Título:	Integration of landsat TM and SPOT HRG Images for vegetation change detection in the Brazilian Amazon.
Autoria:	LU, D. BATISTELLA, M. MORAN, E.
Afiliação:	DENGSHENG LU, Indiana University; MATEUS BATISTELLA, CNPM; EMÍLIO MORAN, Indiana University.
Ano de publicação:	2008
Referência:	Photogrammetric Engineering & Remote Sensing, v. 74, n. 4, p. 421-430, 2008.
Conteúdo:	Traditional change detection approaches have been proven to be difficult in detecting vegetation changes in the moist tropical regions with multitemporal images. This paper explores the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data for vegetation change detection in the Brazilian Amazon. A principal component analysis was used to integrate TM and HRG panchromatic data. Vegetation change/non-change was detected with the image differencing approach based on the TM and HRG fused image and the corresponding TM and HRG multispectral images into thematic maps with three coarse land-cover classes: forest, non-forest vegetation, and non-vegetation lands. A hybrid approach combining image differencing and post-classification comparison was used to detect vegetation change trajectories. This research indicates promising vegetation chance trajectories. This research indicates promising vegetation change techniques especially for vegetation gain and loss, even if very limited reference data are available.
Palavras-chave:	Brazilian Amazon Image collection and preprocessing Vegetation Chance Detection
ISBN:	0099-1112
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (CNPM)