Mapping the spatial extent of agroforestry lands across Africa by characterizing tree spatial arrangements

Agroforestry is a land-use practice that integrates trees into agricultural lands, supporting food security and rural livelihoods while delivering both climate adaptation and mitigation benefits. However, current large-scale assessments often approximate the spatial extent of agroforestry using coarse tree canopy cover or biomass products that cannot characterize tree spatial arrangements within croplands, potentially obscuring structurally distinct agroforestry systems. Here, we produced a 100 m resolution map of the spatial extent of agroforestry lands in Africa for 2019, using 10 m resolution tree canopy cover data derived from PlanetScope, together with Sentinel-2 imagery. This map explicitly delineates croplands where agroforestry systems are practised by distinguishing tree spatial patterns within croplands, focusing on irregularly scattered trees and trees aligned in rows. Scattered trees were identified using tree-pixel distance metrics, whereas trees planted in rows were detected using a U-Net model trained with ∼38,000 manually labelled tree-row features. The identified agroforestry lands with scattered trees and trees in rows on cropland were validated against ground-based sites collected from the literature and Google Earth imagery, yielding an overall accuracy of 77.2%. This agroforestry dataset with explicit spatial tree arrangements can serve as a baseline for assessing the environmental impacts and changes in crop yield associated with agroforestry ecosystems, as well as for supporting the development of process-based agroforestry models.

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Publication Details

Journal
International Journal of Applied Earth Observation and Geoinformation
Published
2026-10-29
DOI
https://doi.org/10.1016/j.jag.2026.105644
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00

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article

Mapping the spatial extent of agroforestry lands across Africa by characterizing tree spatial arrangements

Martin Brandt, David Makowski, Minxuan Sun, Florian Reiner et al.
1 links
International Journal of Applied Earth Observation and Geoinformation
Remote Sensing in Agriculture
article

Mapping the spatial extent of agroforestry lands across Africa by characterizing tree spatial arrangements

Martin Brandt, David Makowski, Minxuan Sun, Florian Reiner, Youngryel Ryu, Yidi Xu, Wei Li, Xu Jianchu, Zhixuan Guo, Xiaomeng Du, P. Ciais, Xin Jiao, Yi Leng, Mengjie Han, Jingbo Chen, Jiaying He
article en

Abstract

Agroforestry is a land-use practice that integrates trees into agricultural lands, supporting food security and rural livelihoods while delivering both climate adaptation and mitigation benefits. However, current large-scale assessments often approximate the spatial extent of agroforestry using coarse tree canopy cover or biomass products that cannot characterize tree spatial arrangements within croplands, potentially obscuring structurally distinct agroforestry systems. Here, we produced a 100 m resolution map of the spatial extent of agroforestry lands in Africa for 2019, using 10 m resolution tree canopy cover data derived from PlanetScope, together with Sentinel-2 imagery. This map explicitly delineates croplands where agroforestry systems are practised by distinguishing tree spatial patterns within croplands, focusing on irregularly scattered trees and trees aligned in rows. Scattered trees were identified using tree-pixel distance metrics, whereas trees planted in rows were detected using a U-Net model trained with ∼38,000 manually labelled tree-row features. The identified agroforestry lands with scattered trees and trees in rows on cropland were validated against ground-based sites collected from the literature and Google Earth imagery, yielding an overall accuracy of 77.2%. This agroforestry dataset with explicit spatial tree arrangements can serve as a baseline for assessing the environmental impacts and changes in crop yield associated with agroforestry ecosystems, as well as for supporting the development of process-based agroforestry models.

International Journal of Applied Earth Observation and GeoinformationVol. 154
University of Copenhagen (DK), Centre National de la Recherche Scientifique (FR), Ministry of Education of the People's Republic of China (CN), Kunming Institute of Botany (CN), Seoul National University (KR), Université de Versailles Saint-Quentin-en-Yvelines (FR), Chinese Academy of Sciences (CN), AgroParisTech (FR), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Université Paris-Saclay (FR), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Laboratoire des Sciences du Climat et de l'Environnement (FR), Aerospace Information Research Institute (CN), Institute of Geographic Sciences and Natural Resources Research (CN), State Key Laboratory of Resources and Environmental Information System (CN), China Agricultural University (CN), Tsinghua University (CN)
National Natural Science Foundation of China, Tsinghua University, Yunnan Provincial Science and Technology Department
Life on land, Zero hunger, Climate action
Openalex Percentile: Top 17%
Remote Sensing in Agriculture
DOIOpen Access PDFOpenAlex
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