Search Results (216)

In recent years, China’s hilly and mountainous areas have faced widespread farmland abandonment. However, research on farmland abandonment and its driving mechanisms in hilly and mountainous regions is limited. This study proposes a transferable methodological framework that integrates Landsat data, Google Earth Engine, a time sliding-window algorithm, and the interpretable XGBoost–Shapley Additive explanation (SHAP) model. The time sliding-window algorithm is used to robustly detect long-term land cover changes across the entire study period. The SHAP quantifies the contributions of key drivers to farmland abandonment, providing transparent insights into the driving mechanisms. Applying this framework, we systematically analyzed the spatiotemporal evolution patterns and driving factors of farmland abandonment in Ji’an City, a typical city located in the hilly and mountainous areas of southern China and ultimately developed a farmland abandonment probability distribution map. The findings demonstrate the following. (1) Methodological validation showed that the random forest classifier achieved a mean overall accuracy (OA) of 91.05% (Kappa = 0.88) and the abandonment maps achieved OA of 91.58% (Kappa = 0.83). (2) Spatiotemporal analysis revealed that farmland area increased by 13.26% over 1990–2023, evolving through three stages: fluctuation (1990–2005), growth (2006–2015), and stability (2016–2023). The abandonment rate showed a long-term decreasing trend, peaking in 1998, whereas the abandoned area reached its minimum in 2007. From a spatial perspective, abandonment was more pronounced in mountainous and hilly regions of the study areas. (3) The XGBoost–SHAP model (R² > 0.85) identified key driving factors, including the potential crop yield, soil properties, mean annual precipitation, population density, and terrain features. By offering an interpretable and transferable monitoring framework, this study not only advances farmland abandonment research in complex terrains but also provides concrete policy implications. The results can guide targeted protection of high-risk abandonment zones, promote sustainable land-use planning, and support adaptive agricultural policies in hilly and mountainous regions. Full article

Natural latex harvest is pushing the boundaries of unmanned agricultural production in rubber milk collection via integrated robots in hilly and mountainous regions, such as the fixed and mobile tapping robots widely deployed in forests. As there are bad working conditions and complex natural environments surrounding rubber trees, the real-time and precision assessment of rubber milk yield status has emerged as a key requirement for improving the efficiency and autonomous management of these kinds of large-scale automatic tapping robots. However, traditional manual rubber milk yield status detection methods are limited in their ability to operate effectively under conditions involving complex terrain, dense forest backgrounds, irregular surface geometries of rubber milk, and the frequent occlusion of rubber milk bowls (RMBs) by vegetation. To address this issue, this study presents an unmanned aerial vehicle (UAV) imagery rubber milk yield state detection method, termed YOLOv8n-RMB, in unstructured field environments instead of manual watching. The proposed method improved the original YOLOv8n by integrating structural enhancements across the backbone, neck, and head components of the network. First, a receptive field attention convolution (RFACONV) module is embedded within the backbone to improve the model’s ability to extract target-relevant features in visually complex environments. Second, within the neck structure, a bidirectional feature pyramid network (BiFPN) is applied to strengthen the fusion of features across multiple spatial scales. Third, in the head, a content-aware dynamic upsampling module of DySample is adopted to enhance the reconstruction of spatial details and the preservation of object boundaries. Finally, the detection framework is integrated with the BoT-SORT tracking algorithm to achieve continuous multi-object association and dynamic state monitoring based on the filling status of RMBs. Experimental evaluation shows that the proposed YOLOv8n-RMB model achieves an AP@0.5 of 94.9%, an AP@0.5:0.95 of 89.7%, a precision of 91.3%, and a recall of 91.9%. Moreover, the performance improves by 2.7%, 2.9%, 3.9%, and 9.7%, compared with the original YOLOv8n. Plus, the total number of parameters is kept within 3.0 million, and the computational cost is limited to 8.3 GFLOPs. This model meets the requirements of yield assessment tasks by conducting computations in resource-limited environments for both fixed and mobile tapping robots in rubber plantations. Full article

