. The results showed good consistent with the water level changes and precipitation. &#160;The high resolution TerraSAR Spotlight images had been used to monitoring the Shuping landslide and Fanjiaping landslide area in Zigui country from 2009~2012,the InSAR results showed good details of the landslide boundary and deformation rate with DInSAR technology.This paper studies several landslide area in the Three Gorge by InSAR technology in the past few years, such as Huangtupo, Huanglashi , Daping and &#160;Baiheping landslide area , etc. al . The high resolution SAR images covered Badong and Wushan area have been collected, including the Sentinel-1, TerraSAR, RadarSAT-2, ALOS-2 SAR images. The high resolution topography in those landslide area have been collected both by UAV lidar and high resolution topography map.The Huangtupo landslide area changed a lot in the past 3 years with the buildings ruins cleared and red soil covered by the local government. The time series results by Sentinel data in this area shows the big changes but could not derive reasonable deformation results.Three Gorges Research Center for Geo-hazards (TGRC) of China University of Geosciences(CUG) built the Badong field test site in Huangtupo landslide area. This test site is composed with a tunnel group and a series of monitoring system including the inside sensors, surface deformation monitoring sensors and so on. In this paper, we mounted several new designed dihedral corner reflectors on the Huangtupo landslide area for high precision deformation monitoring by InSAR. Both the &#160;ascending and the &#160;descending orbit data of RadarSAT-2 high resolution SAR image &#160;and TerraSAR Spotlight images have been collected in this field.The preliminary results from those new acquiring SAR data series show that the traditional landslide area such as Huanglashi , Daping, Baiheping are all moving slowly with good coherence in SAR image series. &#160;The poor vegetation coverage in those landslide area helped to get the credible &#160;InSAR results. The high resolution DEM is the critical elements for the DInSAR techniques in those landslide area. The steep&#160; topography in those landslide area distorted the SAR images correspondingly.Our results shows that it is possible to use ascending and descending high resolution SAR images to monitor the landslide area with mm level precision, while the vegetation is not so dense. High resolution SAR interferometry helped a lot for the landslide boundary detection and detailed analysis. The lower resolution SAR images such as Sentinel-1 still could provide some deformation results in landslide area, but it need more auxiliary data to interpret the results.

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High-Resolution SAR Image Classification Using Multi-Scale Deep Feature Fusion and Covariance Pooling Manifold Network

Remote Sensing ◽

10.3390/rs13020328 ◽

2021 ◽

Vol 13 (2) ◽

pp. 328

Author(s):

Wenkai Liang ◽

Yan Wu ◽

Ming Li ◽

Yice Cao ◽

Xin Hu

Keyword(s):

High Resolution ◽

Image Classification ◽

Feature Fusion ◽

Representation Learning ◽

Sar Image ◽

Gabor Filtering ◽

Feature Maps ◽

Sar Images ◽

Multi Scale ◽

Deep Feature

The classification of high-resolution (HR) synthetic aperture radar (SAR) images is of great importance for SAR scene interpretation and application. However, the presence of intricate spatial structural patterns and complex statistical nature makes SAR image classification a challenging task, especially in the case of limited labeled SAR data. This paper proposes a novel HR SAR image classification method, using a multi-scale deep feature fusion network and covariance pooling manifold network (MFFN-CPMN). MFFN-CPMN combines the advantages of local spatial features and global statistical properties and considers the multi-feature information fusion of SAR images in representation learning. First, we propose a Gabor-filtering-based multi-scale feature fusion network (MFFN) to capture the spatial pattern and get the discriminative features of SAR images. The MFFN belongs to a deep convolutional neural network (CNN). To make full use of a large amount of unlabeled data, the weights of each layer of MFFN are optimized by unsupervised denoising dual-sparse encoder. Moreover, the feature fusion strategy in MFFN can effectively exploit the complementary information between different levels and different scales. Second, we utilize a covariance pooling manifold network to extract further the global second-order statistics of SAR images over the fusional feature maps. Finally, the obtained covariance descriptor is more distinct for various land covers. Experimental results on four HR SAR images demonstrate the effectiveness of the proposed method and achieve promising results over other related algorithms.

