ABSTRACT
Analysis of Vegetation Indices in Urban Areas
from TM-Landsat and HRV-SPOT Orbital Data
Ana Lücia Ramos Carrara
Celina Foresti
Joäo Roberto dos Santos
Instituto de Pesquisas Espaciais - INPE
Coordenaçäo Geral de Observaçôes da Terra (OBT)
Divisäo de Sensoriamento Remoto e Meteorologia Espacial (DSM)
Caixa Postal 515 - 12201 - Säo José dos Campos - SP - BRAZIL
The spatial organization of the brazilian cities in several areas of the country
reflects the impact of the accelerated and disorganized urbanization that has occurred
during the last years. This urbanization without an adequate planning has caused a process
of degradation of the natural urban environment. An imbalance between built up areas and
green areas has occurred where the presence of the vegetation element has grown less. A
quantitative and qualitative survey of urban green areas establishes basic information to
elaborate adequate planning in order to improve the quality of the urban environment.
Through the transformation of orbital data into numerical models, called Vegetation Indices
(VI), it is possible to obtain a qualitative and quantitative indicator of the vegetation
cover relative to built up areas. The main objective of this study is to analyse the
Normalized Difference Vegetation Index (NDVI) in the urban environment obtained from orbital
data. The study area is located in the city of Taubaté in Sào Paulo state. The NDVI is
calculated from orbital data from TM-Landsat (TM3,TM4,TM5) and HRV-SPOT (XS2,XS3)
corresponding to spectral bands in the red, near-infrared and middle-infrared ranges. The
data was taken on 8 August, 1988 and 19 July, 1988, respectively. It is calculated by the
formulae:
VI= gain x NIR - R + offset and VI = gain x MIR - R + offset.
NIR + R MIR + R
The influence of spectral and spatial characteristics from TM-Landsat and HRV-SPOT data was
‘taken into account in analyzing the performance of classification for the VI calculated from
those sensor systems. Ground information and the percent vegetation cover were determined
from panchromatic aerial photographs (in scale 1:10 000) and planimetric maps (in scales
of 1:25 000 and 1:50 000). The different classes of urban land use were discriminated and
classified on the basis of VI. In the results, it was found that the NDVI calculated by TM-
Landsat and HRV-SPOT data allowed a distinct classification associating urban land use and
vegetal cover to be obtained. It is concluded that the VI is a good estimator to compare
green areas with built up areas and it permits a global view of the spatial distribution
and density of vegetal cover.
KEY WORDS: Urban Environment, Vegetation Index, TM-Landsat, HRV-Spot.
1. INTRODUCTION data permit to acquire information about
the targets on the ground in different
In the most brazilian cities occupation of wavelenghts of eletromagnetic spectrum.
the urban land has occurred without an
adequate planning. It shows the great The vegetation index is one of these
urbanization impact through the last years information that through spectral data
, when the urbanization got the percentage permits to. obtain. | qualitative and
of 65.57 around the country (Censo,1980). quantitative indicators of the
distribution and density of. vegetation
The change in the urban spaces has taken inside the urban area, and its proportion
place rapidly, becoming extense build up compared to build up area.
areas occupied with reduced vegetation.
The main objective of this study is to
Through knowledge of cities green cover, analyse the Normalized Difference
urban planners and urbanists can take some Vegetation Index (NDVI) obtained from TM-
directions in the urban planning such as Landsat and HRV-SPOT orbital data, and
determining deficient and preservation the use of it as a tool in the vegetation
areas and the establishment of suitable survey in the urban environment.
areas to be used as green spaces in the
urban area. 2.STUDY AREA
Using information from remote sensing in The work was developed in the city of
orbital level it gives data that makes the Taubaté in Sao Paulo state.
analysis less costly, timely information
and makes the work fatigueless when The study area. includes the urban
compared to traditional methods. continuous agglomeration of the city,
which occupies an area of approximately
Also the orbital data is important because 51.50 Km?. It is situated between ‚the
of when one works in urban areas there is coordinates south latitude 229559 09" to
the need of periodic survey to update the 23°04' 51' and west longitude 49°31 08" to
information since the urbanization process 45°36 43".
has been very dinamic. In addition, these
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