author = "Bustamante, Jorge Alberto and Alval{\'a}, Regina and von Randow, 
                title = "Seasonal Variability of Vegetation and Its Relationship to 
                         Rainfall and Fire in the Brazilian Tropical Savanna",
            booktitle = "Remote Sensing - Applications",
            publisher = "InTech and the evironment",
                 year = "2012",
               editor = "Escalante-Ramirez, Boris",
                pages = "77--98",
             keywords = "Cerrado, Brazilian savanna.",
             abstract = "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.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                  doi = "10.5772/35287",
                  url = "http://dx.doi.org/10.5772/35287",
                 isbn = "978-953-51-0651-7",
             language = "en",
           targetfile = "seasonal-variability-.htm",
        urlaccessdate = "24 jan. 2021"