Search Results (205)

The Qinba Mountains in China span six provinces, characterized by a large population, rugged terrain, steep peaks, deep valleys, and scarce flat land, making large-scale agricultural development challenging. Terraced fields serve as the core cropland type in this region, playing a vital role in preventing soil erosion on sloping farmland and expanding agricultural production space. They also function as a crucial medium for sustaining the ecosystem services of mountainous areas. As a transitional zone between China’s northern and southern climates and a vital ecological barrier, the Qinba Mountains’ terraced ecosystems have undergone significant spatial changes over the past two decades due to compound factors including the Grain-for-Green Program, urban expansion, and population outflow. However, current large-scale, long-term, high-resolution monitoring studies of terraced fields in this region still face technical bottlenecks. On one hand, traditional remote sensing interpretation methods rely on manually designed features, making them ill-suited for the complex scenarios of fragmented, multi-scale distribution, and terrain shadow interference in Qinba terraced fields. On the other hand, the lack of high-resolution historical imagery means that low-resolution data suffers from insufficient accuracy and spatial detail for capturing dynamic changes in terraced fields. This study aims to fill the technical gap in detailed dynamic monitoring of terraced fields in the Qinba Mountains. By creating image tiles from Landsat-8 satellite imagery collected between 2017 and 2020, it employs three deep learning semantic segmentation models—DeepLabV3 based on ResNet-34, U-Net, and PSPNet deep learning semantic segmentation models. Through optimization strategies such as data augmentation and transfer learning, the study achieves 15-m-resolution remote sensing interpretation of terraced field information in the Qinba Mountains from 2000 to 2020. Comparative results revealed DeepLabV3 demonstrated significant advantages in identifying terraced field types: Mean Pixel Accuracy (MPA) reached 79.42%, Intersection over Union (IoU) was 77.26%, F1 score attained 80.98, and Kappa coefficient reached 0.7148—all outperforming U-Net and PSPNet models. The model’s accuracy is not uniform but is instead highly contingent on the topographic context. The model excels in environments that are archetypal for mid-altitudes with moderately steep slopes. Based on it we create a set of tiles integrating multi-source data from RBG and DEM. The fusion model, which incorporates DEM-derived topographic data, demonstrates improvement across these aspects. Dynamic monitoring based on the optimal model indicates that terraced fields in the Qinba Mountains expanded between 2000 and 2020: the total area was 57.834 km² in 2000, and by 2020, this had increased to 63,742 km², representing an approximate growth rate of 8.36%. Sichuan, Gansu, and Shaanxi provinces contributed the majority of this expansion, accounting for 71% of the newly added terraced fields. Over the 20-year period, the center of gravity of terraced fields shifted upward. The area of terraced fields above 500 m in elevation increased, while that below 500 m decreased. Terraced fields surrounding urban areas declined, and mountainous slopes at higher elevations became the primary source of newly constructed terraces. This study not only establishes a technical paradigm for the refined monitoring of terraced field resources in mountainous regions but also provides critical data support and theoretical foundations for implementing sustainable land development in the Qinba Mountains. It holds significant practical value for advancing regional sustainable development. Full article

Land use intensification profoundly impacts ecological quality, with this dynamic relationship being particularly pronounced in China’s Central Yunnan Plateau Lake Basin (CYP-LBs), an ecologically fragile area of significant socioeconomic value. Despite the critical importance of their interaction, existing research has largely overlooked their dynamic interplay—especially within plateau lake basins. To address this gap, this study employs the Remote Sensing Ecological Index (RSEI) to assess the ecological quality dynamics of CYP-LBs from 2005 to 2025 and its association with land use intensity (LUI), revealing spatiotemporal patterns of ecological quality evolution and its linkage to land use. Results indicate that CYP-LBs maintained overall moderate ecological quality (average RSEI ~0.50), exhibiting an initial increase followed by decline, peaking at 0.5519 in 2015. The center of gravity for ecological quality shifted eastward in most watersheds, with Moran’s I index consistently above 0.50 indicating significant spatial autocorrelation. The LUI showed an overall upward trend, with high-intensity areas primarily concentrated in lakeshore zones (e.g., eastern Dianchi Lake, Xingyun Lake) and exhibiting regional expansion over time. RSEI and LUI generally showed a negative correlation, but positive correlations emerged in localized areas of eastern and northern Dianchi Lake due to concurrent urbanization and ecological restoration efforts. Among land types, grasslands and forests were identified as the primary drivers influencing ecological quality changes in CYP-LBs. These findings provide crucial scientific basis for integrated conservation, land use optimization, and sustainable development in ecologically fragile plateau lake basins. Full article

