Download Extraction and Delineation of Alluvial Fans from Digital Elevation

Extraction and Delineation of
Alluvial Fans from
Digital Elevation Models and
Landsat Thematic Mapper Images
G. Ch. Mlllaresls and D. P. Argialas
Abstract
A methodology was designed and computer algorithms were
implemented for the extraction of alluvial fans from digital
elevation models and Landsat TM imagery, and it was demonstrated for the Death Valley region of the southwestern
U.S.A. First, the drainage network was extracted from the
digital elevation model and the outflow points to the basin
floor were detected. Then, region growing of the outflow points
was performed on the basis of the gradient value of the surroundingpixels in the digital elevation model, and a set of fan
polygons was derived. These polygons were then used as seeds
to another region-growing algorithm that extracted each downfan area using a criterion that was based on both ( I ) the
gradient and (2) the difference of the spectral signatures (in
the Landsat TM band 1 ) between the playa deposited in the
basin floor and the down-fan area. The finally extracted fanpolygons were found to be in accordance with the alluvial
fans depicted on the topographic map and the satellite imagery.
Introduction
Digital elevation models and satellite imagery are available for
extensive areas of the Earth while, at the same time, various
techniques have been developed in order to automate the interpretation of terrain related features (Pike, 1993; Pike, 19951,
including the following:
Description of concave-convex curvature of the alluvial fans
from topographic maps (Troeh, 1965),
Discrimination of pediments and alluvial fans from topographic
maps (Doehring, 1970),
Detection of surface-specific points (pass, ridge, pit, peak,
ravine, flat, etc.] from digital
- elevation models (Peuker and
Douglas, 19751,
Categorization of broad areas of terrain (Pike, 1988),
Extraction of drainage networks (O'Calaghan and Mark, 1984;
Band, 1986; Chorowicz et al., 19921,
Automated mapping of land components (Dymond et al., 1995),
Removal of closed depressions from digital elevation models
(Jenson and Domingue, 19881,
Typology of photo-interpretation procedures for the Basin and
Range landforms through knowledge-based approaches (Argialas and Miliaresis, 1996; 1997a; 1997b), and
G. Ch. Miliaresis is with Hellenic Petroleum S.A., Exploration
of Hydrocarbon Division, 38 Tripoleos Str., Athens, 104-42,
Greece (gmilidcentral.ntua.gr),
D.P. Argialas is with the Remote Sensing Laboratory, Dept.
Rural & Surveying Engineering, National Technical University,
9 Heroon Polytechniou Str., Zographos, 157-80, Greece.
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
Extraction and parametric representation of mountain objects
from moderate resolution digital elevation models (Miliaresis
and Argialas, 1998a; 1998b; 1999a; 1999b; 1 9 9 9 ~ ) .
This paper is concerned with the extraction of alluvial fans
from digital elevation models and Landsat Thematic Mapper
(TM) images. While others have developed methods for mountain feature extraction, the extraction of alluvial fans has been
largely unexplored (Graff and Usery, 1993). Alluvial fans are
best developed in arid areas of high mountain ranges such as
those of the southwestern United States where approximately
30 percent of the land is covered by alluvial fans (Lillesandand
Kiefer, 1987). In arid regions, alluvial fans are ground-water
indicators (Pandey, 1987),and their soils provide good foundation conditions for highways and buildings (Way, 1978;Lillesand and Kiefer, 1987).However, the shifting stream channels
and the frequent flooding that often occur on alluvial fans present serious limitations to development (Bull, 1977).For example, major sections of the Alaska Highway have been re-routed
following destruction of the original roads crossing
- alluvial
d Kiefer, 1987r
fans ( ~ i l g s a n and
Given (1) the engineering significance of alluvial fans, (2)
the lack of previous work concerning their automated extraction, and (3) the availability of the Landsat TM imagery and the
one-degree digital elevation model for the United States and
elsewhere in the world, this research is aimed at the design of a
methodology and the implementation of algorithms for the
extraction of alluvial fans from digital elevation models and
satellite imagery.
