4.7 Article

Monitoring the marine invasive alien species Rugulopteryx okamurae using unmanned aerial vehicles and satellites

Journal

FRONTIERS IN MARINE SCIENCE
Volume 9, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fmars.2022.1004012

Keywords

invasive macroalgae; algae monitoring; multispectral sensor; hyperspectral sensor; UAV; Sentinel-2; Landsat-8; machine learning

Funding

  1. Andalusian Regional Government - MCIN/AEI [PY20-00244, OAPN-2715/2021, RTI2018-098784-J-I00, IJC2019-039382-I, EQC2018004275, EQC2019-00572]
  2. ERDF A way of making Europe
  3. Ministry of Universities of the Spanish Government [FPU20/01294, FPU19/04557]
  4. Biodiversity of the Coastal Ocean: Monitoring with Earth Observation (BiCOME) - European Space Agency [4000135756/21/I-EF]

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This study demonstrates the usefulness of multispectral remote sensing techniques and unmanned aerial vehicles (UAVs) in monitoring the invasive macroalgae R. okamurae in coastal areas. The study successfully detected the presence of R. okamurae using multispectral images and highlighted the importance of early warnings generated by satellite data.
Rugulopteryx okamurae is a species of brown macroalgae belonging to the Dictyotaceae family and native to the north-western Pacific. As an Invasive Alien Species (IAS), it was first detected in the Strait of Gibraltar in 2015. Since then, R. okamurae has been spreading rapidly through the submerged euphotic zone, colonizing from 0 to 50 m depth and generating substantial economic and environmental impacts on the Andalusian coasts (southern Spain). More than 40% of marine IAS in the European Union (EU) are macroalgae, representing one of the main threats to biodiversity and ecosystem functioning in coastal habitats. This study presents a monitoring pilot of beached R. okamurae and fresh R. okamurae down to 5 m depth in Tarifa (Cadiz, Spain), combining multispectral remote sensing data collected by sensors on-board Unmanned Aerial Vehicles (UAVs) and satellites, and how this information can be used to support decision-making and policy. We used an UAV flight carried out at Bolonia beach (Tarifa, Spain) on 1(st) July 2021 and Sentinel-2 (S2) and Landsat-8 (L8) image acquisitions close to the drone flight date. In situ data were also measured on the same date of the flight, and they were used to train the supervised classification Super Vector Machine (SVM) method based on the spectral information obtained for each substrate cover. The results obtained show how multispectral images allow the detection of beached R. okamurae, and the classification accuracy for water, land vegetation, sand and R. okamurae depending on the image resolution (8.3 cm/pixel for UAV flight, 10 m/pixel for S2 and 30 m/pixel for L8). While the UAV imagery precisely delimited the area occupied by this macroalgae, satellite data were capable of detecting its presence, and able to generate early warnings. This study demonstrates the usefulness of multispectral remote sensing techniques to be incorporated in continuous monitoring programmes of the marine IAS R. okamurae in coastal areas. This information is also key to supporting regional, national and European policies in order to adapt strategic management of invasive marine macrophytes.

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