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

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luckvogel_vegetation_2016.pdf(2.24 MB)
Date
2016-05
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Publisher
Elsevier
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 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.
Description
The original publication is available at https://www.sciencedirect.com/journal/south-african-journal-of-botany
CITATION: 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.2016.04.010
Keywords
Multispectral imaging, Remote sensing images, Image processing -- Digital techniques, Geographic information systems (GIS), Optical radar, Lidar, RapidEye, SPOT-6, Machine learning, Vegetation mapping
Citation
Luck-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.2016.04.010
URI
http://hdl.handle.net/10019.1/124439
Collections
Research Articles (Geography and Environmental Studies)