Relic landslide, formed over a long period, possess the potential for reactivation, making them a hazardous geological phenomenon. While reliable relic landslide detection benefits the effective monitoring and prevention of landslide disaster, semantic segmentation using high-resolution remote sensing images for relic landslides faces many challenges, including the object visual blur problem, due to the changes of appearance caused by prolonged natural evolution and human activities, and the small-sized dataset problem, due to difficulty in recognizing and labelling the samples. To address these challenges, a semantic segmentation model, termed mask-recovering and interactivefeature- enhancing (MRIFE), is proposed for more efficient feature extraction and separation. Specifically, to address the visual blur problem, a contrastive learning and mask reconstruction approach is designed under the guidance of remote sensing visual interpretation expert knowledge, which states the height variation at the landslide boundary contributing the most to landslide identification. This approach constructs local patches from the landslide boundary and background to perform supervised contrastive learning and applies mask reconstruction to local patches, guiding the model to focus on the landslide boundary and to extract the most contributive local salient features for reliable recognition. Meanwhile, to address the smallsized dataset problem, a self-distillation learning method is introduced, which uses a momentum encoder to update the teacher network with the average of the student network, suppressing overfitting caused by background interference. By constructing contrastive input pairs, the approach increases the diversity and combinations within the contrastive sample space and improves sample utilization. The proposed MRIFE is evaluated on a real relic landslide dataset, and experimental results show that it greatly improves the performance of relic landslide detection. For the semantic segmentation task, compared to the baseline, the precision increases from 0.4226 to 0.5347, the mean intersection over union (IoU) increases from 0.6405 to 0.6680, the landslide IoU increases from 0.3381 to 0.3934, and the F1-score increases from 0.5054 to 0.5646.

Rainfall is the primary landslide triggering factor in China, and the spatial-temporal hazard prediction of rainfall-induced landslides is of great practical significance. Currently, most countries and regions establish landslide hazard prediction systems based on rainfall data only, resulting in low spatial precision of hazard prediction results and a high false alarm rate. This paper proposes a hazard prediction model that considers landslide triggering factors, landslide predisposing environment, and the spatial regularity of historical landslides based on multi-modal earth observation data. The proposed model has significantly improved the spatial-temporal hazard prediction performance of rainfall-induced natural terrain landslides in Hong Kong.

Ice roads serve as vital land transportation during the Arctic winter season. In the context of polar increased warming, there are great uncertainties for human activities on ice roads. In this paper, we integrate remote sensing techniques to quantify the potential ice road evolution in the Pan-Arctic and its impact on land accessibility from 1979 to 2017. We show that the potential ice roads have significantly decreased, with the fastest decrease in March to 2.34×104km2yr−1. Furthermore, the contribution of potential ice roads to port accessibility is most severely reduced in the Canadian Arctic, reaching 0.93 h yr−1. The results demonstrate that warmer winters are imposing severe stress on Arctic land access.

The TomoSAR technique has been applied to forest aboveground biomass (forest AGB) estimation studies, but existing studies make insufficient use of the forest structure information detected by TomoSAR. In this paper, we proposed a forest AGB estimation method based on TomoSAR backscattered power distribution law. The method uses the TomoSAR vertical profiles calculated by the Beamforming spectral analysis algorithm to extract the backscattered power for fitting in order to obtain the power curve. Then the distribution law was summarized by analyzing the variation of backscattered power distribution at different forest AGB levels. Based on the distribution law of backscattered power, two new forest AGB estimation features, BPC-4 and GVPR-19, are proposed. After modeling and validation, the results show that the forest AGB estimation model built with BPC-4 and GVPR-19 as variables can have better accuracy compared to the models built with the features proposed in previous studies.

In this letter, an improved three-component decomposition method for compact PolInSAR under π/4 mode is proposed. In the proposed algorithm, the volume scattering model is refined by the polarimetric interferometric similarity parameter and the volume scattering can be reasonably reduced. Airborne L-band ESAR PolInSAR data are used to simulate the compact PolInSAR data and evaluate the performance of the method. The experimental results demonstrate that the proposed method can be used to characterize the scattering mechanisms of various terrain types and is a complementary approach to the decomposition method for compact PolInSAR.

