Publication
Spectral Unmixing of Coastal Dune Plant Species from Very High Resolution Satellite Imagery
| dc.contributor.author | Kombiadou, Katerina | |
| dc.contributor.author | Costas, Susana | |
| dc.contributor.author | Gallego-Fernández, Juan Bautista | |
| dc.contributor.author | Yang, Zhicheng | |
| dc.contributor.author | Bon de Sousa, Luísa | |
| dc.contributor.author | Silvestri, Sonia | |
| dc.date.accessioned | 2025-12-12T13:00:21Z | |
| dc.date.available | 2025-12-12T13:00:21Z | |
| dc.date.issued | 2025-12-10 | |
| dc.description.abstract | 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. | por |
| dc.description.sponsorship | This research was funded by the Fundação para a Ciência e a Tecnologia (FCT), Portugal, through grant 2022.06615.PTDC (DEVISE project; http://doi.org/10.54499/2022.06615.PTDC). K.K. was supported by the contract https://doi.org/10.54499/CEECINST/00146/2018/CP1493/CT0011; S.C. was supported by the contract https://doi.org/10.54499/CEECINSTLA/00018/2022/CP2967/CT0003; and L.B.d.S. was supported through the project https://doi.org/10.54499/2022.05392.PTDC, all funded by FCT. K.K., S.C., and L.B.d.S. also recognise the funding attributed by FCT to CIMA (https://doi.org/10.54499/UID/00350/2025) and to the Associate Laboratory ARNET (https://doi.org/10.54499/LA/P/0069/2020). Z.Y. was also supported by the Georgia Coastal Ecosystems Long-Term Ecological Research project, which is supported by the National Science Foundation (OCE-1832178). S.S. was supported by the RETURN Extended Partnership and received funding from the European Union Next-GenerationEU (National Recovery and Resilience Plan—NRRP, Mission 4, Component 2, Investment 1.3—D.D. 1243 2 August 2022, PE0000005). | |
| dc.identifier.doi | 10.3390/rs17243991 | |
| dc.identifier.issn | 2072-4292 | |
| dc.identifier.uri | http://hdl.handle.net/10400.1/27947 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | MDPI | |
| dc.relation.ispartof | Remote Sensing | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Dune species | |
| dc.subject | Subpixel remote sensing | |
| dc.subject | WorldView-2 | |
| dc.subject | Multispectral data | |
| dc.subject | Fractional cover | |
| dc.subject | Random forest regressor | |
| dc.title | Spectral Unmixing of Coastal Dune Plant Species from Very High Resolution Satellite Imagery | |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 24 | |
| oaire.citation.startPage | 3991 | |
| oaire.citation.title | Remote Sensing | |
| oaire.citation.volume | 17 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Kombiadou | |
| person.familyName | Costas | |
| person.familyName | Serrão Bon de Sousa | |
| person.givenName | Katerina | |
| person.givenName | Susana | |
| person.givenName | Maria Luísa | |
| person.identifier | 1448818 | |
| person.identifier.ciencia-id | 1813-F159-070B | |
| person.identifier.ciencia-id | AF19-9EEE-7550 | |
| person.identifier.ciencia-id | E218-3C8E-DF78 | |
| person.identifier.orcid | 0000-0003-1199-1236 | |
| person.identifier.orcid | 0000-0002-4005-077X | |
| person.identifier.orcid | 0000-0002-8583-8771 | |
| person.identifier.rid | M-7458-2017 | |
| person.identifier.scopus-author-id | 16029005200 | |
| person.identifier.scopus-author-id | 9043656500 | |
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