TY - JOUR
T1 - Combining unmanned aerial vehicles and satellite imagery to quantify areal extent of intertidal brown canopy-forming macroalgae
AU - Lewis, Pippa H.
AU - Roberts, Benjamin P.
AU - Moore, Pippa J.
AU - Pike, Samuel
AU - Scarth, Anthony
AU - Medcalf, Katie
AU - Cameron, Iain
N1 - Funding Information:
This research was funded by the Knowledge Economy Skills Scholarships (KESS) and Environment Systems to P.H.L. KESS is part‐funded by the European Social Fund (ESF) through the European Union's Convergence Programme (West Wales and the Valleys). P.J.M. was funded by a NERC‐Newton Fund Grant NE/S011692/1. We thank the Ecostructure Project ( www.ecostructureproject.eu ) for the UAV imagery from north and south Wales. The Ecostructure Project is part‐funded by the European Regional Development Fund (ERDF) through the Ireland‐Wales Cooperation Programme 2014–2020.
Funding Information:
This research was funded by the Knowledge Economy Skills Scholarships (KESS) and Environment Systems to P.H.L. KESS is part-funded by the European Social Fund (ESF) through the European Union's Convergence Programme (West Wales and the Valleys). P.J.M. was funded by a NERC-Newton Fund Grant NE/S011692/1. We thank the Ecostructure Project (www.ecostructureproject.eu) for the UAV imagery from north and south Wales. The Ecostructure Project is part-funded by the European Regional Development Fund (ERDF) through the Ireland-Wales Cooperation Programme 2014–2020.
Publisher Copyright:
© 2023 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London.
PY - 2023/8/26
Y1 - 2023/8/26
N2 - Brown macroalgae habitats provide a range of ecosystem services, offering coastal protection, supporting and increasing biodiversity, and more recently have been recognized for their potential role as blue carbon habitats. Consequently, accurate areal estimates of these habitats are vitally important. Satellite imagery is often utilized for areal estimates of vegetated habitats due to their ability to capture vast areas but are disadvantaged by their lower resolution. In contrast, imagery collected by unmanned aerial vehicles (UAV) provide high-resolution datasets but are unable to cover the necessary spatial scale required for calculating areal estimates at regional, national or international scales. This study successfully and accurately corrects the outputs from low-resolution Sentinel 2 imagery to the standard of high-resolution UAV imagery by using a novel brown algae index and a simple regression model to provide accurate spatial estimates. This model was applied to rocky shores across Wales, UK to predict a spatial extent of 6.2 km2 for three fucoid macroalgae species; Ascophyllum nodosum, Fucus vesiculosus and F. serratus. The regression model was validated in two ways. First, the data used to create the regression model was split to train and test (50:50) the model, with a root mean square error of ~8%–14%. Secondly, spatial estimates of fucoids in independent aerial imagery were assessed using aerial photography interpretation and compared to that of the regression model (7% difference). The carbon standing stock of fucoids calculated from the spatial estimate (6.2 km2) was found to be significantly lower than that of other marine carbon stores, indicating that fucoids do not significantly contribute as a blue carbon habitat based on biomass alone. This study produces a robust and accurate remote sensing technique to estimate spatial extent of macroalgae at large spatial scales, with possible worldwide applicability.
AB - Brown macroalgae habitats provide a range of ecosystem services, offering coastal protection, supporting and increasing biodiversity, and more recently have been recognized for their potential role as blue carbon habitats. Consequently, accurate areal estimates of these habitats are vitally important. Satellite imagery is often utilized for areal estimates of vegetated habitats due to their ability to capture vast areas but are disadvantaged by their lower resolution. In contrast, imagery collected by unmanned aerial vehicles (UAV) provide high-resolution datasets but are unable to cover the necessary spatial scale required for calculating areal estimates at regional, national or international scales. This study successfully and accurately corrects the outputs from low-resolution Sentinel 2 imagery to the standard of high-resolution UAV imagery by using a novel brown algae index and a simple regression model to provide accurate spatial estimates. This model was applied to rocky shores across Wales, UK to predict a spatial extent of 6.2 km2 for three fucoid macroalgae species; Ascophyllum nodosum, Fucus vesiculosus and F. serratus. The regression model was validated in two ways. First, the data used to create the regression model was split to train and test (50:50) the model, with a root mean square error of ~8%–14%. Secondly, spatial estimates of fucoids in independent aerial imagery were assessed using aerial photography interpretation and compared to that of the regression model (7% difference). The carbon standing stock of fucoids calculated from the spatial estimate (6.2 km2) was found to be significantly lower than that of other marine carbon stores, indicating that fucoids do not significantly contribute as a blue carbon habitat based on biomass alone. This study produces a robust and accurate remote sensing technique to estimate spatial extent of macroalgae at large spatial scales, with possible worldwide applicability.
KW - Blue carbon
KW - fucoids
KW - habitat extent
KW - marine vegetated habitats
KW - seaweed
KW - Sentinel-2
UR - http://www.scopus.com/inward/record.url?scp=85150611853&partnerID=8YFLogxK
U2 - 10.1002/rse2.327
DO - 10.1002/rse2.327
M3 - Article
SN - 2056-3485
VL - 9
SP - 540
EP - 552
JO - Remote Sensing in Ecology and Conservation
JF - Remote Sensing in Ecology and Conservation
IS - 4
ER -
