Paregiani

ICCSA - June 30th - July 3rd, 2008 Perugia, Italy

Civil and Environmental Engineering Department – University of Rome “Tor Vergata”

““Automated Unsupervised GeomorphometricAutomated Unsupervised Geomorphometric

Classification of Earth Surface for LandslideClassification of Earth Surface for Landslide

Susceptibility AssessmentSusceptibility Assessment””

Alessandro ParegianiAlessandro Paregiani and Maria and Maria IoannilliIoannilli

International Conference on Computational Science and Its Applications International Conference on Computational Science and Its Applications ICCSA 2008ICCSA 2008

June 30th - July 3rd, 2008 Perugia, ItalyJune 30th - July 3rd, 2008 Perugia, Italy

"Geographical Analysis, Urban Modeling, Spatial Statistics""Geographical Analysis, Urban Modeling, Spatial Statistics"

University of Rome “Tor University of Rome “Tor Vergata”Vergata”



OutlineOutline

4.4. Experimented Classification MethodsExperimented Classification Methods

1.1. Landslide Hazard vs. Landslide SusceptibilityLandslide Hazard vs. Landslide Susceptibility

2.2. Purpose of the WorkPurpose of the Work

3.3. Approaches to Landslide Susceptibility AnalysisApproaches to Landslide Susceptibility Analysis

6.6. Integrated Classification MethodIntegrated Classification Method

5.5. Comparison of Intermediate ResultsComparison of Intermediate Results

7.7. Correlation Analysis between Geomorphometric Classes Correlation Analysis between Geomorphometric Classes and Types of Landslideand Types of Landslide

8.8. ConclusionsConclusions



SusceptibilitySusceptibility

HazardHazard

RiskRisk

ProbabilityProbability of of OccurrenceOccurrence of of

Trigger Trigger PhenomenaPhenomena

ElementsElementsat at RiskRisk

Landslides constitute one of the major hazards Landslides constitute one of the major hazards

that cause losses in lives and property.that cause losses in lives and property.

To assess landslide occurrences is a complex To assess landslide occurrences is a complex

analysis, involving multitude of factors and need analysis, involving multitude of factors and need

to be studied systematically in order to evaluate to be studied systematically in order to evaluate

the hazard.the hazard.

There are no universally accepted forecasting There are no universally accepted forecasting

methods of "natural hazard" and in particular of methods of "natural hazard" and in particular of

landslide hazard.landslide hazard.

Landslide HazardLandslide Hazard

R = P x ( V x E )R = P x ( V x E )(UNESCO)(UNESCO)



Purpose of the workPurpose of the work

The definition of an automated method of terrain morphological The definition of an automated method of terrain morphological

classification in order to establish the correlation degree between classification in order to establish the correlation degree between

topographic forms of the territory and landslide phenomena, by using a topographic forms of the territory and landslide phenomena, by using a

Landslide Inventory and a DEM as inputLandslide Inventory and a DEM as input

A Landslide Inventory and a DEMA Landslide Inventory and a DEM

Input DataInput Data

Specific ObjectivesSpecific Objectives

1.1. Identification of the most suitable measures to describe terrain Identification of the most suitable measures to describe terrain

topographic forms and to distinguish among geomorphically different topographic forms and to distinguish among geomorphically different

landscapes (geometric signatures)landscapes (geometric signatures)

2.2. Identification of a classification method, in order to obtain the best Identification of a classification method, in order to obtain the best

segmentation of terrain surface related to landslide phenomenasegmentation of terrain surface related to landslide phenomena

Moving from the current literature:Moving from the current literature:

1.1. Building up the morphological parametersBuilding up the morphological parameters

2.2. Classification by using different methodsClassification by using different methods

3.3. Evaluation of the goodness of each classification, by considering as Evaluation of the goodness of each classification, by considering as

factors the physical meaning of classes and the statistical correlation factors the physical meaning of classes and the statistical correlation

degree between classes and landslide phenomenadegree between classes and landslide phenomena

4.4. Experiment end evaluation of a new integrated classification methodExperiment end evaluation of a new integrated classification method

