Population and energy consumption in Brazilian Amazonia

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Contents

[edit] Introduction

The Brazilian Amazonia supports the world’s largest contiguous area of untouched tropical forest. However, recent estimates show deforestation rates of 10,000 to 30,000 km²/yr for the period of 1991–1999 and the loss of approximately 600,000 km² of forest by 2000.

The deforested area of the Brazilian Amazonia has increased from 100,000 km² in the 1970s to nearly 590,000 km² in 2000, as a consequence of the construction of an extensive road network and government-assisted migration and agrarian projects.

It was showed that 90% of the total deforestation in Amazonia has been concentrated within a 100 km² land zone around major roads, increasing the environmental and social impact in such areas.

[edit] facts

During the last three decades, the Amazonia region has experienced the highest urban growth rates in Brazil. In 1970, urban population comprised 35.5% of the total population. This proportion increased to 44.6% in 1980, to 58% in 1991, to 61% in 1996, and to 68% in 2000.

[edit] Future trends

The increasing diversity in economic activities and the subsequent increase in the population density have reorganized the network of human settlements all over the region. Current 21st century data show patterns and spatial arrangements that reveal a different Amazonia from the last decades. This new Amazonia emerges as a tropical forest with a complex urban system, a perspective that has led some researchers to put forward the claim for an ‘‘urbanized forest’’.

[edit] Census

Measures of urban growth and population in Amazonia however have been dependent on census data, collected typically on a 10-year interval. Additionally, census tracts in the region frequently cover a mixture of urbanized areas and large uninhabited ones, making it difficult to produce realistic representations of the spatial distribution of the population. The spatial and temporal dimensions of the occupation processes in Amazonia suggest the use of remote sensing data provided by the Defense Meteorological Satellite Program (DMSP), with the Operational Linescan System (OLS).

[edit] Results

A total of 749 munićıpios analysed within the state boundaries of Amazonia, 186 were found inside the night-time light foci and 62 were less than 5 km from the foci, totalling up 248 cities noticed by the DMSP/OLS data. Only 30% of the munićıpios were registered by DMSP night-time lights due to the DSMP image temporal mosaic characteristics: only 10 days of September were used to register stable night lights in a very cloudy region, in the burning season, in 1.1 km of spatial resolution. Despite these restrictions and considering the total resident population, there was night-time light from munićıpios with population up to 2000 inhabitants. All the munićıpios with population higher than 100,000 residents were detected. Others, with population lower than 100 thousand inhabitants, were not detected. DMSP night-time light were not registered for 501 munićıpios.

In Amazonia, 60% of the population lives in urban areas, and the DMSP/OLS data register night-time light in a spatial resolution of 1 km². Therefore the comparison of DMSP/OLS night-time light and population was restricted to the urban population data.

[edit] References

  • Alves, D. (1999). An analysis of the geographical patterns of deforestation in Brazilian Amaz^onia in the 1991–1996 period. In Paper presented at 48th annual conference of the center for Latin American studies––patterns and processes of land use and forest change in the Amazon, University of Florida, Gainesville, 23–26 March.
  • De Koning, G. H. J., Veldkamp, A., Verburg, P. H., Kok, K., & Bergsma, A. R. (1998). CLUE: a tool for spatially explicit and scale sensitive exploration of land use changes. Available: [1]
  • Elvidge, C. D., Baugh, K. E., Kihn, E. A., Kroehl, H. W., Davis, E. R., & Davis, C. W. (1997b). Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption. International Journal of Remote Sensing, 18, 1373–1379.