Automated landform
classification using DEMs
Automated classification of geomorphic/
hydrologic spatial entities to support predictive
ecosystem mapping (PEM)
R. A. (Bob) MacMillan
LandMapper Environmental Solutions
Outline
 Introduction and background
 Automated landform classification from
DEMs
 Capturing and applying expert knowledge
 Significance with respect to PEM
 Closing thoughts
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Introduction
700 m
 Who and what am I?
 Soil scientist & mapper
 Soil-landform modeller
 What do I do?
 Terrain analysis and
classification from DEM
EOR Series
800 m
DYD Series
KLM Series
FMN Series
COR Series
15
40
60
OBL
HULG
SZBL
BLSS
SZHG
HULG
OHG
EOR
COR
DYD
KLM
FMN
COR
HGT
 What can I contribute to
this discussion of PEM?

High water level
An outsider’s perspective
Low water level
CHER
LandMapper
Environmental Solutions © 2001
GLEY
CHER
SOLZ
SALINE
GLEY
GLEY
BC PEM Workshop,
April 25-27, 2001
DEM
LANDFORM CLASSIFICATION
Introduction
 What is automated
landform classification?




What does it require?
How does it work?
What can it produce?
What can’t it produce?
LandMapper
Environmental Solutions © 2001
BC PEM Workshop,
April 25-27, 2001
Background
 Automated landform
800 m
800 m
classification


A work in progress
Previous efforts:
AGRICULTURE
• classify farm fields for
precision agriculture
• classify and describe
landforms for soil survey
• LandMapR Program

Forestry sector interest
• potential to classify
forested areas
LandMapper
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FORESTRY
BC PEM Workshop,
April 25-27, 2001
Background
 Not a paradigm shift!
 Merge long established
concepts and procedures for
manual delineation of spatial
entities using API
 With improved data sources &
new or emerging technologies
for processing and classifying
digital data
•
•
•
•
high resolution DEMs (5-10 m)
applied machine vision
fuzzy logic, expert systems, AI
hydrologic & geomorphic
modeling
LandMapper
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800 m
800 m
MANUAL PROCEDURES
800 m
800 m
NEW DATA SOURCES
BC PEM Workshop,
April 25-27, 2001
Situation analysis
 Natural Resource managers  Natural Resource
are facing increasing
challenges:







Growing globalization
Increased competition
Need for cost-effective
operations
Demands for sustainability
Compliance with standards
Expanding obligations for
monitoring & certification
More accurate forecasting
LandMapper
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Inventories are:


At heart of virtually all
natural resource issues
Provide the basis for:
 responsible management
and planning
 applying and extending
knowledge & experience
 applying spatial decision
support models
 ...over space and time
BC PEM Workshop,
April 25-27, 2001
Situation analysis
 Natural Resource
Inventories undergoing
significant change:



Need a new generation of
classification and mapping
systems
These need to draw upon
existing classification &
mapping approaches
New systems must be:
• more dynamic, adaptive
• cheaper, faster, higher
resolution
• able to model processes
LandMapper
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 Expectations for Natural
Resource Inventories:


Digital from start to finish
Provide framework for
multi-scale, nested modeling
of processes
– Ecosystem
– landscape
– watershed


Have known accuracy
Support management re policy, regulations, planning,
operations
BC PEM Workshop,
April 25-27, 2001
Objective


Devise and implement new procedures & an
operational toolkit for automatically defining…
A multi-level hierarchy of nested hydrologically
and geomorphologically oriented spatial entities
• which act as a basic structural framework for different kinds of
natural resource inventories and their interpretations — soil maps,
terrestrial ecosystem, wildlife habitat, forest productivity
• based on physical features that are:
–
–
–
–
distinct & readily identifiable landform entities
logical entities capable of supporting management & planning
able to support definition of linkages & interactions
able to support nesting & aggregation within a hierarchy
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Conceptual design
Source: Band (1986a)
 Geomorphological-Hydrological spatial entities
 Adopt, adapt & integrate previous successful approaches
• Band, Fels and Matson, Graff and Usery, Irwin et al., Pennock et
al., Pike, Skidmore, Wood, Franklin

Incorporate concepts of hydrological connectivity and
hydrologic response units (HRUs)
• Miller, Band & Wood, Band
• ITC system of terrain mapping units — TMUs (Meijerink)

