New Methodology for Intertidal Seaweed Biomass Estimation Using Multispectral Data Obtained with Unoccupied Aerial Vehicles

Abstract

Seaweed assemblages include a variety of structuring species providing habitats, food and shelter for organisms from different trophic levels. Monitoring intertidal seaweed traditionally involves targeting small areas to collect data on species’ biological traits, which is often labour intensive and covers only a small area of the rocky reef under study. Given the various applications for seaweeds and their compounds, there has been an increase in demand for biomass triggered by the development of new markets. Such biomass demand generates new challenges for biomass quantification and the definition of future in-take harvesting commercial quotas by regulating agencies. The use of Unoccupied Aerial Vehicles (UAVs) as a low-cost yet efficient monitoring solution, combined with new sensors such as multispectral cameras, has been proposed for mapping intertidal reefs and seaweed in particular. In this study, a new methodology was developed and validated to quantify intertidal seaweed biomass based on multispectral UAV imagery, which was made available through an easy-to-use QGIS plugin (named SWUAV_BIO) that automates such biomass estimation. This tool was applied to a case study where the standing stock of Fucus spp. beds located at Viana do Castelo rocky shore (northern Portugal) was assessed using UAV multispectral imagery, providing a reference for future UAV-based ecological studies. Although comparison with the in situ assessments showed that biomass was underestimated by 36%, the SWUAV_BIO plugin is a valuable tool, as it provides an expedited (albeit conservative) seaweed standing stock assessment that can be used to monitor seaweed populations, their changes, and assess the effect of harvesting. These data can be used for an informed and sustainable management of seaweed resources by the competent authorities.

Keywords

• supervised classification
• normalized difference vegetation index
• QGIS plugin
• accuracy