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A comparative case study of MTInSAR approaches for deformation monitoring of the cultural landscape of the Shanhaiguan section of the Great Wall

10.21203/rs.3.rs-301999/v1 ◽

2021 ◽

Author(s):

Hang Xu ◽

Fulong Chen ◽

Wei Zhou

Keyword(s):

High Resolution ◽

Linear Structure ◽

Deformation Monitoring ◽

Sar Interferometry ◽

Comparative Case Study ◽

Rock Falls ◽

High Resolution Data ◽

Heritage Sites ◽

Great Wall

Abstract The Great Wall of China is one of the largest architectural heritage sites globally, and its sustainability is a significant concern. However, its large extent and diverse characteristics cause challenges for deformation monitoring. In this study, the Shanhaiguan section of the Great Wall was investigated in a case study to ascertain the damage and potential hazards of the architectural site. Two standard multi-temporal synthetic aperture radar interferometry (MTInSAR) technologies, including persistent scatterer SAR interferometry (PSInSAR) and small baseline subset (SBAS) SAR interferometry, were used for deformation monitoring using high-resolution TerraSAR-X data acquired in 2015–2017. The results of the two MTInSAR approaches revealed the health condition of the Great Wall. The Shanhaiguan section was stable, but local instabilities caused by rock falls were detected in some mountainous areas. In addition, the applicability of PSInSAR and SBAS was evaluated. The performance analysis of the two approaches indicated that a more reliable and adaptable MTInSAR technique needs to be developed for monitoring the Great Wall. This study demonstrates the potential of MTInSAR technology with high-resolution data for the health diagnosis of heritage sites with a linear structure, such as the Great Wall.

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Brief Communication: Rapid mapping of landslide events: the 3 December 2013 Montescaglioso landslide, Italy

Natural Hazards and Earth System Science ◽

10.5194/nhess-14-1835-2014 ◽

2014 ◽

Vol 14 (7) ◽

pp. 1835-1841 ◽

Cited By ~ 43

Author(s):

A. Manconi ◽

F. Casu ◽

F. Ardizzone ◽

M. Bonano ◽

M. Cardinali ◽

...

Keyword(s):

High Resolution ◽

Synthetic Aperture Radar ◽

Southern Italy ◽

Surface Deformation ◽

Synthetic Aperture ◽

Sar Images ◽

Main Road ◽

Rapid Mapping ◽

X Band ◽

Sar Data

Abstract. We present an approach to measure 3-D surface deformations caused by large, rapid-moving landslides using the amplitude information of high-resolution, X-band synthetic aperture radar (SAR) images. We exploit SAR data captured by the COSMO-SkyMed satellites to measure the deformation produced by the 3 December 2013 Montescaglioso landslide, southern Italy. The deformation produced by the deep-seated landslide exceeded 10 m and caused the disruption of a main road, a few homes and commercial buildings. The results open up the possibility of obtaining 3-D surface deformation maps shortly after the occurrence of large, rapid-moving landslides using high-resolution SAR data.

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Offset tracking procedure applied to high resolution SAR data on Viedma Glacier, Patagonian Andes, Argentina

Advances in Geosciences ◽

10.5194/adgeo-35-7-2013 ◽

2013 ◽

Vol 35 ◽

pp. 7-13 ◽

Cited By ~ 18

Author(s):

N. Riveros ◽

L. Euillades ◽

P. Euillades ◽

S. Moreiras ◽

S. Balbarani

Keyword(s):

High Resolution ◽

Large Study ◽

Sar Interferometry ◽

Santa Cruz ◽

Sar Images ◽

Ice Flow ◽

Displacement Maps ◽

Patagonian Andes ◽

Offset Tracking ◽

Differential Sar Interferometry

Abstract. Main aim of this work is to explore the suitability of high resolution SAR images for measuring ice flow velocity within glaciers. Available techniques for this purpose are Differential SAR Interferometry (DInSAR) and Offset Tracking. The former, although theoretically much more precise, is frequently limited by coherence loss (or lacking of coherence) in glacier environment. The latter constitutes an alternative that works well when displacements are large. Study area is the Viedma Glacier (Santa Cruz, Argentina), one of the largest uncovered ice bodies in the South Patagonian Ice (SPI). High resolution COSMO-SkyMed (CSK) acquisitions were processed by estimating range and azimuth offset fields. Useful results, consisting in displacement maps showing areas with different fast-flowing units, were obtained by Offset Tracking processing.