Currently, Multi-Temporal InSAR (MT-InSAR) is extensively employed to predict the trend of ground deformation. The deformation data acquired through MT-InSAR has been utilized as a single parameter in the model for predicting land deformation. Nevertheless, these models still necessitate enhancement in terms of their ability to generalize and accurately predict outcomes. In this paper, a combined Long Short Term Memory (C-LSTM) is proposed to combine groundwater level, rainfall, and surface deformation information from MT-InSAR. We assess the predictive accuracy of single-factor and multi-factor models. The results show that after feature combination optimization, the R2of the C-LSTM subsidence prediction model with multi-feature training improves the prediction results by 9.7%, 0.48%, and 21.82%, respectively, over the prediction results of the single-feature-factor model. By improving the C-LSTM’s feature factors, this method improves the accuracy of the forecast of ground subsidence change areas.

Slow-moving landslide detection is of vital importance in preventing and mitigating geohazards. Extracting abstract features from remote sensing images is crucial for achieving high-precision detection of slow-moving landslides. This study utilizes both activity features and terrain structure features for geohazard detection. We propose a pixel-level and a feature-level image fusion network, and investigate the multi-level fusion cooperative mechanism. We evaluate the performance of the two-level fusion and single-modal data base on the test data. The experimental results demonstrate that fusion of the activity characteristics and topographic characteristics can enhance the accuracy of identifying slow-moving landslides. Feature-level fusion outperforms pixel-level fusion for slow-moving landslides identification.

Recent advancements in Vison Foundation Models (VFMs) like the Segment Anything Model (SAM) have exhibited remarkable progress in natural image segmentation. However, its performance on remote sensing images is limited, especially in some application scenarios that require strong expert knowledge involvement, such as landslide detection. In this study, we proposed an effective segmentation model, namely LandslideNet, which is realized by embedding a tuning layer in a pre-trained encoder and adapting the SAM to the landslide detection scene for the first time. The proposed method is compared with traditional convolutional neural networks (CNN) on two well-known landslide datasets. The results indicate that the proposed model with fewer training parameters has better performance in detecting small-scale targets and delineating landslide boundaries, with an improvement of 6-7 percentage points in accuracy (F1 and mIoU) compared to mainstream CNN-based methods.

Tropospheric delay significantly hinders the accurate acquisition of high-precision surface deformation by Time series Interferometric Synthetic Aperture Radar (InSAR). The main challenge for current InSAR tropospheric delay estimation lies in effectively utilizing the time-dependent characteristics of the tropospheric delay for accurate atmospheric delay estimation. This paper develops a model to estimate the time-dependent and stochastic components of the delay based on periodic and random characteristics. The experiment demonstrates the effectiveness of the proposed method regardless of whether terrain-dependent delays dominate or random delays dominate at Danba-Xiaojin. The Std of the corrected decreases in 89/90 of the interferograms. The average and maximum improvement of Std is more than 40% and 80% respectively. From the time series, the proposed method can effectively suppress the periodic signals in both non-deformation and deformation regions and can obtain smoother time series. Overall, the proposed method outperforms the other four models for InSAR tropospheric delay correction.

In this letter, a multiple-component polarimetric decomposition method with refined volume scattering models is proposed. In the proposed algorithm, the volume scattering models under the assumption of reflection symmetry are refined by employing the orientation angles. In addition, wire scattering component is introduced to overcome the overestimation of volume scattering. ESAR data collected by the German Aerospace Center (DLR) are used to evaluate the performance of the method. The experimental results demonstrate that the proposed method can be used to characterize the scattering mechanisms of various terrain types and the overestimation of volume scattering can be effectively overcome.

