## 4 Discussion  
### Map Accuracy  
The high resolution binary tree cover map was found to have an accuracy of 88%, compared to lidar derived products and visually interpreted high resolution imagery. Visually comparing the map to the source SPOT5 imagery shows that it does a remarkably good job delineating the distribution of tree cover across the wide variety of landscapes present across NSW (Figures 6 and 7).  
Omission of trees was more common in arid shrublands and semi-arid woodlands, where the trees are shorter, have thinner canopies and are spaced further apart. Commission errors (false positive trees) were more common in areas containing tall shrubs, such as saline wetlands, heatlands, and dry sclerophyll forests (shrubby). Revealing this distribution of errors was only possible through the careful sampling of validation data, and the use of lidar derived products related to tree canopy density and height.  
Figure 6: The final 5 m foliage projective cover (FPC) map for New South Wales, Australia. The locations of the examples shown in Figure 7 are labelled (**A–D**).  
Figure 7: Examples of the final 5 m foliage projective cover (FPC) map compared to pan-sharpened false colour SPOT5 imagery. (**A**) Remnant trees in a cleared agricultural landscape; (**B**) Semi-arid open woodland; (**C**) Dry sclerophyll forest with natural and human controlled clearing; (**D**) Dry sclerophyll forest fragmented by cleared agricultural land. Example locations and FPC colour bar are shown in Figure 6.  
The probability scores over wetland areas were highly variable due to fluctuations in inundation and vegetation growth, making it difficult to apply appropriate sub-tile thresholds. The lower accuracies in arid shrubland vegetation types were caused by difficulties in detecting scattered trees with open canopies.  
Over half the errors were identified as being close to the edges of tree pixels. These errors may be due partly to the difficulty in mapping thinner canopies around the edges of forest, but may have been caused by differences in georeferencing between the satellite and lidar data, or the influence of tree shadows. The size of regions containing errors was also found to have an effect, where omission and commission were more likely in smaller regions. This indicates that the SPOT5 tree cover map has more errors in landscapes with fragmented, patchy vegetation, and in clearings within large continuous regions of trees.  
The SPOT5 FPC map showed an excellent agreement with the previous Landsat based map, when both maps were degraded to 100 m pixels. It also correlated very strongly with field measurements of FPC (overstorey and midstorey), made at 100 m diameter sites. These results confirm that the inclusion of multi-temporal data in the method reduced the influence of ground cover on the FPC map. Comparing to these field measurements, the SPOT5 tree cover map had a RMSE of around 9%, which is very similar to that observed in the QLD Landsat FPC model [36]. Future work will focus on acquiring further field and lidar measurements of FPC to allow further analysis of these errors. Deriving FPC from airborne lidar by correcting for bias due to non-foliage canopy elements and varying survey-sensor parameters is the subject of ongoing research.