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

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