Automated Extraction of Landforms from            DEM data             R. A. MacMillan      LandMapper Environmental Solut...
Outline• Rationale for Automated Landform Classification   – Theoretical, methodological, cost and efficiency arguments• C...
Rationale for Automated Landform           Classification  Scientific and theoretical arguments  Business case – costs and...
Rationale: Scientific and Theoretical• Why Delineate Landforms?  – Landforms define boundary conditions for    processes o...
Rationale: Costs and Efficiency• Why Automate the Delineation of Landforms?  – Speed and cost of production     • Never li...
Conceptual Hierarchy of Landforms• Focus here is on two main levels  – Landform elements (facets here)  – Landform pattern...
Conceptual Hierarchy of Landforms                      Source: MacMillan and Shary, (2009)
Rationale: Process-Form Relationships• Landform Elements related to hill slope processes   – Forms are related to processe...
Rationale: Process-Form Relationships                          Source: Ventura and Irwin. (2000)
Rationale: Recognizing Landform Patterns• Landform Patterns Establish Context and Scale  – Different landform patterns exh...
Classification of Landform Elements      Conceptual underpinnings      Implementation examples
Landform Elements: Conceptual             Underpinnings• Many similar ideas on partitioning of hill slopes  – Simplest and...
Landform Elements: Conceptual              Underpinnings• Many similar ideas on partitioning of hill slopes      Ruhe and ...
Landform Elements: Conceptual                Underpinnings• 2D Concepts in 3D  – Ventura & Irwin (2000)     •   Ridge top ...
Landform Elements: Conceptual                Underpinnings• 3D concepts more comprehensive than 2D   – Erosion, deposition...
Landform Elements: Conceptual             Underpinnings• 3D concepts profile and plan curvature                           ...
Landform Elements: Conceptual               Underpinnings• 3D conceptualization              • 3D classes   Source: Pennoc...
Landform Elements: Conceptual             Underpinnings• Complete system of classification by curvature                   ...
Landform Elements: Conceptual               Underpinnings• 3D conceptualization             • 3D classes   – Initially bas...
Landform Elements: Conceptual                Underpinnings• Adding landform position to 3D improves 3D.                   ...
Measures of Absolute Landform Position         Computed by LandMapR • Flow Length N to Peak                    • Vertical ...
Measures of Relative Relief (in Z)           Computed by LandMapR • Percent Z Pit to Peak                    • Percent Z C...
Measures of Relative Slope Length (L)         Computed by LandMapR • Percent L Pit to Peak                    • Percent L ...
Measures of Relative Slope Position          Computed by LandMapR • Percent Diffuse Upslope Area • Percent Z Channel to Di...
Measures of Relative Slope Position          Computed by LandMapR • Percent Diffuse Upslope Area • Percent Z Channel to Di...
Multiple Resolution Landform Position      What you see depends upon how closely you look      Different results with diff...
MRVBF: Multi-resolution valley         bottom flatness• Valley bottom flatness from:  – Flatness (inverse of slope)  – Loc...
MRVBF: Generalise DEM                           Smooth and subsample                    Source: Gallant, 2012    Original:...
MRVBF: Multi-Resolution      Flatness and bottomness at multiple resolutions                                            So...
Calculating MRVBF      MRVBF2      W2 1 VBF2    (1 W2 )VBF1      MRVBF3 W3 2 VBF3         (1 W3 ) MRVBF2                 ...
Multiple Resolution Landform Position    MRVBF Example Outputs                       Broader Scale 9” DEMMRVBF for 25 m DE...
Landform Elements: Other Measures of               Landform Position • SAGA-RHSP: relative      • SAGA-ABC: altitude   hyd...
Landform Elements: Other Measures of           Landform Position• SAGA-MRVBF: valley      • SAGA-Combined RHSP  bottom fla...
Landform Elements: Other Measures of               Landform Position • SAGA-Combined RHSP • SAGA-Combined RHSP   and MRVBF...
Landform Elements: Other Measures of             Landform Position• TOPHAT – Schmidt                 • Slope Position – Ha...
Landform Elements: Other Measures of    Landform Position - Scilands                           Source: Rüdiger Köthe , 2012
Landform Elements: Other Measures of    Landform Position - Scilands                           Source: Rüdiger Köthe , 2012
Landform Elements: Other Measures of    Landform Position - Scilands                           Source: Rüdiger Köthe , 2012
Landform Elements: Implementation       Example - Scilands                         Source: Rüdiger Köthe , 2012
Landform Elements: Implementation       Example - Scilands                         Source: Rüdiger Köthe , 2012
Landform Elements: Implementation       Example - Scilands                         Source: Rüdiger Köthe , 2012
Landform Elements: Implementation       Example - Scilands                         Source: Rüdiger Köthe , 2012
Landform Elements: Landform Elements:  Implementation Example - Scilands                           Source: Rüdiger Köthe ,...
