Satellite-based salt marsh elevation, vegetation height, and species composition mapping using the superspectral WorldView-3 imagery. Issue 17 (2nd September 2018)
- Record Type:
- Journal Article
- Title:
- Satellite-based salt marsh elevation, vegetation height, and species composition mapping using the superspectral WorldView-3 imagery. Issue 17 (2nd September 2018)
- Main Title:
- Satellite-based salt marsh elevation, vegetation height, and species composition mapping using the superspectral WorldView-3 imagery
- Authors:
- Collin, Antoine
Lambert, Natasha
Etienne, Samuel - Abstract:
- ABSTRACT: Very high resolution (VHR) space-borne data are needed to finely and continuously map salt marshes. The WorldView-3 (WV-3) sensor leverages one panchromatic, eight optical, and eight shortwave infrared (SWIR) bands at 0.31, 1.24, and 7.5 m pixel size, respectively. Although eight optical bands have been previously pansharpened, no attempt to use the 16-band superspectral data set at VHR (0.31 m) has been yet reviewed. Here, we propose to reliably pan-sharpen the 16 WV-3 predictors so as to model (artificial neural network, ANN) salt marsh elevation and vegetation height and classify species composition at VHR using calibration/validation handheld vegetation height, airborne lidar elevation, and drone blue-green-red (BGR) responses. Three models have been created over a megatidal bay (Beaussais Bay, Brittany, France) provided with mud flats, salt marshes, and polders. VHR-screened WV-3 bands very satisfactorily predicted salt marsh elevation and vegetation height responses ( r = 0.86, R 2 = 0.71, root mean square error (RMSE) = 0.33 m and r = 0.88, R 2 = 0.77, RMSE = 5.72 cm, respectively). The WV-3 superspectral data set outperformed the eight-band multispectral and four-band traditional data sets to classify 15 salt marsh habitats (OA = 95.47, 82.33, and 69.27%, respectively). Adding WV-3-based salt marsh elevation and vegetation height augmented the 15-class classification of the superspectral and traditional data sets (OA = 97.60 and 77.47%, respectively),ABSTRACT: Very high resolution (VHR) space-borne data are needed to finely and continuously map salt marshes. The WorldView-3 (WV-3) sensor leverages one panchromatic, eight optical, and eight shortwave infrared (SWIR) bands at 0.31, 1.24, and 7.5 m pixel size, respectively. Although eight optical bands have been previously pansharpened, no attempt to use the 16-band superspectral data set at VHR (0.31 m) has been yet reviewed. Here, we propose to reliably pan-sharpen the 16 WV-3 predictors so as to model (artificial neural network, ANN) salt marsh elevation and vegetation height and classify species composition at VHR using calibration/validation handheld vegetation height, airborne lidar elevation, and drone blue-green-red (BGR) responses. Three models have been created over a megatidal bay (Beaussais Bay, Brittany, France) provided with mud flats, salt marshes, and polders. VHR-screened WV-3 bands very satisfactorily predicted salt marsh elevation and vegetation height responses ( r = 0.86, R 2 = 0.71, root mean square error (RMSE) = 0.33 m and r = 0.88, R 2 = 0.77, RMSE = 5.72 cm, respectively). The WV-3 superspectral data set outperformed the eight-band multispectral and four-band traditional data sets to classify 15 salt marsh habitats (OA = 95.47, 82.33, and 69.27%, respectively). Adding WV-3-based salt marsh elevation and vegetation height augmented the 15-class classification of the superspectral and traditional data sets (OA = 97.60 and 77.47%, respectively), but not for the multispectral one (OA = 81.93%). … (more)
- Is Part Of:
- International journal of remote sensing. Volume 39:Issue 17(2018)
- Journal:
- International journal of remote sensing
- Issue:
- Volume 39:Issue 17(2018)
- Issue Display:
- Volume 39, Issue 17 (2018)
- Year:
- 2018
- Volume:
- 39
- Issue:
- 17
- Issue Sort Value:
- 2018-0039-0017-0000
- Page Start:
- 5619
- Page End:
- 5637
- Publication Date:
- 2018-09-02
- Subjects:
- Remote sensing -- Periodicals
Télédétection -- Périodiques
621.3678 - Journal URLs:
- http://www.tandfonline.com/toc/tres20/current ↗
http://www.tandfonline.com/ ↗ - DOI:
- 10.1080/01431161.2018.1466084 ↗
- Languages:
- English
- ISSNs:
- 0143-1161
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4542.528000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 7265.xml