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...
| Autores: | ; ; ; ; ; |
|---|---|
| Tipo de documento: | Artigo |
| Estado: | Published version |
| Data de publicação: | 2025 |
| País: | España |
| Recursos: | Universidad de Sevilla (US) |
| Repositório: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/182199 |
| Acesso em linha: | https://hdl.handle.net/11441/182199 https://doi.org/10.3390/rs17243991 |
| Nível de acesso: | Acesso aberto |
| Palavra-chave: | Dune species Subpixel remote sensing WorldView-2 Multispectral data Fractional cover Random forest regressor |
| _version_ | 1878741282149367808 |
|---|---|
| author | Kombiadou, Katerina |
| author2 | Costas, Susana Gallego Fernández, Juan Bautista Yang, Zhicheng Bon de Sousa, Luisa Silvestri, Sonia |
| author2_role | author author author author author |
| author_browse | Bon de Sousa, Luisa Costas, Susana Gallego Fernández, Juan Bautista Kombiadou, Katerina Silvestri, Sonia Yang, Zhicheng |
| author_facet | Kombiadou, Katerina Costas, Susana Gallego Fernández, Juan Bautista Yang, Zhicheng Bon de Sousa, Luisa Silvestri, Sonia |
| author_role | author |
| collection | idUS. Depósito de Investigación de la Universidad de Sevilla |
| 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. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | ES_e26d6428fc8bbd2584e1c853ea1ea5ad |
| institution | Universidad de Sevilla (US) |
| instname_str | Universidad de Sevilla (US) |
| network_acronym_str | ES |
| network_name_str | España |
| oai_identifier_str | oai:idus.us.es:11441/182199 |
| publishDate | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI |
| publisherStr | MDPI |
| reponame_str | idUS. Depósito de Investigación de la Universidad de Sevilla |
| spelling | Spectral Unmixing of Coastal Dune Plant Species from Very High Resolution Satellite Imagery Kombiadou, Katerina Costas, Susana Gallego Fernández, Juan Bautista Yang, Zhicheng Bon de Sousa, Luisa Silvestri, Sonia Dune species Subpixel remote sensing WorldView-2 Multispectral data Fractional cover Random forest regressor 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. MDPI https://hdl.handle.net/11441/182199 https://doi.org/10.3390/rs17243991 |
| 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 |
| status_str | publishedVersion |
| title | 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_short | 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 |
| topic | Dune species Subpixel remote sensing WorldView-2 Multispectral data Fractional cover Random forest regressor |
| topic_facet | Dune species Subpixel remote sensing WorldView-2 Multispectral data Fractional cover Random forest regressor |
| url | https://hdl.handle.net/11441/182199 https://doi.org/10.3390/rs17243991 |