More than one quarter of Africa’s tree cover is found outside areas previously classified as forest

The consistent monitoring of trees both inside and outside of forests is key to sustainable land management. Current monitoring systems either ignore trees outside forests or are too expensive to be applied consistently across countries on a repeated basis. Here we use the PlanetScope nanosatellite constellation, which delivers global very high-resolution daily imagery, to map both forest and non-forest tree cover for continental Africa using images from a single year. Our prototype map of 2019 (RMSE = 9.57%, bias = −6.9%). demonstrates that a precise assessment of all tree-based ecosystems is possible at continental scale, and reveals that 29% of tree cover is found outside areas previously classified as tree cover in state-of-the-art maps, such as in croplands and grassland. Such accurate mapping of tree cover down to the level of individual trees and consistent among countries has the potential to redefine land use impacts in non-forest landscapes, move beyond the need for forest definitions, and build the basis for natural climate solutions and tree-related studies.

3 nature portfolio | reporting summary March 2021 Behavioural & social sciences study design All studies must disclose on these points even when the disclosure is negative. Briefly describe the study type including whether data are quantitative, qualitative, or mixed-methods (e.g. qualitative cross-sectional, quantitative experimental, mixed-methods case study).
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This study uses 3 m resolution satellite imagery to map tree cover across continental Africa. More than 230 000 individual PlanetScope scenes were combined to form analysis ready mosaics, and then a deep learning method was used to segment tree canopy cover in the images. The model was trained using manually annotated tree canopy samples, and is able to map non-forest trees as individuals. The produced tree cover map was compared with Lidar field data and existing tree cover products, as well as auxiliary datasets such as land cover and rainfall.
The data used for this study consisted of 3 m, 4-band PlanetScope satellite imagery, covering the study area of continental Africa. No restricted sampling strategy was used, as data was prepared to cover all parts of the study area, with the exception of large parts of the Sahara which were masked out, as well as lakes and rivers which were excluded with a water mask. The PlanetScope imagery was obtained from Planet Labs via the NICFI program, with raw scenes downloaded via the Planet API and then merged together in 1 x 1 degree composite mosaics.
No specific sampling strategy was followed as all tree cover in the study area was included.
The imagery data was downloaded from Planet Inc via the Planet API by Florian Reiner and Martin Brandt. Custom python code was developed to download raw scenes from the API to cover the entire study area (see Code Availability section).
The timeframe of the data is 2019, with the majority of scenes downloaded between January 1st 2019, and December 31st 2019. The daterange of each mosaic is specific to the phenology of that region, such that trees are in full foliage while interference from grasses in minimised. The spatial scale of the data covers continental Africa.
Of all PlanetScope images available, scenes were filtered by date range (using the phenology-determined temporal window), and then by quality, using API filters such as percent clouds, haze and visual confidence. Of the selected scenes, only a subset is used to create a gapless mosaic, with the scene footprints being clipped before download to reduce redudant downloads.
With the deep learning framework used, prediction of tree cover for a given mosaic is deterministic, i.e. predicting the same image several times will always give the same result.
NA. All tree cover results were used for the derived statistics by rainfall, land cover etc.
NA. All tree cover results were used for the derived statistics by rainfall, land cover etc.

March 2021
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Palaeontology and Archaeology
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March 2021
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March 2021
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