Reclaimed coastal areas are highly susceptible to uneven subsidence caused by the consolidation of soft marine deposits, which can induce differential settlement, structural deterioration, and systemic risks to urban infrastructure. Further, engineering activities, such as construction and loadings, exacerbate subsidence, impacting infrastructure stability. Therefore, monitoring the integrity and vulnerability of linear urban infrastructure after construction on reclaimed land is critical for understanding settlement dynamics, ensuring safe and reliable operation and minimizing cascading hazards. Subsequently, in the present study, to monitor deformation of the linear infrastructure constructed over decades-old reclaimed land in Mokpo city, South Korea (where 70% of urban and port infrastructure is built on reclaimed land), we analyzed 79 Sentinel-1A SLC ascending-orbit datasets (2017-2023) using the Persistent Scatterer Interferometry (PSInSAR) technique to quantify vertical land motion (VLM). Results reveal settlement rates ranging from -12.36 to 4.44 mm/year, with an average of -1.50 mm/year across 1869 persistent scatterers located along major roads and railways. To interpret the underlying causes of this deformation, Casagrande plasticity analysis of subsurface materials revealed that deep marine clays beneath the reclaimed zones have low permeability and high compressibility, leading to slow pore-pressure dissipation and prolonged consolidation under sustained loading. This geotechnical behavior accounts for the persistent and spatially variable subsidence observed through PSInSAR. Spatial pattern analysis using Anselin Local Moran's I further identified statistically significant clusters and outliers of VLM, delineating critical infrastructure segments where concentrated settlement poses heightened risks to transportation stability. A hyperbolic settlement model was also applied to anticipate nonlinear consolidation trends at vulnerable sites, predicting persistent subsidence through 2030. Proxy-based validation, integrating long-term groundwater variations, lithostratigraphy, effective shear-wave velocity (Vs30), and geomorphological conditions, exhibited the reliability of the InSAR-derived deformation fields. The findings highlight that Mokpo's decades-old reclamation fills remain geotechnically unstable, highlighting the urgent need for proactive monitoring, targeted soil improvement, structural reinforcement, and integrated InSAR-GNSS monitoring frameworks to ensure the structural integrity of road and railway infrastructure and to support sustainable urban development in reclaimed coastal cities worldwide.

We present a high-resolution geologic map of the Rubin crater region, located on Mons Amundsen, which has been identified as a promising site for future lunar exploration (AOI E in Wueller et al., 2024). We developed a design reference mission (DRM) to highlight the region's potential for addressing key lunar science goals, particularly those related to the early lunar bombardment history, lunar crustal rocks, volatiles, impact processes at multiple scales, and regolith properties, as outlined by the National Research Council (2007). The Rubin crater, which formed about 1.58 billion years ago during the Eratosthenian period, excavated material from depths of up to 320 m, potentially reaching the underlying South Pole-Aitken (SPA) massif, Mons Amundsen. This makes the crater's ejecta material, along with the Amundsen ejecta covering the massif, prime targets for sampling SPA-derived materials that can expand our understanding of early Solar System dynamics and the lunar cratering chronology. Additionally, the region hosts several permanently shadowed regions (PSRs), ideal for studying potential lunar volatiles and the processes affecting their distribution. The DRM proposes nine traverse options for exploration via walking EVAs, the Lunar Roving Vehicle (LRV), and LRV-assisted EVAs, with traverse lengths ranging from 3.6 km to 18.2 km. Each traverse is designed to sample diverse geologic units and address multiple scientific objectives. Given its scientific potential and favorable exploration conditions, the Rubin crater region is an ideal location for testing south polar landing operations, potentially paving the way for more complex missions, such as a Shackleton crater landing. (c) 2025 The Author(s). Published by Elsevier B.V. on behalf of COSPAR. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

