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This paper provides the first analysis at the sub-municipality scale of the relationships between population densities and deforestation in the Brazilian Amazon between 2000 and 2010. We use the database on deforestation published by the Brazilian space research center (INPE) and the population census data released by the federal geographical and statistical agency IBGE at their finest scale: the census tract level. By crossing the population density and deforestation variables, we identify ten human settlement patterns in the Amazon. There are low-low and high-high classes of population density and deforestation, but also low-high and high-low classes. This analysis helps understand the low overall relations in the Amazon for population and deforestation. We emphasize the expansion of large-scale agriculture and cattle ranching as causing the depopulation of rural areas while in many regions of the Amazon quite strong population densities coexist with relatively low extents of deforestation. Such findings stress the need to implement case-specific public policies in these regions in order to encourage human presence compatible with the conservation of forest cover and biodiversity. We also confirm the importance of the Amazon urbanization process, including the ‘discrete urbanization’ of rural areas, and the need to better recognize the distinct social and environmental problems of urban areas. [ABSTRACT FROM AUTHOR]

Abstract: The Amazon rainforest is one of the world''s greatest natural wonders and holds great importance and significance for the world''s environmental balance. Around 60% of the Amazon rainforest is located in the Brazilian territory. The two biggest states of the Amazon region are Amazonas (the upper Amazon) and Pará (the lower Amazon), which together account for around 73% of the Brazilian Legal Amazon, and are the only states that are serviced by international airports in Brazil''s north region. The purpose of this paper is to model and forecast sustainable international tourism demand for the states of Amazonas, Pará, and the aggregate of the two states. By sustainable tourism is meant a distinctive type of tourism that has relatively low environmental and cultural impacts. Economic progress brought about by illegal wood extraction and commercial agriculture has destroyed large areas of the Amazon rainforest. The sustainable tourism industry has the potential to contribute to the economic development of the Amazon region without destroying the rainforest. The paper presents unit root tests for monthly and annual data, estimates alternative time series models and conditional volatility models of the shocks to international tourist arrivals, and provides forecasts for 2006 and 2007. [Copyright &y& Elsevier]

This paper studies the dynamics of land-use in the Brazilian Amazon using a structural vector autoregressive (SVAR) model. A fixed effect panel data specification is used to control for the heterogeneity in the data. Meanwhile, spatial autocorrelation is also diagnosed by a statistical methodology that allows us to split the model in subsamples (clusters) of more homogenous municipalities. The clustering analysis shows that there are three clusters whose land-use patterns are strongly different in an economical point of view. The first cluster identifies municipalities dedicated to logging, natural resources exploitation and slash-and-burn cultures; the second cluster shows a more diversified agriculture; while the third cluster presents very developed intensive agriculture municipalities. Another contribution of this article relies on the assessment of contemporaneous causal relation among distinct land-uses areas. This new approach allows us to evaluate the dynamics relations arisen from unexpected innovations in the process of soil occupation. The impulse response functions (IRF) and the forecast error variance decompositions (FEVD) generate the following results: (1) in the opposition direction of previous studies, we find that the demand for cropping does not require to clear new areas of forest.; (2) contrary to previous studies we do not find indication that cattle ranching is the primary driver of deforestation; (3) the impact of a shock of pasture land on itself is virtually null at the initial stages, but increases over time, not requiring to clear extra areas of forest land but rather competing with crop land; (4) it seems that if not for all the Amazon Basin, at least in one cluster, cattle ranching and cropping could be competitive activities; and (5) we find out that in the long run pasture innovation is responsible for the major percent of the forecast error concern all land uses. It probably means that the destiny of distinct categories of land, in cluster one, is endogenously determine by activities connected to cattle. [Copyright &y& Elsevier]

