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Classification of crops, pastures, and tree plantations along the season with multi-sensor image time series in a subtropical agricultural region

de Oliveira Santos Cecília Lira Melo, Lamparelli Rubens Augusto Camargo, Dantas Araújo Figueiredo Gleyce Kelly, Dupuy Stéphane, Boury Julie, dos Santos Luciano Ana Cláudia, da Silva Torres Ricardo, Le Maire Guerric. 2019. Classification of crops, pastures, and tree plantations along the season with multi-sensor image time series in a subtropical agricultural region. Remote Sensing, 11 (3):334, 27 p.

Journal article ; Article de recherche ; Article de revue à facteur d'impact Revue en libre accès total
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Quartile : Q2, Sujet : REMOTE SENSING

Abstract : 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 km2 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.

Mots-clés Agrovoc : Télédétection, Cartographie de l' utilisation des terres, Cartographie de l'occupation du sol, cartographie des fonctions de la forêt, Utilisation des terres, Saison, Forêt, Terre agricole

Mots-clés géographiques Agrovoc : Brésil

Classification Agris : U30 - Research methods
P01 - Nature conservation and land resources
K01 - Forestry - General aspects
L01 - Animal husbandry

Champ stratégique Cirad : CTS 1 (2019-) - Biodiversité

Auteurs et affiliations

  • de Oliveira Santos Cecília Lira Melo, UNICAMP (BRA)
  • Lamparelli Rubens Augusto Camargo, UNICAMP (BRA)
  • Dantas Araújo Figueiredo Gleyce Kelly, UNICAMP (BRA)
  • Dupuy Stéphane, CIRAD-ES-UMR TETIS (REU) ORCID: 0000-0002-9710-5364
  • Boury Julie, AgroParisTech (FRA)
  • dos Santos Luciano Ana Cláudia, UNICAMP (BRA)
  • da Silva Torres Ricardo, UNICAMP (BRA)
  • Le Maire Guerric, CIRAD-PERSYST-UMR Eco&Sols (FRA) ORCID: 0000-0002-5227-958X - auteur correspondant

Source : Cirad-Agritrop (https://agritrop.cirad.fr/592790/)

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