Landslide is one of the most dangerous and frequently occurred natural disasters. The semantic segmentation technique is efficient for wide area landslide identification from high-resolution remote sensing images (HRSIs). However, considerable challenges exist because the effects of sediments, vegetation, and human activities over long periods of time make visually blurred old landslides very challenging to detect based upon HRSIs. Moreover, for terrain features like slopes, aspect and altitude variations cannot be sufficiently extracted from 2-D HRSIs but can be from digital elevation model (DEM) data. Then, a feature-fusion based semantic segmentation network (FFS-Net) is proposed, which can extract texture and shape features from 2-D HRSIs and terrain features from DEM data before fusing these two distinct types of features in a higher feature layer. To segment landslides from background, a multiscale channel attention module is purposely designed to balance the low-level fine information and high-level semantic features. In the decoder, transposed convolution layer replaces original mathematical bilinear interpolation to better restore image resolution via learnable convolutional kernels, and both dropout and batch normalization (BN) are introduced to prevent over-fitting and accelerate the network convergence. Experimental results are presented to validate that the proposed FFS-Net can greatly improve the segmentation accuracy of visually blurred old landslides. Compared to U-Net and DeepLabV3+, FFS-Net can improve the mean intersection over union (mIoU) metric from 0.508 and 0.624 to 0.67, the F1 metric from 0.254 and 0.516 to 0.596, and the pixel accuracy (PA) metric from 0.874 and 0.906 to 0.92, respectively. For the detection of visually distinct landslides, FFS-NET also offers comparable detection performance, and the segmentation is improved for visually distinct landslides with similar color and texture to surroundings.

The geological characteristics of old landslides can provide crucial information for the task of landslide protection. However, detecting old landslides from high-resolution remote sensing images (HRSIs) is of great challenges due to their partially or strongly transformed morphology over a long time and thus the limited difference with their surroundings. Additionally, small-sized datasets can restrict in-depth learning. To address these challenges, this paper proposes a new iterative classification and semantic segmentation network (ICSSN), which can significantly improve both object-level and pixel-level classification performance by iteratively upgrading the feature extraction module shared by the object classification and semantic segmentation networks. To improve the detection performance on small-sized datasets, object-level contrastive learning is employed in the object classification network featuring a siamese network to realize global features extraction, and a sub-object-level contrastive learning method is designed in the semantic segmentation network to efficiently extract salient features from boundaries of landslides. An iterative training strategy is also proposed to fuse features in the semantic space, further improving both the object-level and pixel-level classification performances. The proposed ICSSN is evaluated on a real-world landslide dataset, and experimental results show that it greatly improves both the classification and segmentation accuracy of old landslides. For the semantic segmentation task, compared to the baseline, the F1 score increases from 0.5054 to 0.5448, the mIoU improves from 0.6405 to 0.6610, the landslide IoU grows from 0.3381 to 0.3743, the PA is improved from 0.945 to 0.949, and the object-level detection accuracy of old landslides surges from 0.55 to 0.90. For the object classification task, the F1 score increases from 0.8846 to 0.9230, and the accuracy score is up from 0.8375 to 0.8875.

Extracting roads from high-resolution remote sensing images (HRSIs) is vital in a wide variety of applications, such as autonomous driving, path planning, and road navigation. Due to the long and thin shape as well as the shades induced by vegetation and buildings, small-sized roads are more difficult to discern. In order to improve the reliability and accuracy of small-sized road extraction when roads of multiple sizes coexist in an HRSI, an enhanced deep neural network model termed Dual-Decoder-U-Net (DDU-Net) is proposed in this paper. Motivated by the U-Net model, a small decoder is added to form a dual-decoder structure for more detailed features. In addition, we introduce the dilated convolution attention module (DCAM) between the encoder and decoders to increase the receptive field as well as to distill multi-scale features through cascading dilated convolution and global average pooling. The convolutional block attention module (CBAM) is also embedded in the parallel dilated convolution and pooling branches to capture more attention-aware features. Extensive experiments are conducted on the Massachusetts Roads dataset with experimental results showing that the proposed model outperforms the state-of-the-art DenseUNet, DeepLabv3+ and D-LinkNet by 6.5%, 3.3%, and 2.1% in the mean Intersection over Union (mIoU), and by 4%, 4.8%, and 3.1% in the F1 score, respectively. Both ablation and heatmap analysis are presented to validate the effectiveness of the proposed model. Moreover, the designed small decoder and introduced DCAM can be used as a portable module to be embedded in other U-Net-like models with encoder-decoder structure to enhance the road detection performance, especially for small-sized roads. The high portability of the designed module is validated by embedding in the LinkNet, which greatly improves the road segmentation performance.

