Keynote speaker presentation, TADTM-2006


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Keynote speaker presentation, TADTM-2006

  1. 1. Terrain Modelling for Specific Geomorphologic Processing George MILIARESIS, Department of GEOLOGY, University of PATRAS, GREECE International Symposium on Terrain Analysis & Digital Terrain Modelling, Nanjing, China, 23-25 November 2006,
  2. 2. AIM: To review the global digital elevation data availability for both earth and Mars and the quantitative techniques and methods used in modern digital specific geomorphology from the author’s subjective point of view.
  3. 3. SPECIFIC GEOMORPHOLOGY (definition, processing steps & history)
  4. 4. Definition Specific geomorphology involves subdividing a landscape into landforms based on a terrain segmentation methodology and measurement of their size, shape and relation to each other.
  5. 5. Steps in Specific Geomorphologic processing Defining of a measurable form from a data source. Sampling a population of landforms Choosing descriptive parameters Measuring of enumerating parameters Analyzing the data Making sense of the results.
  6. 6. HISTORY (Geomorphic Features analysed) OROMETRY orometry (19th century) the study of mountain topography •Nowadays part of tectonic geomorphology (terrain evolution models) • Terrain analysis point of view: navigation of airplanes & missiles •Remote sensing point of view: SAR processing chain
  7. 7. HISTORY: PHYSIOGRAPHY   The Physiography was the regional geomorphology of the 20th century aimed on the partition of terrain to physiographic units on the basis of the type and form of component features from topo maps and air-photographs Nowadays is applied to the study of enigmatic landscapes in other planets
  8. 8. HISTORY: TERRAIN ANALYSIS    Terrain analysis: local (medium scale) geomorphology (second half of 20th century) from air-photographs. The systematic study of image patterns relating to the origin, morphologic history & composition of distinct terrain units, called landforms (fluvial landforms). The study of landforms was useful in terrain evaluation studies (civil engineering & military applications) and site selection.
  10. 10. Data sources used in the past Field work. Physiographic maps, topographic maps, airphotography. Physiographic map of Atwood (19th century) for the Great Basin. NOWADAYs the main data sources are DIGITAL ELEVATION MODELS and their derivative products. Shaded relief map of Great Basin
  11. 11. Global DEMs: GTOPO, GLOBE 1 km (30 arc sec) 1:1.000.000 SRTM-1 Spacing: 1’’~ 30m (1:50.000) ICE-Sat Spaceborne Lidar (profiles) Internet DEMs SRTM (SAR) ASTER DEM: spacing 30 m 1:100.0001:250.000 SRTM-3 Spacing: 3 arc sec ~ 92 m (1:250.000)
  12. 12. GTOPO30, GLOBE (1:1.000.000)
  13. 13. ASTER DEMs from USGS
  14. 14. ICE-SAT, through EOS Data Gateway
  15. 15. SRTM-1,
  16. 16. SRTM-3 (void free data):
  17. 17. MOLA DEM for Mars, By the Mars Orbiter Laser Altimeter a 10-Hz pulsed infraredranging instrument, operated in orbit from 1997 to 2001 aboard the Mars Global Surveyor
  18. 18. Modern Digital Specific Geomorphometry      It is the outcome of the advances in digital image processing, terrain modelling, G.I.S. processing, etc. It applies to DEMs that are not perfect or compatible to what we are used to deal with (DEM derived from maps) ! .  horizontal & vertical accuracy (ASTER DEMs) are not compatible to the traditioonal topographic map standards  Orthometric instead geometric heights are evident (SRTM, ICEsat),  directional & slope dependency of accuracy (eg. SRTM) ? It requires a terrain partition framework first, (partition of the landscape to elementary objects) each object should be parametrically represented on the basis of its spatial 3-dimensional arrangement the terrain classification separates and maps landforms that have similar and contrasting ranges of characteristics
  20. 20. Terrain partition schemes Terrain Segmentation Terrain objects (rather) Continous Aspect regions landform based Fluvial landforms Alluvial fans bajadas Mountains basins Artificial objects Local authorities City objects
  21. 21. Aspect regions   They are composed by adjacent DEM points with the same aspect pointing direction (aspect is standardised to the 8 geographic directions defined in a raster image). They are identified by a connected component labeling algorithm
  22. 22. examples Mars (MOLA DEM, 926 m) Earth (DEM 75 m)
  23. 23. LANDFORM region growing segmentation  Require:   seed points region growing criteria (used to grow the seed points by successive iterations) that includes:  growing rule  stopping rule
  24. 24. Seed points selection Landform > Mountains Alluvial fan Bajadas Seeds > ridges outlet point valleys
  25. 25. 1st order landforms  mountain, piedmont slopes and basins: physiographic features extraction ! - {AVI} -
  26. 26. 3rd order landform (bajadas) - {AVI} -
  27. 27. 3rd order landform (aluvial fan) - {AVI} Algorithm for region growing segmentation                Input: Gradient and Seeds matrices (segmented pixels are labeled with 0 while not segmented pixels are labeled with 1). Matrix dimension N,M. GR_min, GR_max gradient thresholds. Output: segmented pixels that are stored in the matrix Seeds. Stopping criterion: segmented pixels (Nopoints). Nopoints=1; while Nopoints <> 0 % repeat until no more points are segmented Nopoints=0; for k = 2 (1) M-1 for l = 2 (1) N-1 % Scan if Seeds(k,l) = 0 % if a not-segmented pixel is found if (Gradient(k,l)<GR_max) and (Gradient(k,l) >GR_min) then 8connectedndess=0; % check for 8connectedness for ii=-1 (1) 1 for jj=-1 (1) 1 if Seeds(k+ii,l+jj) = 1 then 8connectedness=8connectedness+1; end; if 8connectedness <>0 then %adjacency to a previously segmented pixel Seeds(k,l)=1; % label it Nopoints=Nopoints+1; end; end; end; end; end;
  28. 28. Artificial (virtual) objects   Local authorities in Greece establish an artificial terrain partition framework that in many cases contradicts to the geomorphologic and environmental organization of the terrain. Specific geomorphologic processing applied to local authorities objects might estimate vulnerability (sensitivity of a region to the influence of unfavorable & dangerous events).
  29. 29. Artificial (Man-made) objects City objects: building blocks, city parks.
  31. 31. Attribute-value representation scheme   Terrain Objects= {mountains, basins, alluvial fans, bajadas, local authorities, city blocks} Parametric representation on the basis of  Shape descriptors (size, elongation, etc.)  3d arrangement (mean elevation, roughness, mean slope, massiveness, etc.)
  32. 32. Density slicing of attribute domain & mapping - {AVI} -
  33. 33. K-means classification & mapping
  34. 34. DECISION SUPPORT SYSTEMs in specific geomorphology
  35. 35. Object oriented representation A model of the world (spatial relationships, attributes-values)
  36. 36. Linguistic representation of attribute domain Fuzzy rule based expert systems
  37. 37. Decision rules hybrid expert system
  38. 38. Selective APPLICATIONS
  39. 39. Landslides susceptibility Parametric representation of aspect regions combined by knowledge based rules acquired from experts
  40. 40. Exploration of Mars Attribute density slicing for aspect regions and interpretation of endogenic & exogenic processes
  41. 41. Morphotectonic Mapping Mountain building process
  42. 42. CONCLUSION Global DEMs fostered specific geomorphology and terrain modelling at broad spatial scales The broad-scale quantification of topography and the DEM-based analyses transformed specific geomorphology into one of the most active and exciting fields in the Earth sciences.