Search Results (437)

Operational landslide susceptibility mapping (LSM) remains challenging in regions with pronounced geo-environmental heterogeneity, where single global models often overlook spatially variable landslide-environment relationships. Northern Guangdong, China, is a typical humid mountainous region where steep terrain, diverse lithology, and highly variable rainfall produce non-stationary landslide controls. To address this challenge, we develop a cluster-informed LSM framework that integrates unsupervised consensus K-means sub-zoning with localized Random Forest (RF) models and SHapley Additive exPlanations (SHAP). We use a harmonized inventory of 1510 landslides (2011–2022), together with twelve 30 m conditioning factors, for model training and validation. Compared with logistic regression, Support Vector Machines (SVM), and Light Gradient Boosting Machine (LightGBM), RF consistently achieves higher accuracy across clusters, and the cluster-wise RF ensemble attains pooled ACC = 0.8212, F1 = 0.8176, and AUC = 0.8956. SHAP highlights both regionally consistent predictors (e.g., NDVI, distance to road) and distinct cluster-specific controls linked to geomorphic and hydrologic settings. The proposed framework enhances predictive accuracy, produces finer susceptibility gradients, and yields better-calibrated probability estimates than a single global model. These results demonstrate that explicitly accounting for geo-environmental heterogeneity can generate interpretable, spatially adaptive susceptibility outputs. By identifying high-risk zones for priority monitoring, land-use regulation, infrastructure protection, and mitigation planning, the proposed framework provides a practical decision-support tool for sustainable mountain development and disaster risk reduction in heterogeneous mountainous regions. Full article

In UAV-based remote sensing, accurate and efficient image mosaicing is crucial for achieving real-time monitoring. Traditional cloud-centric processing paradigms, however, face core scientific challenges such as high latency, bandwidth bottlenecks, and limited autonomy, making them inadequate for dynamic, real-time scenarios. To address these issues, this paper proposes an edge-computing-enabled UAV image mosaicing system. The system consists of a UAV remote sensing platform and an edge computing terminal, with the core being our novel B-SIFT-ILS algorithm. The algorithm first uses geographic coordinates for unified registration, constructs a Gaussian scale space for multi-resolution representation, and then precisely locates extrema in the Difference of Gaussian (DoG) space using a 3D quadratic function. A BANSAC algorithm is subsequently employed to refine feature points and extract stable SIFT features, and finally, Iterative Least Squares (ILS) are used to achieve seamless mosaicing. Experimental results demonstrate that, compared with classical RANSAC, the proposed method achieves superior feature sampling accuracy (rotation: 0.879, translation: 0.877) and lower latency. The ILS-based smoothing stage effectively eliminates noise and ghosting without introducing gradient reversal, performing comparably to deep learning methods while significantly outperforming direct averaging and Gaussian approaches. On the NVIDIA Jetson Orin NX edge terminal, a single processing instance requires only 1124 ms, highlighting its strong potential for real-time, low-latency, and autonomous mosaicing tasks. Future research will focus on extending the approach to non-planar terrains and implementing adaptive parameter tuning for the BANSAC algorithm. Full article

Landslide susceptibility is often treated as a static assessment of present-day conditions, overlooking the temporal evolution of geomorphological and anthropogenic drivers. This limitation is particularly relevant in rapidly urbanising areas, where land use change continuously alters environmental conditions influencing slope stability. This study examines the temporal evolution of landslide susceptibility in the Grigorescu neighbourhood of Cluj-Napoca, Romania, using environmental datasets representing conditions in 1971, 2009, and 2025, along with a projected land use scenario for 2047. The proposed framework integrates multi-temporal landslide inventories and conditioning factors with Maximum Entropy (MaxEnt) modelling and CA–Markov–MLP land use simulation (MOLUSCE). Results indicate a progressive shift towards higher susceptibility classes over time, accompanied by urban expansion onto increasingly steep terrain. However, slope gradient remained the dominant conditioning factor throughout all analysed periods, while land use change influenced the temporal evolution and spatial redistribution of susceptibility through progressive urban expansion into terrain already predisposed to instability. The 2047 scenario suggests that continued urban expansion may increase the exposure of built-up areas to zones of elevated susceptibility. Model performance was robust (AUC > 0.8; Kappa > 0.9). Beyond site-specific findings, the framework provides a transferable methodology for integrating urban growth dynamics into landslide susceptibility assessment, supporting sustainable spatial planning and risk-informed urban development in rapidly urbanising hilly environments. Full article