This article proposes deep convolutional neural networks to detect and map localized, rapid subsidence caused by mining activities using time-series Sentinel-1 synthetic aperture radar (SAR) images. A deformation detection network (DDNet) is developed to automatically identify rapidly subsiding areas from wrapped interferograms, and a phase unwrapping network (PUNet) is designed to unwrap the cropped interferogram patches centered on the detected subsiding locations. To train the two networks, interferogram simulation strategies are developed to generate various training samples using the distorted 2-D Gaussian surface and fractal Perlin noises. The performance of the DDNet is verified by simulations on a synthetic dataset with 13 large interferograms, while the PUNet is evaluated by simulations using synthetic datasets with different levels of deformation gradients and noises. Compared with the traditional and deep-learning methods, the PUNet exhibits excellent performance and efficiency in unwrapping interferograms with rapid mining-induced deformation. The proposed networks are further verified by applying them to Shanxi province, China, which is characterized by serious ground subsidence hazards caused by long-term coal mining activities. The time-series deformations of 1344 detected subsidence areas are calculated with the vertical velocities ranging from −19.7 to −254.8 cm/year. The results are validated using the ascending and descending Sentinel-1 Interferograms and an L-band ALOS-2 interferogram covering the same area within the acquisition period, showing highly consistent vertical deformation rates. The proposed strategy and methods introduce deep learning to the time-series interferometric SAR (InSAR) processing chain and may have profound implications on the detection and monitoring of localized mining-induced deformation using InSAR.

Phase unwrapping is a critical step of interferometric synthetic aperture radar processing, and its accuracy directly determines the reliability of subsequent applications. Many phase unwrapping methods have been proposed, most of which assume that the phase has spatial continuity, while decorrelation noise and aliasing fringes invalidate the assumptions, resulting in poor performance of these methods. To obtain more reliable unwrapping results, in this article, a deep convolutional neural network, called a discontinuity estimation network (DENet), is proposed for predicting the probabilities of phase discontinuities in interferograms. The main advantages of DENet are: 1) using branching structure to extract detailed and high-level features separately and retain details while making full use of contextual information; 2) using multichannel input, including interferogram, range/azimuthal phase gradients, and residues map, to provide effective guidance for discontinuity prediction; and 3) using a single network to estimate phase discontinuities in both range and azimuth directions simultaneously. To train the network, a dataset simulation strategy is proposed to generate enough training samples. The strategy considers a variety of phase components, such as terrain-related phase, random deformation, atmospheric turbulence, and noise. The phase discontinuity estimated by DENet is then converted to costs in the minimum cost flow (MCF) solver of the statistical-cost, network-flow algorithm for phase unwrapping (SNAPHU) to obtain the final unwrapped phase. Based on validations of simulated and real interferograms, the proposed method exhibits excellent performance compared to traditional and deep learning unwrapping methods. The proposed method can effectively unwrap large-scale, low-quality interferograms, which is expected to significantly improve the accuracy of synthetic aperture radar interferometry (InSAR) applications.

Interferometric synthetic aperture radar (InSAR) technique can periodically measure small deformation over a large area, providing many supports for landslide studies. However, landslide detection with InSAR is still a challenge problem, especially in the efficiency over large-area. In this paper, the workflow of InSAR driven landslide detection and monitoring is optimized based on small baseline sets. It adopts a two-step strategy: (1) regional detection by small baseline InSAR-stacking, (2) continuous monitoring of detected landslides by small baseline sets InSAR (SBAS-InSAR), making a trade-off between efficiency and accuracy. This workflow was applied in the Dongchuan Section of Jinsha River Valley. Over more than 5000 km2, 62 active landslides are detected and mapped. Then for one typical landslide, Dapingdi landslide, deformation time-series are estimated to illustrate its growing process.

Land subsidence is a typical geological disaster caused by local surface elevation changes under natural or man-made action. InSAR technology has been successfully applied to monitor the wide area land subsidence. The SAR pairs after 8 February 2015 are generally effective for ScanSAR-ScanSAR interferometry. For the specific low resolution and wide swath observations, we have a limited understanding of L-band ScanSAR mode for monitoring land subsidence with InSAR technique. This research focuses on eliminating differential ionospheric phase, and then discusses the feasibility of using ScanSAR mode to monitor large-scale land subsidence.

