development history and personal use of landmapr 1984-2012

Development History and Personal use of LandMapR focus on custom extensions and

unusual uses

R. A. MacMillanLandMapper Environmental Solutions Inc.

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

Pre-LandMapR

Background on Reasons for Interest in DEMs and

Landform Classification

• 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

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)

Rationale

Rationale

• Soil-Landform Models– Are the

fundamental basis for soil survey

– Relate soils to landform position

• Catena Concept– Can be

approximated by terrain analysis and classification from DEM

– Wanted to automated classification of landforms

OBL HULG SZBL BLSS SZHG HULG OHG

EOR COR DYD KLM FMN COR HGT

CHER GLEY CHER SOLZ SALINE GLEY GLEY

High water level

Low water level

700 m 800 m

EOR Series DYD Series KLM Series FMN Series

15

40

60

COR Series

My Interest in Automated Soil-Landform Models and DEMs Began in 1984-85

• Conducted Grid Soil Survey– Lacombe Research

Station• Sampled soils on a 50

m grid– Sand, Silt, Clay, – pH, OC, EC, others– 3 depths (0-15, 15-50,

50-100)• 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

020406080

100120140160

SEMI-VARIOGRAM FOR A-HORIZON %SAND

LAG (1 LAG = 30 M)

SEM

I-V

AR

IAN

CE

LACOMBE SITE: A HORIZON %SAND (1985)

800

m

Source: MacMillan, 1985 unpublished

Pre-LandMapR

Origins of LandMapR in Distributed Hydrological Model DISTHMOD 1988-

1993

Intelligent Pit Removal is Legacy of DISTHMOD

• Remove Initial Small Pits– Based on computed pit geometry

• Pit area (remove only small pits)– Typically use value of 10 cells for

5-10 m DEMs• Pit depth (remove if < selected

depth)– Typically use a value of 0.15 m

for 5-10 m DEMs• Treat these pits as errors or

unimportant

• Pit Removal Process– Based on reversing flow

directions• Find pour point for a given pit• Trace down path from pour

point• Reverse flow directions of

cells along path from pour point to pit

• Flow back “up” to pour point and compute new value for upslope area

• Assign all cells to new joined catchment

3 1 (becomes 2) 2 (becomes

new 2)

1 2

2

12

Pour Elevation 1

Pour Elevation 2initial localdirection of

flow

elevation of allcells below pourpoint raised topour elevation

new “reversed”flow directions

Divide

Pit Center

55

55

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

Intelligent Pit Removal is Legacy of DISTHMOD• Remove all Pits in the Most Likely

Fill Order728

727

726

725

724

723

722

721

728

727

726

725

724

723

722

721

to 33

to 121to 39

to 33

to 64

to 64to 23

to 23

to 74to 19

to 37

to 120

to 52 to 33

to 37

to 118

72

71 16 15

64 55 5223

39

33 29 26 36

41

29 27 36 37 21 19

4267 69 70 66

18

74

68 65 58

117118

116

119120

121

124128

130131

132

Ele

vati

on (

m)


DISTHMOD Left Me With the Ability to Flow Across DEMs• Key aspect of flow was ability to

retain pit info 4 53 214 1 2 3 5 6 7 8 9 10 11 12 13 1415 16 18 17 20 21 22 23 24 25 26 27 28 29 31 30 32 33 34 35 36 37 38 39 40 41 42 43 44 19

725

724

723

722

721

725

724

723

722

72116 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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

1

3

2


Key Advantage of LandMapR is Ability to Flow from Cell to Cell & through Pits

0 14 5 8 7 6 25 4 3 2 1

0 1080 100 100 88 75 2063 50 38 25 12

DIVIDE

DIVIDECELL

CELL DRAINAGE DIRECTION (LDD)

PIT CENTRE

CELL DOWNSLOPE LENGTH (LDN)

CELL RELATIVE SLOPE POSITION (PUP)

