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Groundwater resources are crucial to socio-economic development and the ecosystem, and over-extraction can cause the groundwater level to drop, deplete reserves, and trigger geological hazards like land subsidence. The North China Plain (NCP) has experienced both subsidence and groundwater depletion due to over-extraction in the past 70 years. In this study, we used MT-InSAR technology and ascending C-band Sentinel-1 SAR data from 2017 to 2023 to study land deformation in the junction area of Shijiazhuang–Baoding–Cangzhou–Hengshui. We identified multiple subsidence funnels with a maximum rate exceeding −150 mm/year and a total deformation surpassing 600 mm. Seasonal decomposition methods accurately separated seasonal signals in the time-series deformation and groundwater level data. An exponential function model applied to long-term deformation showed no significant decrease in subsidence in severely affected areas. By modeling seasonal deformation and seasonal groundwater levels, we determined the elastic skeletal storage coefficients (Ske) to be in the range of 1.02 × 10−3~6.53 × 10−3 in subsidence areas. We obtained the spatiotemporal evolution of the total groundwater storage (TGWS), irreversible ground storage (IGWS), and recoverable ground storage (RGWS). The TGWS and IGWS decreased annually while the RGWS increased, which is attributable to the implementation of the South-to-North Water Diversion Project (SNWDP) and the issuance of groundwater withdrawal policies in the NCP.

Against the backdrop of global warming and increased rainfall, the hazards and potential risks of landslides are increasing. The rapid generation of a landslide inventory is of great significance for landslide disaster prevention and reduction. Deep learning has been widely applied in landslide identification due to its advantages in terms of its deeper model structure, high efficiency, and high accuracy. This article first provides an overview of deep learning technology and its basic principles, as well as the current status of landslide remote sensing databases. Then, classic landslide deep learning recognition models such as AlexNet, ResNet, YOLO, Mask R-CNN, U-Net, Transformer, EfficientNet, DeeplabV3+ and PSPNet were introduced, and the advantages and limitations of each model were extensively analyzed. Finally, the current constraints of deep learning in landslide identification were summarized, and the development direction of deep learning in landslide identification was analyzed. The purpose of this article is to promote the in-depth development of landslide identification research in order to provide academic references for the prevention and mitigation of landslide disasters and post-disaster rescue work. The research results indicate that deep learning methods have the characteristics of high efficiency and accuracy in automatic landslide recognition, and more attention should be paid to the development of emerging deep learning models in landslide recognition in the future.

Accurate and complete digital elevation models (DEMs) play an important fundamental role in geospatial analysis, supporting various engineering applications, human activities, and scientific research. Interferometric synthetic aperture radar (InSAR) plays an increasingly important role in DEM generation. Nonetheless, owing to its inherent characteristics, gaps often appear in regions marked by significant topographical fluctuations, necessitating an extra void-filling process. Traditional void-filling methods have operated directly on preexisting data, succeeding in relatively flat terrain. When facing mountainous regions, there will always be gross errors in elevation values. Regrettably, conventional methods have often disregarded this vital consideration. To this end, this research proposes a DEM void-filling method based on incorporating elevation outlier detection. It accounts for the detection and removal of elevation outliers, thereby mitigating the shortcomings of existing methods and ensuring robust DEM restoration in mountainous terrains. Experiments were conducted to validate the method applicability using TanDEM-X data from Sichuan, China, Hebei, China, and Oregon, America. The results underscore the superiority of the proposed method. Three traditional methods are selected for comparison. The proposed method has different degrees of improvement in filling accuracy, depending on the void status of the local terrain. Compared with the delta surface fill (DSF) method, the root mean squared error (RMSE) of the filling results has improved by 7.87% to 51.87%. The qualitative and quantitative experiments demonstrate that the proposed method is promising for large-scale DEM void-filling tasks.

In contemporary warfare, radar countermeasures have become multifunctional and intelligent, rendering the conventional jamming method and platform unsuitable for the modern radar countermeasures battlefield due to their limited efficiency. Reinforcement learning has been proven to be a practical solution for cognitive jamming decision-making in the cognitive electronic warfare. In this paper, we proposed a radar-jamming decision-making algorithm based on an improved Q-Learning algorithm. This improved Q-Learning algorithm ameliorated the problem of overestimating the Q-value that exists in the Q-Learning algorithm by introducing a second Q-table. At the same time, we performed a comprehensive design and implementation based on the classical Q-Learning algorithm, deploying it to a Field Programmable Gate Array (FPGA) hardware. We decomposed the implementation of the reinforcement learning algorithm into individual steps and described each step using a hardware description language. Then, the reinforcement learning algorithm can be computed on FPGA by linking the logic modules with valid signals. Experiments show that the proposed Q-Learning algorithm obtains considerable improvement in performance over the classical Q-Learning algorithm. Additionally, they confirm that the FPGA hardware can achieve great efficiency improvement on the radar-jamming decision-making algorithm implementation.

Ship classification using the synthetic aperture radar (SAR) images has a significant role in remote sensing applications. Aiming at the problems of excessive model parameters numbers and high energy consumption in the traditional deep learning methods for the SAR ship classification, this paper provides an energy-efficient SAR ship classification paradigm that combines spiking neural networks (SNNs) with Siamese network architecture, for the first time in the field of SAR ship classification, which is called the Siam-SpikingShipCLSNet. It combines the advantage of SNNs in energy consumption and the advantage of the idea in performances that use the Siamese neuron network to fuse the features from dual-polarized SAR images. Additionally, we migrated the feature fusion strategy from CNN-based Siamese neural networks to the SNN domain and analyzed the effects of various spiking feature fusion methods on the Siamese SNN. Finally, an end-to-end error backpropagation optimization method based on the surrogate gradient has been adopted to train this model. Experimental results tested on the OpenSARShip2.0 dataset have demonstrated the correctness and effectiveness of the proposed SAR ship classification strategy, which has the advantages of the higher accuracy, fewer parameters and lower energy consumption compared with the mainstream deep learning method of the SAR ship classification.

