Interferometric synthetic aperture radar (InSAR) was applied to Sentinel-1 satellite imagery from 2015 to 2024 to monitor long-term deformation of various cross-river bridge types in Jiangsu Province, China. The results demonstrate that InSAR effectively captures cross-river bridge deformation, with monitoring efficacy influenced by geometric structures and material properties of the bridge. Continuous steel truss structures and steel bridge towers exhibit strong backscattering characteristics, enabling dense monitoring point distributions that finely depict periodic deformation characteristics. For cable-stayed and suspension bridges, deformation concentrates at the mid-span and attenuates toward both ends, with notable differences in cumulative deformation magnitude. Integrated temperature data analysis reveals pronounced thermal expansion and temperature shrinkage effects in continuous steel truss bridges, characterized by periodic deformation amplitudes increasing from the mid-span center to both ends. Thermal effects in cable-stayed and suspension bridges are predominantly localized at steel bridge towers.

The fundamental principles of Beidou navigation satellite system (BDS)/global navigation satellite system (GNSS) and interferometric synthetic aperture radar (InSAR) technology were outlined, with a focused review of their theoretical developments since the 21st Ccentury. The latest research advances in their integrated application for deformation monitoring were analyzed in depth. Core issues and potential challenges currently faced by the combined BDS/GNSS and InSAR technology in monitoring engineering deformation and ensuring safety prevention for high-steep slopes were summarized. It is suggested that this integrated approach can achieve centimeter-level accuracy while enabling full-area coverage, significantly enhancing the capability for high-steep slope deformation monitoring. Furthermore, it is pointed out that future efforts should focus on deepening the integration of BDS and domestic SAR satellite data based on an integrated space-air-ground monitoring network, alongside the development of industry-specific large language models tailored for high-steep slope monitoring.

River embankments are designed to defend against floods over coastal and riparian areas. It is important to early detect unexpected damages on embankments before they exacerbate. To continuously monitor the stability of the embankments and efficiently recognize such potential damages, this study takes SAR (Synthetic Aperture Radar) derived deformation as an indicator of the embankment instability, and customizes a multi-temporal InSAR (Interferometric SAR) approach-small baseline subset. Specifically, during InSAR processing, we apply a two-step amplitude difference dispersion threshold method to extract InSAR measurement points, thus improving the point density within the embankment. We applied this method to the Kangshan Embankment (KE) using 147 Sentinel-1 acquired between 2017 and 2021. We categorized KE into Waterside Slope (WS), Embankment Top (ET), and Landside Slope (LS) using InSAR height estimation. Given the dominance of downslope movement, we developed a projection matrix from InSAR-derived deformation in the satellite line-of-sight direction onto WS and LS. The study shows that KE was generally stable during the five-year period, while WS, ET, and LS experienced different deformation processes. For instance, seasonal variation was observed from the deformation time series, especially between every April and November. We applied the principal component analysis to the time-series displacement and analyzed the results in conjunction with the rainfall data of Kangshan Township. It showed that deformation due to rainfall equals 80.93%, 81.30%, and 82.46% of the total deformation for WS, ET, and LS, respectively, indicating that rainfall is one of the environmental driving factors affecting the deformations. We conclude that the proposed methodology is suited for systematic embankment monitoring and identifies major driving forces.

As a consumed and influential natural plant beverage, tea is widely planted in subtropical and tropical areas all over the world. Affected by (sub) tropical climate characteristics, the underlying surface of the tea distribution area is extremely complex, with a variety of vegetation types. In addition, tea distribution is scattered and fragmentized in most of China. Therefore, it is difficult to obtain accurate tea information based on coarse resolution remote sensing data and existing feature extraction methods. This study proposed a boundary-enhanced, object-oriented random forest method on the basis of high-resolution GF-2 and multi-temporal Sentinel-2 data. This method uses multispectral indexes, textures, vegetable indices, and variation characteristics of time-series NDVI from the multi-temporal Sentinel-2 imageries to obtain abundant features related to the growth of tea plantations. To reduce feature redundancy and computation time, the feature elimination algorithm based on Mean Decrease Accuracy (MDA) was used to generate the optimal feature set. Considering the serious boundary inconsistency problem caused by the complex and fragmented land cover types, high resolution GF-2 image was segmented based on the MultiResolution Segmentation (MRS) algorithm to assist the segmentation of Sentinel-2, which contributes to delineating meaningful objects and enhancing the reliability of the boundary for tea plantations. Finally, the object-oriented random forest method was utilized to extract the tea information based on the optimal feature combination in the Jingmai Mountain, Yunnan Province. The resulting tea plantation map had high accuracy, with a 95.38% overall accuracy and 0.91 kappa coefficient. We conclude that the proposed method is effective for mapping tea plantations in high heterogeneity mountainous areas and has the potential for mapping tea plantations in large areas.