Understanding post-fire visible greenness change is important for assessing spatial heterogeneity in mountainous burned landscapes, but satellite observations often cannot capture local variation. This study developed a workflow using Unmanned Aerial Vehicle (UAV) Red–Green–Blue (RGB) imagery for RGB-interpreted burn severity classification and Green Leaf Index (GLI)-derived visible greenness change analysis three years after fire. The workflow integrated object-based Random Forest (RF) classification, bi-temporal GLI difference ($\Delta$GLI) detection, and terrain-stratified analysis under RGB-only conditions. Object-based multi-feature representation, including a 41-dimensional (41D) feature set of color, texture, and gradient metrics, supported local burn severity mapping, although performance gain over the 23-dimensional (23D) set was modest and not statistically significant. The burned area was dominated by high and moderate severity classes. GLI-derived analysis showed limited visible greenness increase (mean $\Delta$GLI = 0.0058), with slightly more than half of pixels being positive; high severity areas had higher $\Delta$GLI, while low severity areas showed limited or negative values. $\Delta$GLI also varied across terrain, being higher on steeper slopes, mid-to-upper elevations, and east-facing aspects. The workflow provides a practical local-scale approach for post-fire analysis using high-resolution UAV RGB imagery, with results interpreted as case-specific visible greenness patterns rather than comprehensive ecological recovery. Full article

Deterministic radio wave propagation models provide high accuracy in complex outdoor environments but remain computationally impractical for large-scale network planning and spectrum management. In contrast, empirical and hybrid models offer low complexity at the expense of reduced accuracy, systematic bias, and limited terrain sensitivity. This paper proposes a unified delta learning framework that enhances fast baseline propagation models by learning a data-driven correction toward a deterministic Parabolic Equation Modeling (PEM) reference. A key novelty lies in a compact, physics-informed feature representation that replaces the full terrain profile with an 18-dimensional vector combining local geometric descriptors, global terrain characteristics, and baseline responses, enabling accurate correction with low-dimensional input. The study also provides the first systematic investigation of delta-based correction across multiple widely used propagation models. The framework is evaluated for free-space propagation, ITU-R P.1546, ITU-R P.1812, and ITU-R P.452 using ridge regression, kernel ridge regression, gradient boosting regression trees, and a neural network model. Model performance is assessed in terms of error reduction, bias mitigation, robustness across learning algorithms, and profile-level generalization to previously unseen propagation paths within the considered terrain categories. Results show substantial error reduction, with up to twofold improvement for simpler baseline models and consistent gains for hybrid models, while preserving computational efficiency. Full article