Accurate wind field modeling over hilly terrain is critical for wind energy, infrastructure safety, and environmental assessment, yet its inherent complexity poses significant simulation challenges. This paper systematically reviews this field’s major advances by analyzing 610 key publications from 2015 to 2024, selected from core databases (e.g., Web of Science and Scopus) through targeted keyword searches (e.g., ‘wind flow’, ‘complex terrain’, ‘CFD’, ‘hilly’) and subsequent rigorous relevance screening. We critique four primary modeling paradigms—field measurements, wind tunnel experiments, Computational Fluid Dynamics (CFD), and data-driven methods—across three key application areas, filling a gap left by previous single-focus reviews. The analysis confirms CFD’s dominance (75% of studies), with a clear shift from idealized 2D to real 3D terrain. Key findings indicate that high-fidelity coupled models (e.g., LES), validated against benchmark field experiments such as Perdigão, can reduce mean wind speed prediction bias to below 0.1 m/s; and optimized engineering designs for mountainous infrastructure can mitigate local wind speed amplification effects by 15–20%. Data-driven surrogate models, represented by FuXi-CFD, show revolutionary potential, reducing the inference time for high-resolution wind fields from hours to seconds, though they currently lack standardized validation. Finally, this review summarizes persistent challenges and outlines future directions, advocating for physics-informed neural networks, high-fidelity multi-scale models, and the establishment of open-access benchmark datasets. Full article

To address the suboptimal leveling performance and insufficient slope stability of existing agricultural machinery chassis in hilly and mountainous regions, this study proposes an innovative omnidirectional leveling system based on a “double-layer frame” crawler-type agricultural chassis. The system employs servo-electric cylinders as its actuation components. A control model for the servo-electric cylinders has been established, accompanied by the design of an adaptive sliding mode controller (ASMC). A co-simulation platform was developed utilizing Matlab/Simulink and Adams to evaluate system performance. Comparative simulations were conducted between the ASMC and a conventional PID controller, followed by comprehensive machine testing. Experimental results demonstrate that the proposed double-layer frame crawler chassis achieves longitudinal leveling adjustments of up to 25° and lateral adjustments of 20°. Through structural optimization and the application of ASMC (in contrast to PID), both longitudinal and lateral leveling response times were reduced by 1.12 s and 0.95 s, respectively. Furthermore, leveling velocities increased by a factor of 1.5 in the longitudinal direction and by a factor of 1.3 in the lateral direction, while longitudinal and lateral angular accelerations decreased by 15.8% and 17.1%, respectively. Field tests confirm the system’s capability for adaptive leveling on inclined terrain, thereby validating the enhanced performance of the proposed omnidirectional leveling system. Full article

Amid expanding higher education and enhancing spatial quality, modern university campuses face challenges including inefficient space utilization and a disconnect from human-centered design. We developed a coupled model that integrates the analytic hierarchy process (AHP) with space syntax theory to identify and address functional fragmentation, limited accessibility, and diminished spatial vitality. The Delphi method was employed to determine weights on visual and traffic influence factors. Through spatial quantitative analysis using Depthmap software, we estimated spatial-efficiency discrepancies across 11 component types, including school gates, teaching buildings, and libraries. A case study was conducted at a university located in the hilly terrain of Conghua District, Guangzhou, China which revealed significant contradictions between subjective evaluations and objective data at components, such as the administrative building and gymnasium. These contradictions led to poor visual permeability, excessive path redundancy, and imbalanced functional layouts. Based on the results of this study, targeted optimization strategies were proposed, including permeable interface designs, path network reconfiguration, and the implementation of dynamic functional modules. These interventions were tailored to accommodate the humid subtropical climate, balancing shading, ventilation, and visual transparency. In this study, methodological support for the renovation of existing campus infrastructure was provided as theoretical and technical references for space renewal in tropical and subtropical academic environments and the enhancement of the quality and resilience of campus spaces. The results also broadened the application of interdisciplinary methods in university planning. Full article