The Yellow River Basin faces high-intensity coal resource development and severe water scarcity. This makes the treatment and use of mine water a critical factor constraining both coal industry development and ecological security for the region. This study uses kernel density estimation and the Standard Deviational Ellipse model to identify the spatial pattern of mine water production. It also combines bibliometric analysis and field investigations to assess research progress and current practice for mine water treatment and use in the basin. Results show that mine water production displays strong spatial clustering, with the center of gravity shifting northward. Research is moving from an engineering-focused stage to a theory-oriented one, emphasizing systematic optimization and sustainable use. Current practices still struggle with non-standardized data, uneven treatment quality, and incomplete management systems. This research underscores the importance of improving the region’s integrated management of mine water and proposes shifting mine water from an environmental burden to a resource asset. Full article

This paper presents an innovative digital twin (DT) model integrated with robust model predictive control (MPC) to enhance the performance of an unmanned air vehicle (UAV) tasked with lifting and transporting irregular-shaped payloads. Traditional UAV control systems face complex challenges in stability and accuracy when dealing with asymmetrical payloads, as such payloads cause continuous shifts in the center of gravity (CoG) and variable inertial forces, which lead to unpredictable flight dynamics. The proposed DT framework enables the creation of a real-time replica of the UAV payload system. It creates an adaptive control environment that anticipates and mitigates disturbances before they impact the stability of the UAV during a mission. By combining a DT with MPC, the control system dynamically adjusts to variations in payload characteristics, namely (a) changes in mass distribution and (b) aerodynamic drag force. As a result, a stable flight path is ensured even under challenging environmental conditions. The DT model continuously forecasts potential destabilizing events and modifies MPC constraints to accommodate complex shifting dynamics, achieving improved control accuracy and energy efficiency. Extensive simulations across various hanging payload configurations and environmental disturbance scenarios validate the effectiveness of the proposed model. The simulation results show that the DT-MPC strategy significantly improves stability, control precision, and energy conservation, outperforming conventional methods. A comparative analysis is also carried out with a conventional control scheme to validate the robustness of the proposed framework. This research work advances the development of intelligent, autonomous UAV systems capable of reliably managing complex and irregularly shaped payloads with varying mass distributions in real-world scenarios, thereby broadening their potential applications in logistics, emergency response, and industrial transportation. Full article

To accurately analyze the dynamic response and driving mechanism of forest carbon sequestration in the core area of the Loess Plateau’s Returning Farmland to Forestry Project, this study takes the Beiluo River Basin as the research area. Using spatial autocorrelation, gravity model, a geodetector, and spatiotemporal geographically weighted regression models, it analyzes the spatiotemporal evolution of forest carbon sequestration and the spatial heterogeneity of its influencing factors based on 2000–2023 data. The results show the following: (1) Forest carbon sequestration in the basin increased by 13.55% from 2000 to 2023; its spatial pattern shifted from “middle reaches concentration” to “stable middle reaches core plus significant upper reaches growth”, with the gravity center moving “southeast then northwest”. (2) Forest carbon sequestration had significant positive spatial correlation, with hotspots in soil–rock mountain forest areas and cold spots in ecologically fragile or high-human-activity areas. (3) Natural ecological factors dominated forest carbon sequestration evolution, socioeconomic factors enhanced synergy, and evapotranspiration and NDVI had significant impacts. (4) Factor impacts had spatiotemporal heterogeneity, such as the decaying positive effect of precipitation and the “positive-negative-equilibrium” change in forestry value-added. This study provides scientific guidance for basin and Loess Plateau ecological restoration and “double carbon” goal achievement. Full article

