Mapping the Abundance of Multipurpose Agroforestry Faidherbia albida Trees in Senegal

Tingting Lu,Xiaoye Tong,Martin Brandt,Louise Leroux,Babacar Ndao,Pierre Hiernaux,Rasmus Fensholt

doi:10.3390/rs14030662

Abstract

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.

Highlights

This is likely because the majority of the sampled pixels in Sentinel-2 images are mixed pixels, and the index value is the result of the reflectance from the tree canopy and a complex set of other covers underneath such as crops and herbaceous vegetation or bare soil, especially in the wet season when the Faidherbia albida trees are defoliated
A 10-m resolution Faidherbia albida canopy map was generated for Senegal based on a time series of Sentinel-2 images from the period of 2017 to 2019, used to form 12 monthly composites covering the phenological phases of the Faidherbia albida tree
Ground observations of tree species from two different regions in Senegal were used to generate a robust model mapping the occurrence of Faidherbia albida trees

Summary

Introduction

Faidherbia albida trees provide resources such as fodder for livestock, fruits, firewood, wood products for construction and traditional medicine [1,2,3,4,5,6]. Faidherbia albida trees contribute to symbiotic nitrogen fixation, and serve multiple ecosystem services, such as water lifting, nutrients recycling and carbon sequestration [7]. What makes Faidherbia albida trees distinct is their unique leaf phenology, which is characterized by leafing out during the dry season and shedding leaves in the rainy season.

Methods

Results

Discussion

Conclusion