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This presentation is co-financed by the European Social Fund and the state budget of the Czech Republic
Corine Land Cover dataset analysis with (geo)computational methods in GIS
Vít Pászto
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
"PLAYLIST"
● Introduction
○ Data used
■ Study Area
● Methods
○ Work-flow diagram
■ Results
● Conclusions
45 MIN.
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
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
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
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 analysis
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
DATA
● Freely available CORINE Land Cover dataset:
○ 1990
○ 2000
○ 2006
● Level 1 of CLC - 5 classes:
○ Artificial surfaces
○ Agricultural areas
○ Forest and semi-natural areas
○ Wetlands
○ Water bodies
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
STUDY AREA
● Olomouc region (800 km2) - 1/2 of London
● More than 944 patches analysed
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
METHODS - Shape & spatial metrics
● Fundamentally based on patch area, perimeter and shape
● Easy-to-obtain metrics & complex metrics
● Software used:
○ FRAGSTATS 4.1
○ Shape Metrics for ArcGIS for Desktop 10.x
● EXAMPLE/EXPLANATION
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
METHODS - Shape & spatial metrics
● Fundamentally based on patch area, perimeter and shape
● Easy-to-obtain metrics & complex metrics
● Software used:
○ FRAGSTATS 4.1
○ Shape Metrics for ArcGIS for Desktop 10.x
● EXAMPLE/EXPLANATION
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
METHODS - Shape & spatial metrics
Shape and spatial metrics
Area index Contiguity index
Perimeter index (FRAGSTATS 4.1)
Core index
Gyrate index Number of Core Areas
Perimeter-area ratio Core Area Index
Shape index Proximity index
Circumscribing index Normalized Proximity index
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
METHODS - Shape & spatial metricsExchange index Girth index
Normalized Exchanged index Normalized Girth index
Spin index Dispersion index
Normalized Spin index Normalized Dispersion index
Perimeter index (Shape Metrics Toolbox) Range index
Normalized Perimeter index (Shape Metrics Toolbox)
Normalized Range index
Depth index Detour index
Normalized Depth index Normalized Detour index
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
METHODS - Shape & spatial metricsExchange index Girth index
Normalized Exchanged index Normalized Girth index
Spin index Dispersion index
Normalized Spin index Normalized Dispersion index
Perimeter index (Shape Metrics Toolbox) Range index
Normalized Perimeter index (Shape Metrics Toolbox)
Normalized Range index
Depth index Detour index
Normalized Depth index Normalized Detour index
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
METHODS - Multivariate statistics
● Principal Component Analysis (PCA) for consequent clustering
● Cluster analysis:
○ DIvisive ANAlysis clustering (DIANA)
○ Partitioning Around Medoids (PAM)
● Software - Rstudio environment using R programming language
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
WORK-FLOW DIAGRAM
CLC (1990, 2000, 2006)
Metrics calculation
PCA Clustering
DIANA
PAM
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
RESULTS - DIANA clustering
Cluster number 1 2 3 4 5
Number of patches 560 273 105 3 3
1 - Agriculture a. (49 %)
2 - Artificial s. (59 %)
3 - Artificial s. (42 %)
4, 5 - not so dominant
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
RESULTS - PAM clustering
Cluster number 1 2 3 4 5
Number of patches 191 255 210 182 6
1 - Artificial s. (43 %)
2 - Agriculture a. (45 %)
3 - Agriculture a.(51 %)
4 - Artificial s. (52 %)
5 - not so dominant
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
CONCLUSIONS
● No significant grouping in Level 1 classes of CLC nomenclature
● One original class does not create its own specific class using metrics and clustering
● It is possible to group patches according to their shape similarity
● Thus, it is needed to analyze patches individually in more detailed level of CLC
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
THE END
Corine Land Cover dataset analysis with (geo)computational methods in GIS
Vít Pászto
