Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1123618
Research center of Embrapa/Collection: Embrapa Informática Agropecuária - Artigo em periódico indexado (ALICE)
Date Issued: 2020
Type of Material: Artigo em periódico indexado (ALICE)
Authors: CECHIM JÚNIOR, C.
JOHANN, J. A.
ANTUNES, J. F. G.
DEPPE, F.
Additional Information: CLÓVIS CECHIM JÚNIOR, Unioeste; JERRY ADRIANI JOHANN, Unioeste; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; FLÁVIO DEPPE, SIMEPAR.
Title: Sugarcane mapping in Paraná State Brazil using MODIS EVI images.
Publisher: International Journal of Advanced Remote Sensing and GIS, v. 9, n. 1, p. 3205-3221, 2020.
Language: Ingles
Keywords: Índice de vegetação
Mapeamento de cana-de-açúcar
Annual agriculture
Timeseries
Description: Abstract Sugarcane cultivated in Brazil deserves attention because it makes the Country the world's largest producer of sugar and ethanol. The aim of this work was to develop and evaluate a methodology for sugarcane mapping in Paraná State, Brazil using temporal series of the MODIS EVI, for 2010/2011 to 2013/2014 crop seasons. The methodology included supervised classification Fuzzy ARTMAP, taking as input variables such as terms of harmonics amplitude and phase, and phenological metrics of culture. Area estimates indicated a moderate and strong correlation (rs), ranging from 0.62 to 0.71 comparing with IBGE official data and from 0.79 to 0.87 with the Canasat data. To assess mapping accuracy, Canasat vector maps were used as reference to build the confusion matrix. The method developed based on Fuzzy ARTMAP proved efficient to map and estimate the acreage of sugarcane in the State of Paraná, due to digital processing techniques used in homogeneous samples, selection of phenological seasonal metrics, and decomposition of images in accordance with harmonics and supervised training. These together minimized the neural network forecast errors. Results indicate that the methodology is appropriate for sugarcane mapping.
Thesagro: Cana de Açúcar
Agricultura
Sensoriamento Remoto
NAL Thesaurus: Agriculture
Sugarcane
Time series analysis
Vegetation index
Remote sensing
Data Created: 2020-07-03
Appears in Collections:Artigo em periódico indexado (CNPTIA)

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