International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004 
  
estimation of its value leads to the determination of flow 
resistance. This is not an easy task which may need extensive 
studies of different circumstances and factors that have a direct 
effect on Manning's n value. 
Chow, (1973) and French (1994) described factors controlling 
the value of n as follows: surface roughness: this factor is 
directly related to the building material of the channel bed, 
whether it is gravel, sand, silt, clay, or any other material. It is 
not enough to estimate the surface roughness as grain size and 
shape although they affect the magnitude of the resistance force 
to the flow. Chow, (1973) states that, commonly fine grain 
materials provide smooth channel and low value of » while 
coarse grain materials give high resistance to the flow and 
relatively higher values of n. LMNO (2000) and Henderson 
(1966) introduce estimations of the values of n for some 
materials. 
3. AERIAL PHOTOGRAPHY AND LIDAR FOR 
CREATING DIGITAL SURFACE MODELS 
Both aerial photography and LiDAR have been used 
extensively for digital surface modelling of the landscape. 
Aerial photography and photogrammetry have had a long 
history of producing DSM's through analogue, analytical and 
digital methods (Mikhail ef a/., 2001). The more recent digital 
techniques have enabled very rapid DSM to be produced 
through automated image matching techniques and the quality 
of these have been assessed by a number of researchers (Smith, 
1996). One of the fundamental differences between LIDAR 
and photogrammetry is that LiDAR is based on a range 
measurement to a point from a single airborne position. 
Photogrammetry however, is based on stereo matching of 
images from two airborne positions. The stereo matching 
process requires the matching of a ‘patch’ of pixels covering a 
small area rather than a discrete point (footprint) as with 
LiDAR. In addition, often the algorithms used in the 
photogrammetry solution have been designed for smooth 
landscape modelling rather than the rapidly changing elevations 
of buildings in an urban environment. With both technologies 
there is the question of what surface is being measured? An 
analysis of these technologies is given in Smith ef al. (2000) 
and in Asal (2003). Before the methodology for producing 
coefficients of roughness could be investigated it was 
considered useful to try and visualise the DSM for different 
land uses. This would help to appreciate, from the information 
that is available from a DSM, the nature of the texture of the 
landscape. So, an analysis to see whether the different DSM’s 
show different textural characteristics was undertaken. A full 
analysis is given in Asal (2003). 
3.1 The Test Site 
The area covered by the test site at Newark-on-Trent includes a 
variety of landscapes. Primarily it is on the flood plain of the 
River Trent but one side of the river rises rapidly to the old 
town of Newark. Typical of many old town centres, it has 
narrow winding streets where it is difficult to see on to the 
ground level from the air. This is particularly difficult for 
photogrammetry as it requires too be able to see the ground 
from two positions for stereo analysis. Along side the old town 
is an industrial area and a relatively new residential area. To 
the north bank of the river and beyond some mixed 
development is a rural flood plan area of mainly hedged 
agricultural fields, small woodlands and a ring road on top of an 
716 
embankment. Aerial photography at 1:25000 scale covers a 
much greater area than the 1:10000 scale but common areas’ 
covered by the photography and LiDAR could be found. 
DSM's were created at 2m postings. Unfortunately it was not 
possible to obtain the different photography and the LiDAR at 
the same time. 
3.2 Analysis and Comparison of Digital Surface Models 
Figures 1, 2 and 3 show typical results obtained. (Red lower, 
green and mauve higher) 
   
Figure 2: DSM from 1:10,000 aerial photography in a rural 
area. 
    
  
  
   
  
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Figure 3: LIDAR DSM in a rural area. 
  
  
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