The three-runway system expansion project of the Hong Kong International Airport (HKIA) began with the land reclamation to the north of the original runway. Understanding its ground deformation is essential for subsequent civil construction and planning at the new land. Synthetic Aperture Radar Interferometry (InSAR) technique is firstly used to investigate the spatiotemporal characteristics of land subsidence after the completion of third runway pavement. Due to the consolidation of underlay materials, the third runway is subject to varying degrees of land subsidence, with the monitored maximum sinking rate to be ~100 mm/year during September 2021 to October 2023. We adopted the Independent Component Analysis (ICA) to separate the underlying sources in order to explore the spatiotemporal characteristics of deformation in the reclaimed land. The results show that there are three distinct deformation sources in the study area, including an exponential decay signal (an exponential decay consolidation process), a periodic signal (thermal effects correlated with buildings and bridges) and a linear signal (a continuous subsiding). Considering the different reclamation methods, the linear deformation component is mainly located in areas with prefabricated vertical drains (PVD), which is strongly associated with the overall subsidence pattern. On the other hand, the land reclaimed by Deep Cement Mixing (DCM) method tends to reach a stable state earlier than those reclaimed by the PVD method, demonstrating the effectiveness of the DCM in reinforcing the reclamations. These results benefit our understanding of the settlement process over the third runway of HKIA and provide reliable suggestions for follow-up reinforcement plans on specific locations if needed.

Analyzing road network subsidence and sinkholes is crucial for ensuring urban traffic and people's safety. InSAR technique is a non-contact measurement technique with high spatiotemporal resolution, wide monitoring range, and unaffected by road conditions, which is of great significance for the analysis of deformation hazards of man-made linear infrastructures, such as road networks and high-speed railways. In this study, we adopt 44 Sentinel-1A images from January 2022 to July 2023 to study the deformation of the road network in Longgang District, Shenzhen, China based on PS-InSAR. The road deformation is further analyzed associated with sinkhole information from field investigation. The results show that the deformation rate of the road network is between -38.3 mm/year and 16.9 mm/year, and the correlation coefficient between the top burial depth of the sinkholes and the maximum deformation rate is 0.58.

Synthetic aperture radar (SAR) is a crucial technique in Earth observation, providing vast amounts of data for monitoring the Earth’s surface. However, SAR’s side-looking imaging characteristics often result in significant geometric distortions in complex terrains such as mountainous gorges. Current methods struggle to accurately compute both active and passive geometric distortions when orbital state vector information is not available. This study aims to address this challenge by concentrating on the Southeastern Tibetan Plateau (SETP) and introducing a DEM-based radar incidence angle-tracking method (Angle-Tracking) based on ray tracing principles. The fundamental aspects of this method include constructing the discrete range direction vector (DRDV) to establish calculation directions, refining grid distribution via cubic-patch cells, and identifying potential topographic occluder points (PTOPs) to minimize redundancy in iterative computations. Through the utilization of this approach, we have acquired and disclosed the distribution of geometric distortion in ascending and descending Sentinel-1 data over the SETP region. Cross validation with results computed from precise orbital data showcases the efficacy of the angle-tracking method in identifying geometric distortions in the absence of satellite state vector information. Furthermore, the angle-tracking method demonstrates effectiveness when implemented on cloud computing platforms such as Google Earth Engine (GEE), thereby enhancing the feasibility of SAR-based research in mountainous areas.

Ground-based interferometric radar (GBIR) is an innovative tool for monitoring land surface subsidence and urban infrastructure deformation caused by rapid urbanization. However, the interferograms of GBIR are often contaminated by severe atmospheric effects, especially in coastal areas. In this study, we use independent component analysis (ICA) to extract atmospheric effects for the interferograms of GBIR. Analysis of the performance of ICA and traditional surface fitting methods have been carried out. The results suggest that the average improved rate of ICA is 93.05%, which is 69.33% higher than that of surface fitting.

