Development History and Personal use of             LandMapR  focus on custom extensions and unusual uses             R. A...
Outline• Pre-LandMapR (1984-1993)   – Rationale and reasons for interest in landform modelling   – Started out as the base...
Pre-LandMapRBackground on Reasons for Interest in DEMs and Landform Classification
Rationale• J.S. Rowe (1996)   – All fundamental variations in landscape ecosystems     can initially (in primary successio...
Rationale• J.S. Rowe (1996)   – Landforms, with their vegetation, modify and shape     their coincident climates over all ...
Rationale                                                700 m                                        800 m• Soil-Landform...
My Interest in Automated Soil-Landform    Models and DEMs Began in 1984-85• Conducted Grid Soil Survey                    ...
Pre-LandMapROrigins of LandMapR in Distributed Hydrological Model DISTHMOD             1988-1993
Intelligent Pit Removal is Legacy of   DISTHMOD• Remove Initial Small Pits                         • Pit Removal Process  ...
Intelligent Pit Removal is Legacy of                DISTHMOD                • Remove all Pits in the Most Likely Fill Orde...
DISTHMOD Left Me With the Ability      to Flow Across DEMs      • Key aspect of flow was ability to retain pit info       ...
Key Advantage of LandMapR is Ability to Flow from Cell to Cell & through Pits• Cell to cell connectivity              CELL...
LandMapR             Version 1  Developed Original LandMapR as aSeries of 19 FoxPro Programs in 1994-99
LandMapR Programs to the End of 1999FoxPro Programs: 19 Separate Programs Run Sequentially
Initial Site Level Studies for PrecisionFarming• Agriculture Canada                 • Dr. W. W. Pettapiece   – Started in ...
Key Outcome: Programs and Definitionof Two Fuzzy Classification Rule Bases• Attribute Rules                     • Classifi...
ARule Table Defines Fuzzy Attributes SORT                                   MODEL            BORDER FILE_IN     ATTR_IN   ...
CRule Table Defines Fuzzy Classes  F                 ATTR   FACET     F                          ATTR   FACET   F         ...
Fuzzy Classification then Assign EachCell to its Most Likely Landform Class
LandMapR Landform Classification• Initial Development                    Stettler Site (800 x 400 m)   – Started with 2 si...
Goddard & Nolan Evaluated Differences in     Soil Properties and Yield at Sites
Coen Checked Soil Property Differences        by Landform Class                                        Hussar             ...
LandMapR Landform Classification Used toRelate Soil Properties to Landform Position
Status of LandMapR at end of 1999• Agriculture Canada                   • Advantages of LandMapR   – Assumed ownership of ...
Evaluation of LandMapR by Other Users• Alberta                               • Saskatchewan   – AAFRD                     ...
LandMapR           Version 2aCollated Original 19 LandMapR FoxProPrograms into a Single FoxPro Program               1999-...
LandMapR Program Beginning in 2000FoxPro Programs: 19 Separate Programs Merged into 1 FoxPro Program in 2000
Early Applications of the Single Revised LandMapR Program• Initial Application Focus   – Small areas equivalent to     ind...
Extensions to LandMapR 1999-2001• Alberta Landforms             • Lessons Learned  – New custom FoxPro             – We go...
Alberta Landforms Project 1999-2000• Morphometric Descriptions  – More than 20 attributes     • Slope, aspect, curvatures,...
Alberta Landforms Project 1999-2000• Morphometric Descriptions for Each Site                          http://www1.agric.go...
Alberta Landforms Project 1999-2000• Landform Type Morphology Summarized                        http://www1.agric.gov.ab.c...
Applications of LandMapR to FieldSized Sites 2000-2001• AgAtlas Project                        • SVAECP Project   – Norwes...
SVAECP Landforms Project 2002• SVAECP  – Soil Variability Analysis    for Crop Production      • 50+ 250 ha farm fields   ...
SVAECP Project: Examples of ClassifiedSites with Complex Hummocky Topography  Turner Valley Site (IUl)   Mundare Site (H1l...
CEMA Landforms Project 2003
LandMapR           Version 2bExtended the Single FoxPro Program by     Adding WeppMapR in 2001
Extensions to LandMapR 2001-2002• WeppMapR Program                    • BC PEM Landforms  – An entirely new module        ...
Wepp Extension to LandMapR in 2001• AAFRD Contract 2000-2001  – Adopted WEPP as their    primary tool     • to investigate...
WeppMapR Extracts Channel Segments   and their Associated Hillslopes     1.80 km• Steps involved             1.55 km   – C...
WeppMapR Computes and Stores  Topological Flow Linkages in a DBF File• WEPP Structure File                • WEPP Structure...
Examples of Wepp Spatial Entities• Salisbury Plain, UK                     • MKMA Region, BC  Mature, eroded well-defined ...
Extension to LandMapR to Allocate Soils to Landform Classes in 2002• Objective   – To automatically link soils to     land...
Use of LandMapR Landform Classes asInput to PEMs in BC in 2001-2002• Advantages of Using  Landform Classes  – Can relate l...
BC: MKMA Forest Region PEM• Broad Valleys in BC   – Need extra context   – Second classification   – Separate crests in   ...
BC: Inveremere Forest Region PEM• Very Large Area   – 172 km EW by 178     km NS (3 M ha)   – 50 Million cells   – Defined...
LandMapR           Version 2c  Major Change to the Single FoxProProgram to Support Ecological Mapping      (PEM) in BC in ...
Major Changes to LandMapR 2002-2003• Split into 4 Modules              • New Ideas and Extensions   – FlowMapR            ...
The New LandMapR PEM Process• Hierarchical Approach              • Hybrid Methodology   – Climatic eco-regionalization    ...
Image Data Copyright the Province of British Columbia, 2003Needed Different Rules and Classes inDifferent Classification Z...
Needed to Construct and Apply DifferentFuzzy Rule Bases• Attribute Rules (arules)   – Concepts like slope position,     we...
Methods• Step1                             • Step 5   – Extract ecological                – Apply fuzzy knowledge rule    ...
