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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]

The identification and counting of grassland degradation indicator ground objects is an important component of grassland ecological monitoring. These steps are also an important basis for developing ecological restoration and management programs for degraded grasslands. Compared with a traditional human survey, the use of remote sensing images can not only achieve dynamic monitoring of a large area, but also improve the efficiency. Recently, most studies regarding ground object classification based on remote sensing images address the development and optimization of classification models for features in several widely used datasets. For the remote sensing of desertified grasslands, remote sensing images with high spatial resolutions are used for studies on small and sparse features in degraded grasslands. The spatial resolution of the above mentioned datasets yields difficulties when attempting to classify small and sparse indicator features for desertified grasslands because generalization becomes limited. Therefore, establishing a lightweight classification model suitable for degraded grassland features with high spatial resolution is important. In this study, a low altitude unmanned aerial vehicle (UAV) hyperspectral remote sensing platform was constructed to collect high spatial resolution remote sensing images of degraded grasslands. The GDIF-3D-CNN classification model was used to classify the pure pixels and all pixels datasets, whose accuracy and efficiency were further improved by optimizing the eight parameters of the model. This study explores the remote sensing ground object classification of thin small plants and a large number of mixed pixels, realizing high precision classification among desertification degradation indicating plant populations of a species, and provides key quantitative data for grassland degradation research. [ABSTRACT FROM AUTHOR]