In many orographically complex monsoon basins, rain gauge networks are sparse and lack the long-term continuous records required for reliable precipitation probability analysis. Traditional regional frequency analysis assumes spatially uniform precipitation across the analysis zone, which is inadequate for basins with steep rainfall gradients and strong seasonal variability. Gridded precipitation products (GPPs) provide spatially continuous, long-term records that enable grid-cell-level probability distribution fitting. However, GPPs may exhibit local biases and errors, and statistical evaluation against gauge observations is necessary before application. This study was conducted in the Phetchaburi–Prachuap Khiri Khan River Basin, western Thailand, a region with steep orographic and coastal rainfall gradients. Four GPPs, namely CHIRPS, CHELSA, WorldClim, and PERSIANN-CCS-CDR, were evaluated against gauge observations. The best-performing product, after monthly bias correction, was then used to generate spatially continuous monthly exceedance probability maps using grid-cell gamma distribution fitting. CHELSA showed the best overall performance across all evaluation metrics (correlation coefficient (r) = 0.908, percent bias (PBIAS) = 7.0%, root mean square error (RMSE) = 48.3 mm), passing the Kolmogorov–Smirnov (KS) goodness-of-fit test at all 96 station-months. CHIRPS and WorldClim showed satisfactory overall performance but exhibited localized biases in complex terrain, whereas PERSIANN-CCS-CDR substantially overestimated wet-season rainfall, limiting its suitability for this basin. Spatial precipitation patterns varied markedly between monsoon regimes, shifting from a dominant west-to-east orographic gradient during the southwest monsoon to a less differentiated advective pattern during the northeast monsoon. Furthermore, analysis at the 75% exceedance probability level showed that mean-based effective rainfall overestimated reliable water supply in high-variance months, leading to underestimation of supplemental irrigation demand. The generated maps provide spatially explicit dependable rainfall estimates across the basin, supporting probabilistic agricultural water management at multiple planning scales in orographically complex monsoon basins. Full article

Trail running imposes highly variable biomechanical demands due to steep, irregular terrain that renders flat-road pacing models inadequate. We present a physics-based digital twin that integrates a terrain-adaptive grade-adjusted pace (GAP) model with individualised physiological calibration to predict finish time across heterogeneous trail-running races. The GAP core applies Minetti’s fifth-degree metabolic cost polynomial to map slope-dependent energy cost across the full range of uphill and downhill gradients encountered in trail racing. Segment-by-segment pace is further modulated by an altitude–VO₂max correction, a Banister TRIMP-based fatigue term, and a progressive pacing-decay factor. Course-elevation profiles are extracted from 1 Hz barometric altimeter data through a five-step normalisation pipeline. Individual parameters (sustainable VT₂ fraction α; pacing-decay slope μ) were calibrated by grid search against 13 race sessions. A sequential validation across four model-complexity stages showed R² increasing from 0.763 to 0.905. Leave-one-out cross-validation (n = 13) yielded R² = 0.864, MAE = 18.2 min, MAPE = 11.1%, and a small positive bias (+2.0 min). The framework demonstrates that integrating biomechanical terrain correction with individual physiological calibration substantially improves race-time prediction for trail running, offering a promising foundation for athlete-specific pre-race simulation. Full article

This study investigates offline trajectory planning strategies for multi-UAV missions in complex 3D environments, with the aim of systematically comparing classical, hybrid, and reinforcement learning-based approaches under unified evaluation conditions. Two simulation scenarios were considered: an uneven terrain environment with elevation-induced constraints and a planar obstacle-rich environment. The evaluated planners include graph-based (A*), sampling-based (RRT, RRT*), gradient-based (APF), a hybrid APF B-RRT* method, and a DQN-based reinforcement learning planner with spatial attention and reward shaping. Performance was assessed using geometric, safety, energetic, and computational metrics. The results show that A* consistently produces the shortest and most stable trajectories with low energy consumption but at increased computational cost in high-resolution environments. Sampling-based planners exhibit higher variability and planning time, while APF achieves computational efficiency but may violate safety margins. The hybrid planner provides improved robustness across scenarios. The reinforcement learning planner demonstrates consistent safety compliance and strong inter-UAV separation in both environments, also with longer trajectories and higher energy usage. Overall, the study highlights trade-offs between determinism, scalability, safety, and adaptability across planning paradigms. Full article