BC PEM Initial Cariboo Pilot Results                     15 km                                       12 km
BC PEM Early Canim Lake Results71 km EW47 km NS10 m GRID33 Million   Cells12 1:20,000Map Sheets
BC PEM Cariboo Pilot Accuracy Assessment• Field Sampling Method         • Final Accuracy Results   – Randomly located radi...
BC PEM Early Experience Conclusions• Reasons for success            • Reasons for error   – There is a relationship       ...
LandMapR       Version 3 (C++) Reprogrammed Single LandMapRFoxPro Program into a Suite of Four   Programs in C++ 2003-2005
Overview of the Structure of the Revised    C++ LandMapR Programs          The LandMapR Toolkit               FlowMapR    ...
Improvements to LandMapR 2003-2005• New C++ Modules                    • New C++ Modules  – FlowMapR                      ...
Extensions to LandMapR 2003-2005• Major Custom Extensions • Major Custom Extensions  – Custom Programs for DSS            ...
FlowMapRComputes Flow Topology
Purpose of FlowMapR• Cell to cell connectivity             CELL DRAINAGE DIRECTION (LDD)   – Wanted to compute            ...
FormMapRComputes Terrain Derivatives
Image Data Copyright the Province of British Columbia, 2003Purpose of FormMapR• Compute Input Data to  Support Classificat...
FacetMapRReads & Applies Fuzzy Classification Rules to Prepared Input Data Sets
Purpose of FacetMapR  • To Provide a Tool for    Classifying Landform-    Based Spatial Entities          – Wanted to use ...
Purpose of New Revised FacetMapR  • Acts as a Classification Engine for    Hierarchical Fuzzy Logic Rules          – Modif...
WeppMapRExtracts Hydrological Spatial Entities          from DEM Data
Purpose of WeppMapR• Extract Hydrological  Spatial Entities   – Wanted a tool to create     WEPP structure files      • Fo...
The Revised LandMapR C++          Programs  Application of the LandMapRKnowledge-Based Approach to PEM    Mapping in BC 20...
BC PEM: Application of the RevisedLandMapR C++ Programs 2003-2008• BC PEM Project History and Hypotheses Tested at each St...
Image Data Copyright the Province of British Columbia, 2003 Fundamental Basis of a LMES PEM• Terrain Analysis   – Partitio...
PEM DSS Classification Using LandMapR                               Normal Mesic                              Moist Foot S...
PEM DSS Final Cartographic Quality Maps
The Revised LandMapR C++          ProgramsApplication of the Revised LandMapR           C++ Programs Mapping Depressions o...
Location and Characterization of all Sagsin the City of Edmonton in 2005-2006
Location and Characterization of all Sagsin the City of Edmonton in 2005-2006
Location and Characterization of all Sagsin the City of Edmonton in 2005-2006
Location and Characterization of all Sagsin the City of Edmonton in 2005-2006
Location and Characterization of all Sagsin the City of Edmonton in 2005-2006
LandMapR       Version 3 C++   Extensions and Add-ons to theLandMapR C++ Programs 2006-2012
Extensions to LandMapR 2006-2012• Major Custom Extensions • Major Custom Extensions  – Landform Entity Programs           ...
Image Data Copyright the Province of British Columbia, 2003Extraction of Peak Sheds and Hill Sheds
Image Data Copyright the Province of British Columbia, 2003Peak Sheds as Initial Landform Objects
Image Data Copyright the Province of British Columbia, 2003Classification of Peak Sheds by Relief
Image Data Copyright the Province of British Columbia, 2003Classified Peak Shed Areas are Different
Image Data Copyright the Province of British Columbia, 2003Peak Sheds Classified by Size and Scale
Image Data Copyright the Province of British Columbia, 2003Zone Map: EcoZone, Landform, PM
Problem with Hill Sheds and Peak Sheds• Slope Breaks Needed to Partition Hill Sheds
New Slope Break Custom Program• Trace Down Flow Paths and Mark Inflections
New Slope Break Custom Program• How Many Slope Breaks is Enough
Nested Pits and Peaks May be Interesting• Add-on to FlowMapR needed for City of Edmonton           Extracts, numbers and m...
Nested Pits and Peaks May be Interesting• Nested Peaks are just pits in the inverted DEM      Might be able to use this to...
Extension to FlowMapR for Nested Pits and Peaks• New and Improved Pit                 • Thoughts on Nested Peaks  Removing...
New Measure of Relative Slope Position: RHSP• Relative Hydrologic Slope Pos • Percent Z Channel to Divide    SENSITIVE TO ...
RHSP: Relative Hydrologic Slope Position as Implemented in SAGA • SAGA-RHSP: relative      • SAGA-RHSP with soil   hydrolo...
FacetMapR Modified to Support PolygonDisaggregation• New Output Option  – Writes out all fuzzy    likelihood values     • ...
New FoxPro Script Computes SoilProperty Values by Weighted Average
New FoxPro Script Computes SoilProperty Values by Weighted Average
Original Map of Clay by Method ofPolygon Averaging
Thank You
Development History and Personal Use of LandMapR 1984-2012
Development History and Personal Use of LandMapR 1984-2012
Development History and Personal Use of LandMapR 1984-2012
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Development History and Personal Use of LandMapR 1984-2012

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Introduction to the development, use and extension of the LandMapR toolkit by the author. R. A. (Bob) MacMillan.

Prepared for the LandMapR User's Workshop
Quebec City, Canada
June 1, 2012

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Development History and Personal Use of LandMapR 1984-2012

  1. 1. Development History and Personal use of LandMapR focus on custom extensions and unusual uses R. A. MacMillan LandMapper Environmental Solutions Inc.
