International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
First Returns Last Returns Flying Height
10 10
5 5 30 m
0 fF. 0 ie |
5 10 5 10
10 10
5 5 50 m
3 lt
o
E 0 0
5 5 10 5 10
= 10 10
o
e
© 5 | 70m
0 0
5 10 5 10
10 10
5 | 90m
0 0
5 10 5 10
Above Ground Height (m)
Figure 4. Histograms of the above ground height of vegetation
returns over a single plot for point clouds captured at above
ground flying heights of a) 30 m, b) 50 m, c) 70 m and d) 90 m.
There is an obvious attenuation of upper canopy returns due to
flight altitude.
12 T
Flying Height
— 30m
E Ex som
= 90m
©
Ss
2
o
©
o 4r
2
o
a
<
0
20 60 80
Above Ground Height Quantile (%)
20 100
Figure 5. Above ground height quantiles measured from various
flying heights for a single plot.
of 50 m these statistics vary by more than 100 %. This varia-
tion in the point clouds captured at greater altitudes suggests that
these point clouds are not capturing the true canopy structure.
As such point clouds from flights with an altitude of more than
50 m above the ground will not be used in any further analysis.
3.2 Horizontal Accuracy
The location of 50 random trees as found in the measurements
of point clouds below 50 m were compared. This resulted in a
minimum of four tree locations being observed for each tree. The
mean repeatability of an individual tree location 0.36 m with a
standard deviation of 0.24 m . The maximum difference between
any two tree locations of 0.96 m was found between two flights at
50 m at a scan angle of approximately 20^ in both flights. This
suggests that accuracy is highly affected by increased scan angles
and flying heights. The sampling densities at flying heights below
502
50 m suggests the top of a tree is likely to be sampled and any
error is likely to be due to errors in the position, orientation or
calibration of the LiDAR system.
3.3 Point Density
The effect of point density on the calculation of quantiles at the
plot level is significantly different to that seen due to the variation
of different flying heights. Decreasing point densities primarily
result in a small decrease in the number of points measured at
the very top of the crown. This is illustrated by a decrease of up
to 1.2 m in the 90 th and 100 th percentiles (when comparing
10 points per m to 77 points per m). As the positively skewed
canopy height distribution tends toward symmetry there is also
an increase of mid-canopy height quantiles. A reduction in point
density from 77 points per m to above 30 points per m (as shown
in Figure 6) was found to have no significant effect on any of the
plot level statistics calculated.
£ 6 a 6 b 6 2 c
+ E
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=
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O2] men 2 den 2b.
BIE oh BE
8 | ated Hr AU ER
2 SEC eed A EES hd = N
0 0 0
=2 0 2 =2 0 2 =2 0 2
Distance (m)
Figure 6. The effect of point density at the level of an individual
tree. Showing a) full density point cloud at 30 m, b) full density
point cloud at 70 m and c) decimated point cloud at 30 m
At the level of an individual grid cell more variation due to to
point density is seen in the calculation of some of the statistics.
This is primarily due to an under sampling of the canopy at this
point density. For point densities below 40 points per m there
is a significant reduction of up to 10 % in the upper level canopy
density metrics. Furthermore, the percentage of canopy returns
vary by up to 50 %.
3.4 Scan Angle
The primary effect of increased scan angle is a shadowing on the
far side of the trees (as demonstrated Figure 7). The statistics for
grid cells in these shadows were not calculated due to the lack of
information. Therefore, the comparison of most statistics from
cells with a sufficient number of returns at different scan angles,
showed only a slight increase in variance for cells with large scan
angle differences.
3.5 Footprint Size
There was no significant variation found due to footprint sizes
(range of 0.6 - 1.3 m) in flights lower than 50 m. As this analysis
was restricted due to the necessity of scan angle similarity there
may exist greater variation towards the edges of the plots due to
increased ranges of footprint sizes.
4 DISCUSSION AND CONCLUSIONS
The results of this study suggest that a repeatable set of statis-
tics can be derived from multiple flights of a UAV-borne LiDAR
system. To achieve this, however, the flying height of the system
needs to be restricted to less than 50 m. The primary effect of
