Historical carbon fluxes in the expanding deforestation frontier of Southern Brazilian Amazonia (1985–2012)

In tropical areas, pioneer occupation fronts steer the rapid expansion of deforestation, contributing to carbon emissions. Up-to-date carbon emission estimates covering the long-term development of such frontiers depend on the availability of high spatial–temporal resolution data. In this paper, we provide a detailed assessment of carbon losses from deforestation and potential forest degradation from fragmentation for one expanding frontier in the Brazilian Amazon. We focused on one of the Amazonia’s hot-spots of forest loss, the BR-163 highway that connects the high productivity agricultural landscapes in Mato Grosso with the exporting harbors of the Amazon. We used multi-decadal (1984–2012) Landsat-based time series on forested and non-forested area in combination with a carbon book-keeping model. We show a 36% reduction in 1984s biomass carbon stocks, which led to the emission of 611.5 TgCO₂ between 1985 and 1998 (43.6 TgCO₂ year⁻¹) and 959.8 TgCO₂ over 1999–2012 (68.5 TgCO₂ year⁻¹). Overall, fragmentation-related carbon losses represented 1.88% of total emissions by 2012, with an increasing relevance since 2004. We compared the Brazilian Space Agency deforestation assessment (PRODES) with our data and found that small deforestation polygons not captured by PRODES had increasing importance on estimated deforestation carbon losses since 2000. The comparative analysis improved the understanding of data-source-related uncertainties on carbon estimates and indicated disagreement areas between datasets that could be subject of future research. Furthermore, spatially explicit, annual deforestation and emission estimates like the ones derived from this study are important for setting regional baselines for REDD+ or similar payment for ecosystem services frameworks.

     

Scenarios of land-use change in a deforestation corridor in the Brazilian Amazon: combining two scales of analysis

Local, regional, and global processes affect deforestation and land-use changes in the Brazilian Amazon. Characteristics are: direct conversions from forest to pasture; regional processes of indirect land-use change, described by the conversion of pastures to cropland, which increases the demand for pastures elsewhere; and teleconnections, fueled by the global demands for soybeans as animal fodder. We modeled land-use changes for two scenarios Trend and Sustainable Development for a hot spot of land-use change along the BR-163 highway in Mato Grosso and Pará, Brazil. We investigated the differences between a coupled modeling approach, which incorporates indirect land-use change processes, and a noncoupled land-use model. We coupled the regional-scale LandSHIFT model, defined for Mato Grosso and Pará, with a subregional model, alucR, covering a selected corridor along the BR-163. The results indicated distinct land-use scenario outcomes from the coupled modeling approach and the subregional model quantification. We found the highest deforestation estimates returned from the subregional quantification of the Trend scenario. This originated from the strong local dynamics of past deforestation and land-use changes. Land-use changes exceeded the demands estimated at regional scale. We observed the lowest deforestation estimates at the subregional quantification of the Sustainable Development story line. We highlight that model coupling increased the representation of scenario outcomes at fine resolution while providing consistency across scales. However, distinct local dynamics were explicitly captured at subregional scale. The scenario result pinpoints the importance of policies to aim at the cattle ranching sector, to increase land tenure registration and enforcement of environmental laws.

     

Multiple environmental burdens and neighborhood-related health of city residents

Urban living environments are known to influence human well-being and health; however, little is known about the multidimensionality of different environmental burdens. The aim of this study is to examine the relations between multiple burdens and self-rated health of city residents in Berlin. A spatial analysis was conducted to determine neighborhood street blocks with high versus low levels of three environmental burdens (traffic noise, air pollution, lack of public green space) as study sites for a cross-sectional household questionnaire. Burden level served as a dichotomous predictor to compare residents' self-reports of neighborhood satisfaction, life satisfaction, health behavior, and psychological and physical health symptoms. Residents from high-burden blocks appraised the environmental conditions more stressful, reported poorer health behavior and were less satisfied with their neighborhood than residents from low-burden blocks. However, they did not differ in regard to more general health symptoms. Three other burdens (behavior-related noise, litter and dirt in public space, lack of urban vegetation), which could not be varied objectively, were assessed by their perceived intensity. Regression analyses of the relations between the perceived levels of all six burdens and outcomes in the total sample revealed the following: Neighborhood satisfaction could be predicted from multiple stressors and resources that co-occur independently, while more general health symptoms were related only to perceived air pollution. The results have implications for both urban planning and public health.     

A model-based assessment of the environmental impact of land-use change across scales in Southern Amazonia

Regional Environmental Change - This article describes the design of a new model-based assessment framework to identify and analyse possible future trajectories of agricultural development and...