Surface soil moisture (SSM) is a key limiting factor for vegetation growth in alpine meadow on the Qinghai-Tibetan Plateau (QTP). Patches with various sizes and types may cause the redistribution of SSM by changing soil hydrological processes, and then trigger or accelerate alpine grassland degradation. Therefore, it is vital to understand the effects of patchiness on SSM at multi-scales to provide a reference for alpine grassland restoration. However, there is a lack of direct observational evidence concerning the role of the size and type of patches on SSM, and little is known about the effects of patches pattern on SSM at plot scale. Here, we first measured SSM of typical patches with different sizes and types at patch scale and investigated their patterns and SSM spatial distribution through unmanned aerial vehicle (UAV)-mounted multi-type cameras at plot scale. We then analyzed the role of the size and type of patchiness on SSM at both patch and plot scales. Results showed that: (1) in situ measured SSM of typical patches was significantly different (P < 0.01), original vegetation patch (OV) had the highest SSM, followed by isolate vegetation patch (IV), small bare patch (SP), medium bare patch (MP) and large bare patch (LP); (2) the proposed method based on UAV images was able to estimate SSM (0-40 cm) with a satisfactory accuracy (R-2 = 0.89, P < 0.001); (3) all landscape indices of OV, with the exception of patch density, were positively correlated with SSM at plot scale, while most of the landscape indices of LP and IV showed negative correlations (P < 0.05). Our results indicated that patchiness intensified the spatial heterogeneity of SSM and potentially accelerated the alpine meadow degradation. Preventing the development of OV into IV and the expansion of LP is a critical task for alpine meadow management and restoration.

The wheat powdery mildew (WPM) is one of the most severe crop diseases worldwide, affecting wheat growth and causing yield losses. The WPM was a bottom-up disease that caused the loss of cell integrity, leaf wilting, and canopy structure damage with these symptoms altering the crop's functional traits (CFT) and canopy spectra. The unmanned aerial vehicle (UAV)-based hyperspectral analysis became a mainstream method for WPM detection. However, the CFT changes experienced by infected wheats, the relationship between CFT and canopy spectra, and their role in WPM detection remained unclear, which might blur the understanding for the WPM infection. Therefore, this study aimed to propose a new method that considered the role of CFT for detecting WPM and estimating disease severity. The UAV hyperspectral data used in this study were collected from the Plant Protection Institute's research demonstration base, Xinxiang city, China, covering a broad range of WPM severity (0-85 %) from 2022 to 2024. The potential of eight CFT [leaf structure parameter (N), leaf area index (LAI), chlorophyll a + b content (Cab), carotenoids (Car), Car/Cab, anthocyanins (Ant), canopy chlorophyll content (CCC) and average leaf angle (Deg)] obtained from a hybrid method combining a radiative transfer model and random forest (RF) and fifty-five narrow-band hyperspectral indices (NHI) was explored in WPM detection. Results indicated that N, Cab, Ant, Car, LAI, and CCC showed a decreasing trend with increasing disease severity, while Deg and Car/Cab exhibited the opposite pattern. There were marked differences between healthy samples and the two higher infection levels (moderate and severe infection) for Cab, Car, LAI, Deg, CCC, and Car/Cab. N and Ant only showed significant differences between the healthy samples and the highest infection level (severe infection). As Cab, Car, and Ant decreased, the spectral reflectance in the visible light region increased. The decrease in N and LAI was accompanied by a reduction in reflectance across the entire spectral range and the near-infrared area, which was exactly the opposite of Deg. The introduction of CFT greatly improved the accuracy of the WPM severity estimation model with R2 of 0.92. Features related to photosynthesis, pigment content, and canopy structure played a decisive role in estimating WPM severity. Also, results found that the feature importance showed a remarkable interchange as increasing disease levels. Using features that described canopy structure changes, such as optimized soil adjusted vegetation index, LAI, visible atmospherically resistant indices, and CCC, the mild infection stage of this disease was most easily distinguished from healthy samples. In contrast, most severe impacts of WPM were best characterized by features related to photosynthesis (e.g., photochemical reflectance index 515) and pigment content (e.g., normalized phaeophytinization index). This study help deepen the understanding of symptoms and spectral responses caused by WPM infection.

