A High-Resolution Global Map of Giant Kelp (<i>Macrocystis pyrifera</i>) Forests and Intertidal Green Algae (<i>Ulvophyceae</i>) with Sentinel-2 Imagery

dc.contributor.authorMora-Soto, Alejandra
dc.contributor.authorPalacios, Mauricio
dc.contributor.authorMacaya, Erasmo C.
dc.contributor.authorGomez, Ivan
dc.contributor.authorHuovinen, Pirjo
dc.contributor.authorPerez-Matus, Alejandro
dc.contributor.authorYoung, Mary
dc.contributor.authorGolding, Neil
dc.contributor.authorToro, Martin
dc.contributor.authorYaqub, Mohammad
dc.contributor.authorMacias-Fauria, Marc
dc.date.accessioned2025-01-23T19:55:08Z
dc.date.available2025-01-23T19:55:08Z
dc.date.issued2020
dc.description.abstractGiant kelp (Macrocystis pyrifera) is the most widely distributed kelp species on the planet, constituting one of the richest and most productive ecosystems on Earth, but detailed information on its distribution is entirely missing in some marine ecoregions, especially in the high latitudes of the Southern Hemisphere. Here, we present an algorithm based on a series of filter thresholds to detect giant kelp employing Sentinel-2 imagery. Given the overlap between the reflectances of giant kelp and intertidal green algae (Ulvophyceae), the latter are also detected on shallow rocky intertidal areas. The kelp filter algorithm was applied separately to vegetation indices, the Floating Algae Index (FAI), the Normalised Difference Vegetation Index (NDVI), and a novel formula (the Kelp Difference, KD). Training data from previously surveyed kelp forests and other coastal and ocean features were used to identify reflectance threshold values. This procedure was validated with independent field data collected with UAV imagery at a high spatial resolution and point-georeferenced sites at a low spatial resolution. When comparing UAV with Sentinel data (high-resolution validation), an average overall accuracy >= 0.88 and Cohen's kappa >= 0.64 coefficients were found in all three indices for canopies reaching the surface with extensions greater than 1 hectare, with the KD showing the highest average kappa score (0.66). Measurements between previously surveyed georeferenced points and remotely-sensed kelp grid cells (low-resolution validation) showed that 66% of the georeferenced points had grid cells indicating kelp presence within a linear distance of 300 m. We employed the KD in our kelp filter algorithm to estimate the global extent of giant kelp and intertidal green algae per marine ecoregion and province, producing a high-resolution global map of giant kelp and intertidal green algae, powered by Google Earth Engine.
dc.fuente.origenWOS
dc.identifier.doi10.3390/rs12040694
dc.identifier.eissn2072-4292
dc.identifier.urihttps://doi.org/10.3390/rs12040694
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/100701
dc.identifier.wosidWOS:000519564600105
dc.issue.numero4
dc.language.isoen
dc.revistaRemote sensing
dc.rightsacceso restringido
dc.subjectgiant kelp
dc.subjectMacrocystis pyrifera
dc.subjectGoogle Earth Engine
dc.subjectUAV
dc.subjectSentinel-2
dc.subjectUlvophyceae
dc.subject.ods14 Life Below Water
dc.subject.ods13 Climate Action
dc.subject.ods15 Life on Land
dc.subject.odspa14 Vida submarina
dc.subject.odspa13 Acción por el clima
dc.subject.odspa15 Vida de ecosistemas terrestres
dc.titleA High-Resolution Global Map of Giant Kelp (<i>Macrocystis pyrifera</i>) Forests and Intertidal Green Algae (<i>Ulvophyceae</i>) with Sentinel-2 Imagery
dc.typeartículo
dc.volumen12
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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