TechnicalTechnical approach approach



Approaches to Landslide Susceptibility AnalysisApproaches to Landslide Susceptibility AnalysisIndirect methodologiesIndirect methodologies

Geomorphometric ApproachGeomorphometric Approach

MethodMethod Statistical ApproachStatistical Approach Heuristic ApproachHeuristic Approach

AlgorithAlgorithmm

LimitsLimitsthis method doesn’t consider the this method doesn’t consider the relationships between instability relationships between instability factorsfactors

high degree of subjectivityhigh degree of subjectivity

F,1F,1

F,nF,n

F,2F,2

LsLs

LsLs

LsLs

CORRELATION CORRELATION ANALYSISANALYSIS



IDENTIFICATION IDENTIFICATION OF RELEVANT OF RELEVANT

PARAMETERS AND PARAMETERS AND RELATIVE RELATIVE WEIGHTSWEIGHTS

CLASSIFICATIONCLASSIFICATION

F,1F,1

F,nF,n

F,2F,2

LsLs

LsLs

LsLs




IDENTIFICATION IDENTIFICATION OF RELEVANT OF RELEVANT

PARAMETERS AND PARAMETERS AND RELATIVE RELATIVE WEIGHTSWEIGHTS

CLASSIFICATIONCLASSIFICATION

Ls

CORRELATI ON ANALYSI S AND

CHECK OF I NI TI AL

HYPOTHESI S

I DENTI FI CATI ON OF RELEVANT

PARAMETERS AND THEI R RELATI VE

WEI GHTSF,1 – P,1F,2 – P,2

.......F,n – P,n

CLASSI FI CATI ON

Ls

CORRELATI ON ANALYSI S AND

CHECK OF I NI TI AL

HYPOTHESI S

I DENTI FI CATI ON OF RELEVANT

PARAMETERS AND THEI R RELATI VE

WEI GHTSF,1 – P,1F,2 – P,2

.......F,n – P,n

CLASSI FI CATI ON



• The “science of quantitative land-surface analysis”The “science of quantitative land-surface analysis”

• It draws upon mathematical, statistical and image-processing techniques to quantify It draws upon mathematical, statistical and image-processing techniques to quantify the shape of the earth at various spatial scalesthe shape of the earth at various spatial scales

• The quantitative analysis of a territory, and in particular of its shape, eliminates the The quantitative analysis of a territory, and in particular of its shape, eliminates the limitations of the qualitative topographic informationlimitations of the qualitative topographic information

• It stems from the need to establish a reliable numerical model in order to describe It stems from the need to establish a reliable numerical model in order to describe the earth shape. The quantitative characterization of topographical shape is a the earth shape. The quantitative characterization of topographical shape is a multidisciplinary technique applicable at any scale of analysismultidisciplinary technique applicable at any scale of analysis

• The geometric signature is an analytic tool of numerical land-surface classificationThe geometric signature is an analytic tool of numerical land-surface classification

• The signature was defined as “a set of measurements sufficient to identify The signature was defined as “a set of measurements sufficient to identify unambiguously an object or a set of objects” [Enzmann, 1966]unambiguously an object or a set of objects” [Enzmann, 1966]

• Natural surface processes create different forms. The geometric signature abstracts Natural surface processes create different forms. The geometric signature abstracts those forms and expresses them numerically.those forms and expresses them numerically.

Geomorphometric ApproachGeomorphometric Approach

Approaches to Landslide Susceptibility AnalysisApproaches to Landslide Susceptibility Analysis

Geomorphometry

Engineeringand AppliedScience

EarthScience

InformationScience

Mathematicsand Statistics

Geomorphometry

Engineeringand AppliedScience

EarthScience

InformationScience

Mathematicsand Statistics

• It considers combinations of instability It considers combinations of instability factors, by introducing the “geometric factors, by introducing the “geometric signature” signature”

• It eliminates the subjectivity of heuristic It eliminates the subjectivity of heuristic approachapproach



Experimented Classification Experimented Classification MethodsMethods

Applying State-of-the-Art Classification Applying State-of-the-Art Classification MethodsMethods

Supervised Classification:Supervised Classification: types of topography are recognized starting types of topography are recognized starting from selected “training samples”from selected “training samples”

Unsupervised Classification:Unsupervised Classification: unconstrained by pre-set conditions, and unconstrained by pre-set conditions, and allow the input data to determine “optimal” allow the input data to determine “optimal” categoriescategories

NestedNested -MeansMeans

DividedDivided

ParametersParameters

ClusteringClustering

•Mean

• S.D.