RELATIVE SLOPE POSITION(Distance down slope from cell to pit Centre as % of maximum)

63

6 2 30

MAXIMUMSLOPE LENGTH

• Cell to cell connectivity– Permits

computation of various measures of:• Absolute &

relative relief • Slope length

– Gives ability to identify• Pits and Peaks• Channels and

Divides• Passes and

Hillslopes– Acts as glue in

classifying

LandMapRVersion 1

Developed Original LandMapR as a Series 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 Precision Farming• Agriculture

Canada– Started in 1995-

96– Wanted to show

that soil-landform models used in Soil Survey had relevance for Precision Farming

– Believed partitioning fields into landform facets would define effective management zones for PF

– Lacked tools to do this• No other suitable

software was available to us

• Dr. W. W. Pettapiece– Former head of

Soil Survey in Canada

– Liked what he saw in models proposed by Pennock et al., 1987• But Pennock

model gave quite noisy results

• Wanted tools to extend, refine and apply models such as Pennock’s

– Contracted LandMapR• to develop new

tools

Key Outcome: Programs and Definition of Two Fuzzy Classification Rule Bases• Attribute Rules

– Arule file (e.g. LM3arule)

– Defines “attributes” of terrain as fuzzy semantic constructs (e.g in words)

– User can define any attribute based on any available input variable

– Have 2 main pre-defined rule sets for landforms• Many for

ecological classes

• Classification Rules– Crule file (e.g.

LM3crule)– Defines user-

defined classes as a weighted combination of fuzzy attributes

– Can define any number of classes based on any number of attributes.

– Have 2 main pre-defined rule sets for landforms

ARule Table Defines Fuzzy Attributes

SORT ORDER FILE_IN ATTR_IN CLASS OUT

MODEL NO B

B LOW B HI B1 B2 D

1 formfile PROF CONVEX_D 4 5.0 0.0 0.0 2.5 0.0 2.52 formfile PROF CONCAVE_D 5 -5.0 0.0 0.0 0.0 -2.5 2.53 formfile PROF PLANAR_D 1 0.0 0.0 0.0 -2.5 2.5 2.54 formfile PLAN CONVEX_A 4 5.0 0.0 0.0 2.5 0.0 2.55 formfile PLAN CONCAVE_A 5 -5.0 0.0 0.0 0.0 -2.5 2.56 formfile PLAN PLANAR_A 1 0.0 0.0 0.0 -2.5 2.5 2.57 formfile QWETI HIGH_WI 4 7.0 0.0 0.0 3.5 0.0 3.08 formfile QWETI LOW_WI 5 0.5 0.0 0.0 0.0 3.5 3.09 formfile SLOPE NEAR_LEVEL 5 0.5 0.0 0.0 0.0 1.0 0.510 formfile SLOPE REL_STEEP 4 2.0 0.0 0.0 1.0 0.0 1.011 relzfile PCTZ2ST NEAR_DIV 4 90.0 0.0 0.0 75.0 0.0 15.012 relzfile PCTZ2ST NEAR_HALF 1 50.0 50.0 50.0 25.0 75.0 25.013 relzfile PCTZ2ST NEAR_CHAN 5 10.0 0.0 0.0 0.0 25.0 15.014 relzfile PCTZ2PIT NEAR_PEAK 4 90.0 0.0 0.0 75.0 0.0 15.015 relzfile PCTZ2PIT NEAR_MID 1 50.0 50.0 50.0 25.0 75.0 25.016 relzfile PCTZ2PIT NEAR_PIT 5 5.0 0.0 0.0 0.0 10.0 5.017 relzfile Z2PIT HI_ABOVE 4 2.0 0.0 0.0 1.0 0.0 1.0

CRule Table Defines Fuzzy Classes

F NAME FUZATTR

ATTR WT

FACET NO

F CODE F NAME FUZATTR

ATTR WT

FACET NO

F CODE F NAME FUZATTR

ATTR WT

FACET NO

F CODE

LCR 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

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

LandMapR Landform Classification

• Initial Development– 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• At those 2 sites

