Automated suitable drainage network extraction from digital

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Automated suitable drainage network extraction from digital
HYDROLOGICAL PROCESSES
Hydrol. Process. (in press)
Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/hyp.5911
Automated suitable drainage network extraction from
digital elevation models in Taiwan’s upstream watersheds
Wen-Tzu Lin,1 Wen-Chieh Chou,2 * Chao-Yuan Lin,3 Pi-Hui Huang4 and Jing-Shyan Tsai5
2
1 Institute of Environmental Planning and Design, Ming Dao University, Changhua County 523, Taiwan
Department of Civil Engineering and Engineering Informatics, Chung Hua University, Hsinchu City 300, Taiwan
3 Department of Soil and Water Conservation, National Chung Hsing University, Taichung City 402, Taiwan
4 Graduate Institute of Civil and Hydraulic Engineering, Feng Chia University, Taichung City 407, Taiwan
5 Department of Landscape Architecture, Chung Hua University, Hsinchu City 300, Taiwan
Abstract:
Automatically extracting drainage networks from digital elevation models coupled with the constant stream threshold
value is a regular method. These extracted networks can be verified by comparing the channel initiation points with
those from real networks. From the results analysed, the differences in channel initiation points will affect the network
geometries, geomorphological indices and hydrological responses. This paper develops two automatic algorithms, the
headwater-tracing method and the fitness index, to trace the flow paths from headwaters to the outlet and to calculate
the reasonable stream threshold. Instead of the method determined by trial and error or field survey, the accurate
channel initiation points can be obtained from airborne photographs coupled with high-resolution SPOT images for
suitable drainage network extraction. Copyright  2005 John Wiley & Sons, Ltd.
KEY WORDS
channel initiation; headwater-tracing method; fitness index; stream network; geomorphological indices;
hydrological responses
INTRODUCTION
A digital terrain model (DTM) is an ordered array of numbers that represents the spatial distribution of terrain
characteristics (Doyle, 1978). When there is only one property, elevation, the term digital elevation model
(DEM) is used (Collins and Moon, 1981). Demel et al. (1982) categorized DEMs as regular grids, digital
contours and triangulated irregular networks according to their data structure. In spite of the fact that DEMs
in the form of triangular irregular networks or digital contour lines seem to be more adequate for representing
terrain morphology (Palacios-Velez and Cuevas-Renaud, 1986; Moore et al., 1991), regular-grid DEMs have
been adopted more often for network extraction (Peucker and Douglas, 1975; Mark, 1984; O’Callaghan and
Mark, 1984; Jenson, 1985; Band, 1986, 1989; Jenson and Domingue, 1988; Fairfield and Leymarie, 1991;
Tarboton et al., 1991; Tribe, 1992). The regular grid is the most popular DEM for terrain because of its simple
and ordered data characteristics. It can be used to derive a wealth of information about the morphology of a
land surface (US Geological Survey, 1987).
A variety of methods have been developed to process raster DEMs automatically to extract drainage
networks and measure their properties (O’Callaghan and Mark, 1984; Band, 1986; Jenson and Domingue,
1988; Tarboton et al., 1991; Martz and Garbrecht, 1992, 1998, 1999). The most commonly used procedures
for extracting drainage networks from raster DEMs are based on O’Callaghan and Mark’s (1984) algorithm
for flow direction determination, coupled with an arbitrary constant value for the minimum contributing
area needed to form and maintain a channel. The stream threshold Ts choice will influence the extracted
* Correspondence to: Wen-Chieh Chou, Department of Civil Engineering, Chung Hua University, Hsinchu City 300, Taiwan.
E-mail: [email protected]
Copyright  2005 John Wiley & Sons, Ltd.
Received 13 February 2003
Accepted 18 January 2005
W.-T. LIN ET AL.
drainage networks. Generally, Ts is assumed as a constant value, based on personal judgment or visual
comparison of the networks generated with the streamlines identified or digitized from a topographical map
(Jenson and Domingue, 1988; Gardner et al., 1991). Other researchers, such as Montgomery and Dietrich
(1988), Tarboton et al. (1991) and Dietrich et al. (1993), have devoted themselves to deriving quantitative
approaches to stream threshold definition according to the link between slope and area power. However,
power law relationship studies should be validated further on the influences between the morphology, soil
and climate to actual channel initiation. Heretofore, using a constant threshold value to extract drainage
networks automatically from a DEM is still the most popular method for calculating the geomorphological
and hydrological information.
