Data/hora: 17/06/2024 11:14:50

Biblioteca(s): 	Embrapa Pantanal.	
Data corrente: 	27/03/1996	
Data da última atualização: 	06/04/2017	
Autoria: 	SILVA, J. dos S. V. da; KUX, H. J. H.	
Afiliação: 	EMBRAPA. Centro de Pesquisa Agropecuaria do Pantanal (Corumba, MS); Instituto Nacional de Pesquisas Espaciais (Sao Jose dos Campos, SP).	
Título: 	Remote sensing techniques to the detection and mapping of flooding dynamics within the Pantanal, Mato Grosso do Sul State, Brazil: preleminary results.	
Ano de publicação: 	1991	
Fonte/Imprenta: 	In: INTERNATIONAL SYMPOSIUM ON REMOTE SENSING OF ENVIRONMENT, 24., 1991, Rio de Janeiro. Sumario... Rio de janeiro: Erim, 1991. p.49-50.	
Idioma: 	Inglês	
Conteúdo: 	The Pantanal, an extensive floodplain region in central-western Brazil, is annually flooded. A relative precise knowledge of the dynamics of flooding of great immediate interest for cattle-raising activities and wildlife management and of the areal extent of the flooded areas can be obtained from the digital classification of TM/Landsat scenes. The objective o this study is to evaluate the performance of classification algorithms (Maximum Likelihood, K-means, Single cell, Slicing) in order to discriminate four thematic classes (open water, flooded areas, wet areas and dry areas) by using TM bands 3, 4 and 5 from two well-defined seasons during the hydrologic year: the dry and flood seasons. The test site is located in the southern Pantanal  (coordinates: S 19o28'49", W Gr 57o09o09o, and S 19o37'29", W Gr 57o18o19o). The native vegetation consists mainly of grass, herbs, woods, and bushes. The soils have a high clay content. The images (size 512x470 pixels) were transformed into reflectance frames and registered. Following the classifiers mentioned above were applied. For the dry and flood seasons five and seven thematic images were generated respectively. From the analysis of several thematic classifications, the classifier on Maximum Likelihood showed for the scene of the dry season, the most consistent results, followed by the Density Slicing classifer. The algorithms Single cell and K-means presented the lowest performance. As for the flood scene, Maximum Likelihood indicated results that were the closest on...	
Palavras-Chave: 	Flooding; Hidrology; Mapeamento; Mapping; Techniques; Tecnica.	
Thesagro: 	Hidrologia; Inundação; Sensoriamento Remoto.	
Thesaurus Nal: 	Pantanal; remote sensing.	
Categoria do assunto: 	--	
Registro original: 	Embrapa Pantanal (CPAP)	
Exemplares: 	CPAP - (31437-1) - UPC - SP - PP - SP13804 -  - 13804 - DIS