Spaceborne differential synthetic aperture radar interferometry (DInSAR) has already proven its potential for mapping ground deformation phenomena, e.g. volcano dynamics. However, atmospheric disturbances as well as phase decorrelation have prevented hitherto this technique from achieving full operational capability. These drawbacks are overcome by carrying out measurements on a subset of image pixels corresponding to pointwise stable reflectors (Permanent Scatterers, PS) and exploiting long temporal series of interferometric data. Results obtained by processing 55 images acquired by the European Space Agency (ESA) ERS SAR sensors over Southern California show that the PS approach pushes measurement accuracy very close to its theoretical limit (about 1 mm), allowing the description of millimetric deformation phenomena occurring in a complex fault system. A comparison with corresponding displacement time series relative to permanent GPS stations of the Southern California Integrated GPS network (SCIGN) is carried out. Moreover, the pixel-by-pixel character of the PS analysis allows the exploitation of individual phase stable radar targets in low-coherence areas. This makes spaceborne interferometric measurements possible in vegetated areas, as long as a sufficient spatial density of individual isolated man-made structures or exposed rocks is available. The evolution of the Ancona landslide (central Italy) was analysed by processing 61 ERS images acquired in the time span between June 1992 and December 2000. The results have been compared with deformation values detected during optical levelling campaigns ordered by the Municipality of Ancona. The characteristics of PS, GPS and optical levelling surveying are to some extent complementary: a synergistic use of the three techniques could strongly enhance quality and reliability of ground deformation monitoring. D 2002 Elsevier Science B.V. All rights reserved.

Monitoring Landslides and Tectonic Motions with the Permanent Scatterers Technique

Investigating landslides and unstable slopes with satellite Multi Temporal Interferometry: Current issues and future perspectives

Landslides represent major natural hazards, which cause every year significant loss of lives and damages to buildings, properties and lifelines. In the last decades, a significant increase in landslide frequency took place, in concomitance to climate change and the expansion of urbanized areas. Remote sensing techniques represent a powerful tool for landslide investigation: applications are traditionally divided into three main classes, although this subdivision has some limitations and borders are sometimes fuzzy. The first class comprehends techniques for landslide recognition, i.e., the mapping of past or active slope failures. The second regards landslide monitoring, which entails both ground deformation measurement and the analysis of any other changes along time (e.g., land use, vegetation cover). The third class groups methods for landslide hazard analysis and forecasting. The aim of this paper is to give an overview on the applications of remote-sensing techniques for the three categories of landslide investigations, focusing on the achievements of the last decade, being that previous studies have already been exhaustively reviewed in the existing literature. At the end of the paper, a new classification of remote-sensing techniques that may be pertinently adopted for investigating specific typologies of soil and rock slope failures is proposed.

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Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives

Large-area landslide detection and monitoring with ALOS/PALSAR imagery data over Northern California and Southern Oregon, USA

Landslide detection from extensive remote-sensing imagery is an important preliminary work for landslide mapping, landslide inventories, and landslide hazard assessment. Aimed at development of an automatic procedure for landslide detection, a new method for automatic landslide detection from remote-sensing imagery is presented in this study. We achieved this objective using a scene classification method based on the bag-of-visual-words BoVW representation in combination with the unsupervised probabilistic latent semantic analysis pLSA model and the k -nearest neighbour k -NN classifier. Given a remote-sensing image, we divided it into equal-sized square sub-images and then described each sub-image as a BoVW representation. The pLSA model was applied to sub-images by using the BoVW representation to discover the object classes depicted in the sub-images, and then a k -NN classifier was used to classify the sub-images into landslide areas and non-landslide areas based on object distribution. We investigated the performance and applicability of the method using remote-sensing imagery from the Ili area. The results show that the method is robust and can produce good performance without the acquisition of three-dimensional 3D topography. We anticipate that these results will be helpful in landslide inventory mapping and landslide hazard assessment in landslide-stricken areas.

