automated landform classification using dems automated classification of geomorphic/ hydrologic...
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Automated landform classification using
DEMs
Automated classification of geomorphic/ hydrologic spatial entities to support predictive
ecosystem mapping (PEM)
R. A. (Bob) MacMillanLandMapper Environmental Solutions
BC PEM Workshop, April 25-27, 2001
LandMapper Environmental Solutions © 2001
Outline
Introduction and background Automated landform classification from
DEMs Capturing and applying expert knowledge Significance with respect to PEM Closing thoughts
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Introduction
Who and what am I? Soil scientist & mapper Soil-landform modeller
What do I do? Terrain analysis and
classification from DEM
What can I contribute to this discussion of PEM?
An outsider’s perspective
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
EOR Series DYD Series KLM Series FMN Series
15
40
60
COR Series
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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
Our Fundamental Assumption
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Introduction
What is automated landform classification?
What does it require? How does it work? What can it produce? What can’t it produce?
DEM LANDFORM CLASSIFICATION
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AGRICULTURE
Background Automated landform
classification A work in progress Previous efforts:
• classify farm fields for precision agriculture
• classify and describe landforms for soil survey
• LandMapR Program Forestry sector interest
• potential to classify forested areas FORESTRY
800 m 800 m
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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
800 m 800 m
800 m
800 m
MANUAL PROCEDURES
NEW DATA SOURCES
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Situation analysis Increasing challenges:
Demands for sustainability Expanding obligations for
monitoring & certification More accurate forecasting
Significant changes: A new generation of classification
and mapping systems New systems must be:
• more dynamic, adaptive• cheaper, faster, higher resolution• able to model processes
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
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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
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Conceptual design
Geomorphological-Hydrological spatial entities Adopt, adapt & integrate previous successful approaches Incorporate concepts of hydrological connectivity and
hydrologic response units (HRUs) Embrace and evolve concepts from traditional forest
inventory• multi-level hierarchies from Ecological Land Classification• landforms provide the basic spatial framework (Rowe)
Source: Band (1986a)
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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
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Conceptual design
• 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– typically used as basis for operational planning and
management
A multi-level, multi-scale hierarchyDEM Resolution and Source
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
5 x 5 m
1 x 1 m
Proposed Name
Physiographic Province
Physiographic Region
Physiographic District
Physiographic System
Unnamed and undefined
Landform Type
Landform Element
Unnamed and undefined
Appropriate Scale
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
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Landformelements Lowest level in hierarchy
expected to exhibit restricted range of
morphological attributes equally restricted range of
internal characteristics• moisture status• soil type• hydrology/lithology
considered landform facets
• differ in shape • landform position• hydrology
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
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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 800 m
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Landformtypes
Second level in hierarchy Characteristic pattern and
scale of repetition Equated to:
• toposequences• catenas• associations
Most commonly mapped physical entity in forestry
• tentative definitions• proposed 34 classes Source: Kocaoglu (1975)
HUMMOCKY LANDFORM TYPE
3D SCHEMATIC
Source: S. Nolan
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Landform types: Implementation
Extending LandMapR program:
Recognize and classify 34 landform types
Recognition based on: Relative size and shape in
3 dimensions• height (relief)• length (longest X)• width (shortest X)
Measures of morphology• gradient, slope length• drainage integration
6 km 7 km
6 km 7 km
3D view illustrating rolling landform type (25 m DEM)
3D view illustrating hummocky landform type 25 m DEM
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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
statistical distributions of:• gradient• slope lengths• landform classes• aspect classes• channels and divides
3D view illustrating rolling landform type (25 m DEM)
3D view illustrating hummocky landform type 25 m DEM
800 m
400 m
800 m800 m
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Classifying areas as landform types
SHORT LONGLENGTH (X)
POTHOLE
LEVEL PLAIN
FLOOD PLAIN
LOW
HIGH
MEDIUM
RE
LIE
F (
Z)
LEVEL TO DEP
INCLINEDUNDULATING
PITTED RIBBED
RIDGED
CLIFF
HUMMOCKY
HUMMOCKY
MOUNTAIN
HILL
DUNED RIDGED
<200 M > 1000 M500 M
NARROW
WIDE
WID
TH (Y
)
<200 M
> 1000 M
500 MBASIN
< 10 M
< 50 M
> 50 M
< 5 M
< 5%
> 5%
> 9%
< 2%
LOW
HIGH
MEDIUM
GR
AD
IEN
T (
%)
LEVEL
INCLINED
ROLLING
Process table to: classify catchment
entities
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75 k m
Physiographic Systems Top-down sub-division and bottom-up agglomeration
Top-down sub-division• Use coarse resolution DEM
– 250 to 500 m grid spacing
• Run LandMapR on DEM– define large regions
Bottom-up agglomeration• Use finer resolution DEM
– 25 m to 100 m grid spacing
• Run LandMapR on DEM– define landform types
120 km
6 km 7 km
500 m DEM 25 m DEM
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710 k m
5 km DEM
1270 km
Physiographic Regions
5 km DEM
710 k m
1270 km
710 k m
1270 km
1270 km
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PhysiographicRegions Better to define manually
Classify 500 - 1000 m DEM Use simple 4 unit LandMapR
classification to help assign boundaries manually
Too few spatial entities to warrant effort of automated classification
Incorporate additional data Consider bedrock & climate 710 k m
1270 km
710 k m
1270 km
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Some useful technical details Role of hydrological
topology Define cells to cell flow
paths Define channels, divides,
hillslopes, patches
Significance of depressions
Real landscape features Need to quantify
Pit characteristics Location, extent, depth Overspill locations
