Search

Study region: The source region of the Yangtze River in the Qinghai-Tibet Plateau, China. Study focus: In the context of global warming, conducting a comprehensive study on the hydrothermal processes and their influencing factors in the permafrost active layer of the Tibetan Plateau is crucial for gaining a better understanding of the ecohydrological processes in alpine grasslands. In this study, we analyzed differences in soil temperature and humidity change patterns in the active layer of four alpine grassland types in the Totuohe Basin of the Yangtze River source area. We aimed to discuss the influence of vegetation, soil, and other factors on the hydrothermal mechanism of the active layer. The main research results are as follows: (1) Significant differences in the active layer's hydrothermal regime, with higher vegetation cover correlating to lower thaw indices and better moisture conditions. (2) Vegetation and water content strongly influence thermal conditions and active layer thickness. In high-cover alpine meadows, ground surface temperature is lower with a 200 cm active layer, while swamp meadows have a shallowest layer at 160 cm. (3) Deeper active layer moisture is influenced by freezing and thawing, while shallower layers are affected by warm-season precipitation and soil texture. (4) Negative heat fluxes in the topsoil of alpine swamp and high-cover meadows indicate substantial heat release, likely contributing to permafrost preservation due to high active layer water content. New hydrological insights for the region: (1) Vegetation cover significantly influences the thermal and moisture conditions of the active layer, with higher vegetation associated with lower thaw indices and better moisture conditions.(2) Soil moisture distribution within the active layer is controlled by both freeze-thaw cycles and warm-season precipitation, indicating complex interactions between seasonal processes and soil properties.

With the increasing demand for global water resources and general deterioration of the ecological environment of the Qinghai-Tibetan Plateau, changes to the water conservation functions of ecosystems and the impact mechanisms have attracted great attention. Currently, the research on water conservation has mainly focused on a single biome type, in particular, forests. Few studies explore the differences in water conservation functions of different biome types. Research on this topic mostly utilizes field investigations and sample plot settings to explore the differences in water conservation capacity of a small number of tree species, but these methods are limited in time and space. Therefore, this study uses MODIS products to evaluate the water conservation function of different biome types in the Qinghai-Tibetan Plateau. Dynamic monitoring of the vegetation and water conservation capacity in the study area and research on the responses of the water conservation functions of different biome types were conducted. The results indicate that the vegetation of the Qinghai-Tibetan Plateau increased slightly from 2000 to 2015; however, due to the dual influence of climate and topographic factors, the water conservation capacity showed a slight decline. The water conservation service function mainly comes from grassland ecosystems, which are closely related to vegetation density and biome types. Therefore, to greatly improve the water conservation service function of the Qinghai-Tibetan Plateau, the management and planting of vegetation should be conducted according to the optimal vegetation coverage area, vegetation quantities and biome types.

Due to the limited band width and congestion of communication channels in the wireless vehicle-to-vehicle (V2V) communication, time delay inevitably arises and dramatically leads to the disturbances for the automated vehicular platoon. This paper focuses on computing the exact time delay stability margin. In this study, we treat this problem as a stability issue of a consensus system with time delay, where each vehicle in the platoon is recognized as a node, and the interconnected information flow is represented as a graph. Then, the distributed controller is designed by combining the states of the vehicle itself and its neighbouring vehicles. Furthermore, the stability of the entire platoon is analysed according to the characteristic equation of the closed-loop system, and a necessary and sufficient condition for the exact time delay stability margin is obtained. Especially, for the automated vehicular platoon with undirected topology, it is revealed that exact time delay stability margin is determined by the largest eigenvalue of the augmented Laplacian matrix. Furthermore, a rapid method for finding exact time delay stability margin is proposed. Finally, numerical simulations demonstrate that this work generates exact and satisfactory time delay stability margin for the automated vehicular platoon.

Vehicle detection in aerial images has attracted great attention as an approach to providing the necessary information for transportation road network planning and traffic management. However, because of the low resolution, complex scene, occlusion, shadows, and high requirement for detection efficiency, implementing vehicle detection in aerial images is challenging. Therefore, we propose an efficient and scene-adaptive algorithm for vehicle detection in aerial images using an improved YOLOv3 framework, and it is applied to not only aerial still images but also videos composed of consecutive frame images. First, rather than directly using the traditional YOLOv3 network, we construct a new structure with fewer layers to improve the detection efficiency. Then, since complex scenes in aerial images can cause the partial occlusion of vehicles, we construct a context-aware-based feature map fusion to make full use of the information in the adjacent frames and accurately detect partially occluded vehicles. The traditional YOLOv3 network adopts a horizontal bounding box, which can attain the expected detection effects only for vehicles with small length−width ratio. Moreover, vehicles that are close to each other are liable to cause lower accuracy and a higher detection error rate. Hence, we design a sloping bounding box attached to the angle of the target vehicles. This modification is conducive to predicting not only the position but also the angle. Finally, two data sets were used to perform extensive experiments and comparisons. The results show that the proposed algorithm generates the desired and excellent performance.