This paper investigates the spatiotemporal dynamics and their changes of the southern limit of latitudinal permafrost (SLLP) and the lower limit of mountain permafrost (LLMP) in Northeast China, emphasizing the roles of climate change and human activities. Permafrost in this region is primarily distributed in the northern parts of the Da and Xiao Xing'anling mountain ranges and in the upper parts of the Changbai Mountains and at the summits of the Huanggangliang Mountains in the southern part of the Da Xing'anling Mountain Range. Permafrost degradation, ongoing since at least the local Holocene Megathermal Period (8.5-6.0 ka BP), has intermittently reversed during cooler climatic intervals but continues to exert significant impacts on regional environments, infrastructure stability, and carbon storage. Notably, the northward retreats of the SLLP since the mid-19th century underscore the sustained nature of this degradation, especially in southern patchy permafrost zones increasingly sensitive to warming and anthropogenic influences. LLMP variability is similarly shaped by a combination of climatic, hydrometeorological, ecological, and topographic factors. The distributions of SLLP and LLMP are further complicated by the presence of relict and sporadic permafrost, as well as the hydrothermal effects of vegetation and snow cover. Addressing the challenges of mapping and modeling boreal permafrost in Northeast China requires comprehensive field investigations, long-term in situ monitoring via station networks, and advanced numerical modeling. Emerging technologies, including satellite and airborne remote sensing (RS), geographic information systems (GIS), unmanned aerial vehicles (UAVs), surface geophysical methods, and big data analytics, offer new possibilities for enhancing permafrost monitoring and mapping. Integrating these tools with conventional field studies can significantly improve our understanding of permafrost dynamics. Continued efforts in monitoring, technological innovation, multidisciplinary collaboration, and international cooperation are essential to meet the challenges posed by permafrost degradation in a changing climate.

The spatial combination of stratigraphic structural elements significantly influences the overburden damage caused by mining. However, existing studies have not yet clearly revealed the specific relationship between these elements and overburden damage, nor have they intuitively demonstrated the spatial distribution characteristics of overburden damage. In response, this paper proposes a comprehensive analysis method that can visually and quantitatively characterize the spatial distribution of overburden damage. This method combines stratigraphic model generalization, damage mechanics modeling, numerical simulation, and color mapping characterization. This method was applied to analyze the mining damage characteristics of different structural overburdens in the Yushenfu mining area. The analysis revealed a prevalent stratigraphic combination pattern of sand layers, soil layers, and two sections of mudstone and fine sandstone interbeds. The study shows that mining height and bedrock-soil ratio are important stratigraphic structural factors that affect the fracture/mining height ratio. The ranking of elastic modulus loss and spatial loss in various damaged areas of the overburden is consistent, in the following order: collapse zone > fracture zone > bending subsidence zone. Furthermore, this method reveals the mechanism of increased residual expansion in the overburden caused by coal mining, which, in turn, leads to surface collapse. This method provides a theoretical basis for implementing targeted engineering disposal and safety measures.