Landform Elements: Implementation         Example: LandMapR• LandMapR 15 Default Landform Classes                         ...
Landform Elements: Implementation         Example: LandMapR• LandMapR 15 Default Landform Classes                         ...
LandMapR: Different Classes in Different Areas                                      Normal Mesic                          ...
Example of Application of Fuzzy K-means     Unsupervised Classification                       From: Burrough et al.,      ...
Supervised Classification Using Fuzzy Logic• Shi et al., 2004                         Fuzzy likelihood of being a broad ri...
Classification of Landform Patterns      Conceptual underpinnings
Rationale: Identify Landscapes of Different         Size, Scale and Context                                  Source: MacMi...
Conceptualization of Landform Patterns• Landform Patterns Tend to Repeat  – Landform patterns are typically of larger size...
Considerations Used in Several Systems of          Classifying Landform Patterns• Hammond (Dikau, 1991)                   ...
Rationale for Classifying Landform Patterns• So, Why Consider These Attributes?  – Slope Gradient     • Steepness relates ...
Conceptualization of Landform Patterns                               Source: MacMillan, 2005
Classification of Landform Patterns      Implementation examples
Landform Patterns: Implementation    Example of the Hammond System• Hammond system; as per Dikau et al., 1991             ...
Source: Zawadzka et al., in prep                               Landform Patterns: Implementation                          ...
Source: Zawadzka et al., in prep      Landform Patterns: Implementation      Example of the Hammond System• Hammond system...
Landform Patterns: Implementation    Example of the Hammond System• Hammond system; as per Dikau et al., 1991             ...
Landform Patterns: Implementation    Example of the Hammond System• Hammond landform underlying 1:650k soil map           ...
Source: Zawadzka et al., in prep        Landform Patterns: Implementation       Example of Iwahashi & Pike (2006) • Implem...
Landform Patterns: Implementation   Example of Iwahashi & Pike (2006)• Iwahashi landform underlying 1:650k soil map       ...
Source: Dobos et al., 2005    8 classes    Landform Patterns: Implementation   Example of eSOTER (Dobos, 2005)• Implemente...
Source: Zawadzka et al., in prep    Landform Patterns: Implementation    Example of Peak Shed Approach• Implemented by Zaw...
Source: Zawadzka et al., in prep    Landform Patterns: Implementation    Example of Peak Shed Approach• Implemented by Zaw...
Source: Zawadzka et al., in prep    Landform Patterns: Implementation    Example of Slope Break Approach• Implemented by Z...
Source: Zawadzka et al., in prepLandform Patterns: Implementation Example   of Homogeneous Objects (eCognition)• Implement...
Landform Patterns: Implementation       Example - Scilands                          Source: Rüdiger Köthe , 2012
Landform Patterns: Implementation Example of Homogeneous Objects vs Meybeck 2001• Implemented by Dragut, (unpublished)    ...
Landform Patterns: Implementation Example     of Meybeck 2001 vs Homogeneous Objects    • Implemented by Dragut, (unpublis...
Source: Drãgut, unpublishedLandform Patterns: Example of Multi-scale      Nested Homogeneous Objects• Implemented by Dragu...
Scilands GMK Classification   Source: Reuter & Bock, 2012                              See: ai-relief.org
Hammond Classification (after Dikau, 1991)   Source: Reuter & Bock, 2012                                             See: ...
Iwahashi & Pike Classification (16 classes) Source: Reuter & Bock, 2012                                               See:...
Scilands GMK Classification   Source: Reuter & Bock, 2012                              See: ai-relief.org
Iwahashi & Pike Classification (16 classes) Source: Reuter & Bock, 2012                                               See:...
Iwahashi & Pike Classification (8 classes)   Source: Reuter & Bock, 2012                                             See: ...
Scilands GMK Classification   Source: Reuter & Bock, 2012                              See: ai-relief.org
Iwahashi & Pike Classification (16 classes) Source: Reuter & Bock, 2012                                               See:...
Iwahashi & Pike Classification (8 classes)   Source: Reuter & Bock, 2012                                             See: ...
Miscellaneous Bits and PiecesSome thoughts and ideas that may or       may not prove useful
We are Really Looking for Discontinuities!                             Source: Minar and Evans. (2008)
We are Really Looking for Discontinuities!                             Source: MacMillan, unpublished
We are Really Looking for Discontinuities!• The more I think about it the clearer it becomes  – We are really looking to l...
There are Special Cases that do not fit in aGeneral Classification (e.g River Valleys)                                    ...