•Variation coefficient

•Symmetry

• Slope gradient

•Texture

•Convexity

-

-

• Slope gradient

•Aspect

•Plan curvature

• Profile curvature

-

MethodMethod

••MeanMean

••S.D.S.D.

••VariationVariation CoefficientCoefficient

••SymmetrySymmetry

••SlopeSlope GradientGradient

••TextureTexture

••ConvexityConvexity

••SlopeSlope GradientGradient

••AspectAspect

••PlanPlan CurvatureCurvature

••ProfileProfile CurvatureCurvature

Types ofTypes of ParametersParameters AuthorsAuthors ParametersParameters --

Single – Single – Cell Cell

TopologicaTopological l

ParameterParameterss

““Context” Context” ParametersParameters(extended (extended

neighborhoneighborhood)od)

EvansEvans(1981)(1981)

Pike – IwahashiPike – Iwahashi(2006)(2006)

PikePike(1971)(1971)



AutomaticMediumHighSlope Unit

AutomaticMediumHighUnique Condition Unit

AutomaticLowHighGrid-Cell

ManualHighLowGeomorphologic Unit

TechniquePhysical MeaningConsistencyTerrain-Unit

AutomaticMediumHighSlope Unit

AutomaticMediumHighUnique Condition Unit

AutomaticLowHighGrid-Cell

ManualHighLowGeomorphologic Unit

TechniquePhysical MeaningConsistencyTerrain-Unit

It depends onIt depends on• the input data typethe input data type• the scale of analysisthe scale of analysis• the desired output data quality and spatial resolutionthe desired output data quality and spatial resolution• the availability of analytic and information toolsthe availability of analytic and information tools

Preliminary ProcessingPreliminary ProcessingThe choice of a terrain-unit of analysisThe choice of a terrain-unit of analysis

Input data analysis and preparationInput data analysis and preparation

a.

d. f .

c.b.

e.

• 30x30m DEM, computed by 30x30m DEM, computed by

interpolating the altitude-points interpolating the altitude-points

extracted from extracted from contour lines (10m contour lines (10m

interval) of the Technical Regional interval) of the Technical Regional

Cartography of LazioCartography of Lazio• Landslide Inventory of Tevere River Basin Landslide Inventory of Tevere River Basin

Authority (PAI), differentiating seven types Authority (PAI), differentiating seven types

of phenomena; the number of events of phenomena; the number of events

totally registered is 351 with a total area totally registered is 351 with a total area

of 19.35 square kilometresof 19.35 square kilometres

as a portion of land surface which contains a as a portion of land surface which contains a

set of ground conditions which differ from the set of ground conditions which differ from the

adjacent units across definable boundariesadjacent units across definable boundaries



Considered ParametersConsidered Parameters

Z = A x2y2 + B x2y + C xy2 + D x2 + E y2 + F xy + G x + H Z = A x2y2 + B x2y + C xy2 + D x2 + E y2 + F xy + G x + H y + Iy + I

A, B, C ecc. are calculated using this polynomial and 9 A, B, C ecc. are calculated using this polynomial and 9 elevation values as input data, as shown: (the reference elevation values as input data, as shown: (the reference

system has its origin-point in the central cell):system has its origin-point in the central cell):A = [(Z1 + Z3 + Z7 + Z9) /4 - (Z2 + Z4 + Z6 + Z8) /2 + A = [(Z1 + Z3 + Z7 + Z9) /4 - (Z2 + Z4 + Z6 + Z8) /2 +