Embrace and evolve concepts from traditional forest
inventory
• multi-level hierarchies from Ecological Land Classification
• landforms provide the basic spatial framework (Rowe)
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Conceptual design
 Evolution not revolution
 Based on capturing and applying expert understanding
• heuristic, rule-based, classification approach
• aim to have a machine replicate and apply human comprehension
–
–
–
–
a form of applied machine vision/artificial intelligence
teach machine to “see” and interpret images as a human might
use fuzzy logic applied to dimensionless semantic constructs
convert absolute terrain measures into relative concepts such as:
» relatively steep, close to mid-slope, relatively convex, etc
– define fuzzy definitions of landform classes (e.g. midslope, crest)
» in terms of relative conceptual attributes (steepness, position)
• finish with landform-based units that would be recognizable to:
– expert human interpreters of air photos and topographic data
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Conceptual design
 A multi-level, multi-scale hierarchy
Appropriate Scale
DEM Resolution and Source
1:5 Million to 1:10 Million
1:1 Million to 1:5 Million
1:250,000 to 1:1 Million
1:125,000 to 1:250,000
1:50,000 to 1:125,000
1:10,000 to 1:50,000
1:5,000
to 1:10,000
1:1,000
to 1:5,000
9 x 9 km (ETOPO5)
1 x 1 km (GTOPO30)
500 x 500 m (DTED)
100 x 100 m (SRTM)
25 x 25 m
10 x 10 m
5x5m
1x1m
Proposed Name
Physiographic Province
Physiographic Region
Physiographic District
Physiographic System
Unnamed and undefined
Landform Type
Landform Element
Unnamed and undefined
• Widely accepted in the forestry and ecological sectors
• Fundamental to Ecological Land Classification
– Rowe, SBLC, Wiken, Boyacioglu
• Primary interest is in lowest 1 or 2 levels in the hierarchy
LandMapper
– typically used as basis for operational planning and
management
Environmental Solutions © 2001
BC PEM Workshop,
April 25-27, 2001
Landform
elements
700 m
 Lowest level in hierarchy
expected to exhibit


restricted range of
morphological attributes
equally restricted range of
internal characteristics
• moisture status
• soil type
• hydrology/lithology

EOR Series
800 m
DYD Series
KLM Series
LandMapper
Environmental Solutions © 2001
COR Series
15
40
60
OBL
HULG
SZBL
BLSS
SZHG
HULG
OHG
EOR
COR
DYD
KLM
FMN
COR
HGT
considered landform
facets
• differ in shape
• landform position
• hydrology
FMN Series
High water level
Low water level
CHER
GLEY
CHER
SOLZ
SALINE
GLEY
GLEY
BC PEM Workshop,
April 25-27, 2001
Landform elements:
Implementation
 Classified using LandMapR
 originally 15 classes
 Identified deficiencies
 Improved recognition of
depressions is required
 Additional elements to
identify:
• stream channel and riparian
entities — active channels,
channel banks, flood plains
800 m
LandMapper
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800 m
BC PEM Workshop,
April 25-27, 2001
Landform
types
 Second level in hierarchy
 Characteristic pattern and
scale of repetition
 Equated to:
• toposequences
• catenas
• associations

Source: S. Nolan
HUMMOCKY LANDFORM TYPE
Most commonly mapped
physical entity in forestry
• tentative definitions
• proposed 34 classes
Source: Kocaoglu (1975)
3D SCHEMATIC
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Landform types:
Conceptualization
 Repeating patterns of landform elements
Source: Dumanski et al.,
(1972)
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Landform types:
Implementation
 Extending LandMapR
program:

Recognize and classify 34
landform types
 Recognition based on:
 Relative size and shape in
3 dimensions
6 km
7 km
3D view illustrating hummocky landform type 25 m DEM
• height (relief)
• length (longest X)
• width (shortest X)

Measures of morphology
• gradient, slope length
• drainage integration
LandMapper
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6 km
7 km
3D view illustrating rolling landform type (25 m DEM)
BC PEM Workshop,
April 25-27, 2001
Classifying areas as
landform types
 Significant challenge
involving:



Pattern analysis
Contextual classification
Object recognition
 Key issue is to define:
 Appropriate search
window to compute
attributes for patches or
regions to assign to
classes
6 km
7 km
3D view illustrating hummocky landform type 25 m DEM
6 km
7 km
3D view illustrating rolling landform type (25 m DEM)
LandMapper
Environmental Solutions © 2001
BC PEM Workshop,
April 25-27, 2001
Classifying areas as
landform types
 Key to success
 Depressional catchments
act as basic entities to
class
 using attributes of:
• size and shape
• length, width, relief

800 m
800 m
3D view illustrating rolling landform type (25 m DEM)
statistical distributions of:
•
•
•
•
•
gradient
slope lengths
landform classes
aspect classes
channels and divides
800 m
400 m
3D view illustrating hummocky landform type 25 m DEM
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Classifying areas as
landform types
 Depressional catchments
 Treated as objects
• attributes recorded in table

data stored in table include:
• statistical summaries of:
– catchment morphology
LandMapper
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800 m
400 m
3D view illustrating hummocky landform type (25 m DEM)
BC PEM Workshop,
April 25-27, 2001
Classifying areas as landform types
 Process table to:
classify catchment
entities
HIGH
LENGTH (X)
500 M
LONG
> 1000 M
> 9%
RIDGED
MOUNTAIN
CLIFF
HUMMOCKY
> 50 M
> 5%
INCLINED
HILL
ROLLING
HUMMOCKY
DUNED
RIDGED
MEDIUM < 50 M
< 5%
RELIEF (Z)
LOW
UNDULATING
PITTED
LEVEL PLAIN
POTHOLE
<5M
LandMapper
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LOW
< 2%
LEVEL
> 1000 M WIDE
RIBBED
< 10 M
MEDIUM
INCLINED
500 M
BASIN
LEVEL
TO DEP
HIGH
GRADIENT (%)

