Autor(es)

Melo de Oliveira Santos, Cecilia Lira ^[1] ; Camargo Lamparelli, Rubens Augusto ^[2] ; Dantas Araujo Figueiredo, Gleyce Kelly ^[1] ; Dupuy, Stephane ^[3] ; Boury, Julie ^[4] ; dos Santos Luciano, Ana Claudia ^{[1, 5]} ; Torres, Ricardo da Silva ^[6] ; le Maire, Guerric ^{[7, 5, 2, 8]}

Afiliação do(s) autor(es)

^[1] Univ Estadual Campinas, UNICAMP, FEAGRI, Sch Agr Engn, BR-13083875 Campinas, SP - Brazil

^[2] Univ Estadual Campinas, UNICAMP, Interdisciplinary Ctr Energy Planning, NIPE, BR-13083896 Campinas, SP - Brazil

^[3] CIRAD, UMR TETIS, F-34398 Montpellier - France

^[4] AgroParisTech, Paris Inst Technol Life Food & Environm Sci, F-75231 Paris - France

^[5] CNPEM, Brazilian Ctr Res Energy & Mat, CTBE, Brazilian Bioethanol Sci & Technol Lab, BR-13083970 Campinas, SP - Brazil

^[6] Univ Estadual Campinas, UNICAMP, Inst Comp, BR-13083852 Campinas, SP - Brazil

^[7] CIRAD, UMR Eco&Sols, BR-13083896 Campinas, SP - Brazil

^[8] Univ Montpellier, Montpellier SupAgro, INRA, CIRAD, IRD, Eco&Sols, F-34000 Montpellier - France

• Tipo de documento: Artigo Científico
• Fonte: REMOTE SENSING; v. 11, n. 3 FEB 1 2019.
• Citações Web of Science: 3

Resumo

Timely and efficient land-cover mapping is of high interest, especially in agricultural landscapes. Classification based on satellite images over the season, while important for cropland monitoring, remains challenging in subtropical agricultural areas due to the high diversity of management systems and seasonal cloud cover variations. This work presents supervised object-based classifications over the year at 2-month time-steps in a heterogeneous region of 12,000 km(2) in the Sao Paulo region of Brazil. Different methods and remote-sensing datasets were tested with the random forest algorithm, including optical and radar data, time series of images, and cloud gap-filling methods. The final selected method demonstrated an overall accuracy of approximately 0.84, which was stable throughout the year, at the more detailed level of classification; confusion mainly occurred among annual crop classes and soil classes. We showed in this study that the use of time series was useful in this context, mainly by including a small number of highly discriminant images. Such important images were eventually distant in time from the prediction date, and they corresponded to a high-quality image with low cloud cover. Consequently, the final classification accuracy was not sensitive to the cloud gap-filling method, and simple median gap-filling or linear interpolations with time were sufficient. Sentinel-1 images did not improve the classification results in this context. For within-season dynamic classes, such as annual crops, which were more difficult to classify, field measurement efforts should be densified and planned during the most discriminant window, which may not occur during the crop vegetation peak. (AU)

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• Modalidade de apoio: Auxílio à Pesquisa - Temático
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• Modalidade de apoio: Auxílio à Pesquisa - Programa de Pesquisa sobre Mudanças Climáticas Globais - PITE