Spectral Unmixing of Coastal Dune Plant Species from Very High Resolution Satellite Imagery

While improvements in the spectral and spatial resolution of satellite imagery have opened up new prospects for large-scale environmental monitoring, this potential has remained largely unrealised in dune ecogeomorphology. This is especially true for Mediterranean coastal dunes, where the highly mix...

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Autores: Kombiadou, Katerina, Costas, Susana, Gallego Fernández, Juan Bautista, Yang, Zhicheng, Bon de Sousa, Luisa, Silvestri, Sonia
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/182199
Acceso en línea:https://hdl.handle.net/11441/182199
https://doi.org/10.3390/rs17243991
Access Level:acceso abierto
Palabra clave:Dune species
Subpixel remote sensing
WorldView-2
Multispectral data
Fractional cover
Random forest regressor
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spelling Spectral Unmixing of Coastal Dune Plant Species from Very High Resolution Satellite ImageryKombiadou, KaterinaCostas, SusanaGallego Fernández, Juan BautistaYang, ZhichengBon de Sousa, LuisaSilvestri, SoniaDune speciesSubpixel remote sensingWorldView-2Multispectral dataFractional coverRandom forest regressorWhile improvements in the spectral and spatial resolution of satellite imagery have opened up new prospects for large-scale environmental monitoring, this potential has remained largely unrealised in dune ecogeomorphology. This is especially true for Mediterranean coastal dunes, where the highly mixed and sparse vegetation requires high resolution satellites and spectral unmixing techniques. To achieve this aim, we employed random forest regressors to predict the fractional cover of dune plant species in two of the sandy barriers of Ria Formosa (S. Portugal) from WorldView-2 imagery (June 2024). The algorithm, tested with spatially upscaled multispectral drone data and satellite imagery, detected the fractional cover of major species (most abundant classes and bushy vegetation) with reasonable to very good accuracy (coefficient of determination, CoD: 0.4 to 0.8) for the former and reasonable to good accuracy (CoD: 0.4 to 0.6) for the latter. Additional tests showed that (a) including the distance to the shoreline can increase model accuracy (CoD by ~0.1); (b) the grouping of species resulted in an insignificant increase in model skill; and (c) testing over independent dune plots showed generalisation beyond the training set and low risk of overfitting or noise. Overall, the approach showed promising results for large-scale observations in highly mixed coastal dunes.MDPIBiología Vegetal y EcologíaFundação para a Ciência e a Tecnologia. PortugalNational Science Foundation (NSF). United StatesEuropean Union (UE)2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/182199https://doi.org/10.3390/rs17243991reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésRemote Sensing, 17 (24), 3991.2022.06615.PTDChttps://doi.org/10.3390/rs17243991info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1821992026-06-17T12:51:07Z
dc.title.none.fl_str_mv Spectral Unmixing of Coastal Dune Plant Species from Very High Resolution Satellite Imagery
title Spectral Unmixing of Coastal Dune Plant Species from Very High Resolution Satellite Imagery
spellingShingle Spectral Unmixing of Coastal Dune Plant Species from Very High Resolution Satellite Imagery
Kombiadou, Katerina
Dune species
Subpixel remote sensing
WorldView-2
Multispectral data
Fractional cover
Random forest regressor
title_short Spectral Unmixing of Coastal Dune Plant Species from Very High Resolution Satellite Imagery
title_full Spectral Unmixing of Coastal Dune Plant Species from Very High Resolution Satellite Imagery
title_fullStr Spectral Unmixing of Coastal Dune Plant Species from Very High Resolution Satellite Imagery
title_full_unstemmed Spectral Unmixing of Coastal Dune Plant Species from Very High Resolution Satellite Imagery
title_sort Spectral Unmixing of Coastal Dune Plant Species from Very High Resolution Satellite Imagery
dc.creator.none.fl_str_mv Kombiadou, Katerina
Costas, Susana
Gallego Fernández, Juan Bautista
Yang, Zhicheng
Bon de Sousa, Luisa
Silvestri, Sonia
author Kombiadou, Katerina
author_facet Kombiadou, Katerina
Costas, Susana
Gallego Fernández, Juan Bautista
Yang, Zhicheng
Bon de Sousa, Luisa
Silvestri, Sonia
author_role author
author2 Costas, Susana
Gallego Fernández, Juan Bautista
Yang, Zhicheng
Bon de Sousa, Luisa
Silvestri, Sonia
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Biología Vegetal y Ecología
Fundação para a Ciência e a Tecnologia. Portugal
National Science Foundation (NSF). United States
European Union (UE)
dc.subject.none.fl_str_mv Dune species
Subpixel remote sensing
WorldView-2
Multispectral data
Fractional cover
Random forest regressor
topic Dune species
Subpixel remote sensing
WorldView-2
Multispectral data
Fractional cover
Random forest regressor
description While improvements in the spectral and spatial resolution of satellite imagery have opened up new prospects for large-scale environmental monitoring, this potential has remained largely unrealised in dune ecogeomorphology. This is especially true for Mediterranean coastal dunes, where the highly mixed and sparse vegetation requires high resolution satellites and spectral unmixing techniques. To achieve this aim, we employed random forest regressors to predict the fractional cover of dune plant species in two of the sandy barriers of Ria Formosa (S. Portugal) from WorldView-2 imagery (June 2024). The algorithm, tested with spatially upscaled multispectral drone data and satellite imagery, detected the fractional cover of major species (most abundant classes and bushy vegetation) with reasonable to very good accuracy (coefficient of determination, CoD: 0.4 to 0.8) for the former and reasonable to good accuracy (CoD: 0.4 to 0.6) for the latter. Additional tests showed that (a) including the distance to the shoreline can increase model accuracy (CoD by ~0.1); (b) the grouping of species resulted in an insignificant increase in model skill; and (c) testing over independent dune plots showed generalisation beyond the training set and low risk of overfitting or noise. Overall, the approach showed promising results for large-scale observations in highly mixed coastal dunes.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/182199
https://doi.org/10.3390/rs17243991
url https://hdl.handle.net/11441/182199
https://doi.org/10.3390/rs17243991
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Remote Sensing, 17 (24), 3991.
2022.06615.PTDC
https://doi.org/10.3390/rs17243991
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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