Geomorphometry: Concepts - Software - Applications

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FIGURE 9 (CHAPTER 1) Geomorphometry then and now: (a) output from late-1980s DOS programme written to display land-surface properties: (left) map of local drainage direction, (right) cumulative upstream drainage elements draped over a DEM rendered in 3-D by parallel profiles. Courtesy of P.A. Burrough; (b) watershed boundaries for the Baranja Hill study area overlaid in Google Earth, an online geographical browser accessible to everyone. (See page 23 of this volume.)

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FIGURE 10 (CHAPTER 1) The “Baranja Hill” datasets. Courtesy of the Croatian State Geodetic Department (http://www.dgu.hr). (See page 27 of this volume.)

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FIGURE 5 (CHAPTER 3) Comparison of DEMs from main sources for Baranja Hill: (a) 90 m resolution SRTM DEM, (b) 30 m resolution SRTM DEM, (c) DEM from 1:50,000 topo-map, and (d) DEM from 1:5000 topo-map. (See page 72 of this volume.)

FIGURE 6 (CHAPTER 3) Example of a 15 ×15 block of 1 arcsec SRTM DEM ordered for Baranja Hill. Courtesy of German Space Agency (DLR). (See page 77 of this volume.)

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FIGURE 7 (CHAPTER 3) Availability of the 1 arcsec SRTM DEMs (C-Band Radar) over the European continent. Missing areas are were not acquired due to an energy shortage at the end of the mission (Rabus et al., 2003). To load the Google Earth map, visit geomorphometry.org. (See page 78 of this volume.)

FIGURE 8 (CHAPTER 3) Availability of the 30 m ASTER DEMs over the European continent (before January 2006). (See page 78 of this volume.)

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FIGURE 3 (CHAPTER 4) An example of local artefacts in part of the GTOPO DEM (1 km resolution). Such artefacts are only visible after careful inspection. (See page 91 of this volume.)

FIGURE 13 (CHAPTER 4) Three approaches to removing spurious sinks: (a) sink filling, (b) carving, and (c) the optimal combination of filling and carving. The detected sinks are indicated black. (See page 107 of this volume.)

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FIGURE 7 (CHAPTER 7) Total catchment area calculated for the Baranja Hill area using three different methods. (See page 183 of this volume.)

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FIGURE 8 (CHAPTER 7) Total catchment area calculated for the Baranja Hill area using MFD and three different dispersion exponents. (See page 184 of this volume.)

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FIGURE 9 (CHAPTER 7) Parts (A)–(C) show the specific contributing area (SCA) calculated for the DEM of a cone sing D8, D-Infinity and MFM. The strong grid bias inherent in D8 is readily visible from the star pattern (A). Part (D) of this figure shows the total contributing area (TCA) calculated using MFM. This counter-intuitive result is correct because of the different flow widths of pixels (see Figure 6). When divided by the flow width, the SCA (C) shows the right circular pattern. (See page 185 of this volume.) © 2005 Rivix LLC, used with permission.

FIGURE 11 (CHAPTER 7) Edge-contaminated areas (white) have been removed from the calculated total contributing area. Both, the flow accumulation as well as the edge-contamination were computed using MFD. Other, less dispersive methods result in a smaller area of edge contamination. (See page 186 of this volume.)

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FIGURE 12 (CHAPTER 7) Wetness index calculated for the Baranja Hill. Values range from 3 (dark) to 20 (yellow); the data is linearly stretched. (See page 187 of this volume.)

FIGURE 13 (CHAPTER 7) Stream power index calculated for the Baranja Hill. Values range from 1 (dark) to 12,000 (yellow); the data is stretched using logarithmic display. (See page 187 of this volume.)

FIGURE 14 (CHAPTER 7) Complete drainage lines for one catchment. In the background, elevation is represented by colour. (See page 188 of this volume.) © 2004 Rivix LLC, used with permission.

