Search Results (2,048)

Land surface temperature (LST) is fundamental for monitoring surface energy balance and environmental dynamics, with remote sensing providing the primary means of acquisition. However, directional anisotropy (DA) introduces systematic bias in satellite-derived LST products, particularly over complex landscapes. This study examines the impact of angular effects on LST retrievals from three leading East Asian geostationary satellites (FengYun 4A, FengYun 4B, and Himawari 9) across Hunan Province, China, using integrated comparison with in situ measurements and reanalysis data. Results show that all products exhibit a systematic cold bias, with FY4B achieving the highest accuracy. Diurnal retrieval precision increases with higher solar zenith angles (SZA), while no consistent relationship is observed between viewing zenith angle (VZA) and retrieval accuracy. Notably, the retrieval bias of the FY4 series increases significantly when the sun and sensor are aligned in azimuth, particularly when the relative azimuth angle (RAA) is less than or equal to 30°. Parametric modeling reveals that emissivity kernel-induced anisotropy is the principal driver of significant LST deviations in central Hunan, while solar kernel effects result in LST overestimation in mountainous regions and underestimation in plains. Increases in elevation or vegetation density reduce emissivity-induced errors but amplify errors caused by shadowing and sunlit effects. Emissivity anisotropy is thus identified as the primary source of LST DA. These findings deepen the understanding of LST DA in remote sensing and provide essential guidance for refining retrieval algorithms and improving the applicability of LST products in complex terrains. Full article

The Weather Research and Forecasting Model (WRF-ARW) version 4.5 was used to simulate the synoptic to mesoscale evolving atmosphere of Tropical Cyclone (TC) Hilary’s (2023) remnant passage over the southwestern United States. The atmospheric dynamic processes conducive to the precursor rain events were extensively studied to determine the effects of mid-level jetogenesis. Concurrently, the dynamics of mesoscale processes related to the interaction of TC Hilary over the complex topography of the western United States were studied with several sensitivity simulations on a nested 2 km × 2 km grid. The differential surface heating between the cloudy California coast and clear/elevated Great Basin plateau had a profound impact on the lower-mid-tropospheric mass field resulting in mid-level jetogenesis. Diagnostic analyses of the ageostrophic flow support the importance of both isallobaric and inertial advective forcing of the mid-level jetogenesis in response to differential surface sensible heating. This ageostrophic mesoscale jet ultimately transported tropical moisture in multiple plumes more than 1000 km poleward beyond the location of the extratropical transition of the storm, resulting in anomalous flooding precipitation within a massive arid western plateau. Full article

Long-term wind speed changes over the Yangtze River waterway have critical implications for inland shipping efficiency, emission dispersion, and renewable energy potential. This study utilizes a high-resolution 5 km gridded reanalysis dataset spanning 1979–2018 to conduct a comprehensive spatiotemporal analysis of surface wind climatology, variability, and trends along China’s primary inland waterway. A pivotal regime shift was identified around 2000, marking a transition from terrestrial stilling to a recovery phase characterized by wind speed intensification. Multiple change-point detection algorithms consistently identify 2000 as a pivotal turning point, marking a transition from the late 20th century “terrestrial stilling” to a recovery phase characterized by wind speed intensification. Post-2000 trends reveal pronounced spatial heterogeneity: the upstream section exhibits sustained strengthening (+0.02 m/s per decade, p = 0.03), the midstream shows weak or non-significant trends with localized afternoon stilling in complex terrain (−0.08 m/s per decade), while the downstream coastal zone demonstrates robust intensification exceeding +0.10 m/s per decade during spring–autumn daytime hours. Three distinct wind regimes emerge along the 3000 km corridor: a high-energy maritime-influenced downstream sector (annual means > 3.9 m/s, diurnal peaks > 6.0 m/s) dominated by sea breeze circulation, a transitional midstream zone (2.3–2.7 m/s) exhibiting bimodal spatial structure and unique summer-afternoon thermal enhancement, and a topographically suppressed upstream region (<2.0 m/s) punctuated by pronounced channeling effects through the Three Gorges constriction. Critically, the observed recovery contradicts widespread basin greening (97.9% of points showing significant positive NDVI trends), which theoretically should enhance surface roughness and suppress wind speeds. Correlation analysis reveals that wind variability is systematically controlled by large-scale atmospheric circulation patterns, including the Northern Hemisphere Polar Vortex (r ≈ 0.35), Western Pacific Subtropical High (r ≈ 0.38), and East Asian monsoon systems (r > 0.60), with distinct seasonal phase-locking between baroclinic spring dynamics and monsoon-thermal summer forcing. These findings establish a comprehensive, fine-scale climatological baseline essential for optimizing pollutant dispersion modeling, and evaluating wind-assisted propulsion feasibility to support shipping decarbonization goals along the Yangtze Waterway. Full article