There are Special Cases that do not fit in a General Classification (e.g River Valleys)• Many General Purpose Classificati...
Source: MacMillan, 2005    Multi-Scale and Multi-ResolutionCalculations are Important but Problematic No single fixed wind...
Multi-Scale and Multi-Resolution are        Important but Problematic• All algorithms and systems that compute  attributes...
Source: Drãgut, unpublished   I Like Top-Down, Divisive, Multi-scale      Fully Nested Hierarchical Objects• Multi-scale O...
I Like Top-Down, Divisive, Multi-scale     Fully Nested Hierarchical Objects• Advantages of multi-scale, hierarchical, nes...
Source: MacMillan, unpublished     The World is Divided into Things that     Stick Up and Things that Stick DownAs a first...
Source: MacMillan, unpublished   The World is Divided into Things that   Stick Up and Things that Stick Down   Extracting ...
The World is Divided into Things that   Stick Up and Things that Stick Down• In the First Instances Many Landform Pattern ...
Are Landform Patterns and Landform        Elements Really Different Things??                     Maybe the only real diffe...
Are Landform Patterns and Landform   Elements Really Different Things?• The More I look, the more that landform  patterns ...
Source: MacMillan, 2005    I Have Personally Found Hierarchical     Classification Useful to Set Context                  ...
Discussion and ConclusionsWhat works and what doesn’t?How can we tell what works? Challenges to be addressed   Future deve...
What Works and What Doesn’t?• All things being equal apply Ockham’s Razor  – If you need to decide between several competi...
How Can We Tell What Works?• How can we evaluate “Truth” for subjective  classifications?  – Hard to decide objectively wh...
Challenges to be Addressed• A diversity of methods and absence of standards  – Classes and results need to be comparable b...
Future Developments• Global standards  – We need global standards to compare results• Free and open-source data and tools ...
Thank YouExtra Slides Follow
Source: MacMillan, 2005                          Image Data Copyright the Province of British Columbia, 2003     Classify ...
Source: MacMillan, 2005                          Image Data Copyright the Province of British Columbia, 2003   Quesnel PEM...
Source: MacMillan, 2005                                       Source: MacMillan, unpublished        I Have Personally Foun...
Source: MacMillan, 2005                          Image Data Copyright the Province of British Columbia, 2003   Quesnel PEM...
Source: MacMillan, unpublished     The World is Divided into Things that     Stick Up and Things that Stick DownAs a first...
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Automated Extraction of Landforms from DEM Data

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Provides an overview of methods of automated landform classification

R. A. (Bob) MacMillan
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Automated Extraction of Landforms from DEM Data

  1. 1. Automated Extraction of Landforms from DEM data R. A. MacMillan LandMapper Environmental Solutions Inc.
  2. 2. Outline• Rationale for Automated Landform Classification – Theoretical, methodological, cost and efficiency arguments• Classification of Landform Elements – Conceptual underpinnings – hill slope segments – Implementation methods – various examples• Classification of Landform Patterns – Conceptual underpinnings – size, shape, scale, context – Implementation methods – various examples• Miscellaneous Bits and Pieces – Thoughts and ideas that may or may not prove useful• Discussion and Conclusions – What works and what doesn’t? – Future developments & challenges to be addressed
  3. 3. Rationale for Automated Landform Classification Scientific and theoretical arguments Business case – costs and efficiency
  4. 4. Rationale: Scientific and Theoretical• Why Delineate Landforms? – Landforms define boundary conditions for processes operative in the fields of: • Geomorphology • Hydrology • Ecology • Pedology • Forestry ... others – Landforms control or influence the distribution and redistribution of water, energy and matter Source: MacMillan and Shary, (2009)
  5. 5. Rationale: Costs and Efficiency• Why Automate the Delineation of Landforms? – Speed and cost of production • Never likely to ever again see investments in large groups of human interpreters to produce global maps • Governments can’t afford and are unwilling to pay for manual interpretation and delineation of landforms – Consistency and reproducibility • Manual human interpretation can never be entirely consistent or reproducible • Automated methods can be constantly improved and re-run to produce updated products. Source: MacMillan and Shary, (2009)
