Charles Darwin University

CDU eSpace
Institutional Repository

 
CDU Staff and Student only
 

An evaluation of alternative image classification techniques for the identification and mapping of tropical savanna landscape in northern Australia

Khwaja, Zulifiquar H., Ahmad, Waqar and Williams, R. J. (2003). An evaluation of alternative image classification techniques for the identification and mapping of tropical savanna landscape in northern Australia. Geocarto International,18(1):33-44.

Document type: Journal Article
Citation counts:
Google Scholar Search Google Scholar

Title An evaluation of alternative image classification techniques for the identification and mapping of tropical savanna landscape in northern Australia
Author Khwaja, Zulifiquar H.
Ahmad, Waqar
Williams, R. J.
Journal Name Geocarto International
Publication Date 2003
Volume Number 18
Issue Number 1
ISSN 1010-6049   (check CDU catalogue open catalogue search in new window)
Start Page 33
End Page 44
Total Pages 12
Place of Publication Hong Kong
Publisher Geocarto International Centre
Field of Research 0499 - Other Earth Sciences
0909 - Geomatic Engineering
HERDC Category C1 - Journal Article (DEST)
Abstract Landsat MSS, TM and SPOT XS imageries were used in conjunction with unsupervised, supervised and hybrid classilication techniques to classify land cover types in semi-arid savannas of Mathison Pastoral Station in the Katherine region of northern Australia. Accuracy assessment was based on field data from 246 ground survey sites over a 745-km2 study area. Of 14 land cover classes identified by traditional mapping means, all combinations of imageries and classification techniques differentiated at least seven land cover types. The overall accuracy for these classifications ranged between 43% and 67%. SPOT XS image delivered the best accuracy followed by TM and MSS; unsupervised classification performed better than supervised and hybrid methods. User's and producer's accuracy of individual land units ranged from 0% to 100%. Riparian woodlands, woodland on limestone slopes, shrubland on clay plains, woodland on limestone plains and shadows were the best-mapped classes. The land units that were associated with undulating hills were not mapped accurately. However, incorporation of a digital elevation model (DEM) in a GIS improved the overall accuracy. The user's and producer's accuracy of dominant land cover types were also enhanced. The classification results and the efficacy of the techniques at Mathison were similar to those found for a nearby semi-arid area (Kidman Springs) about 200 km from Mathison. However, the overall accuracy was lower at Mathison than at Kidman Springs. Spectral classification masks were developed from the SPOT XS and TM imageries at Kidman Springs, and were applied to classify SPOT XS and TM imageries at Mathison. Initial results showed that the classification mask could be successfully extrapolated to map dominant land cover types but only with moderate accuracy (50%).
DOI http://dx.doi.org/10.1080/10106040308542261   (check subscription with CDU E-Gateway service for CDU Staff and Students  check subscription with CDU E-Gateway in new window)
 
Versions
Version Filter Type
Access Statistics: 166 Abstract Views  -  Detailed Statistics
Created: Fri, 12 Sep 2008, 08:35:25 CST by Administrator