Evapotranspiration (ET) is a key component of the terrestrial water and energy cycle, and its long-term dynamics are essential for regional hydrological assessment in subtropical China. In this study, two widely used satellite-based ET products, MOD16 and PML-V2, were selected for intercomparison because they provide consistent spatial (500 m) and temporal (8-day) resolutions. Validation against flux observations showed that PML-V2 performed better than MOD16 and was therefore used for subsequent analysis. Based on the 500 m, 8-day PML-V2 dataset, the spatiotemporal variation in ET in subtropical China during 2001–2020 was examined using the Theil–Sen slope estimator, Mann–Kendall test, and Hurst exponent. To identify the most relevant controls on ET variation, eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) were used to screen environmental factors and rank their relative importance. Multiple linear regression (MLR) was then applied only to the selected dominant factors to quantify their contributions. Residual analysis was used to distinguish climate–vegetation effects from residual influences, which could arise from human activities and unmodeled natural processes. The results showed that annual ET averaged 669 mm and increased significantly at a rate of 2.03 mm yr⁻¹ from 2001 to 2020, with an accelerated increase after 2010. Spatially, ET exhibited clear gradients from south to north and from coastal to inland regions. Downward shortwave radiation (SWDown) and leaf area index (LAI) were the dominant drivers over most of the study area, although their controls varied geographically, with northern subregions being more energy-limited and southern subregions being jointly influenced by vegetation and temperature. Residual ET trends largely coincide with cropland and urbanising areas, indicating a partial influence of human activities, while in subregions such as XM, complex terrain and hydrological heterogeneity suggest that unmodeled natural processes may dominate. These findings enhance understanding of ET dynamics in subtropical China and demonstrate the value of high-resolution remote sensing products for regional hydrological monitoring and driver attribution. Full article

Satellite remote sensing imagery has become an essential resource for large-scale surface water monitoring. Nevertheless, in karst regions, the elongated and fragmented morphology of water bodies, along with terrain shadows and vegetation interference, still leads to limitations in existing methods for small water body detection and accurate boundary delineation. To overcome the aforementioned issues, this paper proposes MESA-Net, a CNN–Mamba hybrid segmentation network for water body extraction in complex karst terrain. The network employs ResNet-18 as an encoder to extract shallow-level features. The decoder primarily consists of three modules: the Cross-Scale Adaptive Feature Fusion (CAFF) module, the Directional Gradient Histogram Edge-Guided Fusion (DGHEF) module, and the Omni-directional Global-Local Mamba Block (OGLMB). Among these, the CAFF module enhances the detection capability for small-scale water bodies by performing cross-scale feature fusion and dynamic weight allocation on the feature outputs from each level of the encoder. The OGLMB integrates an omnidirectional state space model with an 8-directional scanning mechanism and cross-attention guidance, effectively enhancing the ability to represent the structural continuity and global consistency of water bodies. The DGHEF utilizes directional gradient histograms to explicitly model multi-directional boundary information of water bodies, and combines this with a boundary guidance mechanism to enhance the representation of water body boundary features whilst suppressing spurious responses. In addition, the LJ-Water dataset has been constructed for the Lijiang River Basin in Guangxi, which is based on Sentinel-2 imagery. To validate the effectiveness and generalization capability of the method, comparative experiments were conducted on the self-built LJ-Water dataset as well as the publicly available Water-CD and LoveDA datasets. Experimental results demonstrate that MESA-Net consistently outperforms representative CNN-based, Transformer-based, and Mamba-based segmentation networks. On the LJ-Water dataset, it achieves 84.59% IoU and 91.65% F1, whilst on the Water-CD dataset, it attains 92.15% IoU and 95.91% F1, and 69.83% IoU and 82.24% F1 on the LoveDA dataset. Relative to the strongest baseline method, the proposed model achieved IoU gains of 1.51%, 2.34%, and 1.73% on the three datasets, respectively. In summary, MESA-Net demonstrates superior water segmentation performance under complex background conditions. Full article