The current investigation examines the fluctuating behaviour of stiff pavement built on a two-parameter base and is influenced by aircraft loading impacts. This investigation is driven by the necessity for an accurate evaluation of pavement behaviour under elevated stress scenarios caused by aircraft, which can guide pavement design and upkeep. A stochastic numerical model, the vehicle-pavement interaction model (VPI), was created using a comprehensive 3D dynamic model of an aircraft vehicle and stationary runway roughness profiles. The rigid pavement is simulated using a computationally efficient 1D finite element mathematical model incorporating six DOF. The Pasternak model represents the soil medium, incorporating shear interaction between the spring elements. The pavement's irregularities are considered and replicated using a power spectral density (PSD) function. This assembled model was used to investigate the dynamical reaction of concrete pavement vibrations caused by the passing of an aircraft vehicle using MATLAB code. The dynamic governing differential equations of the aircraft's motion are developed and coupled with the pavement system equations. The coupled system is then solved in the time domain using the direct computational integration approach with the Newmark-Beta integration scheme, explicitly utilizing the linear average acceleration method. This approach is employed to resolve the equations that govern and assess the performance of the connected system. The current findings are being compared to existing analytical outcomes to verify the precision of the current coding. The research examined the impact of various pavement and aircraft vehicle behaviors and factors on the dynamic response of pavement, including the speed, main and auxiliary suspension components, mass and the load position of the aircraft, also the damping, random roughness, thickness, span length and elastic constant of the pavement, even, the modulus of subgrade of the foundation, the rigidity modulus of the shear layer. The findings demonstrate notable influences of aircraft speed and pavement surface roughness on various response parameters. Specifically, the results reveal that a higher subgrade modulus leads to decreased deflection, rotation, and bending moments. Conversely, longer span lengths tend to elevate response parameters while simultaneously reducing shear force. In conclusion, the results highlight the significance of critical factors, including velocity and subgrade modulus, in forecasting the performance of pavement subjected to aircraft loads. The present research is confined to the investigation of the dynamic's performance of the VPI simulation of airfield rigid pavement. The findings from this study can be expanded on by paving engineers to improve the structural effectiveness and reliability of the pavement, serving as a basis for subsequent fatigue analysis in response to diverse dynamic loads such as earthquake, temperature and vehicle load.

The excellent grounding performance of tracked mining vehicles (TMVs) is a crucial foundation for the normal operation of the entire deep-sea polymetallic nodule mining system. Based on the weak mechanical properties of deep-sea fluidized sediments, this study conducted model tests to deeply analyze the pressure-sinkage relationship curve characteristics and the soil failure process under the vertical action of the TMV track plates. It identified the influence of soil water content on the failure mode and compaction degree and established a new segmented pressure-sinkage model, verifying its accuracy. The test results showed that the width of the track plates and the water content of the sediments had a significant impact on the pressure-sinkage relationship curve, while the sinkage speed had little effect. The bearing capacity of the sediment was an inherent property of the soil, independent of the track plate width and sinkage speed, and decreased with increasing water content. By combining the changes in soil strength and the movement characteristics of soil particles under vertical load, the pressure-sinkage model was divided into the compaction stage, elastic stage, elastoplastic stage, and plastic stage. Based on the experimental results under various conditions, a predictive model for track sinkage depth that considers sediment water content and track plate width was developed. The findings of this study can provide a scientific theoretical basis for the design optimization of parameters such as vehicle weight and track dimensions, promoting the development of deep-sea polymetallic nodule mining.

Reducing product damage, preserving quality, and enhancing efficiency from harvest to consumption are crucial for sustainable agriculture. The integration of advanced information and communication technologies into agricultural practices plays a vital role in meeting these goals. This study introduces an autonomous transport vehicle designed for the efficient logistics of fruit transportation in agricultural settings. The vehicle's software framework is constructed on the Robot Operating System (ROS) and incorporates an enhanced hybrid navigation system that merges the Extended Kalman Filter (EKF) with Simultaneous Localization and Mapping (SLAM) for precise localization. The A* algorithm facilitates global path planning, whereas the Dynamic Window Approach (DWA) guarantees real-time obstacle avoidance. Essential hardware components comprise high-resolution LIDAR for environmental mapping, an Inertial Measurement Unit (IMU) for motion estimation, and wheel encoders for odometry. The performance evaluation was executed across five distinct terrain types: concrete, fine-tilled soil, coarse-tilled soil, asphalt, and grass. The vehicle attained optimal path-following precision on concrete, exhibiting a deviation of 5.39 cm at a speed of 0.3 m/s with a 200 kg payload, whereas tracking errors escalated on uneven terrains like grass and coarse-tilled soil. Maneuverability assessments verified a turning radius of 60.0 cm for 90 degrees turns and 125.0 cm for 180 degrees turns, ensuring suitability in restricted agricultural environments. Finite element analysis (FEA) evaluated structural durability under diverse loads (2000-4000 N), indicating a minimum safety factor of 1.23, thereby affirming structural stability under static conditions. This study demonstrates the potential of autonomous transport vehicles to revolutionize agricultural logistics by reducing labor dependency, improving operational efficiency, and supporting sustainable farming.

Due to the insufficient burial depth of shallow-buried foundation bridges, foundation voiding easily occurs during floods or rapid water flows. When heavy vehicles pass over these partially voided bridges, the stress state of the foundation deteriorates instantaneously, causing critical components to exceed their load-bearing capacity in a short period, leading to a chain reaction that results in the rapid collapse and overall failure of the bridge structure. Previous numerical simulations of bridge water damage often neglected the strong coupling between water flow, soil, and structure during the scouring process. This paper applies a fluid-solid coupling simulation modeling method for bridge damage behavior under scouring action to study the structural damage behavior of shallow-buried foundation bridges under the combined effects of flood scouring and heavy vehicle load. This method employs point cloud reverse engineering technology to solve the difficult problem of converting the complex scour morphology around the foundation under flood scouring into a structural model, and investigates the multi-hazard damage behavior of shallow-buried foundations by coupling extreme hydraulic effects on the pier surface and placing the most unfavorable heavy vehicle loads on the bridge deck.

