Automated Extraction of Landforms from DEM Data

Automated Extraction of Landforms from
DEM data

R. A. MacMillan
LandMapper Environmental Solutions Inc.

Outline
• Rationale for Automated Landform Classification
– Theoretical, methodological, cost and efficiency arguments
• Classification of Landform Elements
– Conceptual underpinnings – hill slope segments
– Implementation methods – various examples
• Classification of Landform Patterns
– Conceptual underpinnings – size, shape, scale, context
– Implementation methods – various examples
• Miscellaneous Bits and Pieces
– Thoughts and ideas that may or may not prove useful
• Discussion and Conclusions
– What works and what doesn’t?
– Future developments & challenges to be addressed

Rationale for Automated Landform
Classification

Scientific and theoretical arguments
Business case – costs and efficiency

Rationale: Scientific and Theoretical
• Why Delineate Landforms?
– Landforms define boundary conditions for
processes operative in the fields of:
• Geomorphology
• Hydrology
• Ecology
• Pedology
• Forestry ... others
– Landforms control or influence the distribution
and redistribution of water, energy and matter
Source: MacMillan and Shary, (2009)

Rationale: Costs and Efficiency
• Why Automate the Delineation of Landforms?
– Speed and cost of production
• Never likely to ever again see investments in large
groups of human interpreters to produce global maps
• Governments can’t afford and are unwilling to pay for
manual interpretation and delineation of landforms
– Consistency and reproducibility
• Manual human interpretation can never be entirely
consistent or reproducible
• Automated methods can be constantly improved and
re-run to produce updated products.

Conceptual Hierarchy of Landforms

• Focus here is on two main levels
– Landform elements (facets here)
– Landform patterns (repeating landform types)

Source: MacMillan, 2005

Conceptual Hierarchy of Landforms


Rationale: Process-Form Relationships

• Landform Elements related to hill slope processes
– Forms are related to processes and also control them

Source: Skidmore et al. (1991)

Rationale: Process-Form Relationships

Source: Ventura and Irwin. (2000)

Rationale: Recognizing Landform Patterns

• Landform Patterns Establish Context and Scale
– Different landform patterns exhibit differences in
• Relief energy available to drive processes such as runoff,
erosion, mass movement, solar illumination, energy flows
• Size and scale of landform features such as slope lengths,
slope gradients, surface texture, complexity of slopes, degree
of incision of channel networks
• Contextual position in the larger landscape
– Runoff producing or runoff receiving area
– Sediment accumulation or removal area
– Elevated water tables or artesian conditions versus recharge areas


Classification of Landform Elements

Conceptual underpinnings
Implementation examples

Landform Elements: Conceptual
Underpinnings
• Many similar ideas on partitioning of hill slopes
– Simplest and most basic conceptualization
– 2d not 3d partitioning of a hill slope into elements

Underpinnings
• Many similar ideas on partitioning of hill slopes

Ruhe and Reprinted from Ventura
Walker (1968) and Irvin (2000)

Underpinnings
• 2D Concepts in 3D
– Ventura & Irwin (2000)
• Ridge top
• Shoulder
• Backslope
• Footslope
• Toeslope
• Floodplain
– Based solely on slope
and curvature
• No landform position

Source: Ventura and Irvin (2000)

Underpinnings
• 3D concepts more comprehensive than 2D
– Erosion, deposition, and transit are influenced by
both profile and plan (across-slope) curvature

Concepts: Gauss, 1828 Source: Shary et al., (2005)

Underpinnings
• 3D concepts profile and plan curvature

Source: Shary et al., (2000)

Underpinnings
• 3D conceptualization • 3D classes

Source: Pennock et al., (1987)
Source: Pennock et al., (1987)

Underpinnings
• Complete system of classification by curvature

Source: Shary et al., (2005)

Underpinnings
• 3D conceptualization • 3D classes
– Initially based solely on
local surface form
• Convex, concave, planar
– In theory surface form
should reflect
• Landscape position
• Hillslope processes
– Surface shape not
always sufficient
• Need better context
Source: Dikau et al., (1989)

