University of PerugiaDepartment of Man and TerritoryUnit of Rural Landscape Planning

Spatio-temporal analysis using urban-rural gradient modelling and landscape metrics

Marco Vizzari

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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

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Analysis of urbanization

An effective method to analyze the effects of urbanization on ecosystems is to study their patterns and 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

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

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The approach proposed in this study

Comparison of landscape metrics calculated at the same functional position along the gradient and consequently referred to different spatial extents.

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Objective: investigation of spatio-temporal changes induced by urbanization and other anthropogenic factors

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Study area Umbrian municipalities of

Perugia, Corciano, Torgiano and Deruta which encompass an urban and productive tissue of high territorial continuity around the city of Perugia

During the period under investigation (1977-2000) the area is characterized by high urbanization rate and relevant rural transformations

Key steps of methodology

Urban-rural gradient modelling based on

density estimation of settlements

Subsequent landscape subdivision and metrics calculation

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)

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Built-up data cleaning

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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)

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.

∆SDI - Urbanization gradient modifications

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.

Zone shifting and area variations

Spatial shifting of the five landscape zones (z1 – z5) along a hypothetical section of the urban-rural gradient and consequent boundaries modifications

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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

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

Abbreviation

Metric Description Range Level

PLAND Percentage of landscape

The proportion of total area occupied by a specific land use class

0 < PLAND ≤ 100

C

PD Patch density Number of patches on a area basis.

PD > 1 L, C

MPS Mean patch size Average area of patches in a landscape

MPS > 0 L, C

LSI Landscape shape index

Standardized measure of total edge or edge density. Gives a measure of patch shape and indirectly of aggregation or disaggregation.

LSI ≥ 1 L, C

SHDI Shannon’s diversity index

Measure of diversity in landscape.

0 ≤ SHDI < 1 L

Main LCLU changes

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

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+21%+16%-52%

-6%+4%

Metrics at class level for the whole area

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Metrics at landscape level along the 5 zones

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PLAND at class level along the gradient

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PD at class level along the gradient

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MPS at class level along the gradient

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LSI at class level along the gradient

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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

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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

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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

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27I paesaggi periurbani del Perugino: analisi delle trasformazioni dei gradienti insediativi e dell’uso del suolo – Marco Vizzari

Anyway.. Perugia (still!) remains beautiful!

Thank you!!!

University of PerugiaDepartment of Man and TerritoryUnit of Rural Landscape Planning

Marco Vizzarivizzari@unipg.it