Ground subsidence is a common urban geological hazard in several regions worldwide. The settlement of loess fill foundations exhibits more complex subsidence issues under the coupled effects of geomechanical and seepage-driven processes. This study selected 21 ascending Sentinel-1 A radar images from April 2023 to October 2024 to monitor the loess fill foundation in Shaanxi, China. To minimize errors caused by the orbital phase and residual flat-earth phase, this research combined PS-InSAR technology with the three-threshold method to improve the SBAS-InSAR processing workflow, thereby exploring time-series deformation of the loess fill foundation. Compared with conventional SBAS-InSAR technology, the improved SBAS-InSAR technique provided more consistent deformation time-series results with leveling data, effectively capturing the deformation characteristics of the fill foundation. Additionally, geographic information system (GIS) spatial analysis techniques and statistical methods were employed to analyze the overall characteristics and spatiotemporal evolution of the ground surface deformation in the study area. On the other hand, the major drivers of the subsidence in the study area were also discussed based on indoor experiments and engineering geological data. The results showed gradual and temporal shifts of the subsidence center toward areas with the maximum fill depths. In addition, two directions of uneven subsidence were observed within the fill foundation study area. The differences in the fill depth and soil properties caused by the building foundation construction were the main factors contributing to the uneven settlement of the foundations. Foundation deformation was also positively and negatively affected by surface water infiltration. This study integrates remote sensing and engineering geological data to provide a scientific basis for accurately monitoring and predicting loess fill foundation settlement. It also offers practical guidance for regional infrastructure development and geological hazard prevention.

Understanding the spatiotemporal dynamics of microbial communities is essential for predicting their ecological roles and interactions with host plants. In a recent study, Wei and colleagues (Microbiol Spectr 13:e02097-24, 2024) investigated fungal diversity across multiple plant and soil compartments in rubber trees over two seasons and two geographically distinct regions in China. Their findings revealed that alpha diversity was primarily influenced by seasonal changes and physicochemical factors, while beta diversity exhibited a strong geographical pattern, shaped by leaf phosphorus and soil available potassium. These results highlight the role of environmental drivers in shaping within-community diversity, while other factors contribute to the differences between fungal communities across the soil-plant continuum. By distinguishing the effects of temporal and spatial factors, this study provides detailed insights into plant-associated microbiomes and emphasizes the need for further research on the functional implications of microbial diversity in the context of changing environmental and agricultural conditions.

Electrical resistivity tests can potentially be applied in loess damage testing under combined freeze-thaw cycle (FTC) and earthquake conditions, which is crucial for preventing and controlling loess landslides. However, two challenges involving loess electrical resistivity measurements and damage characterization should be addressed. To achieve loess spatial resistivity measurements in extreme environments with low-uncertainty, a novel, multichannel, four-point method utilizing flexible electrodes is proposed. For loess damage characterization, a novel fusion algorithm is developed that integrates the electrical conductivity model with a data-driven process to eliminate the influence of moisture content and temperature on resistivity. To validate this approach, loess resistivity tests and damage characterizations were conducted using a combination of FTCs and earthquakes. The results indicate that the proposed method addresses the challenge of continuous measurement, ensuring that the discrepancy between the calculated and CT test results remains within an acceptable range, where the relative error ranged from 0 to 0.15. In addition, in the top and bottom areas, where considerable soil moisture exists, the calculation error associated with the previous empirical model was reduced considerably, with the relative error primarily ranging from 0.04 to 0.44.

Alpine treelines ecotones are critical ecological transition zones and are highly sensitive to global warming. However, the impact of climate on the distribution of treeline trees is not yet fully understood as this distribution may also be affected by other factors. Here, we used high-resolution satellite images with climatic and topographic variables to study changes in treeline tree distribution in the alpine treeline ecotone of the Changbai Mountain for the years 2002, 2010, 2017, and 2021. This study employed the Geodetector method to analyze how interactions between climatic and topographic factors influence the expansion of Betula ermanii on different aspect slopes. Over the past 20 years, B. ermanii, the only tree species in the Changbai Mountain tundra zone, had its highest expansion rate from 2017 to 2021 across all the years studied, approaching 2.38% per year. In 2021, B. ermanii reached its uppermost elevations of 2224 m on the western aspects and 2223 m on the northern aspects, which are the predominant aspects it occupies. We also observed a notable increase in the distribution of B. ermanii on steeper slopes (> 15 degrees) between 2002 and 2021. Moreover, we found that interactions between climate and topographic factors played a more significant role in B. ermanii's expansion than any single dominant factor. Our results suggest that the interaction between topographic wetness index and the coldest month precipitation (Pre(1)), contributing 91% of the observed variability, primarily drove the expansion on the southern aspect by maintaining soil moisture, providing snowpack thermal insulation which enhanced soil temperatures, decomposition, and nutrient release in harsh conditions. On the northern aspect, the interaction between elevation and mean temperature of the warmest month explained 80% of the expansion. Meanwhile, the interaction between Pre(1) and mean temperature of the growing season explained 73% of the expansion on the western aspect. This study revealed that dominant factors driving treeline upward movement vary across different mountain aspects. Climate and topography play significant roles in determining tree distribution in the alpine treeline ecotone. This knowledge helps better understand and forecast treeline dynamics in response to global climate change.

