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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); 1094 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 1096 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: 1098 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). References Argialas, D.P, and C.A. Harlow, 1990. Computational Image Interpretation Models: An Overview and a Perspective, Photogrammetric Engineering & Remote Sensing, 56(6):871-886. Argialas, D.P., and G.CH. Miliaresis, 1996. Physiographic Knowledge Acquisition: Identification, Conceptualization and Representation, Proceedings, 1996 ACSM/ASPRS Convention, 22-25 April, Baltimore, Maryland, pp. 311-320. , 1997a. Landform Spatial Knowledge Acquisition: Identification, Conceptualization and Representation, Proceedings, 1997 ACSM/ASPRS, Convention, 07-10 April, Seattle, Washington, pp. 733-740. , 1997b. An Object Oriented Representation Model for the Landforms of an Arid Climate Intermontane Basin: Case Study of Death Valley, California, Proceedings, 23rd International Conference of the Remote Sensing Society, 02-04 September, Reading, United Kingdom, pp. 199-205. Band, L.E., 1986. Topographic Partition of Watersheds with Digital Elevation Models, Water Resources Research, 22(1):15-24, Bissenbach, E., 1954. Geology of Alluvial Fans in Semi-Arid Regions, Bulletin of the Geological Society of America, 65:175-190. Bull, W.B., 1977. The Alluvial Fan Environment, Progress i n Physical Geography, 1222-270. Chorowicz, J., C. Ichoku, S. Riazanoff, Y. Kim, and B. Cervelle, 1992. A Combined Algorithm for Automated Drainage Network Extraction, Water Resources Research, 28(5): 1293-1302. Doehring, D., 1970. Discrimination of Pediments and Alluvial Fans from Topographic Maps, Geological Society of America Bulletin, 81:3109-3165. Dymond, J.R., R.C. Derose, and G.R Harmsworth, 1995. Automated Mapping of Land Components from Digital Elevation Data, Earth Surface Processes and Landforms, 20:131-137. Fenneman, N., 1931. Physiography of Western United States, McGrawHill, New York, 534 p. Graff, L.H., and E.L. Usery, 1993. Automated Classification of Generic Terrain Features in Digital Elevation Models, Photogrammetric Engineering & Remote Sensing, 59(9):1409-1417. Hamblin, W.K., and J.D. Howard, 1980. Exercises i n Physical Geology, Fifth Edition, Burgess Publishing Co., New York, N.Y., 225 p. Hunt, B., 1975. Death Valley: Geology, Ecology, Archaeology, University of California Press, Berkeley, California, 234 p. Isaacson, D., and W. Ripple, 1990. Comparison of the 7.5-Minute and 1-degree Digital Elevation Models, Photogrammetric Engineering 6.Remote Sensing, 56(11):1523-1527. Jenson, S., and J. Dominque, 1988. Extracting Topographic Structure from Digital Elevation Data for Geographic Information System Analysis, Photogmmmetric Engineering 6. Remote Sensing, 54(11):1593-1600. Levin, H.L., 1989. Contemporary Physical Geology, Saunders College Publishing, Philadelphia, Pennsylvania, 623 p. PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING Lillesand, T.M., and R.W. Kiefer, 1987. Remote Sensing and Image Interpretation, Second Edition, John Wiley and Sons, New York, 721 p. Mark, D.M., 1984. Automated Detection of Drainage Network from Digital Elevation Models, Cartographica, 21:168-178. Miliaresis, G.CH., and D.P. Argialas, 1998a. Physiographic Feature Extraction from Moderate Resolution Digital Elevation Data, 24th International Conference of the Remote Sensing Society, 09-11 September, Greenwich, United Kingdom, pp. 545-551. -, 1998b. Parametric Representation and Classification of Mountain Objects Extracted from Moderate Resolution Digital Elevation Data, Proceedings, 4th International Conference of the Association for Mathematical Geology, 05-09, October, Ispra, Naples, Italy, pp. 