Search Results (230)

This study presents a comprehensive review and comparative analysis of traditional machine learning (ML) and deep learning (DL) models for land cover classification in agricultural remote sensing. We evaluate the reported successes, trade-offs, and performance metrics of ML and DL models across diverse agricultural contexts. Building on this foundation, we apply both model types to the specific case of almond crop field identification in California’s Central Valley using Landsat data. DL models, including U-Net, MANet, and DeepLabv3+, achieve high accuracy rates of 97.3% to 97.5%, yet our findings demonstrate that conventional ML models—such as Decision Tree, K-Nearest Neighbor, and Random Forest—can reach comparable accuracies of 96.6% to 96.8%. Importantly, the ML models were developed using data from a single year, while DL models required extensive training data spanning 2008 to 2022. Our results highlight that traditional ML models offer robust classification performance with substantially lower computational demands, making them especially valuable in resource-constrained settings. This paper underscores the need for a balanced approach in model selection—one that weighs accuracy alongside efficiency. The findings contribute actionable insights for agricultural land cover mapping and inform ongoing model development in the geospatial sciences. Full article

With the global increase in maritime activities, the frequency of maritime accidents has risen, underscoring the urgent need for faster and more efficient search and rescue (SAR) solutions. This study presents an intelligent unmanned aerial vehicle (UAV)-based maritime rescue system that combines GPS-driven dynamic path planning with vision-based dual-target detection and tracking. Developed within the Gazebo simulation environment and based on modular ROS architecture, the system supports stable takeoff and smooth transitions between multi-rotor and fixed-wing flight modes. An external command module enables real-time waypoint updates. This study proposes three path-planning schemes based on the characteristics of drones. Comparative experiments have demonstrated that the triangular path is the optimal route. Compared with the other schemes, this path reduces the flight distance by 30–40%. Robust target recognition is achieved using a darknet-ROS implementation of the YOLOv4 model, enhanced with data augmentation to improve performance in complex maritime conditions. A monocular vision-based ranging algorithm ensures accurate distance estimation and continuous tracking of rescue vessels. Furthermore, a dual-target-tracking algorithm—integrating motion prediction with color-based landing zone recognition—achieves a 96% success rate in precision landings under dynamic conditions. Experimental results show a 4% increase in the overall mission success rate compared to traditional SAR methods, along with significant gains in responsiveness and reliability. This research delivers a technically innovative and cost-effective UAV solution, offering strong potential for real-world maritime emergency response applications. Full article

Unmanned aerial vehicles (UAVs) autonomous landing plays a key role in cooperative work with other heterogeneous agents. A neglected aspect of UAV autonomous landing on a moving platform is addressed in this study. The landing process is divided into three stages: positioning, tracking, and landing. In the tracking phase, MPCs are designed to implement tracking of the target landing platform. In the landing phase, we adopt a nested Apriltags collaboration identifier combined with the Aprilatags algorithm to design a PID speed controller, thereby improving the dynamic tracking accuracy of UAVs and completing the landing. The experimental data suggested that the method enables the UAV to perform dynamic tracking and autonomous landing on a moving platform. The experimental results show that the success rate of UAV autonomous landing is as high as 90%, providing a highly feasible solution for UAV autonomous landing. Full article

This study presents a cooperative unmanned aerial system (UAS) designed to enable precise autonomous landings in unstructured environments using low-cost onboard vision technology. This approach involves a carrier UAV with a stabilized RGB camera and a neural inference system, as well as a lightweight tailsitter payload UAV with an embedded grayscale vision module. The system relies on visually recognizable landing markers and does not require additional sensors. Field trials comprising full deployments achieved an 80% success rate in autonomous landings, with vertical touchdown occurring within a 1.5 m radius of the target. These results confirm that vision-based marker detection using compact neural models can effectively support autonomous UAV operations in constrained conditions. This architecture offers a scalable alternative to the high complexity of SLAM or terrain-mapping systems. Full article