Soil and water conservation structures are vital for environmental resilience but present maintenance challenges due to their wide distribution and remote locations. To tackle these issues, a method using unmanned aerial vehicles (UAVs) combined with 360 degree photography was developed. UAVs captured images that were processed into panoramic and 3D models, enabling precise inspections of structural damage. These models were integrated into the disaster environment review and update (DER&U) rating system, enhanced by a fuzzy inference classification mechanism for improved damage estimation. Additionally, a management platform was created to boost overall efficiency and provide decision-making support for relevant authorities. The UAV-assisted inspection method demonstrated promising results, though certain limitations were also noted.

Vegetation indices (VIs) are widely applied to estimate leaf area index (LAI) for monitoring vegetation vigor and growth dynamics. However, the saturation issues in VIs caused by crown closure during the growing season pose significant challenges to the application of VIs in LAI estimation, particularly at the individual tree level. To address this, the feasibility of common VIs for LAI estimation at the individual tree level throughout the growing season was analyzed using data from digital hemispherical photography (DHP) and Unmanned Aerial Vehicle (UAV) acquisition. Additionally, the physical mechanisms underlying a generic VI-based estimation model were explored using the PROSAIL model and Global Sensitivity Analysis (GSA). Furthermore, the relationships between observed LAI derived from DHP and UAV-based VIs across different phenological development phases throughout the growing season were analyzed. The results suggested that the normalized difference vegetation index (NDVI) and its faster substitute infrared percentage vegetation index (IPVI) exhibited the best capabilities for LAI estimation (R2 = 0.55 and RMSE = 0.77 for both) across the entire growing season. The LAI-VI relationship varied seasonally due to the saturation issues on VIs, with R2 values increasing from the leaf budburst to the growing stage, decreasing during maturation, and rising again in the senescence stage. This indicated that seasonal effects induced by phenological changes should be considered when estimating LAI using VIs. Additionally, the saturation of VIs was influenced by soil background, leaf properties (especially leaf chlorophyll content [Cab] and dry matter content [Cm]), and canopy structures (especially average leaf inclination angle, ALA). Compared to satellites, UAV-based sensors were more effective at mitigating spectral saturation at finescale due to their finer spatial resolution and narrower bandwidth. The drone-based VIs used in this study provided reliable estimates and effectively described temporal variability in LAI, contributing to a better understanding of VI saturation effects.

Buried steel gas pipelines are increasingly facing safety challenges due to the escalating traffic loads and varying burial depths, which could potentially lead to hazards such as leakage, fire, and explosion. This paper investigates stress mechanisms in buried steel gas pipelines subjected to vehicular loading through integrated analytical approaches. Theoretical modeling incorporates three key components: dynamic vehicle load characteristics, soil-pipeline interaction pressures, and stress distribution angles across pipeline cross-sections. Stress variations are systematically quantified under varying soil conditions and load configurations. A finite-element model was developed to simulate pipeline responses, with computational results cross-validated against theoretical predictions to establish stress profiles under multiple operational scenarios. Additionally, this paper employ fatigue accumulation damage and reliability theories, utilizing Fe-Safe software to evaluate pipeline reliability, determining fatigue life and strength coefficients for various loads and burial depths. Based on these analyses, this paper develop risk control measures and protective methods for buried steel gas pipelines, validated through finite-element and fatigue analyses. Overall, this paper offers insights for preventing and controlling risks to buried steel gas pipelines under vehicle loads.

Uneven frost heave is frequently encountered in the subgrade-bridge transition zones (SBTZ) in seasonally frozen soil regions, which could lead to the deformation of track and even jeopardize running safety of vehicles. To this end, this paper conducts dynamic analysis of a vehicle-track coupled system accounting for the effect of frost heave deformation. Initially, the finite element method is used to obtain the relationship between rail irregularity and frost heave deformation. Then, a vehicle-track vertically coupled dynamics model is established, and its accuracy is validated by the measured data, published results and existing model. The time-domain dynamic responses of a vehicle-track coupled system under typical frost heave are analyzed. Afterwards, parametric analysis of frost heave deformation is conducted. Finally, the control threshold of frost heave is proposed from aspects of vehicle running safety, comfort, and track deformation. Numerical results indicate that the allowable amplitude of frost heave should be respectively restricted to 5, 20, and 25 mm for frost heave wavelengths less than 10 m, between 10 and 15 m, and greater than 15 m. The research findings offer theoretical support for the maintenance and operation of track in the SBTZ in seasonally frozen soil regions.