Methodology
At first, the study area is introduced, then the data used, followed by the geomorphologicknowledge relevant to alluvial
fans. Finally, the detailed steps that describe the extraction
methodology are given.
Study Area
The studv area is located in the Death Valley (California)region
in the socthwestern United States (Figure la) and it is b o u d e d
by latitude 36" 06' to 36" 14' Nand longitude 116" 49' to 116'
40' W. The study area is part of the Great Basin section of the
Photogrammetric Engineering & Remote Sensing
Vol. 66, No. 9, September 2000, pp. 1093-1101.
0099-1112/00/6609-1093$3.00/0
0 2000 American Society for Photogrammetry
and Remote Sensing
September 2000
1093
Baiadas developed in front of the Paramint Range (Figure lb];
and
Salt-crusted, dry lakebeds (playas) which cover the valley floor
(Figures l b and lc).
Eastward tilting of Death Valley has caused the west-side fans
to be rather extensive, and the toes of the east-side fans to be
buried by playa deposits (Bull, 1977).Numerous springs and
wells are evident at the emergence of the alluvial landforms to
the valley floor (Hunt, 1975) and a seasonal shallow lake is
formed occasionally in the Badwater Basin, in the northwest
portion of the study area (Figure lb). Badwater Basin has the
lowest elevation of any area in the United States.
Digltal Data
The digital data used in this study included the one-degree
Defense Mapping Agency digital elevation model of Death Valley at a scale of 1:250,000 (U.S. Geological Survey, 1999)and a
Landsat TM satellite image acquired on 23 June 1984 (U.S. Geological Survey, 1984).The one-degree digital elevation models
are freely available for the whole of the conterminous United
States (U.S. Geological Survey, 1993),and they are adequate for
inclusion with large area surveys incorporating satellite-acquired data (Isaacson and Ripple, 1990).The digital file of the
one-degree digital elevation model for Death Valley was described as a raster ASCII-formattedfile, with a spacing of 3 arc
seconds (a 3-arc-second spacing in latitude corresponds always
to 92.6 m while a 3-arc-second spacing in longitude for this
mid-latitude area equals about 75 m). The digital elevation
model was rescaled, rectified, and then resampled to 75 meters
in both the east to west and north to south directions using the
nearest-neighborhoodmethod (Figure 2a). Finally, the Landsat
TM satellite image (Figures 2b and 2c) was registered to the digital elevation model of the study area.
Geomorphologlc Knowledge Relevant to Alluvlal Fans
Definitions of alluvial fans emphasize the evolution process,
the locus of deposition, and the external shape. Fan initiation
is due to a drastic reduction in gradient between the eroded valley and the receiving plain that causes the deposition of a sediment lead (Figure 3a). Alluvial fans exhibit a characteristic
semi-circular, semi-conical shape. Bissenbach (1954) states
that the main morphological parts of alluvial fans are
Ftgure 1. Study area. (a) Location of Death Valley within the
southwestern United States (Levin, 1989). (b) Study area
on the hydro-geologic map of Death Valley (Hunt, 1975). (c)
Oblique air-photograph showing the alluvial fans of the study
area (Hamblin et a/., 1980) numbered for reference in the
subsequent figures.
Basin and Range Physiographic Province. The Great Basin is
occupied mostly by wide desert plains, generally almost level,
interrupted by great, largely dissected, north-trending, roughly
parallel mountain ranges formed by a series of tilted fault
blocks (Fenneman, 1931).Death Valley forms a closed depression bounded by two mountain ranges (Figure lb), the Paramint Range to the west and the BlackMountains to the east. The
common landforms of Death Valley are
Alluvial fans deposited in front of valley mouths along the
mountain front at the emergence of rivers to the adjacent valley
floor (Figure lc);
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September 2000
the fan-head, the area of the fad closest to the apex (the highest
point of an alluvial fan where the stream emerges from the
mountain valley);
the fan-toe (or fan-base), the outermost or lowest zone of the
fan; and
the mid-fan,the area between the fan head and the fan-toe.
