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

1. Development History and Personal use of
LandMapR
focus on custom extensions and unusual uses
R. A. MacMillan
LandMapper Environmental Solutions Inc.

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. Pre-LandMapR
Background on Reasons for Interest in
DEMs and Landform Classification

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. 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. 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. 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. Pre-LandMapR
Origins of LandMapR in Distributed
Hydrological Model DISTHMOD
1988-1993

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 Center
Source: MacMillan et al., 1993 Landscape Ecology and GIS

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 23 726
Elevation (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
721 721
Source: MacMillan et al., 1993 Landscape Ecology and GIS

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 4
725 725
3
2
724 724
723 1 723
722 722
721 721
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 44
Source: MacMillan et al., 1993 Landscape Ecology and GIS

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. LandMapR
Version 1
Developed Original LandMapR as a
Series of 19 FoxPro Programs in 1994-99

14. LandMapR Programs to the End of 1999
FoxPro Programs: 19 Separate Programs Run Sequentially

15. Initial Site Level Studies for Precision
Farming
• 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. Key Outcome: Programs and Definition
of 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. ARule Table Defines Fuzzy Attributes
SORT MODEL B
ORDER 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. CRule Table Defines Fuzzy Classes
F ATTR FACET F ATTR FACET F ATTR FACET F
NAME FUZATTR WT NO CODE F NAME FUZATTR WT NO CODE F NAME FUZATTR WT NO CODE
LCR NEAR_PEAK 30 11 1 CBS NEAR_HALF 20 23 6 TSL NEAR_CHAN 20 32 11
LCR NEAR_DIV 20 11 1 CBS NEAR_MID 10 23 6 TSL NEAR_PIT 10 32 11
LCR HI_ABOVE 10 11 1 CBS HI_ABOVE 5 23 6 TSL REL_STEEP 10 32 11
LCR NEAR_LEVEL 20 11 1 CBS REL_STEEP 20 23 6 TSL PLANAR_D 25 32 11
LCR PLANAR_D 10 11 1 CBS CONCAVE_A 20 23 6 TSL PLANAR_A 25 32 11
LCR PLANAR_A 5 11 1 CBS PLANAR_D 15 23 6 TSL HIGH_WI 10 32 11
LCR LOW_WI 5 11 1 CBS HIGH_WI 10 23 6 FAN NEAR_CHAN 20 33 12
DSH NEAR_PEAK 30 12 2 TER NEAR_HALF 20 24 7 FAN NEAR_PIT 10 33 12
DSH NEAR_DIV 20 12 2 TER NEAR_MID 10 24 7 FAN REL_STEEP 10 33 12
DSH HI_ABOVE 10 12 2 TER HI_ABOVE 5 24 7 FAN CONVEX_A 25 33 12
DSH CONVEX_D 20 12 2 TER NEAR_LEVEL 30 24 7 FAN PLANAR_D 25 33 12
DSH CONVEX_A 10 12 2 TER PLANAR_D 15 24 7 FAN LOW_WI 10 33 12
DSH LOW_WI 10 12 2 TER PLANAR_A 20 24 7 LSM NEAR_DIV 10 41 13
UDE NEAR_PEAK 30 13 3 SAD NEAR_HALF 20 25 8 LSM NEAR_CHAN 20 41 13
UDE NEAR_DIV 20 13 3 SAD NEAR_MID 10 25 8 LSM NEAR_PIT 10 41 13
UDE HI_ABOVE 10 13 3 SAD HI_ABOVE 5 25 8 LSM NEAR_PEAK 10 41 13
UDE NEAR_LEVEL 10 13 3 SAD NEAR_LEVEL 20 25 8 LSM REL_STEEP 10 41 13
UDE CONCAVE_D 10 13 3 SAD CONCAVE_D 20 25 8 LSM CONVEX_D 15 41 13
UDE CONCAVE_A 10 13 3 SAD CONVEX_A 20 25 8 LSM CONVEX_A 15 41 13
UDE HIGH_WI 10 13 3 MDE NEAR_HALF 20 26 9 LSM LOW_WI 10 41 13
BSL NEAR_HALF 20 21 4 MDE NEAR_MID 10 26 9 LLS NEAR_CHAN 20 42 14
BSL NEAR_MID 10 21 4 MDE HI_ABOVE 5 26 9 LLS NEAR_PIT 20 42 14
BSL HI_ABOVE 5 21 4 MDE NEAR_LEVEL 25 26 9 LLS NEAR_LEVEL 40 42 14
BSL REL_STEEP 20 21 4 MDE CONCAVE_D 10 26 9 LLS PLANAR_D 5 42 14
BSL PLANAR_D 15 21 4 MDE CONCAVE_A 10 26 9 LLS PLANAR_A 5 42 14
BSL PLANAR_A 25 21 4 MDE HIGH_WI 20 26 9 LLS HIGH_WI 10 42 14
BSL LOW_WI 5 21 4 FSL NEAR_CHAN 20 31 10 DEP NEAR_CHAN 20 43 15
DBS NEAR_HALF 20 22 5 FSL NEAR_PIT 10 31 10 DEP NEAR_PIT 30 43 15
DBS NEAR_MID 10 22 5 FSL REL_STEEP 10 31 10 DEP NEAR_LEVEL 20 43 15
DBS HI_ABOVE 5 22 5 FSL CONCAVE_D 20 31 10 DEP CONCAVE_A 10 43 15
DBS REL_STEEP 20 22 5 FSL CONCAVE_A 20 31 10 DEP CONCAVE_D 10 43 15
DBS CONVEX_A 20 22 5 FSL PLANAR_A 10 31 10 DEP HIGH_WI 10 43 15
DBS PLANAR_D 15 22 5 FSL HIGH_WI 20 31 10
DBS LOW_WI 10 22 5

