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

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

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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 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

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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

The approach proposed in this study
Comparison of landscape metrics calculated at the samefunctional 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 Derutawhich 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
30000
25000
20000
1977
15000
2000
10000
5000
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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

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

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
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Metrics at class level for the whole area
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Metrics at landscape level along the 5 zones
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YEAR

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

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

by Geographical Analysis, Urban Modeling, Spatial Statistics on Jun 28, 2011

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