THE GLOBE DEM PARAMETERIZATION OF THE MOUNTAIN FEATURES OF MINOR ASIA George Ch. Miliaresis Department of Topography Technological Educational Institute of Athens 38 Tripoleos Str. Athens, 104-42, Greece Charalampos V.E. Paraschou Dept. Rural & Surveying Engineering National Technical University of Athens 23 Velvedou Str. Athens 113-64, Greece miliaresis@email.com chvparas@central.ntua.gr ABSTRACT In previous research efforts, methodologies were developed for the extraction of mountains from the GTOPO30 digital elevation model (DEM) in Basin Range and in Zagros Ranges (Iran). On the other hand the evaluation of the parametric representation (mountaintop accuracy assessment) of mountains revealed systematic errors in the GTOPO30 DEM. Here, the extraction of mountains within the Minor Asia is presented. The GLOBE DEM is used, that is the most thoroughly designed, reviewed, and documented global DEM today. The study area is bounded by latitude 36 o to 42 o N and longitude 26 o to 45 o E approximately. More specifically, region-growing segmentation was implemented and ridge pixels were considered as seeds while the growing criterion was based on gradient. Valley pixels did not allow participating in the region growing process for region growing to be blocked in NE were steep sloping valleys were evident. Small in size (less than 30 pixels) isolated islands of mountains were removed. Additionally, small islands (less than 24 pixels) of non-mountain aggregates (flat mountaintops) were merged to the mountain terrain class. The extracted 702 mountain objects were in accordance to the features interpreted visually from a shaded relief map of the study area. In a forecoming effort, mountains within Iran, Turkey, and Greece will parametrically represented on the basis of geomorphometric attributes and interpretation models will be provided for the Eurasian-Arabian continental margin on the basis of the morphometry of mountains. INTRODUCTION AND AIM Various research efforts tried to explore the relationship between tectonics and topography [Merits and Ellis, 1995; Summerfield, 2000) and various geomorphometric techniques (Pike, 1995, 2000) have been developed in an effort to automate the interpretation of terrain features from digital elevation models (DEMs). Towards this end a methodology was developed for the extraction of physiographic features from GTOPO30 DEM (US Geological Survey, 1998) and it was implemented in Basin and Range where the crust is under tensional forces, thins by normal faulting, and results in an array of tipped mountain blocks (Miliaresis and Argialas, 1999). In another effort, the mountains were considered to form the elementary morphotectonic units and a) their definition and b) modeling, could characterize the landscape at regional scale (Miliaresis, 2001a). Thus, a method was developed for the extraction of mountains in an area of compressional stress such as the Zagros Ranges (where collision of the Arabian shield with Iran has shortened and thickened the crust to produce a spectacular mountainous physiography) and the landscape of the study area was characterized on the basis of the resulting morphometry of mountains. Zagros Ranges is part of Alpine-Himalayan belt that is extended through Turkey to eastern Greece (Moore and Twiss, 1995). It would be of great scientific interest if the mountain ranges from Iran to Greece were extracted and represented by a set of numerical attributes and the landscape of the Eurasian-Arabian continental margin was characterized on the basis of the morphometry of mountains. On the other hand the evaluation of the parametric representation of mountains in Zagros revealed systematic errors in the GTOPO30 DEM (Miliaresis and Paraschou,
2001). The objective of the present research effort is develop a method for the extraction of the mountain features (DEM to mount transformation) of Turkey (Minor Asia) from the GLOBE 1.1 DEM (Globe, 2001). METHODOLOGY The DEM to Mountain transformation (Miliaresis and Argialas, 1999; Miliaresis, 2001a) integrates a) certain geomorphometric techniques: computation of aspect and gradient, runoff simulation (Mark, 1984) with b) the digital image processing techniques (Pitas, 1993): segmentation, connected components labeling, and small object elimination. It is actually a region-growing segmentation algorithm that uses the ridge pixels as seeds and a growing criterion based on gradient (Graff and Usery, 1993). Note that in Zagros Ranges, the method was modified in order to cope with the specific geomorphometric characteristics of the study area and valley pixels were not allowed to participate in the region growing process (Miliaresis, 2001a). The key points are the determination of a) the initial set of seeds and b) the region-growing criterion. First, the Globe DEM and the study are will be presented and finally the detailed steps of the implementation of the DEM to mountain transformation will be given. Study Area and DEM Horizontal expuls ion is taking place in Asia Minor. Most of the area is extruding westward away from the Arabian-Eurasian collision and toward the small remnant of oceanic crust underlying the eastern Mediterranean Sea (Yeats, Sieh and Allen, 1997; Summerfield, 2000). Within the study area (Figure 1) are a) the North Anatolian Fault (NAF, with total length of 1500 km) and b) the East Anatolia Fault (EAF, with size of 580 km). EAF marks the Anatolian- Arabian plate boundary while Zagros Ranges fold zone is the outcome of the collision between Figure 1. The study area in Minor Asia, NAF, EAF and the study area in Zagros Ranges were outlined in the map of the Alpine-Himalayan belt of Dewey (1977). Figure 2. Globe DEM of the study area. The elevation values (1 to 4,916 m) were rescaled to the interval 255 to 0 (the brightest pixels have lowest elevation) for presentation. The study area (land) occupies 885,260 pixels (1 pixel =1 km 2 ) out of 1,064,000 image pixels.