FIGURE 15 (CHAPTER 7) Drainage lines pruned by Horton–Strahler order. (See page 189 of this volume.) © 2004 Rivix LLC, used with permission.

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FIGURE 12 (CHAPTER 8)

Altitude above channel lines. (See page 217 of this volume.)

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FIGURE 10 (CHAPTER 9) Illustration of landform elements extracted from land-surface parameters: 64 ha site in Alberta, Canada. See further Section 2 in Chapter 24. (See page 243 of this volume.)

FIGURE 12 (CHAPTER 9) Illustration of possibilities and problems with using hillslopes as basic spatial entities for classifying repeating landform types. See text for detailed discussion. (See page 250 of this volume.)

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FIGURE 8 (CHAPTER 11) Landform classification as shown above using (a) pennock97.aml and (b) simplelfabc.aml scripts for the Baranja Hill Case study with a resolution of 10 m. (See page 286 of this volume.)

FIGURE 9 (CHAPTER 11) of this volume.)

Aspect classes calculated for the Baranja Hill DEM TIN. (See page 288

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FIGURE 6 (CHAPTER 12)

Convergence Index. (See page 301 of this volume.)

FIGURE 8 (CHAPTER 12) Hydrological analysis in SAGA: (a) catchment areas (DEMON, each 100th cell), (b) watershed basins, (c) downslope area (FD8) and (d) upslope area (FD8). (See page 303 of this volume.)

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FIGURE 12 (CHAPTER 12) (a) Flood plain map calculated using a threshold buffer, (b) terrain classification using Cluster Analysis. (See page 306 of this volume.)

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FIGURE 3 (CHAPTER 13) Addition of medial axes: (a) original (bulk) contour data; (b) detected medial axes in problematic areas (padi-terraces); (c) extrapolated shape of the land surface; and (d) temporary terrace-free map prior to interpolation of the remaining undefined pixels. (See page 316 of this volume.)

FIGURE 4 (CHAPTER 13) Visualization of the DEMs using the multi illuminated angles in ILWIS. (See page 319 of this volume.)

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FIGURE 8 (CHAPTER 13) Extraction of hydrological parameters and objects using the built-in operations: (a) flow direction, (b) flow accumulation with catchment lines, (c) overland flow length and (d) wetness index. All calculated using the Deterministic-8 algorithm. (See page 323 of this volume.)

FIGURE 12 (CHAPTER 13) volume.)

Study area classified into the generic landforms. (See page 330 of this

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FIGURE 4 (CHAPTER 14) Profile curvature (per 100 m) measured over 75 and 625 m spatial extents. (See page 337 of this volume.)

FIGURE 5 (CHAPTER 14) Profile curvature (per 100 m) measured from the Baranja Hill 5 m DEM at contrasting spatial scales. The square in the bottom centre of each image represents the size of the window used for processing (15 and 275 m respectively). (See page 338 of this volume.)

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FIGURE 6 (CHAPTER 14) Plan curvature (per 100 m) of the Baranja Hill 25 m DEM measured at the 275 m window scale. The image on the left shows only plan curvature. The image on the right shows the same measure but with colour intensity, representing local shaded relief of the underlying surface. (See page 339 of this volume.)

FIGURE 11 (CHAPTER 14) Maximum absolute profile curvature (per 100 m) measured over all scales between 75 m and 1.7 km (window sizes 3 to 35). The image to the right shows the window scale (in pixels) at which the most extreme value of profile curvature occurs. (See page 348 of this volume.)

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FIGURE 1 (CHAPTER 15) The main window of MicroDEM, with standard Windows controls and four active child windows. The centre left window is an index map showing eastern Europe with available Landsat imagery outlined by the large red rectangle, SRTM data shown in green, and the Baranja Hill DEM barely visible at this scale. Selecting the small box in red opened two DEMs, one a merge of 4 SRTM cells, and the satellite image visible in the background. (See page 353 of this volume.)

FIGURE 6 (CHAPTER 15) Sample maps of land-surface parameters created with MicroDEM. From left to right these show three options for colour coding: a continuous colour scale, a greyscale, and a discrete colour scale. These maps also show the options for placement and orientation of legend and scale bar. (See page 358 of this volume.)

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FIGURE 7 (CHAPTER 15) Sample land-surface parameters draped on the Baranja Hill DEM. (See page 358 of this volume.)

FIGURE 11 (CHAPTER 15) Organisation map of North Africa, with colour displaying the degree of organisation (red highly, to blue poorly organized), draped on shaded topography. Note the large void regions where dry sand led to no radar returns. (See page 362 of this volume.)

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FIGURE 2 (CHAPTER 16) TAS can apply a histogram equalisation stretch dynamically as an image is zoomed into. (See page 371 of this volume.)

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FIGURE 4 (CHAPTER 16) Land-surface parameters derived from the Baranja hill SRTM DEM. (See page 374 of this volume.)

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FIGURE 5 (CHAPTER 16) Stream morphometrics calculated for a stream network derived from the Baranja Hill DEM. (See page 376 of this volume.)

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FIGURE 6 (CHAPTER 16) Various means of extracting watersheds for the Baranja Hill DEM. (See page 378 of this volume.)

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FIGURE 7 (CHAPTER 16) Automated landform classification of the Baranja Hill 25 m SRTM DEM, based on the crisp classification scheme of Pennock et al. (1987). The DEM was pre-processed by running a 21×21 mean filter to remove fine-scale topographic variation. (See page 379 of this volume.)

FIGURE 9 (CHAPTER 16) Elevation as a percentage of local relief (EPR) calculated using an 11×11 (a) and a 101×101 (b) filter and a multi-scale landscape position index (c). Images have been derived from the sample script applied to the Baranja Hill 25 m SRTM DEM. (See page 382 of this volume.)

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FIGURE 10 (CHAPTER 16) Results of a Monte-Carlo uncertainty analysis of the watershed area of a group of seed points in the Baranja Hill 25 m SRTM DEM. (See page 383 of this volume.)

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FIGURE 4 (CHAPTER 17) Slope steepness [°]. (See page 395 of this volume.)

FIGURE 6 (CHAPTER 17) Profile curvature [m−1 ]. (See page 395 of this volume.)

FIGURE 8 (CHAPTER 17)

FIGURE 5 (CHAPTER 17) Aspect [°]. (See page 395 of this volume.)

FIGURE 7 (CHAPTER 17) Tangential curvature [m−1 ]. (See page 395 of this volume.)

Mean curvature [m−1 ]. (See page 395 of this volume.)

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FIGURE 9 (CHAPTER 17) Profile curvature [m−1 ] computed directly from SRTM data using r.slope.aspect. (See page 396 of this volume.)

FIGURE 10 (CHAPTER 17) Profile curvature [m−1 ] from smoothed SRTM data using r.resamp.rst. (See page 396 of this volume.)

FIGURE 11 (CHAPTER 17) this volume.)

Flow accumulation [-] generated by r.terraflow. (See page 398 of

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FIGURE 12 (CHAPTER 17) Flowpath lengths [m] and flowlines generated by r.flow. (See page 399 of this volume.)

FIGURE 14 (CHAPTER 17)

Topographic soil erosion index [-]. (See page 401 of this volume.)

FIGURE 16 (CHAPTER 17) Basic land-surface features extracted using r.param.scale. (See page 404 of this volume.)

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FIGURE 17 (CHAPTER 17) this volume.)

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Global solar radiation for spring equinox [Wh/m2 ]. (See page 405 of

FIGURE 18 (CHAPTER 17)

Visibility analysis using r.los. (See page 405 of this volume.)

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FIGURE 19 (CHAPTER 17) Random fractal surface generated by r.surf.fractal. (See page 406 of this volume.)

FIGURE 20 (CHAPTER 17) Baranja Hill aspect maps: (a) DEM25, (b) DEM5K (generated by v.surf.rst), (c) DEM25-SRTM, and (d) a combined polar diagram of all aspect maps from d.polar. (See page 408 of this volume.)