Due to the inherent characteristics of synthetic aperture radar (SAR) imaging, SAR ship features are not obvious and the category distribution is unbalanced, which makes the task of ship recognition in SAR images quite challenging. To address the above problems, a two-level feature-fusion ship recognition strategy combining the histogram of oriented gradients (HOG) features with the dual-polarized data in the SAR images is proposed. The proposed strategy comprehensively utilizes the features extracted by the HOG operator and the shallow and deep features extracted by the Siamese network in the dual-polarized SAR ship images, which can increase the amount of information for the model learning. First, the Siamese network is used to extract the shallow and deep features from the dual-polarized SAR images, and then the HOG feature of the dual-polarized SAR images is also extracted. Furthermore, the bilinear transformation layer is used for fusing the HOG features from dual-polarized SAR images, and the grouping bilinear pooling process is used for fusing the dual-polarized shallow feature and deep feature extracted by the Siamese network, respectively. Finally, the catenation operation is used for fusing the dual-polarized HOG features and dual-polarized shallow feature and deep feature, respectively, which are used for the recognition of the SAR ship targets. Experimental results tested on the OpenSARShip2.0 dataset demonstrate the correctness and effectiveness of the proposed strategy, which can effectively improve the recognition performance of the ship targets by fusing the different level features of the dual-polarized SAR images.

Ground deformation is the direct manifestation of the earth-rock dam's hazard potential. Therefore, it is essential to monitor deformation for dam warning and security evaluation. The Liuduzhai Dam, a clay-core dam of a large reservoir in China, was reinforced with plastic concrete cut-off walls between 13 January 2009 and 29 May 2010, as it was subject to leakage and deformation. However, the deformation development and the mechanism of the dam are still unclear. In this study, the deformation fields before and after the reinforcement of the Liuduzhai Dam were yielded by using the Interferometric Synthetic Aperture Radar (InSAR) technique. Furthermore, a numerical simulation method was employed to obtain the dynamic seepage field of the dam during the InSAR observation period. The results indicated that the average deformation velocity and maximum deformation velocity are −11.7 mm/yr and −22.5 mm/yr, respectively, and the cumulative displacement exceeds 100 mm, which shows typical continuous growth characteristics in a time series. In contrast, the dam deformation tended to be stable after reinforcement, with the average deformation velocity and maximum deformation velocity being −0.4 mm/yr and −1.2 mm/yr, respectively, behaving as cyclical deformation time series. According to the results of InSAR and seepage analysis, it is shown that: (1) dynamic seepage was the main mechanism controlling dam deformation prior to reinforcement; (2) the concentrated load caused by construction and the rapid dissipation of pore water pressure caused by the sudden drop of the infiltration line were the reasons for the acceleration of deformation during and after construction; and (3) the plastic concrete cut-off walls effectively reduced the dynamic seepage field, while the water level fluctuations were the main driving factor of elastic deformation of the dam after reinforcement. This study provides a novel approach to investigating the deformation mechanism of earth-rock dams. Furthermore, it has been confirmed that InSAR can identify the seepage deformation of dams by detecting surface movements. It is recommended that InSAR deformation monitoring should be incorporated into future dam safety programs to provide detailed deformation signals. By analyzing the temporal and spatial characteristics of the deformation signal, we can identify areas where dam performance has degraded. This crucial information aids in conducting a comprehensive dam safety assessment.

Image registration is the basis for the joint interpretation of synthetic aperture radar (SAR) and optical images. However, the significant nonlinear radiation difference (NRD) and the geometric imaging model difference render the registration quite challenging. To solve this problem, both traditional and deep learning methods are used to extract structural information with dense descriptions of the images, but they ignore that structural information of the image pair is coupled and often process images separately. In this paper, a deep learning-based registration method with a co-attention matching module (CAMM) for SAR and optical images is proposed, which integrates structural feature maps of the image pair to extract keypoints of a single image. First, joint feature detection and description are carried out densely in both images, for which the features are robust to radiation and geometric variation. Then, a CAMM is used to integrate both images’ structural features and generate the final keypoint feature maps so that the extracted keypoints are more distinctive and repeatable, which is beneficial to global registration. Finally, considering the difference in the imaging mechanism between SAR and optical images, this paper proposes a new sampling strategy that selects positive samples from the ground-truth position’s neighborhood and augments negative samples by randomly sampling distractors in the corresponding image, which makes positive samples more accurate and negative samples more abundant. The experimental results show that the proposed method can significantly improve the accuracy of SAR–optical image registration. Compared to the existing conventional and deep learning methods, the proposed method yields a detector with better repeatability and a descriptor with stronger modality-invariant feature representation.

The Xi’an region of China has been suffering from groundwater depletion, ground fissure hazards, and surface subsidence for a long time. Due to the complex tectonics and frequent human and natural activities, land deformation in the region is aggravated, posing a threat to infrastructure and human life. This study adopted the multi-orbit and multi-temporal InSAR technology to measure multi-dimensional displacements and time-series displacements in Xi’an City. Through the multi-dimensional deformation verification, it was found that the control of groundwater flow direction by ground fissures is the cause of horizontal deformation. On the contrary, the flow direction of groundwater from west to east was inferred using multi-dimensional deformation. Further analysis was performed by calculating the deformation gradient of the cumulative deformation to obtain differential land subsidence and angular distortions, and it was quantitatively determined that the threshold for the generation of ground fissures caused by differential subsidence is 1/500. Then, through the mutual verification of the time series data and the groundwater level, a positive correlation was obtained. However, due to the inconsistent geological conditions and soil layers at the monitoring positions of Well 2 and Well 3, the lag time was 64 days and 4 days, respectively. Finally, the relationship between the surface deformation and the groundwater in the sustained uplift areas was explored. The Well 1 groundwater-level data with a monitoring period of 22 years and the corresponding monitoring points’ time series data were modeled; it was concluded that, in the future, the groundwater level will continue to rise and surface deformation will mainly increase, without a slowing trend. Therefore, research on the impact of surface uplift on infrastructure should be strengthened. By quantifying the relationship between land subsidence, ground fissures, and the groundwater level in Xi’an, the results of this study provide a reference for groundwater monitoring and management.

