that land and preserve the productive capacity of that land for 
future generations (Figure 7). 
  
(See http://www.derm.qld.gov.au/land/planning/strategic- 
cropping/index.html ). 
s E = 
P d & i Queenstand| 
wil Ë *% 
S Loves 
oY 1500's 
    
pereat 
Strategic Cropping Land Area: Summer 2011 e iometers 
  
  
  
Sc0'E 150°00*E 155-D0"E 
Figure 7. Cropping areas detected by this study within the 
Strategic Cropping Land trigger map during summer 
2011, as indicated by green patches in the map. 
Overlaid is the footprints of 35 Landsat scenes that 
cover the study area. 
The legislation aims to restrict developmental activities on 
cropping areas, which have been cultivated at least three times 
between 1 January 1999 to 31 December 2010 and that meet 
on-ground assessment against the site level SCL criteria. This 
has generated a demand for automated large area crop 
classification. 
The trigger map indicates the location of potential SCL in 
Queensland and is based on soil, land and climate information. 
The SCL area extends to 42-million ha, which is almost one- 
quarter of Queensland and requires 35 Landsat scenes to cover 
the entire area. SVM models were applied on these 35 sets of 
multi-temporal Landsat data to demarcate areas cropped during 
summer 2010 (Figure 7) and winter 2011 (Figure 8) 
  
Strategic Cropping Land Area: Winter 2011 
  
  
  
Figure 8. Cropping areas detected by this study within the 
Strategic Cropping Land trigger map during winter 
2011, as indicated by green patches in the map. 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
4. CONCLUSIONS 
Results of this study demonstrated the distinctive advantage of 
object-based methods over pixel-based methods, considering 
the complexities of within-field spectral heterogeneity and 
spectral mixing. This is well supported by several other studies 
(Castillejo-González et al., 2009; Pefía-Barragán et al., 2011). 
This investigation further combined the superiority of object- 
based data with a powerful non-parametric SVM classifier 
(Boser et al., 1992; Dixon and Candade, 2007; Huang et al., 
2002) to perform automated large-area broadacre crop mapping. 
Comparative analysis clearly revealed that substantially higher 
overall classification accuracy (9596) was observed with the 
object-based SVM, compared with that of traditional pixel- 
based classification (8996). Object-based classification also 
resulted in neater and speckle-free images. Further, object- 
based SVM models were used to classify different broadacre 
crop types for summer and winter seasons. Influence of 
different shape, textural and spectral variables and their weights 
on crop-mapping accuracy was also examined. Temporal 
change in the spectral characteristics, specifically through 
vegetation indices derived from multi-temporal Landsat data, 
was found to be the most critical information that aftected the 
accuracy of classification using SVM models. However, use of 
these variables was constrained by the multi-temporal data 
availability and cloud cover. 
ACKNOWLEDGMENTS 
Authors wish to thank United States Geologic Survey (USGS) 
for providing Landsat images used for this study. 
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