– Sampled to verify classes were different• Soils and Soil

Properties• Moisture, fertility &

yields

Stettler Site (800 x 400 m)

Hussar Site (800 x 800 m)

Goddard & Nolan Evaluated Differences in Soil Properties

and Yield at Sites

Hussar

0

2

4

6

8

10

12

U M L

Landscape Position

% O

M (0

-15

cm)

1997 Original (28 pt)transects

1998 Verification (13 pt)transects

Coen Checked Soil Property Differences by

Landform Class

LandMapR Landform Classification Used to Relate Soil Properties to Landform Position

Status of LandMapR at end of 1999• Agriculture

Canada– Assumed

ownership of LandMapR IP• Took

custodianship of the original 19 FoxPro programs

• Distributed them to internal Ag Canada researchers

• 19 FoxPro Programs– Use Constraints

• Slow to run & Need FoxPro

• Had to run 19 separate programs in correct order

• Difficult to learn & use

• Advantages of LandMapR– Computed a wide

range of terrain derivatives (for 1996)• Relative landform

position indices not easily available in other software at the time

• Less speckle than Pennock’s

– Default Landform Classes• Fuzzy rules

developed– LM_arule,

LM_crule• 15 default

landform classes defined, evaluated & accepted

– Ready to be evaluated

Evaluation of LandMapR by Other Users• Alberta

– AAFRD• T. Goddard & S. Nowlan• Dr. Linda Hall & Ty Faechner• Dr. Len Kryzanowski

– AAFC• Dr. Gerry Coen (Lethbridge)

• Manitoba– U of M

• Grant Manning (MSc.)• Yann Pelcat (MSc.)

– Brandon AAFC & Assiniboine• Dr. Al Moulin• Dr. Ty Faechner

• Saskatchewan– Indian Head Precision

Farm• Yann Pelcat (MSc.)

• Quebec– Dr. Thomas Piekutowski

• Montana– Montana State University

• Dr. Dan Long and others• United Kingdom - Silsoe

– Soil Survey of England & Wales

• Dr. Thomas Mayr• Ontario

– Doug Aspinal (OMAF)

LandMapRVersion 2a

Collated Original 19 LandMapR FoxPro Programs into a Single

FoxPro Program 1999-2003

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

Early Applications of the Single Revised LandMapR Program

Original LandMapR 15 Landform Facets

• Initial Application Focus– Small areas

equivalent to individual farm fields

– Clear agricultural focus

• Applications– Precision farming

research• Alberta, Manitoba,

Ontario, Quebec, Montana, Germany

– Extension (SVAECP)– Commercial service

• Norwest Soils AgAtlas

800 m 800 m

800 m800 m

Extensions to LandMapR 1999-2001• Alberta

Landforms– New custom

FoxPro programs to compute summary statistics for terrain attributes for an entire classified DEM

• SVAECP Project– Used same

programs to compute and report statistics for each site

• CEMA Project– Oil Sands

Landscapes

• Lessons Learned– We got slope

length wrong• Our slope values

were too long– Used Lpit2Peak for

length– Should have used

LStr2Div

– Soil properties not always related to landform class• Field sample data

for 50+ sites– Only about 50%

showed a clear relationship between landform class and soil property values

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

Alberta Landforms Project 1999-2000• Morphometric Descriptions for

Each Site


Alberta Landforms Project 1999-2000• Landform Type Morphology

Summarized


Applications of LandMapR to Field Sized Sites 2000-2001• AgAtlas Project

– Norwest Soil Research– 35 Sites across Canada

• Manitoba to BC• Obtained 5 m DEMs• Applied classification• Prepared maps &

reports• Evaluated visually in

field– All appeared

reasonable– Commercial viability

not proven

• SVAECP Project– CARDF Funded Project– 40+ Sites in Alberta

• ¼ section in size• Obtained 5 m DEMs• Applied classification• Prepared 2D and 3D

maps and images• Sampled sites by

landform position– Created Web Site

• “www.infoharvest.ca/svaecp/”

