The rise in seasonal lifestyle tourism, characterized by winter-escape health and wellness stays and long-term leisure residence, has intensified peak-off-peak imbalances and pressures on the allocation of tourism service supply in tropical island destinations. However, existing research lacks a systematic comparison of seasonal fluctuations and long-term evolution for this subgroup at the city/county level. Therefore, this study aims to characterize the seasonal pattern, long-term trend features, and typological differentiation of seasonal lifestyle tourism at the county level, and to compare differences across types. Using monthly data on seasonal lifestyle tourism for 18 cities/counties in Hainan from 2021 to 2024, we apply TRAMO/SEATS decomposition to identify seasonal structures and measure seasonal amplitude and employ the Hodrick-Prescott (HP) filter to extract trend components and determine their directions of change. We further construct five development types by integrating trend categories and changes in seasonal amplitude and test between-type differences using one-way analysis of variance (ANOVA). Results show that Hainan exhibits a stable "winter-spring peak and summer-autumn trough" pattern (peaks concentrated in January-March and December, with the off-season typically spanning May-October), with strong seasonality and pronounced spatial heterogeneity. The four-year mean seasonal range at the county level is 215.01, with high values clustered in southern Hainan; Haikou remains relatively low, while Wenchang shows an upward trend. Long-term trends are clearly differentiated: 13 counties show sustained growth, 2 show decline, and 3 display a U-shaped recovery (decline followed by rebound). Growth rates also vary substantially, with Qionghai increasing at roughly 27 times the rate of Qiongzhong. Integrating seasonal and trend characteristics yields five types, of which the Robust Development type accounts for the largest share (50%). Between-type differences are mainly reflected in tourism service supply capacity: the number of star-rated hotels (p = 0.033, eta 2 = 0.530) and overnight visitors (p = 0.004, eta 2 = 0.676) differ significantly across types, whereas differences in natural-environment conditions are not significant. This study provides a scientific basis for zoning management and optimizing low-season strategies in Hainan.

The accurate calculation of excavation volume is critical for open-pit mine planning and management. Traditional methods are often inefficient and constrained by operational conditions. In contrast, digital surface model (DSM) differential analysis using stereophotogrammetry enables rapid acquisition of excavation volume, which holds significant value for retrospective excavation process. However, the actual mining process is not a simple matter of “excavation” or “backfilling”, but rather a complex mining pattern involving repeated excavation as new coal seams are exposed. This study utilized multi-source stereo remote sensing data (ZY-3, GF-7 satellite and UAV data) to construct a high-precision DSM time series spanning 2013 to 2025, focusing on analyzing the topographical evolution patterns of three representative mining pits. Research indicates that constructing DSMs during summer and autumn yields higher conformity with actual terrain, RMSE = 1.67 m and ME = −0.07 m. To address diverse mining patterns, we propose two calculation methods: the Cumulative Method (CM), which captures iterative excavation-backfilling cycles, and the First-Last Subtraction Method (FLSM), which mitigates cumulative DSM errors during continuous excavation. For phased mining operations, a hybrid method combining both approaches yields optimal results. Validation in three typical pits showed relative calculation errors of 1.36%, −0.49%, and 1.68%, respectively. The study indicates that the surface morphology changes in open-pit mines exhibit distinct non-linear characteristics. The method proposed herein not only enhances computational accuracy but also provides technical support for tracing historical coal excavation volumes.

