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Discrimination of tropical agroforestry systems in very high resolution satellite imagery using object-based hierarchical classification: A case-study in Cameroon

Discrimination of tropical agroforestry systems in very high resolution satellite imagery using object-based hierarchical classification: A case-study in Cameroon. Lelong Camille, Alexandre Cyprien, Dupuy Stéphane. 2014. South-Eastern European Journal of Earth Observation and Geomatics, 3 (2S) : 255-258.
http://ejournals.lib.auth.gr/seejeog/issue/view/726/showToc

Conference on Geographic Object-Based Image Analysis. 5, Thessaloniki, Grèce, 21 Mai 2014/24 Mai 2014.
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Résumé : Tree crops and agroforestry systems are very representative of tropical agricultural landscape. Automatic recognition and mapping of this typical land cover type is thus a challenge for the use of remote sensing in driving issues in food sustainability. Therefore, this paper presents an attempt to use the potential of the object?based approach in image classification to produce a land?cover map of a complex agroforestry area. This case?study focuses on very high spatial resolution data acquired over the savannah/cocoa/forest transition region of Bokito in Cameroon, providing a large panel of various cropping systems. WorldView2 panchromatic and multispectral data are thus processed through textural indices derivation and principal component analysis, to select the more discriminant attributes for the different land?cover types, resulting in a stack of 32 image layers. The object?based approach is then run on eCognition Developer, combining several levels of multiresolution segmentation and consecutive classifications that involves different criteria at each step. At the end, a land?use map based on 13 classes was produced with 85% of global accuracy, evaluated based on ground?truth data and photointerpretation. Its typology is rather fine, especially for the agroforestry crops displaying complex structures, and that would not have been accurately delimitated nor discriminated with a pixel?based approach. (Résumé d'auteur)

Mots-clés Agrovoc : Télédétection, Analyse d'image, Traitement de l'information, Méthodologie, Cartographie, Couverture végétale, Theobroma cacao, Savane, Agroforesterie, Imagerie par satellite

Mots-clés géographiques Agrovoc : Cameroun

Classification Agris : U40 - Méthodes de relevé
P31 - Levée et cartographie des sols
F01 - Culture des plantes
K10 - Production forestière

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

Auteurs et affiliations

  • Lelong Camille, CIRAD-ES-UMR TETIS (FRA)
  • Alexandre Cyprien
  • Dupuy Stéphane, CIRAD-ES-UMR TETIS (FRA)

Source : Cirad - Agritrop

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