Automated landform classification using DEMs Automated classification of geomorphic/ hydrologic...

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



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



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



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



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



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



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



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



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)



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



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



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



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



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



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



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 hummocky landform type 25 m DEM

800 m

400 m

800 m800 m



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



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



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



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



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



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



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



Hydrological response units

Superimpose HRUs on geomorphic classifications

3.5km

4 km



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



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



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



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)



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



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



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



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)



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 hummocky landform type (25 m DEM)



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



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.



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



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



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



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



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



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



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



Thank you!

Automated landform classification using DEMs Automated classification of geomorphic/ hydrologic...

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