Spatio-temporal analysis using urban-rural gradient modelling and landscape metrics Marco Vizzari - Department of Man and Territory, University of Perugia

1. Universityof Perugia Department of Man and Territory Unit of Rural Landscape Planning Spatio-temporal analysis using urban-rural gradient modelling and landscape metrics Marco Vizzari

2. 2 Landscape gradients Landscapes can be viewed as continua and spatial gradients Urbanization can be considered as a particular environmental gradient Along this gradient, urban-rural fringes represent spaces with fuzzy boundaries

3. 3 Analysis of urbanization An effective method to analyze the effects of urbanization on ecosystems is to study their patternsand processes along the urban to rural gradient The quantification of spatial heterogeneity is necessary to explore relationships between ecological processes and spatial patterns A great variety of metrics for the analysis of landscape structure were developed

4. 1 1 2 2 3 3 4 4 5 5 6 6 7 7 Traditional methods for ecological gradient analysis The transect method was widely used for urban-rural gradient analysis also for spatio-temporal pattern analysis It is based on the comparison of multi-temporal data spatially coincident, but often functionally and ecologically incomparable

6. Key steps of methodology Urban-rural gradient modelling based on density estimation of settlements Subsequent landscape subdivision and metrics calculation

7. Land use data processing LU data was retrieved at a scale of 1:10 000 for the years 1977 and 2000. Extensive processing of built-up data based on morphological analysis methods aimed at segmenting binary patterns of settlements into mutually exclusive categories (Soille and Vogt, 2009)

8. 9 Built-up data cleaning

9. 10 Kernel Density Estimation (KDE) In KDE a moving window (kernel) function is superimposed over a grid of locations and the (distance-weighted) density of point events is estimated at each location, with the degree of smoothing controlled by the kernel bandwidth Bandwidth definition represents the most problematic step, but also the most interesting for exploratory purposes (Jones et al., 1996, Borruso, 2008)

10. KDE application The calculated spatial index (SDI – Settlement Density Index) expresses settlement concentration as the km2 of surface occupied by settlements over the km2 of the territorial surface.

11. ∆SDI - Urbanization gradient modifications

12. Landscape subdivision Five urban zones have been defined, each one characterized by a specific SDI interval Despite their different spatial configuration these contexts were considered ecologically comparable.

13. Zone shifting and area variations 30000 25000 20000 1977 15000 2000 10000 5000 0 z1 z2 z3 z4 z5 Spatial shifting of the five landscape zones (z1 – z5) along a hypothetical section of the urban-rural gradient and consequent boundaries modifications Settlement Density

14. Reti ecologiche e Greenways • Marco Vizzari Landscape metrics Algorithms that quantify specific characteristics of patches, classes of patches, or the entire landscape mosaic. Fall into two general categories (McGarigal and Marks 1995, Gustafson 1998): Landscape composition (no reference to spatial attributes) Landscape configuration, requiring spatial information for their calculation

15. Metrics calculation Was executed using FRAGSTATS (McGarigal et al., 2002): On the entire landscape and through all of the five SDI zones At landscape and at class level For the two periods in analysis

16. Main LCLU changes +21% +16% -52% -6% +4% Increase in built-up areas Conversion of sowable lands with trees into ordinary sowable lands Decrease in vineyards in favour of sowable lands Decrease in olive groves Expansion of woodlands 17

17. Metrics at class level for the whole area 18

18. Metrics at landscape level along the 5 zones 19 YEAR

19. PLAND at class level along the gradient 20

20. PD at class level along the gradient 21

21. MPS at class level along the gradient 22

22. LSI at class level along the gradient 23

23. Specific zone transformations Higher density urban areas (z1) continue to be dominated by aggregated and branched built-up patches Urban fringes (z2), along the gradient, remain the most fragmented and the most vulnerable Outer areas (z3 and z4) continue to be dominated by agricultural land uses, but progressively become more homogeneous Zone 4 is characterized by many fragmented natural and semi-natural elements that increased In zone 5 occurred a moderate expansion of wooded areas 24

24. Key landscape issues of Perugia Consistent irregular expansion of built-up areas generating erosion and fragmentation of traditional periurban agricultural land uses Progressive simplification of the agricultural systems Periurban erosion induced by urbanization, together with simplification of rural land uses, resulted in a consequential loss of landscape diversity along the entire gradient Clear alteration of the characteristics of the typical Umbrian gradients 25

25. Concluding remarks Effectiveness of the combined method of gradient analysis and landscape metrics for interpreting the transformations The settlement density index (SDI), calculated using KDE, allowed the modelling of the urbanization gradient Metrics calculated on the SDI zones showed the structural transformation of landscapes along the gradient Possible improvements: Enhance GIS modelling of urban-rural gradient, but not necessarily! Improve the directionality to the analysis Integrate updated LULC and socio-economic data 26

26. I paesaggi periurbani del Perugino: analisi delle trasformazioni dei gradienti insediativi e dell’uso del suolo – Marco Vizzari 27 Anyway.. Perugia (still!) remains beautiful!Thankyou!!! Universityof Perugia Department of Man and Territory Unit of Rural Landscape Planning Marco Vizzari vizzari@unipg.it