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Improved regional-scale Brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach

Bellon De La Cruz Beatriz, Bégué Agnès, Lo Seen Danny, Lebourgeois Valentine, Evangelista Balbino Antônio, Simoes Margareth, Demonte Ferraz Rodrigo Peçanha. 2018. Improved regional-scale Brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach. International Journal of Applied Earth Observation and Geoinformation, 68 : pp. 127-138.

Journal article ; Article de revue à facteur d'impact
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Quartile : Q1, Sujet : REMOTE SENSING

Abstract : Cropping systems' maps at fine scale over large areas provide key information for further agricultural production and environmental impact assessments, and thus represent a valuable tool for effective land-use planning. There is, therefore, a growing interest in mapping cropping systems in an operational manner over large areas, and remote sensing approaches based on vegetation index time series analysis have proven to be an efficient tool. However, supervised pixel-based approaches are commonly adopted, requiring resource consuming field campaigns to gather training data. In this paper, we present a new object-based unsupervised classification approach tested on an annual MODIS 16-day composite Normalized Difference Vegetation Index time series and a Landsat 8 mosaic of the State of Tocantins, Brazil, for the 2014–2015 growing season. Two variants of the approach are compared: an hyperclustering approach, and a landscape-clustering approach involving a previous stratification of the study area into landscape units on which the clustering is then performed. The main cropping systems of Tocantins, characterized by the crop types and cropping patterns, were efficiently mapped with the landscape-clustering approach. Results show that stratification prior to clustering significantly improves the classification accuracies for underrepresented and sparsely distributed cropping systems. This study illustrates the potential of unsupervised classification for large area cropping systems' mapping and contributes to the development of generic tools for supporting large-scale agricultural monitoring across regions.

Mots-clés géographiques Agrovoc : Brésil

Classification Agris : F08 - Cropping patterns and systems

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

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

  • Bellon De La Cruz Beatriz, CIRAD-ES-UMR TETIS (FRA) - auteur correspondant
  • Bégué Agnès, CIRAD-ES-UMR TETIS (FRA)
  • Lo Seen Danny, CIRAD-ES-UMR TETIS (FRA) ORCID: 0000-0002-7773-2109
  • Lebourgeois Valentine, CIRAD-ES-UMR TETIS (FRA)
  • Evangelista Balbino Antônio, EMBRAPA (BRA)
  • Simoes Margareth, EMBRAPA (BRA)
  • Demonte Ferraz Rodrigo Peçanha, EMBRAPA (BRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/587685/)

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