Mountain Excavation and Land Creation Projects (MELCPs) have emerged as a critical strategy for expanding urban development space in mountainous regions facing land scarcity. Dynamic monitoring and risk management of these projects are essential for promoting sustainable urban development. This study develops an integrated monitoring framework for MELCPs by combining ascending and descending Sentinel-1 SAR data, Sentinel-2 optical imagery, SRTM digital elevation models (DEM), and field survey data. The framework incorporates multi-temporal change detection, random forest classification, and time-series InSAR analysis to systematically capture the spatiotemporal evolution and subsidence mechanisms associated with MELCPs. Key findings include: (1) The use of dual-orbit SAR data significantly improves the detection accuracy of excavation areas, achieving an overall accuracy of 87.1% (Kappa = 0.85) and effectively overcoming observation limitations imposed by complex terrain. (2) By optimizing the combination of spectral, texture, topographic, and polarimetric features using a random forest algorithm, the classification accuracy of MELCPs is enhanced to 91.2% (Kappa = 0.889). This enables precise annual identification of MELCP progression from 2017 to 2022, revealing a three-stage evolution pattern: concentrated expansion, peak activity, and restricted slowdown. Specifically, the reclaimed area increased from 2.66 km² (pre-2018) to a peak of 12.61 km² in 2021, accounting for 34.56% of the total area of the study region, before decreasing to 2.69 km² in 2022. (3) InSAR monitoring from 2017 to 2023 indicates that areas with only filling experience minor shallow subsidence (<50 mm), whereas subsequent building loads and underground engineering activities lead to continuous deep soil consolidation, with maximum cumulative subsidence reaching 333.8 mm. This study demonstrates that subsidence in MELCPs follows distinct spatiotemporal patterns and is predictable, offering important theoretical insights and practical tools for engineering safety management and territorial spatial optimization in mountainous cities. Full article

Off-road vehicle control designs often neglect the complex tire–soil interactions inherent to soft terrain. This paper proposes a Variable Impedance Control (VIC) strategy integrated with a high-fidelity terramechanics model. First, a real-time sinkage estimation algorithm is derived using experimentally identified Bekker parameters and the quasi-rigid wheel assumption to capture the nonlinear feedback between soil deformation and vehicle dynamics. Building on this, the VIC strategy adaptively regulates virtual stiffness, damping, and inertia parameters based on real-time suspension states. Comparative simulations on an ISO Class-C soft soil profile demonstrate that this framework effectively balances ride comfort and safety constraints. Specifically, the VIC strategy reduces the root-mean-square of vertical body acceleration by 46.9% compared to the passive baseline, significantly outperforming the Linear Quadratic Regulator (LQR). Furthermore, it achieves a 48.6% reduction in average power relative to LQR while maintaining suspension deflection strictly within the safe range. Moreover, unlike LQR, the VIC strategy improves tire deflection performance, ensuring superior ground adhesion. These results validate the method’s robustness and energy efficiency for off-road applications. Full article

Efficient route planning technology is the core support for ensuring the successful execution of unmanned aerial vehicle (UAV) flight missions. In this paper, the coordination issue of global route planning and local real-time obstacle avoidance in complex mountainous environments is studied. To deal with this issue, a hierarchical route planning framework is designed, including global route planning and AI-based local route re-planning using deep reinforcement learning, exhibiting both flexible versatility and practical coordination and deployment efficiency. Throughout the entire flight, the local route re-planning task triggered by dynamic threats can be executed in real time. Meanwhile, a multi-model DQN (MMDQN) agent with a Monte Carlo traversal iterative learning (MCTIL) strategy is designed for local route re-planning. Compared to existing methods, this agent can be directly used to generate local obstacle avoidance routes in various scenarios at any time during the flight, which simplifies the complicated structure and training process of conventional deep reinforcement learning (DRL) agents in dynamic, complex environments. Using the framework structure and MMDQN agent for local route re-planning ensures the safety and efficiency of the mission, as well as local obstacle avoidance during global flights. These performances are verified through simulations based on actual terrain data. Full article

