Mapping savannah woody vegetation at the species level with multispecral drone and hyperspectral EnMAP data
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909257214984192 |
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| author | Karakizi, Christina Okujeni, Akpona Sofikiti, Eleni Tsironis, Vasileios Psalta, Athina Karantzalos, Konstantinos Hostert, Patrick Symeonakis, Elias |
| author_facet | Karakizi, Christina Okujeni, Akpona Sofikiti, Eleni Tsironis, Vasileios Psalta, Athina Karantzalos, Konstantinos Hostert, Patrick Symeonakis, Elias |
| contents | Savannahs are vital ecosystems whose sustainability is endangered by the spread of woody plants. This research targets the accurate mapping of fractional woody cover (FWC) at the species level in a South African savannah, using EnMAP hyperspectral data. Field annotations were combined with very high-resolution multispectral drone data to produce land cover maps that included three woody species. The high-resolution labelled maps were then used to generate FWC samples for each woody species class at the 30-m spatial resolution of EnMAP. Four machine learning regression algorithms were tested for FWC mapping on dry season EnMAP imagery. The contribution of multitemporal information was also assessed by incorporating as additional regression features, spectro-temporal metrics from Sentinel-2 data of both the dry and wet seasons. The results demonstrated the suitability of our approach for accurately mapping FWC at the species level. The highest accuracy rates achieved from the combined EnMAP and Sentinel-2 experiments highlighted their synergistic potential for species-level vegetation mapping. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_11404 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mapping savannah woody vegetation at the species level with multispecral drone and hyperspectral EnMAP data Karakizi, Christina Okujeni, Akpona Sofikiti, Eleni Tsironis, Vasileios Psalta, Athina Karantzalos, Konstantinos Hostert, Patrick Symeonakis, Elias Machine Learning Savannahs are vital ecosystems whose sustainability is endangered by the spread of woody plants. This research targets the accurate mapping of fractional woody cover (FWC) at the species level in a South African savannah, using EnMAP hyperspectral data. Field annotations were combined with very high-resolution multispectral drone data to produce land cover maps that included three woody species. The high-resolution labelled maps were then used to generate FWC samples for each woody species class at the 30-m spatial resolution of EnMAP. Four machine learning regression algorithms were tested for FWC mapping on dry season EnMAP imagery. The contribution of multitemporal information was also assessed by incorporating as additional regression features, spectro-temporal metrics from Sentinel-2 data of both the dry and wet seasons. The results demonstrated the suitability of our approach for accurately mapping FWC at the species level. The highest accuracy rates achieved from the combined EnMAP and Sentinel-2 experiments highlighted their synergistic potential for species-level vegetation mapping. |
| title | Mapping savannah woody vegetation at the species level with multispecral drone and hyperspectral EnMAP data |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.11404 |