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Monitoring and mapping the critically endangered Clanwilliam cedar using aerial imagery and deep learning
The critically endangered Clanwilliam cedar, Widdringtonia wallichii, is an iconic tree species endemic to the Cederberg mountains in the Fynbos Biome. Consistent declines in its populations have been noted across its range primarily due to the impact of fire and climate change. Mapping the occurren...
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2022
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| _version_ | 1867613159484817408 |
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| access_status_str | Open Access |
| author | Hadebe, Blessings |
| author2 | Britz, Stefan |
| author_browse | Britz, Stefan Hadebe, Blessings |
| author_facet | Britz, Stefan Hadebe, Blessings |
| author_sort | Hadebe, Blessings |
| collection | Thesis |
| description | The critically endangered Clanwilliam cedar, Widdringtonia wallichii, is an iconic tree species endemic to the Cederberg mountains in the Fynbos Biome. Consistent declines in its populations have been noted across its range primarily due to the impact of fire and climate change. Mapping the occurrences of this species over its range is key to the monitoring of surviving individuals and is important for the management of biodiversity in the region. Recent efforts have focused on the use of freely available Google EarthTM imagery to manually map the species across its global native distribution. This study advances this work by proposing an approach for automating the process of tree detection using deep-learning. The approach involves using sets of high-resolution red, green, blue (RGB) imagery to train artificial neural networks for the task of tree-crown detection. Additional models are trained on colour-infrared imagery, since live vegetation has a red tone on the near-infrared (NIR) spectrum. Preliminary results show that using an intersection-over-union threshold of 0.5 yields an average tree-crown recall of 0.67 with a precision of 0.53, and that the addition of the NIR spectral band does not result in improved performance. The viability of using this approach to regularly update maps of the Clanwilliam Cedar and monitor its population trends in the Cederberg is assessed. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/35622 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:31:43.046Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/35622 Monitoring and mapping the critically endangered Clanwilliam cedar using aerial imagery and deep learning Hadebe, Blessings Britz, Stefan Moncrieff, Glenn Statistical Sciences The critically endangered Clanwilliam cedar, Widdringtonia wallichii, is an iconic tree species endemic to the Cederberg mountains in the Fynbos Biome. Consistent declines in its populations have been noted across its range primarily due to the impact of fire and climate change. Mapping the occurrences of this species over its range is key to the monitoring of surviving individuals and is important for the management of biodiversity in the region. Recent efforts have focused on the use of freely available Google EarthTM imagery to manually map the species across its global native distribution. This study advances this work by proposing an approach for automating the process of tree detection using deep-learning. The approach involves using sets of high-resolution red, green, blue (RGB) imagery to train artificial neural networks for the task of tree-crown detection. Additional models are trained on colour-infrared imagery, since live vegetation has a red tone on the near-infrared (NIR) spectrum. Preliminary results show that using an intersection-over-union threshold of 0.5 yields an average tree-crown recall of 0.67 with a precision of 0.53, and that the addition of the NIR spectral band does not result in improved performance. The viability of using this approach to regularly update maps of the Clanwilliam Cedar and monitor its population trends in the Cederberg is assessed. 2022-01-31T09:06:13Z 2022-01-31T09:06:13Z 2021 2022-01-26T13:29:10Z Master Thesis Masters MSc http://hdl.handle.net/11427/35622 eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | Statistical Sciences Hadebe, Blessings Monitoring and mapping the critically endangered Clanwilliam cedar using aerial imagery and deep learning |
| thesis_degree_str | Master's |
| title | Monitoring and mapping the critically endangered Clanwilliam cedar using aerial imagery and deep learning |
| title_full | Monitoring and mapping the critically endangered Clanwilliam cedar using aerial imagery and deep learning |
| title_fullStr | Monitoring and mapping the critically endangered Clanwilliam cedar using aerial imagery and deep learning |
| title_full_unstemmed | Monitoring and mapping the critically endangered Clanwilliam cedar using aerial imagery and deep learning |
| title_short | Monitoring and mapping the critically endangered Clanwilliam cedar using aerial imagery and deep learning |
| title_sort | monitoring and mapping the critically endangered clanwilliam cedar using aerial imagery and deep learning |
| topic | Statistical Sciences |
| url | http://hdl.handle.net/11427/35622 |
| work_keys_str_mv | AT hadebeblessings monitoringandmappingthecriticallyendangeredclanwilliamcedarusingaerialimageryanddeeplearning |