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FIGURE 21 (CHAPTER 17) Volume interpolation and isosurface visualisation of precipitation (isosurfaces of 1100, 1200, 1250 mm/year are shown) using v.vol.rst. (See page 409 of this volume.)

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FIGURE 2 (CHAPTER 18) A yellow box and crosshairs on a shaded relief image shows the location of a hole (red) in an SRTM DEM for Volcan Baru, Panama. The two images on the right show wire mesh surface plots of the area near the hole, before and after using the Repair Bad Values tool. (See page 417 of this volume.) © 2008 Rivix LLC, used with permission.

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FIGURE 3 (CHAPTER 18) (a) Shaded relief image with labeled contour line overlay; (b) Shaded image of a D8 slope grid; (c) Shaded image of a total contributing area grid, extracted using the mass flux method; (d) Drainage pattern obtained by plotting all D8 flow vectors; (e) Watershed subunits with overlaid contours and channels (blue), using a D8 area threshold of 0.025 km2 ; (f) Shaded image of plan curvature, extracted using the method of Zevenbergen–Thorne. (See page 418 of this volume.) © 2008 Rivix LLC, used with permission.

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FIGURE 3 (CHAPTER 18)

(continued)

FIGURE 5 (CHAPTER 18) A relief-shaded image of a TCA grid for Mt. Sopris, Colorado, that was created using the Mass Flux method. Areas with a large TCA are shown in red while areas with a small TCA value (e.g. ridgelines) are shown in blue and purple. Complex flow paths are clearly visible and results are superior to both the D8 and D-infinity methods. (See page 422 of this volume.) © 2008 Rivix LLC, used with permission.

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FIGURE 8 (CHAPTER 18) High-resolution MOLA (Mars Orbiter Laser Altimeter) DEM displayed in RiverTools: colour shaded relief image for planet Mars shown by the cylindrical equidistant map projection. (See page 426 of this volume.)

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FIGURE 2 (CHAPTER 20) A traditional soil delineation drawn on an aerial photo overlain by contour lines (above) and the derived soil map with soil mapping units (below) for Baranja Hill region (Croatia). The lines are delineated manually and points show the location of soil profile observations. (See page 464 of this volume.)

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FIGURE 3 (CHAPTER 20) Vertical zonation of soils in the Baranja Hill: from deep, drained soils (Kastanozems), to saturated (Gleysoils) and shallow eroded soils (Regosols). (See page 467 of this volume.)

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FIGURE 5 (CHAPTER 21) An automated extraction of land-cover classes: (a) an orthophoto of the Baranja Hill area, overlaid with manually digitised land-cover areas; (b) land-cover classes from the CLC 2000 Croatia (www.azo.hr) and field observations; (c) the land-cover of the study area, predicted using land-surface parameters only; (d) the land-cover of the study area predicted using land-surface parameters plus RS data; (e) the land-cover of the study area, predicted using RS data only. (See page 493 of this volume.)

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FIGURE 2 (CHAPTER 22) Net elevation change on Hintereisferner in the 2001–2002 budget year. Reprinted from Geist and Stötter (2007). Used with permission (http://www. borntraeger-cramer.de). (See page 505 of this volume.)

FIGURE 6 (CHAPTER 22) Training points displayed in geographical (left) and feature (right) space. The false colour composite (DEM, SLOPE, TWI) can be used to interactively select the most typical locations for each landform class (in this case manually delineated units). The values for TWI and SLOPE in the right plot have been stretched to the 0–255 scale. (See page 514 of this volume.)

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FIGURE 7 (CHAPTER 22) Results of supervised classification using maximum likelihood classifier (above) and memberships derived using fuzzy k-means classification (below). Hi111 (Hill summit), Hi112 (Hill shoulder), Hi211 (Escarpment scarp), Hi212 (Escarpment colluvium), Hi311 (Valley slope), Hi312 (Valley bottom), Hi411 (Glacis slope), Pl311 (High terrace) and P411 (Low terrace). Compare with Figure 6. (See page 517 of this volume.)