  2. 2. Outline• Pre-LandMapR (1984-1993) – Rationale and reasons for interest in landform modelling – Started out as the base for a deterministic hydrological model DISTHMOD• LandMapR Version 1 (1994-1999) – Original FoxPro Programs written for a project with Agriculture Canada• LandMapR Version 2 (1999-2003) – Version 2a: Single program applied mainly to small agricultural fields – Version 2b: Extended single program by adding WeppMapR on top – Version 2c: Major change to LandMapR, split into 4 different modules • To Permit hierarchical PEM mapping and consideration of non-DEM inputs• LandMapR Version 3 C++ Programs (2003-2008) – Primarily reprogrammed to permit use for PEM mapping in BC • Demands of PEM mapping of large areas forced development of numerous extensions – Interesting use to map sags in the City of Edmonton• Applications & extensions to C++ Programs 2008-2012
  3. 3. Pre-LandMapRBackground on Reasons for Interest in DEMs and Landform Classification
  4. 4. Rationale• J.S. Rowe (1996) – All fundamental variations in landscape ecosystems can initially (in primary succession) be attributed to variations in landforms as they modify climate • Boundaries between potential ecosystems can be mapped to coincide with changes in those landform characteristics known to regulate the reception and retention of energy and water
  5. 5. Rationale• J.S. Rowe (1996) – Landforms, with their vegetation, modify and shape their coincident climates over all scales • Earth surface energy-moisture regimes at all scales /sizes are the dynamic driving variables of functional ecosystems at all scales/sizes • Climatic regimes are primarily interpreted from visible terrain features known to be linked to the regimes of radiation and moisture (viz. landform and vegetation)
  6. 6. Rationale 700 m 800 m• Soil-Landform Models EOR Series DYD Series KLM Series FMN Series COR Series – Are the fundamental basis 15 for soil survey 40 – Relate soils to landform 60 position• Catena Concept OBL EOR HULG COR SZBL DYD BLSS KLM SZHG FMN HULG COR OHG HGT – Can be approximated by terrain analysis and classification from DEM High water level – Wanted to automated classification of landforms SALINE Low water level CHER GLEY CHER SOLZ GLEY GLEY
  7. 7. My Interest in Automated Soil-Landform Models and DEMs Began in 1984-85• Conducted Grid Soil Survey SEMI-VARIOGRAM FOR A-HORIZON %SAND SEMI-VARIANCE 160 – Lacombe Research Station 140 120 • Sampled soils on a 50 m grid 100 80 60 – Sand, Silt, Clay, 40 20 – pH, OC, EC, others 0 11 13 15 17 19 1 3 5 7 9 – 3 depths (0-15, 15-50, 50-100) LAG (1 LAG = 30 M) • Used custom written software – To compute variograms – Interpolate using the variograms • DEMs and Landform Models – Saw strong soil-landscape pattern – Wanted to quantify relationships and automate elucidation of them LACOMBE SITE: A HORIZON %SAND (1985) Source: MacMillan, 1985 unpublished
  8. 8. Pre-LandMapROrigins of LandMapR in Distributed Hydrological Model DISTHMOD 1988-1993
  9. 9. Intelligent Pit Removal is Legacy of DISTHMOD• Remove Initial Small Pits • Pit Removal Process – Based on computed pit geometry – Based on reversing flow directions • Pit area (remove only small pits) • Find pour point for a given pit – Typically use value of 10 cells for 5-10 m • Trace down path from pour point DEMs • Reverse flow directions of cells along • Pit depth (remove if < selected depth) path from pour point to pit – Typically use a value of 0.15 m for 5-10 m • Flow back “up” to pour point and DEMs compute new value for upslope area • Treat these pits as errors or unimportant • Assign all cells to new joined catchment 3 1 (becomes 2) 2 (becomes new 2) elevation of all Pour Elevation 2 new “reversed” initial local cells below pour flow directions direction of point raised to flow pour elevation Divide Pour Elevation 1 2 1 2 5 5 1 2 5 5 Pit CenterSource: MacMillan et al., 1993 Landscape Ecology and GIS
  10. 10. Intelligent Pit Removal is Legacy of DISTHMOD • Remove all Pits in the Most Likely Fill Order 728 to 64 728 727 72 727 to 64 68 65 58 to 19 to 74 726 to 23 726Elevation (m) to 37 725 71 16 15 to 23 18 725 to 120 to 37 724 74 724 to 52 to 33 132 to 33 131 67 69 70 66 130 723 to 121 128 to 118 42 723 64 55 52 to 39 124 23 120 119 41 118 722 121 722 to 33 117 116 39 33 29 26 36 29 27 36 37 21 19 721 721Source: MacMillan et al., 1993 Landscape Ecology and GIS
  11. 11. DISTHMOD Left Me With the Ability to Flow Across DEMs • Key aspect of flow was ability to retain pit info 3 1 2 4 5 17 19 18 16 15 14 33 34 35 36 37 39 41 44 43 42 40 38 26 28 30 32 31 29 27 25 24 23 22 21 20 9 11 13 12 10 8 7 6 5 1 2 3 4725 725 3 2724 724723 1 723722 722721 721 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44Source: MacMillan et al., 1993 Landscape Ecology and GIS
  12. 12. Key Advantage of LandMapR is Ability to Flow from Cell to Cell & through Pits• Cell to cell connectivity CELL DRAINAGE DIRECTION (LDD) – Permits computation of various measures of: DIVIDE RELATIVE SLOPE POSITION (Distance down slope from cell • Absolute & relative relief to pit Centre as % of maximum) MAXIMUM • Slope length SLOPE LENGTH 63 PIT CENTRE – Gives ability to identify DIVIDE CELL 6 2 30 • Pits and Peaks • Channels and Divides 4 5 8 7 6 5 4 3 2 1 0 1 2 CELL DOWNSLOPE LENGTH (LDN) • Passes and Hillslopes 80 100 100 88 75 63 50 38 25 12 0 10 20 – Acts as glue in classifying CELL RELATIVE SLOPE POSITION (PUP)
