The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
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polygon despite the classification of only a fraction of a tree CrownSize = 12 080-25 ^2e^°' m6{TreeHeighl) 
crown (Figure 3b). 
3.5 Accuracy assessment 
To evaluate accuracy, ground truth tree types and crown maps 
for six plots (Appendix A), each with an area of 50m by 50m, 
were acquired in the field. Existing aerial photography was used 
to design the sample plots and tree information was collected by 
the field survey. There are a total of 61 trees in the six plots. An 
error matrix was generated from the field sample data 
corresponding to the fused results. 
4. RESULTS AND DISCUSSION 
Figure 4 illustrates the close correspondence {r 2 = 0.87, 95% 
Confidence level, standard error = 0.67m) between tree mean 
heights derived from both field measurements and lidar data 
was observed. The comparison suggested, however, that the 
mean height was more reliably estimated for trees with large 
and relatively flat crown areas than those were small and 
pointed crowns. 
Figure 4. Relationship between individual tree mean heights as 
estimated in the field and from lidar. 
Results from previous studies have shown that isolating 
deciduous tree species in lidar data is difficult due to their 
complex structure (Chen et al.,2006). However, the use of the 
marker-controlled watershed segmentation algorithm with the 
lidar data achieved a satisfactory result for eucalypt trees. The 
success of the tree crown extraction algorithm in old growth 
areas was higher than in more juvenile areas where the crowns 
are more scattered. It was also observed that large crowns were 
better delineated than small ones. 
To study the correlation between tree height and crown size, 
tree height and crown size were measured from the crown 
segments. Crown size is the average crown diameter and was 
derived from the shape file generated area and algorithm for the 
relationship of the crown area and radius. From the tabular 
dataset, the trees were randomly sampled over the whole study 
area and the sample size was 100 trees. It was found that crown 
size has larger variability when a tree height is higher, which 
will contradict the assumption of homoscadasticity if a linear 
model is fitted. To avoid this issue, a parameterised non-linear 
model was fitted: 
Using Equation (1) a fitted line was generated through the 
scatter plot as illustrated in Figure 5. In the regression analysis, 
the relationship between crown size and tree height followed a 
non-linear curve and mean squared error is 0.52, implied a low 
level correlation. 
Figure 5. The relationship between crown size and tree height. 
The application of the maximum likelihood classification 
technique involving the original four-band data set led to low 
classification accuracy. The main reason was the confusion 
within classes due to the noise effects such as shadows, 
background vegetation and lack of information. An 
improvement of the classification was achieved with the 
integration of lidar derived height and intensity data. 
Additionally, eight new layers generated from multispectral 
data substantially reduced interclass confusion compared to the 
original four-band data. The class separability was also 
improved for particular tree species by increasing the gap 
between the class means and reducing the class variability. The 
use of the tree polygons derived from watershed segmentation 
markedly improved the classification results through the 
assignment of the most frequent pixel to the particular tree 
polygon as shown in Figure 3b. In this way, only one class 
occupied the entire polygon despite the classification of only a 
fraction of a tree crown. 
The results of the accuracy assessment are summarised in Table 
2. The accuracy was assessed by comparing the classified tree 
crown with the true tree information derived from field survey. 
An average classification accuracy of 86 percent was achieved 
and this procedure outperformed on average the original four- 
band maximum likelihood classification by 23 percent. The 
separation between the classes Black Box and Grey Box was 
improved with the fused 10 layer datasets. This is mainly due to 
the incorporation of the lidar data and the four principal 
components of the mutispectral imagery, into the classification. 
Classification types 
Classification accuracy (%) 
Only multi spectral (4 layer) 
63 
Fused multi spectral and lidar 
data (10 layer) 
86 
Table 2 Comparative accuracy assessments.