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

SVAECP Project: Examples of Classified Sites with Complex Hummocky Topography

Rumsey Site (H1h)

Turner Valley Site (IUl)

Stettler Site (H1m)

Mundare Site (H1l)

CEMA Landforms Project 2003

LandMapRVersion 2b

Extended the Single FoxPro Program by Adding WeppMapR

in 2001

Extensions to LandMapR 2001-2002• WeppMapR

Program– An entirely new

module• Reprocessed

FlowMapR output to extract and characterize Wepp spatial entities automatically

• Soil-Landform Program– FoxPro scripts

• Compute likelihood of each soil in each notional landform position

• Automatically allocate soils to defined landform classes

• BC PEM Landforms– Hierarchical

Classification• Changed core

LandMapR program to allow for different classes and rules in different zones

– New options in LandMapR

• Built, applied and evaluated several new rule bases

– FoxPro Scripts• Tile and then

mosaic overlapping DEM tiles

• To process very large areas

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

WeppMapR Extracts Channel Segments and their Associated Hillslopes• Steps involved– Compute

catchments for each channel segment

– Subdivide into left, right & top hillslope components

1.80 km

1.55 km

WeppMapR Computes and Stores Topological Flow Linkages in a DBF File

• WEPP Structure File

• Number hillslope entities sequentially from 1 to n

• Link hillslopes to channels

• WEPP Structure File• Number channel/ impoundment entities

from n+1 to total number of entities (m)

Examples of Wepp Spatial Entities• Salisbury Plain,

UK

• MKMA Region, BC

Mature, eroded well-defined landscape Young, steep, mountainous landscape

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)

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

BC: MKMA Forest Region PEM

50.0 km

45.0 km

• Broad Valleys in BC– Need extra

context– Second

classification– Separate crests

in broad valleys from crests on mountains

– Beginnings of multi level hierarchical classification

– Need techniques for tiling regions

BC: Inveremere Forest Region PEM

172 km EW

• Very Large Area– 172 km EW by

178 km NS (3 M ha)

– 50 Million cells– Defined 11

Tiles• Different

Landform Types in Different Parts of the Area– Defined 2

Zones– Different Rules

in each zone

178 km NS

LandMapRVersion 2c

Major Change to the Single FoxPro Program to Support

Ecological Mapping (PEM) in BC in 2002-2003

Major Changes to LandMapR 2002-2003• Split into 4

Modules– FlowMapR

• Only compute flow once

– FormMapR• Only need to

compute derivatives once per tile

• New and changed derivatives

– FacetMapR• Needed to support

hierarchical rules and outputs

• Needed to rerun classifier many times

– WeppMapR

• New Ideas and Extensions– Hierarchical

Classification• New option in

LandMapR– Required new DBFs

and creation of a new Zone File

– Required ability to read and apply different rule bases

– Non-DEM Inputs• New Geo File in

FacetMapR– Contains new non-

DEM info– Rules consider non-

DEM info

– FoxPro Scripts• To tile and then

mosaic overlapping DEM tiles

The New LandMapR PEM Process

• Hierarchical Approach– Climatic eco-

regionalization• BEC sub-zones &

variants– Physiographic

sub-division• Size & scale of

landforms– Local climate

variation• Frost

accumulation areas

– Parent material variation• Texture & depth

maps– Topographic

setting• Relative

landform position

• Relative moisture regime

• Slope, orientation, others

• Hybrid Methodology– Manual

methods• Big BEC

localization• JMJ materials

mapping• Ad-hoc custom

inputs– Automated

methods• TRIM DEM

analysis– Hydrological

flow – Hills and

hillslopes– Terrain

Derivatives• Image analysis

– LS7 Satellite images

– Orthoimagery

– Boolean & Fuzzy logic

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)