  13. 13. LandMapR Version 1 Developed Original LandMapR as aSeries of 19 FoxPro Programs in 1994-99
  14. 14. LandMapR Programs to the End of 1999FoxPro Programs: 19 Separate Programs Run Sequentially
  15. 15. Initial Site Level Studies for PrecisionFarming• Agriculture Canada • Dr. W. W. Pettapiece – Started in 1995-96 – Former head of Soil – Wanted to show that soil- Survey in Canada landform models used in – Liked what he saw in Soil Survey had relevance models proposed by for Precision Farming Pennock et al., 1987 – Believed partitioning fields • But Pennock model gave into landform facets quite noisy results would define effective • Wanted tools to extend, refine and apply models management zones for PF such as Pennock’s – Lacked tools to do this – Contracted LandMapR • No other suitable software • to develop new tools was available to us
  16. 16. Key Outcome: Programs and Definitionof Two Fuzzy Classification Rule Bases• Attribute Rules • Classification Rules – Arule file (e.g. LM3arule) – Crule file (e.g. LM3crule) – Defines “attributes” of – Defines user-defined terrain as fuzzy semantic classes as a weighted constructs (e.g in words) combination of fuzzy – User can define any attributes attribute based on any – Can define any number of available input variable classes based on any – Have 2 main pre-defined number of attributes. rule sets for landforms – Have 2 main pre-defined • Many for ecological classes rule sets for landforms
  17. 17. ARule Table Defines Fuzzy Attributes SORT MODEL BORDER FILE_IN ATTR_IN CLASS OUT NO B LOW B HI B1 B2 D 1 formfile PROF CONVEX_D 4 5.0 0.0 0.0 2.5 0.0 2.5 2 formfile PROF CONCAVE_D 5 -5.0 0.0 0.0 0.0 -2.5 2.5 3 formfile PROF PLANAR_D 1 0.0 0.0 0.0 -2.5 2.5 2.5 4 formfile PLAN CONVEX_A 4 5.0 0.0 0.0 2.5 0.0 2.5 5 formfile PLAN CONCAVE_A 5 -5.0 0.0 0.0 0.0 -2.5 2.5 6 formfile PLAN PLANAR_A 1 0.0 0.0 0.0 -2.5 2.5 2.5 7 formfile QWETI HIGH_WI 4 7.0 0.0 0.0 3.5 0.0 3.0 8 formfile QWETI LOW_WI 5 0.5 0.0 0.0 0.0 3.5 3.0 9 formfile SLOPE NEAR_LEVEL 5 0.5 0.0 0.0 0.0 1.0 0.5 10 formfile SLOPE REL_STEEP 4 2.0 0.0 0.0 1.0 0.0 1.0 11 relzfile PCTZ2ST NEAR_DIV 4 90.0 0.0 0.0 75.0 0.0 15.0 12 relzfile PCTZ2ST NEAR_HALF 1 50.0 50.0 50.0 25.0 75.0 25.0 13 relzfile PCTZ2ST NEAR_CHAN 5 10.0 0.0 0.0 0.0 25.0 15.0 14 relzfile PCTZ2PIT NEAR_PEAK 4 90.0 0.0 0.0 75.0 0.0 15.0 15 relzfile PCTZ2PIT NEAR_MID 1 50.0 50.0 50.0 25.0 75.0 25.0 16 relzfile PCTZ2PIT NEAR_PIT 5 5.0 0.0 0.0 0.0 10.0 5.0 17 relzfile Z2PIT HI_ABOVE 4 2.0 0.0 0.0 1.0 0.0 1.0
  18. 18. CRule Table Defines Fuzzy Classes F ATTR FACET F ATTR FACET F ATTR FACET FNAME FUZATTR WT NO CODE F NAME FUZATTR WT NO CODE F NAME FUZATTR WT NO CODELCR NEAR_PEAK 30 11 1 CBS NEAR_HALF 20 23 6 TSL NEAR_CHAN 20 32 11LCR NEAR_DIV 20 11 1 CBS NEAR_MID 10 23 6 TSL NEAR_PIT 10 32 11LCR HI_ABOVE 10 11 1 CBS HI_ABOVE 5 23 6 TSL REL_STEEP 10 32 11LCR NEAR_LEVEL 20 11 1 CBS REL_STEEP 20 23 6 TSL PLANAR_D 25 32 11LCR PLANAR_D 10 11 1 CBS CONCAVE_A 20 23 6 TSL PLANAR_A 25 32 11LCR PLANAR_A 5 11 1 CBS PLANAR_D 15 23 6 TSL HIGH_WI 10 32 11LCR LOW_WI 5 11 1 CBS HIGH_WI 10 23 6 FAN NEAR_CHAN 20 33 12DSH NEAR_PEAK 30 12 2 TER NEAR_HALF 20 24 7 FAN NEAR_PIT 10 33 12DSH NEAR_DIV 20 12 2 TER NEAR_MID 10 24 7 FAN REL_STEEP 10 33 12DSH HI_ABOVE 10 12 2 TER HI_ABOVE 5 24 7 FAN CONVEX_A 25 33 12DSH CONVEX_D 20 12 2 TER NEAR_LEVEL 30 24 7 FAN PLANAR_D 25 33 12DSH CONVEX_A 10 12 2 TER PLANAR_D 15 24 7 FAN LOW_WI 10 33 12DSH LOW_WI 10 12 2 TER PLANAR_A 20 24 7 LSM NEAR_DIV 10 41 13UDE NEAR_PEAK 30 13 3 SAD NEAR_HALF 20 25 8 LSM NEAR_CHAN 20 41 13UDE NEAR_DIV 20 13 3 SAD NEAR_MID 10 25 8 LSM NEAR_PIT 10 41 13UDE HI_ABOVE 10 13 3 SAD HI_ABOVE 5 25 8 LSM NEAR_PEAK 10 41 13UDE NEAR_LEVEL 10 13 3 SAD NEAR_LEVEL 20 25 8 LSM REL_STEEP 10 41 13UDE CONCAVE_D 10 13 3 SAD CONCAVE_D 20 25 8 LSM CONVEX_D 15 41 13UDE CONCAVE_A 10 13 3 SAD CONVEX_A 20 25 8 LSM CONVEX_A 15 41 13UDE HIGH_WI 10 13 3 MDE NEAR_HALF 20 26 9 LSM LOW_WI 10 41 13BSL NEAR_HALF 20 21 4 MDE NEAR_MID 10 26 9 LLS NEAR_CHAN 20 42 14BSL NEAR_MID 10 21 4 MDE HI_ABOVE 5 26 9 LLS NEAR_PIT 20 42 14BSL HI_ABOVE 5 21 4 MDE NEAR_LEVEL 25 26 9 LLS NEAR_LEVEL 40 42 14BSL REL_STEEP 20 21 4 MDE CONCAVE_D 10 26 9 LLS PLANAR_D 5 42 14BSL PLANAR_D 15 21 4 MDE CONCAVE_A 10 26 9 LLS PLANAR_A 5 42 14BSL PLANAR_A 25 21 4 MDE HIGH_WI 20 26 9 LLS HIGH_WI 10 42 14BSL LOW_WI 5 21 4 FSL NEAR_CHAN 20 31 10 DEP NEAR_CHAN 20 43 15DBS NEAR_HALF 20 22 5 FSL NEAR_PIT 10 31 10 DEP NEAR_PIT 30 43 15DBS NEAR_MID 10 22 5 FSL REL_STEEP 10 31 10 DEP NEAR_LEVEL 20 43 15DBS HI_ABOVE 5 22 5 FSL CONCAVE_D 20 31 10 DEP CONCAVE_A 10 43 15DBS REL_STEEP 20 22 5 FSL CONCAVE_A 20 31 10 DEP CONCAVE_D 10 43 15DBS CONVEX_A 20 22 5 FSL PLANAR_A 10 31 10 DEP HIGH_WI 10 43 15DBS PLANAR_D 15 22 5 FSL HIGH_WI 20 31 10DBS LOW_WI 10 22 5
  19. 19. Fuzzy Classification then Assign EachCell to its Most Likely Landform Class
  20. 20. LandMapR Landform Classification• Initial Development Stettler Site (800 x 400 m) – Started with 2 sites • with very different soils and topography (note closed pits) • Farm field size (800 x 800 m) – Developed and refined procedures and rules Hussar Site (800 x 800 m) • At those 2 sites – Sampled to verify classes were different • Soils and Soil Properties • Moisture, fertility & yields
  21. 21. Goddard & Nolan Evaluated Differences in Soil Properties and Yield at Sites
  22. 22. Coen Checked Soil Property Differences by Landform Class Hussar 12% OM (0 -15 cm) 10 8 1997 Original (28 pt) 6 transects 4 1998 Verification (13 pt) transects 2 0 U M L Landscape Position