19. Fuzzy Classification then Assign Each
Cell to its Most Likely Landform Class

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. Goddard & Nolan Evaluated Differences in
Soil Properties and Yield at Sites

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. LandMapR Landform Classification Used to
Relate Soil Properties to Landform Position

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. 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. LandMapR
Version 2a
Collated Original 19 LandMapR FoxPro
Programs into a Single FoxPro Program
1999-2003

27. LandMapR Program Beginning in 2000
FoxPro Programs: 19 Separate Programs Merged into 1 FoxPro Program in 2000

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. 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. 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. Alberta Landforms Project 1999-2000
• Morphometric Descriptions for Each Site
http://www1.agric.gov.ab.ca/soils/soils.nsf

32. Alberta Landforms Project 1999-2000
• Landform Type Morphology Summarized
http://www1.agric.gov.ab.ca/soils/soils.nsf

33. Applications of LandMapR to Field
Sized 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. 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. SVAECP Project: Examples of Classified
Sites with Complex Hummocky Topography
Turner Valley Site (IUl) Mundare Site (H1l)
Stettler Site (H1m) Rumsey Site (H1h)

36. CEMA Landforms Project 2003

37. LandMapR
Version 2b
Extended the Single FoxPro Program by
Adding WeppMapR in 2001

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. 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. 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. 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. Examples of Wepp Spatial Entities
• Salisbury Plain, UK • MKMA Region, BC
Mature, eroded well-defined landscape Young, steep, mountainous landscape

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. Use of LandMapR Landform Classes as
Input 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. 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. 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. LandMapR
Version 2c
Major Change to the Single FoxPro
Program to Support Ecological Mapping
(PEM) in BC in 2002-2003

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. 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. Image Data Copyright the Province of British Columbia, 2003
Needed Different Rules and Classes in
Different 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. Needed to Construct and Apply Different
Fuzzy 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. 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. BC PEM Initial Cariboo Pilot Results
15 km
12 km