Z5] /L4Z5] /L4B = [(Z1 + Z3 - Z7 - Z9) /4 - (Z2 - Z8) /2] /L3B = [(Z1 + Z3 - Z7 - Z9) /4 - (Z2 - Z8) /2] /L3

C = [(-Z1 + Z3 - Z7 + Z9) /4 + (Z4 - Z6)] /2] /L3C = [(-Z1 + Z3 - Z7 + Z9) /4 + (Z4 - Z6)] /2] /L3D = [(Z4 + Z6) /2 - Z5] /L2D = [(Z4 + Z6) /2 - Z5] /L2E = [(Z2 + Z8) /2 - Z5] /L2E = [(Z2 + Z8) /2 - Z5] /L2

F = (-Z1 + Z3 + Z7 - Z9) /4L2F = (-Z1 + Z3 + Z7 - Z9) /4L2G = (-Z4 +Z6) /2LG = (-Z4 +Z6) /2L

H = (Z2 - Z8) /2LI = Z5H = (Z2 - Z8) /2LI = Z5

Curvature = -2 (D + E) * 100Curvature = -2 (D + E) * 100

aa bb cc

dd ee ff

gg hh ii

(dz/dx) = [(a + 2d + g) - (c + 2f + i)] / (8 * L)(dz/dx) = [(a + 2d + g) - (c + 2f + i)] / (8 * L)(dz/dy) = [(a + 2b + c) - (g + 2h + i)] / (8 * L)(dz/dy) = [(a + 2b + c) - (g + 2h + i)] / (8 * L)

Computational procedure to calculate Computational procedure to calculate “curvature”:“curvature”:

Computational procedure to calculate Computational procedure to calculate “local convexity”:“local convexity”:

1.1. Focalmean(DEM)Focalmean(DEM)

Computational procedure to calculate Computational procedure to calculate “texture”:“texture”:

1.1. Focalmedian(DEM)Focalmedian(DEM)

2.2. DEM – Focalmedian(DEM)DEM – Focalmedian(DEM)

“slope gradient” “section curvature”

“plan curvature” “aspect”

“local convexity”

“texture”

Computational procedure to calculate Computational procedure to calculate “slope gradient” in “e” cell“slope gradient” in “e” cell



Convexity isgreater than mean

for the whole area?

Slope gradient issteeper than meanfor the whole area?

Class1

Class2

Class3

Class4

Class5

Class6

Class7

Class8


for the whole area?

Texture is finerthan mean for

the whole area?


the whole area?


the whole area?


the whole area?

yesyes

yesyesyesyesyesyesyesyes

yesyes

yesyes nono

nononono

nono nono nono nono

First Threshold

Third Threshold

Second ThresholdConvexity isgreater than mean

for the whole area?

Slope gradient issteeper than meanfor the whole area?

Class1

Class2

Class3

Class4

Class5

Class6

Class7

Class8


for the whole area?


the whole area?


the whole area?


the whole area?


the whole area?

yesyes

yesyesyesyesyesyesyesyes

yesyes

yesyes nono

nononono

nono nono nono nono

First Threshold

Third Threshold

Second Threshold

Considered Parameters:Considered Parameters:

• Slope Gradient (first Slope Gradient (first

threshold)threshold)

• Local Convexity (second Local Convexity (second


• Surface Texture (third Surface Texture (third


Classification of earth topography from DEMs by a nested-means Classification of earth topography from DEMs by a nested-means algorithm and a three-part geometric signaturealgorithm and a three-part geometric signature

Experiment 1: Nested-Means Multivariate Experiment 1: Nested-Means Multivariate Analysis (Pike – Iwahashi)Analysis (Pike – Iwahashi)

TOPOGRAPHY:TOPOGRAPHY:

• Continuous random surfaceContinuous random surface

• Independent of any spatial orderliness Independent of any spatial orderliness imposed by geomorphic processesimposed by geomorphic processes



Nested-Means Multivariate Analysis (Pike - Nested-Means Multivariate Analysis (Pike - Iwahashi)Iwahashi)The classification underline a remarkable distinction among mountainside surfaces in The classification underline a remarkable distinction among mountainside surfaces in

four different classes characterized by increasing values of elevation and slope gradientfour different classes characterized by increasing values of elevation and slope gradient



