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FUSION OF REMOTELY SENSED MULTISPECTRAL IMAGERY AND LIDAR DATA 
FOR FOREST STRUCTURE ASSESSMENT AT THE TREE LEVEL 
S. S. Ali 3 ’ 1 *„ P. Dare b , S. D. Jones c 
d b School of Geography, Population and Environmental Management, Flinders University, Adelaide, South Australia 
- (sohel.syed, paul.dare)@flinders.edu.au 
c Mathematical & Geospatial Sciences, RMIT University, Melbourne - simon.jones@rmit.edu.au 
Commission VII, WG VII/6 
KEY WORDS: Airborne remote sensing, Classification, Digital photogrammetry, Fusion, Forestry, Lidar, Multispectral imagery 
ABSTRACT: 
A new feature-level fusion is presented for modelling individual trees by applying watershed segmentation and subsequent 
classification, using tree heights and tree crown signatures derived from light detection and ranging (lidar) data and multispectral 
imagery. The study area is part of the Moira State Forest, New South Wales, Australia where the dominant tree species are native 
eucalypts. In this study, airborne lidar data and four band multispectral imagery were acquired. A digital surface model (DSM) was 
generated from the lidar first return data and a digital terrain model (DTM) was derived from the lidar last return data. A tree crown 
model was computed as the difference between DSM and DTM using appropriate height thresholds. A marker-controlled watershed 
segmentation algorithm was used to extract individual tree crowns from the lidar data. The resulting crown polygons were overlaid 
on the four band multispectral imagery to extract the spectral signatures of the tree crowns. A principal components transformation 
was applied to the four-band dataset to replace the highly correlated original bands with those of reduced correlation. In addition, 
two lidar derived texture and height layers were included in the fusion procedure. The application of the maximum likelihood 
technique led to a high classification accuracy. An average classification accuracy of 86 percent was achieved and this procedure 
outperformed the original four-band maximum likelihood classification by 23 percent. The success of the tree crown extraction 
algorithm in old growth areas was higher than in more juvenile areas where the crowns were more scattered. It was also observed 
that large crowns were better delineated than small ones. The results indicate that this fusion modelling strategy may prove suitable 
for estimating and mapping the crown area, height and species of each tree. 
1. INTRODUCTION 
1.1 Motivation 
Individual tree components (both in the horizontal and vertical 
plane) are important parameters for developing a better 
understanding of how forest ecosystems function. Lidar data 
provide accurate measurements of forest structure in the vertical 
plane; however, current lidar sensors have limited coverage in 
the horizontal plane. Conversely, high resolution multispectral 
imagery provides extensive coverage of forest structure in the 
horizontal plane, but is relatively insensitive to variation in the 
vertical plane. Therefore, it is desirable to synergistically use 
both sensors for mapping forest parameters at a fine scale. 
Delineating individual trees and extracting relevant tree 
structure information from fused remotely sensed data have 
significant implications in a variety of applications such as 
reducing fieldwork required for forest inventory (Gong et 
a/., 1999), assessing forest damage (Kelly et al.,2004) and 
monitoring forest regeneration (Clark et al. ,2004). 
1.2 Imagery and lidar data fusion 
There have been several attempts to fuse lidar and high spatial 
resolution imagery for individual tree attributes collection 
(Baltsavias,1999; Leckie et al.,2003). The strong argument of 
fusion is that the lidar measurements do not distribute 
homogeneously and usually have gaps between them. As a 
result, the three-dimensional structure of the objects might not 
be very well defined (Baltsavias,1999). It thus becomes fairly 
complex to obtain a good 3D model of the canopy architecture 
of each tree with a low density of lidar returns. The idea of 
exploiting the complementary properties of lidar and aerial 
imagery is to extract semantically meaningful information from 
the aggregated data for more complete surface description. 
Sua'reza et al.(2005) propose a data fusion analysis with lidar 
and aerial photography to estimate individual tree heights in 
forest stands. The tree canopy model is derived from lidar 
layers as the difference between the first pulse and last pulse 
return. Information about individual trees was obtained by 
object-oriented image segmentation and classification. This 
analysis provided a good method of estimating tree canopies 
and heights. However, the method of segmentation and 
classification are too image dependent. The classification 
parameters were not defined automatically and exhibit no clear 
relationship to allometry factor. Instead, they were defined 
empirically following a trial-error process. Leckie et al. (2003) 
applied the valley following approach in to both lidar and 
multispectral imagery and found that the lidar can easily 
eliminate most of the commission errors that occur in the open 
stands while the optical imagery performs better for isolating 
trees in Douglas-fir plots. 
This study attempts to use a new feature-level fusion 
methodology for modelling individual trees. The method 
* Corresponding author.