Combining unmanned aerial vehicles and satellite imagery to quantify areal extent of intertidal brown canopy‐forming macroalgae
Abstract 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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-08-01
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| Series: | Remote Sensing in Ecology and Conservation |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/rse2.327 |
| _version_ | 1797736683886608384 |
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| author | Pippa H. Lewis Benjamin P. Roberts Pippa J. Moore Samuel Pike Anthony Scarth Katie Medcalf Iain Cameron |
| author_facet | Pippa H. Lewis Benjamin P. Roberts Pippa J. Moore Samuel Pike Anthony Scarth Katie Medcalf Iain Cameron |
| author_sort | Pippa H. Lewis |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-12T13:17:26Z |
| format | Article |
| id | doaj.art-60c3e2d8a18842e4b86d0ee8816f301f |
| institution | Directory Open Access Journal |
| issn | 2056-3485 |
| language | English |
| last_indexed | 2024-03-12T13:17:26Z |
| publishDate | 2023-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Remote Sensing in Ecology and Conservation |
| spelling | doaj.art-60c3e2d8a18842e4b86d0ee8816f301f2023-08-26T15:26:48ZengWileyRemote Sensing in Ecology and Conservation2056-34852023-08-019454055210.1002/rse2.327Combining unmanned aerial vehicles and satellite imagery to quantify areal extent of intertidal brown canopy‐forming macroalgaePippa H. Lewis0Benjamin P. Roberts1Pippa J. Moore2Samuel Pike3Anthony Scarth4Katie Medcalf5Iain Cameron6Department of Geography and Earth Sciences Aberystwyth University Aberystwyth SY23 3FL UKInstitute of Biological Environmental and Rural Science Aberystwyth University Aberystwyth SY23 3FL UKInstitute of Biological Environmental and Rural Science Aberystwyth University Aberystwyth SY23 3FL UKEnvironment Systems Ltd Science Park, Cefn Llan Aberystwyth SY23 3AH UKEnvironment Systems Ltd Science Park, Cefn Llan Aberystwyth SY23 3AH UKEnvironment Systems Ltd Science Park, Cefn Llan Aberystwyth SY23 3AH UKEnvironment Systems Ltd Science Park, Cefn Llan Aberystwyth SY23 3AH UKAbstract 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.https://doi.org/10.1002/rse2.327Blue carbonfucoidshabitat extentmarine vegetated habitatsseaweedSentinel‐2 |
| spellingShingle | Pippa H. Lewis Benjamin P. Roberts Pippa J. Moore Samuel Pike Anthony Scarth Katie Medcalf Iain Cameron Combining unmanned aerial vehicles and satellite imagery to quantify areal extent of intertidal brown canopy‐forming macroalgae Remote Sensing in Ecology and Conservation Blue carbon fucoids habitat extent marine vegetated habitats seaweed Sentinel‐2 |
| title | Combining unmanned aerial vehicles and satellite imagery to quantify areal extent of intertidal brown canopy‐forming macroalgae |
| title_full | Combining unmanned aerial vehicles and satellite imagery to quantify areal extent of intertidal brown canopy‐forming macroalgae |
| title_fullStr | Combining unmanned aerial vehicles and satellite imagery to quantify areal extent of intertidal brown canopy‐forming macroalgae |
| title_full_unstemmed | Combining unmanned aerial vehicles and satellite imagery to quantify areal extent of intertidal brown canopy‐forming macroalgae |
| title_short | Combining unmanned aerial vehicles and satellite imagery to quantify areal extent of intertidal brown canopy‐forming macroalgae |
| title_sort | combining unmanned aerial vehicles and satellite imagery to quantify areal extent of intertidal brown canopy forming macroalgae |
| topic | Blue carbon fucoids habitat extent marine vegetated habitats seaweed Sentinel‐2 |
| url | https://doi.org/10.1002/rse2.327 |
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