Integration of InSAR and LiDAR Technologies for a Detailed Urban Subsidence and Hazard Assessment in Shenzhen, China

An Enhanced Offset Tracking Method: Providing Auxiliary Information for DInSAR Phase Filtering in Urban Areas

In the application of synthetic aperture radar differential interferometry in urban environments, it is easy to regard the phase change in the deformation band of buildings under construction as noise that requires filtering. This introduces an error into the surrounding area while over-filtering, resulting in an error in the magnitude of the deformation measurement results for the entire region and the loss of deformation details in the surrounding area. Based on the traditional DInSAR workflow, this study added a deformation magnitude identification step, determined the deformation magnitude by using enhanced offset tracking technology, supplemented the filtering quality map and removed the construction areas that affect the interferometry in the filtering stage. The enhanced offset tracking technique adjusted the ratio of contrast saliency and coherence via the contrast consistency peak in the radar intensity image, which was used as the basis for adjusting the adaptive window size. The method proposed in this paper was evaluated in an experiment on a stable region using simulated data and in an experiment on a large deformation region using Sentinel-1 data. The experimental results show that the enhanced method has a better anti-noise ability than the traditional method, and the accuracy rate is improved by about 12%. The supplemented quality map can effectively remove the large deformation area to prevent over-filtering while ensuring the filtering quality, and it can achieve better filtering results.

Land Subsidence Monitoring Method in Regions of Variable Radar Reflection Characteristics by Integrating PS-InSAR and SBAS-InSAR Techniques

In the InSAR solution, the uneven distribution of permanent scatterer candidates (PSCs) or slowly decoherent filtering phase (SDFP) pixel density in a region of variable radar reflection feature can cause local low accuracy in single interferometry. PSCs with higher-order coherence in Permanent Scatter InSAR (PS-InSAR) are generally distributed in those point targets of urban built-up areas, and SDFP pixels in Small Baseline Subset InSAR (SBAS-InSAR) are generally distributed in those distributed targets of countryside vegetation areas. According to the respective reliability of PS-InSAR and SBAS-InSAR for different radar reflection features, a new land subsidence monitoring method is proposed, which combines PS-SBAS InSAR by data fusion of different interferometry in different radar reflection regions. Density-based spatial clustering of applications with noise (DBSCAN) clustering analysis is carried out on the density of PSCs with higher-order coherence in PS-InSAR processing to zone the region of variable radar reflection features for acquiring the boundary of data fusion. The vector monitoring data of PS-InSAR is retained in the dense region of PSCs with higher-order coherence, and the vector monitoring data of SBAS-InSAR is used in the sparse region of PSCs with higher-order coherence. The vertical displacements from PS-InSAR and SBAS-InSAR are integrated to obtain the optimal land subsidence. The verification case of 38 SAR images acquired by the Sentinel-1A in Suzhou city indicates that the proposed method can automatically choose a matched interferometry technique according to the variability of radar reflection features in the region and improve the accuracy of using a single interferometry method. The integrated method of the combined field is more representative of overall subsidence characteristics than the PS-InSAR-only or SBAS-InSAR-only results, and it is better suited for the assessment of the impact of land subsidence over the study area. The research results of this paper can provide a useful comprehensive reference for city planning and help decrease land subsidence in Suzhou.

Land Subsidence in Qingdao, China, from 2017 to 2020 Based on PS-InSAR

Land subsidence is a global geological disaster that seriously affects the safety of surface and underground buildings/structures and even leads to loss of life and property. The large-scale and continuous long-time coverage of Interferometric Synthetic Aperture Radar (InSAR) time series analysis techniques provide data and a basis for the development of methods for the investigation and evolution mechanism study of regional land subsidence. Based on the 108 SAR data of Sentinel-1 from April 2017 to December 2020, this study used Persistent Scatterer InSAR (PS-InSAR) technology to monitor the land subsidence in Qingdao. In addition, detailed analysis and discussion of land subsidence combined with the local land types and subway construction were carried out. From the entire area to the local scale, the deformation analysis was carried out in the two dimensions of time and space. The results reveal that the rate of surface deformation in Qingdao from 2017 to 2020 was mainly −34.48 to 5.77 mm/a and that the cumulative deformation was mainly −126.10 to 30.18 mm. The subsidence areas were mainly distributed in coastal areas (along the coasts of Jiaozhou Bay and the Yellow Sea) and inland areas (northeast Laixi City and central Pingdu City). In addition, it was found that obvious land subsidence occurred near the Health Center Station of Metro Line 8, a logistics company in Qingdao, and near several high-rise residential areas and business office buildings. It is necessary for the relevant departments to take timely action to prevent and mitigate subsidence-related disasters in these areas.