  6. 6. Conceptual Hierarchy of Landforms• Focus here is on two main levels – Landform elements (facets here) – Landform patterns (repeating landform types) Source: MacMillan, 2005
  7. 7. Conceptual Hierarchy of Landforms Source: MacMillan and Shary, (2009)
  8. 8. Rationale: Process-Form Relationships• Landform Elements related to hill slope processes – Forms are related to processes and also control them Source: Skidmore et al. (1991)
  9. 9. Rationale: Process-Form Relationships Source: Ventura and Irwin. (2000)
  10. 10. Rationale: Recognizing Landform Patterns• Landform Patterns Establish Context and Scale – Different landform patterns exhibit differences in • Relief energy available to drive processes such as runoff, erosion, mass movement, solar illumination, energy flows • Size and scale of landform features such as slope lengths, slope gradients, surface texture, complexity of slopes, degree of incision of channel networks • Contextual position in the larger landscape – Runoff producing or runoff receiving area – Sediment accumulation or removal area – Elevated water tables or artesian conditions versus recharge areas Source: MacMillan, 2005
  11. 11. Classification of Landform Elements Conceptual underpinnings Implementation examples
  12. 12. Landform Elements: Conceptual Underpinnings• Many similar ideas on partitioning of hill slopes – Simplest and most basic conceptualization – 2d not 3d partitioning of a hill slope into elements
  13. 13. Landform Elements: Conceptual Underpinnings• Many similar ideas on partitioning of hill slopes Ruhe and Reprinted from Ventura Walker (1968) and Irvin (2000)
  14. 14. Landform Elements: Conceptual Underpinnings• 2D Concepts in 3D – Ventura & Irwin (2000) • Ridge top • Shoulder • Backslope • Footslope • Toeslope • Floodplain – Based solely on slope and curvature • No landform position Source: Ventura and Irvin (2000)
  15. 15. Landform Elements: Conceptual Underpinnings• 3D concepts more comprehensive than 2D – Erosion, deposition, and transit are influenced by both profile and plan (across-slope) curvature Concepts: Gauss, 1828 Source: Shary et al., (2005)
  16. 16. Landform Elements: Conceptual Underpinnings• 3D concepts profile and plan curvature Source: Shary et al., (2000)
  17. 17. Landform Elements: Conceptual Underpinnings• 3D conceptualization • 3D classes Source: Pennock et al., (1987) Source: Pennock et al., (1987)
  18. 18. Landform Elements: Conceptual Underpinnings• Complete system of classification by curvature Source: Shary et al., (2005)
  19. 19. Landform Elements: Conceptual Underpinnings• 3D conceptualization • 3D classes – Initially based solely on local surface form • Convex, concave, planar – In theory surface form should reflect • Landscape position • Hillslope processes – Surface shape not always sufficient • Need better context Source: Dikau et al., (1989)
  20. 20. Landform Elements: Conceptual Underpinnings• Adding landform position to 3D improves 3D. Upslope length - cell to peak (cells) 2 1 0 1 2 3 4 5 6 7 8 7 6 Upslope drainage direction (UDIR) PEAK CELL 5 100 Relative slope 4 80 Elevation of each 5m position as 3 3 cells 63 PIT CELL 60 % height cell above pit elevation (m) 2 40 above pit DIVIDE level 1 CELL 5 cells 20 3m Downslope drainage direction (DDIR) 4 5 8 7 6 5 4 3 2 1 0 1 2 Downslope length - cell to pit (cells) 80 100 100 88 75 63 50 38 25 12 0 10 20 Relative slope position as % length upslope Source: MacMillan, 2000, 2005
  21. 21. Measures of Absolute Landform Position Computed by LandMapR • Flow Length N to Peak • Vertical Distance Z to Ridge FLOW UP TO PEAK FROM EVERY CELL FLOW UP TO RIDGE FROM EVERY CELL Image Data Copyright the Province of British Columbia, 2003Source: MacMillan et al., 2007
  22. 22. Measures of Relative Relief (in Z) Computed by LandMapR • Percent Z Pit to Peak • Percent Z Channel to Divide MEASURE OF REGIONAL CONTEXT MEASURE OF LOCAL CONTEXT Image Data Copyright the Province of British Columbia, 2003Source: MacMillan et al., 2007
  23. 23. Measures of Relative Slope Length (L) Computed by LandMapR • Percent L Pit to Peak • Percent L Channel to Divide MEASURE OF REGIONAL CONTEXT MEASURE OF LOCAL CONTEXT Image Data Copyright the Province of British Columbia, 2003Source: MacMillan et al., 2007
  24. 24. Measures of Relative Slope Position Computed by LandMapR • Percent Diffuse Upslope Area • Percent Z Channel to Divide SENSITIVE TO HOLLOWS & DRAWS RELATIVE TO MAIN STREAM CHANNELS Image Data Copyright the Province of British Columbia, 2003Source: MacMillan et al., 2007
  25. 25. Measures of Relative Slope Position Computed by LandMapR • Percent Diffuse Upslope Area • Percent Z Channel to Divide SENSITIVE TO HOLLOWS & DRAWS RELATIVE TO MAIN STREAM CHANNELS Image Data Copyright the Province of British Columbia, 2003Source: MacMillan et al., 2007