Phase unwrapping is a crucial step of InSAR, and its reliability directly determines the feasibility of deformation monitoring. However, severe noise and dense fringes often make the existing unwrapping methods fail. In this work, we propose a convolutional neural network DENet for identifying phase discontinuities and design a data set simulation strategy to generate enough training samples. We combine the traditional cost flow method with the output from DENet to achieve more accurate phase unwrapping. Compared with the GAMMA, the root mean square error of the proposed method on the simulated data set is reduced by 46.4%. We also verified the superior performance of the proposed method on real data sets.

Tremendous underground exploitation as subway tunneling, functional facilities excavation in inner-city environments caused potential damages to buildings and bridges over the construction zone. PSInSAR technique and high resolution SAR data provides a powerful tool for slightly surface deformation in recent years. Till now, it is time to use PSInSAR as an operational and mature technique for geodesy and geodetic purposes. In this work we presented the monitoring of subsidence over subway tunnels and along high speed railway in Beijing city by interferometric time series analysis based on nearly 46 scenes of TerraSAR-X data over the past 4 years. The resulted velocity map described the extent and magnitude of subsidence along the subway lines due to the different ground excavation method. It demonstrated that the total subsidence of PSs over the tunnels caused by open-cutting method is up to 70-80 mm and that caused by tunneling method is only 15-25mm in the full construction cycle of subway tunnels. The subsidence extent is distinguished different relying on the excavation method as well as the load of ground buildings. Under the same condition the subsidence over the built up area is more significant than that over the general area.

Using RADARSAT-2 satellite images from January 2012 to October 2015, this paper employ the PSInSAR(Permanent Scatterer InSAR) technique to acquire coastal lowland subsidence in Zhejiang province. In the Hangzhou Bay Region, the widely distributed silt deposition leads to the entire ground sinking slowly, with 10~20mm/a in rate. Conversely, the ground subsidence distribution of the Eastern Coastal Region appear be funnel-shaped. Its involving area is small, while the settlement gradient of settlement center is comparatively large, whose maximum is more than 20mm/a. It is clear that the over-exploitation groundwater may be the main force in pace with the growth of factories and plenty of population. However, the distribution characteristic of ground subsidence in distinct regions could be determined by different geological structure. Therefore the environmental capacity of Zhejiang's coast is relatively vulnerable, and the ground subsidence is extremely prominent. Nowadays, the main undertaking is to vigorously restrict groundwater extraction and make seawater dilute in order to keep a comfortable environment to live in.

IBIS-L ground based SAR is able to provide accurate and long-term measurements over relative large areas by combining of stepped frequency continuous wave, synthetic aperture radar and interferometric technique. To measure displacements of an open pit slope in Beizhan iron mine, a novel environmental correction method is presented to perform the atmospheric distortion correction of GB-InSAR. GB-InSAR results show that, the entire working area is mainly on a stable condition, only two remarkable displacement areas with the maximum displacement over -15mm locate in the upper and lower parts of quarry rock face, respectively. Considering the displacement evolutions of selected pixels at different sectors, the quarry rock face should be impossible to produce larger geological disasters.

In this paper, 42 X-band COSMO-Skymed (CSK) images are processed using Permanent Scatterer InSAR (PSI) technique in order to monitor and analyze deformation phenomena in Beijing urban areas between January 2009 and August 2011. Italian COSMO constellation composed of four satellites is capable of acquiring SAR data with 8 day repeat cycle with up to 1 meter resolution. The results show significant X-shape subsidence tendency in the east of urban areas. Displacement values of up to 90 mm/ year along the line of sight of are detected in the area near Shuangqiao subway station. The results from CSK data are consistent with those from C-band ENVISAT ASAR data. Spatial characteristics and temporal changes of linear projects in inhomogeneous subsidence region are analyzed to evaluate the safety of subways and railways.

We present in this work the monitoring of terrain displacement along large scale manmade linear features in Shanghai city by integrating medium and high resolution PSInSAR data. Focused on the stability of linear features themselves and infrastructures over the path of linear features, land subsidence along Shanghai Magnetic Levitation (Maglev) and No. 10 subway tunnels have been investigated. In the first case we reported the impact of regional subsidence to Maglev line by analyzing PSInSAR measurements from ENVISAT ASAR data and Cosmo 3m resolution data. Subsidence velocity map derived through Cosmo data indicates that the Maglev line has slightly displacement although regional subsidence rate reaches 2–3 cm per year described by ENVISAT data. The second case dedicated on the assessment of subway tunnels to buildings over the path of No. 10 subway. By time series analysis of the Cosmo SAR data acquired during Nov 2008 to June 2010, terrain subsidence in the construction period and operational period of No. 10 line has been studied. The PSInSAR data demonstrated that subsidence caused by subway tunnels reaches l–2cm and it is accelerated after the running of subways since the dynamic loading effect of vehicles.