Multitemporal InSAR is a widely used geodetic technique for measuring ground deformation. However, assessing the accuracy of InSAR deformation results is challenging, especially when field measurements such as leveling are limited in coverage or unavailable. While many studies have attempted to calculate the uncertainty of deformation using a priori InSAR stochastic models to assess the deformation reliability, these models are often biased by various factors. In this letter, we propose a new method called the Sparse Parameter Model (SPM) for InSAR deformation retrieval and uncertainty assessment when instantaneous deformation is not the focus. The method estimates the sparser deformation time series and leverages redundant SAR observations for the deformation uncertainty assessment and decorrelation noise suppression. The proposed model is tested by both simulated and real Sentinel-1 datasets and the derived deformation was validated with GPS measurements in the real application. The results demonstrated that the overall uncertainty of InSAR deformation, as estimated by the SPM, is 5.4 mm, falling well within the expected range of uncertainty, which highlights the effectiveness of the SPM in retrieving InSAR deformation and assessing uncertainty.

Discriminating leaf and wood components in terrestrial laser scanning (TLS) point clouds is a prerequisite for accurately estimating 3-D structural and biophysical attributes of both individual trees and entire forests. However, most existing separation methods are conducted at local (i.e., individual or plot) level. The local level separation methods need a presegmentation of the acquired point clouds, and the separation accuracy and reliability are greatly influenced by forest occlusion effect and point cloud qualities. A new generalized method merely based on differences in geometric features, including curvature, density, and salient features, is proposed in this study for separating leaf and wood components at the TLS single-scan level. A preliminary separation is conducted using the quantity of normal change rate (i.e., surface variation) given that leaf points often demonstrate sharp local curvature changes. Then, separation is continually conducted on the basis of calibrated density data (i.e., number of points in a given radius) because of the scattered orientations and small sizes of leaves. Finally, a new self-adjusting connectivity segmentation algorithm is proposed to group remaining points into different clusters. Leaf and wood clusters are separated in accordance with salient features and sizes simultaneously. Results indicate that derived geometric quantities from curvature, density, and salient features of individual points and segmented clusters can be jointly used to discriminate leaf and wood components effectively and robustly in single-scan TLS point clouds with a mean overall accuracy of approximately 93%. In addition, results show good performance in terms of the insensitivity to distance, instrument type, occlusion effect, and forest composition of the proposed method.

Phase decorrelation, as one of the main error sources, limits the capability of interferometric synthetic aperture radar (InSAR) for deformation mapping over areas with low coherence. Although several methods have been realized to reduce decorrelation noise, for example, by phase linking and spatial and temporal filters, their performances deteriorate when coherence estimation bias exists. We present an arc-based approach that allows reconstructing unwrapped interval phase time-series based on iterative weighted least squares (WLS) in temporal and spatial domains. The main features of the method are that phase optimization and unwrapping can be jointly conducted by spatial and temporal iterative WLS and coherence matrix bias has negligible effects on the estimation. In addition, the linear formation makes the implementation suitable with small subset of interferograms, providing an efficient solution for future big SAR data. We demonstrate the effectiveness of the proposed method using simulated and real data with different decorrelation mechanisms and compare our approach with the state-of-art phase reconstruction methods. Substantial improvement can be achieved in terms of reduced root-mean-square error (RMSE) in the simulation data and increased density of coherent measurements in the real data.

Multi-temporal interferometric synthetic aperture radar (MT-InSAR), as one of the geodetic techniques, is widely used in geological disaster monitoring and engineering applications. However, modern satellites provide an endless array of SAR imagery for InSAR applications, which has prompted us in exploring more efficient and accurate MT-InSAR methods to monitor the ground stability continuously and early detect the ground hazards. We proposed in this paper a step-by-step estimator for continuous deformation monitoring, aiming to study the ground surface deformation that has occurred in the past few years and monitor the latest developments in the same location continuously. Associated with the existing results of MT-InSAR, when a new SAR image is available, the unwrapped phase vector is sequentially estimated with the ambiguity detection, and then the deformation parameters are updated timely. Through experimental verification over Hong Kong, the proposed estimator shown its capability to detect the abnormal changes and update the deformation sequentially without being affected by the complex deformation histories.