The reconstruction of high-fidelity radio maps is pivotal for wireless network planning but remains challenging due to the tension between physical accuracy and computational efficiency. We propose HiFiRadio, a novel framework that achieves a breakthrough in this balance by integrating centimeter-resolution 3D environmental meshes with semantic-aware propagation modeling. At its core, HiFiRadio introduces a semantic-enhanced 3D indexing structure that efficiently manages complex terrain data, enabling real-time classification of signal paths into line-of-sight, non-line-of-sight, and vegetation-obstructed categories. This classification directly guides a hybrid propagation model, which dynamically applies dedicated loss calculations for buildings and foliage, grounded in physical principles. Extensive experiments demonstrate that HiFiRadio attains an accuracy comparable to commercial ray-tracing tools while being orders of magnitude faster. It also significantly outperforms existing learning-based baselines in both accuracy and scalability, a claim further validated by field measurements. By making high-fidelity, real-time radio map reconstruction practical for large-scale scenes, HiFiRadio establishes a new state of the art with immediate applications in network planning, UAV pathing, and dynamic spectrum access. Full article

With the rapid development of the power Internet of Things (IoT), the traditional manual inspection mode can no longer meet the growing demand for power equipment inspection. Unmanned aerial vehicle (UAV) intelligent inspection technology, with its efficient and flexible features, has become the mainstream solution. The rapid development of computer vision and deep learning (DL) has significantly improved the accuracy and efficiency of UAV intelligent inspection systems for power equipment. However, mainstream deep learning models have complex structures, and manual design is time-consuming and labor-intensive. In addition, the images collected during the power inspection process by UAVs have problems such as complex backgrounds, uneven lighting, and significant differences in object sizes, which require expert DL domain knowledge and many trial-and-error experiments to design models suitable for application scenarios involving power inspection with UAVs. In response to these difficult problems, evolutionary computation (EC) technology has demonstrated unique advantages in simulating the natural evolutionary process. This technology can independently design lightweight and high-precision deep learning models by automatically optimizing the network structure and hyperparameters. Therefore, this review summarizes the development of evolutionary deep learning (EDL) technology and provides a reference for applying EDL in object detection models used in UAV intelligent power inspection systems. First, the application status of DL-based object detection models in power inspection is reviewed. Then, how EDL technology improves the performance of the models in challenging scenarios such as complex terrain and extreme weather is analyzed by optimizing the network architecture. Finally, the challenges and future research directions of EDL technology in the field of UAV power inspection are discussed, including key issues such as improving the environmental adaptability of the model and reducing computing energy consumption, providing theoretical references for promoting the development of UAV power inspection technology to a higher level. Full article

Global environmental changes significantly alter ecosystem services (ESs), particularly in fragile regions like the Tibetan Plateau. While methodological advances have improved spatial assessment capabilities, understanding of how multiple drivers interact to shape ecosystem service heterogeneity remains limited to regional scales, especially across complex alpine landscapes. This study aims to clarify whether multi-factor interactions produce nonlinear enhancements in ES explanatory power and how these driver–response relationships vary across heterogeneous terrains. We quantified spatiotemporal patterns of four key ecosystem services—water yield (WY), soil conservation (SC), carbon sequestration (CS), and habitat quality (HQ)—across the southeastern Tibetan Plateau from 2000 to 2020 using multi-source remote sensing data and spatial econometric modeling. Our analysis reveals that SC increased by 0.43 t·hm⁻²·yr⁻¹, CS rose by 1.67 g·m⁻²·yr⁻¹, and HQ improved by 0.09 over this period, while WY decreased by 3.70 mm·yr⁻¹. ES variations are predominantly shaped by potent synergies, where interactive explanatory power consistently surpasses individual drivers. Hydrothermal coupling (precipitation ∩ potential evapotranspiration) reached 0.52 for WY and SC, while climate–vegetation synergy (precipitation ∩ normalized difference vegetation index) achieved 0.76 for CS. Such climate–restoration synergies now fundamentally shape the region’s ESs. Geographically weighted regression (GWR) further revealed distinct spatial dependencies, with southeastern regions experiencing strong negative effects of land use type and elevation on WY, while northwestern areas showed a positive elevation associated with WY but negative effects on SC and HQ. These findings highlight the critical importance of accounting for spatial non-stationarity in driver–ecosystem service relationships when designing conservation strategies for vulnerable alpine ecosystems. Full article

