The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
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and the multispectral imagery (in 2004). These were the only 
available images for this research project; therefore we had to 
compromise on this issue. The study area is a slow growing 
forest and within this time frame it had not seen any abrupt 
changes such as tree damage by bush fire or logging. However, 
some temporal effects were found, due mainly to the natural 
growth of trees, which is always a challenge to address in high 
resolution data fusion. 
3.2 Watershed segmentation 
The lidar derived nDSM represents the tree canopies of the 
forest. Single and disjoint tree canopies can easily be delineated 
in this process. However, a segmentation procedure is needed to 
isolate trees which are grouped. This study uses marker- 
controlled watershed segmentation for tree canopy isolation. 
Watershed segmentation, first proposed by Beucher and 
Lantuejoul (1979), is a well known image segmentation method 
that incorporates region growing and edge detection techniques 
(Soille,2003). To avoid the over segmentation problem, Meyer 
and Beucher (1990) introduced marker-controlled watershed 
segmentation. The idea is to perform watershed segmentation 
around user-specific markers rather than the local maxima in 
the input image. 
In the watershed segmentation of the nDSM data, the tree 
crown model was treated as a 3D surface, with lateral 
dimensions representing the image plane, and the vertical 
dimension representing the grey values (Figure 2a). Internal 
markers were used to locate the local minima, which were 
associated with high grey values (i.e. selected tree crowns) and 
external markers were pointed to the local maxima, which were 
associated with the background. Through flooding from the 
local minima, the watershed segmentation was performed: 
neighbouring watersheds were merged unless boundaries were 
built to isolate individual tree features (Figure 2c). The process 
of merging regions and building boundaries continued until no 
more region growing could take place. 
(a) (b) (c) 
Figure 2. An illustration of watershed segmentation, (a) A 
canopy model derived from nDSM, (b) 3D view of 
the canopies, and (c) Segmentation results with 
dams (in red) built at the divide line. 
3.3 Data processing 
After segmentation, the resulting crown polygons were overlaid 
on the lidar and multispectral imagery to extract the spectral 
signatures and texture information of the tree crowns for tree 
species discrimination. Firstly, the extracted signatures from 
four of the original multispectral bands were processed with a 
directional convolution filter using a 3x3 window. This filtering 
procedure allowed the suppression of shadow effects within the 
sunlit area of the tree crown. The weighting factors and the 
dimensionality of the filter are primarily dependent on the solar 
direction at the time of over flight, the tree size, and the 
illumination conditions within the tree crown. 
Secondly, image enhancement by principal components 
transformation was applied to the filtered four-band data set. 
The objective being the replacement of the highly correlated 
original bands with those of reduced correlation. The 
transformation resulted in four new components: the brightness, 
the redness, the greenness, and the blue-yellowness, for each of 
the tree types. 
In addition, two more lidar derived layers were included in the 
fusion procure. A ninth layer was generated by a texture 
analysis of the first return lidar intensity and the tenth layer 
from lidar derived nDSM layer. 
3.4 Supervised classification 
A supervised classification of the 10 layer datasets into three 
different categories as listed in Table 1 was carried out. Much 
of the success of the maximum likelihood classifiers depends on 
the choice of training areas. Extensive field survey 
measurements were conducted to collect the training data. The 
processed datasets were also used to redefine the training area 
in order to maximize the classification results. These datasets 
allowed a much better class-specific delineation of the training 
areas involving a reduced sample size for the different tree 
categories. However, the selected training areas still met the 
minimal requirement of 5 x k (no. of layers) pixels from a 
statistical point of view (Kalayeh and Landgrebe,1983). 
Class 
Tree type 
Description 
1 
Black Box 
Rough bark 
2 
Grey Box 
Fine, pale, fibrous bark 
3 
River Red Gums 
Smooth bark 
Table 1. Selected tree classes and associated degree of disease 
3.4.1 Filling the tree polygon: In high spatial resolution 
data fusion, the class variability within tree crown is caused 
mainly by the variability in crown structure (shadow effects), 
crown density (background material) and different tree 
components (bark, needles/leaves) (see figure 3a). In addition, 
the class variability is also affected by the categorisation of the 
tree types with respect to the leave and bark patterns (Table 1). 
( a ) «Black Box (b) 
« River Red Gum 
■ Grey Box 
Figure 3. Refining the tree classification; (a) the classified tree 
crowns; (b) Filling the tree crown area with majority 
species. 
In order to increase the significance of the classification results, 
the entire tree polygon was filled with the most frequent class 
(Figure 3a). In this way, only one class occupied the entire