The most important limiting factor of conventional D-InSAR method is strongly atmospheric wet delay during monitoring small landslides located at rainy and mountainous area. The wet delay phase could even cover deformation field of test site sometimes. In this paper, we found the wet delay phase over Shuping landslide was not only closely related with elevation, but also was a function of distance along radar azimuth or range direction. Therefore, based on least-square method, best-fit-polynomial correction models about wet delay filed over Shuping landslide could be established. By removing the wet delay phase field simulated from unwrapping phase, the deformation field of Shuping landslide could appear immediately. The result showed that this correction method could effectively remove the wet delay phase from differential interferograms, and it will have an important significance for monitoring slowly moving landslide in Three Gorges Area.

In this work we analysis the relationship between the industrial structure and regional subsidence base on the research in past. We mapped the spatial and temporal patterns of the land subsidence near the city of Hangzhou, China by PSI analysis with more than 49 scenes of ERS-1/2 SAR images acquired from 1992 to 2006.[1] According to the PSI results, 13 subsidence centers (express by red dashed circle in fig 1) were found in the Xiaoshan (a district of Hangzhou, China) Economic and Technological Development Zone. Characteristics of each subsidence centers are different. I have found out 86 different enterprises belong to different industrial types (express by red pin tag in Fig 1) by field survey. The Zone was approved as a state-level development zone by the State Council in May, 1993. The structure of the Zone industry Includes 10 main types, Such as petrochemical, power industry, manufacturing and so on. Fig 2 shows the Percentage of different types of enterprises. The main reason of land subsidence in the Zone is groundwater exploitation, which is necessary for the rapid economic development.

PSInSAR has been proved an operational tool for surfaces deformation monitoring due to its millimeters precision and dense spatial samplings. However, PSInAR derived deformation parameters including the average displacement velocity and displacement history can't be compared directly with these measured by the conventional technique such as leveling and GPS since the different reference datum between these measurement system. In this work we present the method to integrate Corner Reflectors(CRs) and PSInSAR technique for regional subsidence monitoring. For reference offset compensation between PSInSAR and leveling, the method using homogenous leveling survey of dedicated CRs was established. Meanwhile, an improved PSInSAR technique based on time series analysis of coherent target within small baseline interferograms was proposed. The proposed method was applied for regional subsidence monitoring in Beijing area with an extent of 100 × 100km2by using the ESA ENVISAT ASAR data collected during the time of 2008 to 2009 year. Seven CRs were installed in test sites for reference compensation and PSInSAR measurement validation. The results demonstrated the accuracy and reliability of PSInSAR for regional subsidence mapping.

Spaceborne Radar Interferometry (InSAR) is well known as an accurate, time saving and low cost technique for monitoring of surface deformation phenomena ranging from land subsidence, volcano displacement, tectonic motion and even slow landslides. The Coherent Point Target SAR Interferometry (CPT-InSAR) overcomes atmospheric delay anomalies and the temporal and geometric decorrelation by the interferometric calculating of point-wise targets which maintain high coherent. The linear and no-linear subsidence can be retrieved by CPT-InSAR, which can be done well even with less than 20 SAR images by integrating the SBAS (Small Baseline Subset) and PSI (Permanent Scatterers InSAR) techniques. The application of the proposed method to monitor seasonal land subsidence in the Dezhou city, a medium city in Shandong province in North China Plain (NCP), is described. The presented results, obtained by processing of ENVISAT ASAR data acquired between Mar, 2004 and Dec, 2008, are highly agreed with the deep groundwater level, including the spatial position and variation tendency.