Montane forests, characterized by complex terrain and diverse climates, serve as critical global biodiversity hotspots, particularly for birds and mammals. However, the patterns and underlying processes of bird and mammal diversity remain insufficiently studied in the montane forests of eastern China. This study employed infrared-triggered camera trapping to conduct a four-year field monitoring of birds and mammals, analyzing the effects of plant diversity and seasonal variations on the diversity of habitat-associated animals. Our results revealed that species-level habitat visit frequency in ground-dwelling birds exhibited a significant phylogenetic signal, particularly in spring and summer. Plant diversity metrics demonstrated significant positive correlations with corresponding bird metrics of species richness (SR), phylogenetic diversity (PD), and the standardized effect size of PD (Phylo SES PD). In contrast, for mammals, plant diversity metrics were significantly positively correlated with corresponding SR, mean pairwise phylogenetic distance (Phylo MPD), and mean nearest phylogenetic taxon distance (Phylo MNTD), as well as community structure metrics, including the net relatedness index (Phylo NRI) and nearest taxon index (Phylo NTI). Furthermore, the plant Shannon–Wiener index showed significant positive correlations with both bird and mammal metrics of SR, PD, and Phylo SES PD but significant negative correlations with Phylo MNTD. Seasonal variations triggered the mean altitudinal migration in ground-dwelling birds and mammals. There were significant differences in the diversity and community structure metrics of birds (Shannon–Wiener, Funct FNND, and PD) and mammals (Shannon–Wiener, Funct MPD, Funct FNND, PD, Phylo MPD, Phylo MNTD, and Phylo SES PD), which varied across different seasons. These findings emphasize that plant diversity and seasonal changes are closely related to the diversity and community structure of birds and mammals. They provide theoretical support for the role of habitat vegetation and seasonal dynamics in maintaining the stability and functioning of montane animal ecosystems, offering important insights for addressing habitat fragmentation and species migratory behavior. Full article

Monitoring steep slopes in mountainous canyon areas has always been a challenging problem, especially during the construction of large hydropower projects. Effective monitoring is crucial for construction safety and operational security. However, under complex terrain conditions, existing monitoring methods have significant limitations and cannot comprehensively and accurately cover steep slopes. To address the above challenges, this study proposes a multi-temporal UAV-based photogrammetric offset tracking (POT) monitoring method assisted by terrestrial laser scanning (TLS), which is primarily applicable to rocky and texture-rich steep slopes. This method utilizes TLS point cloud data to provide supplementary ground control points (TLS-GCPs) for UAV image modeling, effectively overcoming the difficulty of deploying conventional RTK ground control points (RTK-GCPs) on high and steep slopes, thereby significantly improving the accuracy of UAV-based Structure-from-Motion (SfM) models. In a case study at a hydropower station, we employed TLS-assisted UAV modeling to produce high-precision UAV images. Using POT technology, we successfully identified signs of slope deformation between January 2024 and December 2024. Comparative experiments with traditional algorithms demonstrated that in areas where RTK-GCPs cannot be deployed, this method greatly enhances UAV modeling accuracy, fully meeting the monitoring requirements for steep slopes in complex terrains. Full article

Lakes on the Qinghai–Tibet Plateau are important indicators of global climate change, and variations in their water storage strongly influence regional hydrological cycles and ecosystems. However, existing studies have largely focused on relative changes in lake volume, while the precise quantification of absolute water storage remains insufficient, largely due to the lack of long-term, high-accuracy water storage time series. Constrained by harsh natural conditions and limited in situ observations, conventional approaches struggle to achieve the accurate long-term monitoring of lake water storage across the Plateau. To address this challenge, we propose a DEM-based underwater topography extrapolation method. Under the assumption of continuity between surrounding onshore terrain and submerged lakebed morphology, nearshore DEM data are extrapolated to reconstruct lake bathymetry. By integrating multi-source remote sensing observations of lake area and water level, we estimate and reconstruct 30-year absolute water storage time series for 120 Plateau lakes larger than 50 km². This method does not require measured water depth data and is particularly suitable for data-scarce, topographically complex, high-altitude lake regions, effectively overcoming key limitations of conventional methods used for absolute water storage monitoring. Validation shows strong agreement between our estimates and an independent validation dataset, with an overall correlation coefficient of 0.95; the reconstructed time series are highly reliable, with correlation coefficients exceeding 0.6. During the study period, the total lake water storage of the Qinghai–Tibet Plateau exhibited a significant increasing trend, with a cumulative growth of approximately 137.297 billion m³, representing a 20.73% increase, and showing notable spatial heterogeneity. The water storage dataset constructed in this study provides reliable data support for research on water cycles, climate change assessment, and regional water resource management on the Qinghai–Tibet Plateau. Full article

