High-resolution three-dimensional mapping of eelgrass (Zostera marina) habitat and blue carbon using drone-borne LiDAR

Abstract

The accessibility of flying drones (Unoccupied Aerial Vehicles) presents scientists and managers with reproducible and cost-effective methods to monitor submerged aquatic vegetation. In particular, drone-borne topobathymetric LiDAR provides high-resolution (cm-scale), three-dimensional information about the geometry and structure of surveyed areas, allowing for quantification of vegetation volume in addition to bathymetry. For habitat-forming submerged and intertidal vegetation like seagrass, this information can advance research regarding the structure and patchiness of canopies in relation to biodiversity, blue carbon storage, and hydrodynamic processes. Here, we report how drone-borne LiDAR can be used to estimate the habitat volume of eelgrass (Zostera marina) within a sheltered bay in south-eastern Norway. After classifying LiDAR points using a Random Forest model, we created a Digital Terrain Model of the sea floor and a Digital Surface Model of the eelgrass canopy. From these models, we estimated eelgrass canopy volume to range between 862 and 1099 m3 across the small study area. From the volume, we estimated above-ground carbon storage in living eelgrass tissue to range between 96 and 122 kg. To our knowledge, this is the first study to utilise drone-borne LiDAR to quantify the volume and carbon-storage potential of a marine habitat-forming species like eelgrass, thereby demonstrating the potential of drone-borne LiDAR as an efficient tool to provide reproducible and high-resolution data for submerged aquatic habitats, including seagrass meadows.

Plant phenotyping relevance

ドローン搭載LiDARを用いて eelgrass のキャノピー体積という植物形態形質を推定する方法が研究の中心であり、分類、地形・表面モデル作成、再現可能な高解像度測定手法として記述されているため。

Code and data availability

The paper's R analysis code (point cloud cleaning, Random Forest classification, DTM/DSM/canopy height and biomass/carbon computations) is publicly available on the corresponding author's GitHub repository. The underlying LiDAR/field data are only available upon request, so no public data asset qualifies.