This data set consists of a shapefile/kml of mangrove extent for the lower reaches of the West Alligator River Kakadu National Park, with this generated from airborne Compact Airborne Spectrographic Imager (CASI) hyperspectral data data acquired in 2011. Information on the details of the attribute fields for the KakaduNP_2002.shp are located in this document in the section titled 'file.attributes'.
These data can be freely downloaded and used subject to the CC BY licence. Attribution and citation is required as described at http://www.auscover.org.au/citation . We ask that you send us citations and copies of publications arising from work that use these data.
Point of contact
Aberystwyth University/University of New South Wales
Lucas, R.M., Finlaysson, C.M., Bartolo, R., Rogers, K., Mitchell, A., Woodroffe, C.D., Asbridge, E.F. and Ens, E. (2017). Historical perspectives on the mangroves of Kakadu National Park. Marine and Freshwater Research (in press).
Lucas, R.M., Rowlands, A.R., Niemann, O. and Merton, R. (2004). Hyperspectral Sensors: Past, Present and Future. In: Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data, P.K. Varshney and M.K. Arora (Eds), pp. 11-49.
Spatial and Temporal extents
Typical spatial resolution
The original data were acquired at 1 m spatial resolution
UL 201216.00,8638096.0; LR 208238.0,8649006.0
7th July 2002
Sensor(s) and platform
Compact Airborne Spectrographic Imager (CASI).
Spatial representation type
Spatial reference system
EPSG: 32753, WGS 84 UTM Zone 53S
File names and descriptions
West Alligator River 2002 CASI data (corrected to surface reflectance, %; bands 1-5)
Abstract or Summary
From hyperspectral Compact Airborne Spectrographic imager (CASI) data acquired over the mouth of the West Alligator River in Kakadu National Park, mangroves were mapped by first applying a fine scale spectral difference segmentation within eCognition to the blue, red and near infrared wavelength regions. A maximum likelihood (ML) algorithm within the environment for visualizing images (ENVI) software was then used to classify all segments using training areas associated with mangroves, but also water, mud- flats, sandflats, and coastal woodlands. These were identified through visual interpretation of the imagery. With reference to the CASI data, all segments were examined individually and methodically to determine whether they should be reallocated to a non-mangrove class (e.g., mudflats) or as mangrove. Open woodlands dominated by Eucalyptus species were also able to visually identified within CASI data, although their discrimination was assisted by only considering areas where the underlying LiDAR DTM exceeded 10 m, with these associated with the tidally inundated sections.