Intelligent pit removal Establish sequence of
• Overspill and connection Compute and record
• Full depressional topology
• How, when and where pits fill, overspill & connect
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Establishing landform context
Depressional catchments Define local window
• within which to evaluate landform context
• establish landform position Define 1 repeat cycle
• ridge to ridge
• trough to trough
• wavelength of landscape
800 m
400 m
800 m
400 m
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Hydrological response units (HRUs)
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
Importance of HRUs in establishing connectivity:
From cell to cell From upper to mid to
lower slope entities within sub-catchments
From sub-catchment hillslope entities to channel segments
From channel segment to channel segment
From catchment to catchment
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Hydrological response units
Superimpose HRUs on geomorphic classifications
3.5km
4 km
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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 25 m DEM
5 m DEM
900 m 800 m
900 m 800 m
25 m DEM WITH 5 m DEM INSERT
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Discussion - abstraction & smoothing Smoothing is essential
bring out signal reduce local noise
We mainly use: successive mean filters —
7x7 & 5x5 Also have smoothed
using: block kriging thin plate spline with tension
Interested in: wavelets, Fourier
transforms
DEM NOT FILTERED
DEM FILTERED
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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
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Capturing and applying expert knowledge
I n d i v i d u a l s a l i n i t y h a z a r d r a t i n g sfo r ea c h l a y e r
1 0 0 x 1 0 0 m g r id
L a n d s c a p ec u r v a t u r e
V e g e t a t io n
R a in f a l l
G e o lo g y
S o i ls
L a n d s u r f a c e
S a l in i t y h a z a r dm a p
L a y e r w e ig h t in g s
2 x
1 x
2 x
1 x
3 x
T o t a l s a l in i t yh a z a r d r a t in g
Data and observations
Experience and knowledge
Evidence and hypotheses
Beliefs and belief-based rules
Formulae and evidence rules
Place boundaries Classify entities
Field Maps
Source: Searle and Baillie (2000)
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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 computed from evidence
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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
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Relevance of landform elements to PEM
TEM and PEM utilize Terrain, Topography,
Landscape, Soils
Rowe (1996) suggested: Combining terrain and
topographic components into a single coverage
• With coincident boundaries
• Comprehensive descriptions of texture, drainage, depth, mineralogy, slope attributes
Automated landform classification could:
Define combined terrain-topographic units with:
• A single set of boundaries
• Comprehensive descriptions of attributes
Capture a consistent set of automated rules for:
• Delineating boundaries
• Describing areas
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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 Source: Meidinger et al., (2001)
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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– Greater than the sum of its parts.
Landscape associations• a compound mapping unit entity
whose definition includes a predictable pattern of member mapping entities
6 km7 km
6 km 7 km
3D view illustrating rolling landform type (25 m DEM)
3D view illustrating hummocky landform type (25 m DEM)
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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”
– Static attributes of areas
Emerging hydrological entities• Includes “Why” & “What will be”
– “How do/will things change?”
– Dynamic - current status of areas
Source: Maidment, 2000
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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 beliefs regarding:
– The importance of various input maps/variables (weights)
– The strength and direction of relationships between classes of input data and desired prediction.
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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
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
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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;
– 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
Visible salinity over depth to bedrock
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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 )| )
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
Visible salinity over LandSat TM Band 3
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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 Depress
Coulee Bottom Contact
Slough Ring Outcrop Artesian Natural
Canal Seep Map Total
TKp 5937.875 598.875 2894.063 212.625 208.375 49.625 610.813 2133.938 427015.563Khc 1796.563 1581.875 620.813 405.563 49.938 18.938 340.438 188.563 336137.438Kbp 0 0 0 0 0 0 0 0 1574Total 7734.438 2180.750 3514.875 618.188 258.313 68.563 951.250 2322.500 764727
TKp 1.391 0.140 0.678 0.050 0.049 0.012 0.143 0.500 55.839Khc 0.534 0.471 0.185 0.121 0.015 0.006 0.101 0.056 43.955Kbp 0 0 0 0 0 0 0 0 0.206Map Tot 1.011 0.285 0.460 0.081 0.034 0.009 0.124 0.304 100
TKp 100.000 29.801 100.000 41.270 100.000 100.000 100.000 100.000 0Khc 38.436 100.000 27.251 100.000 30.444 48.478 70.804 11.225 0Kbp 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0
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Relevance of allocation of soils to landforms to PEM
Parallels with TEM/PEM Ecosystem map units & Site Series
• Have expected relationships to landform Landform elements
• Could be associated with Site Series– Through similar belief matrices
Landform types• Could be associated with “landscape
associations”– Allows component entities to be described
and placed in landform positions
– Without explicitly mapping them
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Relevance of predictive programs to PEM
Similarity & convergence Predicted output (class) Usually a function (F) of
expert belief 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
Multi-purpose Predictive Calculator (MPC or UPC)
Both possible & desirable Many different processing
options & possible outputs• Many different options for
implementing calculations– Weighted means, Fuzzy
JMF, Boolean, Bayesian, Cross products
• Many possible combinations of inputs & outputs
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Some closing thoughts
J.S. Rowe (1996) Thus, landforms, with their vegetation, modify and
shape their coincident climates over all scales 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
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Thank you!