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MULTI-TEMPORAL SAR INTERFEROMETRY FOR LANDSLIDE MONITORING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-55-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 55-58

Author(s):

R. Dwivedi ◽

A. B. Narayan ◽

A. Tiwari ◽

O. Dikshit ◽

A. K. Singh

Keyword(s):

Surface Deformation ◽

Deformation Monitoring ◽

Sar Interferometry ◽

Line Of Sight ◽

Landslide Monitoring ◽

Deformation Pattern ◽

Mean Velocity ◽

Study Region ◽

Persistent Scatterer ◽

Multi Temporal

In the past few years, SAR Interferometry specially InSAR and D-InSAR were extensively used for deformation monitoring related applications. Due to temporal and spatial decorrelation in dense vegetated areas, effectiveness of InSAR and D-InSAR observations were always under scrutiny. Multi-temporal InSAR methods are developed in recent times to retrieve the deformation signal from pixels with different scattering characteristics. Presently, two classes of multi-temporal InSAR algorithms are available- Persistent Scatterer (PS) and Small Baseline (SB) methods. This paper discusses the Stanford Method for Persistent Scatterer (StaMPS) based PS-InSAR and the Small Baselines Subset (SBAS) techniques to estimate the surface deformation in Tehri dam reservoir region in Uttarkhand, India. Both PS-InSAR and SBAS approaches used sixteen ENVISAT ASAR C-Band images for generating single master and multiple master interferograms stack respectively and their StaMPS processing resulted in time series 1D-Line of Sight (LOS) mean velocity maps which are indicative of deformation in terms of movement towards and away from the satellites. From 1D LOS velocity maps, localization of landslide is evident along the reservoir rim area which was also investigated in the previous studies. Both PS-InSAR and SBAS effectively extract measurement pixels in the study region, and the general results provided by both approaches show a similar deformation pattern along the Tehri reservoir region. Further, we conclude that StaMPS based PS-InSAR method performs better in terms of extracting more number of measurement pixels and in the estimation of mean Line of Sight (LOS) velocity as compared to SBAS method. It is also proposed to take up a few major landslides area in Uttarakhand for slope stability assessment.

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Parameterized Modeling and Calibration for Orbital Error in TanDEM-X Bistatic SAR Interferometry over Complex Terrain Areas

Remote Sensing ◽

10.3390/rs13245124 ◽

2021 ◽

Vol 13 (24) ◽

pp. 5124

Author(s):

Huiqiang Wang ◽

Yushan Zhou ◽

Haiqiang Fu ◽

Jianjun Zhu ◽

Yanan Yu ◽

...

Keyword(s):

High Resolution ◽

High Precision ◽

Phase Error ◽

Test Site ◽

Sar Interferometry ◽

Shanxi Province ◽

Helix Formation ◽

Digital Elevation ◽

Orbital Phase ◽

Parameterized Model

The TerraSAR-X add-on for Digital Elevation Measurements (TanDEM-X) bistatic system provides high-resolution and high-quality interferometric data for global topographic measurement. Since the twin TanDEM-X satellites fly in a close helix formation, they can acquire approximately simultaneous synthetic aperture radar (SAR) images, so that temporal decorrelation and atmospheric delay can be ignored. Consequently, the orbital error becomes the most significant error limiting high-resolution SAR interferometry (InSAR) applications, such as the high-precision digital elevation model (DEM) reconstruction, subway and highway deformation monitoring, landslide monitoring and sub-canopy topography inversion. For rugged mountainous areas, in particular, it is difficult to estimate and correct the orbital phase error in TanDEM-X bistatic InSAR. Based on the rigorous InSAR geometric relationship, the orbital phase error can be attributed to the baseline errors (BEs) after fixing the positions of the master SAR sensor and the targets on the ground surface. For the constraint of the targets at a study scene, the freely released TanDEM-X DEM can be used, due to its consistency with the TanDEM-X bistatic InSAR-measured height. As a result, a parameterized model for the orbital phase error estimation is proposed in this paper. In high-resolution and high-precision TanDEM-X bistatic InSAR processing, due to the limited precision of the navigation systems and the uneven baseline changes caused by the helix formation, the BEs are time-varying in most cases. The parameterized model is thus built and estimated along each range line. To validate the proposed method, two mountainous test sites located in China (i.e., Fuping in Shanxi province and Hetang in Hunan province) were selected. The obtained results show that the orbital phase errors of the bistatic interferograms over the two test sites are well estimated. Compared with the widely applied polynomial model, the residual phase corrected by the proposed method contains little undesirable topography-dependent phase error, and avoids unexpected height errors ranging about from −6 m to 3 m for the Fuping test site and from −10 m to 8 m for the Hetang test site. Furthermore, some fine details, such as ridges and valleys, can be clearly identified after the correction. In addition, the two components of the orbital phase error, i.e., the residual flat-earth phase error and the topographic phase error caused by orbital error, are separated and quantified based on the parameterized expression. These demonstrate that the proposed method can be used to accurately estimate and mitigate the orbital phase error in TanDEM-X bistatic InSAR data, which increases the feasibility of reconstructing high-resolution and high-precision DEM. The rigorous geometric constraint, the refinement of the initial baseline parameters, and the assessment for height errors based on the estimated BEs are investigated in the discussion section of this paper.