Snow avalanches severely damage infrastructure in high-mountain regions. Susceptibility maps show where avalanches may occur but do not quantify hazard intensity, which limits risk-informed decisions. We propose a decision-oriented framework that combines interpretable machine learning with dynamic simulation, applied to the Eastern Himalayan Syntaxis. A remote-sensing inventory of 1233 avalanche events was compiled and validated by field surveys. A Bayesian-optimized XGBoost model produced susceptibility maps. SHAP analysis identified critical thresholds of key drivers. These thresholds were used to automatically map potential release areas under general and extreme scenarios. The release areas were then input into the Rapid Mass Movement Simulation (RAMMS) to generate hazard intensity maps and assess road exposure along the Galongla section of the Zhamo road. The model achieved high predictive performance (AUC = 0.932). High-susceptibility zones concentrated in the Duoxiongla and Galongla corridors. Four dominant factors were NDVI (15.6%), aspect (11.0%), slope (9.1%), and roughness (8.9%). Under general and extreme scenarios, avalanche-affected areas covered 54.6% and 66.2% of the study region, and 16.7% and 25.8% of road segments faced high risk (impact pressure > 500 kPa). The framework provides practical support for hazard zoning, infrastructure prioritization, and scenario-based risk evaluation in data-scarce high-mountain environments.

Under a sustained warming climate, reservoir-area landslides exhibit increasingly complex, non-linear interactions. The cascade "climate or seasonal variability-soil rheology-vegetation response/feedback-impulsewave risk" constitutes an emerging safety threat in alpine gorge reservoirs; yet how such cascades propagate-and how their key links manifest-remains insufficiently resolved. Here, adopting a geomorphological-ecological, multi-feedback perspective, we develop a research paradigm that couples heterogeneous geological modeling with surface-ecosystem diagnostics and analyses of spatiotemporal lag effects, and apply it to an empirical study of the Likan Landslides in the Lijia Gorge Reservoir on the upper Yellow River. We show that the landslide undergoes a northward-deflecting motion as a quasi-rigid block, and we identify and quantitatively characterize three heterogeneous zones with distinct activity signatures: (1) Horizontally dominated displacement zone (0.7 km2; mean thickness 10.6 m; maximum displacement rate 73 mm yr-1) showing thick-slope shear-creep behavior; (2) Downslope displacement-dominated zone (0.3 km2; 7.9 m; 33 mm yr-1) characterized by thin-slope creep and brittle unloading; (3) Severe dual-motion zone (2.2 & times; 10-2 km2; 7.03 m; 33-73 mm yr-1) manifesting block tensile-fracture movement. We further detect a 3-6-month lag between precipitation forcing and landslide response, coupled with a vegetation recovery suppression effect, confirming the system as a hydrologically lag-driven, progressive heterogeneous landslide. Its long-term creep-spatial differentiation-environmental feedback composite mechanism induces cumulative damage to the ground surface and poses the potential to trigger secondary wave surges. The study establishes a multi-field coupling framework of remote sensing that links process analysis with risk-warning decision-making, provides an empirical case for understanding landslide responses under climate change, and offers a transferable paradigm for cascading landslide risk assessment in reservoir regions.

Abstract. Under ongoing climate warming, extreme rainfall is becoming increasingly frequent, intensifying hazard pressure in mountainous regions where short-lived torrent events interact with long-term geomorphic evolution. Yet it remains unclear how external climatic forcing couples with the long-term antagonism between tectonic uplift and fluvial incision, and whether this coupled relationship can help identify hazard-prone geomorphic settings at the catchment scale. Here we investigate the 21 August 2023 torrent disaster in Jinyang County, Sichuan Province, China, as a representative case. Using multi-source data and quantitative analysis, we establish a regional disaster-system framework, DGR, which integrates Dynamics, Geology–meteorology cross-spatial coupling, and Risk phasing. Within this framework, we aim to capture the combined influence of endogenic tectonic forcing, exogenic hydrometeorological forcing, lithological erodibility, and human disturbance on hazard occurrence, and to provide a simplified conceptual mapping of the regional Earth system in tectonically active mountain terrain. Our results show that the disaster site was located in a runoff-incision-dominated reach characterized by disequilibrium between tectonic uplift and fluvial incision. On this basis, we propose the concept of a weak equilibrium window in the uplift–incision system, which may provide a useful geomorphic perspective for identifying potentially hazard-prone settings. Hydro-sedimentary analysis further suggests that the event was primarily flood-dominated, with sediment supplied mainly by proximal erosion rather than by sustained long-distance channel transport. Field-constrained reconstruction also indicates that engineering disturbance on the valley floor may have modified the natural transport corridor and amplified the interaction between human activity and natural hazard processes. More broadly, the results suggest that torrent disasters can serve as short-lived expressions of the long-term antagonism between internal and external geomorphic forcing. In this sense, the DGR framework provides a transferable conceptual and analytical approach for linking long-term uplift–incision evolution with short-term hazard response, and offers a practical basis for hazard identification and risk assessment in tectonically active mountain regions under climate change.