With the fast-growing computer technologies and geographic information systems (GISs), a number of
programs, such as the HYDROLOGY module in ArcView, the DWCON and TERRAIN ANALYSIS modules
in EASI/PACE (Chang, 2002), and TOPAZ (Lacroix et al., 2002), landscape analysis tools were developed to
extract drainage networks by combining the Jenson and Domingue (1988) and Garbrecht and Martz (1997)
algorithms with GISs. The only way to determine a reasonable stream threshold was trial and error. Trial and
error is not only subjective, but also wastes time when determining the reasonable stream threshold. Channel
initiation information depends on the landform and/or climate characteristics of the watershed. The channel
initiation points, therefore, fluctuate.
The extracted stream network in hydrologic analyses is important because the network indirectly determines
the hillslope travel distance and network link lengths. The characteristics of the extracted network depend
extensively on the definition of channel initiations on the digital landscape. Once the channel initiations are
defined, the essential topology and morphometric characteristics of the corresponding downstream drainage
network are implicitly predefined because of their close dependence on channel initiation definition. Thus, the
identification of channel sources is critical for extraction of a representative drainage network from DEMs
(ASCE, 1999). This study develops modified algorithms that can easily extract suitable drainage networks,
especially using channel initiations. Comparisons between networks extracted from different maps or various
stream thresholds were carried out in quantitative terms by calculating the geomorphological indices and the
hydrological responses, represented by the geomorphological instantaneous unit hydrographs (GIUHs) on two
upstream watersheds in central Taiwan.
MATERIALS AND METHODS
Study area
The Chi-Jia-Wang stream watershed (area 7403 ha, altitude 1693–3873 m, average slope 71%) and the
Erh-Wu stream watershed (area 5136 ha, altitude 1633–3447 m, average slope 66%) are located at the
upstream Da-Chia River in Ho-Ping Hsiang, Taichung County, Taiwan, as shown in Figure 1. The climate data
obtained from Taiwan’s Central Climate Bureau shows about 120 days of rain a year with average precipitation
2246 mm, mainly concentrated from February to August. The rainfall types are convective precipitation (as
thunderstorms) and orographic precipitation (for topographic reasons). The geological and geomorphological
characteristics of the watersheds are quite similar. The geological data from Taiwan’s Central Geological
Service shows that the rock formations occurring in the target area are the Da-Tong-Shan and Gan-Gou
Palaeocene stratifications, chiefly formed by slate, shale, gravel, rock and sandstone. They consist dominantly
of fine-grained calcareous sandstone, intercalated with dark-grey shale and interlaminated sandstone and shale.
The soils contain a high percentage of sand and minor silt.
For each watershed, 1 : 10 000- and 1 : 25 000-scale topographic maps, airborne photographs rectified by
6Ð25 m high-resolution SPOT images and a raster DEM (grid size 40 m) were used to obtain drainage
networks. From experience, and the terrain characteristics in Taiwan, the stream threshold used on the DEM
varied from 50 to 500 pixels. In most cases, automatic generation validation methods were carried out using
Copyright  2005 John Wiley & Sons, Ltd.
Hydrol. Process. (in press)
DRAINAGE NETWORK EXTRACTION
drainage network
watershed boundary
0
3
6
Km
Erh-Wu stream
watershed
D
a-
C
hi
a
R
iv
e
r
Chi-Jia-Wang stream
watershed
Figure 1. Study area
visual comparison with the blue lines from medium-scale topographic maps or photo-interpretation (Chorowicz
et al., 1992). This study selected photo-interpretation to validate the new extraction methods.
Methods
This study employed Lin’s (2002) method for calculating the flow directions and developed two modified
algorithms combining the channel initiation points and the flow directions to delineate the proper drainage
networks. Figure 2 shows the flowchart for this study.
