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A remote sensing based approach for optimizing sampling strategies in tree monitoring and agroforestry systems mapping

Ndao Babacar, Leroux Louise, Diouf Abdoul Aziz, Soti Valérie, Sambou Bienvenu. 2019. A remote sensing based approach for optimizing sampling strategies in tree monitoring and agroforestry systems mapping. In : 4th World Congress on Agroforestry. Book of abstracts. Dupraz Christian (ed.), Gosme Marie (ed.), Lawson Gerry (ed.). CIRAD, INRA, World Agroforestry, Agropolis International, MUSE. Montpellier : CIRAD-INRA, Résumé, p. 563. World Congress on Agroforestry. 4, Montpellier, France, 20 May 2019/22 May 2019.

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Abstract : Characterizing agroforestry systems (AFS) at landscape scale is of a great importance for development planning at regional scale in Africa. Therefore, the major constraint to effective AFS mapping with remote sensing is the high diversity within landscapes. To have a robust and representative sample of training data, this study proposes an optimized sampling strategy guided by the AFS functioning and allowing to take into account the landscape diversity. A simple and reproducible approach based on unsupervised classification of remote sensing data and an a priori knowledge on the environment functioning is developed. The study is conducted on AFS of the Senegalese Peanut Basin. Assuming that AFS landscapes with similar trees and crop cover composition will have similar phenological development, a multiresolution segmentation was performed on Sentinel-2 NDVI time series to obtain homogeneous landscape units. Then for each unit, landscape diversity proxies were derived from various geospatial data sources, namely vegetation productivity and its temporal dynamic, actual evapotranspiration, woody cover rates and soil type. Using a hierarchical clustering, four classes of typical unit of the landscape heterogeneity gradient were obtained. On this basis an optimized sampling plan was produced and used to carry out an inventory campaign of tree biodiversity (figure). The results showed a well-defined landscape diversity gradient, confirmed by the field inventory of tree species.

Mots-clés Agrovoc : Agroforesterie, Utilisation des terres, Cartographie de l'occupation du sol, Cartographie de l' utilisation des terres, Télédétection

Mots-clés géographiques Agrovoc : Sénégal

Mots-clés libres : Remote sensing, Agroforestry system, Sampling strategy, Landscape diversity, Landscape classification

Classification Agris : F08 - Cropping patterns and systems
K10 - Forestry production
U30 - Research methods

Auteurs et affiliations

  • Ndao Babacar, CIRAD-PERSYST-UPR AIDA (FRA) - auteur correspondant
  • Leroux Louise, CIRAD-PERSYST-UPR AIDA (SEN) ORCID: 0000-0002-7631-2399
  • Diouf Abdoul Aziz, CSE [Centre de suivi écologique] (SEN)
  • Soti Valérie, CIRAD-PERSYST-UPR AIDA (SEN)
  • Sambou Bienvenu, ISE [Institut des sciences de l'environnement] (SEN)

Autres liens de la publication

Source : Cirad-Agritrop (https://agritrop.cirad.fr/592669/)

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