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For a visual representation, it is also suggested that plots
shown in Figure 7 be part of the data quality reporting pro-
cess. It should be noted that the errors described here assume
that flight lines are parallel and adjacent flight lines are in
the opposing direction. If that is not the case, the pattern of
errors will be different. It should also be noted that not all
systematic errors cannot be traced using the methods out-
lined here, and it is not the intention of this work to do so.
Concluding Remarks
Current accuracy measurement and reporting practices fol-
lowed in the industry and as recommended by data specifica-
tion documents (Heidemann 2014) potentially underestimate
the inter-swath errors, including the presence of predomi-
nantly horizontal errors in lidar data. Hence they pose a risk
to the user in terms of data acceptance (i.e. a higher potential
for of accepting potentially unsuitable data). For example, if
the overlap area is too small or if the sampled locations are
close to the center of overlap, or if the errors are sampled in
flat regions when there are residual pitch errors in the data,
the resultant Root Mean Square Differences (RMSD) can still
be small. To avoid this, the following are suggested to be used
as criteria for defining the inter-swath quality of data:
a) Median Discrepancy Angle
b) Mean and RMSD of Horizontal Errors using DQM mea-
sured on sloping surfaces
c) RMSD for sampled locations from flat areas (defined as
areas with less than 5 degrees of slope)
Test points (user defined number depending on size of
swaths) are uniformly sampled in the overlapping regions
of the point cloud, and depending on the surface roughness,
to measure the discrepancy between swaths. Care must be
taken to sample areas of single return points only. Point-to-
Plane data quality measures are determined for each sample
point. These measurements are used to determine the above
mentioned quality metrics. This document detailed the mea-
surements and analysis of measurements required to deter-
mine these metrics, i.e. Discrepancy Angle, Mean and RMSD
of errors in flat regions and horizontal errors obtained using
measurements extracted from sloping regions (slope greater
than 10 degrees).
References
Habib A, Kersting A. P., Bang K. I., and D. C. Lee. 2010.
“Alternative Methodologies for the Internal Quality
Control of Parallel LiDAR Strips,” in IEEE Transactions
on Geoscience and Remote Sensing, vol. 48, no. 1, pp. 221-
236, Jan. 2010.
doi: 10.1109/TGRS.2009.2026424
Heidemann, H. K., 2014, Lidar base specification version
1.2, U.S. Geological Survey Techniques and Methods,
Book 11, Chapter. B4, 63 p.
Latypov, D., 2002. Estimating relative LiDAR accuracy in-
formation from overlapping flight lines. ISPRS Journal of
Photogrammetry and Remote Sensing, 56 (4), 236–245.
Munjy, Riadh. 2014. “Simultaneous Adjustment of LIDAR
Strips.” Journal of Surveying Engineering 141.1 (2014):
04014012.
Sande, C.; Soudarissanane, S.; Khoshelham, K. 2010. Assess-
ment of Relative Accuracy of AHN-2 Laser Scanning Data
Using Planar Features. Sensors. 2010, 10, 8198-8214.
Acknowledgement
This document represents a collaboration between indus-
try and government partners through a US Geological Sur-
vey (USGS)/ASPRS Lidar Data Quality Working Group, or
sometime referred to as the “ASPRS Lidar Cal/Val Working
Group”. ASPRS and its Lidar Division greatly appreciate the
collaboration and effort provided by the community to com-
plete this guidelines document. Any comments, questions or
suggestions related to these guidelines, associated software,
or associated research can be addressed through the ASPRS
Lidar Division,
.
The ASPRS would also like to recognize the USGS for de-
veloping and providing the software executable, a Python
version of the software for technical decomposition and use,
and the associated software documentation that supports the
process defined in this guideline.
The ASPRS would like to provide a special acknowledgement
and thank you to the Lidar Division and its Airborne Lidar
committee, the Lidar community, and to the US Geological
Survey (USGS)/ASPRS Lidar Data Quality Working Group
which allowed these guidelines and associated software to
be made available. ASPRS provides specific recognition for
the original members and key contributors to this working
group: Mr. Greg Stensaas, Dr. Aparajithan Sampath, Mr.
Karl Heidmann Dr. Jason Stoker, Dr. Ayman Habib, Dr. Val-
erie Brooks, Mr. Matt Bethel and to all those who support
this long term effort.