Flow directions calculation. Currently, the most commonly used procedures for calculating the flow
directions from raster DEMs are based on Jenson and Domingue’s (1988) algorithm. This procedure uses
a depression-filling technique to treat flat areas and depressions. However, two problems were encountered
when this method was applied to realistic landscapes. The first problem is that in some cases more than one
outflow point from a depression or flat area exists after the area is filled (Jenson and Domingue, 1988). The
second problem is that looping depressions located on a flat surface cannot be solved. Martz and Garbrecht
(1998, 1999) proposed the breaching algorithm for treating closed depressions in a DEM. However, this
approach still cannot delineate an adequate watershed boundary when verified in Shihmen watershed of
northern Taiwan (Lin, 2002). A modified method proposed by Lin (2002) was chosen to calculate the flow
directions for improving dead-end problems in depressions and flat areas. The first step is to derive the
incipient flow direction (non-depression flow direction) using an elevation-differentiating method coupled
with a surface-inclining method. The second step is to calculate the optimal outlet and flow direction in
depressions using a depression watershed method with the Preference Ranking Organization Method for
Enrichment Evaluations (PROMETHEE) theory proposed by Brans et al. (1984). It is proved to be a good
way to calculate the optimal outlet and flow direction in depressions (Chou et al., 2004). Figure 3 shows the
differences between these two methods.
Stream network extraction. Drainage networks are traditionally obtained by manually digitizing stream
channels from maps or airborne photographs. With increasing computer capability and DEM availability,
Copyright  2005 John Wiley & Sons, Ltd.
Hydrol. Process. (in press)
W.-T. LIN ET AL.
data collection
1. topographical maps
2. airborne photos
3. SPOT satellite image
digital elevation model
flow direction calculation
WinGrid system
1. fitness index
2. headwaters-tracing method
3. constant stream threshold
drainage networks
Geomorphological indices
hydrological response
results and discussion
Figure 2. Flowchart of this study
clipping DEM
filling depression in a DEM
seeking depression watershed
computing depressionless
flow direction
Lin’s method
Jenson and Domingue’s method
computing non-depression flow direction
computing the outlet of depression
watershed using PROMETHEE
computing depression flow direction
computing the flow accumulation
assigned a constant threshold
delineating watershed
extracting drainage network
Figure 3. Differences in extracting a drainage network between the Jenson and Domingue and the Lin methods
attempts have been made to extract networks from DEMs via computer programs. The ‘constant-threshold’
method was used to calculate the number of upstream elements, i.e. the number of cells that contribute surface
flow to any particular cell. Cells with catchment numbers greater than a given threshold are considered on
the flow path. The smaller the chosen threshold, the more complicated are the channels obtained. Tribe
Copyright  2005 John Wiley & Sons, Ltd.
Hydrol. Process. (in press)
DRAINAGE NETWORK EXTRACTION
(1991) pointed out a problem involved in positioning the drainage network end that would prevent successful
delineation of fully connected drainage networks. Positioning of the ends of drainage networks fluctuates with
the threshold value.
Instead of the visual judgment or trial and error, the fitness index calculates the channel initiation error
length between the observed and calculated values to determine the reasonable stream threshold (Figure 4).
The proposed formula of the fitness index can be written as
n
FD
Li s C
sD1
n
Lr s
sD1
1
LT
where Li is the insufficient stream length, Lr is the redundant stream length, LT is the total stream length
extracted from airborne photographs or electronic maps, s is the specific insufficient or redundant stream, and
n is the total insufficient or redundant stream numbers. If F is a minimum, then the assigned stream threshold
can be defined as the reasonable stream threshold.
Since the thresholds for the real networks are not constant, a modified algorithm that could substitute
the constant threshold value is proposed in this study. The headwater-tracing method procedures involved in
channel initiation point extraction are the flow direction calculation and the spatial analysis for tracing the flow
paths from the channel initiation to the outlet. Figure 5 illustrates the headwater-tracing method architecture.
