Rogers, M.S.J. and Bithell, M. and Brooks, Sue and Spencer, T. (2021) VEdge_Detector: automated coastal vegetation edge detection using a convolutional neural network. International Journal of Remote Sensing 42 (13), pp. 4809-4839. ISSN 0143-1161.
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Abstract
Coastal communities, land covers and intertidal habitats are vulnerable receptors of erosion, flooding or both in combination. This vulnerability is likely to increase with sea level rise and greater storminess over future decadal-scale time periods. The accurate, rapid and wide-scale determination of shoreline position, and its migration, is therefore imperative for future coastal risk adaptation and management. This paper develops and applies an automated tool, VEdge_Detector, to extract the coastal vegetation line from high spatial resolution (Planet's 3 to 5 m) remote sensing imagery, training a very deep convolutional neural network (Holistically-Nested Edge Detection), to predict sequential vegetation line locations on annual to decadal timescales. Red, green and near-infrared (RG-NIR) was found to be the optimum image spectral band combination during neural network training and validation. The VEdge_Detector outputs were compared with vegetation lines derived from ground�45 referenced positional measurements and manually digitised aerial photographs, which were used to ascertain a mean distance error of < 6 m (two image pixels) and > 84% producer accuracy at six out of the seven sites. Extracting vegetation lines from Planet imagery of the rapidly retreating cliffed coastline at Covehithe, Suffolk, United Kingdom has identified a landward retreat rate > 3 m a-1 (2010 to 2020). Plausible vegetation lines were successfully retrieved from images in The Netherlands and Australia, which were not used to train the neural network; although significant areas of exposed rocky coastline proved to be less well recovered by VEdge_Detector. The method therefore promises the possibility of generalising to estimate retreat of sandy coastlines from Planet imagery in otherwise data-poor areas, which lack ground referenced measurements. Vegetation line outputs derived from VEdge_Detector are produced rapidly and efficiently compared to more traditional non-automated methods. These outputs also have the potential to inform upon a range of future coastal risk management decisions, incorporating future shoreline change.
Metadata
| Item Type: | Article |
|---|---|
| Additional Information: | This is an Accepted Manuscript of an article published by Taylor & Francis, available online at the link above. |
| Keyword(s) / Subject(s): | Machine learning, automated edge detection, coastal vegetation, satellite imagery, shoreline change analysis. |
| School: | Birkbeck Faculties and Schools > Faculty of Humanities and Social Sciences > School of Social Sciences |
| Depositing User: | Sue Brooks |
| Date Deposited: | 10 May 2021 12:47 |
| Last Modified: | 02 Aug 2023 18:09 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/44203 |
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