Mapping common and glossy buckthorns (Frangula alnus and Rhamnus cathartica) using multi-date satellite imagery WorldView-3, GeoEye-1 and SPOT-7
Buckthorns (Glossy buckthorn, Frangula alnus and common buckthorn, Rhamnus cathartica) represent a threat to biodiversity. Their high competitivity lead to the replacement of native species and the inhibition of forest regeneration. Early detection strategies are therefore necessary to limit invasiv...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17538947.2022.2162136 |
| _version_ | 1797678514809339904 |
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| author | Fiston Nininahazwe Mathieu Varin Jérôme Théau |
| author_facet | Fiston Nininahazwe Mathieu Varin Jérôme Théau |
| author_sort | Fiston Nininahazwe |
| collection | DOAJ |
| description | Buckthorns (Glossy buckthorn, Frangula alnus and common buckthorn, Rhamnus cathartica) represent a threat to biodiversity. Their high competitivity lead to the replacement of native species and the inhibition of forest regeneration. Early detection strategies are therefore necessary to limit invasive alien plant species’ impacts, and remote sensing is one of the techniques for early invasion detection. Few studies have used phenological remote sensing approaches to map buckthorn distribution from medium spatial resolution images. Those studies highlighted the difficulty of detecting buckthorns in low densities and in understory using this category of images. The main objective of this study was to develop an approach using multi-date very high spatial resolution satellite imagery to map buckthorns in low densities and in the understory in the Québec city area. Three machine learning classifiers (Support Vector Machines, Random Forest and Extreme Gradient Boosting) were applied to WorldView-3, GeoEye-1 and SPOT-7 satellite imagery. The Random Forest classifier performed well (Kappa = 0.72). The SVM and XGBoost's coefficient Kappa were 0.69 and 0.66, respectively. However, buckthorn distribution in understory was identified as the main limit to this approach, and LiDAR data could be used to improve buckthorn mapping in similar environments. |
| first_indexed | 2024-03-11T23:00:55Z |
| format | Article |
| id | doaj.art-cdb66a30bf484b4fa5af9bfdb5d151fc |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-11T23:00:55Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-cdb66a30bf484b4fa5af9bfdb5d151fc2023-09-21T14:57:12ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552023-12-01161314210.1080/17538947.2022.21621362162136Mapping common and glossy buckthorns (Frangula alnus and Rhamnus cathartica) using multi-date satellite imagery WorldView-3, GeoEye-1 and SPOT-7Fiston Nininahazwe0Mathieu Varin1Jérôme Théau2Université de SherbrookeCentre d’enseignement et de recherche en foresterie (CERFO)Université de SherbrookeBuckthorns (Glossy buckthorn, Frangula alnus and common buckthorn, Rhamnus cathartica) represent a threat to biodiversity. Their high competitivity lead to the replacement of native species and the inhibition of forest regeneration. Early detection strategies are therefore necessary to limit invasive alien plant species’ impacts, and remote sensing is one of the techniques for early invasion detection. Few studies have used phenological remote sensing approaches to map buckthorn distribution from medium spatial resolution images. Those studies highlighted the difficulty of detecting buckthorns in low densities and in understory using this category of images. The main objective of this study was to develop an approach using multi-date very high spatial resolution satellite imagery to map buckthorns in low densities and in the understory in the Québec city area. Three machine learning classifiers (Support Vector Machines, Random Forest and Extreme Gradient Boosting) were applied to WorldView-3, GeoEye-1 and SPOT-7 satellite imagery. The Random Forest classifier performed well (Kappa = 0.72). The SVM and XGBoost's coefficient Kappa were 0.69 and 0.66, respectively. However, buckthorn distribution in understory was identified as the main limit to this approach, and LiDAR data could be used to improve buckthorn mapping in similar environments.http://dx.doi.org/10.1080/17538947.2022.2162136invasive alien plant speciesremote sensingbuckthornsmulti-date satellite imagerymachine learning |
| spellingShingle | Fiston Nininahazwe Mathieu Varin Jérôme Théau Mapping common and glossy buckthorns (Frangula alnus and Rhamnus cathartica) using multi-date satellite imagery WorldView-3, GeoEye-1 and SPOT-7 International Journal of Digital Earth invasive alien plant species remote sensing buckthorns multi-date satellite imagery machine learning |
| title | Mapping common and glossy buckthorns (Frangula alnus and Rhamnus cathartica) using multi-date satellite imagery WorldView-3, GeoEye-1 and SPOT-7 |
| title_full | Mapping common and glossy buckthorns (Frangula alnus and Rhamnus cathartica) using multi-date satellite imagery WorldView-3, GeoEye-1 and SPOT-7 |
| title_fullStr | Mapping common and glossy buckthorns (Frangula alnus and Rhamnus cathartica) using multi-date satellite imagery WorldView-3, GeoEye-1 and SPOT-7 |
| title_full_unstemmed | Mapping common and glossy buckthorns (Frangula alnus and Rhamnus cathartica) using multi-date satellite imagery WorldView-3, GeoEye-1 and SPOT-7 |
| title_short | Mapping common and glossy buckthorns (Frangula alnus and Rhamnus cathartica) using multi-date satellite imagery WorldView-3, GeoEye-1 and SPOT-7 |
| title_sort | mapping common and glossy buckthorns frangula alnus and rhamnus cathartica using multi date satellite imagery worldview 3 geoeye 1 and spot 7 |
| topic | invasive alien plant species remote sensing buckthorns multi-date satellite imagery machine learning |
| url | http://dx.doi.org/10.1080/17538947.2022.2162136 |
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