With the current GIS technique and the availability of electronic maps and airborne photographs, the channel
initiation points can be easily obtained by loading the image data source in to any GIS software. After the
channel initiation recognition, using the flow direction calculation method explained in above section, the
stream network extraction can be done by the headwater-tracing method. The stream networks close to the
actual network can then be quickly delineated using the headwater-tracing method. These extracted networks
from airborne photographs and electronic maps were used to compare stream networks generated by different
threshold values. The channel initiation error length caused by redundant and insufficient stream links can be
calculated by the precise initiation coordinates.
the observed
channel initiation
the calculated
channel initiation
the insufficient
stream length (Li)
the redundant
stream length (Lr)
Figure 4. Error length of the channel initiation derived from the difference between observed and calculated value
Copyright  2005 John Wiley & Sons, Ltd.
Hydrol. Process. (in press)
W.-T. LIN ET AL.
map
DEM
GIS
channel initiation points
flow direction
headwater-tracing method
drainage network
Figure 5. The architecture of the headwater-tracing method
Table I. Geomorphological indices of the headwaters watershed
Watershed length
Average altitude
Average slope
Relief
L
H
S
Rf D Hmax Hmin
Relief ratio
Rf
R D d
Hmax
Hmin
d
The highest point in the watershed
The lowest point in the watershed
The horizontal distance from the
highest point to the outlet
Geomorphological indices and hydrological responses. The threshold reflects the river evolution mechanism
and depends on the landform and/or climate characteristics. Because the formation of each headwater
watershed is closely associated with the geomorphological indices, five indices listed in Table I were
calculated to analyse the relationships with the channel initiation threshold. The geomorphological indices
Copyright  2005 John Wiley & Sons, Ltd.
Hydrol. Process. (in press)
DRAINAGE NETWORK EXTRACTION
Table II. Geomorphological indices of the drainage network
Number of headwaters
Number of stream links
Main stream length (m)
Nh
N
L0
Total stream length (m)
LT D
The inlet numbers of first-order stream
n
The total length of stream within a
watershed. Li : stream link length
A number that designates the relative position
of stream segment in a drainage network
A: watershed area
Li
i
Catchment order (unitless)
Drainage density (m1 )
Ds D LAT
Fs D NAw
Drainage frequency (m2 )
The number of stream segments per unit area
of the drainage network (Table II) were also calculated to compare the networks delineated using various
methods.
The width function Wl, which measures the number of stream links (Figure 6) at a given distance l from
the outlet, was used to describe the drainage network. One of the important reasons for the width function
lies in its close relationship to the watershed stream networks. Wl is (Gandolfi and Bischetti, 1997)
Wl D
N
bl; lui , ldi 2
iD1
where N is the number of stream links in the drainage network, lui and ldi (m) are the distances of the upstream
and downstream ends of link i from the outlet, and the function bl is defined as follows:
1 if ldi l < lui
3
bl; lui , ldi D
0
otherwise
Subwatershed
Junction
Initiation
Interior
link
Exterior link
Outlet
or sink
Stream channel link
Drainage divide link
Exterior basin area
Interior basin area
Figure 6. Stream links and divide graph structures (revised from Band (1986))
Copyright  2005 John Wiley & Sons, Ltd.
Hydrol. Process. (in press)
W.-T. LIN ET AL.
In order to compare the width functions of different networks, it is convenient to put Wl in the normalized
form:
WxL
L
4
wx D
LT
where x D l/L, L is the length of the longest flow path and LT is the total stream network length.
The GIUH can be employed to calculate the influence of the channel network on the delay and the
shape of the hydrograph. The GIUH approach was originated by Rodriguez-Iturbe and Valdes (1979), who
rationally interpreted the runoff hydrograph in a travel time distribution framework that explicitly accounted
for the watershed geomorphological structure. Huge efforts were subsequently devoted to the development and
application of GIUH theory (Gupta et al., 1980; Rinaldo et al., 1991; Gandolfi and Bischetti, 1997; Gandolfi
et al., 1999). In the GIUH approach, rainfall excess is assumed to follow different paths on overland areas
and in channels of different stream orders to reach the watershed outlet. A GIUH model was chosen here to
quantify the differences in hydrological response for the networks generated (Mesa and Mifflin, 1986; Rinaldo
et al., 1991). The GIUH will be
N
max
1
li
ft D w
li expf[li ut2 /4DL t]g
5
3
L
4DL t iD1
where li is the distance of the link i from the outlet, Nmax is the number of stream links of the longest flow
path, wli /L is the normalized width function, u (m s1 ) is the flow celerity in the network, and DL (m2 s1 )
is the hydrodynamic dispersion coefficient.