Areas of serpentine in ophiolitic melange zones often trigger large rock avalanches and exhibit strong movement. However, how the mechanism under which they post-failure hypermobility and long runout are unclear. Here, we identify and analyze a representative prehistoric rock avalanche, the Basu rock avalanche, with a large volume and a high mobility, which developed in the Nu River ophiolitic melange zone of the Tibetan Plateau. Based on field investigations, experimental, and Numerical simulation analyses we determined its development background and thus explained why it was hypermobile. This rock avalanche, with a volume of approximately 3.15 x 10(9) m(3), occurred around similar to 187 ka before present (B.P.). It developed on a marble nappe, with serpentine soft rock exposed locally at its base. It may have ultimately been triggered under seismic action, resulting in intense movement. The lubrication effect of fine-grained serpentine particles within the slip zone facilitated the hypermobility of the rock avalanche, resulting in both a large volume and an extended runout distance. This demonstrates that serpentine soft fine particles widely distributed in the suture zone are a typical lubricating material. The hypermobility of this large rock avalanche are striking and emphasizes the need to determine where, how and when these rare but high-magnitude rock avalanche events may occur. We proposed a new perspective on the triggering mechanisms of the rock avalanches and further verified the hypothesis of powder lubrication control effects.

The Baihetan Reservoir filling began on April 15, 2021, triggering extensive landslides and the reactivation of previously deposited slope material after two periods of 825 m trial impoundments. Reactivation events are distinct in the Heishui tributary. It is crossed by the Zemuhe Fault Zone, with broken rock soil mass inducing bank collapse processes associated with long-term tectonic activity. Following two impoundment cycles, three disastrous slope failures occurred in the Heishui tributary, posing significant threats to road and building safety. We aimed to qualitatively and quantitatively evaluate the transient evolution of bank collapse in the Heishui tributary. Terrain-following photogrammetry and innovative dual-controller cooperative UAV flight campaigns will be conducted in 2022 and 2023. Then, digital orthophoto maps (DOMs) and digital surface models (DSMs) were created to establish and analyze comprehensive bank collapse inventory and distribution laws. Next, a geomorphic change detection (GCD) method was used to calculate the erosion caused by catastrophic bank collapses. The results indicated that 80 bank collapses occurred during the initial impoundment, which increased to 90 after the second impoundment. The collapses mainly occurred at the intersection of the faults and bank slope. Catastrophic bank collapses exhibit precursor signs, with the front edge experiencing failure in areas ranging from 7 % to 46 % during the initial impoundment and destruction occurring during periods of high water levels. In addition, the bank slope increases by 2 degrees to 5 degrees. Although some bank collapses became part of the fluctuating zone after impoundment, the collapse process remained prevalent in the Baihetan Reservoir. This case study focuses on bank collapse geomorphological characteristics, distribution laws, and transient evolution, which can help enhance the understanding of disaster prevention in reservoir impoundment regions.