Coherent point (including persistent and distributed scatterers) selection and phase ambiguity treatment (or parameter estimation) are the key tasks involved in multitemporal InSAR (MTInSAR) algorithms, which are usually conducted separately with empirical thresholds. It is not rare to see that due to the discrepancies on threshold setting, even for the same MTInSAR technique with the same data sets, it will raise different (sometimes quite notable) results and affect the applicability of InSAR techniques. We propose here an integrated MTInSAR estimator that combines the coherent point selection and phase vector unwrapping into a single step. Essentially, the estimator aims to recover the unwrapped phase vector at coherent points. Therefore, it could serve as an alternative solution of spatial-temporal phase unwrapping problem. In the estimator, wrapped phase at all pixels in short baseline interferograms are taken as observations. Starting from the phase differences at arcs of a fully connected network of pixels, based on the residual analysis and spatial closure of phase triangularity, the estimator can detect and delete the arcs having unacceptable phase noise and phase ambiguities. By integrating the phase differences at the remained arcs, the unwrapped phase at coherent points in consecutive acquisition intervals can be obtained. Impressively, the estimator is immune to the bias raised by improper deformation model. The performance of the proposed estimator is evaluated via semisynthetic and real data tests. Considering that the phase enhancement algorithms (e.g., phase-linking and Extended Minimum Cost Flow-Small BAseline Subset) that can reconstruct high-quality wrapped phases are gaining popularity, the proposed estimator can also be implemented as a postprocessing module of these algorithms for retrieval of unwrapped phase vectors at coherent points.

Interferometric synthetic aperture radar (InSAR) is one of the most popular geodetic techniques for studying earthquake-related crustal displacements. Satellite SAR signals interact with the ionosphere when they travel through it during the synthetic aperture time. The condition of the ionosphere and its variation can significantly affect spaceborne InSAR measurements. In this letter, we use the Advanced Land Observation Satellite Phase Array-Type L-band SAR data from the 2009 southern Sumatra earthquake to evaluate the effects of the ionospheric artifacts on the slip distribution inversion of earthquake. The split-spectrum method is used to estimate and correct the ionospheric artifacts in the InSAR results. This letter shows that the long-wavelength ionospheric artifacts in the coseismic interferograms can be effectively mitigated. The slip distribution of the earthquake derived from the interferograms corrected for the ionospheric artifacts is presented. The slip distribution pattern and the magnitude of the slip are significantly refined after correcting the ionospheric artifacts.

Ground based interferometric radar (GBIR) is a revolutionary advanced measurement technique for geoscience and engineering geodesy. It is powerful for temporally and spatially dense measurements of highly dynamic target with sub-millimetric accuracy, especially in man-made structures, e.g. buildings, towers, dams and bridges. In this case study, we use a real aperture radar system, the Gamma Portable Radar Interferometer (GPRI-II), to perform near-real-time deformation monitoring of the deck (back side) of a cable-stayed bridge. As a test site, the Ting Kau Bridge at Tsuen Wan, Hong Kong, was continuously measured from two modes of observation, rotated azimuth scanning (RAS) and fixed azimuth scanning (FAS). The results reveal the wind-driven and vehicle-driven non-uniform oscillation of the bridge. The presented works demonstrate the ability of GPRI-II in bridge deformation or oscillation monitoring, which provide a new way for structural health monitoring of bridge.

Terrestrial laser scanning (TLS) can accurately and thoroughly document the actual geometrical deformations and surface conditions of metro tunnel structures using high-density point clouds. In addition, TLS can record the backscattered intensity values of each point simultaneously. Backscattered intensity values are significant measures of the spectral property of scanned objects in the near-infrared region of the electromagnetic spectrum. The intensity values of water leakage regions are theoretically lower than those of the background because of the high water absorption coefficient in the near-infrared spectrum. Thus, the intensity data can be used in water leakage detection and quantitative analysis. This letter investigates the feasibility of a new mobile system called Faro Focus3DX330 for data collection, processing, and intensity correction to detect and extract water leakage spots in metro tunnels. A case study of the Shanghai Metro was conducted. Results show that the corrected intensity value is an effective physical criterion to detect water leakages in metro tunnels. By combing the 3-D point cloud, the location, area, and even water content of the leakage regions can be accurately determined.

The paper researches on the relationship between image quality and the performance of interest point detection. In this paper, we use the image quality metrics and interest point repeatability as the measures of image quality and the performance of interest point detection respectively. Considering the differences of image's scene and quality degradation factor, nine images covering three kinds of classic scenes are selected as the experiment data, and they are respectively contaminated by Gaussian blur and Gaussian noise. The results show that image quality metrics and the repeatability have a certain quantitative relationship, and the repeatability is reduced with the decrease of image quality metrics. Moreover, the relationship can be simulated by some simple functions such as linear and exponential model when the image's scene and degradation factor are fixed.