According to Bull (1977),the fan surface is segmented into
concentric zones with different slope gradients (Figure 3b).
Troeh (1965) states that the slope-gradient in an alluvial fan
tends to decrease with a decrease in elevation, and that the
slope profile is therefore concave upward. Thus, a greater slope
gradient is observed in the fan-head than in the fan-toe.
The overall gradient of the surface depends upon the
length of the radius of the fan; therefore, fans with a long radius
tend to have more gentle slopes. Bissenbach (1954) classified
fans according to their slope into (1)steep (above 5"), (2) gentle
(2" to so),and (3) flat (below ZO),and he observed that slopes
above 5" are found only in the upper reaches of fans. Additionally, Rachocki (1981)states that, in a range of alluvial fan radii
from several hundred meters to 100 km, the gradients rarely
exceeded lo0,averagingbetween 3" and 6". Bull (1977)reserved
the term alluvial cone for smaller forms with gradients greater
than 20 degrees.
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
(a)
Q
(b)
Figure 2. Data used. (a) One-degree digital elevation model (201 rows and 1 7 1 columns) at a scale of 1:250,000
Elevation values (- 72 to 3480 m) were rescaled
in the range 255 to 0.The lighter a pixel, the lower its elevation. (b) Landsat TM band 1with a spectral range of 0.45
to 0.52 pm (blue)designed for water body penetration, shows the playa formation beneath the seasonal playa lake
in the Badwater Basin (the northern part of the valley floor). The identifiers used for the labeling of the alluvial fans
were the same as those in Figure lc. (c) Landsat TM band 5 with a spectral range of 1.55 to 1.75 pm (mid infrared)
shows with darker tones the northern area (Badwater Basin) of the valley floor in contrast to the southern light-toned
valley floor (playa).It is interpreted that higher soil moisture content or even a seasonal playa lake is formed in the north.
(Ptp://edcPtp.cr.usgs.gov/pub/data/DEM/25O/D/death~va~ley-w.gz).
Extmctlon of alluvial fans
A compilation of geomorphologic knowledge of the study area
and the interpretation of the satellite imagery (Figures 2b and
2c) and the digital elevation model (Figure2a) indicated that
Alluvial fans are being developed in the piedmont slope and
around the outflow points of the drainage networks that emerge
in the valley floor (Figure 3a), and
An alluvial fan is a genly sloping feature in comparison to the
flat valley floor (downslope) and to the upslope, high-sloping
mountain front (fault scarp).
These factors suggest the implementation of a segmentation method for the delineation of alluvial fans by a regiongrowing algorithm (Argialas and Harlow, 1990). In this
approach, a set of seed points should be detected and these
points should grow on the basis of a region-growing criterion
(Pitas, 1993).In the present research effort,the region-growing
algorithm uses as seeds the outflow points of the drainage networks emerging into the valley floor and the region-growing
criterion was based on the gradient magnitude. Both the selection of outflow points and the determination of the regiongrowing criteria are examined in the following.
Detection of Outflow Points: Initial Fan Polygons
For the detection of the outflow points, the drainage network of
the study area was extracted as follows. First, the digital elevation model was smoothed twice using a 3 by 3 Gaussian mask in
Zone o f corlsrcence
w l th other fans
A
5 KM
I
1
(a)
(b)
Figure 3. Depositional environment and morphology of alluvial fans. (a) Fan developed in front of a valley mouth at
the adjacent valley floor. (b) Development of an alluvial fan with segmented radial profile after Bull (1977).
