Drone lidar-derived surface complexity metrics as indicators of intertidal oyster reef condition

Abstract

Eastern oysters (Crassostrea virginica) generate structurally complex reef systems that offer diverse ecosystem services. However, there is limited understanding of how reef structure translates into reef condition. This knowledge gap might be better addressed if oyster reef structure could be more rapidly assessed. Conventional in situ monitoring techniques are often time-intensive, invasive, and do not provide spatially continuous information on the reef structure. Unoccupied Aircraft Systems (UAS), commonly referred to as drones, equipped with optical sensors can rapidly and non-invasively map intertidal oyster reef surfaces. We demonstrate how a digital surface model from UAS-based light detection and ranging (lidar) can enable very high-resolution characterization and monitoring of intertidal oyster reef surface morphology. Generalized linear models (GLMs) identified relationships between in situ live oyster counts and surface complexity metrics derived from digital surface models produced from lidar point clouds. Statistically significant relationships between surface complexity metrics (e.g., gray level co-occurrence features, volume to area ratio, skewness of elevation) and live oyster counts suggest that surface complexity provides useful proxies for reef condition. Advancing the application of remote sensing to intertidal oyster reefs can help identify reefs that are prone to degradation and inform conservation and restoration strategies.