Automatic landslide detection from remote-sensing imagery using a scene classification method based on BoVW and pLSA

Although the differential interferometric synthetic aperture radar (DInSAR) technique has the potential for monitoring ground deformation with millimetric accuracy, it still suffers from temporal and geometric decorrelation. In this paper, the corner reflectors differential interferometry synthetic aperture radar (CRDInSAR) technique was used to overcome the limitations of conventional DInSAR. We studied the basic principles of CRDInSAR, discussed the calculation of the flat earth and topographic phases based on the geometry of satellite and corner reflectors, presented the phase unwrapping approach for the sparse grid of corner reflectors, then investigated the construction of, and solution to, the unwrapped phase system equation. Subsequently, we applied CRDInSAR to monitor the displacement of the Shuping landslide in Hubei Province, China. In this study, we processed five SAR images on a descending pass acquired by the Environmental Satellite (ENVISAT) Advanced Synthetic Aperture Radar (ASAR) sensor from September 2005 to March 2006, and compared the achieved results of CRDInSAR with Global Positioning System (GPS) measurements at the same time of the SAR data acquisition assumed as reference. The result indicates a good agreement between the measurements provided by the two different techniques, which shows that CRDInSAR allows monitoring of slow landslide deformation in low coherence areas and provides accurate results.

Landslide monitoring by corner reflectors differential interferometry SAR

Land subsidence is a common geohazards in many countries of the world, which cause damages for many urban areas and civil infrastructure. The development of spaceborne SAR interferometry provides an efficient tool for large spatial scale surface deformation monitoring with a high accuracy and precision. This paper presents a case study of land subsidence investigation along railway by using Permanent Scatterers SAR interferometry (PSI). Based upon the conventional InSAR techniques, PS-InSAR overcomes atmospheric delay anomalies and temporal and geometric decorrelation by exploiting the temporal and spatial characteristics of radar interferometric signatures collected from point-wise targets that preserve phase coherent over time. In this work, a linear model is adopted to retrieval land subsidence rate by using the differential phase series of the permanent scatterers. For the subsidence rate derivation along the railway, a buffer with a width of 10 km is set up and those PS within the buffer is interpolated to generate the subsidence map. The results archived using ENVISAT ASAR images acquired from 2003 to 2004 are validated with the precise leveling data and used to investigate the Jing-Jin railway in north china.

Land Subsidence Investigation Along Railway Using Permanent Scatterers SAR Interferometry

Landslide in threegorge area is a severe geohazard threatening many people. Conventional differential SAR interferometry (DInSAR) and Persistent Scatterers for SAR interferometry (PSInSAR) technique are unsuitable for landslide deformation monitoring in this area due to temporal and lack of natural phase stable point targets. The method of DInSAR using corner reflectors (CRInSAR) is a powerful tool in the vegetation area. The procedure of DInSAR using corner reflectors (CRInSAR) used by this paper is briefly introduced. Using ENVISAT ASAR time series data, the deformation of 12 corner reflectors (CR) in Shuping landslide are analyzed. As to the CR with slow creep deformation, the CRInSAR results are reliable. But as to the CR with nonlinear accelerated deformation, our CRInSAR method still needs to be enhanced.

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CRInSAR for landslide deformation monitoring: A case in threegorge area

Interferometric baseline plays a very important role in InSAR data processing, and it will directly affect the accuracy of interferometric result. In this article, three classical baseline estimation methods were described. By comparing and combining three methods, a new baseline estimation method and its flow were listed. RADARSAT-2 satellite image data and grid digital elevation model ASTER GDEM (30m resolution) in Kailuan area are used as sample data. By analyzing the differential interferometric results generated with different baseline estimation methods, it can be concluded that the new method used by this article is correct and feasible.

A method for InSAR baseline refinement and its application

Landslides are a kind of typical natural disaster in China, which pose serious threats to civil lives, property and living environment. Therefore, the identification, monitoring and prevention of landslides have been considered as a long-term geological work for the public welfare. In this article, 8 TerraSAR-X high resolution strip-map mode images, acquired in the period from January to March 2012 and covering Fanjinping landslide in Zigui county, Hubei province, were used to test the usability in monitoring the deformation of single landslide. The results of two-pass DInSAR sketched the region and the shape of the deformation field of Fanjiaping landslide. Corner reflectors’ linear deformation rate using CRInSAR method could be approximately validated by the in-situ GPS measurements. From the coherent pixels’ linear deformation rate map, it was inferred that the deformation could be more obvious in the tail of the Muyubao landslide while the lowest frontier of this landslide might prevent the slide. Due to its shorter revisiting period and high bandwidth,,the high resolution TerraSAR-X images can keep better coherence than previous satellite SAR data in the test area and provide basic guarantee to monitor the deformation of single landslides.

Xiaofang Guo

Papers

Landslide monitoring by corner reflectors differential interferometry SAR

Land Subsidence Investigation Along Railway Using Permanent Scatterers SAR Interferometry

CRInSAR for landslide deformation monitoring: A case in threegorge area

A method for InSAR baseline refinement and its application

Monitoring of landslide deformation based on the coherent targets of high resolution InSAR data