Search Results (4)

Urban tree detection is fundamental to effective forestry management, biodiversity preservation, and environmental monitoring—key components of sustainable smart city development. This study introduces a deep learning framework for urban tree crown segmentation that exclusively leverages high-resolution satellite imagery from GeoEye-1, WorldView-2, and WorldView-3, thereby eliminating the need for additional data sources such as LiDAR or UAV imagery. The proposed framework employs a Residual U-Net architecture augmented with Attention Gates (AGs) to address major challenges, including class imbalance, overlapping crowns, and spectral interference from complex urban structures, using a custom composite loss function. The main contribution of this work is to integrate data from three distinct satellite sensors with varying spatial and spectral characteristics into a single processing pipeline, demonstrating that such well-established architectures can yield reliable, high-accuracy results across heterogeneous resolutions and imaging conditions. A further advancement of this study is the development of a hybrid ground-truth generation strategy that integrates NDVI-based watershed segmentation, manual annotation, and the Segment Anything Model (SAM), thereby reducing annotation effort while enhancing mask fidelity. In addition, by training on 4-band RGBN imagery from multiple satellite sensors, the model exhibits generalization capabilities across diverse urban environments. Despite being trained on a relatively small dataset comprising only 1200 image patches, the framework achieves state-of-the-art performance (F1-score: 0.9121; IoU: 0.8384; precision: 0.9321; recall: 0.8930). These results stem from the integration of the Residual U-Net with Attention Gates, which enhance feature representation and suppress noise from urban backgrounds, as well as from hybrid ground-truth generation and the combined BCE–Dice loss function, which effectively mitigates class imbalance. Collectively, these design choices enable robust model generalization and clear performance superiority over baseline networks such as DeepLab v3 and U-Net with VGG19. Fully automated and computationally efficient, the proposed approach delivers cost-effective, accurate segmentation using satellite data alone, rendering it particularly suitable for scalable, operational smart city applications and environmental monitoring initiatives. Full article

Unmanned articulated vehicles play a crucial role in the intelligent mine system and have been extensively investigated and implemented in the fields of mine transportation, agriculture and forestry construction. However, the working environment of articulated wheeled vehicles is harsh and the working conditions are changeable. These conditions are often accompanied by load changes, road interference excitation caused by an unstructured environment and the dynamic nonlinear characteristics of articulated wheeled vehicles. The current research on path tracking control methods suitable for traditional wheeled vehicles does not meet the intelligent operation requirements of articulated wheeled vehicles, and it is necessary to combine the specific working environment and its own specific structural model characteristics. In this paper, a composite barrier function sliding mode control method based on an extended state observer is proposed to solve the problem of modeling uncertainty and unknown external disturbance in the path tracking control of unmanned articulated vehicles. Firstly, the mathematical model of the articulated wheeled working vehicle is built to derive the expected heading angle in the prediction horizon. Then, the strong nonlinear lumped disturbance in articulated dynamics is dynamically estimated by combining the composite nonlinear extended state observer. Afterward, based on the error compensation theory, a composite barrier function sliding mode controller suitable for articulated vehicle path tracking is derived. Finally, through simulation analysis and experimental verification, this method can estimate the strong nonlinear lumped disturbance caused by the structural characteristics of the articulated vehicle, and then compensate for the disturbance of the control quantity to achieve stable, robust and accurate path tracking control. Full article

The sensible use of forest resources and the sound management of forests have become increasingly important throughout the years. In keeping with the trend, a composite forestry operation model has emerged. Traditional Chinese culture and forest management are particularly intertwined in China. Thus, use of the forest–medicine compound management model is recommended. The majority of research on the management of forest–medicine compounds has focused on how to grow more effective medicinal plants, ignoring the effects of the chemicals used on the soil environment, particularly the soil micro-environment. A forest–medicine system was established in South China to investigate the impacts of planting Aspidistra elatior on the variety of rhizospheric microorganisms and their ability to use carbon sources. In the plots with or without A. elatior, three dominant plants (Castanopsis hystrix, Psychotria rubra, and Ficus hirta) grew soil rhizosphere microbes, which were analyzed using Biolog EcoPlates. The study found that planting medicinal plants in the understory improved the soil’s nutritional content, increased the inter-root microbial communities of other medicinal plants, and enhanced the microbes’ ability to use soil carbon sources. The forest–medicine complex model, which rationalizes the use of forest clearings and generates economic and ecological benefits, can significantly increase the quantity of dominant microorganisms and enhance the enrichment of other species, resulting in a positive impact on the soil environment. These findings suggest that the forest–medicine compound management model can improve the use of soil carbon sources throughout the forest system. Full article

Planted forests of Sitka spruce, a non-native species from north-west America, are the major forest type in Great Britain and Ireland. Standard management involves even-aged stands, rotations of 40–50 years and a patch clear-felling system with artificial regeneration. However, forest policies support managing these forests for multifunctional objectives with increased diversity of species composition and stand structure. Continuous cover forestry (CCF) is an alternative silvicultural approach used to provide such diversity, but the amount of CCF forest is under 10% of the forest area, and less in Sitka spruce forests; This paper reviews research carried out in the last two decades to support the implementation of CCF in Sitka spruce planted forests; Stand structures and microclimate favouring natural regeneration are understood. Harvesting systems have been adapted for use in CCF stands, a single-tree growth model has been calibrated, comparative costs and revenues have been determined, and operational trials established. The interaction between thinning and wind stability in irregular stands is problematic, together with the lack of suitable species for growing in mixture with Sitka spruce; Introduction of an alternative silvicultural approach may take decades and must overcome technical challenges and cultural resistance. Full article