A High-Resolution Global Map of Giant Kelp (Macrocystis pyrifera) Forests and Intertidal Green Algae (Ulvophyceae) with Sentinel-2 Imagery
1
Biogeosciences Group, School of Geography and the Environment, University of Oxford, Oxford OX1 3QY, UK
2
Instituto de Ciencias Marinas y Limnológicas, Facultad de Ciencias, Universidad Austral de Chile, Campus Isla Teja s/n, Valdivia 5090000, Chile
3
Facultad de Ciencias, Universidad de Magallanes, Punta Arenas 6210427, Chile
4
Programa de Doctorado en Biología Marina, Universidad Austral de Chile, Valdivia 5090000, Chile
5
Centro FONDAP de Investigación en Dinámica de Ecosistemas Marinos de Altas Latitudes (IDEAL), Valdivia 5090000, Chile
6
Laboratorio de Estudios Algales (ALGALAB), Departamento de Oceanografía, Universidad de Concepción, Casilla 160-C, Concepción 4030000, Chile
7
Millenium Nucleus Ecology and Sustainable Management of Oceanic Islands (ESMOI), Coquimbo 1780000, Chile
8
Subtidal Ecology Laboratory, Departamento de Ecología, Estación Costera de Investigaciones Marinas, Pontificia Universidad Católica de Chile, Casilla 114-D, Santiago 8320000, Chile
9
Centre for Integrative Ecology, Life and Environmental Sciences, Deakin University, Princes Hwy, Warrnambool 3280, Australia
10
South Atlantic Environmental Research Institute (SAERI), Stanley FIQQ 1ZZ, Falkland Islands
11
Instituto de Geografía, Pontificia Universidad Católica de Chile, Macul, Santiago 782-0436, Chile
12
IT Services, University of Oxford, Oxford OX2 6NN, UK
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(4), 694; https://doi.org/10.3390/rs12040694
Received: 6 December 2019 / Revised: 13 February 2020 / Accepted: 18 February 2020 / Published: 20 February 2020
(This article belongs to the Special Issue Drone-Based Ecological Conservation)
Giant 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.
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Keywords:
giant kelp; Macrocystis pyrifera; Google Earth Engine; UAV; Sentinel-2; Ulvophyceae
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
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Externally hosted supplementary file 1
Link: https://github.com/BiogeoscienceslabOxford/kelp_forests
Description: Kelp detection algorithm (JS code); box and whisker plot of NDVI, FAI and KD and their thresholds; methodology workflow; high-resolution validation (GEE interface); Drone-KD-CART and RF comparison; low-resolution kelp observations (table).
MDPI and ACS Style
Mora-Soto, A.; Palacios, M.; Macaya, E.C.; Gómez, I.; Huovinen, P.; Pérez-Matus, A.; Young, M.; Golding, N.; Toro, M.; Yaqub, M.; Macias-Fauria, M. A High-Resolution Global Map of Giant Kelp (Macrocystis pyrifera) Forests and Intertidal Green Algae (Ulvophyceae) with Sentinel-2 Imagery. Remote Sens. 2020, 12, 694. https://doi.org/10.3390/rs12040694
AMA Style
Mora-Soto A, Palacios M, Macaya EC, Gómez I, Huovinen P, Pérez-Matus A, Young M, Golding N, Toro M, Yaqub M, Macias-Fauria M. A High-Resolution Global Map of Giant Kelp (Macrocystis pyrifera) Forests and Intertidal Green Algae (Ulvophyceae) with Sentinel-2 Imagery. Remote Sensing. 2020; 12(4):694. https://doi.org/10.3390/rs12040694
Chicago/Turabian StyleMora-Soto, Alejandra; Palacios, Mauricio; Macaya, Erasmo C.; Gómez, Iván; Huovinen, Pirjo; Pérez-Matus, Alejandro; Young, Mary; Golding, Neil; Toro, Martin; Yaqub, Mohammad; Macias-Fauria, Marc. 2020. "A High-Resolution Global Map of Giant Kelp (Macrocystis pyrifera) Forests and Intertidal Green Algae (Ulvophyceae) with Sentinel-2 Imagery" Remote Sens. 12, no. 4: 694. https://doi.org/10.3390/rs12040694
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