  23. 23. LandMapR Landform Classification Used toRelate Soil Properties to Landform Position
  24. 24. Status of LandMapR at end of 1999• Agriculture Canada • Advantages of LandMapR – Assumed ownership of – Computed a wide range of LandMapR IP terrain derivatives (for 1996) • Took custodianship of the • Relative landform position original 19 FoxPro programs indices not easily available in • Distributed them to internal other software at the time Ag Canada researchers • Less speckle than Pennock’s• 19 FoxPro Programs – Default Landform Classes – Use Constraints • Fuzzy rules developed – LM_arule, LM_crule • Slow to run & Need FoxPro • 15 default landform classes • Had to run 19 separate defined, evaluated & accepted programs in correct order – Ready to be evaluated • Difficult to learn & use
  25. 25. Evaluation of LandMapR by Other Users• Alberta • Saskatchewan – AAFRD – Indian Head Precision Farm • T. Goddard & S. Nowlan • Yann Pelcat (MSc.) • Dr. Linda Hall & Ty Faechner • Quebec • Dr. Len Kryzanowski – Dr. Thomas Piekutowski – AAFC • Montana • Dr. Gerry Coen (Lethbridge) – Montana State University• Manitoba • Dr. Dan Long and others – U of M • United Kingdom - Silsoe • Grant Manning (MSc.) • Yann Pelcat (MSc.) – Soil Survey of England & Wales • Dr. Thomas Mayr – Brandon AAFC & Assiniboine • Dr. Al Moulin • Ontario • Dr. Ty Faechner – Doug Aspinal (OMAF)
  26. 26. LandMapR Version 2aCollated Original 19 LandMapR FoxProPrograms into a Single FoxPro Program 1999-2003
  27. 27. LandMapR Program Beginning in 2000FoxPro Programs: 19 Separate Programs Merged into 1 FoxPro Program in 2000
  28. 28. Early Applications of the Single Revised LandMapR Program• Initial Application Focus – Small areas equivalent to individual farm fields – Clear agricultural focus 800 m 800 m• Applications – Precision farming research • Alberta, Manitoba, Ontario, Quebec, Montana, Germany – Extension (SVAECP) – Commercial service 800 m 800 m • Norwest Soils AgAtlas Original LandMapR 15 Landform Facets
  29. 29. Extensions to LandMapR 1999-2001• Alberta Landforms • Lessons Learned – New custom FoxPro – We got slope length wrong programs to compute • Our slope values were too long summary statistics for – Used Lpit2Peak for length terrain attributes for an – Should have used LStr2Div entire classified DEM – Soil properties not always• SVAECP Project related to landform class • Field sample data for 50+ sites – Used same programs to – Only about 50% showed a compute and report clear relationship between statistics for each site landform class and soil property values• CEMA Project – Oil Sands Landscapes
  30. 30. Alberta Landforms Project 1999-2000• Morphometric Descriptions – More than 20 attributes • Slope, aspect, curvatures, slope length, wetness index, slope position, drainage density, percent internal drainage, etc. • Reported cumulative frequency distributions, means, 10% decile values, dominant classes – Landform classifications • 15 and 4 unit classifications • Gave means, dominant classes and decile values for attributes for each landform class http://www1.agric.gov.ab.ca/soils/soils.nsf
  31. 31. Alberta Landforms Project 1999-2000• Morphometric Descriptions for Each Site http://www1.agric.gov.ab.ca/soils/soils.nsf
  32. 32. Alberta Landforms Project 1999-2000• Landform Type Morphology Summarized http://www1.agric.gov.ab.ca/soils/soils.nsf
  33. 33. Applications of LandMapR to FieldSized Sites 2000-2001• AgAtlas Project • SVAECP Project – Norwest Soil Research – CARDF Funded Project – 35 Sites across Canada – 40+ Sites in Alberta • Manitoba to BC • ¼ section in size • Obtained 5 m DEMs • Obtained 5 m DEMs • Applied classification • Applied classification • Prepared maps & reports • Prepared 2D and 3D maps and • Evaluated visually in field images – All appeared reasonable • Sampled sites by landform position – Commercial viability not proven – Created Web Site • “www.infoharvest.ca/svaecp/”
  34. 34. SVAECP Landforms Project 2002• SVAECP – Soil Variability Analysis for Crop Production • 50+ 250 ha farm fields • Classified into 4 classes • Samples taken along transects through classes • Soil properties did not always vary significantly by landform class
  35. 35. SVAECP Project: Examples of ClassifiedSites with Complex Hummocky Topography Turner Valley Site (IUl) Mundare Site (H1l) Stettler Site (H1m) Rumsey Site (H1h)
  36. 36. CEMA Landforms Project 2003
  37. 37. LandMapR Version 2bExtended the Single FoxPro Program by Adding WeppMapR in 2001
  38. 38. Extensions to LandMapR 2001-2002• WeppMapR Program • BC PEM Landforms – An entirely new module – Hierarchical Classification • Reprocessed FlowMapR • Changed core LandMapR output to extract and program to allow for different characterize Wepp spatial classes and rules in different entities automatically zones – New options in LandMapR• Soil-Landform Program • Built, applied and evaluated – FoxPro scripts several new rule bases • Compute likelihood of – FoxPro Scripts each soil in each notional landform position • Tile and then mosaic overlapping DEM tiles • Automatically allocate soils to defined landform classes • To process very large areas
  39. 39. Wepp Extension to LandMapR in 2001• AAFRD Contract 2000-2001 – Adopted WEPP as their primary tool • to investigate runoff from agricultural lands • to quantify amounts and rates of phosphorous release from – Natural sources – Farming operations – Livestock operations – Contracted LandMapper to • Write extension to LandMapR to extract Wepp hydrological entities