Hilly areas serve as critical ecological barriers yet face developmental challenges, drawing increasing attention to how land use transformation affects eco-environmental quality (EEQ). Systematic studies on EEQ drivers in complex terrains remain limited, particularly regarding nonlinear and interactive effects. This study examines Zhejiang’s hilly area—typical of southern China’s hills—using land use data from 2000, 2010, and 2020. Methods including land use transfer matrix, EEQI, hotspot analysis, and XGBoost-SHAP were applied to assess impacts and quantify drivers. Results show a slight but consistent decline in EEQ index (EEQI) (0.7635 to 0.7472), driven primarily by rapid built-up land (BL) expansion (276.41% increase). NDVI was the most influential factor (SHAP: 0.1226, >59%), followed by GDP per unit area and temperature. NDVI showed a threshold effect (>0.65 strengthens benefit), and strong interaction with per capita GDP. A slope-vegetation coupling mechanism was identified: on slopes > 30°, high NDVI significantly amplifies EEQ improvement, highlighting the importance of vegetation conservation on steep slopes. These findings provide a scientific basis for targeted land management in hilly regions of southern China and similar areas. Full article

Achieving energy-efficient aerial coverage remains a significant challenge for UAV-based missions, especially over hilly terrain where consistent ground resolution is needed. Traditional solutions use changes in altitude to compensate for elevation changes, which requires a significant amount of energy. This paper presents a new way to plan coverage paths (CPP) that uses real-time zoom control of a pan–tilt–zoom (PTZ) camera to keep the ground sampling distance (GSD)—the distance between two consecutive pixel centers projected onto the ground—constant without changing the UAV’s altitude. The proposed algorithm changes the camera’s focal length based on the height of the terrain. It only changes the altitude when the zoom limits are reached. Simulation results on a variety of terrain profiles show that the zoom-based CPP substantially reduces flight duration and path length compared to traditional altitude-based strategies. The framework can also be used with low-cost camera systems with limited zoom capability, thereby improving operational feasibility. These findings establish a basis for further development and field validation in upcoming research phases. Full article

This paper presents a bionic extended Kalman filter (EKF) state estimation algorithm for agricultural planters, inspired by the bionic mechanism of migratory birds navigating in complex environments, where migratory birds achieve precise localization behaviors by fusing multi-sensory information (e.g., geomagnetic field, visual landmarks, and somatosensory balance). The algorithm mimics the migratory bird’s ability to integrate multimodal information by fusing laser SLAM, inertial measurement unit (IMU), and GPS data to estimate the position, velocity, and attitude of the planter in real time. Adopting a nonlinear processing approach, the EKF effectively handles nonlinear dynamic characteristics in complex terrain, similar to the adaptive response of a biological nervous system to environmental perturbations. The algorithm demonstrates bio-inspired robustness through the derivation of the nonlinear dynamic teaching model and measurement model and is able to provide high-precision state estimation in complex environments such as mountainous or hilly terrain. Simulation results show that the algorithm significantly improves the navigation accuracy of the planter in unstructured environments. A new method of bio-inspired adaptive state estimation is provided. Full article

The rural border areas of Serbia have been undergoing significant demographic shifts and transformations in land use. Between 2002 and 2022, these regions experienced a continuous population decline, an increase in the average age, and a growing share of single-person households. Simultaneously, there has been a reduction in agricultural land and a noticeable expansion of forested and grassland areas, particularly in hilly and mountainous terrain. This paper aims to explore the interrelationship between demographic indicators and land cover changes in these areas. Pearson’s correlation analysis was applied to data from the national population censuses and the CORINE Land Cover datasets for 1990 and 2018. The strongest positive correlation was found between the decline in the number of households and the reduction in agricultural land. Conversely, the expansion of forested areas showed a negative correlation with most demographic indicators. The findings reflect trends similar to those observed in other Eastern European countries but also reveal specific patterns of spatial marginalization unique to Serbia. In the study, the conclusion leads to the idea that depopulated border areas are in transition between past and future functions that will be influenced by their resource base. Full article

