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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004 
for assisting with determining Manning’s ‘n’ values. Airborne 
remote sensing techniques normally provide better accuracy in 
elevation measurements and more detailed analysis of the 
surface compared to satellite remote sensing methods. There 
are a number of airborne remote sensing techniques that can be 
considered including aerial photography and photogrammetry, 
LiDAR (airborne laser scanning), Synthetic Aperture Radar 
(SAR) and multi-spectral line scanning. 
Digital surface models (DSM's) are standard products from 
aerial — photography using analytical and digital 
photogrammetric techniques. Analytical photogrammetry is a 
well-established mapping technique and can provide reliable 
and accurate measurements (Elfick et al. 1994). However, the 
measuring technique is a manual process which requires skills 
and experience of putting the measuring mark on the stereo 
model surface with every new measurement. This is a very 
slow operation so obtaining a DSM is usually a time consuming 
and very costly process. Digital photogrammetry employs 
image-matching techniques to compute elevation measurements 
from stereo pairs of digital (softcopy) aerial photographs. Then 
creating DSM's can be automatically performed which makes 
the process much faster and very cost effective compared to the 
analytical technique. ^ Unfortunately, the quality of these 
automatically-generated DSMs may not be as high when 
compared with a DSM from analytical techniques. LiDAR is a 
relatively new technology that can provide accurate DSM's, 
with a suggested accuracy of between +10 cm to £20 cm, in a 
relatively short time (Baltsavias, 1999). 
The accuracy of a DSM or a digital terrain model (DTM) can be 
critical in flood risk management in the cases of flat or gentle 
sloping floodplains. Fowler, (2000) suggests that contour line 
maps of the floodplain at one-foot (—30 cm) interval should be 
available for a flooding study. 
This paper presents the results from research in to the use of 
photogrammetry and LiDAR techniques to provide high quality 
DSM's and estimations of Manning's 'n' values. The paper 
outlines the aims, an introduction to hydrodynamic studies, and 
the results from a test site. Further information can be obtained 
from Asal (2003). 
1.1 Aims, Objectives and Methodology 
Aims: 
I. Investigate the use of airborne remote sensing techniques 
for creating DSM's. 
2. Assess the potential of using airborne remote sensing 
techniques including laser scanning systems and aerial 
photography in modelling the landscape in particular for 
the estimation of Manning's coefficient of roughness. 
Objectives: 
l. Create and evaluate DSM's from airborne laser scanning 
and aerial photography. 
Apply comparative analysis of the surface models 
generated from aerial photography and LiDAR to assess 
the potential of each technique in analysing the landscape. 
3. Investigate automatic method(s) for the estimation of the 
coefficient of roughness in large areas such as floodplains. 
N 
Methodology: 
l. To undertake the practical trials a test site has been 
established at Newark-on-Trent to the east of Nottingham, 
UK. LiDAR data was obtained from the Environment 
715 
Agency of England and Wales and the aerial photographs 
were obtained from the National Remote Sensing Centre 
(NRSC) at two common scales, 1:10,000 and 1:25,000. 
These photo scales were chosen as they are commonly 
flown scales in the UK and are readily available from 
archives. This ensures this research has the widest 
potential use. 
The ground control points for the aerial photography and 
field ground truth elevations were measured using Global 
Positioning System (GPS) techniques. 
3. Digital photogrammetry was undertaken using ERDAS 
IMAGINE OrthoMAX. 
4. Analytical photogrammetry was undertaken using a Leica 
SD2000 analytical plotter. 
5. Visual and quantitative analysis of the surface models, and 
the investigations into automatic techniques for estimating 
the coefficient of roughness were investigated using 
ERDAS IMAGINE digital image processing system and 
ArcView GIS. 
n2 
2. HYDRODYNAMIC STUDIES 
2.1 Manning’s Coefficient of Roughness 
Many equations have been developed for the purpose of 
studying open channel flow, Manning’s equation being one of 
the most widely used in this analysis. It is a semi-empirical 
equation and was developed in the 19" century by Manning in 
order to simulate open channel water flow. It was first designed 
for the purposes of studying uniform steady state flows of 
constant discharge, constant velocity and constant channel 
dimensions with time. However, practical experience proved 
that this equation can be successfully applied on gradually 
varied flow, which is the common natural flow. It *is also used? 
in defining the water flow over floodplains (LMNO, 2000). 
Manning's equation takes the following form (Jain, 2001): 
vz(1/n) (R^ s?) (1) 
where: 
v = the mean velocity through the channel in metres per second. 
n ^ Manning's coefficient of roughness. 
S 7 the channel bed slope in metres per metre. 
R = the channel hydraulic radius calculated from: 
R=A/WP (2) 
where: 
A =the cross sectional area of the channel. 
WP = the wetted-perimeter of the channel. 
2.2 Factors that Determine Manning's ‘n° 
Kay (1998) states that depends on the building material of the 
channel and the channel vegetation texture, which impose 
difficulty in estimating it with any degree of accuracy. 
Furthermore, the value of » is not constant with time in the 
same channel due to weed growth and variations of flow 
conditions over time. This can be explained as in case of small 
flow rate grass and weeds tend to be upright which brings about 
bigger resistance to the flow and leads to a bigger value of n. 
The situation is different with high discharges in the same 
channel due to grass and weeds being unable to continue 
standing in high velocity. This leads to their flattening which 
results in smaller resistance to the flow and smaller n value. 
From this it can be seen that z is a variable quantity wii