VEdge_Detector: automated coastal vegetation edge detection using a convolutional neural network.
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| Title: | VEdge_Detector: automated coastal vegetation edge detection using a convolutional neural network. |
|---|---|
| Authors: | Rogers, Martin S. J.1 (AUTHOR) msjr2@cam.ac.uk, Bithell, Mike1 (AUTHOR), Brooks, Susan M.2 (AUTHOR), Spencer, Tom1 (AUTHOR) |
| Source: | International Journal of Remote Sensing. Jul2021, Vol. 42 Issue 13, p4805-4835. 31p. |
| Subjects: | Convolutional neural networks, Remote sensing, Coastal zone management, Detectors, Aerial photographs, Coastal ecosystem health |
| Geographic Terms: | Suffolk (England), Netherlands |
| 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-referenced positional measurements and manually digitized aerial photographs, which were used to ascertain a mean distance error of <6 m (two image pixels) and >84% producer accuracy (PA) 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 year−1 (2010–2020). 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 generalizing 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. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 150283433 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: VEdge_Detector: automated coastal vegetation edge detection using a convolutional neural network. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Rogers%2C+Martin+S%2E+J%2E%22">Rogers, Martin S. J.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> msjr2@cam.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Bithell%2C+Mike%22">Bithell, Mike</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Brooks%2C+Susan+M%2E%22">Brooks, Susan M.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Spencer%2C+Tom%22">Spencer, Tom</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Remote+Sensing%22">International Journal of Remote Sensing</searchLink>. Jul2021, Vol. 42 Issue 13, p4805-4835. 31p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Coastal+zone+management%22">Coastal zone management</searchLink><br /><searchLink fieldCode="DE" term="%22Detectors%22">Detectors</searchLink><br /><searchLink fieldCode="DE" term="%22Aerial+photographs%22">Aerial photographs</searchLink><br /><searchLink fieldCode="DE" term="%22Coastal+ecosystem+health%22">Coastal ecosystem health</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Suffolk+%28England%29%22">Suffolk (England)</searchLink><br /><searchLink fieldCode="DE" term="%22Netherlands%22">Netherlands</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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-referenced positional measurements and manually digitized aerial photographs, which were used to ascertain a mean distance error of <6 m (two image pixels) and >84% producer accuracy (PA) 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 year−1 (2010–2020). 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 generalizing 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/01431161.2021.1897185 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 31 StartPage: 4805 Subjects: – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Coastal zone management Type: general – SubjectFull: Detectors Type: general – SubjectFull: Aerial photographs Type: general – SubjectFull: Coastal ecosystem health Type: general – SubjectFull: Suffolk (England) Type: general – SubjectFull: Netherlands Type: general Titles: – TitleFull: VEdge_Detector: automated coastal vegetation edge detection using a convolutional neural network. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Rogers, Martin S. J. – PersonEntity: Name: NameFull: Bithell, Mike – PersonEntity: Name: NameFull: Brooks, Susan M. – PersonEntity: Name: NameFull: Spencer, Tom IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 01431161 Numbering: – Type: volume Value: 42 – Type: issue Value: 13 Titles: – TitleFull: International Journal of Remote Sensing Type: main |
| ResultId | 1 |