The issue of geotechnical hazards induced by excavation in soft soil areas has become increasingly prominent. However, the retaining structure and surface settlement deformation induced by the creep of soft soil and spatial effect of the excavation sequence are not fully considered where only elastic-plastic deformation is used in design. To understand the spatiotemporal effects of excavation-induced deformation in soft soil pits, a case study was performed with the Huaxi Park Station of the Suzhou Metro Line S1, Jiangsu Province, China, as an example. Field monitoring was conducted, and a three-dimensional numerical model was developed, taking into account the creep characteristics of mucky clay and spatiotemporal response of retaining structures induced by excavations. The spatiotemporal effects in retaining structures and ground settlement during excavation processes were analyzed. The results show that as the excavation depth increased, the horizontal displacement of the diaphragm walls increased linearly and tended to exhibit abrupt changes when approaching the bottom of the pit. The maximum horizontal displacement of the wall at the west end well was close to 70 mm, and the maximum displacement of the wall at the standard reached approximately 80 mm. The ground settlement on both pit sides showed a trough distribution pattern, peaking at about 12 m from the pit edge, with a settlement rate of -1.9 mm/m per meter of excavation depth. The excavation process directly led to the lateral deformation of the diaphragm walls, resulting in ground settlement, which prominently reflected the time-dependent deformation characteristics of mucky soft soil during the excavation process. These findings provide critical insights for similar deep excavation projects in mucky soft soil, particularly regarding excavation-induced deformations, by providing guidance on design standards and monitoring strategies for similar geological conditions.

The fall armyworm (FAW), Spodoptera frugiperda, a major pest in maize production, was assessed for its temporal and spatial distribution in maize fields during both the dry and rainy seasons of 2021 and 2022 in two agroecological regions in Benin (zone 6 and 8). Zone 6 (AEZ 6) called zone of terre de barre (Southern and Central Benin) consisted of ferralitic soils, a Sudano-Guinean climate (two rainy seasons alternating with two dry seasons) with a rainfall ranging between 800 and 1400 mm of rainfall per year; while zone 8 (AEZ 8) called fisheries region (Southern Benin is characterized by coastal gleysols and arenosols with a Sudano-Guinean climate and a rainfall of 900-1400 mm of rainfall per year. In this study, 30 and 50 maize plants were randomly sampled using a W pattern during the dry and rainy seasons, respectively. Larval density, larval infestation rates, and damage severity were monitored over time. Taylor's power law and the mean crowding aggregation index were applied to evaluate the dispersion patterns of the larvae. The results indicate a higher larval infestation rate and larval density in AEZ 8 compared to AEZ 6 during the dry season. In the rainy season, while the percentage of damaged plants was higher in AZE 8, no significant differences in larval density between the two zones were observed. The dispersion analysis revealed moderate aggregation (aggregation index = 1.25) with a basic colony of 2.08 larvae, i.e., an average initial cluster of 2.08 larvae observed per plant, reflecting the aggregation oviposition behavior of FAW. This study provides valuable monitoring data on the FAW's distribution, offering insights for further research on population dynamics and developing predictive models for integrated pest management strategies.

Global climate change and permafrost degradation have significantly heightened the risk of geological hazards in high-altitude cold regions, resulting in severe casualties and property damage, particularly in the Qinghai-Tibet Plateau of China. To mitigate the risk of geological disasters, it is crucial to identify the primary disaster-inducing factors. Therefore, to address this issue more effectively, this study proposes a spatiotemporal-scale approach for detecting disaster-inducing factors and investigates the disaster-inducing factors of geological hazards in high-altitude cold regions, using the Kanchenjunga Basin as a case study. As the world's third-highest peak, Kanchenjunga is highly sensitive to climate fluctuations. This study first integrates the frost heave model and multitemporal interferometric synthetic aperture radar techniques to monitor ascending and descending track line-of-sight deformation of the frozen active layer in the study area. Subsequently, the surface parallel flow constrained model is employed to decompose the 3-D time-series deformation of geological hazards in the basin, with remote sensing imagery and field surveys used to identify a total of 94 disaster sites. In parallel, a database of potential conditioning factors is constructed by leveraging Google Earth Engine remote sensing inversion technology and relevant data provided by the China Geological Survey. Finally, by integrating monitoring results with a database of potential geological conditioning factors, the spatiotemporal-scale approach for detecting disaster-inducing factors proposed in this study is applied to investigate the disaster-inducing factors in the Kanchenjunga Basin. The research results highlight that surface temperature is the primary driving factor of geological hazards in the Kanchenjunga Basin. This research helps bridge the data gap in the region and offers critical support for local government decision-making in disaster prevention, risk assessment, and related areas.

