The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part BI. Beijing 2008 
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Based on the results of this study, we have concluded that POS 
(or any GPS/INS system) data accuracy has the most dominant 
impact on the attainable horizontal accuracy of airborne lidar 
data. Hence, the specified horizontal accuracy numbers of two 
airborne lidar systems equipped with the same or equivalent 
GPS/INS systems must be identical, if these numbers are 
derived by similar methodologies, and if the same or similar 
reference set of operating conditions has been considered. 
3.5 Post-Processing and Data Accuracy 
Another factor that may have a crucial impact on the accuracy 
numbers on the lidar system specification sheet is the data 
processing procedure. Even after thorough consideration 
including a reference set of operating parameters, a reference 
target, and a reference set of data collection conditions, the data 
processing procedure and the various processing algorithms 
applied to the raw lidar data may introduce or reduce errors. 
The data set might be further adjusted, optimized, or smoothed 
by using third-party software. Moreover, additional data 
optimization algorithms could also be applied to data already 
processed and calibrated by the manufacturer’s proprietary 
software. After a series of data processing procedures, the final 
accuracy numbers may look very different as a result. Since 
every commercial lidar system manufacturer uses a unique set 
of proprietary procedures to determine data accuracy, there is 
always a “grey” area around the accuracy numbers on the lidar 
system specification sheet. 
Based on the results of a recent study on the impact of the data 
processing procedure on lidar accuracy numbers (Pokomy et al., 
2008), we have concluded that the optimization algorithms 
applied to the processed lidar data may significantly improve 
the derived accuracy numbers. Table 1 and Table 2 show some 
results of this study, in which two different software tools and 
two different algorithms were used to calculate the RMSE (root 
mean square error) and standard deviation for the vertical 
accuracy of data collected by three different ALTM systems at 
slightly varied flying altitudes of about 1 km and under similar 
operational conditions. 
Software tool 1 
Software tool 2 
Algorithm 
1 
Algorithm 
2 
Algorithm 
1 
Algorithm 
2 
System 1 
0.096 
0.095 
0.087 
0.086 
System 2 
0.053 
0.047 
0.054 
0.047 
System 3 
0.074 
0.054 
0.074 
0.054 
Table 1. Comparison of z-RMSE values for the lidar data 
processed by different software tools and different algorithms 
Software tool 1 
Software tool 2 
Algorithm 
1 
Algorithm 
2 
Algorithm 
1 
Algorithm 
2 
System 1 
0.062 
0.052 
0.061 
0.051 
System 2 
0.043 
0.036 
0.044 
0.035 
System 3 
0.072 
O 
Ö 
L/1 
•-J 
0.072 
0.057 
Table 2. Comparison of standard deviation values for the lidar 
data processed by different software tools and different 
algorithms 
The comparisons in Table 1 and Table 2 show clearly that the 
final accuracy numbers presented on the lidar system 
specification sheet may be improved by 10-30% simply by 
using different processing algorithms, either developed 
internally by the lidar system manufacturer or provided by 
third-party software. 
In addition, since overall lidar data accuracy strongly depends 
on the accuracy of the position and orientation data, post 
processing software tools available in advanced GPS/INS 
systems may also have a significant impact on final data 
accuracy. A prime example is the new POSPac 5.0 processing 
package offered by Applanix/Trimble, which has proved to be 
even more robust than the POSPac 4.4 currently used with 
ALTM/Gemini and is capable of handling steeper banking 
angles without compromising the specified accuracy (Hutton et 
al, 2007). In tests performed at Optech (Boba et al., 2008), 
processing with the POSPac 5.0 has consistently shown 
improved POS data accuracy that, in turn, improved the overall 
accuracy of ALTM/Gemini data. 
Thus, the data accuracy derived immediately after data 
processing may look noticeably different from the numbers 
derived after applying additional processing tools to optimize 
the data. Moreover, the methodology that the data processing 
workflow uses to derive the accuracy numbers may vary from 
one manufacturer to another. Therefore, the final accuracy 
numbers derived by different methodologies would not be 
obviously valid for sensible comparison. 
4. CONCLUSION 
To bridge the gap between the numbers on a lidar specification 
sheet and expected system performance in the field, the lidar 
system user must understand the underlying premises and 
relationships between these numbers and plan an airborne 
survey project accordingly. 
It was shown that in addition to laser PRF, which determines 
data collection efficiency, the scan pattern and beam deflection 
mechanism used in a particular lidar system may influence 
ground point density and area coverage rate and consequently 
affect the operating parameters for a planned mission. The 
dynamic range of intensities that a particular lidar system can 
accommodate may also significantly enhance or reduce 
achievable data quality and accuracy. Hence, to collect 
accurate data without voids over highly variable terrain, the 
user should carefully evaluate a lidar system’s dynamic range 
capabilities and limitations. 
In addition, it was shown that the combined impact of laser 
footprint size and the GPS/INS system on lidar data accuracy 
can make the data collected at very high altitudes and very wide 
scan angles not usable for most practical applications. Also, the 
analysis of the impact of processing algorithms and third-party 
software tools on data accuracy indicated that accuracy 
numbers derived by different processing workflows may look 
noticeably different. Thus, without consensus in the industry 
on how to derive the lidar data accuracy numbers, lidar users 
should remember that the numbers they see on lidar 
specification sheets across different manufacturers may not be 
valid for comparison and may not be applicable to certain 
survey conditions. 
In conclusion, knowing the relationships underlying 
manufacturer-derived lidar specifications and the many factors 
that can alter actual data collection efficiency and quality will