  40. 40. WeppMapR Extracts Channel Segments and their Associated Hillslopes 1.80 km• Steps involved 1.55 km – Compute catchments for each channel segment – Subdivide into left, right & top hillslope components
  41. 41. WeppMapR Computes and Stores Topological Flow Linkages in a DBF File• WEPP Structure File • WEPP Structure File • Number hillslope entities • Number channel/ impoundment sequentially from 1 to n entities from n+1 to total number • Link hillslopes to channels of entities (m)
  42. 42. Examples of Wepp Spatial Entities• Salisbury Plain, UK • MKMA Region, BC Mature, eroded well-defined landscape Young, steep, mountainous landscape
  43. 43. Extension to LandMapR to Allocate Soils to Landform Classes in 2002• Objective – To automatically link soils to landform class to create soil- landform models• Methods – Create expert system rules to link soils to landform position – Apply rules to compute most likely landform position for each soil• Result – New FoxPro programs (scripts)
  44. 44. Use of LandMapR Landform Classes asInput to PEMs in BC in 2001-2002• Advantages of Using Landform Classes – Can relate landform classes to Site Series in PEM rules – Single standardized classes – Don’t have to develop new landform classes for each BGC Sub-zone – Can be applied rapidly and cheaply ($0.004 per cell) – Huge cost reduction relative to traditional manual maps
  45. 45. BC: MKMA Forest Region PEM• Broad Valleys in BC – Need extra context – Second classification – Separate crests in 45.0 km broad valleys from crests on mountains – Beginnings of multi level hierarchical classification – Need techniques for tiling regions 50.0 km
  46. 46. BC: Inveremere Forest Region PEM• Very Large Area – 172 km EW by 178 km NS (3 M ha) – 50 Million cells – Defined 11 Tiles• Different Landform 178 km Types in Different NS Parts of the Area – Defined 2 Zones – Different Rules in each zone 172 km EW
  47. 47. LandMapR Version 2c Major Change to the Single FoxProProgram to Support Ecological Mapping (PEM) in BC in 2002-2003
  48. 48. Major Changes to LandMapR 2002-2003• Split into 4 Modules • New Ideas and Extensions – FlowMapR – Hierarchical Classification • Only compute flow once • New option in LandMapR – FormMapR – Required new DBFs and creation of a new Zone File • Only need to compute – Required ability to read and derivatives once per tile apply different rule bases • New and changed derivatives – Non-DEM Inputs – FacetMapR • New Geo File in FacetMapR • Needed to support – Contains new non-DEM info hierarchical rules and outputs – Rules consider non-DEM info • Needed to rerun classifier – FoxPro Scripts many times • To tile and then mosaic – WeppMapR overlapping DEM tiles
  49. 49. The New LandMapR PEM Process• Hierarchical Approach • Hybrid Methodology – Climatic eco-regionalization – Manual methods • BEC sub-zones & variants • Big BEC localization – Physiographic sub-division • JMJ materials mapping • Size & scale of landforms • Ad-hoc custom inputs – Local climate variation – Automated methods • Frost accumulation areas • TRIM DEM analysis – Hydrological flow – Parent material variation – Hills and hillslopes • Texture & depth maps – Terrain Derivatives – Topographic setting • Image analysis • Relative landform position – LS7 Satellite images • Relative moisture regime – Orthoimagery • Slope, orientation, others – Boolean & Fuzzy logic
  50. 50. Image Data Copyright the Province of British Columbia, 2003Needed Different Rules and Classes inDifferent Classification Zones• Boolean Stratification – Climate and Vegetation • Big BEC Subzones – Physiography • Size and scale of landforms • Frost zones – Parent Material • JMJ focussed bioterrain • Texture classes (coarse)
  51. 51. Needed to Construct and Apply DifferentFuzzy Rule Bases• Attribute Rules (arules) – Concepts like slope position, wetness, exposure, gradient – Direct analogues to concepts used to define Site Series • Different rules for each Zone • Can consider non-DEM data• Class Rules (Site Series) – Class defined by its attributes • Different classes in each zone • Different numbers and types• Changes to DBFs needed – To allow separate classes to be defined and output for each • BGC Sub-zone • Material texture, depth • Relief type, slope position
  52. 52. Methods• Step1 • Step 5 – Extract ecological – Apply fuzzy knowledge rule knowledge from field guides bases to digital data sets• Step 2 • Step 6 – Process DEMs to compute – Tune and refine the model terrain derivatives using local expert knowledge• Step 3 • Step 7 – Relate digital inputs to – Apply final knowledge bases defining concepts to entire area of interest• Step 4 • Step 8 – Construct fuzzy knowledge – Evaluate accuracy of final rule base maps using independent data
  53. 53. BC PEM Initial Cariboo Pilot Results 15 km 12 km
  54. 54. BC PEM Early Canim Lake Results71 km EW47 km NS10 m GRID33 Million Cells12 1:20,000Map Sheets
  55. 55. BC PEM Cariboo Pilot Accuracy Assessment• Field Sampling Method • Final Accuracy Results – Randomly located radial – DDSS method was: arm transects • Most accurate (66%) – Classes identified using • Lowest Cost ($0.47/ha) line intercept methodMethod Accuracy CostSoftCopy Site Series 62% $0.64Softcopy Bioterrain 42% $2.161:15 k Photo Bioterrain 57% $2.34DDSS with TRIM DEM 66% $0.47DDSS with Custom DEM 65% $1.30 Source: Moon (2002)
