Tension cracks in the ground are an early warning sign of active geological processes, such as landslides. This study introduces a novel approach using roots of Aleppo pine (Pinus halepensis Mill.) and olive (Olea europaea L.) to date the formation and widening of tension cracks of a landslide located in Monovar, SE Spain. We evaluated the utility of wood anatomy features for geomorphological hazard assessment. Additionally, we examined the suitability of olive and pine roots for analysing the formation and widening of cracks. Particularly useful features of wood anatomy allowing for the reconstruction of crack formation in the terrain substrates are the decrease in cell lumen size that occurs after root exposure. Additionally, compression wood, documents the widening of the crack after root exposure. The cracks on the studied landslide were formed in 1984 and widened during several successive episodes at a rate of 0.66-2.15 cm/year, providing values very similar to those measured by InSAR. In recent years, the cracks widened intensively, indicating an increasing hazard susceptibility of the studied landslide. The anatomy of pine root wood allows for the dating of crack formation. Moreover, subsequent episodes of crack widening were identified with an accuracy of one year. In contrast, the analysis of olive wood anatomy reveals only the timing of crack formation and widening. The study shows that the wood anatomy of roots growing in tension cracks may serve to assess natural hazards such as landslides.

The Daguangbao (DGB) mega-landslide is the largest-scale landslide triggered by the 2008 Wenchuan Earthquake. The slope failure involved approximately 1.2 × 109 m3, exposing a head scarp with a projected area of 1.85 km2 and a maximum height of 800 m. However, the initiation mechanisms remain inadequately understood due to a lack of detailed structural data on the failure boundaries. This study employs 3D laser scanning to analyze rock bridge distribution and fracture patterns on the earthquake landslide scarp. Results indicate that rock bridge failures constitute 22.49

The use of satellite Differential Synthetic Aperture Radar Interferometry (DInSAR) has transformed the analysis of landslide dynamics by enabling detailed spatiotemporal monitoring of slow and subtle ground deformations. DInSAR enables comprehensive geomorphological characterization and identification of triggering factors. Retrospective applications of DInSAR provide valuable insights into past events and support causal analysis linked to rainfall episodes or piezometric fluctuations. Moreover, integration with numerical modeling enhances predictive capabilities and facilitates the calibration of geotechnical parameters. DInSAR is also instrumental in assessing infrastructure impacts and in the generation of susceptibility, hazard, vulnerability, and risk maps, which are key for land-use planning and risk management. Nevertheless, this technique has inherent limitations that must be carefully considered when interpreting results. Future developments, driven by the integration of artificial intelligence and enhanced computing capacities, are transforming the landscape of InSAR applications in landslide studies. These advancements, combined with upcoming satellite missions, are expected to significantly improve measurement accuracy, temporal resolution, and overall operational potential, paving the way for more robust quasi-early warning systems for landslide prevention. In this work, an overview of the current applications, future trends, and challenges of DInSAR in landslide studies is presented, with particular emphasis on the practical dimension of landslide studies and on the exploitation of DInSAR outcomes to support risk management and mitigation strategies.

Land subsidence poses significant problems in regions facing water management challenges, primarily resulting from unsustainable groundwater abstraction that causes significant drops in piezometric levels. Recognizing the urgent need for effective mitigation measures against land subsidence, this study developed a multicriteria decision-making model on the basis of the analytic hierarchy process (AHP) to evaluate ten mitigation alternatives. Expert opinions were gathered through structured surveys in four Mediterranean areas: the coastal aquifer of Comacchio (Italy), the Alto Guadalentín aquifer (Spain), the Sarıgöl-Alaşehir aquifer in the Gediz River Basin (Türkiye), and the Azraq Wetland Reserve (Jordan). Quantitatively, results of the AHP analysis consistently ranked two alternatives (enhancing water use efficiency and strengthening legal control over groundwater abstraction) as the most preferred to mitigate land subsidence, with statistical tests (Friedman test and Dunn’s post-hoc test) confirming the robustness of these results. The innovative contribution of this work lies in its integration of diverse expert perspectives into a comprehensive, transferable decision-making tool that goes beyond previous approaches by combining technical, economic, environmental, and social dimensions. The results provide valuable, targeted guidance for policymakers and planners, offering sustainable strategies that can be adapted to different regional contexts.