In order to improve the efficiency and adaptability of cognitive radar jamming decision-making, a fusion algorithm (Ant-QL) based on ant colony and Q-Learning is proposed in this paper. The algorithm does not rely on a priori information and enhances adaptability through real-time interactions between the jammer and the target radar. At the same time, it can be applied to single jammer and multiple jammer countermeasure scenarios with high jamming effects. First, traditional Q-Learning and DQN algorithms are discussed, and a radar jamming decision-making model is built for the simulation verification of each algorithm. Then, an improved Q-Learning algorithm is proposed to address the shortcomings of both algorithms. By introducing the pheromone mechanism of ant colony algorithms in Q-Learning and using the ε-greedy algorithm to balance the contradictory relationship between exploration and exploitation, the algorithm greatly avoids falling into a local optimum, thus accelerating the convergence speed of the algorithm with good stability and robustness in the convergence process. In order to better adapt to the cluster countermeasure environment in future battlefields, the algorithm and model are extended to cluster cooperative jamming decision-making. We map each jammer in the cluster to an intelligent ant searching for the optimal path, and multiple jammers interact with each other to obtain information. During the process of confrontation, the method greatly improves the convergence speed and stability and reduces the need for hardware and power resources of the jammer. Assuming that the number of jammers is three, the experimental simulation results of the convergence speed of the Ant-QL algorithm improve by 85.4%, 80.56% and 72% compared with the Q-Learning, DQN and improved Q-Learning algorithms, respectively. During the convergence process, the Ant-QL algorithm is very stable and efficient, and the algorithm complexity is low. After the algorithms converge, the average response times of the four algorithms are 6.99 × 10−4 s, 2.234 × 10−3 s, 2.21 × 10−4 s and 1.7 × 10−4 s, respectively. The results show that the improved Q-Learning algorithm and Ant-QL algorithm also have more advantages in terms of average response time after convergence.

Landslides are geological events that frequently cause major disasters. Research on landslides is essential, but current studies mostly use historical landslide data and do not reflect dynamic, real-time research results. In this study, landslide deformations and land-use changes were used to analyze the landslide distribution in Fengjie County and Wushan County in the Three Gorges Reservoir Area (TGRA) by using interferometric and polarimetric SAR. In this study, the mean annual rate of landslide deformations was obtained using the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) for the ALOS-2 (2014–2019) data. Land-use changes were based on the 2007 and 2017 land-use results from dual-polarization ALOS-1 and ALOS-2 data, respectively. To address the problem of classification accuracy reduction caused by geometric distortion in mountainous areas, we first used texture maps and pseudocolor maps synthesized with dual-polarization intensity maps to perform classification with random forest (RF), and then we used coherence and slope maps to run the K-Means algorithm (KMA). We named this the secondary classification method. It is an improvement on the single classification method, exhibiting a 94% classification accuracy, especially in rugged areas. Combined with land-use changes, GIS spatial analysis was used to analyze the spatial distribution of landslides, and it was found that the landslide rate was significantly correlated with the type after change, with a correlation coefficient of 0.7. In addition, land-use types associated with human activities, such as cultivated vegetation, were more likely to cause landslide deformation, which can be used to guide local land-use planning.

Strong earthquakes can not only trigger many landslides in a short period of time but can also change the stability of slopes in the earthquake area, causing them to be active for a long time after the earthquake. Research on the variation of slow-motion slopes before and after earthquakes can help us to better understand the mechanism of earthquake-affected landslides, which is also crucial for assessing the long-term landslide risk in seismically active areas. Here, L-band ALOS-2 PALSAR-2 images are utilized with the SBAS-InSAR algorithm to monitor and assess the location and activity changes of slow-moving landslides in the Iburi region (Hokkaido, Japan) before and after an earthquake occurred on 6 September 2018. Unlike previous studies, which focused on single typical landslides, we tracked all the landslides within a 33 × 55 km region close to the epicenter. According to the results, the majority of the co-seismic landslides that quickly failed during the earthquake are now stable, and a few of them are still moving. In contrast, due to near-field seismic shaking, certain slopes that did not show substantial surface changes during the earthquake period continued to move and eventually developed into slow-moving landslides. In addition, it can be seen from the spatial distribution of slow-moving landslides after the earthquake that this distribution is not only dependent on strong earthquake seismic vibration or the hanging-wall effect. Far-field weak vibrations can also accelerate landslides. Additionally, we discovered that the earthquake made the unstable slopes move more quickly but also tended to stabilize the slopes that were already in motion before the earthquake. The various response modes of slow-moving landslides to seismic events are related not only to the intensity of seismic vibration but also to the geological conditions of the region and to the size of the landslide itself. These findings are extremely valuable for studying the mechanism of earthquake-affected landslides.

Geosynchronous (GEO) spaceborne–airborne very high-frequency ultra-wideband bistatic synthetic aperture radar (VHF UWB BiSAR) can conduct high-resolution and wide-swath imaging for ocean scenes. However, GEO spaceborne–airborne VHF UWB BiSAR imaging faces some challenges such as the geometric configuration, huge amount of echo data, serious range–azimuth coupling, large spatial variance, and complex motion error, which increases the difficulty of the high-efficiency and high-precision imaging. In this paper, we present an improved bistatic fast factorization backprojection (FFBP) algorithm for ocean scene imaging using the GEO satellite-unmanned aerial vehicle (GEO-UAV) VHF UWB BiSAR, which can solve the above issues with high efficiency and high precision. This method reconstructs the subimages in the orthogonal elliptical polar (OEP) coordinate system based on the GEO satellite and UAV trajectories as well as the location of the imaged scene, which can further reduce the computational burden. First, the imaging geometry and signal model of the GEO-UAV VHF UWB BiSAR are established, and the construction of the OEP coordinate system and the subaperture imaging method are proposed. Moreover, the Nyquist sampling requirements for the subimages in the OEP coordinate system are derived from the range error perspective, which can offer a near-optimum tradeoff between precision and efficiency. In addition, the superiority of the OEP coordinate system is analyzed, which demonstrates that the angular dimensional sampling rate of the subimages is significantly reduced. Finally, the implementation processes and computational burden of the proposed algorithm are provided, and the speed-up factor of the proposed FFBP algorithm compared with the BP algorithm is derived and discussed. Experimental results of ideal point targets and natural ocean scenes demonstrate the correctness and effectiveness of the proposed algorithm, which can achieve near-optimal imaging performance with a low computational burden.

