ESSDD Earth System Science Data Discussions ESSDD Earth Syst. Sci. Data Discuss. 1866-3591 Göttingen, Germany 10.5194/essd-2026-269 Annual forest cover maps in Africa during 2015–2023 by analyses of PALSAR-2, Landsat, and GEDI LiDAR datasets with knowledge-based algorithms Yao Yuan 1 Xiao Xiangming 1 Zhang Chenchen https://orcid.org/0000-0003-0458-6232 1 Tang Hao https://orcid.org/0000-0001-7935-5848 2 3 Qin Yuanwei 4 Meng Cheng 1 Pan Baihong 1 Pan Li 1 Wang Jie https://orcid.org/0000-0003-1866-3999 5 School of Biological Sciences, University of Oklahoma, Norman, OK 73019, USA Department of Geography, National University of Singapore, Singapore 117570, Singapore Centre for Nature-based Climate Solutions, National University of Singapore, Singapore 117546, Singapore Jiangsu Key Laboratory of Soil and Water Processes in Watershed, College of Geography and Remote Sensing, Hohai University, Nanjing 211100, China College of Grassland Science and Technology, China Agricultural University, Beijing 100193, China 24 06 2026 2026 1 27 Copyright: © 2026 Yuan Yao et al. 2026 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://essd.copernicus.org/preprints/essd-2026-269/ The full text article is available as a PDF file from https://essd.copernicus.org/preprints/essd-2026-269/essd-2026-269.pdf

According to the Food and Agriculture Organization of the United Nations (FAO) Global Forest Resources Assessment (FRA) 2020 report, Africa has the highest annual net forest loss rate worldwide during 2010–2020, approximately 50 % higher than that of South America. Multiple high-resolution forest cover data products derived from optical and/or microwave remote sensing data show large discrepancies in forest area estimates and spatial distribution in Africa. To date, few studies have evaluated these datasets using the FAO forest definition and consistent assessment data. Here, we generate annual forest cover maps in Africa at 30 m resolution from 2015 to 2023 using Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2), Landsat imagery, and knowledge-based algorithms. We compare the resulting PALSAR-2/Landsat forest/non-forest (FNF) maps with four widely used forest datasets: (a) Landsat tree canopy cover from Global Forest Watch (Landsat-GFW), (b) PALSAR/PALSAR-2 Forest/Non-Forest Map from the Japan Aerospace Exploration Agency (JAXA FNF4), (c) global map of forest cover 2020 from the European Commission Joint Research Centre (JRC GFC2020 v2), and (d) the FAO FRA 2020 forest statistics. Using the criteria of FAO forest definition (canopy height > 5 m; canopy cover > 10 %), we assess four satellite-based forest cover products for 2020 using canopy height and canopy cover measurements derived from spaceborne light detection and ranging (LiDAR) measurements in the NASA Global Ecosystem Dynamics Investigation (GEDI) mission. We find that PALSAR-2/Landsat FNF, JAXA FNF4, and JRC GFC2020 v2 have high and consistent overall accuracy (OA; approximately 90 %), whereas Landsat-GFW (tree cover > 10 %) has substantially lower accuracy (69 %). Forest area estimates for 2020 from PALSAR-2/Landsat (8.8 × 10⁶ km²), JAXA FNF4 (8.9 × 10⁶ km²), and JRC GFC2020 v2 (7.6 × 10⁶ km²) are larger than the FRA 2020 statistics (6.4 × 10⁶ km²). Forest area and spatial distribution from PALSAR-2/Landsat FNF are most consistent with those from JAXA FNF4, followed by JRC GFC2020 v2. Landsat-GFW (16.7 × 10⁶ km²) differs substantially from the other three products. Our annual forest cover maps complement FRA reporting and support a better understanding of the magnitude, dynamics, and drivers of forest gain and loss across Africa.

 National Science Foundation 1946093