The presence of understory vegetation not only influences slope-scale soil and water conservation but also exerts a profound effect on hydrodynamic characteristics and the processes of runoff and sediment production. Therefore, in this study, different vegetation types and vegetation coverages (bare land, 30%, 60%, and 90%) were set up by simulating rainfall (45, 60, 90, and 120 mm·h⁻¹) to evaluate the runoff-sediment process and the response characteristics of hydrodynamic parameters. The results showed that increasing vegetation cover significantly reduced soil erosion on forest slopes (p < 0.05). When the vegetation cover ranged from 60% to 90%, vegetation pattern C and pattern D were the most effective in suppressing erosion, where increased cover improved runoff stability. Under low-cover conditions, overland flow tended toward turbulent and rapid regimes, whereas under high cover conditions, flow was primarily laminar and slow. Patterns C and D significantly reduced flow velocity and water depth (p < 0.05). Structural equation patterning revealed that, under different vegetation patterns, the runoff power (ω), Reynolds number (Re), and resistance coefficient (f) more effectively characterized the erosion process. Among these, the Reynolds number and runoff power were the dominant factors driving erosion on red soil slopes. By contrast, runoff shear stress was significantly reduced under high-cover conditions and showed weak correlation with sediment yield, suggesting that it was unsuitable as an indicator of slope erosion. Segmental vegetation arrangements and increasing vegetation cover near runoff outlets—especially at 60–90% coverage—effectively reduced soil erosion. These findings provide scientific insight into the hydrodynamic mechanisms of vegetation cover on slopes and offer theoretical support for optimizing soil and water conservation strategies on hilly terrain. Full article

Aiming at key issues in harvesting film-covered potatoes in hilly and mountainous areas—incomplete residual film collection, poor potato–soil separation, and high damage from potato-collecting devices—this study developed a crawler self-propelled potato harvester suitable for these regions. This study first expounds the overall structure and working principle of the potato harvester and then conducts principal analysis and structural design for key components (film-collecting device, digging device, primary conveying and separating device, secondary conveying and separating device, and intelligent potato-collecting device) from the perspectives of material force and movement. Finally, field performance tests were carried out in Huangzhong County, Xining City, Qinghai Province. The test results show that the machine can achieve an operation effect with a potato harvest loss rate of 2.4%, a potato damage rate of 1.4%, an impurity content rate of 2.8%, a skin-breaking rate of 2.7%, and a residual film cleaning rate of 89.6%, meeting the potato harvesting needs of this region. The lightweight self-propelled crawler potato harvester designed in this paper can realize functions such as residual film collection, potato–soil vibration separation, manual auxiliary sorting, and intelligent potato boxing, providing technical and equipment references for the harvesting of film-covered potatoes in complex terrain areas. Full article

This study investigates the dynamic performance of mountainous crawler tractors during small-radius slope steering, providing theoretical support for power machinery design in hilly and mountainous regions. Addressing the mechanization demands in complex terrains and existing research gaps, a steering dynamics model is established. The model incorporates an amplitude-varied multi-peak cosine ground pressure distribution, employs position vectors and rotation matrices to characterize 3D pose variations in the tractor’s center of mass, and integrates slope angle, soil parameters, vehicle geometry, center-of-mass shift, bulldozing resistance, and sinkage resistance via d’Alembert’s principle. Numerical simulations using Maple 2024 analyzed variations in longitudinal offset of the instantaneous steering center, bilateral track traction forces, and bulldozing resistance with slope, speed, and acceleration. Variable-gradient steering tests on the “Soil-Machine-Crop” Comprehensive Experimental Platform demonstrated model accuracy, with <8% mean error and <12% maximum relative error between predicted and measured track forces. This research establishes a theoretical foundation for predicting, evaluating, and controlling the steering performance/stability of crawler tractors in complex slope conditions. Full article