The increasing occurrence of wildfires, driven by climate change and environmental degradation, highlights the importance of studying their effects from the perspective of geotechnics and soil mechanics. During a wildfire in 2023 in Central-Southern Chile, temperatures in eucalyptus forests reached up to 600 °C, resulting in significant physical and chemical alterations in the soil. This dataset focuses on the impact of wildfires on the geomechanical properties of volcanic soils in central Chile, providing data collected through laboratory tests and in-situ survey techniques. The methods employed include: (i) delineation of areas severely affected by the 2023 wildfires in the Biobío region of Chile; (ii) measurement of soil geomechanical parameters in affected areas and comparison with pre-fire values; and (iii) compilation of a dataset to facilitate the evaluation of post-fire changes in soil geomechanical properties. This dataset can serve as a reference for assessing the impacts of wildfire-induced changes in soils, providing valuable insights for identifying areas prone to ground instability and informing civil engineering projects.

InSAR and optical pixel offset tracking (POT) are two efficient tools for monitoring landslide displacements, but limitations in resolving 3D displacements constrain the full exploration of kinematic behaviors, especially for complex landslides exhibiting diverse movement types. In this study, we propose a technical route that combines SAR and optical images to reveal the spatiotemporal evolution of the Lanuza landslide (Spain). In the temporal domain, ascending and descending Sentinel-1 SAR images were acquired to retrieve the line-of-sight (LOS) displacements. STL and cross wavelet transform were integrated to calculate the time lag between displacements and environmental factors. In the spatial domain, a two-stage method combining feature point matching and DeepFlow (FPM-DF) was proposed to retrieve the non-rigid horizontal displacements from optical images. A strain model and Bayesian inversion framework (SM-BIF) were integrated to invert 3D displacement fields. The mass conservation method was subsequently applied to estimate the landslide thickness. The results indicate that (1) the periodic terms of displacement are in phase with the freeze-thaw cycle of solifluction, which can intensify earthflow movement. (2) FPM-DF method is more efficient than the traditional POT method, especially for smallscale displacement fields, achieving reductions of standard deviations by 38 % and 51 % in the EW and NS directions, respectively. (3) the SM-BIF method reduces the maximum standard deviations of the 3D displacement field compared to the SM-VCE method, and the maximum thickness of the earthflow is approximately 22 m. This study can provide valuable insights into comprehensive monitoring of complex landslides with multiplatform remote sensing datasets.

Beijing and Tianjin are two mega-cities in China having exhibited large land subsidence for decades, mainly due to groundwater over-exploitation, which is believed to have induced potential damages to man-made linear infrastructure such as the Beijing–Tianjin (Jingjin) high-speed railway. Spaceborne interferometric synthetic aperture radar (InSAR) has been widely employed to investigate land subsidence in cities in the world including Beijing and Tianjin. However, further research is required to evaluate the service limit state of the high-speed railway derived from high-resolution satellite displacement measurements. This is partly due to the stringent demands of high-speed railways and, more significantly, the absence of damage indices for identifying structural damage. In this paper, high-resolution TerraSAR-X data were used to examine land subsidence in Beijing Plain during the period from April 2010 to December 2019, revealing a maximum mean displacement rate of above 110 mm/year. Three damage indices, namely the slope gradient, the vertical radius of curvature, and the angular distortion, are proposed to identify potential damage in high-speed railways using InSAR-derived displacements. It turns out that the angular distortion is the most appropriate damage index, and areas with potential damage have been identified along the Jingjin high-speed railway, which is consistent with its maintenance records. It is believed that this proposed damage identification method, together with the increasing high-resolution InSAR observations, can be routinely applied to identify potential damages to high-speed railways, which will, in turn, guarantee their safe operation.