Accelerated glacier retreat in the Southeastern Tibetan Plateau (SETP) under global warming has significantly increased threats from ice avalanches (IAs) and glacier lake outburst floods (GLOFs). This study established a comprehensive inventory of IAs and GLOFs in the SETP using multi-source remote sensing data. Hazard assessments were conducted using the Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE). The results indicate that: (1) A total of 1,676 IAs and 100 potential GLOFs were identified, primarily located along the eastern Nyainqentanglha and the western Hengduan Mountains. (2) IAs mostly occur on northfacing slopes with elevations of 4,500-6,000 m and areas of 0.1-0.5 km2, while GLOF-related lakes are mainly distributed at 4,000-5,000 m with areas of 0.1-0.3 km2 and northward outburst directions. (3) Area and average slope are the primary driving factors for IAs, while glacial lake area and parent glacier slope are identified as the key controlling factors for GLOFs. (4) The hazard assessment results identified 111 high-risk IAs and 16 high-risk GLOFs, with the latter experiencing a 154.7 % increase in area since 1985, and 11 of these lakes exhibit cascading IA-GLOF risks. This comprehensive study enhances understanding of IA and GLOF hazards in the SETP and provide a scientific basis for effective disaster risk management.

Mining-induced geological hazards in the mountainous regions of southwestern China are often characterized by wide impact zones and complex subsurface structures, which pose significant challenges for the precise identification of landslides and the analysis of their formation mechanisms. To address this issue, this study introduces an integrated “Space-Air-Ground-Subsurface” collaborative observation system that combines SBAS-InSAR technology, a two-dimensional deformation decomposition model, Unmanned Aerial Vehicle photogrammetry, field geological investigation, and audio magnetotelluric (AMT) sounding. This multi-dimensional framework enables the systematic acquisition of both surface deformation and subsurface structural information of the Leji landslide, thereby elucidating its controlling factors and causative mechanisms. The results reveal that the central parts of Landslide I and Landslide II exhibit the most significant deformation, with surface displacement dominated by downslope subsidence. The maximum annual average subsidence rates range between −60 mm/y and −80 mm/y. The cumulative deformation zones retrieved by SBAS-InSAR closely coincide with the mining areas detected by AMT. Through data fusion, the boundary angles of the mining areas were determined as 77° in the upslope direction and 48° in the downslope direction along the dip, and 77° and 55° in the strike direction. Comprehensive analysis indicates that the Leji landslide is a Quaternary soil creep landslide formed under the combined influence of fault–fold structures, frequent heavy rainfall, and both open-pit and underground mining activities, and it remains in an active state. This study demonstrates that the “Space-Air-Ground-Subsurface” collaborative observation system effectively overcomes the limitations of single techniques in landslide mechanism research, providing a reliable technical pathway and scientific basis for understanding the development mechanisms and disaster risk mitigation of mining-induced landslides.

Reservoir landslides are the focus of geohazards associated with mega hydropower projects and have been extensively studied by monitoring their post-impoundment deformation. However, how landslide deformation changes before, during, and after impoundment is rarely known. Using satellite radar interferometry, we map 200 active landslides with their time-series deformation spanning the impoundment of Baihetan, the secondlargest hydropower project globally. We define the amplitude of seasonal fluctuation (ASF) to analyze the impact of rainfall and water level on seasonal landslide velocities before and after impoundment. Interestingly, although landslides are overall accelerated, a reduction in seasonal fluctuation is apparent after the impoundment. We argue that the project elevated water levels during the dry season, only promoting landslide motion when they were kept stable before impoundment. We also find the 32 newly formed landslides are more likely to develop on slopes with structures related to river flow direction, emphasizing the role of the raised water in triggering new landslides. These findings reveal how landslides respond to mega hydropower projects, facilitating disaster risk management and resettlement policy regulation.

Constructing large hydroelectric power stations in canyon areas is widely accepted as a solution to meet energy demands. However, large-scale water storage elevates water levels, shifts the water-land boundary, increases evaporation, and alters the microclimate, potentially triggering a chain of environmental responses. This raises concerns about whether such changes could increase abnormal precipitation events, thereby stimulating more widespread slope failures and vegetation changes, ultimately disturbing the landscape. The Baihetan Hydropower Station, located on the lower Jinsha River in China, serves as a case study for exploring these effects. By monitoring long-term surface deformations, abnormal precipitation, topography, geomorphological parameters, and vegetation changes, we have gained insights into the macro disturbances caused by water level fluctuations. Since the reservoir began storing water, slope failures have markedly increased, particularly in the form of creeping slopes and bank collapses in the drawdown zone. This period has also seen a reduction in total precipitation, an increase in abnormal precipitation, and slower vegetation growth. Further analysis reveals that while rising water levels primarily destabilize reservoir shore slope-failures, precipitation also significantly influences this instability. The greatest threat to shore stability arises when water levels drop and are followed by heavy rainfall. Although the severity of abnormal precipitation has increased post-impoundment, it has not led to more extreme precipitation events. Vegetation growth on active slopes near the reservoir is mainly controlled by changes in precipitation, with vegetation decline due to slope instability being limited and not widespread. These findings contradict initial assumptions, indicating that landscape disturbances due to water storage are limited and have not led to severe, uncontrollable chain reactions.