System architecture. The WinGrid spatial analysis software was developed (Lin and Lin, 2001) to compute
the flow directions, extract the drainage networks and calculate the geomorphological indices and hydrological
response. In the WinGrid system, the basic data storage unit can be represented as a single layer in a map that
contains information about the location features. The WinGrid system consists of several separate program
components (e.g. GRIDDING for handling boundary, clipping, recoding, etc.; SPATIAL for handling terrain
analysis, watershed delineation, stream network extraction, etc.; WATERSHED for handling geomorphological
indices, hydrological response, stream order, etc.; MODULES for handling remote sensing data, riparian
buffer strip evaluation, vegetation recovery evaluation, etc.; UTILITY for handling grid information and
coordinate transformation; DISPLAY for handling display; IMPORT/EXPORT for handling data from/to
ArcView, AutoCAD, Imagine, SPSS and Surfer). In this study, most of the tasks can be done via the
TERRAIN and WATERSHED components. The former module calculates the flow directions, the extraction of
drainage networks and the delineation of watershed. The latter module executes the analysis and calculation
for the threshold of channel initiation and the corresponding geomorphological indices and hydrological
responses.
RESULTS AND DISCUSSION
Drainage network comparisons delineated by different methods
The suitable threshold for stream delineation. The drainage networks delineated using various stream
thresholds (Ts D 50, 200, 350 and 500) are shown in Figures 7 and 8. As expected, with the increase in
Ts , the number of stream links gradually decreases. This shows that there are significant differences between
the extracted networks. Comparing the extracted networks with blue lines from the 1 : 10 000 and 1 : 25 000
maps and airborne photographs, the suitable threshold values for stream delineation for both Chi-Jia-Wang and
Erh-Wu stream watersheds are listed in Table III. The suitable stream threshold values can also be calculated
using the fitness index, as listed in Table III and illustrated in Figures 9 and 10. The minimum fitness index
values calculated from airborne photographs for the Chi-Jia-Wang and Erh-Wu watersheds are 0Ð30 and 0Ð38
Copyright  2005 John Wiley & Sons, Ltd.
Hydrol. Process. (in press)
DRAINAGE NETWORK EXTRACTION
0
3
6
0
3
6
Km
Ts =50
Km
Ts = 350
0
3
6
0
3
6
Km
Ts = 200
Km
Ts = 500
Figure 7. The drainage networks of the Chi-Jia-Wang watershed generated by various stream thresholds
respectively. The corresponding stream threshold is 215 and 145 pixels respectively. There is a significant
difference in the determination of suitable stream thresholds between the visual judgment and fitness index,
especially for the Erh-Wu stream watershed. Since the fitness index is a quantitative analysis method, the
calculated stream threshold should be more objective and accurate than a visual judgment. Comparing various
results, the networks generated from a 1 : 25 000 map were more unreliable than networks generated from
1 : 10 000 map and airborne photographs because of reality simplification.
Channel initiation threshold analysis. The channel initiation points can be easily obtained by integrating
computer programs with GIS software such as components in ArcView, EASI/PACE or WinGrid. Figures 11
and 12 illustrate the frequency distribution and the associated statistical information for channel threshold
initiation in each watershed. The histograms are significantly positively skewed, i.e. most of the initial
stream thresholds are quite small, especially for those obtained from airborne photographs. Owing to the
high variability between the channel threshold initiation there is no single threshold that could fit the actual
drainage network.