  56. 56. BC PEM Early Experience Conclusions• Reasons for success • Reasons for error – There is a relationship – The relationship is not between landform shape always perfect and and position and soil or predictable ecological classes – The coarse DEMs miss – Even relatively coarse a significant amount of resolution DEMs capture finer resolution terrain some of this relationship variation – Fuzzy heuristic rules can • You can’t classify what capture and apply inexact you can’t see human concepts and – Human constructs are classifications inexact & inconsistent
  57. 57. LandMapR Version 3 (C++) Reprogrammed Single LandMapRFoxPro Program into a Suite of Four Programs in C++ 2003-2005
  58. 58. Overview of the Structure of the Revised C++ LandMapR Programs The LandMapR Toolkit FlowMapR FormMapR FacetMapR WeppMapR GridReadWrite
  59. 59. Improvements to LandMapR 2003-2005• New C++ Modules • New C++ Modules – FlowMapR – FacetMapR • Runs faster on bigger files • Runs faster on bigger files • Still produces incorrect • Big change is ability to apply mm2fl results hierarchical rules • Endless loop can happen • 3 options for output – FormMapR • Different numbers and types of classes for different regions • Runs faster on bigger files • Added option to compute – WeppMapR new measures of flow • An entirely new module length (L2Str, L2Pit, etc) • A bit buggy sometimes • DSS Wetness uses real area • Extracts channels & hillslopes instead of cell count only
  60. 60. Extensions to LandMapR 2003-2005• Major Custom Extensions • Major Custom Extensions – Custom Programs for DSS – Custom Programs for City • Create and fill new GeoFile • Re-compute pit filling • Compute distance to wetlands • Make maps of mm2flood • Create and fill new Zone file • Make maps of nested pond id • Create and fill a Location file – Tiling Programs (watershed) – Tiling Programs (rectangles) • Create master or base files • Create master or base files • Cut base files into tiles • Cut base files into tiles • Rebuild tiles into mosaics by • Rebuild tiles into mosaics global watershed Ids – Landform Entity Programs – Landform Statistics Program • Extract pit, peak & hill sheds • QDL Stats for Ag Canada • Classify pit, peak or hill sheds • CEMA Stats for CEMS
  61. 61. FlowMapRComputes Flow Topology
  62. 62. Purpose of FlowMapR• Cell to cell connectivity CELL DRAINAGE DIRECTION (LDD) – Wanted to compute DIVIDE RELATIVE SLOPE POSITION various measures of: (Distance down slope from cell to pit Centre as % of maximum) • Absolute & relative relief MAXIMUM 63 PIT CENTRE SLOPE LENGTH • Slope length DIVIDE – Wanted to identify CELL 6 2 30 • Pits and Peaks 4 5 8 7 6 5 4 3 2 1 0 1 2 • Channels and Divides CELL DOWNSLOPE LENGTH (LDN) • Passes and Hillslopes 80 100 100 88 75 63 50 38 25 12 0 10 20• Act as glue in classifying CELL RELATIVE SLOPE POSITION (PUP)
  63. 63. FormMapRComputes Terrain Derivatives
  64. 64. Image Data Copyright the Province of British Columbia, 2003Purpose of FormMapR• Compute Input Data to Support Classifications – No single program available to compute all variables of interest for classification – Decided to create an in- house set of programs to support automated landform classification – Full suite of derivatives • Mostly existing algorithms • New relief & slope length
  65. 65. FacetMapRReads & Applies Fuzzy Classification Rules to Prepared Input Data Sets
  66. 66. Purpose of FacetMapR • To Provide a Tool for Classifying Landform- Based Spatial Entities – Wanted to use fuzzy rules to capture and apply expert human heuristic knowledge – Wanted to be able to replicate human devised classification systems • Wanted imposed classesImage Data Copyright the Province of British Columbia, 2003 INVEREMERE, BC 25 m DEM
  67. 67. Purpose of New Revised FacetMapR • Acts as a Classification Engine for Hierarchical Fuzzy Logic Rules – Modified to apply multi-level, hierarchical classifications • Applies different rules for different ecological situations • Needs a zone map to define zones – Modified to be able to use inputs other than DEM derivatives • “External” co-registered data sets • Parent material texture & depth, water, wetlands, rock, imagery, etc.Image Data Copyright the Province of British Columbia, 2003
  68. 68. WeppMapRExtracts Hydrological Spatial Entities from DEM Data
  69. 69. Purpose of WeppMapR• Extract Hydrological Spatial Entities – Wanted a tool to create WEPP structure files • For very large data sets • GeoWepp not available – Reprocess outputs from FlowMapR to extract • Numbered channels • Associated hillslopes • Flow topology Source: Flanagan et al., 2000
  70. 70. The Revised LandMapR C++ Programs Application of the LandMapRKnowledge-Based Approach to PEM Mapping in BC 2003-2008
  71. 71. BC PEM: Application of the RevisedLandMapR C++ Programs 2003-2008• BC PEM Project History and Hypotheses Tested at each Stage – PEM Pilot – 2002/03 (FoxPro Version 2c Programs used) • Automated methods will be less costly than traditional manual ones • Intensive manual interpretation and field sampling will produce more accurate maps than those produced by automated modeling – Canim Lake PEM Operational Scale-up – 2003/04 (FoxPro Version 2c) • Automated predictive methods aren’t scalable for operational mapping • Finer resolution DEM data (5 & 10 vs. 25m) will yield more accurate maps – Quesnel Operational PEM – 2004/05 (Version 3 C++ Programs used) • Unit costs can go down with efficiencies of scale as larger areas are mapped • Single sets of KB rules can apply to entire BEC subzones – East Williams Lake Operational PEM – 2005/06 • Local experts can agree on correct classification in the field at 100% of visited locations • Areas of elevated frost hazard can be predicted to occur in structural hollows – East Quesnel and West Williams Lake Operational PEMs – 2006/08 • Land Cover information from LandSat imagery is not useful for PEMs
