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.

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).

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.

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.

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%.

Flooding is one of the most severe natural hazards, causing widespread environmental, economic, and social disruption. If not managed properly, it can lead to human losses, property damage, and the destruction of livelihoods. The ability to rapidly assess such damages is crucial for emergency management. Near Real-Time (NRT) spatial information on flood-affected areas, obtained via remote sensing, is essential for disaster response, relief, urban and industrial reconstruction, insurance services, and damage assessment. Numerous flood mapping methods have been proposed, each with distinct strengths and limitations. Among the most widely used are machine learning algorithms and spectral indices, though these methods often face challenges, particularly in threshold selection for spectral indices and the sampling process for supervised classification. This study aims to develop an NRT flood mapping approach using supervised classification based on spectral features. The method automatically generates training samples through masks derived from spectral indices. More specifically, this study uses FWEI, NDVI, NDBI, and BSI indices to extract training samples for water/flood, vegetation, built-up areas, and soil, respectively. The Otsu thresholding technique is applied to create the spectral masks. Land cover classification is then performed using the Random Forest algorithm with the automatically generated training samples. The final flood map is obtained by subtracting the pre-flood water class from the post-flood image. The proposed method is implemented using optical satellite images from Sentinel-2, Landsat-8, and Landsat-9. The proposed method's accuracy is rigorously evaluated and compared with those obtained from spectral indices and machine learning techniques. The suggested approach achieves the highest overall accuracy (OA) of 90.57% and a Kappa Coefficient (KC) of 0.89, surpassing SVM (OA: 90.04%, KC: 0.88), Decision Trees (OA: 88.64%, KC: 0.87), and spectral indices like AWEI (OA: 84.12%, KC: 0.82), FWEI (OA: 88.23%, KC: 0.86), NDWI (OA: 85.78%, KC: 0.84), and MNDWI (OA: 87.67%, KC: 0.85). These results underscore the superior accuracy and effectiveness of the proposed approach for NRT flood detection and monitoring using multi-sensor optical imagery.

The 2022 flood events in Quetta, Pakistan, caused severe damage to the economy, properties, and lives. Therefore, flood risk mapping to identify flood-prone areas is essential for planners and decision-makers to take critical protective measures to control the effects of flooding. This study focuses on mapping flood-prone regions in the Quetta district of Pakistan using an analytical hierarchy process (AHP) and a geographic information system (GIS). The factors influencing flood used in the present study were topographic witness index (TWI), elevation, slope, land use, land cover, precipitation, stream distance, drainage density, and soil type. Weights and ranks were allocated separately to all factors through AHP and were interpreted in a GIS environment. The produced flood hazard model of the study area depicted four zones. These zones ranged from low (19.49%), moderate (43.34%), high (28.30%), to very high (8.87%). The model was further validated through previous flood events in the study area. Around 90% of flood hazard events in the past took place mainly in the produced model's very high and high zones, which is why the current model is reliable. Finally, integrating geospatial approaches with AHP in flood hazard mapping is a quick, reliable, and affordable method that may be utilized in the area.

The Andes region exhibits high susceptibility to landslides, leading to significant infrastructure, road, and agricultural damage. This study focuses on Ciudad Victoria, a housing program located in a geologically unstable area of Loja, Ecuador. Loja has experienced frequent landslides in recent decades, with Ciudad Victoria initially affected in 2011 and 2015, intensifying during the rainy season in early 2021. However, a comprehensive understanding of this case is currently lacking. Here, we evaluate terrain instability and structural damages through a comprehensive multi-technical approach. In this study, we integrate advanced techniques such as Differential Interferometric Synthetic Aperture Radar (DInSAR), Uncrewed Aerial Vehicles (UAV)-supported field surveys, geomorphological assessments, Electrical Resistivity Tomography (ERT), and a semi-quantitative evaluation of house damages using a Geographic Information System (GIS). The combined analysis of DInSAR, field surveys, and geomorphological observations reveals that soil instability in Ciudad Victoria is primarily influenced by slow-moving translation-type landslides occurring in the SW-NE and S-N directions. These landslides exhibit an average active displacement of 1-4 cm/yr, which increases during the rainy months, reaching velocities of up to 13 cm per month. ERT, supported by the aforementioned observations, indicates a displaced soil volume of approximately 5 Hm3, 3 , with dimensions of roughly 700 m (length), 400 m (width), and 20 m (thickness). A semi- quantitative evaluation reveals that four houses have irreversible structural damages, while 172 houses exhibit severe damage and 553 houses display moderate damages such as cracks and fractures, which also impact water and sewage pipelines. Additionally, we demonstrate how these building damages can be utilized to determine terrain movement, serving as (1) ground-truth information for validating DInSAR data, and (2) facilitating detailed movement characterization. This study exemplifies the effectiveness of coordination among a multidisciplinary team that utilizes diverse techniques and perspectives, ultimately leading to a more precise diagnosis of unstable areas.