Quantifying deforestation and secondary forest determinants for different spatial extents in an Amazonian colonization frontier (Rondonia)

Spatial patterns of deforested areas and secondary forest are analyzed in terms of the spatial variation in location factors at different spatial extents. The spatial extents considered are old and new agrarian colonization projects and the administrative units of two different municipalities in Rondonia: Vale do Anari and Machadinho d'Oeste. A grid database was constructed including land cover and potential location factors based on biophysical, accessibility, socioeconomic and policy data. Results of the spatial analyses confirmed the hypothesis that different extents yield different relationships between land use/cover patterns and their location factors, particularly between old and new agrarian colonization projects. It emphasizes that current patterns of forest, secondary forest and pasture/agriculture can only be understood with a combination of policy, accessibility, biophysical and socioeconomic factors while accounting for the historical pathways of change. Because we are dealing with different trajectories of land use/cover change, static analysis of the spatial pattern without acknowledging these trajectories will lead to erroneous interpretations of the current and future land use/cover dynamic

Statistical analysis and feedback exploration of land use change determinants at local scale in the Brazilian Amazon

This work focus on two municipalities established first as rural settlements in Rondonia State in 1982. These two municipalities consist on a unique land use change study due to the differences in settlements planning, but also because they are part of recent frontiers of deforestation. The main land use patterns in the study area are mainly related to small farmers. However, medium and big farms, mineral extraction and timber exploration are also expressive. To understand the land use and land change processes, we have investigated and connected these different patterns to their possible causes and driving forces. This was realized by the use of statistical analysis and spatial-temporal modeling. The simulation was useful as learning tool to explore previous feedbacks of deforestation observed during fieldwork campaigns in the are