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Figure 3: The first three principal components for the 
vegetation image. Eigenvector values for each PC are 
presented below the associated image. These graphs 
illustrate the weighting of each WV-2 band (from 1 to 8) 
expressed in each principal component. For example, 
Eigenvectors for PC-2 show low levels in band 5 (red) 
and high in the NIR bands 7 and 8, thus land cover 
components with a strong difference between red and 
near infra red, such as actively growing vegetation are 
highlighted in this image. 
2.6 Spectral Separability 
Results of the PCA of the NDVI masked vegetation were 
used in conjunction with strong local knowledge of the 
area to extract reference spectra from the original 
Worldview-2 scene for Buffel grass in various conditions 
(lush, grazed and burnt) and broadly categorised 
surrounding vegetation including mulga, tree and native. 
Between 5 and 10 spectra for each vegetation cover type 
were collected (Figure 4). Their spectral separability was 
examined using a linear discriminate analysis (LDA). 
LDA is a method used to discriminate between groups of 
samples based on a linear transformation of predictor 
variables, which in this case are the eight image bands 
(Rencher, 2002). LDA was cross validated using the 
leave-one-out technique (Rencher, 2002). To examine the 
importance of the additional bands on the classification, 
the LDA was performed for 4 bands (blue, green, red and 
NIR1) as well as the full 8 bands. Outlying samples were 
excluded and spectral-groups were averaged prior image 
classification. 
2.7 Image classification 
Image classification was conducted using the ENVI 4.8 
Target Detection Wizard. Lush Buffel grass was used as 
the target spectra. Background spectra were also 
identified as native, mulga and tree and included buffel 
grass “grazed” and “burnt”. A Minimum Noise 
Fraction Transformation was applied to the imagery. For 
target detection we chose to explore the use of Mixture 
Tuned Matched Filtering (MTMF), a method often 
applied to hyperspectral imagery (Williams and Hunt, 
2002). The MTMF produced two grey scale images: 
Matched Filtering Score (MFS) and Infeasibility Score 
(IS). Areas most likely to be Buffel grass will have a 
high MFS and a low infeasibility score. Therefore, we 
divided MF by Infeasibility to produce a grey scale 
image of spectral likeness to Buffel grass, where higher 
values are the most like Buffel grass. We classified the 
image based on thresholds of this score to produce a map 
that indicates Buffel grass absence (MFS/IS <0.06), 
Buffel grass presence — division 1 (MFS/IS >0.1) and 
Buffel grass presence — division 2 (MFS/IS >0.06, <0.1), 
where division 1 is most like Buffel grass. 
  
10000 - 
BurntBuffel grass 
se Tree Grazed Buffel grass 
9000 -«- Mulga wane Natives 
+ Lush Buffel grass === 
    
  
8000 - 
7000 - 
6000 - 
5000 - 
4000 - 
3000 - 
2000 
1000 - 
  
  
coast blue green yellow red  rededge niri nir2 
  
  
  
Figure 4: Mean, Maximum and Minimum reflectance 
(nm) for each spectral group collected from Worldview-2 
scene obtained 22 Jan 2011 over a 10x10km area 1km 
west of Alice Springs, Australia. Each spectral grouping 
is offset by 1000nm. Spectral groups include Lush Buffel 
grass, Tree, Mulga, Burnt Buffel grass, Grazed Buffel 
grass and Natives. 
2.8 Ground Validation Data 
To validate the classified image, ground data was 
collected on the 20-22 of March 2011, two months after 
image capture. The presence or absence of Buffel grass at 
low (1-34%), medium (35-84%), and high (85-100%) 
densities was recorded at points accessible by roads 
throughout the study area. Each record represented a 
circular area with a diameter of approximately 10 meters. 
This diameter was selected to account for the spatial 
accuracy of the Worldview-2 product (10.16 metres) as 
well as of the Garmin GPS receiver (2 m). 
Approximately 40 records were collected (Figure 2). 
3. RESULTS 
3.1 Spectral Separability of Buffel grass from 
surrounding vegetation using 4 and 8 bands 
Spectra selected showed high separability based on LDA 
for both 4 and 8 band analysis. Predictions based on