StackAnalysis

Results and Classification

Input Data Computation

StackDefinition

ClusterDefinition

ClassificationCluster

Analysis

ClassificationAnalysis

StackAnalysis

Results and Classification

Input Data ComputationInput Data

ComputationStack

DefinitionStack

Definition

ClusterDefinition

ClusterDefinition

ClassificationClassificationCluster

Analysis

ClassificationAnalysis

Statistical Multivariate Statistical Multivariate AnalysisAnalysis

Cluster methodCluster method• Maximum internal homogeneity and minimum external homogeneityMaximum internal homogeneity and minimum external homogeneity• Statistical Mean of parameter distributions and Covariance among parameter distributionsStatistical Mean of parameter distributions and Covariance among parameter distributions




• Slope gradient (SIMG)Slope gradient (SIMG)

• Local Convexity (CONVEX) Local Convexity (CONVEX)

• Surface Texture (PITPEAK)Surface Texture (PITPEAK)

Experiment 2: Statistical Multivariate Experiment 2: Statistical Multivariate Analysis (Pike - Iwahashi)Analysis (Pike - Iwahashi)



Experiment 3: Statistical Multivariate Experiment 3: Statistical Multivariate Analysis (Evans)Analysis (Evans)


• Slope gradientSlope gradient

• AspectAspect

• Plan CurvaturePlan Curvature

• Profile CurvatureProfile Curvature

normalized as follow:normalized as follow:

i x

x

xX

A remarkable distinction among terrain elements originated by hydrological and wind erosive activities, such as torrential (Class 1) and fluvial (Class 6) riverbeds and ridges (Class 8), with a topological continuity between Classes 1 and 6



Experiment 4: Statistical Multivariate Experiment 4: Statistical Multivariate Analysis (Pike)Analysis (Pike)

Considered Considered Parameters Parameters (statistically (statistically derived):derived):• MeanMean• Standard DeviationStandard Deviation• Variation Variation CoefficientCoefficient• SymmetrySymmetryNot-derived Not-derived Parameters:Parameters:• ElevationElevation• Slope GradientSlope Gradient• CurvatureCurvature



Comparison of Intermediate Comparison of Intermediate ResultsResults

UnacceptableOptimalSufficientGoodTrigger Elements

Distinction

Unacceptable

Unacceptable

Unacceptable

MeanStandard DeviationSymmetryVariation Coefficient

StatisticalMultivariate

Optimal

Good

Good

Slope GradientAspectPlan CurvatureProfile Curvature


GoodGoodClassification

Optimal

Optimal

Slope GradientTextureLocal Convexity

Nested-meansAlgorithm

CorrelationAnalysis

MountainsideSurfacesDistinction

Parameters

Methodof Analysis

Good

Good



UnacceptableOptimalSufficientGoodTrigger Elements

Distinction

Unacceptable

Unacceptable

Unacceptable

MeanStandard DeviationSymmetryVariation Coefficient


Optimal

Good

Good

Slope GradientAspectPlan CurvatureProfile Curvature


GoodGoodClassification

Optimal

Optimal


Nested-meansAlgorithm

CorrelationAnalysis

MountainsideSurfacesDistinction

Parameters

Methodof Analysis

Good

Good





Integrated Classification Integrated Classification MethodMethod

Landslides-ClassesCorrelation Analysis

IntegratedClassification

Procedure

Analysis and Comparison betweenthe Obtained Results

Nested-MeansClassification (Iwahashi)

Multivariate StatisticalClassification (Evans)

GeomorphometricClassification

Mapping

Landslides-ClassesCorrelation Analysis

IntegratedClassification

Procedure

Analysis and Comparison betweenthe Obtained Results

Nested-MeansClassification (Iwahashi)

Multivariate StatisticalClassification (Evans)

GeomorphometricClassification

Mapping

ClassificationClassification 22New New ClassificationClassificationClassificationClassification 11

771111

881212

661010

5599

8888

77

6666

445555

334444

223333

112222

1111

ThresholdThreshold--DividedDividedVariablesVariablesClassesClassesIntegratedIntegrated ClassesClasses