SHORT
<200 M
FLOOD PLAIN
<200 M NARROW
BC PEM Workshop,
April 25-27, 2001
Classifying areas as
landform types
 Not yet implemented
 Expect success given
recent work & analysis
of 100 5m DEMs
Landform
Attribute
Method
slope gradient (%)
relative relief a) descriptive (m)
b) effective (m)
slope length
a) descriptive (m)
b) effective (m)
watershed
a) number / 100 ha
b) off-site %
LandMapper
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M1h: high relief rolling
H1m: moderate hummocky
800 m
800 m
800 m
400 m
Landform type
Landform type
M1h
H1m
(high relief rolling) (mod. hummocky)
11
34
18
600
350
19
59
8
6
3
150
90
87
5
BC PEM Workshop,
April 25-27, 2001
Physiographic Systems
 Top-down sub-division and bottom-up agglomeration
120 km
75 k m
6 km
500 m DEM
 Top-down sub-division
• Use coarse resolution DEM
– 250 to 500 m grid spacing
• Run LandMapR on DEM
– define large regions
LandMapper
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7 km
25 m DEM
 Bottom-up agglomeration
• Use finer resolution DEM
– 25 m to 100 m grid spacing
• Run LandMapR on DEM
– define landform types
BC PEM Workshop,
April 25-27, 2001
Physiographic Regions
710 k m
1270 km
710 k m
1270 km
1270 km
1270 km
710 k m
5 km DEM
LandMapper
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5 km DEM
BC PEM Workshop,
April 25-27, 2001
Physiographic
Regions
 Better to define manually
 Classify 500 - 1000 m DEM
 Use simple 4 unit LandMapR
classification to help assign
boundaries manually
710 k m
1270 km
 Too few spatial entities to
warrant effort of automated
classification
 Incorporate additional data

Consider bedrock & climate
LandMapper
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1270 km
710 k m
BC PEM Workshop,
April 25-27, 2001
Some useful technical details
700 m
LandMapper
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800 m
BC PEM Workshop,
April 25-27, 2001
Role of hydrological
topology
7
8
5
4
• volume, area, depth
• depressions not artifacts,
not “spurious” pits
LandMapper
Environmental Solutions © 2001
1 2
5
ELEVAT
ION
Define depressional
catchments, attributes:
3
1 2 3
3 2 1
• flow over flat cells
• depressions in DEM

6
2
1
 Cell to cell flow paths
 Conventional D8 flow
 Custom treatment of:
9
1
2
3
2 1
4
6
5
9
7
8
BC PEM Workshop,
April 25-27, 2001
Significance of
depressions
 Depressions are considered:
 Real landscape features
• define local top & bottom
where:
– water slows down
– water ponds
– sediments deposited
• Establish local context

800 m
400 m
initial local
direction of
flow
elevation of all
cells below pour
point raised to
pour elevation
new “reversed”
flow directions
Divide
Procedures need to:
• Recognize depressions
– selectively remove
– retain all information about
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5
5
5
5
Pit Center
BC PEM Workshop,
April 25-27, 2001
Computing pit
characteristics
C
 Depressional watersheds

For each watershed record

Pour Elev 1
For each pour point record
• Pour Elevation (m)
• Pour Point Location (row,col)
• Neighbour Location (row,col)
LandMapper
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C
A
For each depression record
• Pit Location (row, col)
• Pit Elevation (m)
• Pit Area (m2) & Volume (m3)
B
Pour Elev 2
• Shed Number & Shed Area
• Shed# of the Shed(s) it drains to

A
B
A
B
SHED SHED PIT LOC PIT PIT
PIT NEXT POUR POINT
NEIGHBOUR
NO AREA ROW COL ELEV AREA VOL SHED ROW COL ELEVROW COL ELEV
58 171
59 134
60
30
61 108
62
8
63 870
64 1389
93 131 726.8
95
4 722..9
96 10 722.8
96 38 722.8
98
7 726.4
98 61 722.8
99 138 722.9
40 6.1
17 1.7
3
0.4
2
0.2
1
0.1
3
0.4
70 10.4
70
59
56
54
53
59
55
96 129
95
1
94 11
94 16
95 38
96
6
92 63
727.1
723.0
723.0
722.9
726.5
723.0
723.1
97
95
93
93
96
95
91
129
1
10
16
37
6
63
727.1
723.0
722.9
722.8
726.5
723.0
723.1
BC PEM Workshop,
April 25-27, 2001
Intelligent pit removal
 Remove pits in sequence
 Intelligent pit removal
based on computed pit
geometry:




Remove from lowest to highest
Remove into lowest neighbor (A>B)
Define new pit C = A+B+C
Compute attributes of new pit C
process based on reversing
flow directions




