Pattern Recognition and Remote Sensing techniques applied to Land Use and Land Cover mapping in the Brazilian Savannah

Abstract

• The Cerrado biome is highly complex in terms of LULC patterns rendering classification a big challenge. • Time series analysis and phenological metrics arise as valuable assets to perform LULC mapping. • Contextual information is a key factor to accurately classify vegetation and deforestation in the Cerrado. • We provide frameworks to integrate machine learning and image processing to perform LULC analysis The Brazilian Savannah, or Cerrado, has gained vital importance in the discussions about sustainable land development after the conversion of half of its natural vegetation. For the last two decades, most of the agricultural expansion in Brazil has occurred in this biome. This is related to technological improvements in agriculture as well as to environmental compliance policies that have effectively reduced soybean expansion in the Brazilian Amazon biome. Therefore, remotely sensed imagery, pattern recognition and image processing techniques have been employed to analyze and monitor the land dynamics over Cerrado. In this work, we present a brief review on Land Use and Land Cover mapping (LULC) in the Cerrado biome from an application perspective: natural vegetation, pastureland, agriculture, and deforestation. In this review we selected some studies whose results could contribute to the development of more detailed and accurate LULC maps for the Cerrado biome.