Distributed scatterers (DSs) are necessary to increase point density in multi-temporal InSAR (MT-InSAR) monitoring. The identification of homogeneous pixels (HPs) is the first and key step for DS processing to overcome the low signal-to-noise ratio condition. Since multi-polarization data are good at describing geometrical structures and dielectric properties of ground objects, they have been applied for HP identification. However, polarimetric information is not enough for identifying areas with similar ground objects but different deformation. We propose a novel DS preprocessing algorithm based on polarimetric interferometric homogeneous pixel (PIHP) identification. Firstly, a novel Polarimetric InSAR (PolInSAR) similarity that combines polarimetric intensity, interferometric coherence, and phase is proposed, which is readily available in multi-baseline and multi-polarization data and flexible by controlling weighting factors. Secondly, based on the binary partition tree (BPT) framework, object-orientated multi-scale PIHP identification is achieved, which is suitable for complex deformation scenes. Tested with simulated quad-polarization data, our method shows improvement in phase quality and point density, especially in the deformed areas, compared with the traditional HP identification method based on the polarimetric homogeneity (PolHom) test and the method with ground object type map. Tested with 30 quad-polarization Radarsat-2 images over Kilauea Volcano, the point density of our method is three times higher than that of the PolHom test in vegetation areas. Our method is proven to be more sensitive and mechanically more advanced to homogeneous pixels identification than the traditional ones, which is helpful for phase optimization, spatial enlargement of monitoring points, and stability of the MT-InSAR algorithm.

The random volume over ground (RVoG) model has been widely used in the field of vegetation height retrieval based on polarimetric interferometric synthetic aperture radar (PolInSAR) data. However, to date, its application in a time-series framework has not been considered. In this study, the logistic growth equation was introduced into the PolInSAR method for the first time to assist in estimating crop height, and an improved inversion scheme for the corresponding RVoG model parameters combined with the logistic growth equation was proposed. This retrieval scheme was tested using a time series of single-pass HH-VV bistatic TanDEM-X data and reference data obtained over rice fields. The effectiveness of the time-series RVoG model based on the logistic growth equation and the convenience of using equation parameters to evaluate vegetation growth status were analyzed at three test plots. The results show that the improved method can effectively monitor the height variation of crops throughout the whole growth cycle and the rice height estimation achieved an accuracy better than when single dates were considered. This proved that the proposed method can reduce the dependence on interferometric sensitivity and can achieve the goal of monitoring the whole process of rice height evolution with only a few PolInSAR observations.

As one of the most prevailing geodetic tools, the interferometric synthetic aperture radar (InSAR) technique can accurately obtain co-seismic displacements, but is limited to the one-dimensional line-of-sight (LOS) measurement. It is therefore difficult to completely reveal the real three-dimensional (3D) surface displacements with InSAR. By employing azimuth displacement observations from pixel offset tracking (POT) and multiple aperture InSAR (MAI) techniques, 3D displacements of large-magnitude earthquakes can be obtained by integrating the ascending and descending data. However, this method cannot be used to accurately realize the 3D surface displacement measurements of small-magnitude earthquakes due to the low accuracies of the POT/MAI-derived azimuth displacement measurements. In this paper, an alternative method is proposed to calculate co-seismic 3D displacements from ascending and descending InSAR-LOS observations with the dislocation model-based displacement direction constraint. The main contribution lies in the two virtual observation equations that are obtained from the dislocation model-based forward-modeling 3D displacements, which are then combined with the ascending/descending InSAR observations to calculate the 3D displacements. The basis of the two virtual observation equations is that the directions of the 3D displacement vectors are very similar for real and model-based 3D displacements. In addition, the weighted least squares (WLS) method is employed to solve the final 3D displacements, which aims to consider and balance the possible errors in the InSAR observations as well as the dislocation model-based displacement direction constraint. A simulation experiment demonstrates that the proposed method can achieve more accurate 3D displacements compared with the existing methods. The co-seismic 3D displacements of the 2020 Nima earthquake are then accurately obtained by the proposed method. The results show that co-seismic displacements are dominated by the vertical displacement, the magnitude of the horizontal displacement is relatively small, and the overall displacement pattern fits well with the tensile rupture.

In recent years, increasing available synthetic aperture radar (SAR) satellite data and gradually developing interferometric SAR (InSAR) technology have provided the possibility for wide-scale ground-deformation monitoring using InSAR. Traditionally, the InSAR data are processed by the existing time-series InSAR (TS–InSAR) technology, which has inefficient calculation and redundant results. In this study, we propose a wide-area InSAR variable-scale deformation detection strategy (hereafter referred to as the WAVS–InSAR strategy). The strategy combines stacking technology for fast ground-deformation rate calculation and advanced TS–InSAR technology for obtaining fine deformation time series. It adopts an adaptive recognition algorithm to identify the spatial distribution and area of deformation regions (regions of interest, ROI) in the wide study area and uses a novel wide-area deformation product organization structure to generate variable-scale deformation products. The Turpan–Hami basin in western China is selected as the wide study area (277,000 km2) to verify the proposed WAVS–InSAR strategy. The results are as follows: (1) There are 32 deformation regions with an area of ≥1 km2 and a deformation magnitude of greater than ±2 cm/year in the Turpan–Hami basin. The deformation area accounts for 2.4‰ of the total monitoring area. (2) A large area of ground subsidence has occurred in the farmland areas of the ROI, which is caused by groundwater overexploitation. The popularization and application of facility agriculture in the ROI have increased the demand for irrigation water. Due to the influence of the tectonic fault, the water supply of the ROI is mainly dependent on groundwater. Huge water demand has led to a continuous net deficit in aquifers, leading to land subsidence. The WAVS–InSAR strategy will be helpful for InSAR deformation monitoring at a national/regional scale and promoting the engineering application of InSAR technology.

For time-series interferometric phases optimization of distributed scatterers (DSs), the SqueeSAR technology used the phase linking (PL) to extract the equivalent single-master (ESM) interferometric phases from the multilooking time-series coherence matrix. The Cramer–Rao lower bound (CRLB) for the PL describes the highest achievable estimation accuracy of the ESM phases, which depends on the number of looks and the time-series coherence magnitude matrix. With the abundance of time-series polarimetric SAR data, many scholars have studied the coherence magnitude-based polarimetric optimization methods for optimizing the DS’s time-series interferometric phases, for example, the widely-used exhaustive search polarimetric optimization (ESPO) algorithm. However, the traditional polarimetric optimization methods select the boundary extremums of the coherence region (CR) as the optimized complex coherence, which is usually biased from the free-noise one. Currently, in the polarimetric InSAR (PolInSAR) technology, Shen et al. innovatively considered polarimetric information as statistical samples and proposed the total power (TP) coherency matrix construction method for increasing the number of looks and reducing the interferometric phase noise. Therefore, to optimize the time-series interferometric phases for DS, this paper proposes performing a polarization stacking and extending the PolInSAR TP construction to the time-series PolInSAR (TSPolInSAR) data configuration, called the time-series TP (TSTP) method. Simulated and real experiments prove that the new TSTP construction method has better performance and higher efficiency than the single polarimetric and the traditional ESPO algorithms.