Anthropogenic activities constitute a significant factor in inducing landslide instability, especially for large landslides in the vicinity of major infrastructures. However, systematic monitoring and risk assessment of the entire slope of such large landslides under engineering disturbances is challenging. In this study, we propose an approach to investigate landslide dynamics from surface to subsurface based on three-dimensional (3D) deformation monitoring and successfully applied it to the Li-Kan Road landslide (LKRL), which was induced by road engineering and located on the right bank of the Lijia Gorge Reservoir in China. We used Sentinel-1 datasets to study the spatiotemporal evolution characteristics of LKRL motion and to determine the sliding depth after inverting 3D deformation fields. Results reveal the significant cumulative displacement (over 2 m) and spatial heterogeneity of LKRL motion over the past 8.5 years. The sliding depth was found to be unevenly distributed, averaging 10.6 m, with a landslide volume of 1.45 × 107 m3. As the landslide is currently in a phase of continuous motion with occasional localized collapses, the failure risk of this large LKRL deserves further close attention in the future. This study represents the systematic survey of LKRL activity using satellite data and provides insights for the mechanistic interpretation and risk management of landslides triggered by human activities.

Excessive groundwater extraction, often leads to changes in aquifer-system layers, causing anthropogenic-triggered land subsidence. The reduction in pore pressure due to groundwater withdrawal acts as an external factor, while soil compressibility serves as the primary internal factor of land subsidence. The interaction between stress and strain within an aquifer-system, or specific layers, is typically represented through stress-strain curves. These curves, illustrating the hydrograph data (stress induced by piezometric level variations) against land subsidence compaction records (strain), offer valuable insights into the geomechanical behaviour of the aquifer-system, such as elastic, plastic, elasto-plastic, or visco-elasto-plastic behaviour. Additionally, these curves can be employed to estimate hydrogeological parameters like the storage coefficients of the aquifer-system. Traditionally, determining storage coefficients from stress-strain curves has relied on subjective visual assessments by skilled researchers. In this study, we proposed a MATLAB© application designed to automate and streamline the analysis of land subsidence datasets. The application facilitates the exploration of potential correlations with piezometric levels and allows for the estimation of storage coefficients. This approach reduces the time-cost of analysis and minimizes potential human-interpretation errors. The developed application integrates temporal series of groundwater levels from observation wells and ground deformation measurements o automatically generate stress-strain curves. To illustrate and validate the effectiveness of the proposed application, the proposed app is applied to diverse aquifer-systems worldwide, each exhibiting distinct geomechanical behaviour. The results showcase the tool's capability in efficiently studying and understanding land subsidence, providing a valuable resource for scientists and researchers investigating the impacts of excessive groundwater extraction on land deformation.

Spaceborne interferometric synthetic aperture radar (InSAR) has been extensively employed to detect surface displacements. However, the automatic extraction of locations and boundaries of active geohazards from surface displacement data remains a significant research challenge. In this study, we propose an improved spatial clustering method to automatically detect active geohazards in Lanzhou City, Gansu Province, China. First, we applied the general atmospheric correction online service for InSAR-assisted InSAR stacking technique to derive the annual surface deformation rate. Then, the C-index was employed to eliminate false deformation signals, and a spatial clustering method was used to delineate the boundaries of active geohazards efficiently. Subsequently, the geohazards were classified, and their spatial distribution characteristics were analyzed. Our results revealed that the annual surface deformation rates in Lanzhou city ranged from -176 to 74 mm/yr. The combination of ascending- and descending-track SAR images increased the observable area from 86.3% (ascending only) and 93.4% (descending only) to 96.8% . In addition, applying the C-index reduced misdetection probabilities by 14.4% and 10.9% for the ascending and descending tracks, respectively. Using the improved spatial clustering method, 775 active geohazards, including 331 active landslides and 444 land subsidence areas, were identified and mapped in Lanzhou City for the first time. Active landslides are predominantly located in the northern and southern hills of the urban area, while land subsidence mainly occurs in areas where hills have been excavated or flattened through land grading and leveling for urban development. The improved spatial clustering approach effectively and automatically extracts, classifies, and characterizes active geohazards, enabling rapid cataloging and providing essential data for geohazard management and risk assessment.

