Nolè, Danese, Murgante, Lasaponara & Lanorte - input 2012

University of Basilicata - DAPITLaboratory of Urban and Regional Territorial Systems Engineering

Gabriele Nolè, Maria Danese, Beniamino Murgante, Rosa Lasaponara, Antonio LanorteCagliari, 10-11-12 Maggio 2012

Analyzing urban growth with spatial autocorrelation in multi-temporal satellite data



A phenomenon already present in Europe France - Investigations on the 22 largest

metropolitan agglomerationsWithin the City, the residential migrations towords

external crowns clearly outweigh those directed towards the center (in thousands). source:Certu, 2000



World Urbanization Prospects The 2007 Revision-Highlights United Nations New York

Urban and rural population of the World, 1950-2050



construction and maintenanceroadways

time required to move

house prices

Urbanization cost

public transport

public services (street lighting, waste management)

consumption of the soil

congestion and increased path home-work

the diseconomies

An unregulated urbanization for the periurban crown: - consumes the soil - denatures the Landscape



URBAN SPRAWL: A PHENOMENA TO BE MONITORED IN ORDER TO CONTROLL IT

Remote Sensing

A critical point for understanding and monitoring urban expansion processes is the availability of both (i) time-series data set (ii) updated information relating to current urban spatial structure to define and to locate evolution trends.

LANDSAT



Dati –> LANDSAT

www.landsat.org

http://earthexplorer.usgs.gov/….

http://earthexplorer.usgs.gov/



Dati –> LANDSAT

www.landsat.org

http://glovis.usgs.gov/….



spectral indices Spatial autocorrelation

To cope with the fact that small changes have to be captured and extracted from TM multi-temporal data sets, we adopted the use of spectral indices to emphasize occurring changes, and geospatial data analysis for revealing spatial patterns.

Global indicator

(Moran, Geary C ratio)

Local indicator

(LISA, Local Geary, Getis e Ord)

Spatial autocorrelation statistics are considered very useful tools in analysing satellite images, since they consider not only pixel value (reflectance, temperature, spectral index) under investigation, butalso the relationship between same pixel and its surrounding pixels in a relevant scope of analysis.

URBAN SPRAWL: A PHENOMENA TO BE MONITORED IN ORDER TO CONTROLL IT



Tobler's First Law of Geography “All things are related, but nearby things are more related than distant things” (1970)

Positive Autocorrelation

Negative Autocorrelation

No Autocorrelation



- Import/export Dati- Elaboration of remote sensing images- Index elaboration (es. NDVI, BAI)

GRASS

Database GRASS and tools

- data analysis- Import/export data- thematic representation - layout

R Project for Statistical Computing

- Import/export data- Elaboration of remote sensing image- Geostatistics (Moran, Getis,..)



Study area



Study Area

Landsat in RGB



Analysis

REDNIR

REDNIR

RR

RR

+−=NDVI

Takes values between -1 and 1, in particular below 0 for the water, slightly higher than 0 for soils, and between 0.4 and 0.7 for vegetation. Only very dense vegetation to reach 0.8.It is intuitive that an increased presence of pigments increases the numerator.The NDVI allows to relate the absorption spectrum of chlorophyll in the red, with the phenomenon of reflection in the near infrared (strongly influenced by the type of leaf structure)

NDVI - Normalized Difference Vegetation Index



AnalysisChange Detection 1999 – 2009 NDVI

REDNIR

REDNIR

RR

RR

+−=NDVI



AnalysisSpatial autocorrelation – Getis Gi

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AnalysisSpatial autocorrelation – Local Geary

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AnalysisSpatial autocorrelation – Lisa

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ConclusionsSpatial autocorrelation – comparison

Getis and Ord's Index, better captures this phenomenon Instead for Geary index, areas in light red pixels are too large and autocorrelated areas include not only urban areas, but also bareterrains.

Lisa index, gives the worst result, because of a spread of pixels of different colours, which means that there is not a strong autocorrelation and urban areas are not captured by such an analysis.



Conclusions- The process has been carried out using free tools and data. Operating system (Linux Ubuntu) and GIS software (GRASS GIS and Quantum GIS) are open source type, while Landsat data are downloadable and ready to use.- The main objectives of the study were: (i) to assess if the variation of urban structure over time can be quantitatively determined using TM images, (ii) to investigate and describe the modification of urban shape and morphology over time.- Analysing and comparing different years, the process of urban intensification has been observed, and the increase of urbanized area wasrevealed. - However, this study is preliminary and quite suggestive and its mainobjective was to present a way of applying autocorrelation analysis to the monitoring of urban area evolution. The need of analysing more timeperiods and a comparative analysis among many urban areas would befruitful, and the application of the geostatistical analysis applied to satellite time series constitutes a major challenge for further investigation.

Technology

Nolè, Danese, Murgante, Lasaponara & Lanorte - input 2012