Image Data Copyright the Province of British Columbia, 2003

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

Methods

• Step1– Extract ecological knowledge from field guides

• Step 2– Process DEMs to compute terrain derivatives

• Step 3– Relate digital inputs to defining concepts

• Step 4– Construct fuzzy knowledge rule base

• Step 5– Apply fuzzy knowledge rule bases to digital data

sets• Step 6

– Tune and refine the model using local expert knowledge

• Step 7– Apply final knowledge bases to entire area of

interest• Step 8

– Evaluate accuracy of final maps using independent data

BC PEM Initial Cariboo Pilot Results

15 km

12 km

BC PEM Early Canim Lake Results

71 km EW

47 km NS

10 m GRID

33 Million Cells

12 1:20,000 Map Sheets

BC PEM Cariboo Pilot Accuracy Assessment

Method Accuracy Cost

SoftCopy 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

• Field Sampling Method– Randomly located

radial arm transects

– Classes identified using line intercept method

• Final Accuracy Results– DDSS method

was:• Most accurate

(66%)• Lowest Cost

($0.47/ha)

Source: Moon (2002)

BC PEM Early Experience Conclusions• Reasons for

success– There is a

relationship between landform shape and position and soil or ecological classes

– Even relatively coarse resolution DEMs capture some of this relationship

– Fuzzy heuristic rules can capture and apply inexact human concepts and classifications

• Reasons for error– The

relationship is not always perfect and predictable

– The coarse DEMs miss a significant amount of finer resolution terrain variation• You can’t

classify what you can’t see

– Human constructs are inexact & inconsistent

LandMapRVersion 3 (C++)

Reprogrammed Single LandMapR FoxPro Program

into a Suite of Four Programs in C++ 2003-2005

Overview of the Structure of the Revised C++

LandMapR Programs

The LandMapR ToolkitFlowMapRFormMapRFacetMapRWeppMapR

GridReadWrite

Improvements to LandMapR 2003-2005• New C++

Modules– FlowMapR

• Runs faster on bigger files

• Still produces incorrect mm2fl results

• Endless loop can happen

– FormMapR• Runs faster on

bigger files• Added option to

compute new measures of flow length (L2Str, L2Pit, etc)

• DSS Wetness uses real area instead of cell count only

• New C++ Modules– FacetMapR

• Runs faster on bigger files

• Big change is ability to apply hierarchical rules

• 3 options for output• Different numbers

and types of classes for different regions

– WeppMapR• An entirely new

module• A bit buggy

sometimes• Extracts channels

& hillslopes

Extensions to LandMapR 2003-2005• Major Custom Extensions– Custom Programs

for DSS• Create and fill new

GeoFile• Compute distance

to wetlands• Create and fill new

Zone file• Create and fill a

Location file– Tiling Programs

(rectangles)• Create master or

base files• Cut base files into

tiles• Rebuild tiles into

mosaics– Landform Entity

Programs• Extract pit, peak &

hill sheds• Classify pit, peak

or hill sheds

• Major Custom Extensions– Custom Programs

for City• Re-compute pit

filling• Make maps of

mm2flood• Make maps of

nested pond id– Tiling Programs

(watershed)• Create master or

base files• Cut base files into

tiles• Rebuild tiles into

mosaics by global watershed Ids

– Landform Statistics Program• QDL Stats for Ag

Canada• CEMA Stats for

CEMS

FlowMapR

Computes Flow Topology

Purpose of FlowMapR

0 14 5 8 7 6 25 4 3 2 1

0 1080 100 100 88 75 2063 50 38 25 12

DIVIDE

DIVIDECELL

CELL DRAINAGE DIRECTION (LDD)

PIT CENTRE

CELL DOWNSLOPE LENGTH (LDN)

CELL RELATIVE SLOPE POSITION (PUP)

RELATIVE SLOPE POSITION(Distance down slope from cell to pit Centre as % of maximum)

63

6 2 30

MAXIMUMSLOPE LENGTH

• Cell to cell connectivity– Wanted to

compute various measures of:• Absolute &

relative relief • Slope length

– Wanted to identify• Pits and Peaks• Channels and

Divides• Passes and

Hillslopes

• Act as glue in classifying

FormMapR

Computes Terrain Derivatives

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


FacetMapR

Reads & 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

fuzzy rules to capture and apply expert human heuristic knowledge

– Wanted to be able to replicate human devised classification systems• Wanted imposed

classes

INVEREMERE, BC 25 m DEMImage Data Copyright the Province of British Columbia, 2003

• 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.


