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Table 3: Accuracy assessment of Buffel grass mapping 
comparing on ground data to Worldview-2 image 
classification of Buffel grass presence and absence 
  
  
  
  
Imagery Omiss Commis Mapping 
AES Pros Total ions sions Accuracy 
ent ent Possible 
Abs 6 2 8 25.0 50 50 
ent 
Pres 15 19 34 44.1 11.7 44.1 
ent 
Tota 21 21 42 
Ground 
1 
Overall Accuracy = (6 + 16)/ 42 *100 = 59.5% 
  
4. DISCUSSION 
Monitoring grass invasion is crucial for effective control. 
Remote sensing presents as a cost effective means to do 
this. However, for species which are spectrally similar to 
their surroundings remote detection can be challenging. 
We have presented a method for detecting lush (highly 
photosynthetic) Buffel grass in a diverse central 
Australian environment using 8-band multispectral 
imagery, Worldview-2. We used NDVI to mask out non- 
photosynthetic land cover and apply Mixed Mixture 
Tuned Matched Filtering to classify the image. Absence 
of Buffel grass is mapped with 50% accuracy, and error 
is mostly attributed to Commission. Presence of Buffel 
grass is mapped with 44.1% accuracy, omission error 
44.1% while the commission error is 11.7%. It is likely 
that some of the error in the classification of Buffel grass 
presence relates to the 2 month time lag between image 
capture and field data collection, during which there was 
considerable rainfall and highly active growth of all 
ephemeral plants. In addition, Buffel grass can be 
observed growing beneath trees particularly Mulga trees 
in the field. This may result in under classification of 
Buffel grass on the imagery. 
To our surprise, linear discriminate analysis of spectra 
using 8-bands and 4-bands (blue, green, red and NIR1) of 
the Worldview-2 imagery does not indicate a benefit in 
using the additional 4 bands. This is probably due to a 
high level of variation within the spectral groups, which 
is particularly observable in the NIR2 and Yellow bands 
(Figure 4). In this instance, we feel that that the 8-bands 
may significantly improve spectra separability, under 
different seasonal conditions. Here, we examined an 
image dominated by high volumes of photosynthetically 
active, green vegetation, and thus Green and Blue bands 
present as the most effective discriminators. We consider 
that had the image been captured during a dry season, 
the Yellow band may have been a significant contributor 
to the effective discrimination of Buffel grass from 
surrounding vegetation. 
ACKNOWLEDGEMENTS 
This research was undertaken as a part of PhD studies at 
the University of Adelaide, Australia, supported by an 
Australian Postgraduate Award and funding from the 
Alinitjara Wiluara Natural Resource Management Board. 
Thanks to the many valued individuals who shared their 
knowledge, especially Peter Latz whose personal tours of 
the Alice Springs landscape where invaluable and my 
PhD supervisors Associate Professors Dr. Megan Lewis 
and Dr. Bertram Ostendorf. This imagery obtained free 
of charge via the Digital Globe 8-Band Challenge 2010. 
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