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FIGURE 10 (CHAPTER 22) Extraction of landform elements for the 10×10 km Ebergötzen study area, Germany using the 25 m DEM (a) and the 90 m SRTM DEM (b). (See page 520 of this volume.)

FIGURE 12 (CHAPTER 22) Landforms extracted using unsupervised fuzzy k-means classification with 3 and 7 classes in the FuZME package. Because the classification is unsupervised, the legend can be constructed only a posteriori. (See page 522 of this volume.)

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FIGURE 1 (CHAPTER 23) The track and deposits left by the June 2001 flow of debris that overwhelmed the Swiss village of Täsch. Reproduced by permission of SWISSTOPO (BA081244). (See page 535 of this volume.)

FIGURE 3 (CHAPTER 23) Modelling H/L angles using the MSF (top) and the MFD (bottom) models. Map and DEM reproduced by permission of SWISSTOPO (BA081244). (See page 537 of this volume.)

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FIGURE 4 (CHAPTER 23) Deposition and the total volume of flow as modelled by the MFD deposition approach (map and DEM data reproduced by permission of SWISSTOPO). (See page 538 of this volume.)

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FIGURE 7 (CHAPTER 23) Features illustrating preparation of a landslide-susceptibility map for a part of the city of Oakland, California (Pike et al., 2001); the area shown in the four maps is about 2 km across. (A) Geology, showing 21 of the 25 map units in Table 1; the NNW-striking Hayward Fault Zone lies along the eastern edge of unit KJfm. (B) Inventory of old landslide deposits (orange polygons) and locations of post-1967 landslides (red dots) on uplands east of the fault and on gentler terrain to the west; shaded relief is from a 10 m DEM. (C) Old landslide deposits and recent landslides overlain on 1995 land use (100 m resolution): yellow, residential land; green, forest; tan, scrub vegetation; blue, major highway; pink, school; orange, commercial land; brown, public institution; white, vacant and mixed-use land; road net in grey. (D) Values of relative susceptibility at 30-m resolution mapped in eight intervals from low to high as grey, 0.00; purple, 0.01–0.04; blue, 0.05–0.09; green, 0.10–0.19; yellow, 0.20–0.29; light-orange, 0.30–0.39; orange, 0.40–0.54; red, 0.55. Low to moderate values 0.05–0.20 predominate in this 9 km2 sample of the study area. (See page 543 of this volume.)

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FIGURE 3 (CHAPTER 24) An illustration, from the small Baranja Hill data-set, of several of the more frequently used land-surface parameters in the PEM process. (See page 564 of this volume.)

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FIGURE 4 (CHAPTER 24) An illustration of the results of applying a hypothetical set of ecological–landform classification rules to the small data set from Baranja Hill. See Table 3 for an explanation of legend classes. (See page 571 of this volume.)

FIGURE 5 (CHAPTER 24) Part of a 1:20,000 scale predictive ecosystem map (PEM) produced for an area in the former Cariboo Forest Region of BC, Canada. (See page 575 of this volume.)

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FIGURE 4 (CHAPTER 25) Flow lines for the small basin near the north edge of the Baranja DEM, as extracted from a DEM by the D8 method. The flow lines are overlaid on a colour image that shows flow distance to the basin outlet. (See page 596 of this volume.)

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FIGURE 2 (CHAPTER 26) Model orography (height in m) for the global model ECHAM4, resolution about 250 km (top), and for the present regionalisation simulation with MM5, resolution 60 km (bottom). (See page 613 of this volume.)

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FIGURE 3 (CHAPTER 26) Distribution of the sea level pressure in hPa for April 5th 1991, 00 UTC, in the global simulation with ECHAM4 (top) and in the regional simulation with MM5 (bottom). (See page 614 of this volume.)