Accurate estimation of hourly near-surface air temperature (NSAT) is critical for climate analysis, environmental monitoring, and urban thermal studies. However, existing temperature datasets remain constrained by coarse spatial resolution and limited hourly accuracy. This study systematically evaluates four widely used land surface temperature (LST) datasets—MODIS, ERA5-Land, FY-2F, and CGLS—and five machine learning models (RF, MDN, DNN, XGBoost, and GTNNWR) for NSAT estimation across two contrasting regions in Shaanxi, China: a complex-terrain region in southwestern Shaanxi and the urban area of Xi’an. Results demonstrate that single-source LST inputs outperform multi-source LST stacking, largely due to compounded systematic biases across heterogeneous datasets. MODIS provides the best performance in the mountainous region, while CGLS excels in the urban environment. Among all models, GTNNWR—which explicitly captures spatiotemporal non-stationarity—consistently achieves the highest accuracy, reducing RMSE by 44.8% and 44.2% relative to the second-best model in the two study areas, respectively, whereas the remaining four models exhibit broadly comparable performance. This work identifies effective data–model configurations for generating high-resolution hourly NSAT products and provides methodological insights for climate and environmental applications in regions with complex terrain or strong urban heterogeneity. Full article

Remote sensing provides an efficient approach for monitoring ecosystem dynamics in the arid and semi-arid regions of Xinjiang, yet traditional forest-land extraction methods (e.g., spectral indices, threshold segmentation) show limited adaptability in complex environments affected by terrain shadows, cloud contamination, and spectral confusion with grassland or cropland. To overcome these limitations, this study used three convolutional neural network-based models (FCN, DeepLabV3+, and PSPNet) for accurate forest-land extraction. Four tri-band training datasets were constructed from Sentinel-2 imagery using combinations of visible, red-edge, near-infrared, and shortwave infrared bands. Results show that the FCN model trained with B4–B8–B12 achieves the best performance, with an mIoU of 89.45% and an mFscore of 94.23%. To further assess generalisation in arid landscapes, ESA WorldCover and Dynamic World products were introduced as benchmarks. Comparative analyses of spatial patterns and quantitative metrics demonstrate that the FCN model exhibits robustness and scalability across large areas, confirming its effectiveness for forest-land extraction in arid regions. This study innovatively combines band combination optimization strategies with multiple deep learning models, offering a novel approach to resolving spectral confusion between forest areas and similar vegetation types in heterogeneous arid ecosystems. Its practical significance lies in providing a robust data foundation and methodological support for forest monitoring, ecological restoration, and sustainable land management in Xinjiang and similar regions. Full article

The repeated extraction of coal seams in the Loess Plateau mining region has greatly increased the severity of surface deformation and associated damage. Accurately characterizing the spatio-temporal evolution of subsidence and the underlying mechanisms is a critical engineering challenge for mining safety. Taking the Dafosi Coal Mine located in the loess gully region as a case study, this paper thoroughly examines the variations in surface deformation and damage characteristics caused by single and repeated seam mining. The analysis integrates surface movement monitoring data, global navigation satellite system (GNSS) dynamic observations, field surveys, unmanned aerial vehicle (UAV) photogrammetry, and numerical simulation methods. Notably, to ensure the accuracy of prediction parameters, a refined Particle Swarm Optimization (PSO) algorithm incorporating a neighborhood-based mechanism was employed specifically for the inversion of probability integral parameters. The results indicate that the subsidence factor and horizontal movement factor increase markedly following repeated mining. The maximum surface subsidence velocity also increases substantially, and this acceleration remains evident after normalizing by mining thickness and face-advance rate. The fore effective angle is smaller in repeated mining than in single-seam mining, and the duration of surface movement is substantially extended. Repeated mining fractured key strata and caused a functional transition from the classic three-zone response to a two-zone connectivity pattern, while the thick loess cover responds as a disturbed discontinuous-deformation layer, which together aggravates step-like and slope-related damage. The severity of surface damage is strongly influenced by topographic features and geotechnical properties. These findings demonstrate that the proposed integrated approach is highly effective for geological hazard assessment and provides a practical reference for engineering applications in similar complex terrains. Full article