A combination of Sentinel-1 RADAR and Sentinel-2 multispectral data improves classification of morphologically similar savanna woody plants
The co-existence of diverse plant forms in densely vegetated savanna environments presents a challenge when mapping species diversity using single remotely sensed data type that carries either optical or structural information. In the present study, Sentinel-1 RADAR and Sentinel-2 multispectral data...
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| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | European Journal of Remote Sensing |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/22797254.2022.2083984 |
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| author | Emmanuel Fundisi Solomon G. Tesfamichael Fethi Ahmed |
| author_facet | Emmanuel Fundisi Solomon G. Tesfamichael Fethi Ahmed |
| author_sort | Emmanuel Fundisi |
| collection | DOAJ |
| description | The co-existence of diverse plant forms in densely vegetated savanna environments presents a challenge when mapping species diversity using single remotely sensed data type that carries either optical or structural information. In the present study, Sentinel-1 RADAR and Sentinel-2 multispectral data were combined to classify morphologically similar woody plant species (n =27) and three coexisting land cover types using Deep Neural Network (DNN). The fused image recorded a higher overall classification accuracy (76%) than the sole use of Sentinel-2 (72%) and Sentinel-1 RADAR data (71%). Slightly more species (15) recorded accuracies exceeding 75% using fused image compared to Sentinel-2 and Sentinel-1 data (13 species >75%). Analysis of relative band contributions resulted in high importance from Sentinel-1 C-band ratio of VH/VV polarization (8.6%) as well as a select Sentinel-2 bands (Near infrared (9.86%), Shortwave (9.5%), and Vegetation red edge (8%)). Parallel to continual efforts to improve the accuracies of fused RADAR–optical data, the services of such data for regional-scale applications should be explored to inform timely biodiversity assessments. |
| format | Article |
| id | doaj-art-bd8aa33aefb546ee9d3affda56251250 |
| institution | DOAJ |
| issn | 2279-7254 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj-art-bd8aa33aefb546ee9d3affda562512502025-08-20T03:05:26ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542022-12-0155137238710.1080/22797254.2022.2083984A combination of Sentinel-1 RADAR and Sentinel-2 multispectral data improves classification of morphologically similar savanna woody plantsEmmanuel Fundisi0Solomon G. Tesfamichael1Fethi Ahmed2Department of Geography, Environmental Management and Energy Studies, University of Johannesburg, Johannesburg, South AfricaDepartment of Geography, Environmental Management and Energy Studies, University of Johannesburg, Johannesburg, South AfricaGeography, Archaeology and Environmental Studies, University of Witwatersrand, Johannesburg, South AfricaThe co-existence of diverse plant forms in densely vegetated savanna environments presents a challenge when mapping species diversity using single remotely sensed data type that carries either optical or structural information. In the present study, Sentinel-1 RADAR and Sentinel-2 multispectral data were combined to classify morphologically similar woody plant species (n =27) and three coexisting land cover types using Deep Neural Network (DNN). The fused image recorded a higher overall classification accuracy (76%) than the sole use of Sentinel-2 (72%) and Sentinel-1 RADAR data (71%). Slightly more species (15) recorded accuracies exceeding 75% using fused image compared to Sentinel-2 and Sentinel-1 data (13 species >75%). Analysis of relative band contributions resulted in high importance from Sentinel-1 C-band ratio of VH/VV polarization (8.6%) as well as a select Sentinel-2 bands (Near infrared (9.86%), Shortwave (9.5%), and Vegetation red edge (8%)). Parallel to continual efforts to improve the accuracies of fused RADAR–optical data, the services of such data for regional-scale applications should be explored to inform timely biodiversity assessments.https://www.tandfonline.com/doi/10.1080/22797254.2022.2083984Savannawoody plant species diversitydata fusionSentinel-1 C-bandSentinel-2Deep Neural Network algorithm |
| spellingShingle | Emmanuel Fundisi Solomon G. Tesfamichael Fethi Ahmed A combination of Sentinel-1 RADAR and Sentinel-2 multispectral data improves classification of morphologically similar savanna woody plants European Journal of Remote Sensing Savanna woody plant species diversity data fusion Sentinel-1 C-band Sentinel-2 Deep Neural Network algorithm |
| title | A combination of Sentinel-1 RADAR and Sentinel-2 multispectral data improves classification of morphologically similar savanna woody plants |
| title_full | A combination of Sentinel-1 RADAR and Sentinel-2 multispectral data improves classification of morphologically similar savanna woody plants |
| title_fullStr | A combination of Sentinel-1 RADAR and Sentinel-2 multispectral data improves classification of morphologically similar savanna woody plants |
| title_full_unstemmed | A combination of Sentinel-1 RADAR and Sentinel-2 multispectral data improves classification of morphologically similar savanna woody plants |
| title_short | A combination of Sentinel-1 RADAR and Sentinel-2 multispectral data improves classification of morphologically similar savanna woody plants |
| title_sort | combination of sentinel 1 radar and sentinel 2 multispectral data improves classification of morphologically similar savanna woody plants |
| topic | Savanna woody plant species diversity data fusion Sentinel-1 C-band Sentinel-2 Deep Neural Network algorithm |
| url | https://www.tandfonline.com/doi/10.1080/22797254.2022.2083984 |
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