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ENVIRONMENTAL MONITORING USING ERS-1 SAR DATA OF THE
AMAZON REGION (IRANDUBA-ITACOATIARA MUNICIPALITIES), BRAZIL
Francesco Lucarelli
P. Murino, R. Somma
P. Neri, P. De Stefano
F. Pasquali, L. Castellano
A. Siciliano
Facoltá Economia e Comercio
Via Cintia (Monte S. Angelo)
Napoli 80125 Italia
ISPRS Commission VII / Working Group 3
ABSTRACT
During recent years there has been a great immigration to the Amazon zone causing a rapid expansion of
agriculture. The exploitation of some areas of the Amazon Region has been varied passing from traditional
agricultural systems (shifting cultivation) to the utilization of areas, for cattle pasture and cutting the trees for
power generating.
The deforested areas can be exposed to a dynamic process, the result of which could be either the
environmental regeneration or degradation; in the first case the area regeneration favours the formation of the
secondary vegetation, while, in the second case, it is evident the formation of degraded areas, having a low
biological productivity and then excluded for this reason from those taken in consideration for a possible
agricultural exploitation.
The above transformation process concerns large areas of the Amazon forest
The sites which have been selected for this study are: Iranduba Municipality and Itacoatiara Municipality.
Iranduba Municipality, intersection of two rivers, the Rio Negro and the Rio Solimoes, has the highest
concentration of deforestation.
Itacoatiara Municipality: has been selected to be an example of applied ecology. Placed approximately 180
km from Manaus, it is a large area of agricultural development within an area of ancient degraded pasture.
To perform the proposed study, data of various nature have to be used, such as: cartography, satellite data,
ground truth.
ERS-1 provides systematic and reliable SAR, data acquisition, considering the particular weather conditions
existing in the Amazon forest zone.
The main study objectives, of this paper are:
- Identification of agricultural and forestry development for these areas, using space remote sensing data.
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