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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B2. Istanbul 2004
use of them. Educated users, especially research organisations,
will always be keen to look at new products, but users more
familiar with photographic products will take more time. Some
of the major users of both airborne LiDAR and IfSAR have
been new users, for example power generation companies for
powerline survey, and insurance companies for assessing flood
risk. But there can be problems with such users not
understanding the characteristics of the data, nor the accuracy
which can be expected.
LiDAR can produce a high density of points, and although this
is an advantage in some situations, it can also cause problems,
for example in the volume of data to handle. High density
might be necessary to identify detail on the ground, small
gullies or crash barriers on highways for example, but not on
the main carriage way. Thus there is a problem on how to this
the data to retain only what is needed. Intensity images may
appear to be useful in order to make it unnecessary to fly a
camera as well, but their quality is not as good, and there is no
standard for measuring intensity. On the other hand flying a
camera with the LiDAR can be a disadvantage as it means that
the lighting conditions must be good enough for the camera,
whilst the LIDAR could operate in poorer lighting conditions.
IfSAR is a complex system and users do not need know the
intricacies of the processing, but they do need to understand
that SAR samples a footprint which is quite large and that
different types of land cover give different responses. They also
need to understand the meaning of orthocorrection, (terrain
orthoimages and true orthoimages), the need for compatibility
of projection and datum, and the significance of error statistics.
In other words the users need to be educated to some degree and
the data provider needs to ensure that they are.
In the United Kingdom, the Highways Agency has produced a
specification for LIDAR surveys which has been produced in
close consultation between the data providers and the client.
This ensures that the client gets what is needed, for example in
terms of data formats, and visualisation of products to help new
users, and the provider understands what is required.
13. APPLICATIONS
13.1 Introduction
There are now a great many applications for DEMs from
LiDAR and IfSAR data and it is beyond the scope of this paper
to deal with all of them. We will therefore briefly review some
of the innovative applications and concentrate on those which
involve the use of data from more than one source.
13.2 Regional and global mapping
IfSAR has proven itself for low cost DEM generation over large
areas. The prime example is SRTM, but large areas have also
been mapped with ERS data, for example the Radarmap of
Germany (Kosmann et al, 1994). Airborne systems have been
used for generation of DEMs and orthoimages over large areas
such as the Nextmap Britain project (Mercer, 2003a, Dowman
and Fischer, 2003). The Nextmap data was originally
commissioned for an insurance company for flood risk analysis,
but is now being used more widely than that, and is
complementary to LiDAR, which is useful in denser urban
areas. Intermap have carried out IfSAR surveys in many parts
of the world including Malaysia and Indonesia, and are starting
on a coverage of the whole of the USA.
907
13.3 Environmental applications
À major application for environmental use is forestry. The
ScandLaser Scientific Workshop of Airborne Laser Scanning of
Forest, held from September 3-4, 2003 in Umea, Sweden, gives
a very detailed view of the current status of LiDAR for forestry,
e.g Wulder (2003), Naesset (2003), Hyyppa et al (2003).
Hamdan (personal communication) has noted that Dubayah and
Drake (2000) listed the key forest characteristics that can be
measured directly or indirectly by LiDAR. Among the
parameters that can be retrieved directly are canopy and tree
height, timber volume, forest mixtures according to tree species,
natural age classes, forest canopy closure, decision of forest /
non-forest and sub canopy topography. Beside this, above
ground biomass and volume, basal area, mean stem diameter,
vertical foliar profiles, canopy volume and large tree density can
either be modelled or inferred from LiDAR measurements.
Other important parameters for forest such as canopy cover, leaf
area index (LAI) and life form diversity need aifferent approach
where data fusion from lidar and other sensor is essential. In this
case, the vertical component provided by LiDAR should be
fused with information from passive optical, hyperspectral,
thermal and radar remote sensing (Hill et al.,2003). Apart from
that, LIDAR data like other optical remote sensing techniques
are restricted by clouds and dense atmospheric haze. This can
attenuate the signal before it reaches the ground. Another
limitation of LiDAR is the lack of algorithms and data
processing expertise required for operational use of the data. All
these enhance the integration of this data with other satellite
system.
An interesting new development is the combination of airborne
LiDAR with terrestrial LiDAR for forestry and the creation of
virtual forest environments. (Evans, 2003). Off shore tidal area
are anther important application area. A LiDAR survey has
been done for Willapa Bay in Washington, USA, demonstrating
the utility of the technique in intertidal areas.
13.4 Engineering applications
LiDAR has been used for engineering work such as railways,
powerlines and highways because of its high vertical accuracy
and the density of points. The application for power lines,
(Silver, 2001) and the ability to accurately determine the
position of the cables is an excellent indication of the usefulness
of LiDAR.
When a camera is flown with the LiDAR, even if a non metric
camera, then large scale mapping can be carried out. Figure 7
shows a plot of a highway intersection with detail and contours.
Compiled from the LIDAR DEM and a digital image acquired
at the same time as the LiDAR data.
The use of LiDAR for the generation of 3D city models is well
established and some techniques are discussed in section 13.5.
High density point clouds can be used to extract buildings and
roof detail by fitting planes to the points. TerraScan provides
tools for creating fully dimensional vectorised models of
buildings from LiDAR data based on identification of planar
roof surfaces. Chayakula (2004) has investigated the use of
airborne IfSAR in urban areas and shown that useful
information can be extracted. Houshmand and Gamba (2001)
have also worked on this topic (see below).