Underpinnings
• Adding landform position to 3D improves 3D.
Upslope length - cell to peak (cells)
2 1 0 1 2 3 4 5 6 7 8 7 6
Upslope drainage direction (UDIR)

PEAK CELL
5 100 Relative
slope
4 80
Elevation of each 5m position as
3 3 cells 63 PIT CELL 60 % height
cell above pit
elevation (m) 2 40 above pit
DIVIDE level
1 CELL 5 cells 20
3m

Downslope drainage direction (DDIR)

4 5 8 7 6 5 4 3 2 1 0 1 2
Downslope length - cell to pit (cells)
80 100 100 88 75 63 50 38 25 12 0 10 20
Relative slope position as % length upslope
Source: MacMillan, 2000, 2005

Measures of Absolute Landform Position
Computed by LandMapR
• Flow Length N to Peak • Vertical Distance Z to Ridge

FLOW UP TO PEAK FROM EVERY CELL FLOW UP TO RIDGE FROM EVERY CELL

Image Data Copyright the Province of British Columbia, 2003
Source: MacMillan et al., 2007

Measures of Relative Relief (in Z)
• Percent Z Pit to Peak • Percent Z Channel to Divide

MEASURE OF REGIONAL CONTEXT MEASURE OF LOCAL CONTEXT


Measures of Relative Slope Length (L)
• Percent L Pit to Peak • Percent L Channel to Divide

MEASURE OF REGIONAL CONTEXT MEASURE OF LOCAL CONTEXT


Measures of Relative Slope Position
• Percent Diffuse Upslope Area • Percent Z Channel to Divide

SENSITIVE TO HOLLOWS & DRAWS RELATIVE TO MAIN STREAM CHANNELS



Multiple Resolution Landform Position

What you see depends upon how closely you look
Different results with different window sizes and grid resolutions
Relative position is always relative to something and varies across
an area

Source: Geng et al., 2012

MRVBF: Multi-resolution valley
bottom flatness
• Valley bottom flatness from:
– Flatness (inverse of slope)
– Local lowness (ranking in a 6 cell circular region)
• Multi-resolution:
– Compute valley bottom flatness at different
resolutions
• Smooth and subsample the DEM

Source: Gallant, 2012

MRVBF: Generalise DEM
Smooth and subsample Source: Gallant, 2012

Original: 25 m Generalised: 75 m Generalised 675 m
Flatness Flatness

Bottomness Bottomness

Valley Bottom Valley Bottom
Flatness Flatness

MRVBF: Multi-Resolution
Flatness and bottomness at multiple resolutions

25
m

75
m

675
m

Flatness Bottomness Valley Bottom Flatness

Calculating MRVBF
MRVBF2 W2 1 VBF2 (1 W2 )VBF1
MRVBF3 W3 2 VBF3 (1 W3 ) MRVBF2

MRVBF5 W5 4 VBF5 (1 W5 ) MRVBF4

Weight function Wn
gives abrupt transition,
depends on n
W
2*W2
5*W5

VBF

Multiple Resolution Landform Position
MRVBF Example Outputs

Broader Scale 9” DEM

MRVBF for 25 m DEM


Landform Elements: Other Measures of
Landform Position
• SAGA-RHSP: relative • SAGA-ABC: altitude
hydrologic slope position above channel

Source: C. Bulmer, unpublished
Calculation based on: MacMillan, 2005 Source: C. Bulmer, unpublished

Landform Position
• SAGA-MRVBF: valley • SAGA-Combined RHSP
bottom flatness index and MRVBF


Landform Position
• SAGA-Combined RHSP • SAGA-Combined RHSP
and MRVBF vs Soil Map and MRVBF vs Soil Map

Calculation based on: MacMillan, 2005 Source: C. Bulmer, unpublished

Landform Position
• TOPHAT – Schmidt • Slope Position – Hatfield
and Hewitt (2004) (1996)

Source: Schmidt & Hewitt, (2004) Source: Hatfield (1996)