Table III. The suitable threshold for stream delineation
Maps
Stream threshold
Chi-Jia-Wang stream
1 : 10 000 topographic map
1 : 25 000 topographic map
Airborne photographs
Copyright  2005 John Wiley & Sons, Ltd.
Erh-Wu stream
Visual
judgment
Fitness
index
Visual
judgment
Fitness
index
250
400
250
235
540
215
250
450
250
170
780
145
Hydrol. Process. (in press)
W.-T. LIN ET AL.
0
3
6
0
3
6
Km Ts =50
Km Ts =350
0
3
6
0
3
6
Km Ts =200
Km Ts =500
Figure 8. As Figure 7, but for the Erh-Wu watershed
2.4
the fitness index compared
with airborne photos
the fitness index compared
with 1:10000 topographic map
the fitness index compared
with 1:25000 topographic map
Fitness Index
2
1.6
1.2
0.8
235
0.4
540
215
0
50
150
250
350
450
Threshold
550
650
Figure 9. The analysis of the fitness index for the Chi-Jia-Wang watershed
As shown in Figures 13 and 14, the drainage networks were delineated using the headwater-tracing method
coupled with channel initiation points extracted from different data sources. Compared with map digitizing or
trial and error, the headwater-tracing method is more simple, accurate and applicable for network extraction.
Overlaying the networks generated from a single stream threshold and the headwater-tracing method, in
Copyright  2005 John Wiley & Sons, Ltd.
Hydrol. Process. (in press)
DRAINAGE NETWORK EXTRACTION
2.5
the fitness index compared
with airborne photos
the fitness index compared
with 1:10000 topographic map
the fitness index compared
with 1:25000 topographic map
Fitness Index
2
1.5
1
170
0.5
145
780
0
50
150 250 350 450 550 650 750 850
Threshold
Figure 10. As Figure 9, but for the Erh-Wu watershed
1:10000 topographic map
20
Mean=46.82
Std. Deviation=84.57
CV=180.63
Skewness=3.71
N=89
50
Frequency
Frequency
60
Mean=39.00
Std. Deviation=52.10
CV=133.59
Skewness=3.04
N=57
10
40
30
20
1:25000 topographic map
10
6
4
2
10
0
0
0
0
50 100 150 200 250 300
Mean=282.22
Std. Deviation=230.1
CV=81.53
Skewness=0.98
N=22
8
Frequency
airborne photos
30
0
Threshold
100 200 300 400 500
0
Threshold
200
400
600
800
Threshold
Figure 11. Frequency distribution of the thresholds of channel initiation for the Chi-Jia-Wang watershed
Mean=47.8
Std. Deviation=67.52
CV=141.26
Skewness=2.23
N=60
30
20
10
10
40
80 120 160 200 240
Threshold
6
4
2
0
0
Mean=575.67
Std. Deviation=809.1
CV=71.15
Skewness=1.86
N=15
8
Frequency
40
Mean=53.50
Std. Deviation=50.92
CV=95.17
Skewness=1.22
N=56
Frequency
Frequency
1:25000 topographic map
1:10000 topographic map
airborne photos
16
14
12
10
8
6
4
2
0
0
0
50 100 150 200 250 300
Threshold
0
1000
2000
Threshold
Figure 12. As Figure 11, but for the Erh-Wu watershed
Figures 15 and 16 the bold blue lines represent the coincidental links, the red lines represent the insufficient
links and the green lines represent the redundant stream links. The coincidental analyses are illustrated in
Figures 17 and 18. Calculated using the fitness index, the drainage networks extracted from Ts D 215 have
the highest percentage of coincidental stream links (71%). However, the topographical map at a 1 : 25 000
scale has the lowest percentage of coincidental stream links (only 57%).
Copyright  2005 John Wiley & Sons, Ltd.
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W.-T. LIN ET AL.
(b)
(a)
(c)
Figure 13. The drainage networks for the Chi-Jia-Wang watershed were generated by the headwaters-tracing method coupled with channel
initiation points extracted from (a) airborne photographs, (b) 1 : 10 000 topographic map, and (c) 1 : 25 000 topographic map
(b)
(a)
(c)
Figure 14. As Figure 13, but for the Erh-Wu watershed
The channel threshold initiation will be strongly affected by the morphology, soil and climate (Dietrich
et al., 1993). Although some previous studies indicated that the slope would affect the network channel
initiation, no strong relationship was evident in the results analysed.