C
A
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
Retain original elevations in pit area
B
initial local
direction of
flow
Pour Elev 2
Pour Elev 1
A
A
elevation of all
cells below pour
point raised to
pour elevation
new “reversed”
flow directions
Divide
C
B
B
5
5
5
5
Pit Center
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Establishing landform
context
 Depressional catchments
 Define local window
• within which to evaluate
landform context
• establish landform position

800 m
400 m
Define 1 repeat cycle
• ridge to ridge
• trough to trough
• wavelength of landscape
800 m
400 m
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Establishing landform
context
 Flow paths establish:
 Hydrological
connectivity
• follow flow down to pit
or channel
• follow flow up to peak or
divide

Landform position —
location of cell relative
to:
• pits and peaks
• channels and divides
• catchment max and min
LandMapper
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CELL DRAINAGE DIRECTION (LDD)
DIVIDE
RELATIVE SLOPE POSITION
(Distance down slope from cell
to pit Centre as % of maximum)
MAXIMUM
SLOPE LENGTH
63
DIVIDE
CELL
4
5
8
7
6
5
PIT CENTRE
6
2
4
3
30
2
1
0
1
2
0
10
20
CELL DOWNSLOPE LENGTH (LDN)
80 100 100 88 75 63 50 38 25 12
CELL RELATIVE SLOPE POSITION (PUP)
BC PEM Workshop,
April 25-27, 2001
Hydrological response
units
 Establish interactions & flows
 Feature that is lacking in solely
geomorphic classifications
 Essential for modeling
ecological and hydrological
processes — flows of energy,
matter, water; in response to
gravitational gradients
 Important framework for nesting
and agglomeration, rolling
spatial entities up
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3.5k
m
4 km
BC PEM Workshop,
April 25-27, 2001
Hydrological response
units
 Importance of HRUs in
establishing connectivity:





From catchment to
catchment
From channel segment to
channel segment
From sub-catchment entity
to channel segment
From upper to mid to lower
to depressions within subcatchments
From cell to cell
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800 m
C
800 m
A
B
Pour Elev 2
Pour Elev 1
C
A
A
B
B
BC PEM Workshop,
April 25-27, 2001
Hydrological response units
 Superimpose HRUs on geomorphic classifications
3.5km
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4 km
BC PEM Workshop,
April 25-27, 2001
Discussion - DEM
resolution
 Require DEMs of:
 5 – 10 m horizontal
 0.3 – 0.5 m vertical to
adequately capture landform
features of interest
 DEMs of :
 25-100 m horizontal
 1-10 m vertical generalize
& abstract the landscape too
much; fail to capture
significant features of
interest
LandMapper
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25 m DEM
WITH 5 m
DEM
INSERT
900 m
800 m
5 m DEM
900 m
800 m
25 m DEM
BC PEM Workshop,
April 25-27, 2001
25 m DEMs
WITH 5 m
DEMs AS
INSERTS
100 M
DTED
5M
800 m
50 M
1:20 K
25 M
1:20 K
25 m
What we have!
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10 M
Optimal resolution for most
natural landscapes
What we need!
BC PEM Workshop,
April 25-27, 2001
Discussion - abstraction &
smoothing
 Smoothing is essential
 bring out signal
 reduce local noise
 We mainly use:
 successive mean filters —
7x7 & 5x5
 Also have smoothed
DEM NOT FILTERED
using:


block kriging
thin plate spline with
tension
 Interested in:
 wavelets, Fourier
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DEM FILTERED
BC PEM Workshop,
April 25-27, 2001
Discussion – human vs.
machine strengths
 Classifying landform elements versus landform types
800 m
800 m
Source: Kocaoglu (1975)
 Landform elements
 Landform types
nd lowest level in
 Lowest level in the
 2
hierarchy
hierarchy
 machine recognition is easy
 human recognition is easy
 human recognition is often
 machine recognition is
tedious and error prone
challenging BC PEM Workshop,
LandMapper
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April 25-27, 2001
Conclusions
 Developing a tool kit
 Still in initial stages
 conceptualization
 proof of concept
programming
 Intent to utilize new data
 LIDAR, Radar, SRTM
 Significant features are:
 multi-scale outputs
 multiple scales of DEM
 nested hierarchy
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Capturing and applying
expert knowledge
Data and
observations
Field
Maps
In d iv id u a l s a lin ity h a z a rd ra tin g s
fo r e a c h la y e r
1 0 0 x 1 0 0 m g rid
L a y e r w e ig h tin g s
Landscape
c u rv a tu re
2 x
Experience and
knowledge
Evidence and
hypotheses
Ve g e ta tio n
1 x
R a in fa ll
2 x
G e o lo g y
1 x
S o ils
Beliefs and
belief-based rules
Formulae and
evidence rules
Place boundaries
Classify entities
LandMapper
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3 x
L a n d s u rfa c e
To ta l s a lin ity
h a z a rd ra tin g
S a lin ity h a z a rd
m ap
Source: Searle and Baillie (2000)
BC PEM Workshop,
April 25-27, 2001
Spatial reasoning:
My examples
 Landform classification
 Expert knowledge & belief
• Captured using Fuzzy logic
 Association of mapped soils
with landform position