[Objective]Slope instability triggered by reservoir water-level fluctuations represents a prevalent geohazard in mountainous regions and canyons undergoing large-scale hydropower development.Since the 21st century,accelerated hydropower development has necessitated enhanced methodologies for identifying such specific-type geohazard potentials.In recent years,InSAR observations have largely addressed the challenge of identifying large-scale,multi-target deformation;however,due to limitations in real-time monitoring capabilities,this technique cannot detect latent hazards that have not yet manifested as deformations.Therefore,there is an urgent need to establish geomorphic signatures of reservoir-induced slope failures to improve hazard identification specificity.The large-scale impoundment of the Baihetan Reservoir since 2021 has triggered a series of slope instabilities,providing an exceptional opportunity to define the geomorphic signatures.[Methods]We integrated InSAR observations,geomorphic parameters,and optical imagery.Specifically,we utilize 228 ascending and 234 descending Sentinel-1A datasets(2020-2023)processed with DS-InSAR to identify deformed slopes triggered by reservoir water-level fluctuations.[Results]The results demonstrate the explanatory power of geomorphic parameters such as toe height,slope,aspect,and roughness in relation to disaster triggers.Furthermore,the analysis reveals correlations between lithological variations,slope structures,precipitation,and reservoir water-level fluctuations.[Conclusion]The strength of lithology,slope structure,and geomorphometric parameters in the Baihetan Reservoir area,along with their corresponding numerical ranges,form composite geomorphic signatures that can be used to identify hazards associated with reservoir water-level-induced slope instability early on.Additionally,we discovered that,beyond the effects of water-level fluctuations,precipitation events also play a significant role in triggering slope instability in the reservoir area,highlighting the importance of this factor as a driving force.[Significance]These insights significantly advance risk mitigation strategies for hydropower projects,facilitating optimal site selection and operation of hydropower stations,while providing a reference framework for assessing other slope instability mechanisms.

Mountain tunnels are usually vulnerable due to the existence of fault fracture zones and various types of slope instability. Existing research primarily focuses on tunnelling through fault zones and structural damage caused by fault movements. The research on the deformation of built tunnels caused by reservoir-induced slope deformation remains limited. This study aims to investigate the deformation mechanism of the Dawanzi tunnel after reservoir filling in the Baihetan hydropower station and analyze the impact of different mechanisms on the governance decision of the tunnel. The integrated methods, including the field investigation, airborne light detection and ranging (LiDAR) survey, and interferometry synthetic aperture radar (InSAR) observation, ensured the detailed interpretation of the geological, geomorphologic and surface deformation characteristics of the slope. A three-dimensional numerical model of a tunnel crossing a fault fracture zone was established to analyze the displacement and stress condition of the lining under reservoir filling. Results show that the fault fracture zone plays a vital role in tunnel damage. It provided favorable geological conditions for the deep-seated gravitational slope deformation (DSGSD) induced by reservoir filling. A local ancient landslide developed on the right front side of the DSGSD with a larger deformation magnitude. Several pieces of evidence, including geomorphic features, deformation characteristics, and drilling, indicate that the DSGSD caused the tunnel damage rather than the landslide movement. No matter the spatial position of lining damage and shear stress distribution, simulated results based on the geological model can well correspond to the actual situation, which verifies the correctness of the proposed tunnel deformation mechanism. The research result can provide helpful information on the supporting design and governance decisions of the Dawanzi tunnel.

