Towards an automated approach for monitoring vegetation green-up dynamics using vehicle dashcams in urban environments
Phenological events are highly sensitive to climatic variation, and temporal phenological shifts have significant impact on ecosystem function. Vegetation in urban environments holds significant value in providing ecosystem services, of which will become increasingly important as urban populations g...
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
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2020
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| Online Access: | https://eprints.nottingham.ac.uk/63790/ |
| _version_ | 1848800057738919936 |
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| author | Crudge, Sally Jane |
| author_facet | Crudge, Sally Jane |
| author_sort | Crudge, Sally Jane |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Phenological events are highly sensitive to climatic variation, and temporal phenological shifts have significant impact on ecosystem function. Vegetation in urban environments holds significant value in providing ecosystem services, of which will become increasingly important as urban populations grow. Insights into vegetation phenological transitions have typically long been monitored through satellite imaging analysis and ground-based field measurements, but these methods are limited by financial costs and coarse resolutions, both spatially and temporally. Despite an increase in the growth of fixed digital camera networks for monitoring vegetation phenology, there still exists a data gap in urban settings. Findings of this study showcased that time series imagery of street level trees in urban environments is obtainable from vehicle dashcams. The YOLOv3 deep learning algorithm demonstrated suitability for automating stages of processing towards deriving a greenness metric. However, further work is required to determine an optimum sized detector training dataset, which also proportionally represents trees across the phenological cycle. Questions remain as to how error caused by scene illuminance variation can be mitigated and as to how full automation from raw data to the final green-up metric can be reached. |
| first_indexed | 2025-11-14T20:45:31Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-63790 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:45:31Z |
| publishDate | 2020 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-637902025-02-28T15:07:04Z https://eprints.nottingham.ac.uk/63790/ Towards an automated approach for monitoring vegetation green-up dynamics using vehicle dashcams in urban environments Crudge, Sally Jane Phenological events are highly sensitive to climatic variation, and temporal phenological shifts have significant impact on ecosystem function. Vegetation in urban environments holds significant value in providing ecosystem services, of which will become increasingly important as urban populations grow. Insights into vegetation phenological transitions have typically long been monitored through satellite imaging analysis and ground-based field measurements, but these methods are limited by financial costs and coarse resolutions, both spatially and temporally. Despite an increase in the growth of fixed digital camera networks for monitoring vegetation phenology, there still exists a data gap in urban settings. Findings of this study showcased that time series imagery of street level trees in urban environments is obtainable from vehicle dashcams. The YOLOv3 deep learning algorithm demonstrated suitability for automating stages of processing towards deriving a greenness metric. However, further work is required to determine an optimum sized detector training dataset, which also proportionally represents trees across the phenological cycle. Questions remain as to how error caused by scene illuminance variation can be mitigated and as to how full automation from raw data to the final green-up metric can be reached. 2020-12-11 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en arr https://eprints.nottingham.ac.uk/63790/1/CRUDGE_MResDissertation.pdf Crudge, Sally Jane (2020) Towards an automated approach for monitoring vegetation green-up dynamics using vehicle dashcams in urban environments. MRes thesis, University of Nottingham. Phenology; Digital cameras; Video recording Equipment and supplies; Trees in cities; |
| spellingShingle | Phenology; Digital cameras; Video recording Equipment and supplies; Trees in cities; Crudge, Sally Jane Towards an automated approach for monitoring vegetation green-up dynamics using vehicle dashcams in urban environments |
| title | Towards an automated approach for monitoring vegetation green-up dynamics using vehicle dashcams in urban environments |
| title_full | Towards an automated approach for monitoring vegetation green-up dynamics using vehicle dashcams in urban environments |
| title_fullStr | Towards an automated approach for monitoring vegetation green-up dynamics using vehicle dashcams in urban environments |
| title_full_unstemmed | Towards an automated approach for monitoring vegetation green-up dynamics using vehicle dashcams in urban environments |
| title_short | Towards an automated approach for monitoring vegetation green-up dynamics using vehicle dashcams in urban environments |
| title_sort | towards an automated approach for monitoring vegetation green-up dynamics using vehicle dashcams in urban environments |
| topic | Phenology; Digital cameras; Video recording Equipment and supplies; Trees in cities; |
| url | https://eprints.nottingham.ac.uk/63790/ |