Download - Www.geoinformatics.upol.cz On Shape Metrics in Landscape Analyses Vít PÁSZTO Department of Geoinformatics, Faculty of Science, Palacký University in Olomouc

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On Shape Metrics in Landscape Analyses

Vít PÁSZTO

Department of Geoinformatics, Faculty of Science, Palacký University in Olomouc

Reg. č.: CZ.1.07/2.3.00/20.0170


Presentation schedule

• Introduction

• Data used

• Study area

• Methods

• Case study 1 (Results)

• Case study 2 (Results)

• Case study 3 (Initial idea)

• Conclusions


Introduction

• Computer capabilities used by landscape ecologists

• Quantification of landscape patches

• Via various indexes and metrics

• Prerequisite to the study pattern-process relationships (McGarigal and Marks, 1995)

• Progress faciliated by recent advances in computer processing and GIT


Introduction

• Shape and spatial metrics are exactly those methods for quantitative description

• In combination with multivariate statistics, it is possible to evaluate, classify and cluster patches

• Available metrics were used (as many as possible)

• Unusual approach in CLC and city footprint analysis


Methods - Shape & spatial metrics

• Fundamentally based on patch area, perimeter and shape

• Easy-to-obtain metrics & complex metrics

• Software used:o FRAGSTATS 4.1o Shape Metrics for ArcGIS for Desktop 10.x

• EXAMPLE/EXPLANATION



Convex hull

Detour index


Case study 1 - Data

• Freely available CORINE Land Cover dataset:o 1990o 2000o 2006

• Level 1 of CLC - 5 classes:o Artificial surfaceso Agricultural areaso Forest and semi-natural areas

o Wetlandso Water bodies


Case study 1 - Study area

• Olomouc region (800 km2) - 1/2 of London

• More than 944 patches analyzed


Case study 1 - Methods

• Principal Component Analysis (PCA) for consequent clustering

• Cluster analysis:o DIvisive ANAlysis clustering (DIANA)o Partitioning Around Medoids (PAM)

• Software - Rstudio environment using R programming language


Case study 1 - Workflow Diagram

CLC (1990, 2000, 2006)

Metrics calculation

PCA Clustering

DIANA

PAM


Case study 1 – no. of clusters


Results – DIANA clustering

• Hierarchichal clustering

• Tree structured dendrogram

• One starting cluster divided until each cluster contains one single object


Results – Diana clustering


Results – PAM clustering

• Non-hierarchichal clustering

• „Scatterplot“ groups• Using medoids• Similar to K-means• More robust than K-

means


Case study 2 - Data

• Urban Atlas:o Year 2006o Only Artificial surfaceso Digitized to have urban footprintso All EU member states capital cities


Case study 2


• Fractal Dimension Index• Bruxelles (1.0694) • Vienna (1.1505)

• Cohesion Index• Bruxelles (0,948875) • Tallin (0,636262)

Results


Results

• Elbow diagram (no. of clusters):


Results


• An idea (to be done)• Church of st. Maurice

Case study 3 – what about cartography


Case study 3 – what about cartography


Conclusions & Discussion

• Shape Metrics are useful from quantitative point of view

• Tool for (semi)automatic shape recognition via clustering

• Double-edged and difficult interpretation• Strongly purpose-oriented• Geographical context is needed• Input data (raster&vector) sensitivity


Conclusions & Discussion

• Not many reference studies to validate the results

• Shape metrics correlations• There is no consensus about shape metrics

use among the scientists• Proximity and Cohesion index – for centrality

analysis• Fractal dimension, Perim-area, Shape Index –

for line complexity evaluation


The End

Vít PÁ[email protected]

On Shape Metrics in Landscape Analyses

Download - Www.geoinformatics.upol.cz On Shape Metrics in Landscape Analyses Vít PÁSZTO Department of Geoinformatics, Faculty of Science, Palacký University in Olomouc

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