The excessive extraction and recharge of groundwater lead to long-time seasonal land subsidence in Los Angeles, USA, and especially in the Santa Ana basin. The rate of land subsidence in the Santa Ana basin has been rising, which could pose a danger to infrastructure and human lives. However, the most recent research on land surface deformation in the area was conducted using the traditional parameter estimation method, resulting in little understanding of the regional spatiotemporal characteristics. The parametric method consists of a least square linear inversion, using the pre-defined mathematical geometric or geophysical theoretical models to describe groundwater deformation, and it requires precise external environmental variables and accurate geophysical parameters, which are more difficult to implement. In this study, multitemporal InSAR-derived deformation time series are analyzed by using 69 descending C-band Sentinel-1A SAR scenes acquired from 2015 to 2018. A method based on independent component analysis (ICA) is applied to characterize the spatial pattern and temporal evolution of land subsidence in the Santa Ana basin. The results reveal two different spatial and temporal deformation patterns in the basin. First, a widespread seasonal deformation is identified by the first component, related to annual seasonal groundwater level changes, and the overall deformation shows a concentrated spatial pattern. The second component captures a long-term signal with a large-scale spatial pattern. For quantitative assessment, the obtained deformation time series are compared with the GNSS data, validating an accuracy of millimeters. We further calculate the cross-correlation coefficient and the elastic skeletal storage coefficient from the ICA-derived seasonal deformation and groundwater level, which reveals that the deformation responds quickly (i.e., a lag of 8 days) to the change in groundwater and the Santa Ana aquifer retains almost the same elasticity for at least 15 years. Quantifying the spatiotemporal characteristics of the deformation in the Santa Ana basin can provide a reference for the monitoring and managing of groundwater.

In the past few decades, the demand for reliable and robust systems capable of monitoring unmanned aerial vehicles (UAVs) increased significantly due to the security threats from its wide applications. During UAVs surveillance, birds are a typical confuser target. Therefore, discriminating UAVs from birds is critical for successful non-cooperative UAVs surveillance. Micro-Doppler signature (m-DS) reflects the scattering characteristics of micro-motion targets and has been utilized for many radar automatic target recognition (RATR) tasks. In this paper, the authors deploy local mean decomposition (LMD) to separate the m-DS of the micro-motion parts from the body returns of the UAVs and birds. After the separation, rotating parts will be obtained without the interference of the body components, and the m-DS features can also be revealed more clearly, which is conducive to feature extraction. What is more, there are some problems in using m-DS only for target classification. Firstly, extracting only m-DS features makes incomplete use of information in the spectrogram. Secondly, m-DS can be observed only for metal rotor UAVs, or large UAVs when they are closer to the radar. Lastly, m-DS cannot be observed when the size of the birds is small, or when it is gliding. The authors thus propose an algorithm for RATR of UAVs and interfering targets under a new system of L band staring radar. In this algorithm, to make full use of the information in the spectrogram and supplement the information in exceptional situations, m-DS, movement, and energy aggregation features of the target are extracted from the spectrogram. On the benchmark dataset, the proposed algorithm demonstrates a better performance than the state-of-the-art algorithms. More specifically, the equal error rate (EER) proposed is 2.56% lower than the existing methods, which demonstrates the effectiveness of the proposed algorithm.

Southeast Asia is one of the largest biomass burning (BB) regions in the world, and the air pollutants generated by this BB have an important impact on air pollution in southern China. However, the mechanism of the cross-border transport of BB pollutants to neighboring regions is yet to be understood. Based on the MODIS remote sensing products and conventional observation data of meteorology and the environment, the WRF-Chem and FLEXPART-WRF models were used to simulate a typical PM2.5 pollution episode that occurred during 24–26 March 2017 to analyze the mechanism of cross-border transport of BB pollutants over Yunnan Plateau (YP) in southwest China. During this air pollution episode, in conjunction with the flourishing BB activities over the neighboring Indo-China Peninsula (ICP) regions in Southeast Asia, and driven by the southwesterly winds prevailing from the ICP to YP, the cross-border transport of pollutants was observed along the transport pathway with the lifting plateau topography in YP. Based on the proximity to the BB sources in ICP, YP was divided into a source region (SR) and a receptor region (RR) for the cross-border transport, and the negative and positive correlation coefficients (R) between PM2.5 concentrations and wind speeds, respectively, were presented, indicating the different impacts of BB emissions on the two regions. XSBN and Kunming, the representative SR and RR sites in the border and hinterland of YP, respectively, have distinct mechanisms that enhance PM2.5 concentrations of air pollution. The SR site is mainly affected by the ICP BB emissions with local accumulation in the stagnant meteorological conditions, whereas the RR site is dominated by the regional transport of PM2.5 with strong winds and vertical mixing. It was revealed that the large PM2.5 contributions of ICP BB emissions lift from the lower altitudes in SR to the higher altitudes in RR for the regional transport of PM2.5. Moreover, the contributions of regional transport of PM2.5 decrease with the increase in transport distance, reflecting an important role of transport distance between the source–receptor areas in air pollution change.

The 8 January 2022 Mw6.7 Menyuan earthquake was generated in the transition zone between the western Lenglongling fault and the eastern Tuolaishan fault, both being part of the Qilian–Haiyuan fault system with an important role in the adjustment of the regional tectonic regime. In this study, four pairs of SAR (synthetic aperture radar) data from Sentinel-1 and ALOS-2 (Advanced Land Observation Satellite-2) satellites were used to derive the surface displacement observations along the satellite line-of-sight (LOS) and azimuth directions using the differential interferometric SAR (InSAR, DInSAR), pixel offset-tracking (POT), multiple aperture InSAR (MAI), and burst overlap InSAR (BOI) methods. An SM-VCE method (i.e., a method for measuring three-dimensional (3D) surface displacements with InSAR based on a strain model and variance component estimation) was employed to combine these derived SAR displacement observations to calculate the 3D co-seismic displacements. Results indicate that the 2022 Menyuan earthquake was dominated by left-lateral slip, and the maximum horizontal and vertical displacements were 1.9 m and 0.6 m, respectively. The relative horizontal surface displacement across the fault was as large as 2–3 m, and the fault-parallel displacement magnitude was larger on the southern side of the fault compared with the northern side. Furthermore, three co-seismic strain invariants were also investigated, revealing that the near-fault area suffered severe deformation, and two obviously expanding and compressed zones were identified. We provide displacements/strains derived in this study in the prevailing geotiff format, which will be useful for the broad community studying this earthquake; in addition, the SM-VCE code used in this study is open to the public so that readers can better understand the method.

