Autoria: BEUCHLE, R.; GRECCHI, R. C.; SHIMABUKURO, Y. E.; SELIGER, R.; EVA, H. D.; SANO, E. E.; ARCHARD, F.

Resumo: Abstract - The main objective of our study was to provide consistent information on land cover changes between the years 1990 and 2010 for the Cerrado and Caatinga Brazilian seasonal biomes. These areas have been overlooked in terms of land cover change assessment if compared with efforts in monitoring the Amazon rain forest. For each of the target years (1990, 2000 and 2010) land cover information was obtained through an object-based classification approach for 243 sample units (10 km × 10 km size), using (E)TM Landsat images systematically located at each full degree confluence of latitude and longitude. The images were automatically pre-processed, segmented and labelled according to the following legend: Tree Cover (TC), Tree Cover Mosaic (TCM), Other Wooded Land (OWL), Other Land Cover (OLC) and Water (W). Our results indicate the Cerrado and Caatinga biomes lost (gross loss) respectively 265,595 km2 and 89,656 km2 of natural vegetation (TC + OWL) between 1990 and 2010. In the same period, these areas also experienced gain of TC and OWL. By 2010, the percentage of natural vegetation cover remaining in the Cerrado was 47% and in the Caatinga 63%. The annual (net) rate of natural vegetation cover loss in the Cerrado slowed down from ?0.79% yr?1 to ?0.44% yr?1 from the 1990s to the 2000s, while in the Caatinga for the same periods the rate increased from ?0.19% yr?1 to ?0.44% yr?1. In summary, these Brazilian biomes experienced both loss and gains of Tree Cover and Other Wooded Land; however a continued net loss of natural vegetation was observed for both biomes between 1990 and 2010. The average annual rate of change in this period was higher in the Cerrado (?0.6% yr?1) than in the Caatinga (?0.3% yr?1).

Ano de publicação: 2015

Tipo de publicação: Artigo de periódico

Unidade: Embrapa Cerrados

Palavras-chave: Caatinga, Cerrado, Cobertura do solo, Deforestation, Desmatamento, Land cover change, Remote sensing, Sampling approach, Sensoriamento remoto