In the semi-desert aeolian sand areas of Northern China, surface deformation monitoring with SAR is challenged by loss of coherence due to mobile dunes, seasonal vegetation changes, and large-gradient, nonlinear subsidence from underground mining. This study utilizes PALSAR-2 (L-band, 3 m resolution) and Sentinel-1 (C-band, 30 m resolution) data, applying InSAR and Offset tracking methods combined with differential, Stacking, and SBAS techniques to analyze deformation monitoring effectiveness and propose an efficient dynamic monitoring strategy for the Shendong Coalfield. The main conclusions can be summarized as follows: (1) PALSAR-2 data, which has advantages in wavelength and resolution (L-band, multi-look spatial resolution of 3 m), exhibits better interference effects and deformation details compared to Sentinel-1 data (C-band, multi-look spatial resolution of 30 m). The highly sensitive differential-InSAR (D-InSAR) can promptly detect new deformations, while Stacking-InSAR can accurately delineate the range of rock strata movement. SBAS-InSAR can reflect the dynamic growth process of the deformation range as a whole, and SBAS-Offset is suitable for observing the absolute values and morphology of the surface moving basin. The combined application of Stacking-InSAR and Stacking-Offset methods can accurately acquire the three-dimensional deformation field of mining-induced strata movement. (2) The spatiotemporal process of surface deformation caused by coal mining-induced strata movement revealed by InSAR exhibits good correspondence with both the underground mining progress and the development of ground fissures identified in UAV images. (3) The maximum displacement along the line of sight (LOS) measured in the mining area is approximately 2 to 3 m, which is close to the 2.14 m observed on site and aligns with previous studies. The calculated advance influence angle of the No. 22308 working face in the study area is about 38.3°. The influence angle on the solid coal side is 49°, while that on the goaf side approaches 90°. These findings further deepen the understanding of rock movement and surface displacement parameters in this region. The dynamic monitoring strategy proposed in this study is cost-effective and operational, enhancing the observational effectiveness of InSAR technology for surface deformation due to coal mining in this area, and it enriches the understanding of surface strata movement patterns and parameters in this region.

Toppling is among the most common deformation types in steeply bedded rock slopes. With the construction of high dams in large rivers, various toppling deformations have occurred in reservoir areas, and it is still unclear how toppling deformation varies with long-term water-level fluctuations. To identify the deformation characteristics and different responses to the water-level fluctuations of the two types of toppling in underdip bedding and anti-dip bedding slopes, stacking interferometric synthetic aperture radar (stacking-InSAR) and small baseline subset InSAR (SBAS-InSAR) technologies were used based on Sentinel-1 SAR data from 8 years following reservoir impoundment. Initially, topplings that deformed locally after impoundment and those that deformed later exhibited complete deformation. According to the stacking-InSAR deformation profile and the deformation characteristics of typical permanent scatter (PS) points from SBAS-InSAR, both Xingguangsanzu (XGSZ) and Yanwan (YW) toppling instabilities can be divided into two deformation zones. According to the annual stacking-InSAR results and the deformation rates of the two zones of topplings, the deformation mode of the XGSZ toppling instability was retrogressive and that of the YW toppling instability was progressive. The crack distributions were related to the surface deformation and the slope topography, and the main tension cracks were very consistent with the large deformation area revealed by InSAR. In terms of long-term deformation, the XGSZ toppling instability mainly suffered from collapse of the front edge before reservoir impoundment, which turned into overall deformation after the first impoundment, while the YW toppling instability deformed after impoundment. The deformation area of the XGSZ toppling instability expanded faster than that of the YW toppling instability in the first 3 years after impoundment. Regarding the relationship with water-level fluctuations, the SBAS-InSAR results showed that the impact of water-level drawdown on the YW toppling instability was more significant than that on the XGSZ toppling instability. The elevation of the YW toppling instability affected by water-level fluctuations was higher than that of the XGSZ toppling instability, and it was speculated that the strong water conductivity of the fault fracture zone in the middle of the slope affected the deformation of the YW toppling instability.

