Estimation of grassland canopy cover at quadrat and plot scales using multi-scale UAV imagery

Grassland canopy cover acts as an essential metric for gauging the vitality and ecological functions of grassland. Unmanned aerial vehicles (UAVs) provide stable and reliable data for estimating grassland canopy cover. However, conventional approaches primarily rely on samples from ground surveys and visual assessments, where data consistency is often affected by variations in survey techniques and personnel expertise. By contrast, UAVs provide consistent multi-scale grassland canopy data. Thus, effectively harnessing the strengths of multi-scale UAV imagery can markedly improve the efficiency and precision of canopy cover estimation. This study uses high-resolution UAV imagery for semantic segmentation to derive precise quadrat-scale canopy cover as ground truth. Subsequently, a deep regression network is developed using UAV orthophotos to estimate canopy cover at the plot scale. The findings indicate that semantic segmentation models leveraging deep learning techniques provide accurate vegetation segmentation and canopy cover estimation at the quadrat level, with UNet++ delivering the highest performance, marked by a mean intersection over union (MIoU) of 0.81 and an F1-score of 0.88. The canopy cover results derived from UNet++ segmentation exhibit a coefficient of determination ( R 2 ) of 0.98 and a root mean square error (RMSE) under 3.6%, surpassing conventional methods like Canopeo and Random Forest (RF). At plot scale, models based on convolutional neural networks (CNNs) and vision transformer (ViT) architecture show enhanced capabilities in predicting canopy cover, with the Swin transformer-based model achieving the greatest accuracy ( R 2 = 0.90, RMSE = 5.48%). In meadow, typical, and desert steppe, the Swin tansformer-based model consistently delivers high-precision canopy cover estimates. This study highlights the potential of integrating multi-scale UAV imagery with advanced deep learning techniques for efficient and accurate grassland vegetation monitoring. Future research should focus on optimizing model performance, extending applications to diverse ecosystems, and incorporating additional data sources to enhance robustness and precision.