Título:	Monitoramento e detecção de desmatamento no bioma Cerrado matogrossense utilizando imagens de multisensores.
Autoria:	SILVA, G. B. S. da FORMAGGIO, A. R. SHIMABUKURO, Y. E. SANO, E. E.
Afiliação:	GUSTAVO BAYMA SIQUEIRA DA SILVA, CNPM; ANTONIO ROBERTO FORMAGGIO, INPE; YOSIO EDEMIR SHIMABUKURO, INPE; EDSON EYJI SANO, CPAC.
Ano de publicação:	2011
Referência:	In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 15., 2011, Curitiba. Anais... São José dos Campos: INPE, 2011.
Páginas:	p. 2849-2855.
Conteúdo:	In the last decades, Brazil has become a global agricultural power and the Cerrado biome (Brazilian savanna) has been playing an important role in the Brazilian agriculture growth. To better analyze the biome human disturbance dynamics, it is necessary to develop and adopt effective methods of assessment and monitoring of land use and land cover changes. The goal is to provide adequate land cover classifications and implement an operational monitoring system in the Cerrado biome, since there is only a few attempts to control the degradation of this biome. This monitoring system can be accomplished using MODIS images, as this sensor has great potential for studies about the seasonal dynamics of Cerrado vegetation phytophysiognomies. Due to this new dynamics, the main objective of this work was to apply the PRODES and DETER like methodologies to detect and map deforestation in the Cerrado biome of Mato Grosso State, Brazil, using Landsat and MODIS data. The proposed methodology was able to detect correctly 65% of all MODIS detected polygons; this represented 74% of estimated area of deforestation. Also, it showed suitability to identify new deforested areas in both shrubland and forestland areas with a tendency to misclassify smaller polygons (< 50 ha) of deforestation.
Thesagro:	Cobertura Vegetal
Palavras-chave:	Monitoramento operacional Formação florestal e savânica Modelo linear de mistura espectral
Tipo do material:	Artigo em anais e proceedings
Acesso:	openAccess
Aparece nas coleções:	Artigo em anais de congresso (CNPM)