The Qilian Mountains, located on the northeastern edge of the Qinghai-Tibet Plateau, are characterized by unique high-altitude and cold-climate terrain, where permafrost and seasonally frozen ground are extensively distributed. In recent years, with global warming and increasing precipitation on the Qinghai-Tibet Plateau, permafrost degradation has become severe, further exacerbating the fragility of the ecological environment. Therefore, timely research on surface deformation and the freeze-thaw patterns of alpine permafrost in the Qilian Mountains is imperative. This study employs Sentinel-1A SAR data and the SBAS-InSAR technique to monitor surface deformation in the alpine permafrost regions of the Qilian Mountains from 2017 to 2023. A method for spatiotemporal interpolation of ascending and descending orbit results is proposed to calculate two-dimensional surface deformation fields further. Moreover, by constructing a dynamic periodic deformation model, the study more accurately summarizes the regular changes in permafrost freeze-thaw and the trends in seasonal deformation amplitudes. The results indicate that the surface deformation time series in both vertical and east-west directions obtained using this method show significant improvements in accuracy over the initial data, allowing for a more precise reflection of the dynamic processes of surface deformation in the study area. Subsidence is predominant in permafrost areas, while uplift mainly occurs in seasonally frozen ground areas near lakes and streams. The average vertical deformation rate is 1.56 mm/a, with seasonal amplitudes reaching 35 mm. Topographical (elevation; slope gradient; aspect) and climatic factors (temperature; soil moisture; precipitation) play key roles in deformation patterns. The deformation of permafrost follows five distinct phases: summer thawing; warm-season stability; frost heave; winter cooling; and spring thawing. This study enhances our understanding of permafrost deformation characteristics in high-latitude and high-altitude regions, providing a reference for preventing geological disasters in the Qinghai-Tibet Plateau area and offering theoretical guidance for regional ecological environmental protection and infrastructure safety.

Under the interference of climate warming and human engineering activities, the degradation of permafrost causes the frequent occurrence of geological disasters such as uneven foundation settlement and landslides, which brings great challenges to the construction and operational safety of road projects. In this paper, the spatial and temporal evolution of surface deformations along the Beihei Highway was investigated by combining the SBAS-InSAR technique and the surface frost number model after considering the vegetation factor with multi-source remote sensing observation data. After comprehensively considering factors such as climate change, permafrost degradation, anthropogenic disturbance, and vegetation disturbance, the surface uneven settlement and landslide processes were analyzed in conjunction with site surveys and ground data. The results show that the average deformation rate is approximately -16 mm/a over the 22 km of the study area. The rate of surface deformation on the pavement is related to topography, and the rate of surface subsidence on the pavement is more pronounced in areas with high topographic relief and a sunny aspect. Permafrost along the roads in the study area showed an insignificant degradation trend, and at landslides with large surface deformation, permafrost showed a significant degradation trend. Meteorological monitoring data indicate that the annual minimum mean temperature in the study area is increasing rapidly at a rate of 1.266 degrees C/10a during the last 40 years. The occurrence of landslides is associated with precipitation and freeze-thaw cycles. There are interactions between permafrost degradation, landslides, and vegetation degradation, and permafrost and vegetation are important influences on uneven surface settlement. Focusing on the spatial and temporal evolution process of surface deformation in the permafrost zone can help to deeply understand the mechanism of climate change impact on road hazards in the permafrost zone.

An approach based on a Physics-Informed Neural Network (PINN) is introduced to tackle the two-dimensional (2D) rheological consolidation problem in the soil surrounding twin tunnels with different cross-sections, under exponentially time-growing drainage boundary. The rheological properties of the soil are modelled using a generalized viscoelastic Voigt model. An enhanced PINN-based solution is proposed to overcome the limitation of traditional PINNs in solving integral-differential equations (IDEs) equations. In particular, two key elements are introduced. First, a normalization method is employed for the spatio-temporal coordinates, to convert the IDEs governing the consolidation problem into conditions characterized by unit-duration time and unit-area geometric domain. Second, a conversion method for integral operators containing function derivatives is devised to further transform the IDEs into a set of second-order constant-coefficient homogeneous linear partial differential equations (PDEs). By using the TensorFlow framework, a series of PINN-based models is developed, incorporating the residual adaptive sampling method to address the 2D consolidation equations of soft soils surrounding tunnels with different burial depths and cross-sections. Comparative analyses between the PINNbased solutions, and either finite element or analytical solutions highlight that the aforementioned normalization stage empowers PINNs to solve the PDEs across different spatial and temporal scales. The integral operator transformation method facilitates the utilization of PINNs for solving intricate IDEs.