Constructing a Large-Scale Urban Land Subsidence Prediction Method Based on Neural Network Algorithm from the Perspective of Multiple Factors

The existing neural network model in urban land-subsidence prediction is over-reliant on historical subsidence data. It cannot accurately capture or predict the fluctuation in the sequence deformation, while the improper selection of training samples directly affects its final prediction accuracy for large-scale urban land subsidence. In response to the shortcomings of previous urban land-subsidence predictions, a subsidence prediction method based on a neural network algorithm was constructed in this study, from a multi-factorial perspective. Furthermore, the scientific selection of a large range of training samples was controlled using a K-shape clustering algorithm in order to produce this high-precision urban land subsidence prediction method. Specifically, the main urban area of Kunming city was taken as the research object, LiCSBAS technology was adopted to obtain the information on the land-subsidence deformation in the main urban area of Kunming city from 2018–2021, and the relationship between the land subsidence and its influencing factors was revealed through a grey correlation analysis. Hydrogeology, geological structure, fault, groundwater, high-speed railways, and high-rise buildings were selected as the influencing factors. Reliable subsidence training samples were obtained by using the time-series clustering K-shape algorithm. Particle swarm optimization–back propagation (PSO-BP) was constructed from a multi-factorial perspective. Additionally, after the neural network algorithm was employed to predict the urban land subsidence, the fluctuation in the urban land-subsidence sequence deformation was predicted with the LSTM neural network from a multi-factorial perspective. Finally, the large-scale urban land-subsidence prediction was performed. The results demonstrate that the maximum subsidence rate in the main urban area of Kunming reached −30.591 mm a−1 between 2018 and 2021. Moreover, there were four main significant subsidence areas in the whole region, with uneven distribution characteristics along Dianchi: within the range of 200–600 m from large commercial areas and high-rise buildings, within the range of 400–1200 m from the under-construction subway, and within the annual average. The land subsidence tended to occur within the range of 109–117 mm of annual average rainfall. Furthermore, the development of faults destroys the stability of the soil structure and further aggravates the land subsidence. Hydrogeology, geological structure, and groundwater also influence the land subsidence in the main urban area of Kunming. The reliability of the training sample selection can be improved by clustering the subsidence data with the K-shape algorithm, and the constructed multi-factorial PSO-BP method can effectively predict the subsidence rate with a mean squared error (MSE) of 4.820 mm. The prediction accuracy was slightly improved compared to the non-clustered prediction. We used the constructed multi-factorial long short-term memory (LSTM) model to predict the next ten periods of any time-series subsidence data in the three types of cluster data (Cluster 1, Cluster 2, and Cluster 3).The root mean square errors (RMSE) were 0.445, 1.475, and 1.468 mm; the absolute error ranges were 0.007–1.030, 0–3.001, and 0.401–3.679 mm; the errors (mean absolute error, MAE) were 0.319, 1.214, and 1.167 mm, respectively. Their prediction accuracy was significantly improved, and the predictions met the measurement specifications. Overall, the prediction method proposed from the multi-factorial perspective improves large-scale, high-accuracy urban land-subsidence prediction.

Building Extraction from Airborne LiDAR Data Based on Multi-Constraints Graph Segmentation

Building extraction from airborne Light Detection and Ranging (LiDAR) point clouds is a significant step in the process of digital urban construction. Although the existing building extraction methods perform well in simple urban environments, when encountering complicated city environments with irregular building shapes or varying building sizes, these methods cannot achieve satisfactory building extraction results. To address these challenges, a building extraction method from airborne LiDAR data based on multi-constraints graph segmentation was proposed in this paper. The proposed method mainly converted point-based building extraction into object-based building extraction through multi-constraints graph segmentation. The initial extracted building points were derived according to the spatial geometric features of different object primitives. Finally, a multi-scale progressive growth optimization method was proposed to recover some omitted building points and improve the completeness of building extraction. The proposed method was tested and validated using three datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). Experimental results show that the proposed method can achieve the best building extraction results. It was also found that no matter the average quality or the average F1 score, the proposed method outperformed ten other investigated building extraction methods.