Satellite based observations of the dynamic expansion of urban areas in Southern Italy using geospatial analysis

Gabriele Nolè1,2, Rosa Lasaponara 1,3

1 IMAA-CNR C.da Santa Loja, zona Industriale, 85050 Tito Scalo, Potenza- Italy

2 DAPIT Università degli Studi della Basilicata Macchia Romana Potenza - Italy

3 DIFA - Università degli Studi della Basilicata Macchia Romana Potenza – Italy

Outline

• Research aims

• Satellite time series

• Study area

• Geospatial analysis

• Case study

• Results

Research aims

• Understanding the size distribution and dynamic expansion of urban areas is a key issue for the management of city growth and the mitigation of negative impacts on environment and ecosystems. Satellite time series offer great potential for a quantitative assessment of urban expansion, urban sprawl and the monitoring of land use changes and soil consumption.

• This study deals with the spatial characterization of the expansion of urban area by using geospatial analysis applied to multidate data, such as Thematic Mapper (TM) satellite images. The investigation was focused on several very small towns close to Bari, one of the biggest city in the southern of Ital

Time-series data setA critical point for the understanding and monitoring urban expansion processes is the availability of

both:

• (i) time-series data set and

• (ii) updated information relating to the current urban spatial structure a to define and locate the evolution trends.

In such a context, an effective contribution can be offered by satellite remote sensing technologies, which are able to provide both historical data archive and up-to-date imagery.

• Landsat MSS, TM• ASTER

can be downloaded free of charge from the NASA web site.

Satellite time series available free of chargeSatellite data Resolutions availability Multispectral

NOAA/AVHRR

Spatial resolutions5 channels

1 km 1980th

630-690 nm (red)

760-900 nm (near IR)

2 Thermal channels

3700 nm

Landsat /TM

Spatial resolutions7 channels

30 m 1970th

450-520 nm (blue)

520-600 nm (green)

630-690 nm (red)

760-900 nm (near IR)

SPOT/VEGETATION

Spatial resolutions 4 channels

1 km 1998

450-520 nm (blue)

625-695 nm (red)

760-900 nm (near IR)

nm (near IR)

ATSR Spatial resolutions 1990th4 channels

-

1 km Red, NIR and thermal

MODIS

Spatial resolutions 2001 36 channels

1 km, 500m, 250m

VHR SatelliteSatellite data Resolutions Panchromatic Multispectral

IKONOS (1999)

Spatial resolutions 1 mt 4 mt

Spectral range 450-900 nm

445-516 nm (blue)

506-595 nm (green)

632-698 nm (red)

757-853 nm (near IR)

QuickBird (2001)

Spatial resolutions 0,61 mt 2,44 mt

Spectral range 450-900 nm

450-520 nm (blue)

520-600 nm (green)

630-690 nm (red)

760-900 nm (near IR)

GeoEye (2008)

Spatial resolutions 0,41 mt 1,65 mt

Spectral range 450-900 nm

450-520 nm (blue)

520-600 nm (green)

625-695 nm (red)

760-900 nm (near IR)

WorldView1 (2007)Spatial resolutions 0,50 mt -

Spectral range 450-900 nm -

WolrldView-2 (2009)

Spatial resolutions 0,46 mt 1,84 mt

Spectral range 450-780 nm

400 - 450 nm (coastal)

450-520 nm (blue)

520-585 nm (green)

585 - 625 nm (yellow)

630-690 nm (red)

705 - 745 nm (red edge)

760-900 nm (near IR1)

860 - 1040 nm (near IR1)

Spectral reflectance in relation with pheonological state of vegetation (crop, weed)

Spectral reflectance of a given vegetation for different moisture contents

SPECTRAL SIGNATURES

bluegreen

NIR

red

Spectral reflectance of soil for different moisture contents

Satellite-based variable• Single channels or spectral indices suitable/or specifically

designed for environmental areas mapping were analysed.– Blue, Green, Red – near-Infrared (NIR) – short-wave infrared (SWIR)

• Spectral combinations of different bands is widely used– albedo– Normalized Difference of Vegetation Index (NDVI)– Normalized Difference of Infrared Index (NDII)– NDWI (Moisture index)– GEMI– SAVI– Burned Area Index (BAI)– NBAI

VEGETATION INDICES

NDVI (normalized difference vegetation index) = (NIR-RED)/(NIR+RED)

Green NDVI = (NIR-GREEN)/( NIR+GREEN) Gitelson et al. (1996)

ALBEDO=(NIR+RED)/2 Saunders (1990).

SR (simple ratio) = NIR/RED

SAVI (soil adjusted vegetation indices)=(1 + L) *(NIR - RED)/ (NIR+RED + L) where the term L can vary from 0 to 1 depending on the amount of visible soil SAVI reduces soil background influence

Huete (1988) and Heute et al, (1994)

GEMI=g(1-0.25 g)-(RED-0.125)/(1-RED) where g=(2(NIR2-RED2)+1.5 NIR+0.5 RED)/(NIR+RED+0.5) GEMI by non-linearly combining single band reflectances minimize the influence of

atmospheric effects

Pinty and Verstraete (1992)

EVI (enhanced vegetation index )= (1 +L) * (NIR - RED)/(NIR+ C1*RED- C2*BLUE + L)

Where C1, C2, and L are constants empirically determined. The currently used values are as C1=; 6.0; C2= 7.5; and L= 1

EVI: developed in order to optimize the vegetation signal from deserts to rainforests while minimizing aerosol and canopy background sources of uncertainty.

