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Natural forests of the world - a 2020 baseline for deforestation and degradation monitoring

Natural forests of the world - a 2020 baseline for deforestation and degradation monitoring

This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint.

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Authors

Maxim Neumann , Anton Raichuk, Radost Stanimirova, Michelle Sims , Sarah Carter, Elizabeth Goldman, Melanie Rey, Yuchang Jiang, Keith Anderson, Petra Poklukar, Katelyn Tarrio, Myroslava Lesiv, Steffen Fritz, Nicholas Clinton, Charlotte Stanton, Dan Morris, Drew Purves

Abstract

Informed decisions to reduce deforestation, protect biodiversity, and curb carbon emissions require not just knowing where forests are, but understanding their composition. Identifying natural forests, which serve as critical biodiversity hotspots and major carbon sinks, is particularly valuable. We developed a novel global natural forest map for 2020 at 10 m resolution. This map can support initiatives like the European Union's Deforestation Regulation (EUDR) and other forest monitoring or conservation efforts that require a comprehensive baseline for monitoring deforestation and degradation. The globally consistent map represents the probability of natural forest presence, enabling nuanced analysis and regional adaptation for decision-making. Evaluation using a global independent validation dataset demonstrated an overall accuracy of about 92%.

DOI

https://doi.org/10.31223/X5ZX6P

Subjects

Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Environmental Sciences, Geographic Information Sciences, Remote Sensing

Keywords

Natural forests, forest mapping, global forest monitoring, Earth Observation, remote sensing, EU Deforestation Regulation (EUDR), machine learning, Deep learning, time series

Dates

Published: 2025-04-29 13:04

Last Updated: 2025-04-29 13:04

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License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
None