density for the 72 outer points. The TLS is scheduled to be
captured for the centre point of this grid for later comparison.
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Figure 2: Gridded sampling design for the Rushworth plot.
An initial comparison was conducted for LAI between the LAI-
2200 and the DHP for 72 points sampled at the 4m density
(Figure 2). The mean values for the LAI-2200 and the DHP are
0.95 and 0.50 respectively (Table 1).
LAI Values
13 f! 16 13 ib 135 22 25 JR 31 13 32 30 43 26 49 $2 55 5% DI 63 67 A873
Measurement Number
Figure 3: LAI comparison of LAI-2200 and DHP
LAI (2200) LAI (DHP)
Mean 0.95 0.50
Standard Error 0.02 0.01
Std Deviation 0.18 0.08
Range 0.87 0.41
Minimum 0.65 0.34
Maximum 1.52 0.75
Table 1: LAI summary statistics for LAI-2200 and DHP
A large discrepancy was found between the mean LAI values.
The graph in Figure 3 demonstrates the bias between the two
technologies. The DHP is consistently providing an LAI value
lower than the LAI-2200. However, correlating the two
technologies produced an R? of 0.46 (linear regression
intercepting LAI-2200 vertical axis at 0.23) which indicates an
inconsistent bias.
The results for LAI produced by the DHP and LAI-2200 are
different from the comparison reported by Coops et al., (2004).
Coops ef al. (2004) found that the DHP and LAI-2000
produced higher R? value of 0.65 for a linear regression with an
intercept at the LAI-2000 axis at 0.72.
Further differences between the two studies may be explained
by vegetation type, number of measurements, measurement
aggregation, and different DHP processing software to derive
LAL The dominant tree species found in the Coops ef al,
(2004) were the Eucalyptus delegatensis and the Eucalyptus
dalrympleana. These species are significantly taller and
produce a more uniform upper canopy to the species found in
Rushworth. Furthermore, the mean LAI values reported in
Coops et al., (2004) are greater than the study area in
Rushworth, and are indicative of the lower biomass of the tree
species found in Rushworth. Coops et al., (2004) also
aggregated plot measurements, whereas this study compared
individual measurements over one plot. Lastly, different
software packages were used to derive LAI from DHP in the
two studies. Coops et al., (2004) used HemiView 2.1 software
which requires the user to manually define a threshold for
classifying the image into sky and non-sky elements for gap
fraction analysis. Conversely, the DHP software used in this
study automatically thresholds each image.
2.5 Further Analysis to be completed at Rushworth
The gridded sampling strategy provided an increased coverage
area for a similar time cost by modifying the measurement
spacing. The strategy will allow for a comparison of results at a
variety of sampling densities and designs (Nelder, 1962). The
design allows for comparison at the point level of a variety of
metrics, and also enables comparisons to be undertaken at a
variety of scales through aggregation of measurements.
The comparison of metrics at the individual point scale will be
extended to MTA, FAPAR and FCOVER metrics for both the
DHP and CI-110 data. Comparing the same metrics from
different technologies is important for determining the degree
of consistency within each study area. Different metrics will be
correlated against each other to determine the degree of
relatedness.
Another factor of significance for LAI is the ratio of woody to
non-woody vegetation. The FPC transect will enable the ratio
of woody to non-woody vegetation to be calculated. This ratio
could be utilised when determining the link between LAI and
PAL
The aggregation of point scale measurements for comparison
enables different metrics to be correlated with each other over
multiple scales. Aggregation is relevant as each instrument has
a varying FOV and sample radius. For example, FCOVER
produced by the passive imaging devices is derived over a FOV,
which allows for the FPC value to be aggregated of the same
FOV for comparison. Furthermore, FCOVER can be
aggregated and averaged at the plot scale and then compared to
the site’s aggregated FPC value.
The gridded sample design, which allows for testing of various
sampling designs at different densities, will enable the
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