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Pászto, V: Corine Land Cover dataset analysis with (geo)computational methods in GIS
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Pászto, V: Corine Land Cover dataset analysis with (geo)computational methods in GIS

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  • 1. Corine Land Cover dataset analysis with (geo)computational methods in GIS Vít PásztoThis presentation is co-financed by theEuropean Social Fund and the statebudget of the Czech Republic
  • 2. "PLAYLIST"● Introduction ○ Data used ■ Study Area ● Methods ○ Work-flow diagram ■ Results ● Conclusions 45 MIN. First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  • 3. 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
  • 4. 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
  • 5. DATA● Freely available CORINE Land Cover dataset: ○ 1990 ○ 2000 ○ 2006● Level 1 of CLC - 5 classes: ○ Artificial surfaces ○ Agricultural areas ○ Wetlands ○ Forest and semi-natural areas ○ Water bodies First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  • 6. 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
  • 7. 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
  • 8. 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
  • 9. METHODS - Shape & spatial metrics Shape and spatial metrics Area index Contiguity index Perimeter index (FRAGSTATS Core index 4.1) 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
  • 10. METHODS - Shape & spatial metrics Exchange 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 Normalized Range index Toolbox) Depth index Detour index Normalized Depth index Normalized Detour index First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  • 11. METHODS - Shape & spatial metrics Exchange 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 Normalized Range index Toolbox) Depth index Detour index Normalized Depth index Normalized Detour index First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  • 12. 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
  • 13. WORK-FLOW DIAGRAM CLC (1990, 2000, 2006) DIANA Metrics calculation PAM PCA Clustering First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
  • 14. 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
  • 15. 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
  • 16. 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
  • 17. THE END Vít Pászto vit.paszto@gmail.com Corine Land Cover dataset analysis with (geo)computational methods in GIS First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc

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