Figure 4. Computed gradient and aspect images. (a) Gradient values
expressed in degrees (0°, 85') were rescaled in the range 255 to 0. The
lighter a pixel, the lower its gradient. (b) Aspect pointing downslope, was
quantified to eight directions (East = 1,Northeast = 2, North = 3, Northwest = 4, West = 5, Southwest = 6, South = 7, Southeast = 8).Zero
The aspect
labels (black greytone)were used for flat terrain (gradient< lo).
image was histogram equalized for presentation (the pixels with the darkest
tone point east and those with the brightest tones point southeast).
order to remove the artifacts (pits and peaks) forming runoff
traps that interrupt surface flow in the low-relief areas.
Although there are more appropriate techniques to remove pits
(Jenson and Domingue, 1988),Gaussian filtering was found to
be sufficient and very fast in this particular case study. Then,
the gradient (Figure 4a) and the aspect (Figure 4b) were computed on the basis of the Sobel operator defined within a 3 by
3 mask.
Finally, a runoff simulation technique was implemented,
whereby a single water unit was imported into every pixel of
the digital elevation model and allowed to travel in the downslope aspect direction while the water units imported into
each pixel were counted and the derived values were used to
represent the runoff (Mark, 1984). Pixels with surface runoff
greater than 33 (the mean runoff value per pixel of the digital
elevation model) were assigned to the drainage network segments. Thus, each drainage segment consisted of a connected
chain of points in which runoff was sufficiently concentrated
(Qian et al., 1990).
The resulting segments were skeletonized to one-pointwide lines using an iterative, local operator preserving 8-connectedness (Band, 1986). In each iteration only north, south,
east, and west border points were considered for deletion while
points were removed if and only if they were not end-points
and their removal would not disconnect a contiguous path of
points. Then, a connected component labeling algorithm (Pitas,
1993)was applied and a unique integer identifier was assigned
to every point that belonged to a certain drainage segment.
The next step searched for the end-points of the open segments (points with only one marked neighboring component)
within the drainage segments (Band, 1986). Subsequently, the
downstream outflow points were defined as the minimum in
elevation among the end-points that belonged to a specific
drainage segment. Finally, the outflow points were activated to
begin draining into successively lower elevation pixels in the
digital elevation model until either another drainage segment
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September 2000
or a pit or flat terrain was encountered. After extending all segments, the image was thinned again to a final one-point-wide
line representation of the drainage networks and the outflow
points were redefined (Table 1).It was determined, through
visual interpretation, that all the outflow points were located
on the piedmont slope and, in particular, on the alluvial fan
regions (Figure 5a).
Determination of the Region-Growing Criterion
In order to determine the region-growing criterion, training
areas were delineated on the digital elevation model within the
alluvial fans and gradient statistics were computed. The
selected training areas are shown on the Landsat TMi image
(Figure 5b). More specifically, the mean and standard deviation
of the gradient were computed as 2.7" and l.ZO,respectively.
The minimum and the maximum values were equal to O0 and
7", respectively. It was assumed that the gradient of the valley
floor (where the playa and the playa lake were located) was less
than 2". Thus, the gradient values within the alluvial fans were
TABLE1. DRAINAGE
NETWORK (FIGURE
5A) LENGTH(IN PIXELS)
AND NUMBEROF
OPENSEGMENTS
Drainage Network
Identification Number
Length (in
pixels)
Number of Open
Segments
1
2
3
4
62
88
21
2
90
5
3
27
5
81
6
5
1
7
509
25
8
18
2
2
9
50
3
PHOTOGRAMMETRIC ENGINEERING 81REMOTE SENSING
(4
(b)
Figure 5. Extracted drainage networks and training areas. (a) Nine drainage
networks identified by the connected component-labeling algorithm. End
points of the open segments appear darker than the network. (b) Seven
training areas (white polygons) within the alluvial fans and two training areas
within the playa (black polygons).
determined to be within the interval [2", 7'1 and are consistent
with the values noted earlier by Bissenbach (1954) and
Rachocki (1981).
