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Spaceborne interferometric synthetic aperture radar (InSAR) techniques are important for landslide detection and monitoring; however, several limitations and uncertainties, such as the unique north–south flying direction and side-look radar observing geometry, currently limit the ability of InSAR to credibly detect landslides, especially those related to high and steep slopes. Here, we conducted experimental and statistical analysis on the feasibility of time-series InSAR monitoring for steep slopes using ascending and descending SAR images. First, the theoretical (TGNSS), practical (PGNSS), and terrain (Hterrain) (T-P-H) indices for sensitivity evaluations of the slope displacement monitoring results from time-series InSAR were proposed for slope monitoring. Subsequently, two experimental and statistical studies were conducted for the cases with and without Global Navigation Satellite System (GNSS) monitoring data. Our experimental results of two high and steep open-pit mines showed that the defined theoretical and practical sensitivity indices can quantitatively evaluate the feasibility of ascending and descending InSAR observations in steep-slope deformation monitoring with GNSS data, and the terrain sensitivity index can qualitatively evaluate the feasibility of landslide monitoring results from ascending and descending Sentinel-1 satellite data without GNSS data. We further demonstrate the generalizability of these proposed indices using four landslide cases with both public GNSS and InSAR monitoring data and 119 landslide cases with only InSAR monitoring data. The statistical results indicated that greater indices correlated with higher reliability of the monitoring results, suggesting that these novel indices have wide suitability and applicability. This study can help to improve the practice of slope deformation monitoring using spaceborne InSAR, especially for high and steep slopes. [ABSTRACT FROM AUTHOR]

The safety retaining wall is a critical infrastructure in ensuring the safety of both rock removal vehicles and personnel. However, factors such as precipitation infiltration, tire impact from rock removal vehicles, and rolling rocks can cause local damage to the safety retaining wall of the dump, rendering it ineffective in preventing rock removal vehicles from rolling down and posing a huge safety hazard. To address these issues, this study proposed a safety retaining wall health assessment method based on modeling and analysis of UAV point-cloud data of the safety retaining wall of a dump, which enables hazard warning for the safety retaining wall. The point-cloud data used in this study were obtained from the Qidashan Iron Mine Dump in Anshan City, Liaoning Province, China. Firstly, the point-cloud data of the dump platform and slope were extracted separately using elevation gradient filtering. Then, the point-cloud data of the unloading rock boundary was obtained via the ordered crisscrossed scanning algorithm. Subsequently, the point-cloud data of the safety retaining wall were extracted using the range constraint algorithm, and surface reconstruction was conducted to construct the Mesh model. The safety retaining wall mesh model was isometrically profiled to extract cross-sectional feature information and to compare the standard parameters of the safety retaining wall. Finally, the health assessment of the safety retaining wall was carried out. This innovative method allows for unmanned and rapid inspection of all areas of the safety retaining wall, ensuring the safety of rock removal vehicles and personnel. [ABSTRACT FROM AUTHOR]

In this study, the probability integral method, Synthetic Aperture Radar Interferometry (InSAR), and the Okada dislocation model were collaboratively used to analyze deformation in the Alhada Pb-Zn mine. The predicted deformation values of the subsidence centers in three subsidence areas were 107 mm, 120 mm, and 83 mm, respectively, as predicted using the probability integral method. The coherent scatterer InSAR technique was used to analyze the time-series deformation of the mining area, and the same subsidence center locations and similar deformation values were observed. The Okada dislocation model was used to invert the optimal parameters of the underground-mining ore body causing the surface subsidence, indicating that the surface subsidence is mainly caused by the mining of ore bodies in the 888 and 848 middle sections. We further simulated ground deformation using the multi-source Okada model. The results showed that the predicted and modeled deformations are highly correlated with the observed deformation. Through the analysis and comparison of the InSAR results, it was concluded that the three subsidence areas do not threaten the stability of the main buildings in the mining area. Using theoretical, observational, and modeling methods, the development and evolution of the subsidence area in mines can be established, which could provide basic data for subsidence control work and guarantee mine production safety. [ABSTRACT FROM AUTHOR]