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An Accurate Geocoding Method for GB-SAR Images Based on Solution Space Search and Its Application in Landslide Monitoring

Remote Sensing ◽

10.3390/rs13050832 ◽

2021 ◽

Vol 13 (5) ◽

pp. 832

Author(s):

Jialun Cai ◽

Hongguo Jia ◽

Guoxiang Liu ◽

Bo Zhang ◽

Qiao Liu ◽

...

Keyword(s):

Coordinate System ◽

Local Coordinate System ◽

Solution Space ◽

High Accuracy ◽

Deformation Monitoring ◽

Sar Interferometry ◽

Transformation Model ◽

Landslide Monitoring ◽

Surface Model ◽

Sar Images

Although ground-based synthetic aperture radar (GB-SAR) interferometry has a very high precision with respect to deformation monitoring, it is difficult to match the fan-shaped grid coordinates with the local topography in the geographical space because of the slant range projection imaging mode of the radar. To accurately identify the deformation target and its position, high-accuracy geocoding of the GB-SAR images must be performed to transform them from the two-dimensional plane coordinate system to the three-dimensional (3D) local coordinate system. To overcome difficulties of traditional methods with respect to the selection of control points in GB-SAR images in a complex scattering environment, a high-resolution digital surface model obtained by unmanned aerial vehicle (UAV) aerial photogrammetry was used to establish a high-accuracy GB-SAR coordinate transformation model. An accurate GB-SAR image geocoding method based on solution space search was proposed. Based on this method, three modules are used for geocoding: framework for the unification of coordinate elements, transformation model, and solution space search of the minimum Euclidean distance. By applying this method to the Laoguanjingtai landslide monitoring experiment on Hailuogou Glacier, a subpixel geocoding accuracy was realized. The effectiveness and accuracy of the proposed method were verified by contrastive analysis and error assessment. The method proposed in this study can be applied for accurate 3D interpretation and analysis of the spatiotemporal characteristic in GB-SAR deformation monitoring and should be popularized.

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A comparative case study of MTInSAR approaches for deformation monitoring of the cultural landscape of the Shanhaiguan section of the Great Wall

Heritage Science ◽

10.1186/s40494-021-00543-y ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Hang Xu ◽

Fulong Chen ◽

Wei Zhou

Keyword(s):

High Resolution ◽

Linear Structure ◽

Deformation Monitoring ◽

Sar Interferometry ◽

Comparative Case Study ◽

Rock Falls ◽

High Resolution Data ◽

Heritage Sites ◽

Great Wall

AbstractThe Great Wall of China is one of the largest architectural heritage sites globally, and its sustainability is a significant concern. However, its large extent and diverse characteristics are challenges for deformation monitoring. In this study, the Shanhaiguan section of the Great Wall was investigated in a case study to ascertain the damage and potential hazards of the architectural site. Two standard multi-temporal synthetic aperture radar interferometry (MTInSAR) technologies, including persistent scatterer SAR interferometry (PSInSAR) and small baseline subset (SBAS) SAR interferometry, were used for deformation monitoring using high-resolution TerraSAR-X data acquired in 2015–2017. The results of the two MTInSAR approaches reveal the health condition of the Great Wall. The Shanhaiguan section was stable, but local instabilities caused by rock falls were detected in some mountainous areas. In addition, the applicability of PSInSAR and SBAS was evaluated. The performance analysis of the two approaches indicated that a more reliable and adaptable MTInSAR technique needs to be developed for monitoring the Great Wall. This study demonstrates the potential of MTInSAR technology with high-resolution data for the health diagnosis of heritage sites with a linear structure, such as the Great Wall.

