A validated 3D point‐cloud analysis workflow for the creation of seagrass canopy height models

Abstract Seagrasses support highly productive ecosystems in both temperate and tropical waters and provide essential ecosystem services. Despite their importance, the vertical structure of seagrasses (i.e. the canopy) remains poorly characterised at the microscale, even though it is a strong predictor of ecological processes that influence both biological and physical interactions in coastal environments. This study develops and validates a practical, scalable workflow to map canopy height (CH) across a large Posidonia oceanica meadow using low‐cost underwater Structure‐from‐Motion (SfM) photogrammetry acquired by Scuba Diver Propulsion Vehicle (DPV). Three‐dimensional georeferenced point clouds (PCLs) were analysed to develop an effective workflow, encompassing acquisition settings and optimal processing steps for accurately classifying PCLs and generating SfM‐based canopy height models (CHMs) of seagrass meadows. A testing site was identified to evaluate 3D positioning accuracies and three automated ground‐filtering methods (Metashape, CSF, CANUPO). Results were compared with manually classified PCL and in situ canopy measurements. An analysis of patch morphology, volume, and canopy height, based on model residuals, was conducted to provide an in‐depth assessment of accuracy and method effectiveness before expanding the workflow to a meadow‐wide scale (12,800 m 2 ). Automated classifiers (CANUPO) yielded CHMs and patch metrics that were statistically similar to those from manual filtering. The ground‐filtered PCLs produced centimetre‐scale CHMs with accurate georeferencing, yielding 3D RMSE values of ~0.13–0.22 m depending on survey extent and ground‐control‐point configuration. The application of the proposed method at the meadow‐wide scale reported a higher mean CH at the Cala di Mezzo than at the Subielli bank site, indicating natural variability in seagrass associated with the depth gradient and substratum type. This study presents a new and scalable workflow for generating CHMs from SfM‐derived PCLs, enabling direct analysis of meadow canopy complexity and for deriving key seagrass descriptors, including mean and maximum CH, total canopy area and volume. This approach represents a significant advancement in ecological monitoring, offering the potential to streamline fieldwork and reduce reliance on labour‐intensive manual surveys. Overall, the findings highlight the promise of cost‐effective, open‐source tools for improving efficiency and replicability in underwater monitoring of marine seagrass ecosystems.