coastTrain: A Global Reference Library for Coastal Ecosystems

Murray, Nicholas J., Bunting, Pete, Canto, Robert F., Hilarides, Lammert, Kennedy, Emma V., Lucas, Richard M., Lyons, Mitchell B., Navarro, Alejandro, Roelfsema, Chris M., Rosenqvist, Ake, Spalding, Mark D., Toor, Maren, and Worthington, Thomas A. (2022) coastTrain: A Global Reference Library for Coastal Ecosystems. Remote Sensing, 14 (22). 5766.

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Estimating the distribution, extent and change of coastal ecosystems is essential for monitoring global change. However, spatial models developed to estimate the distribution of land cover types require accurate and up-to-date reference data to support model development, model training and data validations. Owing to the labor-intensive tasks required to develop reference datasets, often requiring intensive campaigns of image interpretation and/or field work, the availability of sufficiently large quality and well distributed reference datasets has emerged as a major bottleneck hindering advances in the field of continental to global-scale ecosystem mapping. To enhance our ability to model coastal ecosystem distributions globally, we developed a global reference dataset of 193,105 occurrence records of seven coastal ecosystem types—muddy shorelines, mangroves, coral reefs, coastal saltmarshes, seagrass meadows, rocky shoreline, and kelp forests—suitable for supporting current and next-generation remote sensing classification models. coastTrain version 1.0 contains curated occurrence records collected by several global mapping initiatives, including the Allen Coral Atlas, Global Tidal Flats, Global Mangrove Watch and Global Tidal Wetlands Change. To facilitate use and support consistency across studies, coastTrain has been harmonized to the International Union for the Conservation of Nature’s (IUCN) Global Ecosystem Typology. coastTrain is an ongoing collaborative initiative designed to support sharing of reference data for coastal ecosystems, and is expected to support novel global mapping initiatives, promote validations of independently developed data products and to enable improved monitoring of rapidly changing coastal environments worldwide.

Item ID: 77603
Item Type: Article (Research - C1)
ISSN: 2072-4292
Keywords: deep learning, feature set, machine learning, mangroves, mudflats, occurrence records, tidal flats, tidal marshes, tidal wetlands, training set
Copyright Information: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Funders: Australian Research Council (ARC)
Projects and Grants: ARC DE190100101
Date Deposited: 23 Feb 2023 00:13
FoR Codes: 41 ENVIRONMENTAL SCIENCES > 4104 Environmental management > 410402 Environmental assessment and monitoring @ 100%
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