Eurasian (Iran) and Arabian plate. The study area is bounded by four points expressed as latitude, longitude pairs: (42 o N, 26 o E), (42 o N, 45 o E), (36 o N, 44 o 15 E), (36 o N, 26 o E). The Global Land One-kilometer Base Elevation (GLOBE) DEM is the most thoroughly designed, reviewed, and documented global DEM today (Hastings and Dunbar, 1998). It comprises a global 30" latitude-longitude array (referenced to World Geodetic System 84) with land areas populated with integer elevation data. The data source of GTOPO30 partially overlaps with Globe data but uses different interpolation methods to extract resampled height values from the original source Globe DEM has a number of improved characteristics such as additional improved elevation accuracy and multiple sources including contour lines and raster-grid data (Hastings and Dunbar, 1998). The Globe DEM of the study area was reprojected to a rectangular grid for the spacing to be 1000 m in both N- S and E-W directions and resampled (by nearest neighbor). Finally, the DEM of the study area consists of 665 rows and 1,600 columns (Figure 2). The mean elevation is 1,100.3 ± 664.9 m. Figure 3. Gradient image. The pixels (with values in the range 0 o to 42 o ) were rescaled to the interval 255 to 0 (the brightest pixels have lowest gradient). Figure 4. Aspect (pointing in upslope direction). The pixels were in the range 0 to 8 (0 is used for flat terrain, if gradient<2 o ), but the image was histogram equalized for presentation Region Growing Criterion and Seeds Gradient and aspect (Figure 3 and 4 respectively) were computed per pixel on the basis of the Z-operator and the Sobel operator (Miliaresis and Argialas, 1999). Mean gradient is 5.98 o ± 5.6 o and aspect was standardized to the eight geographic directions defined in a raster image: East=1, Northeast=2, North=3, Northwest=4, West=5, Southwest=6, South=7, Southeast=8, and zero labels were used for flat terrain, (Table 1).
Table 1. Aspect (pointing downslope) frequency distribution. Flat terrain 208,490 pixels (23.6%) Aspect Pixels % Aspect Number % N 107,037 12.1 S 106,995 12.1 NE 78,197 8.8 SW 89,988 10.2 E 63,036 7.1 W 69,210 7.8 SE 78,515 8.9 NW 83,792 9.5 Training areas indicated that the gradient of the non-mountain areas is less than 6 o in general. An exception is observed in Eastern part of the study area where narrow canyons with steeper sides are observed. Then, runoff simulation (Mark, 1984) was implemented for the identification of ridge (upslope flow) and valley (downslope flow) pixels. 54 iterations were needed for the integration of the downslope runoff simulation. The mean downslope runoff value was 6.01 (see pixels were excluded). A total number of 191,233 (21.6 %) pixels were flagged are valley pixels with downslope runoff greater than 6 (Figure 5). 55 iterations were needed for the integration of the upslope runoff simulation. The mean upslope runoff value was 6.52 (sea pixels were excluded). A Figure 5. Ridge pixels. Figure 6. Valley pixels. total number of 158,394 (17.9 %) pixels were flagged are ridge pixels with upslope runoff greater than 7 (Figure 6).