892-897. -, 1999a. Segmentation of PhysiographicFeatures from the Global Digital Elevation Model / GTOP030, Computers b Geosciences, 25(7):715-728. -, 1999b. Formalization of the Photo-Interpretation Process by a Fuzzy Set Representation of Mountain Objects in the Geomorphic Context of the Great Basin Section, Proceedings, 25th Conference of the Remote Sensing Society, 08-10 September, Cardiff, United Kingdom, pp. 745-750. -, 1999c. Fuzzy Pattern Recognition of Compressional Mountain Ranges in Iran, Proceedings, 5th International Conference of the Association for Mathematical Geology, 06-11 August, l'kondheim. Norway, pp. 227-232. O'Callaghan, J., and D. Mark, 1984. The Extraction of Drainage Networks from Digital Elevation Data, Computer Vision, Graphics and Image Processing, 28:323-344. Pmdey, S.N., 1987. Principles and Applications of Photogeology,Wiley Eastern Limited, New Delhi, 366 p. Peuker, T., and D. Douglas, 1975. Detection of Surface-Specific Points by Local Pardel Processing of Discrete Terrain Elevation Data, Computers Graphics and Image Processing, 4:375-387. Pike, R. J., 1988. The Geometric Signature: Quantifying LandslideTerrain Types from Digital Elevation Models, Mathematical Geology, 20(5):491-511. ANY SET O F 12 ISSUES ----------------For USA Addresses (postage included) Non-USA Addresses: Add $35 for postage. RESOURCE '99 ----------------------------- ANY 1999 SPECIAL ISSUE-------------OTHER SINGLE ISSUES ------------------Add $3.00 postage per issue for Non-USA addresses. GST is chaqed to residents of Canada only (GST #135193065). Tax is calculated at 7% x(subtota1 + shipping charges). Availability: 1 993 - 1999 Out of Print: January 1998, October & December 1997; June 1996; January 1994; March, July, August, September, & October 1993 T O ORDER, CONTACT: ASPRS Distribution Center Annapolis Junction, MD 20701-0305 tel: 301 -61 7-781 2; fax: 301 -206-9789 e-mail: [email protected] ,1993. A Bibliography of Geomorphometry, US. Geological Survey, Open-File Report 93-262-A, Menlo Park, California, 132 p. , 1995, Geomorphometry-Progress, Practice and Prospect, Advances in Geomorphometry: Zeitschrift fir Geomorphologie, (R.J. Pike and Richard Dikau, editors) Supplementband 101, pp. 221-238. Pitas, I., 1993. Digital Image Processing Algorithms, Prentice Hall, London, 362 p. Qian, J., R.W. Ehrinch, and J.B. Campell, 1990. DNESYS- An Expert System for Automatic Extraction of Drainage Networks From Digital Elevation Data, BEE Thns. on Geoscience and Remote Sensing, 20(1):29-45. Rachocki, A., 1981. Alluvial Fans, John Wiley and Sons, New York, 161 p. Rolando, L.J., R.C. Edward, K. Yunjin and S. Yushen, 1996. Design of Shuffle Radar Topography Mapper (SRTM), Technical Report 960520, Jet Propulsion Laboratory, Pasadena, California, 8 p. Short, N.M., and R.W. Blair 1986. Geomorphologyfrom Space, A Global Overview of Regional Landforms, NASA SP-486, U.S. Government Printing Office, Washington, D.C., 709 p. lkoeh, F.R., 1965. Landform Equations Fitted to Topographic Maps, American Journal of Science, 263:616-627. U.S. Geological Survey, 1970. Topographic Map of the Death ValleyCalifornia, Scale 1:250,000. -, 1984. Landsat-ThematicMapper lmage of Death Valley-California, acquired on the 23rd of June 1984, Order: U.S.G.S. 0119612270019. , 1993. Digital Elevation Models: Data Users Guide 5, National Mapping Program, Reston, Virginia, 51 p. , 1998. GTOP030: 30 Arc-Seconds Global Digital Elevation Model, http://edcwww,cr.usgs.gov/lmddaac/gtopo30/ gtopo30.html , 1999. 3 Arc-Second Digital Elevation Model of Death ValleyCalifornia, ftp://edcftp.cr.usgs.gov/pub/data/DEM/250/D/ death-valley-w.gz Way, D. S., 1978. Termin Analysis, McGraw-Hill, New York, 438 p. (Received 22 March 1999; accepted 08 September 1999; revised 06 October 1999) -