Grazing intensity is one of the crucial anthropogenic activities on alpine grasslands. However, how grazing intensity affects soil microorganism diversities and their co-occurrence networks in alpine steppe remains uncertain. We carried out a controlled grazing experiment (null grazing, CK; moderate grazing, MG; and heavy grazing, HG) on a typical alpine steppe in the Lhasa River Basin, Central Tibet, China. We used high-throughput sequencing to find the sequences of bacterial 16S rRNA and fungal ITS gene amplicons. Then, we analyzed their alpha and beta diversities and set up co-occurrence networks that show how often they occur together. MG significantly increased the bacterial Shannon index and changed the bacterial community structure. In contrast, HG decreased the fungal ACE and Chao1 indices and also changed the fungal community structure (p < 0.05). Linear mixed-effect model revealed that available phosphorus in soil significantly impacted on soil bacterial Shannon, ACE, and Chao1 indices across grazing intensities, while total carbon in subsoil significantly affected these indicators of soil fungi. Moreover, MG increased the complexity of the co-occurrence network in the bacterial community, while HG simplified it. However, both MG and HG made the co-occurrence networks in the fungal community less complicated. This shows that the intensity of grazing has different impacts on how microbes interact with each other. Therefore, sustainable grazing intensity necessitates a deeper understanding of biodiversity conservation in alpine grasslands.

Due to the high risk factors in the electric power industry, the safety of power system can be improved by using the surveillance system to predict and warn the operators’ nonstandard and unsafe actions in real time. In this paper, aiming at the real-time and accuracy requirements in video intelligent surveillance, a method based on edge computing architecture is proposed to judge unsafe actions of electric power operations in time. In this method, the service of unsafe actions judgment is deployed to the edge cloud, which improves the real-time performance. In order to identify the action being executed, the end-to-end action recognition model proposed in this paper uses the Temporal Convolutional Neural Network (TCN) to extract local temporal features and a Gate Recurrent Unit (GRU) layer to extract global temporal features, which increases the accuracy of action fragment recognition. The result of action recognition is combined with the result of equipment target recognition based on the yolov3 model, and the classification rule is used to determine whether the current action is safe. Experiments show that the proposed method has better real-time performance, and the proposed action cognition is verified on the MSRAction Dataset, which improves the recognition accuracy of action segments. At the same time, the judgment results of unsafe actions also prove the effectiveness of the proposed method.

Detailed knowledge of landscape dynamics is crucial for many applications, from resource management to ecosystem service assessments. However, identifying the spatial distribution of the landscape using optical remote sensing techniques is difficult in mountainous areas, primarily due to cloud cover and topographic relief. Our study uses stable classification samples from mountainous areas to investigate an integrated approach that addresses large volumes of cloud-cover data (with associated data gaps) and extracts landscape time series (LTS) with a high time–frequency resolution. We applied this approach to map LTS in a typical cloudy mountainous area (Erhai watershed in northwestern Yunnan, China) using dense Landsat stacks, and then we also used the classified results to investigate the spatial–temporal landscape changes in the study area at biennial intervals. The overall accuracy of the landscape classification ranged from 81.75% to 88.18%. The results showed highly dynamic processes in the landscape throughout the study period. Forest was the main land cover type, covering approximately 39.19% to 41.68% of the total study area. Alpine meadow showed fluctuating trends, with a net loss of 11.22% and an annual reduction rate of −0.4%. Shrub cover increased by 1.26%, and water bodies showed a small decrease in area, resulting in an overall net change of −0.03%. Built-up land and farmland areas continued to expand, and their annual growth rates were 1.52% and 1.06%, respectively. Bare land showed the highest loss, with a net change of 228.97 km². In the Erhai watershed, all the landscape classes changed or transitioned into other classes, and a substantial decrease in bare land occurred. The biennial LTS maps allow us to fully understand the spatially and temporally complex change processes occurring in landscape classes; these changes would not be observable at coarse temporal intervals (e.g., 5–10 years). Our study highlights the importance of increasing the temporal resolution in landscape change studies to support sustainable land resource management strategies and integrate landscape planning for environmental conservation.