After the initial impoundment of the Baihetan Reservoir in April 2021, the water level in front of the dam rose about 200 m. The mechanical properties and effects of the bank slopes in the reservoir area changed significantly, resulting in many bank collapses. This study systematically analyzed the bank slope of the head section of the reservoir, spanning 30 km from the dam to Baihetan Bridge, through a comprehensive investigation conducted after the initial impoundment. The analysis utilized UAV flights and ground surveys to interpret the bank slope’s distribution characteristics and failure patterns. A total of 276 bank collapses were recorded, with a geohazard development density of 4.6/km. The slope gradient of 26% of the collapsed banks experienced an increase ranging from 5 to 20° after impoundment, whereas the remaining sites’ inclines remained unchanged. According to the combination of lithology and movement mode, the bank failure mode is divided into six types, which are the surface erosion type, surface collapse type, surface slide type, bedding slip type of clastic rock, toppling type of clastic rock, and cavity corrosion type of carbonate rock. It was found that the collapsed banks in the reservoir area of 85% developed in the reactivation of old landslide deposits, while 15% in the clastic and carbonate rock. This study offers guidance for the next phase of bank collapse regulations and future geohazards prevention strategies in the Baihetan Reservoir area.

The present study proposes a preliminary analysis method for rock mass joint acquisition, analysis, and slope stability assessment based on unmanned aerial vehicle (UAV) photogrammetry to extract the joint surface attitude in Geographic Information Systems (GIS). The method effectively solves the difficulties associated with the above issues. By combining terrain-following photogrammetry (TFP) and perpendicular and slope surface photogrammetry (PSSP), the three-dimensional (3D) information can be efficiently obtained along the slope characteristics’ surface, which avoids the information loss involved in traditional single-lens aerial photography and the information redundancy of the five-eye aerial photography. Then, a semi-automatic geoprocessing tool was developed within the ArcGIS Pro 3.0 environment, using Python for the extraction of joint surfaces. Multi-point fitting was used to calculate the joint surface attitude. The corresponding attitude symbols are generated at the same time. Finally, the joint surface attitude information is used to perform stereographic projection and kinematic analysis. The former can determine the dominant joint group, and the latter can obtain the probability of four types of failure, including planar sliding, wedge sliding, flexural toppling, and direct toppling. The integrated stability evaluation method studied in this paper, which combines a 3D interpretation of UAV and GIS stereographic projection statistical analysis, has the advantages of being efficient and user-friendly, and requires minimal prior knowledge. The results can aid in the geological surveys of slopes and guide engineering practices.

As underground coal mining activities are increasing in the southwestern mountainous areas of China, the geological safety issues related to ground subsidence and slope deformation have emerged as significant concerns. These issues have started to impact the routine mining operations. Monitoring deformation and analyzing the mechanical behavior of mining areas can help reveal the deformation patterns at surface level and on typical slopes in mountainous coalfields, ultimately reducing the risk of landslides. Taking the Guangfeng coal mine as a case study, this paper employs Interferometric Synthetic Aperture Radar (InSAR) and numerical simulation to analyze the influence of surface deformation caused by underground mining activities on slope deformation. The InSAR results indicate the presence of two distinct subsidence areas, labeled as I and II. The maximum annual subsidence rate in area I reaches 72 mm/a, while area II shows an annual deformation rate of 59 mm/a. The subsidence around the mined-out area has triggered sliding deformation in the slopes, which aligns with the numerical simulation results obtained from the fast Lagrangian analysis of continua in three dimensions (FLAC3D). As mining advances further into the working face, greater tensile stress develops at the rear edge of the slope, causing the subsidence at the center of surface which gradually shifts towards the top of the slope. The process of slope deformation related to underground coal mining in mountainous regions can be divided into four stages: the original slope stage, the early stage of underground mining, the evolution stage of composite slope deformation, and the stage of increased surface subsidence in the slope. By combining InSAR technology with FLAC3D numerical simulations, surface movements in mountainous mining areas can be accurately and reliably monitored and analyzed. This approach provides effective guidance for deformation monitoring and prediction of slope stability in mining regions.