In automated sorting and grasping of livestock meat cuts, the ideal assumption of symmetric mass distribution is often violated due to irregular morphology and soft tissue deformation. Under the combined effects of gripping forces and gravity, the originally balanced configuration evolves into an asymmetric state, resulting in dynamic shifts of the center of gravity (CoG) that undermine the stability and accuracy of robotic grasping. To address this challenge, this study proposes a CoG trajectory prediction method tailored for meat-cut grasping tasks. First, a dynamic model is established to characterize CoG displacement during grasping, quantitatively linking gripping force to CoG shift. Then, the prediction task is reformulated as a nonlinear state estimation problem, and a Small-Target Bayesian–Probability Hypothesis Density (STB-PHD) algorithm is developed. By incorporating historical error feedback and adaptive covariance adjustment, the proposed method compensates for asymmetric perturbations in real time. Extensive experiments validated the effectiveness of the proposed method: the Optimal Sub-Pattern Allocation (OSPA) metric reached 4.82%, reducing the error by 4.35 percentage points compared to the best baseline MGSTM (9.17%). The task completion time (TC Time) was 6.15 s, demonstrating superior performance in grasping duration. Furthermore, the Average Track Center Distance (ATCD) reached 8.33%, outperforming the TPMBM algorithm (8.86%). These results demonstrate that the proposed method can accurately capture CoG trajectories under deformation, providing reliable control references for robotic grasping systems. The findings confirm that this approach enhances both stability and precision in automated grasping of deformable objects, offering valuable technological support for advancing intelligence in meat processing industries. Full article

Amid the rapid evolution of the digital economy reshaping global competitiveness, China has advanced regional coordination through the Digital China initiative and the “Data Elements ×” Three-Year Action Plan (2024–2026). To further integrate digital transformation with high-quality growth in the urban agglomerations of the middle and lower Yellow River, this study aims to strengthen regional competitiveness, expand digital industries, foster new productivity, refine the development pathway, and safeguard balanced economic, social, and ecological progress. Taking the Yellow River urban clusters as the research object, a comprehensive assessment framework encompassing seven subsystems is established. By employing a mixed-weighting approach, entropy-based TOPSIS, hotspot analysis, coupling coordination models, spatial gravity shift techniques, and grey relational methods, this study investigates the spatiotemporal dynamics between the digital economy and high-quality development. The findings reveal that: (1) temporally, the coupling–coordination process evolves through three distinct phases—initial fluctuation and divergence (1990–2005), synergy consolidation (2005–2015), and high-level stabilization (2015–2022)—with the average coordination index rising from 0.21 to 0.41; (2) spatially, a persistent “core–periphery” structure emerges, while subsystem coupling consistently surpasses coordination levels, reflecting a pattern of “high coupling but insufficient coordination”; (3) hot–cold spot analysis identifies sharp east–west contrasts, with the gravity center shift and ellipse trajectory showing weaker directional stability but greater dispersion; and (4) grey correlation results indicate that key drivers have transitioned from economic scale and infrastructure inputs to green innovation performance and data resource allocation. Overall, this study interprets the empirical results in both temporal and spatial dimensions, offering insights for policymakers seeking to narrow the digital divide and advance sustainable, high-quality development in the Yellow River region. Full article

The resilience of the tourism economy plays a pivotal role in sustaining regional economic stability across the nine provinces along the Yellow River. This study examines the spatio-temporal evolution and configurational pathways of tourism economic resilience across the nine provinces along the Yellow River during 2012–2022 by applying the Standard Deviation Ellipse and Fuzzy Set Qualitative Comparative Analysis. The results showed that: (1) From 2012 to 2019, the tourism economic resilience exhibited a steady upward tendency overall, with a slight fluctuation in the short term in 2020. (2) High and relatively high-level regions experienced a belt-like high-value zone, eventually extending to Sichuan Province, Henan Province, and Shandong Province. (3) The standard deviation ellipse exhibited a distribution pattern along the northeast-southwest axis, with its center of gravity situated in the middle reaches of the Yellow River, having shifted a total of 146.81 km. (4) Four driving pathways were identified: resistance-dominated, recovery-dominated with restructuring synergy, renewal-driven, and multi-resilience synergy-driven. Three barriers also appeared: renewal-constrained, restructuring-lagged, and overall resilience-deficient. Full article

Maize, one of the world’s three major food crops, plays a vital role in global food security. Analyzing the spatiotemporal patterns of maize cultivation in Hunan Province and their influencing factors contributes to enhancing planting quality and efficiency, optimizing production patterns, and supporting provincial food security initiatives. Utilizing maize cultivation data from Hunan Province (2001–2023), this study employed the standard deviation ellipse, center of gravity shift model, and principal component analysis to examine production patterns and their drivers. Key findings include the following: (1) The maize planting area exhibited an overall increasing trend from 2001 to 2023, with a spatial convergence from the northwest towards the east. Cultivation hot spots were identified in Shaoyang, Loudi, and Changde. Maize cultivation was predominantly concentrated in areas with gentle slopes (0–3°) and gradually shifted eastward towards similar terrain. (2) The provincial maize production center of gravity followed a “Z”-shaped trajectory, moving eastward and southward with Loudi City as its core. While the spatial distribution pattern shifted from “northwest–southeast” to “west–east”, the core concentration area maintained its “northwest–southeast” orientation. Concurrently, the fragmentation of cultivated land within the maize planting landscape increased. (3) Maize planting hot spots expanded from the northwest towards the central and eastern regions, extending southward. Cold spot areas shifted from the central region towards the northeast. By the study’s end, the central region had emerged as the core maize planting area. (4) Agricultural production conditions and policy factors were identified as the main drivers of spatiotemporal changes in maize acreage within Hunan Province. Full article