The Eastern Himalayan Syntaxis in the southeastern margin of the Tibetan Plateau is a tectonically active, deeply incised, high-relief region with frequent landslides. However, the long-term evolution of landslide susceptibility and the temporal behavior of its dominant conditioning factors remain insufficiently understood. This study compiled a 30-year inventory of 1350 landslides from multi-source remote-sensing data and divided it into three periods: P1 (1991–2000), P2 (2001–2010), and P3 (2011–2020). Period-specific random forest models were developed for susceptibility mapping, and Tree-SHAP was used to interpret temporal changes in dominant factors and their nonlinear responses. The models showed reliable performance, with AUC values of 0.887, 0.848, and 0.900, respectively. Susceptibility patterns showed broad temporal stability with localized reorganization, with unchanged areas accounting for 55.62%, 51.62%, and 58.51% of the P1–P2, P2–P3, and P1–P3 transitions, respectively. High and very high susceptibility zones were persistently concentrated along the Yarlung Tsangpo–Parlung Tsangpo–Yigong Tsangpo river system and major tributary junctions. SHAP results identified elevation, slope gradient, terrain curvature, NDVI, and annual precipitation as the persistent core factor group, whereas drainage proximity, the seismic disturbance proxy, and road proximity showed stronger period-dependent effects. Nonlinear SHAP responses revealed threshold-saturation, overall decreasing or distance-decay, threshold-transition, and inverted U-shaped patterns. These findings indicate that susceptibility evolution reflects the coupling between persistent geomorphic predisposition and stage-dependent environmental and disturbance-related modifiers, providing a basis for identifying persistent and stage-specific high-susceptibility zones in high-relief valley regions. Full article

Cropland non-grain use has become an important challenge for food security and cropland governance in rapidly urbanising agricultural regions, yet its trajectory heterogeneity and the divergence between current spatial patterns and long-term-change mechanisms remain insufficiently understood. Taking the Changsha–Zhuzhou–Xiangtan (CZT) urban agglomeration in China as a case, this study quantified the cropland non-grain rate (NGR) on a 1 km grid for 2000, 2010, and 2020, classified grid-level transition trajectories, and developed three temporally structured eXtreme Gradient Boosting (XGBoost) models with spatial block cross-validation, Shapley additive explanations (SHAP) interpretation, and geographically explicit SHAP (GeoSHAP) local attribution. The results show that low-NGR and stable grids formed the dominant regional background, while recent NGR increases were mainly concentrated along the urban development corridor and metropolitan fringe. Current NGR status and long-term NGR change showed divergent explanatory structures. The current spatial pattern was mainly associated with terrain constraints and contemporary urban pressure, whereas long-term change was more strongly conditioned by baseline urbanisation and subsequent urban–environmental changes. Nonlinear dependence analysis further identified model-derived response zones related to slope, impervious surface conditions, hydrothermal change, and hydrological proximity. GeoSHAP mapping revealed that locally dominant mechanisms varied substantially across the study area, indicating that cropland non-grain use was shaped by spatially heterogeneous combinations of terrain, urbanisation, hydrothermal background, and hydrological context. These findings support a shift from aggregate status monitoring toward trajectory-specific and mechanism-differentiated cropland management in urban agglomerations. Full article

The dryline is a sharp boundary between moist air from the Gulf of Mexico and dry air from the desert Southwest. In West Texas, this boundary often surges east during the day and retreats west at night. Understanding exactly why it moves back and forth is critical for predicting where severe thunderstorms will form. Yet the physical drivers of dryline life cycle remain poorly understood and frequently under-predicted. This study utilizes a variable-resolution Model for Prediction Across Scales (MPAS) configuration (3–60 km) with the YSU non-local planetary boundary layer (PBL) scheme to investigate a representative dryline event from April 2017. The control simulation was validated against NWS Surface Analysis, demonstrating a high spatial correlation in both synoptic-scale pressure distributions and mesoscale moisture gradients, successfully resolving a nocturnal retrogression of approximately 170 km, with the dryline retreating from its peak afternoon surge at 100.7° W to a recovery point of 102.5° W between 0000 UTC and 0600 UTC 10 April. This recovery occurred at an average speed of 28.3 km/h, consistently constrained beneath a resilient capping inversion. To decouple the environmental drivers of this motion, two targeted sensitivity experiments were conducted: (1) Mechanical Forcing: A 50% reduction in regional topography confirms that the West Texas sloping ramp acts as a “topographic pump.” Without this gradient, the hydrostatic pressure falls were insufficient to drive the nocturnal retreat, causing the boundary to stall eastward. (2) Thermodynamic Regulation: A 50% reduction in soil moisture revealed an “energy swap,” the near-total partitioning of net radiation into sensible heat drove the planetary boundary layer to a higher peak value—a 600 m increase over the control simulation. These results provide a comprehensive physical framework for dryline mobility, demonstrating that while terrain plays an important role in the extent of the diurnal oscillation, soil moisture governs the vertical structure and moisture gradient intensity. Our findings suggest that high-resolution vertical layering and accurate land-surface initialization are prerequisites for capturing the inversion layer dynamics essential for dryline forecasting. However, these findings are based on a single event and require validation across a broader range of dryline cases. Full article