Mapping landslides in urbanised areas is usually challenging as anthropic alterations can hide or erase their morphological imprint on the natural terrain. When landslide features cannot be recognised currently, geomorphological surveys based on historical aerial images become highly valuable. Similarly, remote sensing techniques like Differential Interferometric Synthetic Aperture Radar (DInSAR) can reveal ground displacement related to a landslide despite the absence of surface morphologies. In this work, we aim to highlight the complementarities and limitations of these methods for mapping urban landslides, evaluating the specific challenges of identifying landslides in urban contexts. This discussion is illustrated with two case examples from the town of Alcoy (SE Spain), a location known for its historical landslide impacts. The two selected landslides were defined for the first time in this study: San Pancracio and Viaduct Park landslides, both causing damage to buildings and infrastructure. Ground displacement in both landslides was evidenced by DInSAR data, showing rates of 10 to 30 mm/yr. This data was crucial to spotlight the San Pancracio landslide, whose geomorphological features are currently indiscernible but can be more clearly inferred from historical aerial images. In contrast, DInSAR displacement in the Viaduct Park landslide was limited to a specific sector, while the entire landslide body was easily visible in historical images and partially evident in the present day. Moreover, in both landslides, damage to buildings and infrastructure supported the validation of ground movement potentially associated with the landslide, although construction-related factors may also contribute.

Limestone powder waste is a by-product generated in the polishing and cutting activities of the natural stone industry. In this research, the effect of limestone powder waste as an additive, either alone or in combination with hydrated lime, on the geotechnical properties of three clayey soils has been studied. Moreover, the suitability of limestone powder waste as a standalone material for embankment construction has been assessed. The geotechnical properties were measured in the laboratory by the Proctor, free swell, CBR, unconfined compressive strength and oedometer tests. A full-scale embankment was also constructed with four different sections combining natural soil, limestone powder waste, and lime. A final section with only limestone powder was also constructed. The strength and deformability of these sections were assessed by the plate load test, the dynamic probe test and the footprint test. The laboratory test results indicate a general improvement in the strength and deformability of the soil when mixed with limestone powder waste. The strength increased by up to 88%, while the deformation was reduced by 32% when the by-product was added to the natural soil. When added to the soil and lime samples the strength increased by up to 59% and the deformation was reduced by 15%. The in-situ tests confirmed a reduction in deformability of up to 83% and an increase in soil strength when the by-product was added. Finally, the section with only limestone powder showed less deformability than the others, indicating that this by-product can be used for road embankment construction.

Swelling pressure is a key geotechnical property that influences the behaviour and stability of engineering structures built on expansive clayey soils. This pressure can be measured directly through laboratory tests or estimated using indirect methods. This paper analyses a dataset of undisturbed clay samples from southeastern Spain using advanced symbolic regression techniques, namely: deep symbolic regression (PhySO), high-performance symbolic regression (PySR), multi-objective symbolic regression (MOSR), and physics-guided symbolic regression (PGSR). These methods provide interpretable results as equations, unlike standard machine learning models. All generated equations showed high performance (R2 > 0.91 and MAE < 23 kPa) and simplicity, making them suitable for practical engineering applications. PySR yielded the best overall metrics (R2 = 0.933, MAE = 20.49 kPa), particularly excelling in high-pressure ranges, while PhySO demonstrated the most balanced performance, especially for low to medium pressures. MOSR minimized edge-case bias, and PGSR, despite lower overall performance, remained competitive. The plasticity index (PI) was identified as the most influential factor in all models, followed by the percentage of fines. The use of undisturbed samples enhanced the reliability of the findings, and the resulting equations enable a flexible estimation of swelling pressure based on commonly available geotechnical parameters.