We examined three decades of changes in the mammal fauna of Estonia, Latvia, and Lithuania in the context of climate variability, land use transformation, and anthropogenic pressures. We compiled distributional, abundance, and status data from publications, atlases, official game statistics, and long-term monitoring programs, and we evaluated trends using compound annual growth rates or temporal indices. Our review identified losses such as regional extinctions of garden dormice and European mink, declines in small insectivores (e.g., pond bats and shrews) and herbivores (e.g., Microtus voles), and the contraction of boreal specialists (e.g., Siberian flying squirrels). However, we also identified gains, including increases in ungulate numbers (e.g., roe deer, red deer, fallow deer, moose, and wild boars before African swine fewer outbreak) and the recovery of large carnivores (e.g., wolves and lynxes). Invasions by non-native species (e.g., American mink, raccoon dog, and raccoon) and episodic disturbances, such as African swine fever and the “anthropause” caused by the SARS-CoV-2 pandemic, have further reshaped community composition. The drivers encompass climatic warming, post-socialist forest succession, intensified hunting management, and rewilding policies, with dispersal capacity mediating the responses of species. Our results underscore the dual legacy of historical land use and contemporary climate forcing in structuring the fauna dynamics of Baltic mammal communities in the face of declining specialists and invasive taxa. Full article

The autonomous landing of unmanned aerial vehicles (UAVs) on moving platforms has potential applications across various domains. However, robust landing remains challenging because the detection reliability of UAVs decreases when the UAV is close to a moving platform. To address this issue, this paper proposes a novel landing strategy that ensures a high detection rate. First, a robust detectable region was established by considering the sensing range and maneuverability limitations of the UAV. Second, a vector field was designed to guide the UAV to the moving platform while remaining in a robust detectable region. Next, safe and accurate landings were achieved by considering the current velocity and vector field. The landing strategy was validated through outdoor flight experiments. A quadrotor equipped with a gimbal-mounted camera was used, and a fractal marker was attached to the moving platform for detection and tracking. When the moving platform moved at a speed of 2–4.3 m/s, the UAV successfully landed on the platform with a distance error of 0.4 m. Because of the robust detectable region and vector field, the detection was conducted with a high success rate (94.9%). Full article

Remotely sensed cropland abandonment monitoring is crucial for providing spatially explicit references for maintaining sustainable agricultural practices and ensuring food security. However, abandoned cropland is commonly detected based on multi-date classification or the dynamics of a single vegetation index, with the interactions between vegetation and soil time series often being neglected, leading to a failure to understand its full-life-cycle succession processes. To fill this gap, we propose a new full-life-cycle modeling framework based on the interactive trajectories of vegetation–soil-related endmembers to identify abandoned and reclaimed cropland in Jinan from 2000 to 2022. In this framework, highly accurate annual fractional vegetation- and soil-related endmember time series are generated for Jinan City for the 2000–2022 period using spectral mixture models. These are then used to integrally reconstruct temporal trajectories for complex scenarios (e.g., abandonment, weed invasion, reclamation, and fallow) using logistic and double-logistic models. The parameters of the optimization model (fitting type, change magnitude, start timing, and change duration) are subsequently integrated to develop a rule-based hierarchical identification scheme for cropland abandonment based on these complex scenarios. After applying this scheme, we observed a significant decline in green vegetation (a slope of −0.40% per year) and an increase in the soil fraction (a rate of 0.53% per year). These pathways are mostly linked to a duration between 8 and 15 years, with the beginning of the change trend around 2010. Finally, the results show that our framework can effectively separate abandoned cropland from reclamation dynamics and other classes with satisfactory precision, as indicated by an overall accuracy of 86.02%. Compared to the traditional yearly land cover-based approach (with an overall accuracy of 77.39%), this algorithm can overcome the propagation of classification errors (with product accuracy from 74.47% to 85.11%), especially in terms of improving the ability to capture changes at finer spatial scales. Furthermore, it also provides a better understanding of the whole abandonment process under the influence of multi-factor interactions in the context of specific climatic backgrounds and human disturbances, thus helping to inform adaptive abandonment management and sustainable agricultural policies. Full article