Gully erosion is one of the major global environmental threats that frequently affects semi-humid to arid Mediterranean regions and contributes to a wide range of ecological problems. Recognizing vulnerable areas to gully erosion and creating a comprehensive gully erosion susceptibility map (GESM) can assist in the lessening of land degradation and damage to numerous infrastructures. The primary goal of this research is to build a random subspace-based function tree (RSFT), i.e., an ensemble model, and compare it with other standard models such as Fisher's linear discriminant analysis (FLDA), Nave Bayes tree (NBTree), J48 Decision Tree, and random forest (RF) models in order to identify which model generates the most accurate outcomes. Overall, a total number of 489 gully sites were utilised for modelling and validation purpose, with 377 (70 %) used for modelling and 112 (30 %) used for validation. Fourteen salient gully erosion conditioning factors (GECFs) were implemented for constructing the GESMs. The efficacy and significance of several GECFs were assessed through the random forest, or RF, model for gully erosion modelling. Using the GES maps, we computed the success rate curve (SRC) and prediction rate curve (PRC), as well as their areas under the curves (AUC). The AUC (SRC, PRC) scores for the RSFT model were 0.906 and 0.916, consequently, while the outcomes for the RF, NBTree, FLDA, and J48 models were 0.875 and 0.869, 0.861 and 0.859, 0.792 and 0.816, and 0.779 and 0.811. AUC findings indicated that the RSFT model delivered the most precise predictions, trailed by the RF, NBTree, FLDA, and J48 models. In terms of RMSE, each of the models performed adequately; however, RSFT exhibits the lowest RMSE values of all models, with 0.31 (training dataset) and 0.29 (validation dataset), which shows that RSFT is substantially more accurate than other models in forecasting gully erosionThus, the results of this research can be used by local managers and planners for environmental management. The results from our study suggests that all of the GESM models have high efficiency, and can be employed to formulate adequate measures for safeguarding of soil and water. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Permafrost, a major component of the cryosphere, is undergoing rapid degradation due to climate change, human activities, and other external disturbances, profoundly impacting ecosystems, hydroclimate, engineering geological stability, and infrastructure. In Northeast China, the thermal dynamics of Xing'an permafrost (XAP) are particularly complex, complicating the accurate assessment of its spatial extent. Many earlier mapping efforts, despite significant progress, fall short in accounting for some key local geo-environmental factors. Thus, this study introduces a new approach that incorporates four key driving factors-biotic, climatic, physiographic, and anthropogenic-by integrating multisource datasets and in situ observations. Four machine learning (ML) models [random forest (RF), support vector machine (SVM), logistic regression (LR), and extreme gradient boosting (XGB)] are applied to simulate permafrost distribution and probability, as well as to evaluate their performance. The results indicate that models' accuracy, ranked from highest to lowest, is as follows: RF (area under the curve (AUC) =0.88 and accuracy =0.81), XGB (0.86 and 0.77), LR (0.81 and 0.73), and SVM (0.76 and 0.66), with RF emerging as the most effective model for permafrost mapping in Northeast China. Analysis of the relationships between predictors and permafrost occurrence probability (POP) indicates that vegetation and snow cover exert nonlinear effects on permafrost, while human activities significantly reduce POP. Additionally, finer soil textures and higher soil organic matter content are positively correlated with increased POP. The modeling results, combined with field survey data, also show that permafrost is more prevalent in lowlands than in uplands, confirming the symbiotic relationship between permafrost and wetlands in Northeast China. This spatial variation is influenced by local microclimates, runoff patterns, and soil thermal properties. The primary sources of model error are uncertainties in the accuracy of multisource datasets at different scales and the reliability of observational data. Overall, ML models demonstrate great potential for mapping permafrost in Northeast China.

Extreme flooding is becoming a more serious hazard to the world's infrastructure, especially in high-risk locations, and is linked to global warming and human activity. This research employs an analytical hierarchy process (AHP) model and geographic information system (GIS) analysis to delineate flood risk zones. An eight-factor multiparametric method to flood risk susceptibility mapping includes precipitation, distance to river, the slope, elevation, land use/cover, topographic wetness index, type of soil, and curvature. An urban flood risk index (UFRI) is established based on vulnerability mapping, revealing that approximately 33% of Haripur District, Khyber Pakhtunkhwa, Pakistan, is prone to floods. Additionally, land use cover analysis indicates that 23% of the crop area in Haripur District is at risk from flood disasters. Recognizing the potential for costly damage to infrastructure, flood hazard mapping serves as a valuable tool to prioritize risk areas for urban and agricultural development. The outcomes of this study are anticipated to significantly contribute to predisaster flood control management in the studied area.

Estimating the landscape and soil freeze-thaw (FT) dynamics in the Northern Hemisphere (NH) is crucial for understanding permafrost response to global warming and changes in regional and global carbon budgets. A new framework for surface FT-cycle retrievals using L-band microwave radiometry based on a deep convolutional autoencoder neural network is presented. This framework defines the landscape FT-cycle retrieval as a time-series anomaly detection problem, considering the frozen states as normal and the thawed states as anomalies. The autoencoder retrieves the FT-cycle probabilistically through supervised reconstruction of the brightness temperature (TB) time series using a contrastive loss function that minimizes (maximizes) the reconstruction error for the peak winter (summer). Using the data provided by the Soil Moisture Active Passive (SMAP) satellite, it is demonstrated that the framework learns to isolate the landscape FT states over different land surface types with varying complexities related to the radiometric characteristics of snow cover, lake-ice phenology, and vegetation canopy. The consistency of the retrievals is assessed over Alaska using in situ observations, demonstrating an 11% improvement in accuracy and reduced uncertainties compared to traditional methods that rely on thresholding the normalized polarization ratio (NPR).