54. BC PEM Early Canim Lake Results
71 km EW
47 km NS
10 m GRID
33 Million
Cells
12 1:20,000
Map Sheets

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 method
Method Accuracy Cost
SoftCopy Site Series 62% $0.64
Softcopy Bioterrain 42% $2.16
1:15 k Photo Bioterrain 57% $2.34
DDSS with TRIM DEM 66% $0.47
DDSS with Custom DEM 65% $1.30
Source: Moon (2002)

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. LandMapR
Version 3 (C++)
Reprogrammed Single LandMapR
FoxPro Program into a Suite of Four
Programs in C++ 2003-2005

58. Overview of the Structure of the Revised
C++ LandMapR Programs
The LandMapR Toolkit
FlowMapR
FormMapR
FacetMapR
WeppMapR
GridReadWrite

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. 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. FlowMapR
Computes Flow Topology

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. FormMapR
Computes Terrain Derivatives

64. Image Data Copyright the Province of British Columbia, 2003
Purpose 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. FacetMapR
Reads & Applies Fuzzy Classification
Rules to Prepared Input Data Sets

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 classes
Image Data Copyright the Province of British Columbia, 2003
INVEREMERE, BC 25 m DEM

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. WeppMapR
Extracts Hydrological Spatial Entities
from DEM Data

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. The Revised LandMapR C++
Programs
Application of the LandMapR
Knowledge-Based Approach to PEM
Mapping in BC 2003-2008

71. BC PEM: Application of the Revised
LandMapR 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. 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

74. 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

75. PEM DSS Final Cartographic Quality Maps

76. The Revised LandMapR C++
Programs
Application of the Revised LandMapR
C++ Programs
Mapping Depressions or ` Sags` in the City of
Edmonton (2005-2006)

77. Location and Characterization of all Sags
in the City of Edmonton in 2005-2006

78. Location and Characterization of all Sags
in the City of Edmonton in 2005-2006

79. Location and Characterization of all Sags
in the City of Edmonton in 2005-2006

80. Location and Characterization of all Sags
in the City of Edmonton in 2005-2006

81. Location and Characterization of all Sags
in the City of Edmonton in 2005-2006

82. LandMapR
Version 3 C++
Extensions and Add-ons to the
LandMapR C++ Programs 2006-2012

83. 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

84. Image Data Copyright the Province of British Columbia, 2003
Extraction of Peak Sheds and Hill Sheds

85. Image Data Copyright the Province of British Columbia, 2003
Peak Sheds as Initial Landform Objects

86. Image Data Copyright the Province of British Columbia, 2003
Classification of Peak Sheds by Relief

87. Image Data Copyright the Province of British Columbia, 2003
Classified Peak Shed Areas are Different

88. Image Data Copyright the Province of British Columbia, 2003
Peak Sheds Classified by Size and Scale

89. Image Data Copyright the Province of British Columbia, 2003
Zone Map: EcoZone, Landform, PM

90. Problem with Hill Sheds and Peak Sheds
• Slope Breaks Needed to Partition Hill Sheds

91. New Slope Break Custom Program
• Trace Down Flow Paths and Mark Inflections

92. New Slope Break Custom Program
• How Many Slope Breaks is Enough

93. Nested Pits and Peaks May be Interesting
• Add-on to FlowMapR needed for City of Edmonton
Extracts, numbers and maps nested pits

94. 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

95. 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

96. 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, 2003
Source: MacMillan, 2005

97. RHSP: Relative Hydrologic Slope Position
as Implemented in SAGA
• SAGA-RHSP: relative • SAGA-RHSP with soil
hydrologic slope position polygons overlaid
Source: C. Bulmer, unpublished
Calculation based on: MacMillan, 2005

98. FacetMapR Modified to Support Polygon
Disaggregation
• 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

99. New FoxPro Script Computes Soil
Property Values by Weighted Average

100. New FoxPro Script Computes Soil
Property Values by Weighted Average

101. Original Map of Clay by Method of
Polygon Averaging

102. Thank You