Preprints
https://doi.org/10.5194/essd-2026-269
https://doi.org/10.5194/essd-2026-269
24 Jun 2026
 | 24 Jun 2026
Status: this preprint is currently under review for the journal ESSD.

Annual forest cover maps in Africa during 2015–2023 by analyses of PALSAR-2, Landsat, and GEDI LiDAR datasets with knowledge-based algorithms

Yuan Yao, Xiangming Xiao, Chenchen Zhang, Hao Tang, Yuanwei Qin, Cheng Meng, Baihong Pan, Li Pan, and Jie Wang

Abstract. 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.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
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Yuan Yao, Xiangming Xiao, Chenchen Zhang, Hao Tang, Yuanwei Qin, Cheng Meng, Baihong Pan, Li Pan, and Jie Wang

Status: open (until 31 Jul 2026)

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Yuan Yao, Xiangming Xiao, Chenchen Zhang, Hao Tang, Yuanwei Qin, Cheng Meng, Baihong Pan, Li Pan, and Jie Wang

Data sets

Annual forest cover maps for Africa from 2015 to 2023 at 30 m spatial resolution by analyses of PALSAR-2 and Landsat datasets Y. Yao et al. https://doi.org/10.5281/zenodo.19464248

Model code and software

The Google Earth Engine code for generating annual foerst cover map in Africa Y. Yao et al. https://doi.org/10.5281/zenodo.19477789

Yuan Yao, Xiangming Xiao, Chenchen Zhang, Hao Tang, Yuanwei Qin, Cheng Meng, Baihong Pan, Li Pan, and Jie Wang
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Latest update: 25 Jun 2026
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Short summary
Africa has experienced rapid forest loss, yet existing maps often disagree. Here, we produce annual forest cover maps at 30 m resolution from 2015 to 2023 using multiple satellite data and expert knowledge and compare them with other forest datasets. We find that forest area estimates of satellite-based products are larger than the Global Forest Resource Assessment statistics. Our results improve confidence in forest monitoring and support better understanding of forest change across Africa.
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