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

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dc.contributor.author Lück-Vogel, Melanie
dc.contributor.author Rautenbach, K
dc.contributor.author Adams, J
dc.contributor.author Van Niekerk, Lara
dc.contributor.author Mbolambi, Cikizwa
dc.date.accessioned 2017-06-07T08:04:43Z
dc.date.available 2017-06-07T08:04:43Z
dc.date.issued 2016-05
dc.identifier.citation Lück-Vogel, M., Mbolambi, C., Rautenbach, K. et al. 2016. Vegetation mapping in the St Lucia estuary using very high-resolution multispectral imagery and LiDAR. South African Journal of Botany, vol. 107: 188-199. DOI: 10.1016/j.sajb.2016.04.010 en_US
dc.identifier.issn 0254-6299
dc.identifier.uri DOI: 10.1016/j.sajb.2016.04.010
dc.identifier.uri http://www.sciencedirect.com/science/article/pii/S0254629916303374
dc.identifier.uri http://hdl.handle.net/10204/9254
dc.description Copyright: 2016 Elsevier. Due to copyright restrictions, the attached PDF file contains the post-print version of the article. For access to the published version, kindly consult the publisher's website. en_US
dc.description.abstract This 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 from the WorldView-2, RapidEye, and SPOT-6 sensors in 2 m and 5 m resolution, 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_US
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.relation.ispartofseries Worklist;17334
dc.subject Estuary en_US
dc.subject RapidEye en_US
dc.subject Remote sensing en_US
dc.subject SPOT-6 en_US
dc.subject St Lucia en_US
dc.subject WorldView-2 en_US
dc.title Vegetation mapping in the St Lucia estuary using very high-resolution multispectral imagery and LiDAR en_US
dc.type Article en_US
dc.identifier.apacitation Lück-Vogel, M., Rautenbach, K., Adams, J., Van Niekerk, L., & Mbolambi, C. (2016). Vegetation mapping in the St Lucia estuary using very high-resolution multispectral imagery and LiDAR. http://hdl.handle.net/10204/9254 en_ZA
dc.identifier.chicagocitation Lück-Vogel, Melanie, K Rautenbach, J Adams, Lara Van Niekerk, and Cikizwa Mbolambi "Vegetation mapping in the St Lucia estuary using very high-resolution multispectral imagery and LiDAR." (2016) http://hdl.handle.net/10204/9254 en_ZA
dc.identifier.vancouvercitation Lück-Vogel M, Rautenbach K, Adams J, Van Niekerk L, Mbolambi C. Vegetation mapping in the St Lucia estuary using very high-resolution multispectral imagery and LiDAR. 2016; http://hdl.handle.net/10204/9254. en_ZA
dc.identifier.ris TY - Article AU - Lück-Vogel, Melanie AU - Rautenbach, K AU - Adams, J AU - Van Niekerk, Lara AU - Mbolambi, Cikizwa AB - This 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 from the WorldView-2, RapidEye, and SPOT-6 sensors in 2 m and 5 m resolution, 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. DA - 2016-05 DB - ResearchSpace DP - CSIR KW - Estuary KW - RapidEye KW - Remote sensing KW - SPOT-6 KW - St Lucia KW - WorldView-2 LK - https://researchspace.csir.co.za PY - 2016 SM - 0254-6299 T1 - Vegetation mapping in the St Lucia estuary using very high-resolution multispectral imagery and LiDAR TI - Vegetation mapping in the St Lucia estuary using very high-resolution multispectral imagery and LiDAR UR - http://hdl.handle.net/10204/9254 ER - en_ZA


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