Landslides are common hazards in reservoir areas and significantly affect dam operation and human lives. For the prevention and management of landslides, accurate assessment of the factors influencing their generation is essential. This study evaluated the key external factors influencing horizontal and vertical displacements of Luobogang Reservoir Slope in Hanyuan County, China. Displacements had been monitored by a surface-displacement-monitoring system consisting of 118 GPS stations during 2012–2015. To identify the external driving factors, their influence zones, and slope responses, we analyzed 32 months of displacement measurements and other multi-source datasets using the empirical orthogonal function. Overall, the results show that slope aging effect, rainfall, and reservoir water levels are three main driving factors. For horizontal displacement, aging effect is the most critical factor and predominantly affects the edges of landslides, the gob cave, and the public building zones. The secondary factor is the reservoir water level, which mainly acts on the boundary between the slope and reservoir water surface. The closer the slope zone is to the reservoir water, the more significant the impact is. Regarding vertical displacement, the most important factor is rainfall. The vertical displacement caused by rainfall accounts for 56.76% of the total vertical displacements. However, rainfall induces elastic displacements that generally cause less damage to the slope. The secondary factor is aging effect, and the vertical displacement caused by aging effect accounts for 9.42%. However, seven individual zones are highly affected by slope aging effect, which is consistent with the distribution of public buildings.

The Global Navigation Satellite Systems (GNSS) broadcast radio signals are continuously at two or more frequencies in the L-band, and the multipath signals from sea surface recorded by off-the-shelf geodetic receivers have been demonstrated they can be used to estimate sea level, using a technology called GNSS multipath reflectometry (GNSS-MR). Before proceeding to estimate reflection parameters, the azimuth range and elevation angle range are needed to be defined, as only with suitable azimuths and elevation angles the sensing zones can be guaranteed on water. So, this study presents an angle dependence analysis method to jointly select the azimuth range and elevation angle range based on wavelet analysis which can describe the non-stationary power of different sinusoidal oscillations changed with elevation angle. The key of this method is to use one grid model to screen the spectrum power of multipath oscillation on different elevation angles and azimuths in this work. Then the elevation angles and the azimuths can be determined by searching grids with greater power. The GPS and GLONASS data of two Multi-GNSS Experiment (MGEX) stations named BRST and MAYG was analyzed and used to retrieve. Firstly, the angle dependence analysis was carried out to determine the elevation range and azimuth range. Secondly, the sea levels were retrieved from individual signals. Finally, the retrievals of individual signals are combined to form a 10-min sea level retrieval series. The RMSEs of the combined retrievals are both less than 15 cm. The results show the effectiveness of the selection of angle range based on the angle dependence analysis method.

The expansion of research and applications of Global Navigation Satellite Systems (GNSS) has revealed the information of reflecting surface in inherent multipath errors. GNSS signals, usually used to measure position, have been demonstrated that they can be used to retrieve water properties including water level, soil moisture, snow depth, and vegetation water content, which are important for climate analysis and water resources monitoring. Reflected GNSS signals with different azimuths can carry information of the corresponding reflecting zone, which means every reflected signal has distinct “signal-to-noise ratio (SNR) characteristics” influenced by specific reflecting zones—and the parameter named “Reflector Height (RH)” deduced from SNR frequency is focused on in this study. Thus, after interpolation of a series of reflector height by coordinates of the footprint, products describing highly detailed terrain over a reflecting footprint can be produced. Data of three GNSS sites in EarthScope Plate Boundary Observatory, named P025, P351 and P101, was used to evaluate the terrain after calculating the terrain slopes and correcting the footprint following the slopes. A comparison of the results with a digital elevation model (DEM) showed that it is possible to retrieve terrain by GNSS-Interferometric Reflectometry (GNSS-IR); and the comparison with terrain slopes from DEMs in previous research also validated its potential.