Wetland ecosystems in the Qinghai–Tibet Plateau are pivotal for global ecology and regional sustainability. This study investigates the dynamic changes in wetland ecosystems within the Chaidamu Basin and their response to drought, aiming to foster sustainable wetland utilization in the Qinghai–Tibet Plateau. Using Landsat TM/ETM/OLI data on the Google Earth Engine platform, we employed a random forest (RF) method for annual long-term land cover classification. Standardized precipitation evapotranspiration indices (SPEI3, SPEI6, SPEI9, and SPEI12) on different time scales were used to assess meteorological drought conditions. We employed a Pearson correlation analysis to examine the relationship between wetland changes and various SPEI scales. The BFASAT method was used to evaluate the impact of SPEI12 trends on the wetlands, while a cross-wavelet analysis explored teleconnections between SPEI12 and atmospheric circulation factors. Our conclusion is as follows: The wetlands, including lake, glacier, and marsh wetlands, exhibited a noticeable increasing trend. Wetland expansion occurred during specific periods (1990–1997, 1998–2007, and 2008–2020), featuring extensive conversions between wetlands and other types, notably the conversion from other types to wetlands. Spatially, lake and marsh wetlands predominated in the low-latitude basin, while glacier wetlands were situated at higher altitudes. There were significant negative correlations between the SPEI at various scales and the total wetland area and types. SPEI12 displayed a decreasing trend with non-stationarity and distinct breakpoints in 1996, 2002, and 2011, indicating heightened drought severity. Atmospheric circulation indices (ENSO, NAO, PDO, AO, and WP) exhibited varying degrees of resonance with SPEI12, with NAO, PDO, AO, and WP demonstrating longer resonance times and pronounced responses. These findings underscore the significance of comprehending wetland changes and drought dynamics for effective ecological management in the Chaidamu Basin of the Qinghai–Tibet Plateau.

Reservoir impoundment significantly impacts the hydrogeological conditions of reservoir bank slopes, and bank slope deformation or destruction occurs frequently under cyclic impoundment conditions. Ground deformation prediction is crucial to the early warning system for slow-moving landslides. Deep learning methods have developed rapidly in recent years, but only a few studies are on combining deep learning and landslide warning. This paper proposes a slow-moving landslide displacement prediction method based on the Informer deep learning model. Firstly, the Sentinel-1 (S1) data are processed to obtain the cumulative displacement time-series image of the bank slope by the Small-BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) method. Then, combining data on rainfall, humidity, and horizontal and vertical distances of pixel points from the water table line, this study created a dataset with landslide displacement as the target feature. After that, this paper improves the Informer model to make it applicable to our dataset. This study chose the Dawanzi landslide in the Baihetan reservoir area, China, for validation. After training with 50-time series deformation data points, the model can predict the displacement results of 12-time series deformation data points using 12-time series multi-feature data, and compared with the monitoring values, its Mean Square Error (MSE) was 11.614. The results show that the multivariate dataset is better than the deformation univariate data in predicting the displacement in the large deformation zone of bank slopes, and our model has better complexity and prediction performance than other deep learning models. The prediction results show that among zones I–IV, where the Dawanzi Tunnel is located, significant deformation with the maximum deformation rate detected exceeding –100mm/year occurs in Zones I and III. In these two zones, the initiation of deformation relates to the drop in water level after water storage, with the deformation rate of Zone III exhibiting a stronger correlation with the change in water level. It is expected that deformation in Zone III will either remain slow or stop, while deformation in Zone I will continue at the same or a decreased rate. Our proposed method for slow-moving landslide displacement forecasting offers fast, intuitive, and economically feasible advantages. It can provide a feasible research idea for future deep learning and landslide warning research.