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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Generalized space-time classifiers for monitoring sugarcane areas in Brazil

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dos Santos Luciano, Ana Claudia [1, 2] ; Araujo Picoli, Michelle Cristina [3] ; Rocha, Jansle Vieira [2] ; Junqueira Franco, Henrique Coutinho [1] ; Sanches, Guilherme Martineli [1] ; Lima Verde Leal, Manoel Regis [1, 4] ; le Maire, Guerric [1, 4, 5, 6]
Número total de Autores: 7
Afiliação do(s) autor(es):
[1] Brazilian Ctr Res Energy & Mat CNPEM, Brazilian Bioethanol Sci & Technol Lab CTBE, BR-13083970 Campinas, SP - Brazil
[2] State Univ Campinas UNICAMP, Fac Agr Engn FEAGRI, BR-13083875 Campinas, SP - Brazil
[3] Natl Inst Space Res INPE, BR-12227010 Sao Jose Dos Campos - Brazil
[4] Univ Estadual Campinas, Interdisciplinary Ctr Energy Planning NIPE, BR-13083896 Campinas, SP - Brazil
[5] CIRAD, UMR Eco & Sols, Campinas, SP - Brazil
[6] Univ Montpellier, CIRAD, INRA, Eco & Sols, IRD, Montpellier SupAgro, Montpellier - France
Número total de Afiliações: 6
Tipo de documento: Artigo Científico
Fonte: REMOTE SENSING OF ENVIRONMENT; v. 215, p. 438-451, SEP 15 2018.
Citações Web of Science: 4

Spatially and temporally accurate information on crop areas is a prerequisite for monitoring the multiannual dynamics of crop production. Satellite images have proven their high potential for mapping crop areas at large scales, even at the crop-species level, when a classifier is calibrated on the same image with reference data corresponding to the same period. For operational monitoring purposes, however, it is critical to develop generalized classification methodologies applicable to large scales and different years. Generalized classifiers were presented in this study as follows: a) simple cross-year calibration and application (M1); b) multiyear calibrations (M2); and c) map updating through change detection with multiyear calibrations (M3). These three methods were developed in a classical frame of object-based classifications for a time series of Landsat images with the Random Forest machine learning algorithm. Therein, we tested these methods for sugarcane classification in Sao Paulo state, Brazil, as sugarcane is an economically important crop that has developed substantially in the past decades. Eight years of sugarcane reference maps were used to calibrate and validate the classifiers at four different sites. The cross-year application of M1 provided a low average accuracy Dice coefficient (DC) of 0.84, while it was, on average, 0.94 for the classical same-year calibration. When the classifier was trained on a multiyear dataset (M2), the accuracies achieved average values of 0.91 in independent years. The map updating method M3 showed promising results but was not able to reach the accuracy of visual interpretation methods for detecting annual sugarcane land use change. The multiyear classifier M2 was applied to four contrasting sites and provided reliable results for new sites and years for sugarcane classification. Calibration of the machine learning algorithm on a multiyear dataset of standardized and gap-filled satellite images and reference data proved to give an accurate and space-time generalized classifier, reducing the time, cost and resources for mapping sugarcane areas at large scales. (AU)

Processo FAPESP: 14/50715-9 - Characterizing and predicting biomass production in sugarcane and eucalyptus plantations in Brazil
Beneficiário:Rubens Augusto Camargo Lamparelli
Linha de fomento: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE