The shortage of land resources on the Loess Plateau has long been a thorny problem. Many high-fill projects are carried out, accompanied by a series of geological hazards, which threaten the ecological environment and personal safety. The creep characteristics of loess are an essential part of loess landslide research. The microstructural changes are closely related to creep behavior. By conducting triaxial creep experiments on Malan loess from the Yan’an area in China, scanning electron microscope (SEM) experiments on loess in different creep stages were carried out. Using qualitative and quantitative analyses of the microstructural characteristics of intact loess and remodeled loess during creep, the microstructural parameters were compared, and finally, the microscopic mechanisms during creep were analyzed. The qualitative analysis of remodeled loess during creep returned significantly higher results than it did for intact loess. During the creep process, among the microstructural parameters of loess change, the change in pore and particle size and shape were the most obvious, while the complexity of other microstructural parameters and orientation changed little. During the creep process of loess, the soil skeleton changed continuously, but the loess microstructure featured different changes at different levels of deviator stress.

Due to the complex terrain and intense tectonic activity, and harsh climate in the Qinling-Daba Mountains, many landslides occur in the area. Most of these landslides are extremely active, posing a serious threat to the safety and property of local residents. As a mature deformation-monitoring technology, InSAR has been widely used in landslide detection, but the steep terrain and dense vegetation in the Qinling-Daba Mountains make detection challenging. Hence, it is important to choose suitable data sources and methods for landslide detection via InSAR in this area. This study was the first to collect ALOS/PALSAR−2 and Sentinel−1A images to detect landslides in the Qinling-Daba Mountains, applying a method combining IPTA and SBAS. In total, 88 landslides were detected and validated. The results show that the deformation-detection error rate of Sentinel−1A is 2% higher than that of ALOS/PALSAR−2 and that its landslide-recognition rate is 47.7% lower than that of ALOS/PALSAR−2. Upon comparing and analyzing the visibility, coherence, closed−loop residuals, and typical time series of landslide deformation from the two kinds of data, it was found that the extremely low quality of available Sentinel−1 A summer data is a major factor influencing that system’s performance. ALOS/PALSAR−2 is more likely to detect landslides in areas with high vegetation coverage, meeting more than 90% of the monitoring needs. It is thus highly suitable for landslide detection in the Qinling–Daba Mountains, where seasonality is significant. In this paper, for the first time, multiple data sources are compared in detail with regard to their utility in landslide detection in the Qinling–Daba Mountains. A large number of accuracy metrics are applied, and the results are analyzed. The study provides important scientific support for the selection of data sources for future landslide monitoring in the Qinling–Daba Mountain area and similar areas and for the selection of methods to evaluate the accuracy of InSAR monitoring.

The LuTan-1 (LT-1) satellite, launched in 2022, is China’s first L-band full-polarimetric Synthetic Aperture Radar (SAR) constellation, boasting interferometry capabilities. However, given its limited use in subsidence monitoring to date, a comprehensive evaluation of LT-1’s interferometric quality and capabilities is necessary. In this study, we utilized the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique to analyze mining-induced subsidence results near Shenmu City (China) with LT-1 data, revealing nine subsidence areas with a maximum subsidence of −19.6 mm within 32 days. Furthermore, a comparative analysis between LT-1 and Sentinel-1 data was conducted focusing on the aspects of subsidence results, interferometric phase, scattering intensity, and interferometric coherence. Notably, LT-1 detected some subsidence areas larger than those identified by Sentinel-1, attributed to LT-1’s high resolution, which significantly enhances the detectability of deformation gradients. Additionally, the coherence of LT-1 data exceeded that of Sentinel-1 due to LT-1’s L-band long wavelength compared to Sentinel-1’s C-band. This higher coherence facilitated more accurate capturing of differential interferometric phases, particularly in areas with large-gradient subsidence. Moreover, the quality of LT-1’s monitoring results surpassed that of Sentinel-1 in root mean square error (RMSE), standard deviation (SD), and signal-to-noise ratio (SNR). In conclusion, these findings provide valuable insights for future subsidence-monitoring tasks utilizing LT-1 data. Ultimately, the systematic differences between LT-1 and Sentinel-1 satellites confirm that LT-1 is well-suited for detailed and accurate subsidence monitoring in complex environments.