Purpose of New Revised FacetMapR

WeppMapR

Extracts Hydrological Spatial Entities from DEM Data

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

The Revised LandMapR C++ Programs

Application of the LandMapRKnowledge-Based Approach

to PEM Mapping in BC 2003-2008

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

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• Ancillary

environmental conditions


Source: Steen and Coupé, 1997

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

PEM DSS Final Cartographic Quality Maps

The Revised LandMapR C++ Programs

Application of the Revised LandMapR C++ Programs

Mapping Depressions or ` Sags` in the City of Edmonton (2005-2006)

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

LandMapRVersion 3 C++

Extensions and Add-ons to the LandMapR C++ Programs

2006-2012

Extensions to LandMapR 2006-2012• Major Custom Extensions– Landform Entity

Programs• Extract pit, peak &

hill sheds– LF_Types Script

• Classify pit, peak or hill sheds

– Slope Break Script• Extract nested pits

(or peaks)– Potentially useful?

– New Slope Position (2005)• Relative

Hydrologic Slope Position (RHSP)

– Upslope accumulation area

– Downslope dispersal area

– Divide one by sum of both

• Major Custom Extensions– Polygon

Disaggregation• Extend

FacetMapR– Revise to write out

fuzzy likelihood values for all classes at all grid cells

– Hierarchical – any number of classes of any type in any defined domain or zone

• New Weighted Average Prog

– Computes weighted average values for every soil property and depth at every grid cell location

– Considers 1-N classes

Extraction of Peak Sheds and Hill Sheds


Peak Sheds as Initial Landform Objects


Classification of Peak Sheds by Relief


Classified Peak Shed Areas are Different


Peak Sheds Classified by Size and Scale


Zone 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

EdmontonExtracts, numbers and maps nested pits

Nested Pits and Peaks May be Interesting• Nested Peaks are just pits in the

inverted DEMMight be able to use this to partition uplands from lowlands

Extension to FlowMapR for Nested Pits and Peaks

• New and Improved Pit Removing Approach– Copies data for

only grid cells located in depressions• Cells below pour

elevation– Only works with

this subset of the full DEM when:• Removing Pits• Computing Pit

Statistics– Many times faster

and more efficient then present• Works with much

smaller files

• Thoughts on Nested Peaks– Presently

equivalent to lowest closed contour around any prominence• Functional

definition of a hill– Use modified

elevation data• Replace original

elevation with elevation to channel

– All stream elevations are 0

• Invert elevation to channel

• Compute nested peaks

• De-trended nested peaks


New Measure of Relative Slope Position: RHSP

• Relative Hydrologic Slope Pos

• Percent Z Channel to Divide

RELATIVE TO MAIN STREAM CHANNELSSENSITIVE TO HOLLOWS & DRAWS

Source: MacMillan, 2005

RHSP: Relative Hydrologic Slope Position as Implemented in SAGA• SAGA-RHSP:

relative hydrologic slope position

• SAGA-RHSP with soil polygons overlaid

Calculation based on: MacMillan, 2005

Source: C. Bulmer, unpublished

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

New FoxPro Script Computes Soil Property Values by Weighted Average

Original Map of Clay by Method of Polygon Averaging

Thank You

development history and personal use of landmapr 1984-2012

Technology

pit centre

pit depth

given pit

pit centersource

pit info

intelligent pit removal

pits cell

pour point