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High-precision statistics on desertification of grassland features are an important part of grassland ecosystem research. The traditional manual survey is inefficient, and satellite remote sensing has very limited statistical precision, so high-spectral remote sensing by lowaltitude drones is preferable. Here, we report a hyperspectral remote-sensing system for unmanned aerial vehicles (UAVs). We used the vegetation and soil of typical desertified grassland in Inner Mongolia as research objects to collect vis-NIR hyperspectral data on desertification using a deep belief network (DBN), 2D convolutional neural network (2D-CNN), and 3D convolutional neural network (3D-CNN). The results show that these typical deep learning models can effectively classify hyperspectral data on desertified grassland features. The highest classification accuracy was achieved by 3D-CNN, with an overall accuracy of 86.36%. This study enriches the spatial scale of remote sensing research on grassland desertification, and provides a basis for further high-precision statistics and inversion of remote sensing of grassland desertification., The term desertification was first proposed by the French scientist A. Aubreville in 1949. At the 1977 United Nations conference on desertification, it was defined as the decline or destruction [...]

In recent years, the desertification of grasslands has increased due to various factors, including both global warming and human activities. It is an essential basis for grassland degradation monitoring to monitor the dynamic change of desert grassland vegetation communities and distribution statistics. Although unmanned aerial vehicle (UAV) remote sensing images have allowed us to achieve dynamic real-time grassland monitoring, the distribution of desert grassland ground objects can be random and narrow, thus increasing the difficulty of sample labelling of remote sensing imagery. Therefore, to reduce the number of samples required for the model, this research proposes a convolutional transformer attention network (CTAN) to identify desert grassland ground objects and validate it on a self-collected desert grassland dataset. The network utilizes the transformer model to enhance its learning of global pixels so that it suppresses the transmission of background pixels within the network. Furthermore, the edge convolution module is designed to strengthen the network's learning for edge pixels, improving its identification effect. The results show that the network provides 97.22% of overall accuracy (OA), 94.35% of average accuracy (AA), and 0.9398 of Kappa for ground object recognition in desert grassland. The model improves OA by 2.36–9.85% points compared to methods in the same field and 0.8–6.35% points compared to methods in hyperspectral imagery classification. The experimental results show the superior performance of the CTAN model for recognizing desert grassland objects, which helps the management and restoration of desert grasslands. [ABSTRACT FROM AUTHOR]

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]

This paper established a mathematical method for the spectral feature extraction of ratholes, based on UAV hyperspectral imaging technology. The degradation of grasslands is a major challenge to terrestrial ecosystems. Rodents not only promote soil erosion and accelerate the process of grassland degradation, but also carry diseases that can easily cause epidemics. The calculation of the number of rodent holes and grassland vegetation cover is an important indicator for monitoring and evaluating grassland degradation. Manual surveys have drawbacks in efficiently monitoring large areas and are human- and material-costly, hardly meeting the current needs of grassland degradation monitoring. Therefore, there is an urgent need to conduct real-time dynamic monitoring of grassland rathole distributions and grassland degradation processes. In this study, a low-altitude remote sensing platform was constructed by integrating a hyperspectral imager with a UAV to collect spectral data of the desert steppes in central Inner Mongolia Autonomous Region, China. Then, the spectral features of ratholes were extracted via radiation correction, noise reduction, and principal component analysis (PCA). Meanwhile, the spectral features of vegetation and bare soil were extracted based on the normalized difference vegetation index (NDVI), which was inputted to calculate the vegetation cover. The results showed that the single-band map extracted based on PCA could effectively determine the location of ratholes, where the overall accuracy and kappa coefficient were 97% and 0.896, respectively. Therefore, the method proposed in this study can accurately identify the location of desert steppe rodent holes. It provides a high-precision technical means for scientific and effective control of grassland rodent infestation and also provides a higher technical means for grassland degradation. [ABSTRACT FROM AUTHOR]

Identifying grass species in grasslands based on unmanned aerial vehicle hyperspectral remote sensing is the basis and premise of hyperspectral remote sensing when applied to grassland degradation monitoring and research. The small targets and mixed pixels involved grass species identification in grasslands creates problems, making identification cumbersome and classification accuracy difficult. This study involved the construction of an unmanned aerial vehicle hyperspectral remote sensing system using hyperspectral data of grass species in desert habitats that had been collected under natural light. A multi‐resolution combined with a 1 × 1 feature map was formed by multiscale convolution, and grass species data were extracted from hyperspectral fine‐grained feature data from grasslands. A recognition and classification model for degradation indicator species CNN was constructed using max pooling to retain the maximum amount of feature detail and up‐sampling, reconstructing the feature space and feature fusion to smooth the edge texture of the data and enhance the weak data to alleviate the imbalance among samples. The results showed that the overall identification accuracy of the model for grassland species reached 98.78%, and the kappa coefficient reached 0.92, realizing the high‐precision identification of grassland species, which laid the foundation for grassland species detection and research based on unmanned aerial vehicle hyperspectral imagery. In addition, the proposed degradation indicator species CNN model provides a useful reference for the identification and classification of small targets with mixed pixels. [ABSTRACT FROM AUTHOR]