Northern Territory University, Darwin
More specifically, the invasive weed, Mimosa pigra and native
vegetation in the Oenpelli floodplain were assessed.
The review of Landsat TM's ability to map land cover types led
to the image classification comprising bands 2, 3, 4 and 5. The
classification process involved the hybrid method. Two algorithms,
the minimum distance to means (MDM) and the maximum likelihood
classifiers (MLC) were used to classify herbaceous vegetation and
bare clay soil areas.
The effectiveness of chemical control was analysed. The
distribution of Mimosa infestations inhabiting the floodplain were
destroyed after the application of herbicides. This allowed native
vegetation to slowly recuperate in the flood plains. Thus, the
monitoring of these vegetation changes were observed. The area of
change of native floodplain vegetation and bare soil was estimated
in relation to the rate of Mimosa expansion.
Actual field data could not be collected. This was compensated
by visual interpretations of the final land cover classification
maps made by an expert who had field knowledge of the study site.
In addition, the accuracy of both the MDM and MLC algorithms were
compared by error matrices.
The accuracy assessments for six of the eight images were highly
acceptable, with overall mapping assessments greater than 85'7o.
Accuracy assessment for both the 1994 images were substantially
lower. Overall mapping accuracies of 79@'o and 77@'o were
calculated for the MDM classification and MLC, respectively.
Sources of errors were identified, primarily due to TM's inability
to distinguish between floodplain vegetation types. However,
analysis of the results concluded that Landsat TM has high
potential for meeting the current and future needs in relation to
mapping and monitoring land cover types.
Professor Greg Hill, Northern Territory University
Dr Waqar Ahmad, Northern Territory University
Dr Garry Cook, CSIRO W&E