  26. 26. Multiple Resolution Landform Position What you see depends upon how closely you look Different results with different window sizes and grid resolutions Relative position is always relative to something and varies across an area Source: Geng et al., 2012
  27. 27. MRVBF: Multi-resolution valley bottom flatness• Valley bottom flatness from: – Flatness (inverse of slope) – Local lowness (ranking in a 6 cell circular region)• Multi-resolution: – Compute valley bottom flatness at different resolutions • Smooth and subsample the DEM Source: Gallant, 2012
  28. 28. MRVBF: Generalise DEM Smooth and subsample Source: Gallant, 2012 Original: 25 m Generalised: 75 m Generalised 675 m Flatness Flatness Bottomness BottomnessValley Bottom Valley Bottom Flatness Flatness
  29. 29. MRVBF: Multi-Resolution Flatness and bottomness at multiple resolutions Source: Gallant, 201225m75m675 m Flatness Bottomness Valley Bottom Flatness
  30. 30. Calculating MRVBF MRVBF2 W2 1 VBF2 (1 W2 )VBF1 MRVBF3 W3 2 VBF3 (1 W3 ) MRVBF2  MRVBF5 W5 4 VBF5 (1 W5 ) MRVBF4 Weight function Wngives abrupt transition, depends on n W 2*W2 5*W5 VBF Source: Gallant, 2012
  31. 31. Multiple Resolution Landform Position MRVBF Example Outputs Broader Scale 9” DEMMRVBF for 25 m DEM Source: Gallant, 2012
  32. 32. Landform Elements: Other Measures of Landform Position • SAGA-RHSP: relative • SAGA-ABC: altitude hydrologic slope position above channelSource: C. Bulmer, unpublishedCalculation based on: MacMillan, 2005 Source: C. Bulmer, unpublished
  33. 33. Landform Elements: Other Measures of Landform Position• SAGA-MRVBF: valley • SAGA-Combined RHSP bottom flatness index and MRVBF Source: C. Bulmer, unpublished
  34. 34. Landform Elements: Other Measures of Landform Position • SAGA-Combined RHSP • SAGA-Combined RHSP and MRVBF vs Soil Map and MRVBF vs Soil MapSource: C. Bulmer, unpublishedCalculation based on: MacMillan, 2005 Source: C. Bulmer, unpublished
  35. 35. Landform Elements: Other Measures of Landform Position• TOPHAT – Schmidt • Slope Position – Hatfield and Hewitt (2004) (1996)Source: Schmidt & Hewitt, (2004) Source: Hatfield (1996)
  36. 36. Landform Elements: Other Measures of Landform Position - Scilands Source: Rüdiger Köthe , 2012
  37. 37. Landform Elements: Other Measures of Landform Position - Scilands Source: Rüdiger Köthe , 2012
  38. 38. Landform Elements: Other Measures of Landform Position - Scilands Source: Rüdiger Köthe , 2012
  39. 39. Landform Elements: Implementation Example - Scilands Source: Rüdiger Köthe , 2012
  40. 40. Landform Elements: Implementation Example - Scilands Source: Rüdiger Köthe , 2012
  41. 41. Landform Elements: Implementation Example - Scilands Source: Rüdiger Köthe , 2012
  42. 42. Landform Elements: Implementation Example - Scilands Source: Rüdiger Köthe , 2012
  43. 43. Landform Elements: Landform Elements: Implementation Example - Scilands Source: Rüdiger Köthe , 2012
  44. 44. Landform Elements: Implementation Example: LandMapR• LandMapR 15 Default Landform Classes Source: MacMillan et al, 2000
  45. 45. Landform Elements: Implementation Example: LandMapR• LandMapR 15 Default Landform Classes Source: MacMillan, 2003
  46. 46. LandMapR: Different Classes in Different Areas Normal Mesic Moist Foot Slope Warm SW Slope Shallow Crest Organic Wetland Wet Toe Slope Cold Frosty Wet Permanent Lake Source: MacMillan et al., 2007
  47. 47. Example of Application of Fuzzy K-means Unsupervised Classification From: Burrough et al., 2001, Landscsape Ecology Note similarity of unsupervised classes to conceptual classes
  48. 48. Supervised Classification Using Fuzzy Logic• Shi et al., 2004 Fuzzy likelihood of being a broad ridge – Used multiple cases of reference sites – Each site was used to establish fuzzy similarity of unclassified locations to reference sites – Used Fuzzy-minimum function to compute fuzzy similarity – Harden class using largest (Fuzzy- maximum) value – Considered distance to each reference site in computing Fuzzy-similarity Source: Shi et al., 2004
  49. 49. Classification of Landform Patterns Conceptual underpinnings
  50. 50. Rationale: Identify Landscapes of Different Size, Scale and Context Source: MacMillan, 2005
  51. 51. Conceptualization of Landform Patterns• Landform Patterns Tend to Repeat – Landform patterns are typically of larger size and scale and display greater complexity and variation • Hills, mountains, plains, plateaus, tablelands – Landform patterns usually, but not always, exhibit full or partial cycles of repetition of forms • Hills and mountains exhibit a full range of landform positions, slope gradients, curvatures (mostly positive) • Valleys and plains can exhibit undulations or cyclic variations OR they may be asymmetric.