Rapid socioeconomic development has continuously driven urban land expansion at the expense of other land types, leading to significant changes in land use and environment. However, existing studies still lack fine-resolution, long-term projections of urban land. Using seven periods of land use data from 1990 to 2020, this study projects urban land in the Yangtze River Delta (YRD) region under the framework of Shared Socioeconomic Pathways (SSPs). A multiple linear regression model and the land use change scenario simulation model (GeoSOS-FLUS) were employed to make projection at a high spatial resolution of 1 km. The findings are as follows: (1) From 1990 to 2020, the rate of urban land expansion in the study area showed a pattern of initial acceleration followed by deceleration, with the average annual expansion rate decreasing from 1.36 × 10³ km² to 0.24 × 10³ km². The center of gravity shifted toward the southeast. (2) Future urban land expansion is projected to increase by 14 × 10³ km² (SSP3) to 48 × 10³ km² (SSP5). The northern and central parts of the region will experience more significant growth, and the center of gravity is projected to shifting northwest. (3) Under SSP2 and SSP5, the urban land will increase continuously. The findings can offer a valuable insight for regional planning and sustainable development. Full article

A quantitative study on the spatial structure and spatiotemporal variation characteristics of net carbon sinks in regional farmland ecosystems is of significant importance for uncovering the multifunctional roles of farmland ecosystems and formulating region-specific agricultural policies and management strategies. Based on the measurement of net carbon sinks in county-level farmland ecosystems across Hunan Province from 2005 to 2020, this research employs methodologies, including the standard deviational ellipse (SDE), spatial autocorrelation, and exploratory spatiotemporal data analysis (ESTDA) to investigate the spatiotemporal evolution characteristics of net carbon sinks in Hunan’s county-level farmland ecosystems. The results show that the net carbon sinks of county-level farmland ecosystems in Hunan Province exhibits a “northeast–southwest” spatial distribution pattern, with a trend toward spatial agglomeration during contraction, and the center of gravity of net carbon sinks has generally shifted northwestward over time. A significant positive spatial correlation exists globally in the net carbon sinks of Hunan’s county-level farmland ecosystems, and the degree of spatial agglomeration has gradually intensified amid fluctuations. The dynamic evolution of local spatial patterns of net carbon sinks in county-level farmland ecosystems in Hunan Province varied significantly, showing strong stability in both local spatial structure and spatial dependence direction. In contrast, eastern and central Hunan exhibited more dynamic local spatial structures compared to southern and northern regions. The local spatial association patterns of the net carbon sinks in county-level farmland ecosystems remained relatively stable, with weak spatial synergy and a pronounced path-dependent locking effect in spatial agglomeration. Full article

In recent years, the rapid expansion of urban rail transit has significantly improved travel efficiency, yet it has also exacerbated spatial inequality in service coverage. Accessibility, as a fundamental metric for evaluating the equity of service distribution, remains limited by three major shortcomings in current assessment methods: the neglect of actual road network characteristics, reliance on a single static scale, and the absence of quantitative mechanisms to assess accessibility equity. These deficiencies hinder a comprehensive understanding of how equity evolves with the spatiotemporal dynamics of rail systems. To address the aforementioned issues, this study proposes an innovative spatiotemporally dynamic and multi-scale analytical framework for evaluating urban rail accessibility and its equity implications. Specifically, we develop a network-based buffer decay model to refine service population estimation by incorporating realistic walking paths, capturing both distance decay and road network constraints. The framework integrates multiple spatial analytical techniques, including the Gini coefficient, Lorenz curve, global and local spatial autocorrelation, center-of-gravity shift, and standard deviation ellipse, to quantitatively assess the equity and evolutionary patterns of accessibility across multiple spatial scales. Taking the central urban area of Hangzhou as a case study, this research investigates the spatiotemporal patterns and equity changes in metro station accessibility in 2019 and 2023. The results indicate that the expansion of the metro network has partially improved overall accessibility equity: the Gini coefficient at the TAZ (Traffic Analysis Zone) scale decreased from 0.56 to 0.425. Nevertheless, significant inequality remains at finer spatial resolutions (grid-level Gini coefficient = 0.404). In terms of spatial pattern, the core area (e.g., Wulin Square) forms a ‘high-high’ accessibility agglomeration area, while the urban fringe area (e.g., northern Yuhang) presents a ‘low-low’ agglomeration, and the problem of local ‘accessibility depression’ still exists. Additionally, the accessibility centroid has consistently shifted northwestward, and the long axis of the standard deviation ellipse has rotated from an east–west to a northwest-southeast orientation, indicating a growing spatial polarization between core and peripheral zones. The findings suggest that improving equity in urban rail accessibility cannot rely solely on expanding network size; rather, it requires coordinated strategies involving network structure optimization, branch line development, multimodal integration, and the construction of efficient transfer systems to promote more balanced and equitable spatial distribution of rail transit resources citywide. Full article

