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Title: Mapping salt-marsh land cover vegetation using high-spatial and hyper-spectral satellite data to assist wetland inventory
Contributor(s): Kumar, Lalit (author)orcid ; Sinha, Priyakant (author)
Publication Date: 2014
DOI: 10.1080/15481603.2014.947838
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Abstract: Information on wetland condition can be used for various decision-making processes for better management of this vital resource. Salt marshes are complex ecosystems that are not well mapped and understood. This research was conducted to assess the potential of high-spatial and high-spectral resolution satellite data to map and monitor salt-marsh vegetation communities of Micalo Island of New South Wales, Australia. The aim of the study was to determine whether different salt-marsh vegetation species could be differentiated using high-spectral and high-spatial resolution imagery and whether these could be linked to wetland condition. To compare sensor capabilities in discriminating salt-marsh vegetation, high-spatial data sets from Quickbird and highspectral data sets from Hyperion were used. A hybrid unsupervised and supervised classification procedure was used to assess the wetland mapping potential of the Quickbird and Hyperion data. The supervised classification results had greater overall and within-class accuracies and showed greater promise. Most of the vegetation species were identified and mapped correctly. One area of concern was the misclassification of 'Sporobolus' into grass categories while using Quickbird imagery, mainly where the 'Sporobolus' was tall and dry. They look very similar to the tall reedy grass. The mapping results can be useful in establishing baseline information for subsequent studies involving change detection of salt-marsh ecosystems.
Publication Type: Journal Article
Source of Publication: GIScience and Remote Sensing, 51(5), p. 483-497
Publisher: Taylor & Francis
Place of Publication: United Kingdom
ISSN: 1943-7226
Field of Research (FOR): 050206 Environmental Monitoring
090903 Geospatial Information Systems
090905 Photogrammetry and Remote Sensing
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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