Accurate surface wind speed data are vital for atmospheric science, climatology, and energy applications. European Centre for Medium-Range Weather Forecasts Reanalysis v.5 (ERA5), as one of the most widely used global reanalysis datasets, has insufficient assessment of its applicability across diverse landform types. Using the gridded observational dataset over China (CN05.1) and the Global Basic Landform Units dataset, this study evaluated the surface wind speed data from ERA5 over various altitudinal zones and undulating terrains in China via root-mean-square differences (RMSD) and mean absolute percentage error (MAPE) against CN05.1 observations. Results reveal significant regional variations, with ERA5 effectively capturing the spatial distribution of mean wind speeds but systematically underestimating magnitudes, particularly in plateau and mountainous regions. ERA5 reanalysis fails to reproduce the observed altitudinal increase in surface wind speed. Elevation-dependent biases are prominent, with RMSD and MAPE increasing from low-altitude to high-altitude areas. Terrain complexity exacerbates errors, showing maximum deviations in high-relief mountains and minimum deviations in hilly regions. These biases evolve seasonally, peaking in spring and reaching minima in winter. In summary, discrepancies between observations and ERA5 vary with altitude, topographic relief, and season. The most significant deviations occur for spring surface winds in high-altitude, high-relief mountains, with mean RMSD reaching 3.3 m/s and MAPE 553%. The findings highlight the limitations of ERA5 reanalysis data in scientific and operational contexts over complex terrains. Full article

Gravity erosion is one of the main physical processes of soil erosion and sediment sources in catchments, and its spatiotemporal patterns and driving mechanisms are seriously understudied, mainly due to the the great difficulties in monitoring and quantifying. This study obtained gravity erosion amounts by runoff scouring experiments on the field slope of the hilly–gully region of the Chinese Loess Plateau. The terrain point cloud before and after gravity erosion was obtained based on the TLS, SfM and the fusion of single-scan TLS and SfM, and then the gravity erosion was estimated by four terrain change detection algorithms (DoD, C2C, C2M and M3C2). Results showed that the M3C2 algorithm plus fused data had the highest quantization accuracy among all the algorithms and data sources, with a relative error of 14.71%. The fused data combined with M3C2 algorithm performed much better than other algorithms and data sources for the different gravity erosion magnitudes (mean relative error < 17.00%). The DoD algorithm plus TLS data were preferable for collapse areas, while the M3C2 algorithm plus TLS was suitable for the alcove area. This study provides a useful reference for the monitor and quantitative research of gravity erosion in complex topographic areas. Full article

The Yangtze River Delta urban agglomeration, characterized by high population density, an advanced transportation system, and a concentration of industrial activity, is one of the regions severely affected by O₃ pollution in central and eastern China. Using data collected from 251 monitoring stations between 2015 and 2025, this paper analyzed the spatio-temporal variation of 8 h O₃ concentrations and instances of exceedance. On the basis of exploring the influence of meteorological factors on regional 8 h O₃ concentration, the potential source contribution areas of pollutants under the exceedance condition were investigated using the HYSPLIT model. The results indicate a rapid increase in the 8 h O₃ concentration at a rate of 0.91 ± 0.98 μg·m⁻³·a⁻¹, with the average number of days exceeding concentration standards reaching 41.05 in the Yangtze River Delta urban agglomeration. Spatially, the 8 h O₃ concentrations were higher in coastal areas and lower in inland regions, as well as elevated in plains compared to hilly terrains. This distribution was significantly distinct from the concentration growth trend characterized by higher levels in the northwest and lower levels in the southeast. Furthermore, it diverged from the spatial characteristics where exceedances primarily occurred in the heavily industrialized northeastern region and the lightly industrialized central region, indicating that the growth and exceedance of 8 h O₃ concentrations were influenced by disparate factors. Local human activities have intensified the emissions of ozone precursor substances, which could be the key driving factor for the significant increase in regional 8 h O₃ concentrations. In the context of high temperatures and low humidity, this has contributed to elevated levels of 8 h O₃ concentrations. When wind speeds were below 2.5 m·s⁻¹, the proportion of 8 h O₃ concentrations exceeding the standards was nearly 0 under almost calm wind conditions, and it showed an increasing trend with rising wind speeds, indicating that the potential precursor sources that caused high O₃ concentrations originated occasionally from inland regions, with very limited presence within the study area. This observation implies that the main cause of exceedances was the transport effect of pollution from outside the region. Therefore, it is recommended that the Yangtze River Delta urban agglomeration adopt economic and technological compensation mechanisms within and between regions to reduce the emission intensity of precursor substances in potential source areas, thereby effectively controlling O₃ concentrations and improving public living conditions and quality of life. Full article