The Arctic-boreal zone (ABZ) is warming due to climate change. Current spaceborne remote sensing techniques and retrieval methodologies need to be complemented to improve systematic monitoring of the cryosphere. To that end, this article presents a new investigation of the use of the global navigation satellite system reflectometry (GNSS-R) remote sensing technique by a SmallSat constellation. A new freeze/thaw (F/T) seasonal multithresholding algorithm (STA) is developed using high-inclination orbit near-Nadir Spire Global GNSS-R data acquired through the National Aeronautics and Space Administration (NASA) Commercial Smallsat Data Acquisition (CSDA) Program. Five different soil surface reflectivity Gamma models are proposed to account for the impact of vegetation cover and small-scale surface roughness on Earth-reflected GNSS signals. The sensitivity of the Gamma models to F/T surface state transitions is evaluated, and the optimum model is selected to construct a seasonal scale factor. Then, a multithresholding matrix is obtained for F/T classification using a specific threshold for every surface grid cell. Results for the annual frozen soil duration (days yr(-1)) are compared with those by the Soil Moisture Active Passive (SMAP) mission. Additionally, freezing and thawing periods are analyzed to determine when the moisture exchange with the atmosphere is locked, which is an important climatic factor. A novel metric is introduced to characterize the freeze intensity moving beyond classical F/T binary classifications. Results are evaluated using air and soil temperature, snow depth and temperature, and soil moisture content (SMC) provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5-Land reanalysis product.

Landslides are significant geological hazards in mountainous regions, arising from both natural forces and human actions, presenting serious environmental challenges through their extensive damage to properties and infrastructure, often leading to casualties and alterations to the landscape. This study employed GIS-based techniques to evaluate and map the landslide susceptibility in the Bekhair structure located within the Zagros mountains of Kurdistan, northern Iraq. An inventory map containing 282 landslide occurrences was compiled through intensive field investigations, as well as the interpretation of remote sensing data and Google Earth images. Ten potential influencing factors, including elevation, rainfall, lithology, slope, curvature, aspect, LULC, NDVI, distance to roads and rivers, were selected to construct susceptibility maps by integrating the frequency ratio (FR) and analytical hierarchy process (AHP) approaches, with the goal of understanding how these factors relate to landslides occurrence. The Bekhair core area was divided into 5 hazard zones on the landslide susceptibility maps. The regions classified as very low and low hazard zones are mainly occur in flat or gently sloping plains that characterized by resistant rocks, dense vegetation, minimal rainfall, shallow valleys, and are distant from riverbanks and roads. The areas designated as high and very high hazard zones are found in steep slopes and rough terrain with bare soil, intense weathering, high rainfall, sparse vegetation, highly fractured rocks, deep valleys, and close proximity to construction projects. The moderate hazard zones are mainly located between the other 4 zones across the Bekhair anticline. Results of the susceptibility analysis indicate that the occurrence of landslides in Kurdistan mountains are primarily controlled by factors related to the tectonic structure, surface characteristics and environmental conditions, such as rock lithology (competency), terrain slope, rainfall intensity, and human impacts. The delineation of landslide hazard zones offers important guides for government decision-makers engaged in regional planning, infrastructure development, and the formulation of strategies to mitigate landslides and protect lives and properties in Kurdistan. The accuracy of susceptibility maps was evaluated using the R-index and the AUC-ROC curve. The landslide susceptibility index (LSI) values allocated to different susceptibility classes derived from both FR and AHP models are consistent with the values obtained from the R-index. Moreover, the FR model demonstrated superior performance compared to the AHP model, with a success rate of 85.3% and a predictive rate of 81.2%, in contrast to the AHP model's success rate of 75.2% and predictive rate of 72.4%.