Tacit expert knowledge
• Captured using weighted belief
matrices
 Prediction of salinity hazard
 Analysis of spatial evidence
• Captured using probabilities
LandMapper computed from evidence
Environmental Solutions © 2001
BC PEM Workshop,
April 25-27, 2001
Landform
classification
 Compute a series of
terrain derivatives

We computed 22
• Only used 12
 Convert terrain
derivatives into fuzzy
landform attributes

Change absolute values
• Into relative values
• Based on expert beliefs
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Landform classification
 Convert terrain derivatives into fuzzy landform attributes
TERRAIN DERIVATIVE
Profile Curvature
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FUZZY LANDFORM ATTRIBUTE
Likelihood of being concave in profile
BC PEM Workshop,
April 25-27, 2001
Landform
classification
 Fuzzy attributes computed from hard terrain derivatives
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Landform
classification
 Develop a rule-base
 Based on expert beliefs
• Expressed in semantic terms
 Apply the rule base
 To convert
• fuzzy landform attributes to
• fuzzy landform classes
k
JMF  W j * MF Aj
j 1
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BC PEM Workshop,
April 25-27, 2001
Landform
classification
k
JMF  W j * MF Aj
j 1
 Convert fuzzy landform attributes into landform classes
Fuzzy landform attributes
Fuzzy landform class (DSH)
Final hardened landform classes
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Landform
classification
 Fuzzy landform classes from fuzzy landform attributes
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BC PEM Workshop,
April 25-27, 2001
Allocating soils to landform
classes
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BC PEM Workshop,
April 25-27, 2001
Allocating soils to landform
classes: Background
 Soil survey is a paradigm-based science

Most soil survey knowledge exists as informal tacit
knowledge (Hudson, 1992)
• Soil survey is deficient in not expressing scientific
knowledge in a more formal way
• Knowledge is not easily conveyed to others or used until
it is expressed semantically and formally
• A significant portion of the value of soil survey is lost if
tacit knowledge acquired during mapping is not recorded
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Allocating soils to landform
classes: Background
 Fundamental assumption of soil survey

The distribution of soils in the landscape is
predictable (Arnold, 1979, 1988)
• Function of the 5 soil forming factors of Jenny (1941)
• Topography plays a dominant role locally in influencing
the distribution of soils at field scale
• Research in Alberta has successfully defined landform
segments with different soil regimes
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Allocating soils to landform
classes: Objectives
 To develop a generic procedure for capturing the tacit
knowledge of expert soil surveyors

Relate the distribution of soil attributes and Soil Series to 4
landform positions
• Upper, Mid-slope, Lower-slope, Depressions
 To apply the procedure to the AGRASID database
 Assign each soil in the Alberta SNF file a value for
likelihood of occurring in each of the 4 landform positions
 Link all soils in every AGRASID polygon to their most
likely landform position or positions
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Allocating soils to landform
classes: Methods
 Used SNF as source of data

Selected 6 attributes from the SNF
• Variant, drainage, calcareous, salinity, parent
material & Subgroup classes
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BC PEM Workshop,
April 25-27, 2001
Allocating soils to
landform classes
 Captured expert beliefs and knowledge
 Experts assigned each class of each of the 6 attributes a
likelihood value
• Each class has a likelihood of occurring in each of the 4 landform positions