Accurate, high-resolution gridded air temperature forecasts are essential, particularly in mountainous areas with complex terrain. This study proposes a two-stage processing framework DOWN + BC to downscale Global Forecast System (GFS) temperature forecasts and correct their bias. The approach first employs a random forest (RF) model to geographically downscale 3-hourly 0.25° GFS forecasts to a 30 m resolution (DOWN), followed by bias correction (BC) using a first-order adaptive Kalman filter (AKF). The accuracy of the DOWN + BC-processed forecasts was evaluated against both automatic weather station (AWS) observations and high-resolution air temperature fields derived from an extreme gradient boosting model (XGB-derived). The results indicate that (1) the DOWN step effectively refines the spatial detail of temperature distribution, though it yields limited improvement in accuracy compared to the raw GFS forecasts; (2) the combined DOWN + BC method substantially enhances forecast accuracy. At AWS locations, the root mean square error (RMSE) of GFS forecasts decreased by 37.84% in January 2020 and 41.16% in July 2023. Relative to the XGB-derived temperature distribution, RMSE was reduced by 47.27% and 33.79% for the respective periods. Full article

Accurate surface downward shortwave radiation (SDSR) is essential for solar resource assessment, photovoltaic applications, and land–atmosphere studies. Although ERA5 is widely used in radiation-related research, its SDSR estimates over China still show considerable uncertainties under complex topographic and climatic conditions. Using hourly observations from the 162-station China Meteorological Administration (CMA) radiation network during April 2024–March 2025, of which 160 stations were retained after quality control, this study systematically evaluated ERA5 SDSR and developed a physics-guided Light Gradient Boosting Machine (LightGBM) correction framework. Raw ERA5 exhibits a strong systematic positive bias (PBIAS = 57.40%, ME = 124.2 W/m²) together with a pronounced nonlinear structural bias, characterized by overestimation under low-radiation conditions and underestimation under high-radiation conditions. The largest errors occur in the Southern Monsoon region in summer and the Northwest Arid region in spring, indicating the combined effects of cloud extinction, aerosol attenuation, and terrain-related representativeness differences. To address these mechanisms, the correction model incorporates physically relevant predictors from ERA5 and Copernicus Atmosphere Monitoring Service (CAMS), including cloud microphysical variables, aerosol optical depth, solar geometry, and elevation. SHapley Additive exPlanations (SHAP) analysis shows that the learned correction behavior is broadly consistent with known radiative-transfer processes. On the independent station hold-out test set, the correction increases the Pearson correlation coefficient from 0.8680 to 0.8967 and reduces RMSE from 173.1 to 100.8 W/m², while substantially suppressing the strong positive bias of raw ERA5. Additional robustness tests, including season-blocked validation, interpolation-sensitivity analysis, ablation experiments, and multi-model comparison, further support the stability of the framework. External benchmarking against FY-4B and Himawari also shows that the corrected ERA5 substantially narrows the gap relative to independent geostationary satellite products. Overall, the proposed framework provides an effective and physically interpretable approach for improving ERA5 SDSR over China. Full article