Landform Position - Scilands

Source: Rüdiger Köthe , 2012

Landform Elements: Implementation
Example - Scilands


Landform Elements: Landform Elements:
Implementation Example - Scilands


Example: LandMapR
• LandMapR 15 Default Landform Classes

Source: MacMillan et al, 2000

Example: LandMapR
• LandMapR 15 Default Landform Classes


LandMapR: Different Classes in Different Areas
Normal Mesic

Moist Foot Slope

Warm SW Slope

Shallow Crest

Organic Wetland

Wet Toe Slope

Cold Frosty Wet

Permanent Lake

Source: MacMillan et
al., 2007

Example of Application of Fuzzy K-means
Unsupervised Classification

From: Burrough et al.,
2001, Landscsape
Ecology

Note similarity of unsupervised
classes to conceptual classes

Supervised Classification Using Fuzzy Logic
• Shi et al., 2004 Fuzzy likelihood of being a broad ridge
– Used multiple cases of reference
sites
– Each site was used to establish
fuzzy similarity of unclassified
locations to reference sites
– Used Fuzzy-minimum function to
compute fuzzy similarity
– Harden class using largest (Fuzzy-
maximum) value
– Considered distance to each
reference site in computing
Fuzzy-similarity

Source: Shi et al., 2004

Classification of Landform Patterns

Conceptual underpinnings

Rationale: Identify Landscapes of Different
Size, Scale and Context


Conceptualization of Landform Patterns

• Landform Patterns Tend to Repeat
– Landform patterns are typically of larger size and
scale and display greater complexity and variation
• Hills, mountains, plains, plateaus, tablelands
– Landform patterns usually, but not always,
exhibit full or partial cycles of repetition of forms
• Hills and mountains exhibit a full range of landform
positions, slope gradients, curvatures (mostly positive)
• Valleys and plains can exhibit undulations or cyclic
variations OR they may be asymmetric.

Considerations Used in Several Systems of
Classifying Landform Patterns
• Hammond (Dikau, 1991) • Iwahashi & Pike (2006)
– Considerations – Considerations
• Slope gradient; percentage of gentle • Local slope gradient (3x3 window)
slopes (4 classes) in search window • Texture - Local relief intensity
• Local relief within a search window assessed as number of pits and peaks
of fixed dimensions (6 classes) within a fixed window of 10 cells
• Profile type; percentage of cells • Curvature; calculated as percentage of
classed as gentle slope in lowland convex cells in a 10 cell radius
versus upland locations (4 classes)
• eSOTER (Dobos et al., 2005)
• SOTER (van Engelen & Wen, – Considerations
1995) • Majority slope gradient (of 7 classes
– Considerations in 900 m block, then smoothed)
• Dominant slope gradient • Relief intensity (max-min elevation
within a radius of 5 cells, 990 m
• Relief intensity
classified into 4 classes & smoothed)
• Hypsometry (elevation asl)
• Hypsometry (elevation asl, 10 classes)
• Degree of dissection
• Dissection (# channel cells in radius)

Rationale for Classifying Landform Patterns
• So, Why Consider These Attributes?
– Slope Gradient
• Steepness relates to energy, erosion, deposition, context
– Relief Intensity (or texture or local relief)
• Provides an indication of amplitude of landscape,
amount of energy available for erosion, slope lengths,
size and scale of hill slopes
– Profile Type (or shape, curvature, hypsometry)
• Helps to differentiate uplands (convex) from lowlands
• Helps to establish broader landscape context

Conceptualization of Landform Patterns


Classification of Landform Patterns

Implementation examples

Landform Patterns: Implementation
Example of the Hammond System
• Hammond system; as per Dikau et al., 1991


Source: Zawadzka et al., in prep

Normal Relief Index
Modified Relief Index

9600m square window 9600m circular window 900m circular window


900m circular window 18000 m circular window

Hammond approach is Hammond approach
very sensitive to tends to produce
differences in window concentric rings
size and shape or grid related to how the
resolution search window
observes the data

9200m circular window


Source: MacMillan, (unpublished)

• Hammond landform underlying 1:650k soil map

Source: Reuter, H.I. (unpublished)


