Wind Erosion in Western Queensland Australia

Chapter 6 – Field Assessments and Model ValidationThe model output was then compared with visual assessments of land erodibility acquiredfrom 11 to 21 September 2006. The time difference between validation data collection andmodel simulations was unavoidable and dictated by access to the study area (September) andLandsat image acquisition dates (October). Evaluation of 5 x 5 km grass cover data from theAussieGRASS pasture growth model (Carter et al., 1996a,b) revealed that mean cover change(a net decrease) over the study area was ~2 % between 1 September and 30 October 2006, sothe effect of this time difference on the validation outcome is not believed to be significant.A point-to-pixel extraction method (ArcGIS 9.2, ESRI) was used to retrieve values frommodel output pixels coincident with the transect point observations (Figure 6.1). Modeloutput values were then plotted against the visual assessments of land erodibility. Thevalidation relies on a comparison of categorical (point observations) and continuous (modeloutput) data, so summary statistics (mean and standard deviation) of the model output werecomputed for each validation data class (highly erodible to not erodible). Model performancewas then evaluated by visual assessment of the distributions of predicted values across thevalidation data classes.6.4 Results and DiscussionFigure 6.4 presents plots of predicted versus observed land erodibility for the five outputscenes. For each scene the rate of change in the equation for the line of best fit can be used asa qualitative indicator of agreement in the data. For the Mt Dot, Bedourie and Cadell scenesthere is agreement in the data, with predicted and observed values increasing from low tohigh. The trend indicates that for the area covered by these scenes (Figure 6.1) AUSLEMcaptures the susceptibility of the landscape to wind erosion. There is a weak positive trend inthe data for the Windorah scene. Data for the Quilpie scene shows poor agreement betweenpredicted and observed values. Here the model over-predicts land erodibility for the lowerodibility class and consistently under-predicts across the moderate and high classes.The variability in predicted land erodibility values for each observation class differs betweenthe classes and output scenes (Figure 6.4). There is significant overlap in the modelpredictions between observation classes, and this was expected in comparing the continuous164