How to solve the contradiction between coal mining and soil and water conservation is a key scientific issue in the achievement of high-quality development in the middle reaches of the Yellow River. In this paper, the northern Shaanxi mining area in the middle reaches of the Yellow River is taken as the research area, and the surface loess micro-topography is taken as the entry point. The numerical simulation test and soil loss model calculation are used to reveal the different types of loess natural slope morphology (straight slope, concave slope, convex slope, and composite slope) and the natural slopes (5°, 15°, 25°, 35°, 45°). The influence characteristics and laws of the same mining on the surface loess slope morphology in the coal mining subsidence area are analyzed, and the soil erosion effect on the slope scale is analyzed. The results show that: (1) Coal mining subsidence will lead to an increase in the slope of the loess slope, and the smaller the natural slope, the greater the increase in slope. Among them, the influence of coal mining subsidence on the ‘concave loess slope with natural slope of 15°’ is the most significant, and the natural slope of 15° is the key dividing point for the transformation of the sensitive slope shape of the loess slope in the coal mining subsidence area of northern Shaanxi. (2) Coal mining subsidence will lead to the decrease in slope length of a loess natural slope, and the smaller the natural slope, the greater the decrease in slope length. Among them, coal mining subsidence has the most significant impact on the ‘concave loess slope with a natural slope of 25°’. The natural slope of 25° is the key point of the sudden change rate of the slope length of the loess slope in the coal mining subsidence area of northern Shaanxi. (3) Coal mining subsidence will lead to the increase in the soil erosion modulus on the surface loess slope under the scale of ‘annual erosion rainfall’ and ‘typical field erosion rainfall’, and the smaller the natural slope, the greater the increase in the soil erosion modulus. The natural slopes of 15° and 25° are the key points of the abrupt change in soil erosion intensity on the loess slope in the coal mining subsidence area of northern Shaanxi under the scales of ‘annual erosion rainfall’ and ‘typical erosion rainfall’, respectively. Under the scale of annual erosion rainfall, the increment of the 15° slope was 1.65 times, 1.12 times, 1.11 times, and 1.02 times that of the 5°, 25°, 35°, and 45° slopes, respectively. Under the typical erosion rainfall scale, the increment of the 25° slope was 4.22 times, 1.32 times, 1.04 times, and 1.15 times that of the 5°, 15°, 35°, and 45° slopes, respectively. (4) For the loess subsidence slope with any slope shape, the increase in slope gradient is the main factor for the increase in the soil erosion modulus. Under the annual erosion rainfall scale, the contribution of slope increase was 92.9%. Under the typical erosion rainfall scale, the contribution of slope increase was 79.1%. The research results can provide scientific guidance for soil erosion and control in the northern Shaanxi mining area in the middle reaches of the Yellow River Basin.

A large landslide struck the Zhenba Dahekou area of Shaanxi Province, China, on 9 September 2021. To accurately extract landslide displacement in emergency situations, in this study, we explore the feasibility and effectiveness of using satellite images and post-failure emergency UAV images to investigate the large displacement of the landslide through the pixel offset tracking (POT) method and evaluate four different POT methods, including NCC operated in the spatial domain (NCC), the orientation correlation method in ImGRAFT software (ImGRAFT), the multi-pass method in GIV software (GIV), and the frequency method in COSI-Corr software (COSI-Corr). It is found that the Zhenba landslide has moved southwest by about 74.3 m~96.4 m with the sliding direction between 231°~258°. The southward displacement of the landslide gradually decreases from southeast to northwest, and the westward displacement on the west side is greater than that on the east side. The relative matching precision of the POT methods in stable areas reached 0.8 m, superimposed on an image registration RMSE of 1.2 m. Under the experimental conditions of this study, ImGRAFT demonstrated robust overall performance. In terms of matching ability, ImGRAFT and NCC outperform GIV and COSI-Corr. In terms of displacement gradients expression ability, ImGRAFT and COSI-Corr outperform NCC and GIV; in terms of matching efficiency, COSI-Corr, GIV, and ImGRAFT are far superior to NCC. This study expands the application of multi-source optical data to investigate landslides and provides suggestions for the displacement extraction of large-displacement landslides, which will be helpful for the emergency investigation and research of landslides in the future.