Automatic instream large wood detection and wood load estimation using machine learning from high-resolution aerial imagery

Large wood (LW) is vital for river ecosystems, influencing hydraulics, sediment dynamics, and habitat complexity. Quantifying LW storage is crucial for river management, habitat restoration, and flood mitigation but is challenging due to its spatial and temporal variability. Traditional field surveys are labour-intensive and limited in scope, while manual mapping of aerial imagery, though detailed, is equally time-consuming. This study presents an automated approach to detect and measure LW using high-resolution imagery and convolutional neural networks (CNNs). Two models, YOLOv10 for wood detection and YOLOv8 for wood segmentation, were trained using data from eight rivers in the Swiss Alps and Argentinean Andes. A separate river dataset was used for independent testing. The detection model achieved 90 % accuracy in estimating wood volume compared to field data and detected 97 % of large wood pieces at a 0.3 confidence threshold. The segmentation model achieved a mean Average Precision of 70 %. Although wood diameters were underestimated, the method reliably captured spatial distributions of wood. Expanding the training dataset with more diverse examples could improve performance. This automated approach offers a scalable and efficient tool for monitoring riverine wood, overcoming the limitations of traditional field-based surveys.