Generalized space-time classifiers for monitoring sugarcane areas in Brazil

dos Santos Luciano Ana Cláudia, Araújo Picoli Michelle Cristina, Vieira Rocha Jansle, Franco Henrique Coutinho Junqueira, Martineli Sanches Guilherme, Lima Verde Leal Manoel Regis, Le Maire Guerric. 2018. Generalized space-time classifiers for monitoring sugarcane areas in Brazil. Remote Sensing of Environment, 215 : 438-454.

Quartile : Outlier, Sujet : ENVIRONMENTAL SCIENCES / Quartile : Outlier, Sujet : REMOTE SENSING / Quartile : Outlier, Sujet : IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

Liste HCERES des revues (en SHS) : oui

Thème(s) HCERES des revues (en SHS) : Géographie-Aménagement-Urbanisme-Architecture

Résumé : 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.

Mots-clés Agrovoc : canne à sucre, cartographie de l'utilisation des terres, rendement des cultures, classification, télédétection

Mots-clés géographiques Agrovoc : Brésil

Classification Agris : U30 - Méthodes de recherche
F01 - Culture des plantes

Champ stratégique Cirad : Axe 1 (2014-2018) - Agriculture écologiquement intensive

Auteurs et affiliations

• dos Santos Luciano Ana Cláudia, UNICAMP (BRA)
• Araújo Picoli Michelle Cristina, INPE (BRA)
• Vieira Rocha Jansle, UNICAMP (BRA)
• Franco Henrique Coutinho Junqueira, Brazilian Center for Research in Energy and Materials (BRA)
• Martineli Sanches Guilherme, Brazilian Center for Research in Energy and Materials (BRA)
• Lima Verde Leal Manoel Regis, Brazilian Center for Research in Energy and Materials (BRA)
• Le Maire Guerric, CIRAD-PERSYST-UMR Eco&Sols (BRA) ORCID: 0000-0002-5227-958X

Source : Cirad-Agritrop (https://agritrop.cirad.fr/590011/)

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