Segmentation of Mountains An iterative region-growing segmentation was implemented. In order to segment the closely spaced mountain objects in the North and East where high gradient values are observed in between the valleys, the valley pixels did not allowed to participate in the region growing segmentation process. The ridge pixels formed the initial set of seeds. In each successive iteration, if a non-mountain pixel satisfied the following three conditions (a) it s gradient was > 6 o, (b) it was an 8-connected neighbor to a pixel that already belonged to the current set of mountain pixels and (c) it was not labeled as a valley pixel then it was flagged as a new mountain pixel and the current set of mountain pixels was updated (Miliaresis, 2001a). The segmentation stopped after 13 iterations and 322,762 (36.46 %) pixels were classified to the mountain terrain class. Figure 7. The pixels labeled white within the study area belong to the mountain terrain class. The resulting image was a bit noisy, due to the very small isolated islands of mountain pixels that represent either small remnants or artificial error peaks. Additionally the occasional small islands of non-mountain pixels on mountaintops represent flat or gently sloping areas (gradient< 6 o ). To correct these artifacts, a connected component labeling algorithm (Pitas, 1993) was applied and both foreground (mount terrain class) and background objects (non-mount terrain class) were identified. The foreground pixels (arranged in 2,252 objects) with size less than 30 pixels were removed and 8,600 mountain terrain pixels were assigned to the non-mountain terrain class. Then the background pixels (arranged in 2,165 objects, one with size 745,132 that includes the sea and the adjacent flat plains while the rest had size less or equal than 24 pixels) with size less or equal than 24 pixels were assigned to the mountain terrain class. 4,834 pixels were added to the mountain terrain class. A connected component-labeling algorithm identified 702 mountain objects in the mountain terrain class. Figure 8. Shaded relief. The location of the simulated sun is 50 o above the horizon at NW.
Evaluation Discussion of Results The segmentation was judged from the visual interpretation of the computer-shaded relief map of the study area (Figure 8). Digital shaded-relief maps are a valuable tool for the computer visualization of landscape morphometry, allowing the surface features to viewed in a broad regional context (Pike, 1995). Borderlines of the mo untain objects were delineated and superimposed on the shaded-relief map of (Figure 9). It was observed that (a) the majority of the mountain features interpreted from the shaded-relief map were also extracted by the DEM-to-Mount transformation and (b) the algorithm reproduced their shape. Exceptions were some very small mountain objects (erased during post-processing) observed mainly in central Minor Asia (inland where extensive peneplains are evident). The methodology was implemented in a computer progra m named GeoLogic Shell (Miliaresis, 2001b) suitable for the processing of both DEMs and satellite imagery Figure 9. The borderlines (shown black) of the mountains were superimposed on the shaded relief map. CONCLUSION The extracted mountain objects were interpreted to be in accordance to the mountain features interpreted visually from the shaded relief map. The methodology allowed the extraction of mountain features in Mnor Asia and 702 mountain objects were identified. In an upcoming paper the mountains of the morphotectonic zone from Iran to Greece will be extracted and represented by a set of numerical attributes. This fact will allow the landscape characterization of the Eurasian-Arabian continental margin on the basis of the morphometry of mountains. REFERENCES Dewey, J. (1977). Suture zone complexities, a review. Tectonophysics, 40:69-100. Globe (2001). Global Land One-km Base Elevation digital elevation model, (version 1.1). http://www.ngdc.noaa.gov/seg/topo/globe.shtml. Graff, L. and E. Usery (1993). Automated classification of generic terrain features in digital elevation models. Photogrammetric Engineering & Remote Sensing, 59: 1409-1417 Hastings, D. and P. Dunbar (1998). Development & assessment of the Global Land One-km Base Elevation digital elevation model (GLOBE). ISPRS Archives, 32(4): 218-221 Mark, D. (1984). Automated detection of drainage network from digital elevation models. Cartographica, 21:168-178. Meritts, D. and M. Ellis (1994). Introduction to special section on tectonics and topography. Journal of Geophysical Research, 99 (B6): 12,135-41. Miliaresis, G. (2001a). Geomorphometric mapping of Zagros Ranges at regional scale. Computers & Geosciences, 27 (7): 775-786.
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