Extraction and Delineation of Alluvial Fans by Region
Growing Segmentation
An iterative region-growing segmentation algorithm (Pitas,
1993)was implemented in order to extract and delineate the
alluvial fan regions. The seeds (outflow points) formed the initial set of alluvial fan points. In each iteration, if a pixel of the
digital elevation model with gradient greater than or equal to 2"
and less than or equal to 7" was an %connected neighbor to the
current set of alluvial fan points, then it was flagged as a new
alluvial fan point. The segmentation stopped if no more points
were added during the current iteration. Finally, 932 points
(2.71 percent of the total area) were flagged after 12 iterations
(Table 21.
Figure 6a shows the points that were finally classified into
the alluvial fan feature class. These points formed a set of polygons that were developed around the outflow points in the fanhead and possibly in the mid-fan area.
In order to test the sensitivity of the methodology,the segmentation was performed again with a new region-growing criterion to include gradient values in the interval (2", 11").The
TABLE
OF POINTSADDED
2. NUMBER
IN
EACH~TERATIONOFTHE
increase of the upper limit of gradient from 7" to 11"resulted in
an increase of the alluvial fan regions in the upslope direction
up to and including the downslope region (border) of the
mountain front (Figure 6b). Consequently, the alluvial fanpolygons appeared to be coalescing. Both segmentations failed to
extract the fan-toe.
In order to extract the fan-toe, where the gradient was less
than 2", the difference in spectral response between the playa
(white photo-tone) and the alluvial fans (light gray photo-tone)
in the Landsat TM band 1was taken into account (Table 3). In
this approach, the black-tone alluvial fan points of Figure 6a
were used as seeds, and two region-growing criteria were
applied simultaneously to the digital elevation model and to
the Landsat TM band 1image. More specifically, a pixel of the
digital elevation model that was an 8-connected neighbor to the
current set of alluvial fan points was flagged as an alluvial fan
point if its gradient was less than 2", and its Landsat TM band 1
digital number was in the interval 120 to 200 (Table 3). The second criterion stopped the alluvial fan growing process when
the spectral response (in Landsat TM band 1)of the examined
point approached that of the playa. The first criterion limited
the region growing process only within the valley floor. Finally,
2283 points (6.64 percent) were flagged and assigned to the fantoe, after five iterations (Table 4). The resulting image is shown
in Figure 6c and the borderlines of the extracted fan polygons
are given in Figure 6d.
Discussion of Results
Iteration
Points added
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
599
107
58
50
30
33
20
11
8
6
5
5
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
The results were evaluated by comparing the boundaries of the
extracted fan polygons to those interpreted from both the Landsat Thematic Mapper satellite image (acquired on 23 June
1984) and a topographic map at the scale of 1:250,000(U.S.
Geological Survey, 1970).
The fan polygons extracted from the digital elevation
model (Figure 6a) were overlaid on the Landsat TM image (Figure 7a). It was determined that these fan polygons corresponded to the area around the valley mouth (fan-head, midfan) where the apex of the fan was located.
In the topographic map of the study area (Figure 7b),there
are no contour lines within the mid-fan and down-fan areas.
Thus, a direct comparison was not possible. An indirect evaluaSeptember 2000
1097
f
4
I
i
C,
(a)
b
(dl
(c)
Figure 6. Output of the segmentation algorithm. (a) Alluvial fan points labeled
by the segmentation algorithm (region-growingcriterion: gradient in the interval 2" to 7"). (b) Alluvial fan points labeled by the segmentation algorithm
(region-growing criterion: gradient in the interval 2" to 11"). (c) Extracted fan
~
The black tone
polygons (darker tones) derived from the Landsat T M image.
alluvial fan points of Figure 6a were used as seeds. Two region-growing
criteria were considered simultaneously (see text). (d) The borderlines of the
extracted fan-polygons.