Monitoring and governing the regional land subsidence along the highway and other linear projects has become an important basic work for the normal construction and operation in these regions. In this paper, by using the method of multi-track PS-InSAR integration, the land subsidence velocity varying with time along the JingHu highway (Beijing-Hebei) was studied with adjacent orbits ENVISAT ASAR data from 2008 to 2010. At the same time, the map of subsidence velocity along the highway was successfully extracted as well as the subsidence profile map. And 9 subsidence centers were verified along the highway according to these maps, which were consistent with the previous study. The results showed that the method not only unified the coordinate system of adjacent tracks, but also made cross-track, multi-image, large-scale PS-InSAR monitoring possible. Therefore, PS-InSAR monitoring results could provide a scientific reference for the subsidence control of highway and other linear projects, and protect the safe operation of the linear projects.

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.

With a spatial resolution of up to 1 m, the German radar satellite TerraSAR-X has significantly improved the applicability of spaceborne SAR interferometry (InSAR) technique for fast ground motion monitoring due to its high spatial resolution and short time interval. In this work we present the first Permanent Scatterer Interferometry analysis with TerraSAR-X data for urban subsidence mapping in Tianjin city in China. Totally 17 scenes strip mode SAR images have been collected from Feb to Oct 2009 to perform the PSI analysis. The resulted average subsidence velocity demonstrates the ability of high resolution data for detailed monitoring of urban subsidence. Comparison between PSI result of TerraSAR-X and ENVISAT show the potential of TerraSAR-X data for urban motion as well as large scale manmade linear infrastructure monitoring.

The Permanent Scatterer Interferometry (PSI) technique is usually applied for surface deformation mapping at local area from 1 up to a maximum of 100 km2. Although the ability to provide deformation map of regional area exceeding 10,000 km2, the processed area of interest is mostly limited to SAR acquisitions in a single satellite track and frame. In this work we present the study of merging multi-track PSI results for land subsidence monitoring over very extended area. Apart from the description of the PSI method used for long strip SAR data processing, datum connection of multiple adjacent tracks, including conversion of a common coordinate system and the connection of the PSI derived velocity maps are demonstrated. The application of the proposed method to monitor large coverage land subsidence in the central North China Plain (NCP) is described. The presented results obtained by merging 3 adjacent tracks of ENVISAT ASAR data acquired between Jan, 2007 and Dec, 2009, with a coverage of 200×260km2are very significant and indicate the effectiveness and potential of this technique for land subsidence mapping over very extended area.

In this work we mapped the spatial and temporal patterns of the land subsidence near the city of Hangzhou, China by PSI analysis with 49 scenes of ERS-1/2 SAR images acquired from 1992 to 2006 to detect and retrieve the subsidence due to economic development. The main reason of land subsidence in Hangzhou is groundwater exploitation, which is necessary for the rapid economic development, especially in China. Xiaoshan Economic and Technological Development Zone was approved as a state-level development zone by the State Council in May, 1993. Since then the zone has been suffering land subsidence. There have been more than 300 overseas-funded enterprises with investors from 26 countries and regions by the year 2006. The development of this area can be divided into three periods according to its pace: construction period (1993-1996), stable increase period (1996-2001) and high-speed period (2001-2006).

Land subsidence in Beijing is supposed to be caused by over-exploitation of ground water, which is leading to a rapid decline of water levels, drying out clay layers that finally result in land subsidence. The Interferometric Point Target Analysis (IPTA) is an advanced method to monitor vertical motion of the land surface over time. IPTA identifies backscattering objects, named as coherent points or points targets, at the ground surface that persistently reflect radar radiation emitted by the SAR antenna. The core component of the IPTA technique is the iterative estimation of phase differences for all measurement points over the sets of the SAR data using a linear model. In this paper, IPTA technique was used to retrieve the phase history, extract the linear deformation information from interferometry phase and weaken atmosphere phase delay in Beijing. 20 ENVISAT ASAR images acquired between June-18-2003 and March-14-2007 have been selected. The intention of this article is to demonstrate how IPTA technique could be used to extract valuable information in Beijing area.

The Permanent Scatterers (PSs) SAR Interferometry has become an operational tool in the context of spaceborne SAR interferometry for monitoring surface deformation with millimetric accuracy. In this contribution we presented a case study in Xi'an city for land subsidence monitoring and ground fissures detection by using PS InSAR. We applied a linear regression model to retrieval land subsidence velocity by using a series interferometric phase of the coherent target. For the displacement of ground fissures monitoring, which caused by the nonuniform displacement of land subsidence and fault motion, a time series interferometric analysis of coherent target has been carried out to retrieve the history of displacement. The results archived from ENVISAT ASAR images acquired from 2005 to 2008 has demonstrated the distribution and the magnitude of the land subsidence and ground fissures. We compared the result from PS InSAR with the field surveying data and the results shown good agreement.

