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However, a further adjustment was applied, to enhance this 
registration result. Therefore, RISCAN PRO offers the Multi 
Station Adjustment, which is based on the Iterative Closest 
Point (ICP) Algorithm (Besl & McKay, 1992). The position and 
orientation of each scan position can be modified in multiple 
iterations. 
Subsequently, the Area Of Interest (AOI) was manually 
extracted from the merged point cloud of each date. For an 
easier orientation and the distinction between fields and dikes, 
the point clouds were colorized from the recorded photos. 
Nevertheless, the point clouds still contain noise, caused by 
reflections on water or on small particles in the air. To reduce 
this, a further filtering based on reflectance, measured for each 
point during the data acquisition, was performed. 
In a next step, the cleaned point clouds were interpolated in 
order to receive a digital terrain model (DTM), with a spatial 
resolution of 0.01 m. For the calculation of the plant heights 
and the comparison of the results from the different dates a 
common reference surface is required. Usually a high accurate 
Digital Surface Model (DSM), achievable from scanning the 
AOI without any vegetation, is used. However, since such data 
were not obtained in this present study, we applied another 
method. In order to receive a reference surface similar to the 
real ground, the lowest parts in the point cloud from the first 
date, accordingly containing the least dense vegetation, were 
selected manually to interpolate a DSM. As it can be seen in 
Figure 2, the rice plants were small enough to clearly identify 
points on the ground. Finally, the Crop Surface Models (CSM) 
as the difference between DTM and DSM were established for 
each date. Likewise, the differences between the CSMs were 
calculated for growth monitoring. 
Moreover, the height values, stored in the CSMs, were used to 
calculate the mean plant height and growth of each plot. In the 
following, growth is always defined as a difference in height. 
Additionally, the manually measured heights were averaged for 
each plot and correlated with the values from the CSMs to 
validate the results. 
  
PER un : P i 
Figure 2. Photo of one corner of the investigated field, 
showing the least dense vegetation. 
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 
3. RESULTS 
3.1 Point Clouds 
As a first result, point clouds with high density and accuracy are 
derived. The merged and filtered datasets contain six to ten 
Mio. points for each date, just covering the AOI. This data was 
used to create the CSMs and calculate the mean plant height 
and growth for each plot. 
3.2 Crop Surface Models 
The received CSMs can be visualized as maps of height growth. 
Therefore, the height was calculated above the common 
reference surface, considered as DSM in this study. Figure 3 
shows the maps for four selected plots of the same treatment. 
On the left side, two repetition plots of Kongyul31, on the right 
side two repetition plots of Longjing21 are shown. For the 
presentation, the point clouds were interpolated, using the 
Inverse Distance Weighting (IDW) algorithm. The results are 
stored in raster data sets with a resolution of 0.01 m. 
The following statements can be made from the maps 
(Figure 3): 
e The plant height of Longjing21 is - especially at the last 
date - higher than Kongyu131. 
e In plot 163 the linear structure of the fields can be clearly 
seen. 
e [In plot 262 pronounced height differences within the plot 
can be detected. 
The spatial distribution of the height difference between the 
CSMs, showing the plant growth, can also be visualized by the 
calculation of a new raster. As a result, varieties within the 
fields can be detected. Figure 4 shows the calculated height 
difference between two consecutive CSMs of two plots and 
hence the change in plant height between the first and second, 
respectively second and third date. For both plots an evenly 
distributed increase in height can be concluded. Moreover, a 
stronger increase of height can be detected for Longjing21 
(plot 232), especially for the period between the second and 
third date. 
3.3 Mean Plant Height and Biomass 
From the CSMs, the mean plant height for each single plot was 
calculated as the height above the common reference surface. 
Furthermore, the averaged manually measured plant heights 
were checked against the heights from the CSMs. For the 
statistical analyses 18 plots (nine of each rice variety), well 
distributed over the whole field, were chosen. Since the 
measurements from all dates were used, the analyses are based 
on 54 values. The height values comprise a great range (cf. 
Figure 5), which makes the regression more reliable. The 
correlation between the mean plant height achieved from the 
CSMs and the average measured plant height is very good 
(R2=0.91).