This study investigates the role of urban centrality in shaping urban form and function, with a specific focus on the city of Hail, Saudi Arabia as a representative case. Urban centrality—defined as the spatial and functional concentration of economic, social, and cultural activities—plays a critical role in determining the vibrancy, resilience, and adaptability of cities. Through a mixed-methods approach combining spatial analysis, field observations, and stakeholder insights, this research explores the dynamics of Hail’s central areas, examining how they navigate the tension between preserving historical identity and accommodating contemporary urban growth. The findings reveal that successful urban centrality requires a balanced integration of heritage conservation, economic diversification, and spatial accessibility, supported by inclusive governance and strategic infrastructure investments. The study highlights both the challenges and opportunities associated with the evolution of central urban spaces, particularly in medium-sized cities experiencing rapid socio-economic and spatial transformations, defined in this study as urban regions undergoing annual population growth rates exceeding 3% and substantial changes in land-use, infrastructure development, and governance structures within a decade. By providing actionable insights into the interplay between permanence and change, this research contributes to the broader discourse on urban resilience and sustainable development, offering practical guidance for planners and policymakers aiming to cultivate adaptive and inclusive urban environments capable of responding to future challenges. Full article

The China Baijiu Golden Triangle (BGT) serves as the core production hub of China’s Baijiu industry, where the ecological environment plays a pivotal role in ensuring the industry’s sustainable development. However, urbanization, industrial expansion, and climate change pose potential threats to the region’s vegetation dynamics. Utilizing Landsat remote sensing data from 2002 to 2022, this study integrates Theil–Sen trend analysis, the Mann–Kendall (MK) test, coefficient of variation (CV) analysis, and the Geodetector model (GD model) to investigate the spatiotemporal evolution of the Normalized Difference Vegetation Index (NDVI) and its underlying driving mechanisms within the BGT. The findings reveal an overall upward trend in vegetation NDVI, with the annual mean NDVI increasing from 0.45 to 0.67, corresponding to a growth rate of 0.49%. Spatially, areas of high vegetation cover are predominantly located in mountainous forest zones with favorable ecological conditions, whereas regions of low vegetation cover are concentrated in zones of urban expansion. Precipitation and topographic factors (elevation and slope) emerge as the primary natural drivers of vegetation change, while land use change and the night-time light index stand out as the most influential human-induced factors. Further analysis uncovers a nonlinear interactive enhancement effect between natural and anthropogenic factors, with the interaction between the night-time light index and precipitation being particularly pronounced. This suggests that urbanization not only directly impacts vegetation but may also exert indirect effects on the ecosystem by altering regional hydrological and climatic processes. The results indicate that ecological protection policies in the BGT have yielded some success; however, vegetation fragmentation and ecological pressures stemming from urban expansion remain significant challenges. Moving forward, optimizing land use policies and promoting eco-friendly development models will be essential to achieving ecosystem stability and sustaining industrial growth. Full article

Background/Objectives: The impact of the proximal landing zone has not been investigated in fenestrated and branched endovascular aortic arch repair (f/bTEVAR). This study aimed to analyze the f/bTEVAR outcomes in patients with non-native (nNPAL) vs. native proximal aortic landing (NPAL). Methods: The STROBE statement was followed in order to conduct a single-center retrospective analysis of patients with nNPAL vs. NPAL managed, from 1 September 2011 to 30 June 2022, with f/bTEVAR. The primary outcomes were technical success, 30-day mortality and stroke. Results: A total of 83 patients with nNPAL vs. 126 patients with NPAL were included. Among the nNPAL group, 34 (39.7%) underwent previous aortic arch replacement and the remaining underwent an ascending aortic replacement. The nNPAL patients were more commonly treated for chronic dissections (nNPAL: 70.6% vs. NPAL: 21.6%, p < 0.001), presented a more proximal disease (zone 0: nNPAL: 27.7% vs. NPAL: 7.1%, p < 0.001; zone 1: nNPAL: 50.6% vs. NPAL: 10.2%, p < 0.001) and received more triple-branch devices (nNPAL: 16.9% vs. NPAL: 3.2%, p < 0.001), with a higher rate of Ishimaru zone 0 landing (nNPAL: 86.8% vs. NPAL: 51.6%, p < 0.001). Technical success (nNPAL: 98.8% vs. NPAL: 94.4%, p = 0.07) and 30-day mortality (nNPAL: 6.0%, vs. NPAL: 11.9%, p = 0.16) were similar. Stroke was lower among nNPAL patients (nNPAL: 4.8% vs. NPAL: 13.5%, p = 0.04). A multivariate regression analysis confirmed nNPAL as an independent protector for stroke (p = 0.002). Survival (log rank: p = 0.02) was higher within the nNPAL group at 24 months. Conclusions: f/bTEVAR in patients with nNPAL zone showed encouraging outcomes. Despite more proximal landing in zone 0, stroke was significantly lower when compared to NPAL patients. Full article