Landslides stand as a prevalent geological risk in mountainous areas, presenting substantial danger to human habitation. The slip surface (SSF), volume, type and evolution of landslides constitute crucial information from which to understand landslide mechanisms and assess landslide risk. However, current methods for obtaining this information, relying primarily on field surveys, are usually time-consuming, labor-intensive and costly, and are more applicable to individual landslides than large-scale landslide groups. To tackle these challenges, we present a novel method utilizing multi-orbit Synthetic Aperture Radar (SAR) data to deduce the SSF, volume and type of active landslides. In this method, the SSF of landslides over a wide area is determined from three-dimensional deformation fields by assuming that the most authentic direction of the landslide movement aligns parallel to the SSF, on the basis of which the volume and type of active landslides can also be inferred. This approach was utilized with landslide groups in Gongjue County (LGGC), situated in the eastern Tibetan Plateau, which pose grave peril to community members and critical construction along the upstream/downstream of the Jinsha River. Firstly, SAR images were gathered and interferometrically processed from four separate platforms, spanning the period from July 2007 to August 2022. Then, three-dimensional displacement time series were inverted based on Interferometric Synthetic Aperture Radar (InSAR) observations and a topography-constrained model, from which the SSF, volume and type were determined using our proposed method. Finally, the Tikhonov regularization method was applied to reconstruct 15-year displacement time series along the sliding surface, and potential driving factors of landslide motion were identified. Results indicate that 53 landslides were detected in the LGGC region, of which similar to 70 % were active and complex landslides with maximum cumulative displacement along the sliding surface reaching 1.5 m over the past similar to 15 years. In addition, the deepest SSF of these landslides was found to reach 114 m, with volumes ranging from 1.66 x 10(5) m(3) to 1.72 x 10(8) m(3). Independent in-situ measurements validate the reliability of the SSF obtained in this study. More particularly, we found that the 2018 failure of the Baige landslide (approximately 50 km from LGCC) had caused persistent acceleration to those wading landslides, highlighting the prolonged impact of external factors on landslide evolution. These insights provide a deeper understanding of landslide dynamics and mechanisms, which is crucial when implementing early warning systems and forecasting future failure events.

Groundwater level variations induce aquifer–system deformation, either elastic or inelastic, which can be assessed through stress‒strain curves. These curves, which represent piezometric level variations against ground deformation, provide key insights into the mechanical behaviour of aquifer systems and allow the estimation of storage coefficients. Traditionally, their interpretation relies on visual assessment, making it subjective and time-consuming. This work presents 2 S-TOOL, an open-access MATLAB© application that automates stress‒strain curve analysis and storage coefficient estimation, reducing manual effort and bias. The tool integrates groundwater level data from observation wells and ground deformation measurements from in situ or remote sensing techniques, automatically identifying elastic and inelastic segments and calculating the S_ke and S_kv coefficients. 2 S-TOOL has been tested via real-world case studies, which demonstrate its ability to streamline analysis and improve consistency. The software provides a reproducible, efficient, and objective method for stress‒strain curve interpretation, enhancing hydrogeological modelling and groundwater management applications.

PAZ mission is an X-band synthetic aperture radar (SAR) satellite launched by Spain in February 2018, capable of routinely acquiring images with high spatio-temporal resolution. In this paper, we explore the potential of spotlight-mode (HS) PAZ images for monitoring small-scale ground deformation with millimeter-level accuracy, utilizing 21 HS PAZ images acquired over the Alcoy basin (SE Spain) between September 2019 and February 2021. Phase unwrapping and long-wavelength atmospheric delays significantly impede high-accuracy estimation of small-scale ground deformation in the study area. To address these issues, we first propose an approach for correcting phase unwrapping errors in interferograms by incorporating constraints from both spatial and temporal domains. Subsequently, we propose a block-based correction algorithm based on principal component analysis (PCA) to mitigate long-wavelength errors in the interferograms. Our results demonstrate that the proposed method can effectively eliminate long-wavelength errors in interferograms after both traditional phasebased method and GACOS corrections, reducing the standard deviation of the interferograms by up to a maximum of 68.4 %, and a value of 37.7 % on average. External validations from GPS and topographic measurements confirm that the deformation time series derived from the proposed method show an accuracy higher than 3 mm. This means millimeter-level precision is achieved in small-scale ground deformation measurements using PAZ SAR images. The analysis of the ground deformation derived from PAZ images reveals 41 active deformation areas in the Alcoy basin between September 2019 and February 2021, each that exhibits a deformation rate exceeding 5 mm/year. Through independent component analysis (ICA) and k-means clustering, we identify three distinct temporal evolution patterns corresponding to landslide activities, land subsidence, and land settlements. This study serves as a methodological blueprint for high-accuracy ground deformation estimation using high-resolution PAZ imagery, offering valuable complementary data to conventional mediumresolution SAR systems (e.g., Sentinel-1) in ground deformation monitoring applications.

