An airport is a critical infrastructure in disaster relief, and a strong earthquake inevitably affects an airport near the epicenter. The repair of quake-induced airport damage and the airport surface deformation caused by the quake still needs to be understood to maintain its safe operation. In this study, the differential interferometric synthetic aperture radar (D-InSAR) technique was used to assess the surface deformation of Jiuzhai-Huanglong Airport and Chengdu Shuangliu International Airport with PALSAR data after the 2008 Wenchuan quake (Mw= 7.9) of China. The earthquake affected both airports. The influence on the surface deformation of Jiuzhai-Huanglong Airport is greater, although it is farther from the epicenter. The causeresult is closely related to the geological environments (e.g., setting and structure in strata) of the location and the earthwork in the airport construction process.

Due to the rugged terrain and high seismic activities in the Qinghai-Tibet Plateau region in western China, highway and railroad track construction is challenging. The airport is alternatively a critical transportation infrastructure. To create a flat area large enough for an airport, one has to fill/remove a significant amount of earth materials. The frequency and intensity of earthquakes are high in this area as well. Thus, it is significant to study the different effects of seismic activities on a high-filling/removing airport compared to nearly a non-filling/removing airport. With the small baseline subset (SBAS) InSAR technique and multi-temporal Sentinel-1 SAR data, we studied the impact of the 2017 Jiuzhaigou earthquake (Mw= 7.0) on the surface deformation near two airport areas. One (i.e., Jiuzhai-Huanglong Airport) is high-filling/removing, and the other (Aba-Hongyuan Airport) is nearly nonfilling/removing. Qualitative and quantitative comparative analyses show that seismic activities greatly influence the high-filling/removing earth materials airport.

The phase unwrapping (PU) for Permanent Scatterers (PS) is a key step to obtaining accurate urban mapping results (e.g., a height of a high-rise building) in the time-series InSAR workflow. The phase continuity assumption restricts the existing single-baseline (SB) PU algorithm, so it cannot obtain accurate results in an urban area with high-rise buildings. At the same time, the multi-baseline (MB) method has poor noise robustness for no use of global interferometric phase information. Here, an MB PU algorithm is proposed based on the two-stage programming approach (TSPA) and the general SB PU workflow for the sparse permanent scatterers. Then, we implemented the proposed method into the PSInSAR procedure and estimated building heights in urban areas of southern Chengdu, China. Satisfactory results were obtained for buildings with various heights as assessed by the Google Earth®image and in situ height measurements. Thus, the developed method is valid and effective in building height retrievals in urban areas.

Two-dimensional (2-D) phase unwrapping (PU) is an essential step in interferometric synthetic aperture radar (InSAR) analysis. Although theL0-norm PU method is desired, it is nondeterministic polynomial (NP)-hard. Thus, many PU methods have been proposed to find near-optimal solutions of theL0-norm. As PU is an ill-posed problem, it is difficult to choose the method with the most accurate solution unless reference data are available. In this letter, we prove that the NP-hardness of theL0-norm is weak, suggesting that the α-approximation algorithm of theL0-norm can be devised, i.e., the obtained near-optimal solutions can be within a factor of α of theL0-norm optimal value. The primary contribution of the proof is that the α value of each obtained PU solution can be considered as a new index to assess the PU performance without using reference data. The validity and effectiveness of the α-based index have been verified using simulated and acquired interferometric datasets and three PU methods.

The multi-baseline (MB) interferometric synthetic aperture radar (InSAR) technique is not subjected to the Itoh condition, i.e., phase continuity. The technique is particularly suitable to unwrap the wrapped interferometric phases and create a digital elevation model (DEM) in rugged terrain, where the permanent scatterers (PS) are usually scarce. This study shows that the technique satisfactorily unwrapped the wrapped phases with sparsely distributed PS and created a DEM for an area in Himalaya Mountain Range, China. In comparing three DEMs, two created by the single-baseline InSAR approach and one by the MB InSAR technique, the technique outputs the best DEM, as evaluated with a reference DEM.

A polarimetric interferometric synthetic aperture radar (PolInSAR) approach can estimate tree heights, but underestimation happens. There is a need to mitigate the underestimating. With the Freeman-Durden polarimetric SAR (PolSAR) decomposition algorithm, one identifies the volume, single, and double scattering components from PolSAR data. Here, the PolSAR decomposition and PolInSAR techniques are combined to estimate a tree height. The decomposed volume scattering phase center replaces the canopy phase center and the double scattering phase center the ground phase center. The tree height is then calculated using the phase difference of the two phase centers and effective vertical wavenumber. Using simulated PolSAR datasets, the algorithm combining the decomposition and PolInSAR approaches output tree heights with an average of 8.6 m and one standard deviation of 2.1 m. The mean and one standard deviation of tree heights are 5.1 m and 2.4 m after the PolInSAR technique. Thus, an improvement of 3.5 m in the mean height is achieved.

The surface deformation of the eastern section of the third-ring bypass as well as its surrounding areas, Chengdu, China was studied. The Permanent Scatterer (PS) interferometric synthetic aperture radar (InSAR) technique and Sentinel-1 multi-temporal datasets acquired along the ascending and descending orbits were used. With sixty-two ascending orbit datasets (from July of 2015 to May of 2018) and sixty-nine descending orbit datasets (between October of 2014 and May of 2018), the subsidence occurred in the study area mainly. The overall spatial patterns of the deformation derived from the ascending and descending SAR observations were similar. Five locations with significant subsidence were identified and analyzed. The causes for the subsidence were attributed to the construction and the operation of the subway system and highway overpasses, and the local commercial activities.