for state-wide mapping of cropping history in Queensland,
Australia, is also investigated.
2. METHODS
2.1 Study area
Queensland is the 2" largest state of Australia, covering over
1.7 million square kilometres, and a broad range of climate
zones, topography, vegetation communities, geological
landforms and soils. The study focussed on a Landsat scene
area (path 90 and row 79) covering about 352,456 hectares
(Figure 1).
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sum— Kilometers
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18m 9'0"E 15#0'0"E
Figure 1. Location map of study area. Green areas indicate
cropping regions for summer 2010.
The growing season for summer crops is from December to
April; the growing season for winter crops is from June to
November. The study analysed satellite data for two crop
seasons; summer 2010 (December 2010-April 2011) and winter
2011 (June 2011-November 2011). For summer 2010, 13
Landsat images and for winter 2011, 14 Landsat images were
downloaded from USGS (http://glovis.usgs.gov/).
2.2 Pre-processing of Landsat data
Landsat 5TM and Landsat 7ETM+ data have spatial resolution
of 30m with a 16 day revisit period. The swath width is 185 km
with 7 spectral bands in visible, near infrared, mid infrared and
thermal infrared (NASA, 2011).
Radiometrically calibrated and orthocorrected images were
acquired from USGS and an empirical radiometric correction
was then applied to reduce the combined effects of surface and
atmospheric bidirectional reflectance distribution function
(BRDF)(Danaher, 2002; de Vries et al., 2007). This method
incorporates conversion from radiance to top-of-atmosphere
reflectance with a modified version of the Walthall empirical
BRDF model (Walthall et al., 1985), which was parameterised
using pairs of overlapping ETM- images.
2.3 Cloud masking and image compositing
Automated cloud detection and masking were carried out on
each image using cloud-detection techniques developed at the
Queensland Remote Sensing Centre (Goodwin et al., 2011).
The approach locates anomalies in the reflectance time series
(large differences in reflectance between cloud affected and
predicted non-cloud affected observations) and incorporates
region-growing filters to spatially map the extent of the cloud /
cloud shadow
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
A cloud-free image composite was generated by selecting a
primary image and replacing cloud-affected pixels with cloud-
free pixels from images as close in time to the primary image as
possible. For the summer growing season, an image acquired in
February was selected as the primary image, whereas an image
acquired in September was chosen as the primary image for the
winter growing season.
2.4 Segmentation
The multiresolution segmentation algorithm was applied to
Landsat image composites, to partition the image into objects,
using eCognition Developer 8.64.0 (Trimble, München,
Germany) (Figure 2). The multiresolution segmentation
algorithm is a bottom-up segmentation algorithm, based on a
pairwise region-merging technique. This is an optimization
procedure which, for a given number of image objects,
minimizes the average heterogeneity and maximizes their
respective homogeneity (Trimble, 2010).
Figure 2. Segmenation of Landat image using eCoginition.
Yellow lines indicate segment delineation. The
images is visualised as false colour composite by
projecting near infrared, red and green bands as red,
green and blue, respectively.
The segmentation procedure starts with single image objects of
one pixel and repeatedly merges them in several loops in pairs
to larger units as long as an upper threshold of homogeneity is
not exceeded locally. This homogeneity criterion is defined as a
combination of spectral homogeneity and shape homogeneity.
The ‘scale’ parameter influences this calculation, with higher
values resulting in larger image objects, smaller values in
smaller image objects (Trimble, 2010). Colour and shape
(smoothness and compactness) parameters define the
percentage that the spectral values and the shape of objects,
respectively, will contribute to the homogeneity criterion
(Castillejo-González et al., 2009). This study applied values 90,
0.7, 0.3, 0.5 and 0.5, for scale, colour, shape, smoothness and
compactness, respectively, to generate meaningful image
segments encompassing agricultural fields.
2.5 Support Vector Machine (SVM) classification
Classification of the image objects obtained from the
segmentation procedure was carried out using the SVM
technique. In the first phase, the study attempted to classify the
objects into four major classes: fallow, crop, pasture and woody
vegetation. In the following phase, the potential of SVM to
classify different crop types was examined.
A SVM optimally separates the different classes of data by a
hyperplane (Karatzoglou and Meyer, 2006; Kavzoglu and
Colkesen, 2009; Vapnik, 1998). The points lying on the
boundaries are called support vectors and the middle of the
margin is the optimal separating hyperplane (Meyer, 2001;
Mountrakis et al., 2011) (Figure 3).