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DEFORMATION MONITORING USING SAR INTERFEROMETRY AND ACTIVE AND PASSIVE REFLECTORS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2020-287-2020 ◽

2020 ◽

Vol XLIII-B3-2020 ◽

pp. 287-292

Author(s):

M. Crosetto ◽

G. Luzi ◽

O. Monserrat ◽

A. Barra ◽

M. Cuevas-González ◽

...

Keyword(s):

Phase Stability ◽

Low Cost ◽

Test Site ◽

Deformation Monitoring ◽

Sar Interferometry ◽

Key Characteristics ◽

First Results ◽

Active Reflector

Abstract. This paper is focused on SAR interferometry for deformation monitoring, based on the use of passive and active reflectors. Such reflectors are needed in all cases where a sufficient response from the ground is not available. In particular, the paper describes the development of a low-cost active reflector. This development was carried out in an EU H2020 project called GIMS. The paper summarizes the key characteristics of the developed active reflector. The reflector was tested in two main experiments: the first one located in the campus of CTTC and the second one in a GIMS test site located in Slovenia. The experiments demonstrate the visibility of the active reflectors and provide the first results concerning the phase stability of such devices.

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Temporal Multi-Looking of SAR Image Series for Glacier Velocity Determination and Speckle Reduction

10.5194/egusphere-egu2020-3643 ◽

2020 ◽

Author(s):

Silvan Leinss ◽

Shiyi Li ◽

Philipp Bernhard ◽

Othmar Frey

Keyword(s):

High Resolution ◽

Velocity Field ◽

Cross Correlation ◽

Surface Velocity ◽

Sar Image ◽

Sar Images ◽

Glacier Surface ◽

Radar Images ◽

Glacier Velocity ◽

Offset Tracking

The velocity of glaciers is commonly derived by offset tracking using pairwise cross correlation or feature matching of either optical or synthetic aperture radar (SAR) images.&#160; SAR images, however, are inherently affected by noise-like radar speckle and require therefore much larger images patches for successful tracking compared to the patch size used with optical data. As a consequence, glacier velocity maps based on SAR offset tracking have a relatively low resolution compared to the nominal resolution of SAR sensors. Moreover, tracking may fail because small features on the glacier surface cannot be detected due to radar speckle. Although radar speckle can be reduced by applying spatial low-pass filters (e.g. 5x5 boxcar), the spatial smoothing reduces the image resolution roughly by an order of magnitude which strongly reduces the tracking precision. Furthermore, it blurs out small features on the glacier surface, and therefore tracking can also fail unless clear features like large crevasses are visible.In order to create high resolution velocity maps from SAR images and to generate speckle-free radar images of glaciers, we present a new method that derives the glacier surface velocity field by correlating temporally averaged sub-stacks of a series of SAR images. The key feature of the method is to warp every pixel in each SAR image according to its temporally increasing offset with respect to a reference date. The offset is determined by the glacier velocity which is obtained by maximizing the cross-correlation between the averages of two sub-stacks. Currently, we need to assume that the surface velocity is constant during the acquisition period of the image series but this assumption can be relaxed to a certain extend.As the method combines the information of multiple images, radar speckle are highly suppressed by temporal multi-looking, therefore the signal-to-noise ratio of the cross-correlation is significantly improved. We found that the method outperforms the pair-wise cross-correlation method for velocity estimation in terms of both the coverage and the resolution of the velocity field. At the same time, very high resolution radar images are obtained and reveal features that are otherwise hidden in radar speckle.As the reference date, to which the sub-stacks are averaged, can be arbitrarily chosen a smooth flow animation of the glacier surface can be generated based on a limited number of SAR images. The presented method could build a basis for a new generation of tracking methods as the method is excellently suited to exploit the large number of emerging free and globally available high resolution SAR image time series.

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