204
March 2014
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
the horizontal and vertical accuracy of lidar data. The
targets must be designed such that they can be extracted
from lidar data. The targets shown in Figures 4 (a) and (b)
have been used for lidar horizontal accuracy assessments
(Csnayi and Toth 2007; Bethel et. al., 2006, respectively)
while the target shown in Figure 4(c) (Stoker, unpublished,
2011) is in the design phase, and will be tested in 2014.
The use of targets is not new to the geospatial indus-
try as they have been used in conventional surveying,
photogrammetry and also microwave/Synthetic Aperture
Radar (SAR) based mapping. Alternatively, planar fea-
tures of as-built structures can be measured using a
total station instrument, and the surface be used as a
target for measuring absolute accuracy. Another method
of using GCPs surveyed in open terrain (both horizontal
and sloping terrain) is currently being investigated.
Sensor Model Based System Calibration
While the above two processes are recommended for QC of
lidar data, for Quality Assurance (QA), it is recommended
that a lidar system be calibrated using rigorous or semi-rig-
orous sensor modeling. Rigorous calibration methods are
based on determining parameters describing the sensor
model completely. Since many parameters associated with
a complete sensor model are proprietary, software to per-
form rigorous calibration can only be provided by the in-
strument manufacturer. Rigorous methods of calibration
are often a two-step process, decoupling the georeferencing
portion (lever arm) from the range and boresight measure-
ments. The rigorous calibration approach is robust, and
since the process is automated the resulting swaths of data
are consistent with each other and with external control.
A semi-rigorous sensor model calibration assumes a ge-
neric sensor model, and depends on the instrument manu-
facturer to convert parameters of their proprietary sensor
model to parameters of the generic model. Examples in-
clude the Universal Lidar Error Model (ULEM) developed
by NGA (NGA 2012), and Quasi Rigorous sensor model
developed by Habib (Habib et. al., 2010). However, these
generic models may not have the ability to completely
capture all the intricacies of the original sensor model.
Current Status and Concluding Remarks
The USGS led ASPRS Cal/Val Working Group recognizes
that the proposed QA/QC procedures are a departure from
the currently practiced process. Before recommending these
procedures to be adopted for data procurement, they have
to be tested against real data sets. Hence, a prototype soft-
ware tool has been developed that implements the DQMs
over natural surfaces. A comprehensive test plan has been
prepared and distributed to data vendors. Currently, this
tool is being tested on different data sets, collected under
different conditions, instruments, and by different vendors.
The goal of the testing process is to test the efficiency and
validity of DQMs as indicators of the goodness of fit from li-
dar system calibration. The lidar system calibration can have
(a)
(b)
(c)
Figure 4. Design of targets (a) Painted Targets (b) Elevated Targets and (c) 3D Targets.