Step-feature lines are one of the important geometrical elements for drawing the status quo maps of open-pit mines, and the efficient and accurate automatic extraction and updating of step-feature lines is of great significance for open-pit-mine stripping planning and analysis. In this study, an automatic extraction method of step-feature lines in an open-pit mine based on unmanned-aerial-vehicle (UAV) point-cloud data is proposed. The method is mainly used to solve the key problems, such as low accuracy, local-feature-line loss, and the discontinuity of the step-feature-line extraction method. The method first performs the regular raster resampling of the open-pit-mine cloud based on the MLS algorithm, then extracts the step-feature point set by detecting the elevation-gradient change in the resampled point cloud, further traces the step-feature control nodes by the seed-growth tracking algorithm, and finally generates smooth step-feature lines by fitting the space curve to the step-feature control nodes. The results show that the method effectively improves the accuracy of step-feature-line extraction and solves the problems of local-feature-line loss and discontinuity. [ABSTRACT FROM AUTHOR]

The durability design of reinforced concrete (RC) structures that are exposed to aggressive environmental attacks (e.g., corrosion due to chloride ingress in marine environment) plays a vital role in ensuring the structural serviceability within a reference period of interest. Existing approaches for the durability design and assessment of RC structures have, for the most part, not considered the spatial distribution of corrosion-related structural properties. In this paper, a closed-form approach is developed for durability assessment of RC structures, where the structural dimension, spatial variability, and correlation of structural properties such as the concrete cover thickness and the chloride diffusion coefficient are taken into account. The corrosion and crack initiations of an emerged tube tunnel segment that was used in the Hong Kong-Zhuhai-Macau bridge project were assessed to demonstrate the applicability of the proposed approach. The accuracy of the method was verified through a comparison with Monte Carlo simulation results based on two-dimensional random field modeling. The proposed method can be used to efficiently assess the durability performance of RC structures in the marine environment and has the potential to become an efficient tool to guide the durability design of RC structures subjected to corrosion. [ABSTRACT FROM AUTHOR]

To address the global phenomenon of the salinisation of large land areas, a quantitative inversion model of the salinity of saline soils and soil visible–near-infrared (NIR) spectral data was developed by considering saline soils in Zhenlai County, Jilin Province, China as the research object. The original spectral data were first subjected to Savitzky–Golay (SG) smoothing, multiplicative scattering correction (MSC) pre-processing, and a combined transformation technique. The pre-processed spectral data were then analysed to construct the difference index (DI), ratio index (RI), and normalised difference index (NDI), and the Spearman rank correlation coefficient (r) between these three spectral indices and the salt content in the samples was calculated, while a combined spectral index (r > 0.8) was eventually selected as a sensitive spectral index. Finally, a quantitative inversion model for the salinity of saline soils was developed, and the model's accuracy was evaluated based on partial least squares regression (PLSR), the random forest (RF) algorithm, and the radial basis function (RBF) neural network algorithm. The results indicated that the inversion of soil salt content using the selected combination of spectral indices based on the RBF neural network algorithm was the most effective, with the prediction model yielding an R2 value of 0.950, a root mean square error (RMSE) of 1.014, and a relative percentage deviation (RPD) of 4.479, which suggested a good prediction effect. [ABSTRACT FROM AUTHOR]

Copper (Cu) is essential for many physiological processes in phytoplankton, including electron-transfer reactions and high-affinity Fe transport systems. However, at high concentrations, Cu can have a toxic effect on phytoplankton. Phytoplankton's tolerance to certain toxicants in marine ecosystems cannot always be solely attributed to the presence of compounds, as various environmental factors including temperature can also indirectly influence their effects on organisms. In this study, we investigated the effects of different Cu concentrations (0, 19.6, 160, 800, and 8000 nM) on the growth and physiological changes in the centric diatom Thalassiosira pseudonana (CCMP 1335) when simultaneously applied with temperature cultivation of 20 °C and 25 °C. At low (0 nM) and high (8000 nM) Cu concentrations, the growth rate of T. pseudonana was inhibited, though an increase in temperature lessened this inhibition. There were no significant changes in the POC:PON ratio during all of the treatments. However, increasing the temperature significantly decreased the POC: POP, PON: POP and BSi: POP ratios of T. pseudonana. The intracellular Cu content of T. pseudonana varied from 0.13 to 13.28 fg cell−1 in response to increases in ambient Cu concentrations. Lastly, an increase in the Cu concentration decreased the transcriptional expression of CTR (copper transporter), 3Hfcp (photosynthetic protein), and Sit1 (silica shell formation) encoding genes. In conclusion, our results indicated that T. pseudonana can adapt to physiological processes and molecular mechanisms in response to varying Cu concentrations and ambient temperatures. [ABSTRACT FROM AUTHOR]