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

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  1. 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

  2. Presentation schedule • Introduction • Data used • Study area • Methods • Case study 1 (Results) • Case study 2 (Results) • Case study 3 (Initial idea) • Conclusions

  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

  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 and city footprint analysis

  5. 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

  6. Methods - Shape & spatial metrics

  7. Methods - Shape & spatial metrics

  8. Methods - Shape & spatial metrics

  9. Methods - Shape & spatial metrics

  10. Convex hull Detour index Methods - Shape & spatial metrics

  11. Case study 1 - 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

  12. Case study 1 - Study area • Olomouc region (800 km2) - 1/2 of London • More than 944 patches analyzed

  13. Case study 1 - Methods • Principal Component Analysis (PCA) for consequent clustering • Cluster analysis: • DIvisive ANAlysis clustering (DIANA) • Partitioning Around Medoids (PAM) • Software - Rstudio environment using R programming language

  14. Case study 1 - Workflow Diagram DIANA CLC (1990, 2000, 2006) Metricscalculation PAM PCA Clustering

  15. Case study 1 –no. of clusters

  16. Results – DIANA clustering • Hierarchichal clustering • Tree structured dendrogram • One starting cluster divided until each cluster contains one single object

  17. Results – DIANA clustering

  18. Results – Diana clustering

  19. Results – PAM clustering • Non-hierarchichal clustering • „Scatterplot“ groups • Using medoids • Similar to K-means • More robust than K-means

  20. Results – PAM clustering

  21. Results – PAM clustering

  22. Case study 2 - Data • Urban Atlas: • Year 2006 • Only Artificial surfaces • Digitized to have urban footprints • All EU member states capital cities

  23. Case study 2

  24. Results • Fractal Dimension Index • Bruxelles (1.0694) • Vienna (1.1505) • Cohesion Index • Bruxelles (0,948875) • Tallin (0,636262)

  25. Results • Elbow diagram (no. of clusters):

  26. Results – DIANA clustering

  27. Results – PAM clustering

  28. Results

  29. Case study 3 – whataboutcartography An idea (to be done) Church of st. Maurice

  30. Case study 3 – whataboutcartography

  31. Case study 3 – whataboutcartography

  32. 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

  33. 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

  34. The End On Shape Metrics in Landscape Analyses Vít PÁSZTO vit.paszto@gmail.com