Publicações

The Brazilian savanna, named locally Cerrado, is the second largest Brazilian biome, covering approximately two million km2, especially in the Central Highlands (Ratter et al., 1997). This biome is composed predominantly of tropical savanna vegetation and is considered as one of the world’s biodiversity hotspots, a priority area for biodiversity conservation in the world (Myers et al., 2000). The Cerrado region is considered the last agricultural frontier in the world (Borlaug, 2002), which has been converted in the last 50 years especially for agriculture and pasture purposes, where natural and mainly anthropogenic annual burning is a common practice. Currently, around 50% of natural vegetation in the Cerrado region has been converted to pastures and crops (PROBIO-MMA, 2007).This conversion has impacted the biological diversity, the hydrological cycle, the energy balance, the climate and the carbon dynamics at local and regional scales due to habitat fragmentation, invasive alien species, soil erosion, pollution of aquifers, degradation of ecosystems and changes in fire regimes (Klink & Machado, 2005; Aquino & Miranda, 2008). The knowledge of spatial distribution, temporal dynamics and biophysical characteristics of the vegetation types, are important elements to improve the understanding of what is the interaction like between vegetation, precipitation and fire. The objective of this study is to determine the relationship of environmental variables, such as precipitation and fire, with spatial and temporal distribution patterns of main vegetation type of the Brazilian tropical savanna. Thus, we seek to answer the question: how environmental variables, like rain and fire, influence the main vegetation types, like herbaceous, shrubs, deciduous trees and evergreen trees, in the Cerrado biome taking in account the seasonal patterns of the variables involved? In this study, the potential of multi-temporal satellite data, like TRMM data for precipitation, MODIS vegetation indices products for land cover mapping, and others sensors like GOES and MODIS for fire detection is explored by the use of remote sensing and geographic information systems (GIS) techniques.