  52. 52. Considerations Used in Several Systems of Classifying Landform Patterns• Hammond (Dikau, 1991) • Iwahashi & Pike (2006) – Considerations – Considerations • Slope gradient; percentage of gentle • Local slope gradient (3x3 window) slopes (4 classes) in search window • Texture - Local relief intensity • Local relief within a search window assessed as number of pits and peaks of fixed dimensions (6 classes) within a fixed window of 10 cells • Profile type; percentage of cells • Curvature; calculated as percentage of classed as gentle slope in lowland convex cells in a 10 cell radius versus upland locations (4 classes) • eSOTER (Dobos et al., 2005)• SOTER (van Engelen & Wen, – Considerations 1995) • Majority slope gradient (of 7 classes – Considerations in 900 m block, then smoothed) • Dominant slope gradient • Relief intensity (max-min elevation within a radius of 5 cells, 990 m • Relief intensity classified into 4 classes & smoothed) • Hypsometry (elevation asl) • Hypsometry (elevation asl, 10 classes) • Degree of dissection • Dissection (# channel cells in radius)
  53. 53. Rationale for Classifying Landform Patterns• So, Why Consider These Attributes? – Slope Gradient • Steepness relates to energy, erosion, deposition, context – Relief Intensity (or texture or local relief) • Provides an indication of amplitude of landscape, amount of energy available for erosion, slope lengths, size and scale of hill slopes – Profile Type (or shape, curvature, hypsometry) • Helps to differentiate uplands (convex) from lowlands • Helps to establish broader landscape context
  54. 54. Conceptualization of Landform Patterns Source: MacMillan, 2005
  55. 55. Classification of Landform Patterns Implementation examples
  56. 56. Landform Patterns: Implementation Example of the Hammond System• Hammond system; as per Dikau et al., 1991 Source: MacMillan and Shary, (2009)
  57. 57. Source: Zawadzka et al., in prep Landform Patterns: Implementation Example of the Hammond System • Hammond system; as per Dikau et al., 1991Normal Relief Index Modified Relief Index 9600m square window 9600m circular window 900m circular window
  58. 58. Source: Zawadzka et al., in prep Landform Patterns: Implementation Example of the Hammond System• Hammond system; as per Dikau et al., 1991 900m circular window 18000 m circular window Hammond approach is Hammond approach very sensitive to tends to produce differences in window concentric rings size and shape or grid related to how the resolution search window observes the data 9200m circular window
  59. 59. Landform Patterns: Implementation Example of the Hammond System• Hammond system; as per Dikau et al., 1991 Source: MacMillan, (unpublished)
  60. 60. Landform Patterns: Implementation Example of the Hammond System• Hammond landform underlying 1:650k soil map Source: Reuter, H.I. (unpublished)
  61. 61. Source: Zawadzka et al., in prep Landform Patterns: Implementation Example of Iwahashi & Pike (2006) • Implemented by Zawadzka et al., (in prep) 8 classes 12 classes 16 classesIwahashi & Pike classes needto be labelled and interpreted
  62. 62. Landform Patterns: Implementation Example of Iwahashi & Pike (2006)• Iwahashi landform underlying 1:650k soil map Terrain Classes Fine texture, Terrain Series High convexity 1 5 9 13 Fine texture, Low convexity 3 7 11 15 Coarse texture, High convexity 2 6 10 14 Coarse texture, Low convexity 4 8 12 16 steep gentle Source: Reuter, H.I. (unpublished)
  63. 63. Source: Dobos et al., 2005 8 classes Landform Patterns: Implementation Example of eSOTER (Dobos, 2005)• Implemented by Dobos et al., (in 2005) Manual - yellow Manual - yellow eSOTER - red eSOTER - red
  64. 64. Source: Zawadzka et al., in prep Landform Patterns: Implementation Example of Peak Shed Approach• Implemented by Zawadzka et al., (in prep) Peak shed entities classified by clustering algorithm. Resulting entities need to be labelled and interpreted
  65. 65. Source: Zawadzka et al., in prep Landform Patterns: Implementation Example of Peak Shed Approach• Implemented by Zawadzka et al., (in prep) Peak shed entities labelled according to Hammond
  66. 66. Source: Zawadzka et al., in prep Landform Patterns: Implementation Example of Slope Break Approach• Implemented by Zawadzka et al., (in prep) Run 2 Run 3
  67. 67. Source: Zawadzka et al., in prepLandform Patterns: Implementation Example of Homogeneous Objects (eCognition)• Implemented by Zawadzka et al., (in prep)
  68. 68. Landform Patterns: Implementation Example - Scilands Source: Rüdiger Köthe , 2012