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FIGURE 4 (CHAPTER 26) Model orographies in the nested regional MM5-simulations: (top) simulation S1 with a horizontal resolution of 19.2 km and (bottom) simulation S2 with a resolution of 4.8 km. (See page 615 of this volume.)

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FIGURE 5 (CHAPTER 26) Annual precipitation sum (in mm) for the year 1990 as calculated by the MM5-simulations S1 (top) and S2 (bottom). (See page 616 of this volume.)

FIGURE 6 (CHAPTER 26) Scale reduced difference (S2 − S1 ) of the annual precipitation sum (in mm) for the year 1990. (See page 617 of this volume.)

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FIGURE 7 (CHAPTER 26) Simulation domains for small-scale air quality study in complex terrain following a one-way nesting strategy. The horizontal spatial resolution is 54 km (domain D1 , top frame), 6 km (domain D2 , middle frame), and 1 km (domain D3 , bottom frame). (See page 618 of this volume.)

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FIGURE 7 (CHAPTER 26)

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FIGURE 8 (CHAPTER 26) The figure shows for August 19, 1996, 1900 GMT, for the whole D3 (see also Figure 7) the horizontal winds at 1000 m above sea level (arrows, length indicating strength and arrowheads indicating the direction of the flow) and the ozone concentration at the same height (colours, red: high, green: medium, blue: low, white: terrain height higher than 1000 m above sea level). Black lines in white areas give terrain height in 400 m interval (first line: 1200 m above sea level). (See page 620 of this volume.)

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FIGURE 2 (CHAPTER 27) Simulated spatial distribution of solar radiation relative to a neighbouring automatic weather station for the field site Sportkomplex. (See page 628 of this volume.)

FIGURE 6 (CHAPTER 27) this volume.)

Distribution of landforms at the field site Bei Lotte. (See page 632 of

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FIGURE 1 (CHAPTER 28) Geomorphometry on-the-fly. Laser sensors (top) scan terrain in front of a robotic vehicle as it moves, recording x, y, z location, distance, and time. The resulting data are integrated into 3-D point clouds (bottom) and continuously sorted by the changing relation among the three variables to separate drivable terrain from potential obstacles. Reprinted from Thrun et al. (2006a). (See page 639 of this volume.) © 1996 John Wiley & Sons Limited. Reproduced with permission.

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FIGURE 3 (CHAPTER 28) Out-of-this-world geomorphometry of volcanoes, canyons and, craters. Shaded relief image of Mars from 40°N to 40°S and 60°W to 180°W colour-coded by elevation, from the MOLA 1/128° DEM (available via http://mars.google.com). Image credit: NASA/JPL/GSFC/Arizona State University. (See page 642 of this volume.) © 2007 Google.

FIGURE 5 (CHAPTER 28) Mapping flood risk by geomorphometry. Inundation of southern European coast (in red), given a potential 3-m rise in sea level, estimated from the 1-km GLOBE DEM (http://www.cresis.ku.edu/). Image credit: Center for Remote Sensing of Ice Sheets. (See page 648 of this volume.)

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FIGURE 6 (CHAPTER 28) Page from a future geomorphometric atlas? Elevation kurtosis computed for Africa from the SRTM 3” data set in regions measuring 2.5’×2.5’. Zero kurtosis indicates a more or less statistically normal (bell-shaped) distribution of elevations; positive kurtosis denotes fewer-than-normal very high and low points, whereas negative kurtosis indicates the opposite — more high and low elevations than normal but fewer points in between. The highest values of kurtosis (red) occur in river deltas like the Nile, Mesopotamia, and isolated dune fields in the Sahara and the Empty Quarter of the Arabian peninsula. The lowest values (blue/purple) occur in fields of uniform linear dunes where most topography is either crest or trough. The SRTM data allow creation of global maps (like this), but their detail can only be appreciated when visualised at continental or finer scales. Courtesy of P.L. Guth. (See page 649 of this volume.)