department of geoinformatics, faculty of science, palacký university in olomouc

www.geoinformatics.upol.cz

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

department of geoinformatics, faculty of science, palacký university in olomouc

Documents