Evidence map›Paper›PMID 41233391›Full record

ArticleScientific data2025

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

Maxim Neumann, Anton Raichuk, Yuchang Jiang, Mélanie Rey, Radost Stanimirova, Michelle J Sims, Sarah Carter, Elizabeth Goldman, Keith Anderson, Petra Poklukar and 7 more

Abstract readDataset
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Maxim NeumannGoogle DeepMind, Zurich, Switzerland. maximneumann@google.com.ORCID 0000-0002-1967-318X
Anton RaichukGoogle DeepMind, Zurich, Switzerland.
Yuchang JiangGoogle DeepMind, Zurich, Switzerland.
Mélanie ReyGoogle DeepMind, Zurich, Switzerland.
Radost StanimirovaWorld Resources Institute, Washington DC, USA.ORCID 0000-0001-9617-5830
Michelle J SimsWorld Resources Institute, Washington DC, USA.ORCID 0009-0003-3771-6424
Sarah CarterWorld Resources Institute, Washington DC, USA.
Elizabeth GoldmanWorld Resources Institute, Washington DC, USA.
Keith AndersonGoogle DeepMind, Zurich, Switzerland.
Petra PoklukarGoogle, Mountain View, CA, USA.
Katelyn TarrioGoogle, Mountain View, CA, USA.
Myroslava LesivInternational Institute for Applied Systems Analysis, Laxenburg, Austria.ORCID 0000-0001-9846-3342
Steffen FritzInternational Institute for Applied Systems Analysis, Laxenburg, Austria.ORCID 0000-0003-0420-8549
Nicholas ClintonGoogle, Mountain View, CA, USA.
Charlotte StantonGoogle Research, Mountain View, CA, USA.
Dan MorrisGoogle Research, Mountain View, CA, USA.
Drew PurvesGoogle DeepMind, Zurich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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%.

Indexed as

Conservation of Natural ResourcesEnvironmental MonitoringForestsBiodiversity

Identifiers

PMID41233391
PMCPMC12615789

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.