  69. 69. Landform Patterns: Implementation Example of Homogeneous Objects vs Meybeck 2001• Implemented by Dragut, (unpublished) See: ai-relief.org Implemented by: Reuter and Nelson Method: Meybeck et al., 2001Source: Drãgut & Eisank, 2011 Source: http://eusoils.jrc.ec.europa.eu/projects/landform/
  70. 70. Landform Patterns: Implementation Example of Meybeck 2001 vs Homogeneous Objects • Implemented by Dragut, (unpublished) See: ai- relief.org Method:Meybeck et al., 2001 Source: Reuter Source: and Drãgut , Nelson unpublished
  71. 71. Source: Drãgut, unpublishedLandform Patterns: Example of Multi-scale Nested Homogeneous Objects• Implemented by Dragut, (unpublished)
  72. 72. Scilands GMK Classification Source: Reuter & Bock, 2012 See: ai-relief.org
  73. 73. Hammond Classification (after Dikau, 1991) Source: Reuter & Bock, 2012 See: ai-relief.org
  74. 74. Iwahashi & Pike Classification (16 classes) Source: Reuter & Bock, 2012 See: ai-relief.org
  75. 75. Scilands GMK Classification Source: Reuter & Bock, 2012 See: ai-relief.org
  76. 76. Iwahashi & Pike Classification (16 classes) Source: Reuter & Bock, 2012 See: ai-relief.org
  77. 77. Iwahashi & Pike Classification (8 classes) Source: Reuter & Bock, 2012 See: ai-relief.org
  78. 78. Scilands GMK Classification Source: Reuter & Bock, 2012 See: ai-relief.org
  79. 79. Iwahashi & Pike Classification (16 classes) Source: Reuter & Bock, 2012 See: ai-relief.org
  80. 80. Iwahashi & Pike Classification (8 classes) Source: Reuter & Bock, 2012 See: ai-relief.org
  81. 81. Miscellaneous Bits and PiecesSome thoughts and ideas that may or may not prove useful
  82. 82. We are Really Looking for Discontinuities! Source: Minar and Evans. (2008)
  83. 83. We are Really Looking for Discontinuities! Source: MacMillan, unpublished
  84. 84. We are Really Looking for Discontinuities!• The more I think about it the clearer it becomes – We are really looking to locate abrupt boundaries where the slope, texture, relief and context change • If we are looking for boundaries it makes sense to try to extract vector objects • It makes less sense to classify grid cells then agglomerate them, then de-speckle them, then vectorize them • This argues in favor of approaches like Object Extraction (Dragut) or perhaps Scilands (Kothe) Source: Minar and Evans. (2008)
  85. 85. There are Special Cases that do not fit in aGeneral Classification (e.g River Valleys) I like this! Source: Rüdiger Köthe , 2012
  86. 86. There are Special Cases that do not fit in a General Classification (e.g River Valleys)• Many General Purpose Classifications Need to be Extended to Handle Special Cases – River Valleys are a case in point • They have forms and patterns that are not cyclical • They have special features that have special interpretation – Active flood plain, levee, low terrace, high terrace, inter-terrace scarp, ox-bow lake, abandoned channel, dry islands – Other Special Cases no doubt exist too • Think of mineral and organic wetlands, deserts, playas
  87. 87. Source: MacMillan, 2005 Multi-Scale and Multi-ResolutionCalculations are Important but Problematic No single fixed window size fits all landscapes – Need to be locally adaptive
  88. 88. Multi-Scale and Multi-Resolution are Important but Problematic• All algorithms and systems that compute attributes within a fixed window are flawed – No single window fits all landscapes – User’s frequently adjust window size subjectively to fit local landscape features – no longer universal! – Windows that don’t fit the landscape produce artifacts and unrealistic classes or values – Need to use multiple windows and average (like MRVBF) or make windows self-adjusting
  89. 89. Source: Drãgut, unpublished I Like Top-Down, Divisive, Multi-scale Fully Nested Hierarchical Objects• Multi-scale Objects of Dragut, (unpublished)
  90. 90. I Like Top-Down, Divisive, Multi-scale Fully Nested Hierarchical Objects• Advantages of multi-scale, hierarchical, nested vector objects – They nest, or fit, within higher level objects exactly – There is less arbitrary sliver removal, filtering, speckle removal, smoothing and manipulation – They seem to produce fewer artifacts and outright errors – They produce consistent and comparable results for all similar terrains Source: Drãgut, unpublished
  91. 91. Source: MacMillan, unpublished The World is Divided into Things that Stick Up and Things that Stick DownAs a first step we should always strive to separate erosional uplands from lowlands
  92. 92. Source: MacMillan, unpublished The World is Divided into Things that Stick Up and Things that Stick Down Extracting nested peaks may be a way to separate uplands from lowlandsMight work even better if applied to DEM of inverted Height Above Channel (Z2St)