The extracted network effect on the geomorphological indices and hydrological responses. Table IV lists the
geomorphological indices for the extracted networks using different methods. The geomorphological index
Copyright  2005 John Wiley & Sons, Ltd.
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DRAINAGE NETWORK EXTRACTION
(a)
(b)
(c)
(d)
Figure 15. The drainage networks for the Chi-Jia-Wang watershed that overlaid the networks from airborne photographs with: (a) that from
1 : 10 000 topographic maps, (b) that from 1 : 25 000 topographic maps, (c) the Ts D 215 networks, (d) the Ts D 250 networks
(a)
(b)
(c)
(d)
Figure 16. As Figure 15, but for the Erh-Wu watershed and for (c) Ts D 145
values from topographic maps do not match the values obtained from airborne photographs better than the
networks generated, where the threshold was determined using the fitness index. The networks extracted from
the 1 : 25 000 map showed an impressive number of network segment omissions. Conversely, the networks
from the 1 : 10 000 map were overestimated. All of the geomorphological indices are affected by the stream
Copyright  2005 John Wiley & Sons, Ltd.
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W.-T. LIN ET AL.
0.8
percentage
0.6
overlaid with the networks
extracted from 1:10000 maps
overlaid with the networks
extracted from 1:25000 maps
overlaid with the networks
extracted from Ts=250
overlaid with the networks
extracted from Ts=215
0.4
0.2
0
the insufficient
stream links
the redundant
stream links
the coincidental
stream links
Figure 17. The percentage of the overlaid drainage networks in the insufficient, redundant and coincidental stream links for the Chi-Jia-Wang
watershed
0.8
percentage
0.6
overlaid with the networks
extracted from 1:10000 maps
overlaid with the networks
extracted from 1:25000 maps
overlaid with the networks
extracted from Ts=250
overlaid with the networks
extracted from Ts=145
0.4
0.2
0
the insufficient
stream links
the redundant
stream links
the coincidental
stream links
Figure 18. As Figure 17, but for the Erh-Wu watershed
threshold values. Most of the indices decreased by increasing the stream threshold Ts , except for the catchment
order . When Ts rose from 50 to 500 pixels, dropped from 5 to 4 for both watersheds. Similar to the
fitness index, the total stream length LT could also be used to judge a single stream threshold roughly. The
proper stream threshold resembling LT for the Chi-Jia-Wang ranged from 150 to 200 and the Erh-Wu ranged
from 100 to 150.
Rigon et al. (1993) indicated that the basin shape significantly controls the shape of the width function. The typical shape of major watersheds is reflected in the very small and possibly negatively skewed
corresponding width function (Gupta et al., 1986; Gandolfi and Bischetti, 1997). In Figures 19 and 20,
both watersheds show this behaviour. There are significant differences between the width functions for
Copyright  2005 John Wiley & Sons, Ltd.