Grassland degradation poses a serious threat to ecosystem stability and the sustainable development of human societies. In this study, we propose a framework for grassland degradation risk assessments and early warning based on key ecological factors (KEFs) in Xilingol. The NDVI, NPP, and grass yield were selected as KEFs to represent vegetation coverage, ecosystem productivity, and actual biomass, respectively. By constructing a grassland degradation index (GDI) and integrating K-means clustering, the average curvature, and a gravity center shift analysis, we quantified the degradation risk levels and identified the threshold values for different grassland types. The results showed the following: (1) the grass yield was the most sensitive indicator of grassland degradation in Xilingol, with high-risk thresholds decreasing from 115.67 g·m⁻² in the temperate meadow steppes (TMSs) to 73.27 g·m⁻² in the temperate typical steppes (TTSs), and further to 32.30 g·m⁻² in the temperate desert steppes (TDSs); (2) the TDSs exhibited the highest curvature value (2.81 × 10⁻⁴) in the initial stage, indicating a higher likelihood of rapid early-stage degradation, whereas the TMSs and TTSs reached peak curvature in the latest stages; and (3) the TTSs had the largest proportion of high-risk areas (33.02%), with a northeast–southwest distribution and a probable westward expansion trend. This study provides a practical framework for grassland degradation risk assessments and early warning, offering valuable guidance for ecosystem management and sustainable land use. Full article

The operational stability of agricultural tractors is directly influenced by the mass distribution between axles, particularly when using mounted implements with variable loads. This study aimed to evaluate how different masses of a mounted sprayer (550 kg, 850 kg, and 1150 kg) and tire inflation pressures (151.7–193.1 kPa) affect the load distribution between axles, tire contact area, center of gravity (CG) displacement, and tractor lead ratio. A 3 × 4 factorial design was adopted with a statistical analysis of key parameters across 12 experimental combinations. The results demonstrated that increasing implement mass significantly shifted the load toward the rear axle, reducing the front axle load by up to 46% and displacing the CG rearward by more than 11 cm, thereby compromising stability. Tire pressure, as well as the interaction between mass and pressure, also exhibited statistically significant influence on load distribution and CG positioning while modulating the tire contact area. The lead ratio increased linearly with mass, exceeding the recommended 5% threshold when the sprayer was at full capacity. These findings indicate that while the implement mass exerts a dominant effect, tire pressure management represents a statistically relevant factor for stability, requiring integrated management that considers the interaction between ballasting and tire inflation to mitigate operational risks. Full article

Falls from height are a critical safety concern in the construction industry, underscoring the need for effective identification of high-risk worker behaviors near hazardous edges for proactive accident prevention. This study aimed to address this challenge by developing an improved action recognition model. We propose a novel dynamic spatio-temporal graph convolutional network (CoG-STGCN) that incorporates a center of gravity (CoG)-aware mechanism. The method computes global and local CoG using anthropometric priors and extracts four key dynamic CoG features, which a Multi-Layer Perceptron (MLP) then uses to generate modulation weights that dynamically adjust the skeleton graph’s adjacency matrix, enhancing sensitivity to stability changes. On a self-constructed dataset of eight typical edge-related hazardous behaviors, CoG-STGCN achieved a Top-1 accuracy of 95.83% (baseline ST-GCN: 93.75%) and an average accuracy of 94.17% in fivefold cross-validation (baseline ST-GCN: 92.91%), with significant improvements in recognizing actions involving rapid CoG shifts. The CoG-STGCN provides a more effective and physically informed approach for intelligent unsafe behavior recognition and early warning in built environments. Full article