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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. [ABSTRACT FROM AUTHOR]

Grassland ecosystems are an important part of terrestrial ecosystems and are important for building ecological barriers, promoting the pastoral economy, and maintaining social stability. In recent decades, grasslands in northern China have undergone extensive degradation due to the combined effects of global climate change and the anthropogenic overuse of grasslands. An understanding of the spatial distribution of grassland degradation species is helpful for evaluating the process of grassland degradation and formulating appropriate protective measures. This is important for grassland degradation monitoring. To address the limitations of traditional ground surveys and realize intelligent remote sensing grassland degradation monitoring tasks, we use unmanned aerial vehicle (UAV) hyperspectral remote sensing technology to collect data on vegetation species in desert grasslands. In this paper, we propose a local-global feature enhancement network (LGFEN) for the classification of desert grassland species. The method uses the local feature enhancement (LFE) module and global feature enhancement (GFE) module to extract local and spatial information from hyperspectral images (HSIs), respectively. In addition, the introduction of the convolutional block attention module (CBAM) refines the features of HSIs, improving the stability of the classification performance. The results show that the proposed method has superior classification performance compared with existing HSI classification methods. With only 10 training samples per class, the overall accuracy, average accuracy, and kappa coefficient of the proposed method were 98.61%, 97.61%, and 0.9815, respectively. The proposed method provides a new approach for high-precision and high-efficiency dynamic monitoring of grassland ecosystems. • A local-global feature enhancement network (LGFEN) is proposed. • Improved efficiency of grassland degradation monitoring. • The LFE module is proposed to implement global feature extraction. • Compared with existing methods, LGFEN has better performance. [ABSTRACT FROM AUTHOR]