TABLE3. STATISTICS
FOR THE SPECTRAL
RESPONSE
OF THE ALLUVIAL
FANAND
THE PLAYA
TRAININGAREASIN THE LANDSAT
TM BAND1(FIGURE58)
Spectral Response in
Landsat TM b a n d 1
Statistics
A l l u v i a l Fan
Playa
Minimum
Maximum
Mean
St. Deviation
tion, however, was made by comparing (1)the track of the road
designed parallel to the mountain front and being characterized
as a secondary, all weather hard surface road (U.S. Geological
Survey, 1970) to (2) the downslope border of the extracted fan
polygons. This comparison is meaningful for the following
reasons:
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September 2000
TABLE4. NUMBER
OF POINTSADDEDIN EACH~NTERAT~ON
OF THE ~TERATIVE
REGION-GROWING
SEGMENTATION
ALGORITHM
APPLIED
TO BOTH THE LANDSANT
TM BAND1 IMAGE AND THE DIGITALELEVATION
MODEL
Iteration
Points added
1.
2.
3.
4.
5.
2132
69
44
19
19
The road i s a linear engineering structure designed to provide
the shortest distance between t w o given points and to minimize
construction and maintenance costs (Pandey, 1987);
In alluvial fans, roads are b u i l t o n the same level as the alluvial
fan surface because roads that are lower than the surface o f the
fan tend to be buried and roads that are higher may be destroyed
b y floods (Bull, 1977); and
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
+='
I
Ir
i;
J
I
i
4 I
I"
(c)
(dl
Figure 7. Evaluation. (a)Extracted drainage network and alluvial fans (labeled
white) superimposed over the Landsat T M I image. (b) The U.S. Geological
Survey topographic map of the Death Valley region, at the scale of 1:250,000
registered to the digital elevation model of the study area. The arrows point
out a road designed almost parallel to the mountain front. Alluvial fans
extracted from the digital elevation model are labeled black. (c) Extracted
alluvial fans from the digital elevation model (depicted with lighter tones)
and from the Landsat T M 1 image (depicted with darker tones) superimposed
over the latter. (d) Borderlines of extracted fan-polygons superimposed over
the Landsat TM band 1image.
In the study area, the road should have been designed to avoid
the seasonal playa lake f~-ed in the *adwater Basin (Figure 2c).
The comparison of the road to the border of the fan polygens indicated the following. In the topographic map (Figure
7b) it was observed that the extracted fan polygons were located
east of the road and that the shape of the road followed the west
border of these polygons. The last observation indicated that
the road was closest to the alluvial fan surface in order to avoid
the temporal playa lakes and flooding. In addition, the road has
followed a path of minimum slope (less than two degrees) and
has tried to avoid the topographic obstacles posed by the fans.
The above observations lead to the conclusion that the
extracted fan polygons derived from the digital elevation model
match the topographic expression of the alluvial fans.
PHOTOGRAMMETRIC ENGINEERING 81REMOTE SENSING
The fan-toe and the zone of coalescence with other fans
[Figure 3b) failed to be extracted from the representation of
relief in the one-degree digital elevation model because the
gradient of these features was quite similar to the gradient of
the valley floor. These features, however, were extracted with
the combination of spectral and topographic information, as
is shown in Figure 7c, where there was a satisfactory match of
the fan-toe with the alluvial fan polygons interpreted from
the satellite imagery. The downslope boundary of the fan
polygons (Figure 7d) is fuzzier in the south than in the Badwater Basin (north).The fuzzy border is caused by the continuous deposition of alluvium and by the aeolian processes
while the variation of the seasonal playa-lake level observed
in the Badwater Basin tends to eliminate the fuzzy appearance of the border.
Conclusion and Prospects
A methodology was designed for the extraction of alluvial fans
from one-degree digital elevation models and Landsat TM
imagery and it was demonstrated for the Death Valley region.
