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A generalized space-time OBIA classification scheme to map sugarcane areas at regional scale, using Landsat images time-series and the random forest algorithm

dos Santos Luciano Ana Cláudia, Picoli Michelle Cristina Araújo, Vieira Rocha Jansle, Garbellini Duft Daniel, Camargo Lamparelli Rubens Augusto, Lima Verde Leal Manoel Regis, Le Maire Guerric. 2019. A generalized space-time OBIA classification scheme to map sugarcane areas at regional scale, using Landsat images time-series and the random forest algorithm. International Journal of Applied Earth Observation and Geoinformation, 80 : pp. 127-136.

Journal article ; Article de revue à facteur d'impact
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Quartile : Q2, Sujet : REMOTE SENSING

Abstract : The monitoring of sugarcane areas is important for sustainable planning and management of the sugarcane industry in Brazil. We developed an operational Object-Based Image Analysis (OBIA) classification scheme, with generalized space-time classifier, for mapping sugarcane areas at the regional scale in São Paulo State (SP). Binary random forest (RF) classification models were calibrated using multi-temporal data from Landsat images, at 10 sites located across SP. Space and time generalization were tested and compared for three approaches: a local calibration and application; a cross-site spatial generalization test with the RF model calibrated on a site and applied on other sites; and a unique space–time classifier calibrated with all sites together on years 2009–2014 and applied to the entire SP region on 2015. The local RF models Dice Coefficient (DC) accuracies at sites 1 to 8 were between 0.83 and 0.92 with an average of 0.89. The cross-site classification accuracy showed an average DC of 0.85, and the unique RF model had a DC of 0.89 when compared with a reference map of 2015. The results demonstrated a good relationship between sugarcane prediction and the reference map for each municipality in SP, with R² = 0.99 and only 5.8% error for the total sugarcane area in SP, and compared with the area inventory from the Brazilian Institute of Geography and Statistics, with R² = 0.95 and –1% error for the total sugarcane area in SP. The final unique RF model allowed monitoring sugarcane plantations at the regional scale on independent year, with efficiency, low-cost, limited resources and a precision approximating that of a photointerpretation.

Mots-clés Agrovoc : Canne à sucre, Saccharum officinarum, Utilisation des terres, Imagerie par satellite, Landsat, Plantation, fouille de données, Échantillonnage aléatoire

Mots-clés géographiques Agrovoc : Brésil

Classification Agris : U30 - Research methods
A01 - Agriculture - General aspects

Champ stratégique Cirad : CTS 2 (2019-) - Transitions agroécologiques

Agence(s) de financement européenne(s) : European Commission

Programme de financement européen : FP7

Projet(s) de financement européen(s) : Stimulating Innovation for Global Monitoring of Agriculture and its Impact on the Environment in support of GEOGLAM

Auteurs et affiliations

  • dos Santos Luciano Ana Cláudia, UNICAMP (BRA) - auteur correspondant
  • Picoli Michelle Cristina Araújo, UNICAMP (BRA)
  • Vieira Rocha Jansle, UNICAMP (BRA)
  • Garbellini Duft Daniel, Brazilian Center for Research in Energy and Materials (BRA)
  • Camargo Lamparelli Rubens Augusto, UNICAMP (BRA)
  • Lima Verde Leal Manoel Regis, Brazilian Center for Research in Energy and Materials (BRA)
  • Le Maire Guerric, CIRAD-PERSYST-UMR Eco&Sols (FRA) ORCID: 0000-0002-5227-958X

Source : Cirad-Agritrop (https://agritrop.cirad.fr/592792/)

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