In this work we presented the first experimental results of land subsidence mapping for large areas by using Coherent Point Target SAR interferometry with a reduced set of images in the North China Plain (NCP). Since the limitations of the classical Permanent Scatterer InSAR (PSI) for short temporal span surface deformation monitoring due to the dependency on large volumes data availability, we combine the classical PS InSAR and Small Baseline Subset (SBAS) technique in the data processing chain for large coverage InSAR data processing with a reduced set images. The starting point of our study is the generation of small baseline interferograms of the continuous frames in the same track. Following that, each stack of interferograms are processed with CPT InSAR so as to minimize the effect of phase ramp caused by inaccuracy baseline estimation. For large scale land subsidence mapping, all the mean velocity map are merged into a long strip and the subsidence rate of each coherent point are retrieved. The algorithm are tested with 15 ENVISAT ASAR images collected during the period from Jun, 2007 to Nov, 2008 with an extent of 100 × 400km2 in the NCP for land subsidence mapping. The presented results indicates the large scale land subsidence in central NCP and demonstrates the effectiveness the approach.

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.

This work presents the application of SAR Interferometry for coal mining induced land subsidence investigation and monitoring. Since mining subsidence characterized with, e. g., high rate, relatively small extent and significant nonlinear temporal evolution, it requires a dense spatial sample and a temporal frequent measurement. For SAR data processing, a small baseline interferogram strategy is adopted and the SAR data pairs with a small spatial baseline and short time span are combined to generate differential interferomgrams with good coherence, and consequently to measure the subsidence and derive the temporal evolution process. We use the available ENVISAT ASAR data acquired from 2004 to 2005 for analyzing the subsidence of Kailuan coal field. The InSAR derived subsidence is compared with the result from the classical mining subsidence prediction model and the result shows a good agreement. For operational purpose, the relation between InSAR parameter and the deformation spatial-temporal pattern are discussed.

In this paper, an improved approach for surface subsidence monitoring with SAR interferometry by using coherent point target is presented. A joint criterion of amplitude dispersion index and high spectral correlation criterion are proposed for coherent point target identification, which enable a flexible target selection in case of small or large volume of SAR data. Further more, in order to avid the decorelation caused by long temporal or spatial baseline, interferogram stack are generated from the combination of those images with a small baseline. For subsidence rate calculation, a spatial and temporal regression is exploited to unwrap the differential phase of each coherent point target and a linear model is adopted for the estimation of deformation parameters. Case studies in two different test sites demonstrate the advantage and limitations of the algorithm.

By identifying temporarily stable natural reflectors or persistent scatterers (PS), PSInSAR (Persistent Scatterers for SAR Interferometry) technique can analyze this subset of pixels in SAR images, even with long temporal and space baselines, to get high accuracy deformation measurements. We implement the PSInSAR process that is briefly summarized in this paper and apply this method in Tianjin area to detect the deformation phenomena using ENVISAT ASAR images. Calibration of ASAR images helps us select more PSC and using calibrated backscattering coefficient threshold we can discard the pixels whose amplitude are relatively stable while whose backscattered signals are weak and incoherent. Results obtained by processing 14 images show the distribution and the relative deformation value of the displacement field. The estimated linear velocities of PS are not accurate enough because of the relatively small number of images.

Decorrelation caused by temporal changes influences phase unwrapping of differential interferogram in repeat-pass D-InSAR phase delay due to atmosphere disturbance degrades the accuracy of D-InSAR for small deformation monitoring. In this paper, we present a stacking D-InSAR approach using multi-baseline differential interferograms to estimate the linear deformation based on Rank Defect Free Network Adjustment Model (RDFNA) and to increase the deformation temporal sampling rate and estimate the linear deformation accurately. The Minimum Cost Flow (MCF) algorithm based on Delaunay triangulation network generated with sparse grids is adapted for phase unwrapping of individual interferogram. Scatterers with high coherence values over a given threshold in the interferogram stack are selected for the network generation. Therefore, with the multi-baseline differential interferogram stack, the linear deformation rate can be calculated with the unwrapped phase of each point accurately.