Land subsidence is a worldwide threat that may cause irreversible damage to the environment and the infrastructures. Thus, identifying and mapping areas prone to land subsidence with accurate methods such as Land Subsidence Susceptibility Index (LSSI) mapping is crucial for mitigating the adverse impacts of this geohazard. Also, Machine Learning (ML) is now becoming a powerful tool to analyze vast and different datasets such as those necessary for LSSI mapping. In this study, we use the conventional Frequency Ratio (FR) method and ML models to generate LSSI maps of the region of Murcia (Spain) where land subsidence occurred in the past due to groundwater overdraft. A LSSI map was initially generated with known FR. Then, additional Conditioning Factors (CFs) with increased spatial resolution were used to train several ML models and generate a new LSSI map. The Extra-Trees Classifier (ETC) outperformed the other approaches, achieving the best performance with a weighted average precision and F1-Score of 0.96, after optimizing its hyperparameters. Then, a third LSSI map was calculated using the FR method and observations of land subsidence from InSAR (Interferometric Synthetic Aperture Radar). This study shows that the effectiveness of using several CFs depends on the added information of each layer. Moreover, the comparison between the different LSSI maps and InSAR data highlights the crucial role of the spatial resolution for accurate mapping, thus enhancing land subsidence risk assessment.

Monitoring of mining-induced subsidence dynamics enables to the exploration and analysis of changes in the direction of surface deformation caused by the extraction of underground resources. Both human activities and geological environments affect these changes to a large extent. LuTan-1 (LT-1) satellite as the first SAR mission with L-band bistatic spaceborne in China, provides continuous imagery for analyzing surface deformations through differential SAR interferometry, offering valuable velocity results using stacking techniques. The results of the subsidence bowl obtained from LT-1 closely align with those from Sentinel-1 in the almost same period in Datong, China. Furthermore, we validated the DInSAR results of LT-1 and Sentinel-1 using continuous GNSS data from the corresponding time frames. Notably, we observed a significant improvement in the quality and accuracy of LT-1 datasets due to the in-orbit performance test. Finally, we explored the surface deformation dynamics pertinent to mining activities using DInSAR results obtained from four different time periods. These results revealed substantial changes on the shape and spatial location of the subsidence bowls over time. This indicates a maximum horizontal displacement in the directional change of approximate 1.429 km and an average face advance rate between 0.724 and 6.355 m/day within about a one-year time period. Lastly, velocity and accumulated displacement gradient maps, derived from stacking results, were overlaid to the distribution of critical infrastructures in the study areas to assess their exposure to mining subsidence, and a validation process was carried out using GF-7 optical images. This study emphasizes the potential of LT-1 to monitor mining subsidence dynamics and its capability for joint analysis with the exposure of critical infrastructures.

This study analyzes the effects of high temperatures on the strength, mineralogy, and color of a cretaceous limestone. A decrease in strength of over 90% has been observed at exposure temperatures over 800 degrees C. The natural color turns reddish at 400 degrees C, becomes gray at 600 degrees C, and the rock exhibits a whitish coloration at 900 degrees C. These significant color changes are linked to variations in the mineralogical composition of the rock and is particularly important for historical buildings affected by fires. By considering mineralogy, color, and strength, it becomes possible to assess the impact of a fire on a historical monument. Evaluating color, it is possible to estimate the maximum temperature to which it has been exposed and to identify the areas of the building affected by varying degrees of thermal damage. The relationship between strength and color enables the evaluation of the decrease in strength for each zone.