Example of Iwahashi & Pike (2006)
• Implemented by Zawadzka et al., (in prep)
8 classes 12 classes 16 classes

Iwahashi & Pike classes need
to be labelled and interpreted

Example of Iwahashi & Pike (2006)
• Iwahashi landform underlying 1:650k soil map

Terrain Classes
Fine texture,
Terrain Series
High convexity
1 5 9 13
Fine texture,
Low convexity 3 7 11 15
Coarse texture,
High convexity 2 6 10 14
Coarse texture,
Low convexity 4 8 12 16

steep gentle

Source: Reuter, H.I. (unpublished)

Source: Dobos et al., 2005
8 classes

Example of eSOTER (Dobos, 2005)
• Implemented by Dobos et al., (in 2005)

Manual - yellow Manual - yellow
eSOTER - red eSOTER - red


Example of Peak Shed Approach
Peak shed entities
classified by
clustering
algorithm.

Resulting entities
need to be labelled
and interpreted


Example of Peak Shed Approach

Peak shed entities labelled according to Hammond


Example of Slope Break Approach
Run 2 Run 3


Landform Patterns: Implementation Example
of Homogeneous Objects (eCognition)

Example - Scilands


of Homogeneous Objects vs Meybeck 2001
• Implemented by Dragut, (unpublished)

See: ai-relief.org
Implemented by:
Reuter and Nelson
Method: Meybeck
et al., 2001
Source: Drãgut & Eisank, 2011 Source: http://eusoils.jrc.ec.europa.eu/projects/landform/

of Meybeck 2001 vs Homogeneous Objects

See: ai-
relief.org

Method:
Meybeck et
al., 2001

Source:
Reuter Source:
and Drãgut ,
Nelson unpublished

Source: Drãgut, unpublished

Landform Patterns: Example of Multi-scale
Nested Homogeneous Objects

Scilands GMK Classification Source: Reuter & Bock, 2012
See: ai-relief.org

Hammond Classification (after Dikau, 1991) Source: Reuter & Bock, 2012
See: ai-relief.org

Iwahashi & Pike Classification (16 classes) Source: Reuter & Bock, 2012
See: ai-relief.org

Iwahashi & Pike Classification (8 classes) Source: Reuter & Bock, 2012
See: ai-relief.org

Miscellaneous Bits and Pieces

Some thoughts and ideas that may or
may not prove useful

We are Really Looking for Discontinuities!

Source: Minar and Evans. (2008)


Source: MacMillan, unpublished

• The more I think about it the clearer it becomes
– We are really looking to locate abrupt boundaries
where the slope, texture, relief and context change
• If we are looking for boundaries it makes sense to try to
extract vector objects
• It makes less sense to classify grid cells then agglomerate
them, then de-speckle them, then vectorize them
• This argues in favor of approaches like Object Extraction
(Dragut) or perhaps Scilands (Kothe)

Source: Minar and Evans. (2008)

There are Special Cases that do not fit in a
General Classification (e.g River Valleys)

I like this!


There are Special Cases that do not fit in a
General Classification (e.g River Valleys)
• Many General Purpose Classifications Need to
be Extended to Handle Special Cases
– River Valleys are a case in point
• They have forms and patterns that are not cyclical
• They have special features that have special interpretation
– Active flood plain, levee, low terrace, high terrace, inter-terrace
scarp, ox-bow lake, abandoned channel, dry islands
– Other Special Cases no doubt exist too
• Think of mineral and organic wetlands, deserts, playas


Multi-Scale and Multi-Resolution
Calculations are Important but Problematic

No single fixed window size fits all landscapes – Need to be locally adaptive

Multi-Scale and Multi-Resolution are
Important but Problematic
• All algorithms and systems that compute
attributes within a fixed window are flawed
– No single window fits all landscapes
– User’s frequently adjust window size subjectively to
fit local landscape features – no longer universal!
– Windows that don’t fit the landscape produce
artifacts and unrealistic classes or values
– Need to use multiple windows and average (like
MRVBF) or make windows self-adjusting