It is crucial for a ballistic missile defense system to discriminate the true warhead from decoys. Although a decoy has a similar shape to the warhead, it is believed that the true warhead can be separated by its micro-Doppler features introduced by the precession and nutation. As is well known, the accuracy of the phase-derived range method, to extract micro-Doppler curves, can reach sub-wavelength. However, it suffers from an inefficiency of energy integration and high computational costs. In this paper, a novel phase-derived range method, using high-order multi-frame track-before-detect is proposed for micro-Doppler curve extraction under a low signal-to-noise ratio (SNR). First, the sinusoidal micro-Doppler range sequence is treated as the state, and the dynamic model is described as a Markov chain to obtain the envelopes and then the ambiguous phases. Instead of processing the whole frames, the proposed method only processes the latest frame at an arbitrary given time, which reduces the computational costs. Then, the correlation of all pairs of adjacent pulses is calculated along the slow time dimension to find the number of cells that the point scatterer crosses, which can be further used in phase unwrapping. Finally, the phase-derived range method is employed to get the micro-Doppler curves. Simulation results show that the proposed method is capable of extracting the micro-Doppler curves with sub-wavelength accuracy, even if SNR = −15 dB, with a lower computational cost.

Micro-motion parameters extraction is crucial in recognizing ballistic missiles with a wideband radar. It is known that the phase-derived range (PDR) method can provide a sub-wavelength level accuracy. However, it is sensitive and unstable when the signal-to-noise ratio (SNR) is low. In this paper, an improved PDR method is proposed to reduce the impacts of low SNRs. First, the high range resolution profile (HRRP) is divided into a series of segments so that each segment contains a single scattering point. Then, the peak values of each segment are viewed as non-stationary signals, which are further decomposed into a series of intrinsic mode functions (IMFs) with different energy, using the ensemble empirical mode decomposition with the complementary adaptive noise (EEMDCAN) method. In the EEMDCAN decomposition, positive and negative adaptive noise pairs are added to each IMF layer to effectively eliminate the mode-mixing phenomenon that exists in the original empirical mode decomposition (EMD) method. An energy threshold is designed to select proper IMFs to reconstruct the envelop for high estimation accuracy and low noise effects. Finally, the least-square algorithm is used to do the ambiguous phases unwrapping to obtain the micro-curve, which can be further used to estimate the micro-motion parameters of the warhead. Simulation results show that the proposed method performs well with SNR at −5 dB with an accuracy level of sub-wavelength.

The continuous melting of valley glaciers can impact the water levels of glacial lakes and create glacial lake outburst floods (GLOFs). The Xixabangma massif is one of the most populated areas in the Himalayas and has suffered from multiple GLOFs. To estimate the glacier melting rate in the past four decades and analyze the outburst risk of glacial lakes in the Xixabangma massif, we determined changes in glacier mass balance, glacier area and glacial lake area based on KH-9 images, TanDEM-X images, Landsat images, SRTM DEM and ICESat-2 elevations. Our results show that, from 1974 to 2018, the total glacier area shrank from 954.01 km2 to 752.46 km2, whereas the total glacial lake area grew from 20.90 km2 to 38.71 km2. From 1974 to 2000, 2000 to 2013 and 2013 to 2018, the region-wide glacier mass balance values were −0.16 m w.e./a, −0.31 m w.e./a and −0.29 m w.e./a, respectively. Three glacial lakes, named Gangxico, Galongco and Jialongco, respectively, expanded by 127.14%, 373.45% and 436.36% from 1974 to 2018, and the mass loss rates of their parent glaciers from 2000 to 2013 increased by 81.72%, 122.22% and 160.00% relative to those during 1974 to 2000. The dams of these three lakes are unstable, and their drainage valleys directly connect to a major town and its infrastructure. Due to current high-water levels, possible external events such as ice collapse, landslide, heavy rainfall and earthquakes can easily trigger GLOFs. Hence, we deemed that the Gangxico, Galongco and Jialongco glacial lakes are dangerous and require special attention.

As a by-product of Interferometric Synthetic Aperture Radar (SAR, InSAR) technique, interferometric coherence is a measure of the decorrelation noise for InSAR observation, where the lower the coherence value, the more serious the decorrelation noise. In the densely vegetated area, the coherence value could be too low to obtain any valuable signals, leading to the degradation of InSAR performance and the possible waste of expensive SAR data. Normalized Difference Vegetation Index (NDVI) value is a measure of the vegetation coverage and can be estimated from the freely available optical satellite images. In this paper, a multi-stage model is established to quantitatively estimate the decorrelation noise for vegetable areas based on Landsat-derived NDVI prior to the acquisition of SAR data. The modeling process is being investigated with the L-band ALOS-1/PALSAR-1 data and the Landsat-5 optical data acquired in the Meitanba area of Hunan Province, China. Furthermore, the reliability of the established model is verified in the Longhui area, which is situated near the Meitanba area. The results demonstrate that the established model can quantitatively estimate InSAR decorrelation associated with the vegetation coverage.

The Interferometric Synthetic Aperture Radar (InSAR) technique has been widely used to obtain the ground surface deformation of geohazards (e.g., mining subsidence and landslides). As one of the inherent errors in the interferometric phase, the digital elevation model (DEM) error is usually estimated with the help of an a priori deformation model. However, it is difficult to determine an a priori deformation model that can fit the deformation time series well, leading to possible bias in the estimation of DEM error and the deformation time series. In this paper, we propose a method that can construct an adaptive deformation model, based on a set of predefined functions and the hypothesis testing theory in the framework of the small baseline subset InSAR (SBAS-InSAR) method. Since it is difficult to fit the deformation time series over a long time span by using only one function, the phase time series is first divided into several groups with overlapping regions. In each group, the hypothesis testing theory is employed to adaptively select the optimal deformation model from the predefined functions. The parameters of adaptive deformation models and the DEM error can be modeled with the phase time series and solved by a least square method. Simulations and real data experiments in the Pingchuan mining area, Gaunsu Province, China, demonstrate that, compared to the state-of-the-art deformation modeling strategy (e.g., the linear deformation model and the function group deformation model), the proposed method can significantly improve the accuracy of DEM error estimation and can benefit the estimation of deformation time series.