Kaufman and Tanrè, 1992

Examples of time series per pixel

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NDVI

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GVMI

1. Image differencing: a new image containing changes is created by subtracting

pixel by pixel two images under investigation

2. Image rationing: new image containing changes is created by dividing pixel by pixel two images under investigation

3. Change vector analysis: spectral or spatial differences are employed to evaluate changes plotting two images against each other on a graph..

4. Classification comparisons: classifications are carried out on two different dates and then compared to assess the variations.

CHANGE DETECTION TECHNIQUES

• Change detection Map

Evaluatiing urban expansion using TM Study area

• Fig. 1. RGB of TM images acquired in 1999 (right) and 2009 (left) note that light spots are urban areas.

• The investigation herein presented was focused on the assessment of the expansion of several very small towns very close to Bari (in southern Italy), which is one of the biggest city in Southern Italy.

• Bari is the second largest city of Southern Italy, located in the Apulia (or Puglia) Region. It faces the Adriatic Sea and has one of the major seaports in Italy.

• Bari is the fifth largest province (more than 5,000 square kilometers) in Italy and also the most populated with around 1,600,000 inhabitants as of 2007. The city has around 400,000 inhabitants. The area of concern is characterized by an active and dynamic local economy mainly based on small and medium enterprises operative in the commerce, industry and services

Study area

Change detectionOver the years, different techniques and algorithms were developed for change

detections from the simplest approach based on

• 1. Image differencing: a new image containing changes is created by subtracting pixel by pixel two images under investigation

• 2. Image rationing: new image containing changes is created by dividing pixel by pixel two images under investigation

• 3. Change vector analysis: spectral or spatial differences are employed to evaluate changes plotting two images against each other on a graph.

• 4. Classification comparisons: classifications are carried out on two different dates and then compared to assess the variations.

NDVI map from the TM images acquired in 1999, note that light spots are urban areas.

NDVI map from the TM images acquired in 2009, note that light spots are urban areas.

NDVI difference map from the TM images acquired in 1999 and 2009, note that white pixels are urban areas.

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

Positive Autocorrelation(or attraction)

Negative Autocorrelation(or repulsion)

No Autocorrelation(or random)

Events : near and similar (clustered distribution)

between events when, even if they are near, they are not similar (uniform distribution)

no spatial effects, neither about the position of events, neither their properties

called “event” the number of spatial occurrences in the considered variable,

spatial autocorrelation measures the degree of dependency among events,

considering at the same time their similarity and their distance relationships

First order effects(Absolute location)

Second order effects(Relative location)

ds

dsYEs

ds

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0

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dsYdsYEss

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0,

Properties of a spatial distribution*

*Gatrell et al. (1996)

ds = the neighbourhood each point (s)E() = expected meanY(ds) : events number in the neighbourhood

Large scale variation in the mean value of a spatial process (global trend)

Small-scale variation around the gradient or Local dependence of a spatial process (local clustering)

Spatial autocorrelation : the nature of the problem

Quantitative nature of dataset

•understand if events are similar or dissimilar (define the intensity of the spatial process, how strong a variable happens in the space )

Geometric nature of dataset

• the conceptualization of geometric relationships (..at which distance are events that influence each other (distance band))

Calculation method : Euclidean distance . 2)(2)(),( jyiyjxixjsisEd

Direction considered : or contiguity methods (tower c., bishop c., queen c.)

dist

ance

Definition of spatial event 1

2

3

Global indicators of autocorrelation just measure if and how much the dataset is autocorrelated.

Global indicators of Autocorrelation

Moran’s index

i j i iij

i j jiij

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2)()(

))((

where, N is the total pixel number, Xi and Xj are intensity in i and j points (with i≠j), Xi is the average value, wij is an element of the weight matrix

I Є [-1; 1] if I Є[-1; 0) there’s negative autocorrelation; if I Є (0; 1] there’s positive autocorrelation; if I converges to o there’s null autocorrelation.

Geary’s C

where symbols have the same meaning than the Moran’s index expression

C [0; 2]; if C [0; 1) there’s positive autocorrelation; if C(0; 2] there’s negative autocorrelation; if C converges to 1 there’s null autocorrelation

i iij

i j jiij

XXw

XXwNC

2

2

)((2

)()1( (Geary, 1954),

(Moran, 1948)

LISA allow us to understand where clustered pixels are, by measuring how much are homogeneous features inside the fixed neighbourhood

Local Indicators of Spatial Autocorrelation (LISA)

Local Moran’s index

high value of the Local Moran’s index means positive correlation both for high values both for low values of intensity (reflectance value)

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(Anselin, 1995),

Local Geary’s C index

Detection of areas of dissimilarity of events (pixel reflectance value)

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(Cliff & Ord, 1981)

Getis and Ord’s Gi index

high value of the index means positive correlation for high values of intensity, while low value of the index means positive correlation for

low values of intensity (Getis and Ord, 1992; Illian et al., 2008)

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▪ wij is an element of the weights matrix

X

•Local Moran's I index has values that typically range from approximately +1, representing complete positive spatial autocorrelation, to approximately 1, representing ‑complete negative spatial autocorrelation

•the Local Geary's C index allows us to identify edges and areas characterized by a high variability between a pixel value and its neighboring pixels,

• the Getis-Ord Gi index permits the identification of areas characterized by very high or very low values (hot spots) compared to those of neighboring pixels.

Results from satellite data

Conclusion• Satellite based observations of the dynamic expansion of urban areas in

Southern Italy using geospatial analysis provide an improved estimation of dynamic of urban expansion

• Satellite data can be profitably used as inputs for models (such as SLEUTH )adopted for predicting cumulative trend of the area towards the urban development

Thank you!!!