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DRAINAGE NETWORK EXTRACTION
Table IV. Geomorphological indices of the networks extracted
Ts (pixels)
Chi-Jia-Wang
50
100
150
200
250
300
350
400
450
500
1 : 10 000 map
1 : 25 000 map
Airborne photographs
Erh-Wu
50
100
150
200
250
300
350
400
450
500
1 : 10 000 map
1 : 25 000 map
Airborne photographs
Nh
Nw
L0 (km)
LT (km)
W
Ds
Fs
245
129
76
57
41
36
28
24
24
22
89
22
57
482
256
151
113
81
71
55
47
47
43
175
43
112
17Ð87
17Ð69
17Ð47
17Ð47
17Ð31
17Ð1
16Ð93
16Ð93
16Ð93
16Ð85
17Ð26
16Ð51
16Ð74
172Ð11
119Ð93
96Ð3
85Ð98
76Ð75
71Ð52
66Ð93
63Ð67
62Ð02
59Ð75
103Ð42
56Ð69
97Ð57
5
4
4
4
4
4
4
4
4
4
4
4
4
2Ð32
1Ð62
1Ð3
1Ð16
1Ð04
0Ð97
0Ð9
0Ð86
0Ð84
0Ð81
1Ð4
0Ð77
1Ð32
3Ð31
1Ð74
1Ð03
0Ð77
0Ð55
0Ð49
0Ð38
0Ð32
0Ð32
0Ð3
1Ð2
0Ð3
0Ð77
181
98
55
38
32
29
26
22
20
19
60
15
56
355
195
109
75
63
57
51
43
39
37
119
29
109
15Ð66
15Ð49
15Ð49
15Ð32
15Ð14
15Ð01
14Ð82
14Ð82
14Ð82
14Ð57
15Ð62
15Ð11
15Ð62
119Ð72
81Ð17
64Ð49
57Ð92
53Ð75
50Ð25
47Ð09
43Ð7
41Ð53
39Ð88
67Ð5
35Ð33
71Ð96
5
5
4
4
4
4
4
4
4
4
4
3
4
2Ð33
1Ð58
1Ð26
1Ð13
1Ð05
0Ð98
0Ð92
0Ð85
0Ð84
0Ð78
1Ð31
0Ð69
1Ð4
3Ð52
1Ð91
1Ð07
0Ð74
0Ð62
0Ð56
0Ð51
0Ð43
0Ð39
0Ð37
1Ð17
0Ð29
1Ð09
the networks generated. This is because the highest order channels are well recognized by all methods. Many discrepancies exist in lower order channel identification, especially in channel initiation point
identification (Gandolfi and Bischetti, 1997). Similar to the width functions, such discrepancies are also
represented in the shape of the GIUHs, as shown in Figures 21 and 22. For a given watershed, the differences between the GIUHs are entirely due to the differences in the corresponding network geometries
(Gandolfi and Bischetti, 1997). The GIUHs for the networks generated (Ts D 215 for the Chi-Jia-Wang and
Ts D 145 for the Erh-Wu) fit the networks extracted from airborne photographs well. Different selected
thresholds or maps generate different network geometries, which significantly affects the hydrological
responses. Therefore, it is very important to extract suitable drainage networks using appropriate methods
or maps.
CONCLUSIONS
Suitable drainage network extraction depends primarily on the accuracy of the maps and the channel initiation identification. The discrepancies in the channel initiation points, whether extracted from different
maps or various stream thresholds, affect not only the network geometries, but also the geomorphological indices and hydrological responses. Two modified algorithms were employed in this study, the
headwater-tracing method and the fitness index, to delineate suitable drainage networks rapidly based
on the channel initiation points and the flow directions. The more accurate the channel initiation points
provided, the more suitable the drainage network generated. In recent years, with the fast progress in
Copyright  2005 John Wiley & Sons, Ltd.
Hydrol. Process. (in press)
W.-T. LIN ET AL.
3
DEM generated
1:25000 maps generated
1:10000 maps generated
airborne photos generated
w(x)
2
1
0
0
0.2
0.4
0.6
0.8
1
x
Figure 19. Normalized width functions for the Chi-Jia-Wang watershed
3
DEM generated
1:25000 maps generated
1:10000 maps generated
airborne photos generated
w(x)
2
1
0
0
0.2
0.4
0.6
0.8
1
x
Figure 20. Normalized width functions for the Erh-Wu watershed
computer technology and GIS techniques, acquiring large-scale electronic maps and/or high-resolution satellite images has become more convenient, facilitating applications for the algorithms developed in this
work.
ACKNOWLEDGEMENTS
This research was supported by a grant from the National Science Council, R.O.C. (NSC 92-2313-B-451-003).
Copyright  2005 John Wiley & Sons, Ltd.
Hydrol. Process. (in press)
DRAINAGE NETWORK EXTRACTION
Figure 21. Effects of the identification method on the network GIUHs of the Chi-Jia-Wang watershed (u D 0Ð5 m s1 , DL D 1000 m2 s1 )
Figure 22. As Figure 21, but for the Erh-Wu watershed
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