The Antarctic is one of the most sensitive areas to climate change, and ice velocity is a fundamental parameter for quantitatively assessing the glacier mass balance. Interferometric synthetic aperture radar (InSAR), a powerful tool for monitoring surface deformation with the advantages of having high precision and wide coverage, has been widely used in determining ice velocity in the Antarctic. However, the mapping of complete three-dimensional (3D) ice velocities is greatly limited by the imaging geometries and digital elevation model (DEM)-induced errors. In this study, we propose the integration of multibaseline and multiaperture InSAR measurements from the ENVISAT ASAR datasets to derive complete 3D ice velocities in the Grove Mountains area of the Antarctic. The results show that the estimated complete 3D ice velocities are in good agreement with MEaSUREs and GPS observations. Compared with the conventional 2D and quasi-3D ice velocities, the complete 3D ice velocities can effectively eliminate the effects of DEM errors and elevation changes and are also capable of retrieving the thickness change of the ice, which provides important information on the origin of mass transition.

As an essential parameter in synthetic aperture radar (SAR) images, the equivalent number of looks (ENL) not only indicates the speckle noise level in multi-look SAR data but also can be used for evaluating the region homogeneity level. Currently, time-series polarimetric (interferometric) SAR (TSPol(In)SAR) data are increasingly abundant, but traditional equivalent number of looks (ENL) estimators only use polarimetric information from a mono-temporal observation and do not consider the temporal characteristics or interferometric coherence of ground targets. Therefore, this paper puts forward four novel ENL estimators to overcome the restrictions of inadequate observation information. Firstly, based on the traditional trace moment estimator for polarimetric SAR data (TM-PolSAR), we extend it to both PolInSAR and TSPolInSAR data and then propose both TM-PolInSAR and TM-TSPolInSAR estimators, respectively. Secondly, for both TSPolSAR and single-reference TSPolInSAR data, we estimate the ENL by stacking the trace moments (STM) of multitemporal coherency matrices, called STM-TSPolSAR and STM-TSPolInSAR estimators, respectively. Therefore, these proposed ENL estimators can effectively deal with most of the requirements of TSPol(In)SAR data types in practical applications, mainly including statistical distribution modeling and region homogeneity evaluation. The simulation and real experiments detailedly compare the proposed four ENL estimators to the classical TM-PolSAR estimator and quantitatively analyze the estimation performance. The proposed estimators have obtained the ENL with less bias and standard deviation than the traditional estimator, especially in case of small spatial samples coherency matrices. Additionally, these STM-TSPolSAR, STM-TSPolInSAR, and TM-TSPolInSAR estimators have provided more effective statistical characteristics with the increase of the time-series size. It has been demonstrated that the proposed STM-TSPolSAR estimator considers the time-varying polarimetric characteristics of the crop and detects many edges that the traditional estimator cannot discover, which means a superior capability of region homogeneity evaluation.

Monitoring slope instability is of great significance for understanding landslide kinematics and, therefore, reducing the related geological hazards. In recent years, interferometric synthetic aperture radar (InSAR) has been widely applied to this end, especially thanks to the prompt evolution of multi-temporal InSAR (MTInSAR) algorithms. In this paper, temporarily-coherent point InSAR (TCPInSAR), a recently-developed MTInSAR technique, is employed to investigate the slow-moving landslides in Oso, U.S., with 13 ALOS/PALSAR images. Compared to other MTInSAR techniques, TCPInSAR can work well with a small amount of data and is immune to unwrapping errors. Furthermore, the severe orbital ramps emanated from the inaccurate determination of the ALOS satellite’s state vector can be jointly estimated by TCPInSAR, resulting in an exhaustive separation between the orbital errors and displacement signals. The TCPInSAR-derived deformation map indicates that the riverside slopes adjacent to the North Fork of the Stillaguamish River, where the 2014 mudslide occurred, were active during 2007 and 2011. Besides, Coal Mountain has been found to be experiencing slow-moving landslides with clear boundaries and considerable magnitudes. The Deer Creek River is also threatened by a potential landslide dam due to the creeps detected in a nearby slope. The slope instability information revealed in this study is helpful to deal with the landslide hazards in Oso.

Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technique has proven to be a powerful tool for the monitoring of time-series ground deformations along the line-of-sight (LOS) direction. However, the one-dimensional (1-D) measurements cannot provide comprehensive information for interpreting the related geo-hazards. Recently, a novel method has been proposed to map the three-dimensional (3-D) deformation associated with underground fluid flows based on single-track InSAR LOS measurements and the deformation modeling associated with the Green’s function. In this study, the method is extended in temporal domain by exploiting the MT-InSAR measurements, and applied for the first time to investigate the 3-D time series deformation over Sebei gas field in Qinghai, Northwest China with 37 Sentinel-1 images acquired during October 2014–July 2017. The estimated 3-D time series deformations provide a more complete view of ongoing deformation processes as compared to the 1-D time series deformations or the 3-D deformation velocities, which is of great importance for assessing the possible geohazards. In addition, the extended method allows for the retrieval of time series of fluid volume changes due to the gas extraction in the Sebei field, which agrees well with those from the PetroChina Qinghai Oilfield Company Yearbooks (PQOCYs). This provides a new way to study the variations of subsurface fluids at unprecedented resolution.

Due to the side-looking imaging geometry of the current synthetic aperture radar (SAR) sensors, only ground deformation along the radar’s line-of-sight (LOS) and azimuth directions can be potentially obtained from a single amplitude pair (SAP) of SAR using offset tracking (OT) procedures. This significantly hinders the accurate assessment of mining-related hazards and better understanding of the mining subsidence mechanism. In this paper, we propose a method for completely retrieving three-dimensional (3-D) mining-induced displacements with OT-derived observations of LOS deformation from a single amplitude pair of SAR (referred to as OT-SAP hereinafter). The OT-SAP method first constructs two extra constraints at each pixel of the mining area based on the proportional relationship between the horizontal motion of the mining area and the gradients of the vertical subsidence in the east and north directions. The full 3-D mining-induced displacements are then solved by coupling the two constructed extra constraints with the OT-derived observations of the LOS deformation. The Daliuta coal mining area in China was selected to test the proposed OT-SAP method. The results show that the maximum 3-D displacements of this mining area were about 4.3 m, 1.1 m, and 1.3 m in the vertical, east, and north directions, respectively, from 21 November 2012 to 6 February 2013. The accuracies of the retrieved displacements in the vertical and horizontal directions are about 0.201 m and 0.214 m, respectively, which are much smaller than the mining-induced displacements in this mining area and can satisfy the basic requirements of mining deformation monitoring.

