80
| from
Additionally, at each date plant samples from selected plots
were taken and dry biomass (stem and leaf) was measured.
Thus, the statistical analyses are based on 22 values. A very
good correlation between plant height and dry biomass was
achieved (R>=0.88) and the regression line fits well (cf.
Figure 6).
16 =
R? = 0.8763 Ju
14 7
12 e v4
: d
Weight steam & leaf [g]
00
ed oo
e
Bui.
20 30 40 50 60 70
Mean plant height from CSM [cm]
Figure 6. Regression of mean plant height calculated from
CSM and biomass of stem and leaf (n-22).
3.4 Monitoring Approach
The change in plant height was monitored by the calculation of
differences between each CSM, similar to the calculation of the
plant height above the reference surface. In order to verify the
results, the average manually measured plant growth was
calculated and compared to the outcomes from the CSMs. The
coefficient of determination (R?=0.86) is again very good. As it
can be seen in Figure 7, the calculation of the regression line is
based on values of a great range from 5 to 30 cm, which
reinforces the plausibility.
R? - 0.8600
E 30
e. o ;
© A
2 9 o 7$
e 625 e
t5 ° ©
= o
c o
= o
S. 20 9 x
=
3 yr m
© md
q e -
E 4 e
20 o
® o
S 8
z 10 677605
5 f 1 T i i I
5 10 35 20 25 30 35
Mean plant growth between two CSMs [cm]
Figure 7. Regression of mean plant growth increase
between two consecutive CSMs and average measured plant
growth increase (n=36).
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
4. DISCUSSION
First of all, the data acquisition with the laser scanner in the
field worked very well. A major advantage of the TLS system is
the easily achievable and fast data acquisition of the whole
field, compared to Casanova etal. (1998), using a hand-held
radiometer. Anyway, an accurate differentiation between each
plot is possible with a higher spatial resolution than achievable
with ALS (McKinion et al., 2010).
However, the compact and lightweight build-up of the Riegl
VZ-1000 is therefore quite helpful. Ehlertetal. (2009) and
Lumme et al. (2009) complain about problems with noise in the
point clouds. This problem is always linked with TLS, due to
wind and other effects. However, the time-of-flight scanner,
used in this study, reduces this problem by the high measuring
speed. Earlier studies with a comparable set-up
(Hoffmeister et al., 2011), show already the usability of this
method, but further improvement is still desirable. Nevertheless,
some minor problems, like the transportation of the scanner on
the dikes, have to be solved.
For the registration and merging of the scan positions, RISCAN
PRO offers appropriate tools. The indirect registration based on
the tie points can be considerably improved by the ICP
algorithm. By employing the MSA, the standard deviation error
was decreased from 0.1 m to 0.05 m. Furthermore, filtering
based on the reflectance was helpful to remove noise from the
point clouds. Thus, the time consuming manual postprocessing
was remarkably accelerated. Furthermore, the scan positions
where the setting could be build up on the trailer, profit from
the greater height. This could be seen e.g. in the CSMs of plot
163 (cf. Figure 3). The plot was at the south edge of the
investigated field, close to the scan positions with the trailer.
Due to the higher perspective, the linear structure of the field is
strongly visible.
Beside the well working method, the results show that our
approach seems to be suitable for rice growth monitoring. The
good correlation between the mean plant height calculated from
the CSMs and the manually measured plant height (R>=0.91) as
well as the correlation with the dry biomass (R?-0.88) show the
accuracy of the achieved models. However, the accuracy of the
data is comparable with those from the mentioned studies with
cereals (Ehlert et al., 2008; 2009). Moreover, the correlation
concerning the plant growth (R2—0.86) confirms the suitability
for the monitoring approach. The already mentioned study of
Hoffmeister et al. (2011) with a comparable set-up, was carried
out on sugar-beet fields. In contrast to rice, the more complex
structure of the sugar-beet leaves impairs the correlation
between mean height and dry biomass.
Moreover, the spatial distribution of variances within the CSM
of one plot and between different CSMs can be detected. First,
the CSMs from various repetitions can be compared. Secondly,
for monitoring, CSMs from several time steps can be compared
to receive information about plant growth. Again, compared to
the studies on sugar-beet fields (Hoffmeister et al., 2010; 2011)
spatial differences in height were detectable as well.
5. CONCLUSION AND OUTLOOK
The results presented in this contribution show the applicability
for accurate capturing and monitoring of rice growth in terms of
changes in plant height and biomass. These spatial patterns of