I Like Top-Down, Divisive, Multi-scale
Fully Nested Hierarchical Objects
• Multi-scale Objects of Dragut, (unpublished)

I Like Top-Down, Divisive, Multi-scale
Fully Nested Hierarchical Objects
• Advantages of multi-scale, hierarchical, nested
vector objects
– They nest, or fit, within higher level objects exactly
– There is less arbitrary sliver removal, filtering,
speckle removal, smoothing and manipulation
– They seem to produce fewer artifacts and outright
errors
– They produce consistent and comparable results for
all similar terrains


The World is Divided into Things that
Stick Up and Things that Stick Down

As a first step we should always strive to separate erosional uplands from lowlands


Extracting nested peaks may be a way to separate uplands from lowlands

Might work even better if applied to DEM of inverted Height Above Channel (Z2St)

• In the First Instances Many Landform Pattern
Classifications are Binary (upland vs lowlands)
– Systems of Iwahashi and Pike, eSOTER, Hammond
Scilands all recognize this in their own way
– Maybe we should be making a point of finding ways
to explicitly separate erosional uplands from
aggrading lowlands as a first step in any classification
– I have fooled around with the idea of extracting
nested pits from an inverted DEM as a way to
extract uplands Source: MacMillan, 2005

Are Landform Patterns and Landform
Elements Really Different Things??
Maybe the only real difference is one of scale?

Many classifications of Landform Patterns look a lot like Hillslope Elements on a
large scale
Source: Rüdiger Köthe , 2012 Source: MacMillan, unpublished

Are Landform Patterns and Landform
Elements Really Different Things?
• The More I look, the more that landform
patterns begin to look like landform elements
computed over larger areas and at a coarser scale
– Maybe we need to look at approaches like MRVBF
that compute values at multiple scales then average
them to produce a final value or class
• Similarities to the work of Jo Wood.
• We still want to first separate hills from valleys and
uplands from lowlands, then landform elements within
these larger scale features.


I Have Personally Found Hierarchical
Classification Useful to Set Context
I first classified areas into 3-4 relief classes

Then I developed and applied different classification rules for each relief class

Discussion and Conclusions

What works and what doesn’t?
How can we tell what works?
Challenges to be addressed
Future developments

What Works and What Doesn’t?

• All things being equal apply Ockham’s Razor
– If you need to decide between several competing
methods and none is clearly superior to others
• Pick the one that is simplest, fastest and easiest to
implement
– Fewest input variables
– Fewest processing steps
– Fewest tuneable parameters
– Fewest subjective decisions
• This points towards selection of one of the following
– Iwahashi and Pike, Dragut or Scilands

How Can We Tell What Works?

• How can we evaluate “Truth” for subjective
classifications?
– Hard to decide objectively which classification
method to use when all classifications appear partly
useful and partly incorrect
• Need objective criteria and methods of computing them
to assess different classifications and identify the most
useful
• Should be based on the ability of the classification to
predict ancillary environmental properties or conditions
of interest Source: MacMillan, 2005

Challenges to be Addressed
• A diversity of methods and absence of standards
– Classes and results need to be comparable between
different areas
• This argues for selecting and applying one method
universally and not applying different methods in
different regions
• Need to objectively compare methods and then select one
to use widely (everywhere?).
• Method almost certainly has to be multi-scale, hierarchical
and locally adaptive
• Method needs to be parsimonious and easy to apply

Future Developments
• Global standards
– We need global standards to compare results
• Free and open-source data and tools on-line
– I see both data & tools increasingly available on-line
• Incorporation of ancillary (remotely sensed) data
to infer parent material attributes for landforms
– Once delineated, objects need to be attributed for pm
• Innovations in multi-scale hierarchical analysis
– Way forward will undoubtedly be multi-scale

Thank You

Extra Slides Follow


Classify Landforms by Size and Scale


Quesnel PEM Landform Classification

Source: MacMillan, 2005 Source: MacMillan, unpublished

I Have Personally Found Hierarchical
Classification Useful to Set Context
I first classified areas into 3-4 relief classes

Then I developed and applied different classification rules for each relief class

Automated Extraction of Landforms from DEM Data

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