<span style="mso-ansi-language: EN-GB;">Extraction of Photosynthetic and Non-Photosynthetic Vegetation Cover in Typical Grasslands via Deep Learning Applied to UAV Data
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Photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) play significant roles in ecosystem functions and ecological succession. Accurate monitoring of the coverage and distribution of PV and NPV in the grasslands of semi-arid regions is crucial for understanding the environment and addressing climate change. This study examined the Hengshan grassland site in China's semi-arid regions using imagery from unmanned aerial vehicles (UAVs), constructing a semantic segmentation label database via multiscale parameter optimisation, feature indicator selection, and manual correction methods. Three deep learning semantic segmentation models — PSPNet, DeepLabV3+, and U-Net—were employed to extract and compare the PV and NPV to determine the optimal semantic segmentation model. The experimental results showed that the PSPNet model exhibited a superior performance, with an overall classification accuracy of 89.2% and a Kappa coefficient of 0.80. These values were 0.8% and 3.9% higher and 0.02 and 0.07 higher than the corresponding values for DeepLabV3+ and U-Net, respectively. Further generalisability tests indicated that PSPNet achieved an overall classification accuracy of 87.5%–91.5% and a Kappa coefficient of 0.77–0.93 in different scenarios, effectively extracting the PV and NPV in various scenes of Hengshan grassland. Additionally, compared to estimates based on Sentinel-2A imagery, the UAV-based estimates of the fractional PV (fPV) and fractional NPV (fNPV) were closer to the results of field surveys. The method proposed in this study effectively extracted PV and NPV in China’s Hengshan grassland and demonstrated high reliability and applicability for long-term grassland monitoring. Therefore, the proposed approach can significantly contribute to the intelligent protection and sustainable management of grassland ecosystems in semi-arid areas.