Experts assigned each attribute a weight
ATTRIBUTE
DRAINAGE
DRAINAGE
DRAINAGE
DRAINAGE
DRAINAGE
DRAINAGE
DRAINAGE
CLASS
VR
R
W
MW
I
P
VP
LandMapper
Environmental Solutions © 2001
UP
100
95
85
50
20
5
1
MID
80
80
90
65
30
20
10
LOW DEP
20
1
35
5
60 10
80 40
85 70
70 95
25 100
ATTR
NO. ATTRIBUTE CLASS
6
6
6
6
6
6
6
SG
SG
SG
SG
SG
SG
SG
UP MID LOW DEP
O.R
100
R.BL
97
CA.BL
70
O.DB
90
O.BL
85
O.HG
10
HU.LG 20
80
60
20
70
95
20
50
40
10
40
20
50
75
87
1
2
5
1
15
87
83
BC PEM Workshop,
April 25-27, 2001
Allocating soils to
landform classes
 Processed the rules against the SNF to:
 Created a database relating soil names to landform position
 New database is identical to the original Soil Names File
• Except that each soil now has an associated value for likelihood of
occurring in each of the 4 landform positions
NEW
SYMBOL
KSR
LET
WNY
ZGW
SERIES
SG SCA
KESSLER
O.DB
3
LETHBRIDGE O.DB
3
WHITNEY
O.DB
3
MISC.GLEYSOL O.HG 99
LandMapper
Environmental Solutions © 2001
SLM
ZONE
2
2
2
2
MAS
PM
C3
M2
L3
U0
UPS
85.2
72.7
73.8
25.9
MID LOW DEP
82.3 54.2 21.5
76.5 60.0 26.2
77.7 56.5 25.0
30.9 66.4 77.1
BC PEM Workshop,
April 25-27, 2001
Allocating soils to
landform classes
 Processed the revised SNF against AGRASID data
 Created a database relating soil names to landform position
•
•
•
•
For every soil listed as occurring in every AGRASID polygon
Processed over 25,000 polygons
Considered over 1800 different soil series
Each soil also linked with morphological attributes of landform
SOIL SCA
MU
LF
POLY NO. NAME
POS
41901135 3 KSLE2/H1l UPS
41901135 3 KSLE2/H1l MID
41901135 3 KSLE2/H1l MID
41901135 3 KSLE2/H1l LOW
41901135 3 KSLE2/H1l DEP
LandMapper
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LF FACET NEW LIKELI EXTENT SLP 50 SLP
PCT ORD SYMBOL HOOD
(%)
(%) LEN
30
1
KSR
85.2
30
3
45
40
2
WNY
77.7
30
4
60
40
2
LET
76.5
10
4
60
20
3
LET
60
20
3
30
10
4
ZGW
77.1
10
0.5
15
BC PEM Workshop,
April 25-27, 2001
Allocating soils to landform
classes: Results
 Soil-landform models for every AGRASID polygon
 Models place soils in landform positions
• And relate each soil to its most likely associated landform attributes
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Predicting potential salinity
hazard (PSH)
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BC PEM Workshop,
April 25-27, 2001
Methods: typical application
of Multi Criteria Evaluation
 Usually based on expert belief
 Estimates “suitability” of a site
• Weighted linear combination
• Factor scores * Factor weights

Where:
• PSH = likelihood that a site will
develop salinity
• Wti = expert’s judgement of relative
importance of a map
• FSi = expert’s judgement of
likelihood of salinity given a
particular class on a map
LandMapper
Environmental Solutions © 2001
In d iv id u a l s a lin ity h a z a rd ra tin g s
fo r e a c h la y e r
1 0 0 x 1 0 0 m g rid
L a y e r w e ig h tin g s
Landscape
c u rv a tu re
2 x
Ve g e ta tio n
1 x
R a in fa ll
2 x
G e o lo g y
1 x
S o ils
3 x
L a n d s u rfa c e
S a lin ity h a z a rd
m ap
To ta l s a lin ity
h a z a rd ra tin g
Source: Searle and Baillie (2000)
BC PEM Workshop,
April 25-27, 2001
Methods: MCE requires 2
things
 Estimate of FSi





Criteria scores for factor i
Factor enhances or detracts
from suitability of site for a
result (i.e. becoming saline)
Factors usually continuous
numbers
Scaled from 0-100 or 0-255
Example used here:
• Shallow depth to bedrock is
more likely to result in
salinity
LandMapper
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 Estimate of Wti




Weighting factor for map i
Weighting factors sum to 1
Measure of the information
content or usefulness of map
i for predicting outcome S
Usually computed from
• Pairwise comparisons of
relative weights
• Relative weights assigned
based on expert opinion
BC PEM Workshop,
April 25-27, 2001
Methods: weight of evidence
 Replace belief with evidence

Maps of visible salinity (1:100,000)
Analyze spatial correspondence between:
• a map depicting presence/absence of a
phenomenon of interest (e.g. salinity)
– Maps of visible soil salinity available
• n maps depicting the spatial distribution
of factors considered to influence the
phenomenon of interest (e.g. salinity)
– e.g. soils, surficial geology, bedrock
geology, depth to bedrock, depth to
water table, land use, hydro-geology,
landform position, landform shape
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Methods: Computing factor
scores
 Analyze the evidence to:
 Determine the likelihood of
• Salinity of type k occurring
• Given a specific environmental condition
– e.g. shallow depth to bedrock

Compute the likelihood as:
• FSk,i,j = P(Hk,i,j | Ei.j) where;
Visible salinity over depth to bedrock
– Hk,i,j is the absolute extent of salinity of
type k found in areas mapped as j on i
– Ei,j is the absolute extent of areas on
map i belonging to class j
» e.g. shallow to bedrock
LandMapper
Environmental Solutions © 2001
BC PEM Workshop,
April 25-27, 2001
Methods: Computing
weighting factors
 Analyze the evidence to:
 Determine relative utility of map i
• How useful is map i in predicting
– occurrence of salinity of type k

Compute the relative weight as:
• Wtk,i = ( |P(Ek,i,j|Hk,i) - P(Hk,i,|Ei )| )
LandMapper
Visible salinity over LandSat TM Band 3
where;
– Ek,i,j is the absolute extent of areas on
map i belonging to class j on that occur
in areas mapped as salinity class k
– Hk,i is the total absolute extent of
salinity of type k that occurs on map i
– Ei is the total absolute extent of map i
Environmental Solutions © 2001
BC PEM Workshop,
April 25-27, 2001
Methods: Computing PSH by
salinity type
 Resolve MCE equation
 PSHk =
(FSk,i,j*Wtk,i)
• for each of k types of salinity
(k= 8 here)

