International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B2, 2012 
XXII ISPRS Congress, 25 August - 01 September 2012, Melbourne, Australia 
57 
ANALYSIS OF THE RELATIONSHIP BETWEEN INTRA-URBAN VEGETATION CHANGE AND SOCIO-ECONOMIC 
DATA 
L. M. G. Fonseca a , G. A. Boggione a,b , A. M. V. Monteiro \ R. Santos a 
a INPE, National Institute tor Space Research, Brazil - (leila, giovanni, miguel)@dpi.inpe.br, rafael.santos@lac.inpe.br 
1 1FG, Federal Institute of Goiâs, Brazil 
Commission II, WG II/3 
KEY WORDS: Data mining, Fusion, Vegetation, Urban, Change. 
ABSTRACT: 
Understanding the vegetation dynamics in urban areas in both quantitative and qualitative aspects is essential to population welfare 
and also to economic, social and environmental development. Flowever, it is necessary appropriate tools for monitoring and analysis 
of the landscape dynamic in a systematic way. Therefore, this study proposes a methodology to analyze the relationship between 
intra-urban vegetation and the social-economic data using the integrated techniques of remote sensing and GIS as well as data 
mining. This research intends to answer questions such as: Is it possible to extract the intra-urban vegetation as well as identify the 
intra-urban vegetation changes from medium spatial resolution images and digital image processing techniques? Is it possible to 
establish a relationship between the intra-urban vegetation changes and social-economic information using data mining techniques? 
1. INTRODUCTION 
The lack of effective policies for giving order to the 
development of cities and their rapid growth are related, in most 
cases, to the many consequences of urbanization. The quest in 
understanding the diversity in the aspects of urban space, related 
to their physical-territorial dimensions and their inhabitants has 
become a concern for urban management and planning. In this 
sense, studies related to urban environmental quality (UEQ) has 
been increasingly frequent. Among all the variables used to 
evaluate UEQ, vegetation is recognized as the key one for many 
reasons: filtering air, water, and sunlight; cooling urban heat; 
recycling pollutants; moderating local urban climate; providing 
shelters to animals and recreational areas for people (Liang and 
Weng, 2011]. 
The integration of remote sensing technology and Geographic 
Information Systems (GIS) has been of paramount importance, 
since they allow the investigation of the landscape dynamic and 
the alleged correlation with social and economic variables. 
Therefore, this research aims at proposing a methodology to 
identify the current conditions of the existing intra-urban 
vegetation. This study was carried out under the following 
assumptions: 
• The intra-urban vegetation can be observed and quantified 
from orbital remote sensing images that are processed to be set 
to suitable conditions to the investigation on the basis of their 
spectral and spatial characteristics 
• The effects of seasonal climate affect the concentration of 
water of the vegetation. This effect may be noticed in satellite 
images recorded in the dry and rainy seasons. If the vegetation 
coverage is receiving periodic care, these seasonal variations 
should be softer than those of vegetation that do not receive 
artificial care. 
In this way, it becomes possible to better diagnose the situation 
of the city in relation to its intra-urban vegetation. With this 
diagnosis, spatial indicators that address the management 
condition of the vegetation coverage can be defined. 
To carry on the analysis, a case study in the city of Goiania, 
Brazil, between the years of 2008 and 2009 was performed. The 
methodological approach involves the data integration of 
Remote Sensing, GIS and Data Mining techniques to generate a 
scenario that permits an exploratory analysis in the relation 
between intra-urban vegetation and the socioeconomic 
conditions in the city of Goiania, Brazil. The digital image 
processing techniques are used to improve the visual quality of 
the images as well to highlight all the information of interest for 
the use of the human analyst, in turn leading to a range of 
applications. 
2. METODOLOGY 
The city of Goiania (Figure 1) is located approximately 190 km 
from the Capital, between the coordinates 49027' W, 16050' S 
e 49004' W, 16027' S, as can be seen in Figure 1. It occupies a 
total area of 740.53 km2, of which about 40% of the city’s area 
is already urbanized. 
With a total resident population of 1,093,007 inhabitants (IBGE, 
2000) the city of Goiania suffered a significant population boom 
from the 1950s to the 1980s. At this time, the population of the 
city nearly doubled every ten years, probably due to the change 
of the capital to Brasilia, and also due to the government 
projects of infrastructure and incentives for the use and 
occupation of the Cerrado biome for agricultural practice. After 
the 1980s, the population growth has remained high in Goiania, 
with about 20% every 10 years. 
The rapid population growth that occurred in Goiania also 
induced the rapid growth of the urban area. What is most 
interesting is that although most of the city still belongs to the 
rural zone (60%), virtually the entire population of Goiania 
(99.34%) of the total population resides in urban areas, and the 
rest of the population, only (0.66%) resides in rural areas. 
The remote sensing images used in this study were downloaded 
through the Catalog provided by National Institute for Space 
Research (http://www.dgi.inpe.br), described below: 
• Satellite image from LANDSAT-5 satellite, TM sensor, 
band 2 (green), 3 (red), and 4 (infrared), Point Orbits 222/071 
and 222/072, corresponding to the Goiania region; acquisition 
date on 03/03/2009, rainy season, spatial resolution of 30m. 
• Satellite image from CBERS 2B, CCD sensor, bands 2 
(green) 3 (red), and 4 (infrared), Point Orbit 158/119;