Data/hora: 08/06/2024 01:29:25

Biblioteca(s): 	Embrapa Pesca e Aquicultura; Embrapa Solos.	
Data corrente: 	21/03/2018	
Data da última atualização: 	03/06/2019	
Autoria: 	BELLÓN, B.; BÉGUÉ, A.; LO SEEN, D.; LEBOURGEOIS, V.; EVANGELISTA, B. A.; SIMÕES, M.; FERRAZ, R. P. D.	
Afiliação: 	BEATRIZ BELLÓN, CIRAD; AGNES BEGUE, CIRAD; DANNY LO SEEN, CIRAD; VALENTINE LEBOURGEOIS, CIRAD; BALBINO ANTONIO EVANGELISTA, CNPASA; MARGARETH GONCALVES SIMOES, CNPS; RODRIGO PEÇANHA DEMONTE FERRAZ, UERJ.	
Título: 	Improved regional-scale Brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach.	
Ano de publicação: 	2018	
Fonte/Imprenta: 	International Journal of Applied Earth Observation and Geoinformation, V. 68, p. 127-138, Jun. 2018.	
ISSN: 	0303-2434	
DOI: 	10.1016/j.jag.2018.01.019	
Idioma: 	Inglês	
Conteúdo: 	Cropping systems' maps at fine scale over large areas provide key information for further agricultural production and environmental impact assessments, and thus represent a valuable tool for effective land-use planning. There is, therefore, a growing interest in mapping cropping systems in an operational manner over large areas, and remote sensing approaches based on vegetation index time series analysis have proven to be an efficient tool. However, supervised pixel-based approaches are commonly adopted, requiring resource consuming field campaigns to gather training data. In this paper, we present a new object-based unsupervised classification approach tested on an annual MODIS 16-day composite Normalized Difference Vegetation Index time series and a Landsat 8 mosaic of the State of Tocantins, Brazil, for the 2014-2015 growing season. Two variants of the approach are compared: an hyperclustering approach, and a landscape-clustering approach involving a previous stratification of the study area into landscape units on which the clustering is then performed. The main cropping systems of Tocantins, characterized by the crop types and cropping patterns, were efficiently mapped with the landscape clustering approach. Results show that stratification prior to clustering significantly improves the classification accuracies for underrepresented and sparsely distributed cropping systems. This study illustrates the potential of unsupervised classification for large area cropping systems' mapping and contributes to the development of generic tools for supporting large-scale agricultural monitoring across regions.	
Palavras-Chave: 	Geographic object-based image analysis.	
Thesagro: 	Mapa; Sensoriamento remoto; Sistema de cultivo.	
Thesaurus Nal: 	Cluster analysis; Cropping systems; Landsat; Moderate resolution imaging spectroradiometer; Spectroradiometers.	
Categoria do assunto: 	P Recursos Naturais, Ciências Ambientais e da Terra	
Registro original: 	Embrapa Pesca e Aquicultura (CNPASA)	
Exemplares: 	CNPASA - (650-1) - UPC - AP - DD - CNPASA_AP04 -  - 2018.04 - DIS
CNPS - (19839-1) - UPC - AP - DD -  -  - 2018.00106 - DIS