Land use changes under natural and anthropogenic driving factors have spatiotemporal ecological consequences, and these need to be identified to protect biodiversity and the robustness of ecosystems. While driving factor research has mainly focused on the impacts of univariate statistical correlation, the analysis of the natural and anthropogenic compound driving factors and the spatiotemporal correspondence between the dynamic characteristics of ecological function evolution and the natural and anthropogenic driving processes has been ignored. On the basis of land use change, spatiotemporal ecosystem services and natural and anthropogenic driving process trajectories were linked and characterized in this study. In the Kaduna River Basin (KRB), Nigeria, an important river basin the country, land use change during 2000–2020 caused by both natural and anthropogenic processes significantly changed the ecosystem services. The single anthropogenic driving trajectories were 1.3 times greater than the single natural driving trajectories and 2.02 times greater than the compound driving trajectories. Carbon storage has increased by 15.6% (8.5 × 10⁶ t) and is growing at a decreasing rate, whereas urbanization and reverse succession are the main drivers of carbon stock decline. Water yield has steadily increased but is threatened by the decline induced by restoration, reverse succession, and urbanization. Habitat quality initially increased (0.03) but then decreased (0.01), with urbanization and reclamation being the main drivers of its degradation throughout the study period. This study integrates land use, driving processes, and ecosystem services into a cohesive analytical framework, thereby overcoming the limitations of previous research that examined land use in conjunction with each of the other two elements separately. New developments and methodological steps in watershed management can indicate directions to reconcile and mitigate the conflict between socioeconomic growth and improved ecological functioning in watershed ecosystems. Full article

The livestock sector is crucial for global food security and economic development, particularly in developing nations, as it supports the livelihoods of approximately 1.3 billion people. However, with the global population expected to reach 9.2 billion by 2050, the sector must address increasing demand for livestock products while ensuring environmental sustainability. This study used the available literature to evaluate the economic viability of sustainable pasture and rangeland management practices to enhance livestock production. The key findings demonstrate that strategies such as rotational grazing and nitrogen fertilization can decrease winter feed costs by up to 40% while simultaneously improving pasture productivity and animal weight gains. Initial investments in these improved forage practices offer high internal rates of return, indicating their profitability. To guide sustainable pasture production and rangeland management, we propose a conceptual framework that balances cultivated pastures and natural rangelands. This framework assesses critical factors, including input costs, expected outputs (enhanced biodiversity and livestock production), and interventions to mitigate land degradation. For successful adoption of these practices, targeted policies are essential. Governments should develop financial support mechanisms for smallholder farmers, improve transportation infrastructure for efficient feed logistics, and provide technical assistance to educate producers on sustainable practices. Engaging stakeholders to align policies with local needs is also vital. By implementing these strategic interventions, the resilience of livestock systems can be strengthened, contributing to long-term sustainability and supporting food security and rural community well-being. Full article