The seasonal variation of land cover and the large deformation gradients in coal mining areas often give rise to severe temporal and geometrical decorrelation in interferometric synthetic aperture radar (InSAR) interferograms. Consequently, it is common that the available InSAR pairs do not cover the entire time period of SAR acquisitions, i.e., temporal gaps exist in the multi-temporal InSAR observations. In this case, it is very difficult to accurately estimate mining-induced dynamic subsidence using the traditional time-series InSAR techniques. In this investigation, we employ a logistic model which has been widely applied to describe mining-related dynamic subsidence, to bridge the temporal gaps in multi-temporal InSAR observations. More specifically, we first construct a functional relationship between the InSAR observations and the logistic model, and we then develop a method to estimate the model parameters of the logistic model from the InSAR observations with temporal gaps. Having obtained these model parameters, the dynamic subsidence can be estimated with the logistic model. Simulated and real data experiments in the Datong coal mining area, China, were carried out in this study, in order to test the proposed method. The results show that the maximum subsidence in the Datong coal mining area reached about 1.26 m between 1 July 2007 and 28 February 2009, and the accuracy of the estimated dynamic subsidence is about 0.017 m. Compared with the linear and cubic polynomial models of the traditional time-series InSAR techniques, the accuracy of dynamic subsidence derived by the logistic model is increased by about 50.0% and 45.2%, respectively.

Although the past few decades have witnessed the great development of Synthetic Aperture Radar Interferometry (InSAR) technology in the monitoring of landslides, such applications are limited by geometric distortions and ambiguity of 1D Line-Of-Sight (LOS) measurements, both of which are the fundamental weakness of InSAR. Integration of multi-sensor InSAR datasets has recently shown its great potential in breaking through the two limits. In this study, 16 ascending images from the Advanced Land Observing Satellite (ALOS) and 18 descending images from the Environmental Satellite (ENVISAT) have been integrated to characterize and to detect the slow-moving landslides in Zhouqu, China between 2008 and 2010. Geometric distortions are first mapped by using the imaging geometric parameters of the used SAR data and public Digital Elevation Model (DEM) data of Zhouqu, which allow the determination of the most appropriate data assembly for a particular slope. Subsequently, deformation rates along respective LOS directions of ALOS ascending and ENVISAT descending tracks are estimated by conducting InSAR time series analysis with a Temporarily Coherent Point (TCP)-InSAR algorithm. As indicated by the geometric distortion results, 3D deformation rates of the Xieliupo slope at the east bank of the Pai-lung River are finally reconstructed by joint exploiting of the LOS deformation rates from cross-heading datasets based on the surface–parallel flow assumption. It is revealed that the synergistic results of ALOS and ENVISAT datasets provide a more comprehensive understanding and monitoring of the slow-moving landslides in Zhouqu.

In this study, we mapped the co-seismic deformation of the 2015 Mw 8.3 Illapel, Chile earthquake with multiple Sentinel-1A TOPS data frames from both ascending and descending geometries. To meet the requirement of very high co-registration precision, an improved spectral diversity method was proposed to correct the co-registration slope error in the azimuth direction induced by multiple Sentinel-1A TOPS data frames. All phase jumps that appear in the conventional processing method have been corrected after applying the proposed method. The 2D deformation fields in the east-west and vertical directions are also resolved by combing D-InSAR and Offset Tracking measurements. The results reveal that the east-west component dominated the 2D displacement, where up to 2 m displacement towards the west was measured in the coastal area. Vertical deformations ranging between −0.25 and 0.25 m were found. The 2D displacements imply the collision of the Nazca plate squeezed the coast, which shows good accordance with the geological background of the region.

Ground deformation contains important information that can be exploited to look into the dynamics of a geothermal system. In recent years, InSAR has manifested its strong power in the monitoring of ground deformation. In this paper, a multi-temporal InSAR algorithm, WLS InSAR, is employed to monitor and characterize the Yangbajing geothermal field in Tibet, China, using 51 ENVISAT/ASAR images acquired from two overlapping descending tracks. The results reveal that the WLS InSAR algorithm can suppress the adverse effects of seasonal oscillations, associated with the freezing-thawing cycle of the permafrost in the Qinghai-Tibet Plateau. Deformations of up to 2 cm/yr resulting from the exploitation of the geothermal resource have been detected in the southern part of the Yangbajing field between 2006 and 2010. A source model inversion of the subsurface geothermal fluids was carried out based on the elastic half-space theory using the accumulated deformations. It was found that most geothermal fluid loss has occurred in the southern part of the shallow reservoir as the pore space beneath the northern part of field was recharged by the ascending flow from the deep layers of the reservoir through well-developed faults in the region. [ABSTRACT FROM AUTHOR]

Monitoring slope instability is of great significance for understanding landslide kinematics and, therefore, reducing the related geological hazards. In recent years, interferometric synthetic aperture radar (InSAR) has been widely applied to this end, especially thanks to the prompt evolution of multi-temporal InSAR (MTInSAR) algorithms. In this paper, temporarily-coherent point InSAR (TCPInSAR), a recently-developed MTInSAR technique, is employed to investigate the slow-moving landslides in Oso, U.S., with 13 ALOS/PALSAR images. Compared to other MTInSAR techniques, TCPInSAR can work well with a small amount of data and is immune to unwrapping errors. Furthermore, the severe orbital ramps emanated from the inaccurate determination of the ALOS satellite's state vector can be jointly estimated by TCPInSAR, resulting in an exhaustive separation between the orbital errors and displacement signals. The TCPInSAR-derived deformation map indicates that the riverside slopes adjacent to the North Fork of the Stillaguamish River, where the 2014 mudslide occurred, were active during 2007 and 2011. Besides, Coal Mountain has been found to be experiencing slow-moving landslides with clear boundaries and considerable magnitudes. The Deer Creek River is also threatened by a potential landslide dam due to the creeps detected in a nearby slope. The slope instability information revealed in this study is helpful to deal with the landslide hazards in Oso. [ABSTRACT FROM AUTHOR]