Understory fires, which burn the floor of standing forests, are one of the most important types of forest impoverishment in the Amazon, especially during the severe droughts of El Niño–Southern Oscillation (ENSO) episodes. However, the authors are aware of no estimates of the areal extent of these fires for the Brazilian Amazon and, hence, of their contribution to Amazon carbon fluxes to the atmosphere. In this paper, the area of forest understory fires for the Brazilian Amazon region is calculated during an El Niño (1998) and a non–El Niño (1995) year based on forest fire scars mapped with satellite images for three locations in eastern and southern Amazonia, where deforestation is concentrated. The three study sites represented a gradient of both forest types and dry season severity. The burning scar maps were used to determine how the percentage of forest that burned varied with distance from agricultural clearings. These spatial functions were then applied to similar forest/climate combinations outside of the study sites to derive an initial estimate for the Brazilian Amazon. Ninety-one percent of the forest area that burned in the study sites was within the first kilometer of a clearing for the non-ENSO year and within the first four kilometers for the ENSO year. The area of forest burned by understory forest fire during the severe drought (ENSO) year (3.9 × 106 ha) was 13 times greater than the area burned during the average rainfall year (0.2 × 106 ha), and twice the area of annual deforestation. Dense forest was, proportionally, the forest type most affected by understory fires during the El Niño year, while understory fires were concentrated in transitional forests during the year of average rainfall. The estimate here of aboveground tree biomass killed by fire ranged from 0.049 to 0.329 Pg during the ENSO and from 0.003 to 0.021 Pg during the non-ENSO year.
The spatial distribution of human activities in forest frontier regions is strongly influenced by transportation infrastructure. With the planned paving of 6000 km of highway in the Amazon Basin, agricultural frontier expansion will follow, triggering potentially large changes in the location and rate of deforestation. We developed a landcover change simulation model that is responsive to road paving and policy intervention scenarios for the BR-163 highway in central Amazonia. This corridor links the cities of Cuiabá, in central Brazil, and Santarém, on the southern margin of the Amazon River. It connects important soybean production regions and burgeoning population centers in Mato Grosso State with the international port of Santarém, but 1000 km of this road are still not paved. It is within this context that the Brazilian government has prioritized the paving of this road to turn it into a major soybean exportation facility. The model assesses the impacts of this road paving within four scenarios: two population scenarios (high and moderate growth) and two policy intervention scenarios. In the 'business-asusual' policy scenario, the responses of deforestation and land abandonment to road paving are estimated based on historical rates of Amazon regions that had a major road paved. In the 'governance' scenario, several plausible improvements in the enforcement of environmental regulations, support for sustainable land-use systems, and local institutional capacity are invoked to modify the historical rates. Model inputs include data collected during expeditions and through participatory mapping exercises conducted with agents from four major frontier types along the road. The model has two components. A scenario-generating submodel is coupled to a landscape dynamics simulator, 'DINAMICA', which spatially allocates the land-cover transitions using a GIS database. The model was run for 30 years, divided into annual time steps. It predicted more than twice as much deforestation along the corridor in business-as-usual vs. governance scenarios. The model demonstrates how field data gathered along a 1000 km corridor can be used to develop plausible scenarios of future land-cover change trajectories that are relevant to both global change science and the decision-making process of governments and civil society in an important rainforest region.
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