Assessing rural habitat suitability and its connection to land response is a vital tool for understanding the socio-economic and environmental challenges in rural areas tailored to local contexts. This study fills existing research gaps by examining the suitability of rural habitats in Anhui Province, opening pathways to reveal how rural sustainability may connect to land. Using the autoregressive distributed lag (ARDL) model, it analyzes the short- and long-term effects of socio-economic and environmental factors on rural suitability across various counties. Additionally, a descriptive analysis explores the pathways linking rural suitability to land use responses. The findings reveal that rural greening, village planning, and housing area per resident positively influence rural habitat suitability in both the short and long term. However, agricultural income growth shows a negative impact, potentially due to structural issues in the sector. Environmental factors like temperature and rainfall have a limited influence on rural suitability. The study underscores the importance of suitable rural infrastructure, namely enhancing rural greening rate, implementing village plans, and improving housing for sustainable rural development. Regional variations in rural habitat suitability across Anhui Province are also evident. While some cities, such as Huaibei and Anqing, demonstrate success in revitalization, others, like Suzhou and Chizhou, face challenges. The results highlight the need for region-specific strategies that account for local environmental, economic, and infrastructural contexts. Tailored approaches are essential to achieving long-term, effective rural development in the province. Full article

Background/Objectives: Football poses a high risk of sustaining lower-limb injuries, particularly anterior cruciate ligament (ACL) injuries, owing to the frequent jumping and landing movements. Identifying risk factors for these injuries is crucial to successful prevention. Two-dimensional (2D) video analysis is a commonly employed tool for assessing movement patterns and determining injury risk in clinical settings. This study aims to investigate whether variations in the camera frame rate impact the accuracy of key angle measurements (knee valgus, hip adduction (HADD), and lateral trunk flexion (LTF)) in male football players during high-risk functional tasks such as single-leg landing and 45° side-cutting. Methods: This laboratory-based cross-sectional study included 29 football players (mean (SD) age: 24.37 [3.14] years). The frontal plane projection angle (FPPA), HADD, and LTF during single-leg landing and side-cutting tasks were measured using two different camera frame rates: 30 frames per second (fps) and 120 fps. The 2D kinematic data were analyzed using Quintic Biomechanics software. Results: Significant differences in FPPA scores during single-leg landing were observed between the 30 fps and 120 fps for both the dominant (mean difference = 2.65 [95% confidence interval [CI]: 0.76–4.55], p = 0.008) and non-dominant leg (3.53 [1.53–5.54], p = 0.001). Additionally, the FPPA of the right leg during the side-cutting task showed significant differences (2.18 [0.43–3.93], p = 0.016). The LTF of the right leg during side-cutting displayed a significant variation between frame rates (−2.69 [−5.17–−0.22], p = 0.034). No significant differences in HADD were observed. Conclusions: Compared with a 30 fps camera, a high-speed (120 fps) camera demonstrated a superior performance in delivering accurate kinematic assessments of lower-limb injury risk factors. This improved precision supports injury screening, rehabilitation monitoring, and return-to-play decision-making through determining subtle biomechanical deficits crucial for lower-limb injury prevention and management. Full article

The coconut (Cocos nucifera L.), a valuable tropical crop, is rapidly declining in genetic diversity due to natural disasters, pest and disease attack, and land clearing for other crops. Seed banking is impractical for coconut conservation due to its large, recalcitrant seed, and maintaining field gene bank collections is costly and vulnerable to environmental pressures. Cryopreservation offers a promising alternative method for conserving coconut genetic diversity, but the success in recovering cryopreserved materials remains limited, with few studies consistently reporting high rates of recovery. This highlights the need for improved cryopreservation protocols, particularly in tissue dehydration, which is one of the critical steps in the process of cryopreservation and plant recovery. A desiccator was developed that enabled rapid embryo dehydration with ultra-dry airflow. The desiccator reduced embryo moisture content to 20% (the predetermined viability threshold) within 6 h representing a 2-h improvement when compared to a previous dehydration approach, while maintaining a high germination rate (71%). Smaller embryos (500 to 550 mg fresh weight) desiccated faster than larger embryos (800 to 900 mg fresh weight) but germination was reduced (30%), making small embryos unsuitable for cryopreservation. A 5-day sucrose (0.4 M) pre-treatment further reduced the dehydration time to 4 h, while maintaining a high germination rate (70%). These advances in the use of a sucrose pre-treatment, the rapid embryo dehydration, and selection of large embryos size will help to enhance the success of coconut embryo cryopreservation and recovery. Full article