On Shape Metrics in
Landscape Analyses
Vít PÁSZTO
Reg. č.: CZ.1.07/2.3.00/20.0170
Department of Geoinformatics, Faculty of Science, Palacký University in Olomouc
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Presentation schedule
• Introduction
• Data used
• Study area
• Methods
• Case study 1 (Results)
• Case study 2 (Results)
• Case study 3 (Initial idea)
• Conclusions
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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
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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
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Methods - Shape & spatial metrics
•
Fundamentally based on patch area, perimeter and
shape
•
•
Easy-to-obtain metrics & complex metrics
Software used:
o
FRAGSTATS 4.1
o
Shape Metrics for ArcGIS for Desktop 10.x
•
EXAMPLE/EXPLANATION
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Methods - Shape & spatial metrics
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Methods - Shape & spatial metrics
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Methods - Shape & spatial metrics
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Methods - Shape & spatial metrics
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Methods - Shape & spatial metrics
Convex
hull
Detour index
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Case study 1 - Data
• Freely available CORINE Land Cover dataset:
o
1990
o
2000
o
2006
• Level 1 of CLC - 5 classes:
o Artificial surfaces
o Agricultural areas
o Forest and semi-natural areas
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o Wetlands
o Water bodies
Case study 1 - Study area
• Olomouc region (800 km ) - 1/2 of London
• More than 944 patches analyzed
2
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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
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Case study 1 - Workflow Diagram
CLC (1990, 2000, 2006)
DIANA
Metrics calculation
PAM
PCA
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Clustering
Case study 1 – no. of clusters
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Results – DIANA clustering
• Hierarchichal
clustering
• Tree structured
dendrogram
• One starting cluster
divided until each
cluster contains one
single object
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Results – DIANA clustering
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Results – Diana clustering
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Results – PAM clustering
• Non-hierarchichal
clustering
• „Scatterplot“ groups
• Using medoids
• Similar to K-means
• More robust than Kmeans
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Results – PAM clustering
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Results – PAM clustering
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Case study 2 - Data
• Urban Atlas:
o
o
o
o
Year 2006
Only Artificial surfaces
Digitized to have urban footprints
All EU member states capital cities
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Case study 2
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Results
• Fractal Dimension Index
• Bruxelles (1.0694)
• Vienna (1.1505)
• Cohesion Index
• Bruxelles (0,948875)
• Tallin (0,636262)
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Results
• Elbow diagram (no. of clusters):
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Results – DIANA clustering
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Results – PAM clustering
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Results
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Case study 3 – what about cartography
• An idea (to be done)
• Church of st. Maurice
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Case study 3 – what about cartography
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Case study 3 – what about cartography
1.00000000000
0.95000000000
0.90000000000
nCohesion
nProximity
0.85000000000
nExchange
nSpin
0.80000000000
nPerimeter
0.75000000000
nDepth
nGirth
0.70000000000
nDispers_
nRange
0.65000000000
nDetour
nTraversal
0.60000000000
0.55000000000
0.50000000000
original
1m
2m
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3m
4m
5m
13m
14m
16m
24m
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
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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
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The End
On Shape Metrics in
Landscape Analyses
Vít PÁSZTO
vit.paszto@gmail.com
www.geoinformatics.upol.cz