  72. 72. Image Data Copyright the Province of British Columbia, 2003 Fundamental Basis of a LMES PEM• Terrain Analysis – Partition space into fundamental spatial entities on the basis of: • Landform size & scale • Landform position • Moisture regime • Landform shape/slope • Landform orientation • Hydrological context Source: Steen and Coupé, 1997 • Ancillary environmental conditions
  73. 73. PEM DSS Classification Using LandMapR Normal Mesic Moist Foot Slope Warm SW Slope Shallow Crest Organic Wetland Wet Toe Slope Cold Frosty Wet Permanent Lake
  74. 74. PEM DSS Final Cartographic Quality Maps
  75. 75. The Revised LandMapR C++ ProgramsApplication of the Revised LandMapR C++ Programs Mapping Depressions or ` Sags` in the City of Edmonton (2005-2006)
  76. 76. Location and Characterization of all Sagsin the City of Edmonton in 2005-2006
  77. 77. Location and Characterization of all Sagsin the City of Edmonton in 2005-2006
  78. 78. Location and Characterization of all Sagsin the City of Edmonton in 2005-2006
  79. 79. Location and Characterization of all Sagsin the City of Edmonton in 2005-2006
  80. 80. Location and Characterization of all Sagsin the City of Edmonton in 2005-2006
  81. 81. LandMapR Version 3 C++ Extensions and Add-ons to theLandMapR C++ Programs 2006-2012
  82. 82. Extensions to LandMapR 2006-2012• Major Custom Extensions • Major Custom Extensions – Landform Entity Programs – Polygon Disaggregation • Extract pit, peak & hill sheds • Extend FacetMapR – LF_Types Script – Revise to write out fuzzy • Classify pit, peak or hill sheds likelihood values for all classes at all grid cells – Slope Break Script – Hierarchical – any number • Extract nested pits (or peaks) of classes of any type in any – Potentially useful? defined domain or zone – New Slope Position (2005) • New Weighted Average Prog • Relative Hydrologic Slope – Computes weighted Position (RHSP) average values for every soil property and depth at – Upslope accumulation area every grid cell location – Downslope dispersal area – Considers 1-N classes – Divide one by sum of both
  83. 83. Image Data Copyright the Province of British Columbia, 2003Extraction of Peak Sheds and Hill Sheds
  84. 84. Image Data Copyright the Province of British Columbia, 2003Peak Sheds as Initial Landform Objects
  85. 85. Image Data Copyright the Province of British Columbia, 2003Classification of Peak Sheds by Relief
  86. 86. Image Data Copyright the Province of British Columbia, 2003Classified Peak Shed Areas are Different
  87. 87. Image Data Copyright the Province of British Columbia, 2003Peak Sheds Classified by Size and Scale
  88. 88. Image Data Copyright the Province of British Columbia, 2003Zone Map: EcoZone, Landform, PM
  89. 89. Problem with Hill Sheds and Peak Sheds• Slope Breaks Needed to Partition Hill Sheds
  90. 90. New Slope Break Custom Program• Trace Down Flow Paths and Mark Inflections
  91. 91. New Slope Break Custom Program• How Many Slope Breaks is Enough
  92. 92. Nested Pits and Peaks May be Interesting• Add-on to FlowMapR needed for City of Edmonton Extracts, numbers and maps nested pits
  93. 93. Nested Pits and Peaks May be Interesting• Nested Peaks are just pits in the inverted DEM Might be able to use this to partition uplands from lowlands
  94. 94. Extension to FlowMapR for Nested Pits and Peaks• New and Improved Pit • Thoughts on Nested Peaks Removing Approach – Presently equivalent to – Copies data for only grid lowest closed contour cells located in depressions around any prominence • Cells below pour elevation • Functional definition of a hill – Only works with this subset – Use modified elevation data of the full DEM when: • Replace original elevation • Removing Pits with elevation to channel – All stream elevations are 0 • Computing Pit Statistics • Invert elevation to channel – Many times faster and more • Compute nested peaks efficient then present • De-trended nested peaks • Works with much smaller files
  95. 95. New Measure of Relative Slope Position: RHSP• Relative Hydrologic Slope Pos • Percent Z Channel to Divide SENSITIVE TO HOLLOWS & DRAWS RELATIVE TO MAIN STREAM CHANNELS Image Data Copyright the Province of British Columbia, 2003Source: MacMillan, 2005
  96. 96. RHSP: Relative Hydrologic Slope Position as Implemented in SAGA • SAGA-RHSP: relative • SAGA-RHSP with soil hydrologic slope position polygons overlaidSource: C. Bulmer, unpublishedCalculation based on: MacMillan, 2005
  97. 97. FacetMapR Modified to Support PolygonDisaggregation• New Output Option – Writes out all fuzzy likelihood values • For every grid cell • For all defined classes – Classes can vary by cell • Every cell can have different numbers and types of fuzzy classes • Controlled by a Map Zone identifier • Rules by Map_Zone
  98. 98. New FoxPro Script Computes SoilProperty Values by Weighted Average
  99. 99. New FoxPro Script Computes SoilProperty Values by Weighted Average
  100. 100. Original Map of Clay by Method ofPolygon Averaging
  101. 101. Thank You

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