Abstract: The Brazilian Amazon is a vast territory with an enormous need for mapping and monitoring of renewable and non-renewable resources. Due to the adverse environmental condition (rain, cloud, dense vegetation) and difficult access, topographic information is still poor, and when available needs to be updated or re-mapped. In this paper, the feasibility of using Digital Surface Models (DSMs) extracted from TerraSAR-X Stripmap stereo-pair images for detailed topographic mapping was investigated for a mountainous area in the Carajás Mineral Province, located on the easternmost border of the Brazilian Amazon. The quality of the radargrammetric DSMs was evaluated regarding field altimetric measurements. Precise topographic field information acquired from a Global Positioning System (GPS) was used as Ground Control Points (GCPs) for the modeling of the stereoscopic DSMs and as Independent Check Points (ICPs) for the calculation of elevation accuracies. The analysis was performed following two ways: (1) the use of Root Mean Square Error (RMSE) and (2) calculations of systematic error (bias) and precision. The test for significant systematic error was based on the Student’s-t distribution and the test of precision was based on the Chi-squared distribution. The investigation has shown that the accuracy of the TerraSAR-X Stripmap DSMs met the requirements for 1:50,000 map (Class A) as requested by the Brazilian Standard for Cartographic Accuracy. Thus, the use of TerraSAR-X Stripmap images can be considered a promising alternative for detailed topographic mapping in similar environments of the Amazon region, where available topographic information is rare or presents low quality. [ABSTRACT FROM AUTHOR]

Mapping aboveground forest biomass is of fundamental importance for estimating CO2 emissions due to land use and land cover changes in the Brazilian Amazon. However, existing biomass maps for this region diverge in terms of the total biomass estimates derived, as well as in the spatial patterns of mapped biomass. In addition, no regional or location-specific measure of reliability accompanies most of these maps. In this study, 330 one-hectare plots from the RADAMBRASIL survey, acquired over and along areas adjacent to the state of Rondônia, were used to generate a biomass map over the entire region using geostatistics. The RADAMBRASIL samples were used to generate a biomass map, along with a measure of reliability for each biomass estimate at each location, using kriging with external drift with elevation, vegetation type and soil texture considered as biomass predictor variables. Cross-validation was performed using the sample plots to compare the performance of kriging against a simple biomass estimation using the sample mean. Overall, biomass varied from 225 to 486Mgha−1, with a local standard deviation ranging from 62 to 202Mgha−1. Large uncertainty values were obtained for regions with low sampling density, in particular in savanna areas. The geostatistical method adopted in this paper has the potential to be applied over the entire Brazilian Amazon region to provide more accurate local estimates of biomass, which would aid carbon flux estimation, along with measures of their reliability, and to identify areas where more sampling efforts should be concentrated. [Copyright &y& Elsevier]

The Brazilian Amazon contains the most active rainforest frontier in the world, and its socioeconomic, demographic and spatial dynamism has been a topic of interest for academics and policy makers for decades. In this paper, we use spatial statistical modeling to examine the context of migration in the Brazilian Amazon by investigating its socioeconomic, demographic, spatial and environmental heterogeneities at the municipal level between 2000 and 2010. First, we visualized the spatial distribution of net-migration, in-migration and out-migration rates among municipalities in the Brazilian Amazon. Then, we explored the presence of spatial autocorrelation using Global Moran ' s I Index, and use spatial modeling techniques to investigate the associations between response variables (in-migration and out-migration) and selected explanatory variables. We identified several in-migration frontiers in the region, especially in Center Mato Grosso and Southeast Pará, while out-migration seems more diffuse in the Amazonia territory. Global Moran ' s I scores indicate that most of the selected variables exhibit spatial dependence, and the spatial regression models present better estimates of the coefficients by incorporating the spatially lagged autoregressive parameter. Our results also confirm the spatial heterogeneity and multidimensional character of in-migration and out-migration in the Brazilian Amazon. Economic growth, regional inequality and the environmental dynamism of the rainforest frontier appear to be closely associated with the intensity of migration flows in the region. We also find that less-populated municipalities have a central role in regional migration dynamics, forming relevant in-migration frontiers and ensuring territorial robustness for migration in the region. • There are several in-migration frontiers in the Brazilian Amazon. • Out-migration seems more diffuse in the Amazonia territory. • There is significant spatial patterning in in-migration and out-migration rates. • Socioeconomic and environmental variables are closely associated with migration. [ABSTRACT FROM AUTHOR]