  93. 93. The World is Divided into Things that Stick Up and Things that Stick Down• In the First Instances Many Landform Pattern Classifications are Binary (upland vs lowlands) – Systems of Iwahashi and Pike, eSOTER, Hammond Scilands all recognize this in their own way – Maybe we should be making a point of finding ways to explicitly separate erosional uplands from aggrading lowlands as a first step in any classification – I have fooled around with the idea of extracting nested pits from an inverted DEM as a way to extract uplands Source: MacMillan, 2005
  94. 94. Are Landform Patterns and Landform Elements Really Different Things?? Maybe the only real difference is one of scale? Many classifications of Landform Patterns look a lot like Hillslope Elements on a large scaleSource: Rüdiger Köthe , 2012 Source: MacMillan, unpublished
  95. 95. Are Landform Patterns and Landform Elements Really Different Things?• The More I look, the more that landform patterns begin to look like landform elements computed over larger areas and at a coarser scale – Maybe we need to look at approaches like MRVBF that compute values at multiple scales then average them to produce a final value or class • Similarities to the work of Jo Wood. • We still want to first separate hills from valleys and uplands from lowlands, then landform elements within these larger scale features. Source: MacMillan, 2005
  96. 96. Source: MacMillan, 2005 I Have Personally Found Hierarchical Classification Useful to Set Context I first classified areas into 3-4 relief classesThen I developed and applied different classification rules for each relief class
  97. 97. Discussion and ConclusionsWhat works and what doesn’t?How can we tell what works? Challenges to be addressed Future developments
  98. 98. What Works and What Doesn’t?• All things being equal apply Ockham’s Razor – If you need to decide between several competing methods and none is clearly superior to others • Pick the one that is simplest, fastest and easiest to implement – Fewest input variables – Fewest processing steps – Fewest tuneable parameters – Fewest subjective decisions • This points towards selection of one of the following – Iwahashi and Pike, Dragut or Scilands Source: MacMillan, 2005
  99. 99. How Can We Tell What Works?• How can we evaluate “Truth” for subjective classifications? – Hard to decide objectively which classification method to use when all classifications appear partly useful and partly incorrect • Need objective criteria and methods of computing them to assess different classifications and identify the most useful • Should be based on the ability of the classification to predict ancillary environmental properties or conditions of interest Source: MacMillan, 2005
  100. 100. Challenges to be Addressed• A diversity of methods and absence of standards – Classes and results need to be comparable between different areas • This argues for selecting and applying one method universally and not applying different methods in different regions • Need to objectively compare methods and then select one to use widely (everywhere?). • Method almost certainly has to be multi-scale, hierarchical and locally adaptive • Method needs to be parsimonious and easy to apply Source: MacMillan, 2005
  101. 101. Future Developments• Global standards – We need global standards to compare results• Free and open-source data and tools on-line – I see both data & tools increasingly available on-line• Incorporation of ancillary (remotely sensed) data to infer parent material attributes for landforms – Once delineated, objects need to be attributed for pm• Innovations in multi-scale hierarchical analysis – Way forward will undoubtedly be multi-scale Source: MacMillan, 2005
  102. 102. Thank YouExtra Slides Follow
  103. 103. Source: MacMillan, 2005 Image Data Copyright the Province of British Columbia, 2003 Classify Landforms by Size and Scale
  104. 104. Source: MacMillan, 2005 Image Data Copyright the Province of British Columbia, 2003 Quesnel PEM Landform Classification
  105. 105. Source: MacMillan, 2005 Source: MacMillan, unpublished I Have Personally Found Hierarchical Classification Useful to Set Context I first classified areas into 3-4 relief classes Then I developed and applied different classification rules for each relief class
  106. 106. Source: MacMillan, 2005 Image Data Copyright the Province of British Columbia, 2003 Quesnel PEM Landform Classification
  107. 107. Source: MacMillan, unpublished The World is Divided into Things that Stick Up and Things that Stick DownAs a first step we should always strive to separate erosional uplands from lowlands

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