mapping aquatic vegetation in a tropical wetland using high spatial resolution multispectral satellite imagery

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ID: 215862
2015
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Abstract
Vegetation plays a key role in the environmental function of wetlands. The Ramsar-listed wetlands of the Magela Creek floodplain in Northern Australia are identified as being at risk from weeds, fire and climate change. In addition, the floodplain is a downstream receiving environment for the Ranger Uranium Mine. Accurate methods for mapping wetland vegetation are required to provide contemporary baselines of annual vegetation dynamics on the floodplain to assist with analysing any potential change during and after minesite rehabilitation. The aim of this study was to develop and test the applicability of geographic object-based image analysis including decision tree classification to classify WorldView-2 imagery and LiDAR-derived ancillary data to map the aquatic vegetation communities of the Magela Creek floodplain. Results of the decision tree classification were compared against a Random Forests classification. The resulting maps showed the 12 major vegetation communities that exist on the Magela Creek floodplain and their distribution for May 2010. The decision tree classification method provided an overall accuracy of 78% which was significantly higher than the overall accuracy of the Random Forests classification (67%). Most of the error in both classifications was associated with confusion between spectrally similar classes dominated by grasses, such as Hymenachne and Pseudoraphis. In addition, the extent of the sedge Eleocharis was under-estimated in both cases. This suggests the method could be useful for mapping wetlands where statistical-based supervised classifications have achieved less than satisfactory results. Based upon the results, the decision tree method will form part of an ongoing operational monitoring program.
Reference Key
whiteside2015remotemapping Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Timothy G. Whiteside;Renée E. Bartolo
Journal Journal of pharmacological sciences
Year 2015
DOI
10.3390/rs70911664
URL
http://www.mdpi.com/2072-4292/7/9/11664
Keywords
remote sensing lidar random forests decision tree classification wetland vegetation

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