StatisticalStatisticalMultivariate Multivariate

ClassesClasses

ClassificationClassification 22New New ClassificationClassificationClassificationClassification 11

771111

881212

661010

5599

8888

77

6666

445555

334444

223333

112222

1111

ThresholdThreshold--DividedDividedVariablesVariablesClassesClassesIntegratedIntegrated ClassesClasses

StatisticalStatisticalMultivariate Multivariate

ClassesClasses

12 1

9

2

3

4

5

86

11

10

• grid-cell based analysis• homogeneity between input data (30x30m

cells)• selection of relevant classes by a conditional

function



Integrated Classification Integrated Classification MethodMethodA particular of the classification discriminating mountainside surfacesOverlapping of the three classes representing ridges, fluvial and torrential riverbedsA particular of the new integrated classification that considers both

mountainside surfaces and hydrological factors



Correlation Analysis Geomorphometric Correlation Analysis Geomorphometric Classes/LandslidesClasses/Landslides

Integrated Classification Integrated Classification MethodMethod

1

1

m

iinkj

ii

AreaC

Area

1 2 3 4 5 6 70%

20%

40%

60%

80%

100%

Class 1 Class 2 Class 3 Class 4 Class 5 Class 6 Class 8 Class 9 Class 10 Class 11 Class 12

Perc

enta

ge o

f co

rrel

atio

n

Types of landslides

6Debris Flow

7Complex

5Rotational Slides

4Translational Slides

3Surface Deformation

2Falls or Topples

1Flows

NumberNumberTypesTypes of of LandslidesLandslides

6Debris Flow

7Complex

5Rotational Slides

4Translational Slides

3Surface Deformation

2Falls or Topples

1Flows

NumberNumberTypesTypes of of LandslidesLandslides

Classes\Landslides 1 2 3 4 5 6 7

1 21,9% 7,7% 16,0% 14,4% 7,7% 20,3% 19,4%

2 6,0% 9,9% 11,0% 10,8% 7,3% 2,5% 7,6%

3 17,7% 19,0% 6,4% 6,0% 18,2% 20,6% 18,0%

4 6,2% 6,5% 7,2% 3,6% 11,2% 2,9% 5,2%

5 17,8% 19,6% 12,1% 7,3% 29,0% 33,9% 24,1%

6 9,6% 6,3% 13,1% 14,1% 14,4% 7,7% 8,9%

8 6,7% 20,8% 7,0% 2,6% 10,0% 8,4% 6,9%

9 1,4% 1,0% 11,3% 14,9% 0,0% 0,0% 3,6%

10 1,3% 5,5% 2,3% 1,8% 0,3% 2,8% 0,9%

11 2,3% 0,9% 7,7% 17,2% 1,4% 0,3% 2,8%

12 9,0% 3,0% 6,0% 7,3% 0,6% 0,4% 2,6%

K: type of landslideJ: geomorphometric classM: number of k-type landslides in class jN: total number of k-type landslides



ConclusionsConclusions Identification of the most suitablemost suitable parameters to describe terrain

topographic forms related to landslide susceptibility Slope gradient constitutes the main parameter in discriminating

different classes with a clear physical meaning related to landslide susceptibility analysis

Single – Cell Topological Parameters discriminate local physical terrain features

“Context” Parameters discriminate global physical terrain features Identification of a new classification method, in order to obtain the best segmentation of terrain surface related to the landslide phenomena

The method working by the nested-means algorithm allows to identify global features

Local features, such as fluvial and torrential riverbeds, have been identified by using the statistical multivariate method

The goodness of each classification has been evaluated by considering as factors the physical meaning of classes and the statistical correlation degree between classes and landslide phenomena

The results of this evaluation show that the integration of both classification methods allows to correctly classify the territory and to establish correlation degrees between geomorphometric classes and landslide phenomena

This method could represent a useful tool in territorial-scale landslide susceptibility analysis. In fact, the application of this repeatable and reliable procedure may return the best results in a short time and with low economic resources.



Thanks for your attentionThanks for your attention

Paregiani

Economy & Finance

Transcript of Paregiani