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The classification accuracies achieved are comparable to those
produced by visual interpretation. Map scatter plots of lidar
return combined with multispectral imagery and field data
enabled, in some cases, visual discrimination at the individual
tree level between Black Box and Grey Box. While a clear
distinction between these two species was not always visually
obvious at the individual tree level, due to other extraneous
sources of variation in the dataset, the observation was
supported in general at the site level. Sites dominated by Black
Box generally exhibited a lower proportion of singular lidar
returns compared to sites dominated by Grey Box. River Red
Gums can easily be distinguished from others by their unique
spatial distribution. This species is largely populated throughout
the forest surrounding the Riverine wetlands that are subject to
periodic inundation (see Appendix A).
The fusion procedure proposed in this study demonstrates the
usefulness of the five main processing steps to cope with the
classification of very hight spatial resolution lidar and
multispectral imagery. This approach can be used in principle
for species classification of high spatial resolution data.
However, sensor-specific modifications to these different
processing steps have to be made in order to maximise the
fusion results.
5. CONCLUSIONS
The investigation presented in this paper has been conducted to
establish an automated procedure for forest species
identification at the tree level from high spatial resolution
imagery and lidar data. For this purpose, four-band
multispectral imagery and lidar data were used to develop a
feature-level fusion approach. This technique consists of five
steps: the preprocessing of the lidar and multispectral data; tree
crown polygon extraction using marker-control watershed
segmentation; masking of spectral, height and textural
information using the crown polygons; classification of the
polygon data; and, the accuracy assessment.
In contrast to the original four-band multispectral data sets, the
average classification accuracy was considerably improved
through the generation of additional features using the principal
component transformation, filtering techniques, and texture
analysis. Principal component transformation of the
multispectral imagery added more layers to separate different
tree species. The addition of the height and texture features
derived from lidar data resulted in an improved discrimination
of the tree classes.
The superposition of the watershed derived crown polygon on
to the images was essential for achieving good classification
results and for a more standardised classification. The proposed
procedure can be used as a model for fusing high spatial
resolution multispectral imagery and lidar data for assessing
forest attributes at the tree level. In addition, this fusion
procedure has the potential to minimise human interaction in
the interpretation of forest attributes.
ACKNOWLEDGEMENT
This work was supported by the Australian Research Council
(ARC) under Discovery Project DP0450889. The Ultracam-D
data set was provided by IFMS Germany
(http: //www. arc forest. com/).
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