International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
  
       
M Gully sensitive area 
No-gully area 
  
  
  
  
  
   
  
   
  
  
  
  
  
  
  
  
   
   
  
  
  
  
  
  
   
   
  
  
  
   
   
   
     
areas. 
Figure 7. Model prediction vs. actual mapp 
areas; however, prediction within high prob 
needs to be refined. 
Figure 6. Spatial distribution of gu/ly sensitive and no-gully 
  
iiit 
ed gullies. Dark 
green is no-gully area. Gullies rarely occur on low probability 
ability areas still 
  
LL] Major subcatchment 
Gully presence 
  
Very low 
  
  
  
Figure 8. Gully presence map. Extensive gullying in several 
areas mainly in the Upper Burdekin, northern Suttor and Bowen 
Broken Bogie subcatchments. 
4. DISCUSSION AND CONCLUSIONS 
This study provides a metholodogy that could be applied to 
extensive areas where the mapping of all individual gullies is 
not feasible. For such large areas, it is important to first 
acknowledge the importance of identifying areas where gullies 
are less likely to occur. The no-gully area in the Burdekin 
covered an area of more than 61,000 km - this is an area twice 
the size of Belgium that could now be omitted from further 
analysis. Identifying the no-gully area allowed better targeting 
of gullied areas for mapping and could be used in the future for 
gully modelling as well as policy-making and land management 
purposes. 
Analysis of observed cells in the gully presence map against 
extents of no-gully areas showed that the latter are in fact gully 
free. In the predictive model, most of the uncertainty still 
remains in the high probability areas. Although these cover only 
about 20% of the Burdekin, gullies only occur at a fraction of 
this area. Consequently, we can assume the low probability 
prediction to be relatively accurate, yet further refinement of the 
prediction ability is needed before the same could be assumed 
for the high probability areas. 
With the increasing availability of high-resolution data it is now 
easier to visually identify gullies. Google Earth has proven to be 
a reliable platform for mapping gullies as it holds high- 
resolution data while allowing fast browsing coupled with 
digitization tools that can be easily exported back to the local 
GIS. Nevertheless, similarly to previous works (Eustace et al, 
2011; Prosser et al., 2002), this study shows that remote sensing 
product such as imagery or DEM products can only go so far 
    
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