(Webster et al., 2004). Specifically, they were related to differences in flying altitude
between the calibration flights and the survey flights. The laser range calibrations were
carried out at the planned survey altitude of 900 m. However, because of the power loss
problems, the actual survey was flown at an altitude nearer 600 m, introducing a range
bias. The problem was initially difficult to assess, because the LIDAR data showed a
good match in the calibration areas used by the data acquisition team (usually near the
airport where the GPS base station was established). To confirm this interpretation,
several lines of the LIDAR data have been reprocessed by applying a range scale factor
and offset, resulting in agreement of the orthometric heights in overlapping lines and a
better match to the GPS data.
The above analysis demonstrates the necessity for independent validation of
LIDAR data. The expected level of accuracy of this technology is such that most
existing data (e.g. 1:10,000 scale topographic maps) are inadequate for this purpose.
Therefore carrier-phase GPS data must be collected specifically for validation purposes.
3. DEM Construction from LIDAR
3.1 INTERPOLATION METHODS AND CLASSIFICATION OF THE LIDAR
POINT CLOUD
As mentioned above, it is standard practice to classify the LIDAR returns into
ground and non-ground points to enable the production of a bald earth DEM. An
accurate representation of the ground surface is critical for coastal zone flood risk
mapping from storm-surge events. As outlined in Maune (2001), several methods have
been developed for constructing DEMs from point data. Most involve interpolation
between the LIDAR points to generate a continuous surface. For this project two
approaches were used: 1) for both the Charlottetown and North Shore survey areas, a
DEM was constructed by direct gridding of the LIDAR points; and 2) for the
Charlottetown area, a DEM was constructed through interpolation.
In the first approach, public domain GRASS software was used to build a grid from
the LIDAR ground points. This is the method we have been using for a number of years
to process multibeam bathymetric data to generate digital seabed models and shadedrelief imagery (cf. Courtney and Shaw, 2000). A 2 m grid was overlaid on the LIDAR
points and each grid cell was assigned the mean orthometric height for the point(s)
lying in the cell. This produces a DEM without interpolation, but areas of sparse or
missing LIDAR points will not be assigned a value in the DEM. Limited interpolation
can be used to fill small gaps in the resulting grid. Shaded-relief images derived from
the resulting DEMs were reproduced in Forbes and Manson (2002) and Forbes et al.
(2004).
In the second approach, an Arc/Info geographic information system (GIS) was
used to construct a triangular irregular network (TIN) from the LIDAR ground points.
A 2 m grid was then built from the TIN using the quintic interpolation method (5
th order
polynomial). Although it is computationally more intensive than linear interpolation,
this method ensured a smooth surface that honored all data points. The DEM grid was
then transferred to the PCI Geomatica suite of image processing tools for visualization
and modelling. A colour shaded-relief model was constructed from the DEM and used
for qualitative assessment and flood modelling. At this stage, two problems were
identified:
171
Airborne Laser Altimetry
between the calibration flights and the survey flights. The laser range calibrations were
carried out at the planned survey altitude of 900 m. However, because of the power loss
problems, the actual survey was flown at an altitude nearer 600 m, introducing a range
bias. The problem was initially difficult to assess, because the LIDAR data showed a
good match in the calibration areas used by the data acquisition team (usually near the
airport where the GPS base station was established). To confirm this interpretation,
several lines of the LIDAR data have been reprocessed by applying a range scale factor
and offset, resulting in agreement of the orthometric heights in overlapping lines and a
better match to the GPS data.
The above analysis demonstrates the necessity for independent validation of
LIDAR data. The expected level of accuracy of this technology is such that most
existing data (e.g. 1:10,000 scale topographic maps) are inadequate for this purpose.
Therefore carrier-phase GPS data must be collected specifically for validation purposes.
3. DEM Construction from LIDAR
3.1 INTERPOLATION METHODS AND CLASSIFICATION OF THE LIDAR
POINT CLOUD
As mentioned above, it is standard practice to classify the LIDAR returns into
ground and non-ground points to enable the production of a bald earth DEM. An
accurate representation of the ground surface is critical for coastal zone flood risk
mapping from storm-surge events. As outlined in Maune (2001), several methods have
been developed for constructing DEMs from point data. Most involve interpolation
between the LIDAR points to generate a continuous surface. For this project two
approaches were used: 1) for both the Charlottetown and North Shore survey areas, a
DEM was constructed by direct gridding of the LIDAR points; and 2) for the
Charlottetown area, a DEM was constructed through interpolation.
In the first approach, public domain GRASS software was used to build a grid from
the LIDAR ground points. This is the method we have been using for a number of years
to process multibeam bathymetric data to generate digital seabed models and shadedrelief imagery (cf. Courtney and Shaw, 2000). A 2 m grid was overlaid on the LIDAR
points and each grid cell was assigned the mean orthometric height for the point(s)
lying in the cell. This produces a DEM without interpolation, but areas of sparse or
missing LIDAR points will not be assigned a value in the DEM. Limited interpolation
can be used to fill small gaps in the resulting grid. Shaded-relief images derived from
the resulting DEMs were reproduced in Forbes and Manson (2002) and Forbes et al.
(2004).
In the second approach, an Arc/Info geographic information system (GIS) was
used to construct a triangular irregular network (TIN) from the LIDAR ground points.
A 2 m grid was then built from the TIN using the quintic interpolation method (5
th order
polynomial). Although it is computationally more intensive than linear interpolation,
this method ensured a smooth surface that honored all data points. The DEM grid was
then transferred to the PCI Geomatica suite of image processing tools for visualization
and modelling. A colour shaded-relief model was constructed from the DEM and used
for qualitative assessment and flood modelling. At this stage, two problems were
identified:
171
Airborne Laser Altimetry