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/

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

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

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

Methods - Shape & spatial metrics Fundamentally based on patch area, perimeter and shape Easy-to-obtain metrics & complex metrics Software used: o FRAGSTATS 4.1 o Shape Metrics for ArcGIS for Desktop 10.x EXAMPLE/EXPLANATION

Methods - Shape & spatial metrics

Methods - Shape & spatial metrics

Methods - Shape & spatial metrics

Methods - Shape & spatial metrics

Methods - Shape & spatial metrics Convex hull Detour index

Case study 1 - Data Freely available CORINE Land Cover dataset: o 1990 o 2000 o 2006 Level 1 of CLC - 5 classes: o Artificial surfaces o Agricultural areas o Forest and semi-natural areas o Wetlands o Water bodies

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

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

Case study 1 - Workflow Diagram CLC (1990, 2000, 2006) Metrics calculation PCAClustering DIANA PAM

Case study 1 – no. of clusters

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

Results – DIANA clustering

Results – Diana clustering

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

Results – PAM clustering

Results – PAM clustering

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

Case study 2

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

Results Elbow diagram (no. of clusters):

Results – DIANA clustering

Results – PAM clustering

Results

An idea (to be done) Church of st. Maurice Case study 3 – what about cartography

Case study 3 – what about cartography

Case study 3 – what about cartography

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

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

The End Vít PÁSZTO On Shape Metrics in Landscape Analyses