Mapping the abundance of multipurpose agroforestry Faidherbia albida trees in Senegal

Lu Tingting, Brandt Martin, Tong Xiaoye, Hiernaux Pierre, Leroux Louise, Ndao Babacar, Fensholt Rasmus. 2022. Mapping the abundance of multipurpose agroforestry Faidherbia albida trees in Senegal. Remote Sensing, 14 (3), n.spéc. Mapping Tree Species Diversity:662, 16 p.

https://doi.org/10.3390/rs14030662

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Url - jeu de données - Dataverse Cirad : https://doi.org/10.18167/DVN1/OAYDV3 / Url - jeu de données - Dataverse Cirad : https://doi.org/10.18167/DVN1/RKZ5DN

Résumé : Multi-purpose Faidherbia albida trees represent a vital component of agroforestry parklands in West Africa as they provide resources (fodder for livestock, fruits and firewood) and support water lifting and nutrient recycling for cropping. Faidherbia albida trees are characterized by their inverse phenology, growing leaf flowers and pods during the dry season, thereby providing fodder and shedding leaves during the wet season, which minimizes competition with pastures and crops for resources. Multi-spectral and multi-temporal satellite systems and novel computational methods open new doors for classifying single trees and identifying species. This study used a Multi-Layer Perception feedforward artificial neural network to classify pixels covered by Faidherbia albida canopies from Sentinel-2 time series in Senegal, West Africa. To better discriminate the Faidherbia albida signal from the background, monthly images from vegetation indices were used to form relevant variables for the model. We found that NDI54/NDVI from the period covering onset of leaf senescence (February) until end of senescence (leaf-off in June) to be the most important, resulting in a high precision and recall rate of 0.91 and 0.85. We compared our result with a potential Faidherbia albida occurrence map derived by empirical modelling of the species ecology, which deviates notably from the actual species occurrence mapped by this study. We have shown that even small differences in dry season leaf phenology can be used to distinguish tree species. The Faidherbia albida distribution maps, as provided here, will be key in managing farmlands in drylands, helping to optimize economic and ecological services from both tree and crop products.

Mots-clés Agrovoc : télédétection, distribution géographique, cartographie de l'occupation du sol, agroforesterie, systèmes agroforestiers, Faidherbia albida, modélisation environnementale, phénologie

Mots-clés géographiques Agrovoc : Sénégal

Mots-clés libres : Agroforestry system, Parklands, Remote Sensing, Senegal, Ecological modelling

Classification Agris : U30 - Méthodes de recherche
F70 - Taxonomie végétale et phytogéographie
F40 - Écologie végétale

Champ stratégique Cirad : CTS 5 (2019-) - Territoires

Agences de financement européennes : European Research Council, European Commission

Programme de financement européen : H2020

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