Research on Classification of Grassland Degeneration Indicator Objects Based on UAV Hyperspectral Remote Sensing and 3D_RNet-O Model.

Real-time and high-precision land cover classification is the foundation for efficient and quantitative research on grassland degradation using remote sensing techniques. In view of the shortcomings of manual surveying and satellite remote sensing, this study focuses on the identification and classification of grass species indicating grassland degradation. We constructed a UAV-based hyperspectral remote sensing system and collected field data in grassland areas. By applying artificial intelligence technology, we developed a 3D_RNet-O model based on convolutional neural networks, effectively addressing technical challenges in hyperspectral remote sensing identification and classification of grassland degradation indicators, such as low reflectance of vegetation, flat spectral curves, and sparse distribution. The results showed that the model achieved a classification accuracy of 99.05% by optimizing hyperparameter combinations based on improving residual block structures. The establishment of the UAV-based hyperspectral remote sensing system and the proposed 3D_RNet-O classification model provide possibilities for further research on low-altitude hyperspectral remote sensing in grassland ecology.

The relationship between health belief and sleep quality of Chinese college students: The mediating role of physical activity and moderating effect of mobile phone addiction.

Background: Poor sleep quality has become a common health problem encountered by college students. Methods: Health belief scale (HBS), physical activity rating scale (PARS-3), mobile phone addiction tendency scale (MPATS) and Pittsburgh sleep quality index (PSQI) were adopted to analyze the data collected from survey questionnaires, which were filled out by 1,019 college students (including 429 males and 590 females) from five comprehensive colleges and universities from March 2022 to April 2022. The data collected from survey questionnaires were analyzed using SPSS and its macro-program PROCESS. Results: (1) Health belief, physical activity, mobile phone addiction and sleep quality are significantly associated with each other (P < 0.01); (2) physical activity plays a mediating role between health belief and sleep quality, and the mediating effects account for 14.77%; (3) mobile phone addiction can significantly moderate the effect size of health belief (ß = 0.062, p < 0.05) and physical activity (ß = 0.073, P < 0.05) on sleep quality, and significantly moderate the effect size of health belief on physical activity (ß = -0.112, p < 0.001). Conclusion: The health belief of college students can significantly improve their sleep quality; college students' health belief can not only improve their sleep quality directly, but also improve their sleep quality through physical activity; mobile phone addiction can significantly moderate the effect size of health belief on sleep quality, the effect size of health belief on physical activity, and the effect size of physical activity on sleep quality.

Identification and Classification of Small Sample Desert Grassland Vegetation Communities Based on Dynamic Graph Convolution and UAV Hyperspectral Imagery.

Desert steppes are the last barrier to protecting the steppe ecosystem. However, existing grassland monitoring methods still mainly use traditional monitoring methods, which have certain limitations in the monitoring process. Additionally, the existing deep learning classification models of desert and grassland still use traditional convolutional neural networks for classification, which cannot adapt to the classification task of irregular ground objects, which limits the classification performance of the model. To address the above problems, this paper uses a UAV hyperspectral remote sensing platform for data acquisition and proposes a spatial neighborhood dynamic graph convolution network (SN_DGCN) for degraded grassland vegetation community classification. The results show that the proposed classification model had the highest classification accuracy compared to the seven classification models of MLP, 1DCNN, 2DCNN, 3DCNN, Resnet18, Densenet121, and SN_GCN; its OA, AA, and kappa were 97.13%, 96.50%, and 96.05% in the case of only 10 samples per class of features, respectively; The classification performance was stable under different numbers of training samples, had better generalization ability in the classification task of small samples, and was more effective for the classification task of irregular features. Meanwhile, the latest desert grassland classification models were also compared, which fully demonstrated the superior classification performance of the proposed model in this paper. The proposed model provides a new method for the classification of vegetation communities in desert grasslands, which is helpful for the management and restoration of desert steppes.