Vegetation mapping in the St Lucia estuary using very high-resolution multispectral imagery and LiDAR

Simple item page
dc.contributor.authorLuck-Vogel, Melanieen_ZA
dc.contributor.authorMbolambi, C.en_ZA
dc.contributor.authorRautenbach, K.en_ZA
dc.contributor.authorAdams, J.en_ZA
dc.contributor.authorvan Niekerk, L.en_ZA
dc.date.accessioned2022-04-11T12:27:50Z
dc.date.available2022-04-11T12:27:50Z
dc.date.issued2016-05
dc.descriptionThe original publication is available at https://www.sciencedirect.com/journal/south-african-journal-of-botanyen_ZA
dc.descriptionCITATION: Luck-Vogel, M. et al. 2016. Vegetation mapping in the St Lucia estuary using very high-resolution multispectral imageray and LiDAR. South African Journal of Botany, 107:188-199. https://doi.org/10.1016/j.sajb.2016.04.010en_ZA
dc.description.abstractThis paper examines the value of very high-resolution multispectral satellite imagery and LiDAR-derived digital elevation information for classifying estuarine vegetation types. Satellite images used are fromtheWorldView-2, RapidEye, and SPOT-6 sensors in 2mand 5mresolution, respectively, acquired between 2010 and 2014. Ground truthing reference is a GIS-derived vegetation map based on field data from 2008. Supervised maximum likelihood classification produced satisfactory overall accuracies between 64.3% and 77.9% for the SPOT-6 and the WorldView-2 image, respectively,while the RapidEye-based classifications produced overall accuracies between 55.0% and 66.8%. The reasons for the misclassifications are mainly based on the highly dynamic environmental conditions causing discrepancies between the field data and satellite acquisition dates rather than technical issues. Dynamics in water levels and salinity caused rapid change in vegetation communities. Further, weather impacts such as floods and wind events caused water turbidity and led to bias in the reflective properties of the satellite images and thus misclassifications. These results show, however, that the spatial and spectral resolution of modern very high-resolution imagery is sufficient to satisfactory map estuarine vegetation and to monitor small-scale change. They emphasise, however, the importance of synchronisation of ground truthing data with actual image acquisition dates in these highly dynamic environments in order to achieve high classification accuracies. The results also highlight the importance of ancillary data for accurate interpretation of observed classification discrepancies and vegetation dynamics.en_ZA
dc.description.versionPublisher's versionen_ZA
dc.format.extent12 pages : illustrations
dc.identifier.citationLuck-Vogel, M., Mbolambi, C., Rautenback, K., Adams, J. & Van Niekerk, L. 2016. Vegetation mapping in the St Lucia estuary using very high-resolution multispectral imageray and LiDAR. South African Journal of Botany, 107:188-199. https://doi.org/10.1016/j.sajb.2016.04.010en_ZA
dc.identifier.issn0254-6299
dc.identifier.otherdoi:10.1016/j.sajb.2016.04.010en_ZA
dc.identifier.urihttp://hdl.handle.net/10019.1/124439
dc.language.isoenen_ZA
dc.publisherElsevieren_ZA
dc.rights.holderSouth African Association of Botanists -- SAAB
dc.subjectMultispectral imagingen_ZA
dc.subjectRemote sensing imagesen_ZA
dc.subjectImage processing -- Digital techniquesen_ZA
dc.subjectGeographic information systems (GIS)en_ZA
dc.subjectOptical radaren_ZA
dc.subjectLidaren_ZA
dc.subjectRapidEyeen_ZA
dc.subjectSPOT-6en_ZA
dc.subjectMachine learningen_ZA
dc.subjectVegetation mappingen_ZA
dc.titleVegetation mapping in the St Lucia estuary using very high-resolution multispectral imagery and LiDARen_ZA
dc.typeArticleen_ZA
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
luckvogel_vegetation_2016.pdf
Size:
2.24 MB
Format:
Adobe Portable Document Format
Description:
Download article
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Research Articles (Geography and Environmental Studies)