Estimation of Seaweed Biomass in Shallow Coastal Waters Using UAV Bathymetric LiDAR and Automated 3D Point Cloud Segmentation

Abstract

Accurate and wide-area estimation of seaweed biomass is essential for evaluating blue carbon. Conventional diver surveys and two-dimensional (2D) aerial imagery analysis face challenges such as intensive labor and biomass underestimation. While Unmanned Aerial Vehicle-based Light Detection and Ranging (UAV-LiDAR) provides dense 3D spatial data, classifying point clouds in extremely shallow coastal waters with dense kelp and artificial structures remains difficult. This study establishes a high-accuracy biomass estimation method using UAV-LiDAR and PointNet. A heuristic hybrid filtering approach combining physical constraints and local statistics was developed to automatically generate high-quality reference data. The trained PointNet successfully segmented complex point clouds into four classes with an overall accuracy of 94.2%. To calculate biomass, we introduced a volume correction model based on point cloud density (coverage) to mitigate overestimation caused by internal canopy gaps. This correction yielded estimated wet weights nearly identical to the in situ measurements (an approximate 3% difference), confirming highly accurate biomass reproduction. Furthermore, while the conventional 2D maximum likelihood method underestimated total biomass, our 3D point cloud analysis successfully quantified the dense, overlapping canopy. This framework significantly improves the efficiency and accuracy of blue carbon monitoring.

Plant phenotyping relevance

UAV-LiDARとPointNetによる海藻の3D点群分割および biomass 推定手法を開発・検証しており、植物体のバイオマスという形質の取得が研究の中心である。

Code and data availability

The supplied article blocks describe UAV-LiDAR point clouds, diver survey data, a PointNet model, and HHF filtering code used for seaweed biomass estimation, but contain no data or code availability statement, no public repository, and no author-provided URL for any dataset, point clouds, trained model, or analysis脚本.

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