The drainage network was extracted and the outflow points to
the basin floor were detected. Then, region-growing of the outflow points was performed on the basis of the gradient value of
the surrounding pixels in the digital elevation model, and a set
of fan polygons was derived. Comparison of the derived alluvial fan polygons to the alluvial fan segments interpreted from
the Landsat Thematic Mapper imagery showed that only the
area around the apex of each fan (the fan-head and the mid-fan
zones) was extracted from the one-degree digital elevation
models. The fan toe and the zone of coalescence with other fans
failed to be extracted because their gradient approached the
gradient of the valley floor.
Satellite imagery, in addition to a digital elevation model,
was used for the extraction of the fan-toe zones because there
was an obvious difference in the spectral response of the alluvial fans and the playa covering the valley floor. More specifically, the difference in the digital values in Landsat TM band 1
between the playa deposited in the valley floor and the downfan area deposited in the piedmont slope was used. The initially derived fan polygons were used as seeds to an algorithm
that extracted the down-fan area on the basis of region-growing
criteria that were functions of both spectral and topographic
information (the last limited the search within the valley floor).
The derived alluvial fan polygons were found to be in accordance with the fan features depicted on the topographic map
and the Landsat TM imagery. This fact evaluated the information content of the one-degree digital elevation model and
pointed out its value and applications to image interpretation,
to geomorphometric analysis, and to geomorphologicmapping.
The method should work in similar physiographic conditions where (1)alluvial fans emerge from a gorge to a plain and
(2) playa deposits are evident in the plain. These conditions are
common in arid lands where mountainous physiography is
developed, such as the Basin and Range Province in the United
States and the Makran and Zagros Ranges in Iran (Short and
Blair, 1986).
A key point in the developed methodology was the identification of seed points to initiate the region-growing process.
Seed points were defined as the outflow points that were
located within the piedmont slope region. It should be emphasized that if some of the outflow points are not located on the
piedmont slope, but instead are located on the basin floor, they
cannot be used as seeds, because the region-growing procedure
would fail to extract the alluvial fans. The outflow points in
our case study were detected automatically, but it was found,
through visual interpretation, that all of them were located on
the piedmont slope and, therefore, they could be used as seed
points. In order to avoid the latter visual interpretation step
and to further automate the alluvial fan extraction process from
digital elevation models, the piedmont slope polygons surrounding the mountain features should be extracted first. In
earlier research efforts of the authors (Miliaresis and Argialas,
1999a1, piedmont slope polygons were extracted from the
GTOP030, Global Digital Elevation Model (U.S. Geological Survey, 1998).Thus, a spatial rule could be implemented to find
those outflow points which are located within the piedmont
slope region and to characterize them as the seed points for the
alluvial fan segmentation algorithm.
The 7.5-minute digital elevation models with 30 meters
spacing corresponding to the U.S. Geological Survey 1:24,000scale topographic map series (U.S. Geological Survey, 1993)
could be used in the future. These high-resolution digital elevation models would allow the extraction of the topographic
expression of alluvial fans in greater detail and the derivation
a
0
0
September 2000
of a quantitative parametric representation of the fan polygons.
Similarly, one could use, in the near future, the digital terrain
data from the radar interferometric topography mapper that is
designed to acquire digital elevation maps of the Earth's surface from the Space Shuttle. This mission is planned for the
1999 and the system will be capable of acquiring digital elevation models (with a 3-arc-second spacing and an absolute
height accuracy of 16 meters) of all regions between 54"s and
60°N latitude (Rolando et al., 1996).
Acknowledgments
The authors are grateful for the suggestions of the anonymous
reviewers. Thanks are expressed to NASA (Goddard DAAC) for
providing the c~version of the "Geomorphology from Space"
an out-of-print 1986 publication, edited by Dr. N. M. Short and
Dr. Robert W. Blair (http://daac.gsfc.nasa.gov/DAAC-DOCS/
geomorphology/GEO~HOMEMEPAGE.html).
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Baltimore, Maryland, pp. 311-320.
, 1997a. Landform Spatial Knowledge Acquisition: Identification, Conceptualization and Representation, Proceedings, 1997
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