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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. Presentation schedule. Introduction Data used Study area Methods Case study 1 (Results) Case study 2 (Results)

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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