Every cell has a PSH value
Mapped contact salinity over contact salinity PSH
• represents relative likelihood
that cell may exhibit that
kind of salinity
– White = high PSH (100)
– Dark = low PSH (0)
LandMapper
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Coulee bottom salinity over coulee bottom PSH
BC PEM Workshop,
April 25-27, 2001
Methods: Computing
maximum PSH
 Compare 8 PSH maps
 Identify maximum PSH
value for each cell
• record maximum PSH
value
• record type of salinity
associated with maximum
PSH value

Compare maximum PSH
Maximum PSH overlain with actual mapped salinity
• to distribution of actual
visible salinity of all types
LandMapper
Environmental Solutions © 2001
BC PEM Workshop,
April 25-27, 2001
Discussion:
PSH
Slope gradient
LandSat TM
Slope position
Bedrock type
Depth to bedrock
Soil type
 Data mining
 Making the most of data that
are currently available
• uses existing, widely available
data sets
• merges different types and
scales of data

Using data to improve
knowledge
• systematic procedures uncover
LandMapper
– spatial inter-relationships
– test assumptions/hypotheses
– enhance the knowledge base Landform curvature
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Surficial geology
BC PEM Workshop,
April 25-27, 2001
How does all this relate to
TEM and PEM?
 Landform classification
 Landform elements
 Landform types
 Hydrological response units
 Predictive programs
 belief based (LandMapR)
 evidence based (PSH)
 Allocation of soils to
landform positions
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Relevance of landform
elements to PEM?
Soil & =
Vegetation
(
Parent Material
Climate
Relief / Topography
Organisms
Time
Ecological
Map
=
Delineations
)
(
• texture
• drainage Landform
• depth
• minerology classes
• organic matter depth...
LandMapper
• species composition
• density / stocking
• height
• age...
forest /vegetation cover
terrain / soil map units
topographic features
Digital Base & Terrain Model
• elevation
• hydrography
• slope, position, configuration
• aspect...
Source: K. Jones personal communication
Environmental Solutions © 2001
)
BC PEM Workshop,
April 25-27, 2001
Relevance to
PEM
 TEM and PEM
utilize




Terrain
Topography
Landscape
Soils
 Could
consolidate into

Landform units
Source: EWG/RIC (1998)
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Relevance to
PEM
 PEM uses vector overlay
 BGC subzone
 Elevation
 Slope/Aspect
 Forest cover (primary)
 Terrain
 Overlay produces
 Spaghetti
 Knowledge not used to
define boundaries
LandMapper
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Source: Meidinger et al.,
(2001)
BC PEM Workshop,
April 25-27, 2001
Relevance to
PEM
 PEM vector overlay
produces



Spaghetti
Knowledge not used to
define boundaries
No protocols to reconcile
boundary conflicts
 Landform classes
 Could be used to set
primary boundaries
LandMapper
Environmental Solutions © 2001
Source: Meidinger et al.,
(2001)
BC PEM Workshop,
April 25-27, 2001
Relevance of landform
types to PEM
 Mapping entities/standards
 Workshop: July, 1999
• Treatments often prescribed at the
ecosite (site series) level
• Often implemented at the
landscape level (association)
• Interpretive value of an association
6 km
7 km
3D view illustrating hummocky landform type (25 m DEM)
– Greater than the sum of its parts.

Landscape associations
• a compound mapping unit entity
whose definition includes a
predictable pattern of member
mapping entities
LandMapper
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6 km
7 km
3D view illustrating rolling landform type (25 m DEM)
BC PEM Workshop,
April 25-27, 2001
Relevance of hydrological
connectivity (HRUs) to PEM
 Hydrological framework
 Increasingly important
• ArcGIS Hydro, WEPP, Band
 Static versus dynamic
 Current TEM/PEM approach
• Focus is on “What is where” and
“Where is what”
Source: Maidment, 2000
– Static attributes of areas

Emerging hydrological entities
• Includes “Why” & “What will be”
LandMapper
– “How do/will things change?”
– Dynamic - current status of areas
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BC PEM Workshop,
April 25-27, 2001
Relevance of hydrological
connectivity (HRUs) to PEM
 Current expectations of PEM
 Assign attributes to areas
• current vegetation community
• expected climax vegetation
• environmental/edaphatic conditions
Source: Flanagan et al.,
2000
– drainage, texture, slope, carbon
 Emerging expectations
 Predict and model change
 Provide spatial framework for
modeling
• Support model operation
LandMapper
– Linkages and flows
Environmental Solutions © 2001
BC PEM Workshop,
April 25-27, 2001
Relevance of hydrological
connectivity (HRUs) to PEM
 ArcGIS
Hydro




Data
model
Dynamic
modeling
Off-site
effects
New
standard
?
LandMapper
Environmental Solutions © 2001
Source: Maidment, 2000
BC PEM Workshop,
April 25-27, 2001
Relevance of predictive
programs to PEM
 Belief based
 LandMapR landform classification
• Captures and codifies expert beliefs
about where and how to define
landform boundaries and attributes
 Evidence based (PSH)
 Systematic analysis of evidence
• Provides a method to both establish and
test/evaluate/refine
LandMapper
– Beliefs regarding the importance of
various input maps/variables (weights)
– Beliefs regarding strength and direction
of relationships between classes of
input data and desired prediction.
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BC PEM Workshop,
April 25-27, 2001
Relevance of landform
classification to PEM
 Landform classification rules
 Formalize and systematize the rules for drawing boundaries
• For recognizing and attributing fundamental spatial entities
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Relevance of analysis of
evidence methods to PEM
Table 1. Analysis of spatial correspondence between 8 kinds of visible salinity and 3 bedrock types for 82P
Map
Class
TKp
Khc
Kbp
Total
TKp
Khc
Kbp
Map Tot
TKp
Khc
Kbp
Depress
5937.875
1796.563
0
7734.438
1.391
0.534
0
1.011
100.000
38.436
0.000
Coulee
Slough
Bottom Contact Ring Outcrop Artesian
598.875 2894.063 212.625 208.375 49.625
1581.875 620.813 405.563 49.938 18.938
0
0
0
0
0
2180.750 3514.875 618.188 258.313 68.563
0.140
0.678
0.050
0.049
0.012
0.471
0.185
0.121
0.015
0.006
0
0
0
0
0
0.285
0.460
0.081
0.034
0.009
29.801 100.000 41.270 100.000 100.000
100.000
27.251 100.000 30.444 48.478
0.000
0.000
0.000
0.000
0.000
LandMapper
Environmental Solutions © 2001
Natural
610.813
340.438
0
951.250
0.143
0.101
0
0.124
100.000
70.804
0.000
Canal
Seep Map Total
2133.938 427015.563
188.563 336137.438
0
1574
2322.500
764727
0.500
55.839
0.056
43.955
0
0.206
0.304
100
100.000
0
11.225
0
0.000
0
BC PEM Workshop,
April 25-27, 2001
Relevance of predictive
programs to PEM
 Similarity and convergence
 Predicted output (class)
 Usually a function (F) of:
• Expert belief about:
• Or quantitative evidence about:
– Importance of input variable in
predicting output class (Weight)
– Strength and direction of relationship
between input variable value and each
output class to be predicted

Do we need many custom programs?
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Relevance of predictive
programs to PEM
 Multi-purpose Predictive Calculator (MPC or UPC)
 May be both possible and desirable
 Many different processing options and possible outputs
• Many different options for implementing calculations
– Weighted means, Fuzzy JMF, Boolean, Bayesian, Cross products
• Almost any possible combination of inputs & outputs
– Continuous or classed input or output
Input
Variable Transformation Computation Variable Transformed Output
Variable Value
Method
Method
Wieght Value
Type
Output Output
Variable Value
Slope
Curvature
Texture
Species
FAN
MID
Mv/R
SWck
4.5
-7
CL
Lp 60
Fuzzy model
Fuzzy model
MCE
None
LandMapper
Environmental Solutions © 2001
Weighted mean
Cross product
Weighted mean
Boolean
0.3
1
0.2
1
Landform
Landform
Terrain
Ecosite
78
24
61
82
BC PEM Workshop,
April 25-27, 2001
Relevance of allocation of
soils to landforms to PEM
 Parallels with TEM/PEM
 Ecosystem map units & Site Series
• Have expected relationships to landform

My landform elements
• Could be associated with Site Series
– Through similar belief matrices

My landform types
• Could be associated with “landscape
associations”
– Allows component entities to be described
and placed in landform positions
– Without explicitly mapping them
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Some closing thoughts
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Back to
Basics
 J.S. Rowe (1996)



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)
Thus, landforms, with their vegetation, modify and
shape their coincident climates over all scales
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Back to
Basics
 J.S. Rowe (1996)


Fortunately, two of the enduring or slowly changing
terrain features that are visible at the earth's surface landforms and the drainage patterns that help to reveal
them - are also among the most important for
understanding ecosystems and their sites
All fundamental variations in landscape ecosystems
can initially (in primary succession) be attributed to
variations in landforms as they modify climate
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Back to
Basics
 J.S. Rowe (1996)


Different kinds of landforms are climatically
different, signifying important differences in the
coincident ecosystems of which they are parts.
Boundaries are recognized by perceived changes in
the ecological relationships of vegetation, landform,
drainage and soil, from whose expression climate is
inferred.
LandMapper
Environmental Solutions © 2001
BC PEM Workshop,
April 25-27, 2001
Back to
Basics
 J.S. Rowe (1996)


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
For example, at the local scale, the change in contour
from convex-upward to concave-upward, from the runoff to the run-in position on hill slopes, is always
ecologically significant
LandMapper
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BC PEM Workshop,
April 25-27, 2001
Thank you!
LandMapper
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BC PEM Workshop,
April 25-27, 2001