the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 11 Aug 2025
GFC2020: A Global Map of Forest Land Use for year 2020 to Support the EU Deforestation Regulation
Abstract. Earth observation (EO) data are used to map tree cover extent, estimate canopy height, detect disturbances, and classify land cover and land use. However, comprehensive global information on forest cover, capturing both physical characteristics and land use components as defined by the United Nations Food and Agriculture Organization (FAO), remains limited. Here, we present a harmonized and globally consistent map of forest presence or absence at 10 meter spatial resolution for the year 2020, hereafter referred to as GFC2020. Our approach combines multiple spatial datasets, primarily derived from EO, to harness their complementary strengths within a transparent, flexible, and open science framework. GFC2020 maps 4,562 million hectares (Mha) of forests globally, which is 12 % more than the estimate from latest FAO Global Forest Resources Assessment (FRA). Approximately 11 % (~578 Mha) of tree cover is excluded from forest area in GFC2020, primarily because it does not meet the height threshold or occurs on agricultural or urban land. Conversely, around 0.6 % (~25 Mha) of the area classified as forest in GFC2020 is unstocked, due to forest management practices or natural disturbances such as fire. Based on the reinterpretation of an existing reference set of 21,752 sample units, GFC2020 achieves an overall accuracy of 91 %, with 18 % probability of overestimating the forest area and 8 % for underestimation. Future improvements in EO products, such as better detection of trees in dry and open landscapes, distinguishing natural from human drivers of forest disturbance, mapping tree crops at high spatial resolution or identifying agroforestry systems, will contribute to enhancing future versions of GFC2020. The shift from tree cover to forest cover mapping is not only essential for ecological and climate related applications but also provides new opportunities to support policy needs. GFC2020 (https://forobs.jrc.ec.europa.eu/GFC) is one of many tools to inform the deforestation risk assessments under supply chain oriented regulations such as the European Union Deforestation Regulation (EUDR). This map is not mandatory, not exclusive and not legally binding.
- Preprint
(4122 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 21 Oct 2025)
- CC1: 'Comment on essd-2025-351', Meine van Noordwijk, 15 Aug 2025 reply
-
RC1: 'Comment on essd-2025-351', Anonymous Referee #1, 09 Sep 2025
reply
This paper describes a dataset which is hugely valuable to a wide range of users in light of the forthcoming EUDR implementation. The paper is well written and the methods and approach are robust and fit for purpose. The authors have identified the most appropriate datasets for the purpose of building a map which describes forest land use in the year 2020 – with some known limitations. The use of only datasets which are global in scope is an understandable approach, but the map could probably be improved if local/regional/national datasets were utilized, and this highlights the value of any global datasets which themselves incorporate local data bring to this mapping challenge.
Here are a few minor comments and reflections:
Line 23-24 - consider rephrasing to say "GFC does not include 578Mha of tree cover (11% of the global tree cover area) because it does not meet the height threshold or occurs on agricultural or urban land"
Would it be worth explaining the benefit of a conservative approach (higher commission vs omission error) in light of a more cautious approach for ensuring that products conform to EUDR? While acknowledging that this makes it more challenging for those who are demonstrating that their products are deforestation free.
Line 40 - would a broader term indigenous peoples and local communities make sense here?
Line 140 - can we add 'continued collaborations' because they are already ongoing
Is it possible to provide some more clarity on the forest land use interpretation - for example forest/logging roads, fire breaks, skid trails and other forest related activities. Are they marked as forest in your interpretation and in the validation?
It seems clear to me that the majority of the commission errors are in areas where potential production for EUDR commodities will occur. I wonder whether this needs to be more prominently mentioned.
Very high commission error in Africa – can be done about this in future?
Perhaps in the discussion you could mention the value of specific information on tree crops’ year of planting, to mitigate related limitations in the map.
Line 389 - thematic ambiguity by the interpreter - not sure what this completely means – can you elaborate or rephrase?
Perhaps worth a mention that EUDR also has a legality component which cannnot be addressed by this map?
Citation: https://doi.org/10.5194/essd-2025-351-RC1 -
RC2: 'Comment on essd-2025-351', Anonymous Referee #2, 10 Sep 2025
reply
General comments
This manuscript presents the GFC2020 map of forest land use for 2020 and provides a globally consistent 10 m resolution presence / absence product. The manuscript is very well written with methods and results thoroughly and clearly described. The description of the map and associated methodology is accompanied by a detailed and rigorous accuracy assessment. The accuracy assessment is based on a stratified sampling design and the authors report overall accuracy, user’s and producer’s accuracies, omission and commission error rates, and standard errors globally and by region. The effort and quality of the assessment are top tier.
I have a few minor technical suggestions or requests for clarification that I hope will aid the authors to further improve an already excellent manuscript. I will list the comments in order of appearance in the manuscript by line number rather than by importance.
Line26: This is very minor but the word “existing” is unnecessary because if the reference set does not exist, then it obviously cannot be used. Perhaps replace “exist” with “previously collected”?
L27: The phrasing “… with 18% probability of overestimating the forest area and 8% for underestimating” does not seem correct. First, it is unclear what the setting is for this probability. It would be plausible to frame this as selecting a unit (pixel) at random and asking, “What is the conditional probability that a randomly selected pixel that has reference class of forest is an omission error of forest?” I would claim that that conditional probability is 0.082 from the Table 2 error matrix (omission error rate of Forest). But the statement in the text is specifically about the probability of overestimating forest AREA. I don’t think there is a probability associated with overestimating forest area. The map shows 33.6% forest area and the reference data show 30.0% forest area, so the map overestimates forest area by 3.6% and there is no probability statement associated with that outcome.
L230-L235: Some additional details are needed to clarify the stratification, perhaps to allow the reader to know what was done without having to access Tsendbazar et al. (2020 and 2021). First, was the stratification applied to the 100m x 100m PSUs? Second, do the strata also include a feature related to the distribution of forest and non-forest? For example, at L232 it is stated that “Additional sample units were drawn for rare land cover classes.” Were these added from strata defined based on prevalence of these rare classes? A key feature of the stratification is that the strata are not simply forest and non-forest and this is what motivates use of Stehman (2014) for the estimation formulas rather than, say, Olofsson et al. (2014).
L292-293: This might not merit adding text to explain but I am confused why a sample unit cannot be associated with a strata class.
L294: replace “of” with “by”
L296-297: consider revising to “… and to account for unequal inclusion probabilities of sampled units.”
L306: It might be relevant to provide a formula for these weights? Also, if the pixels differ in area according to a latitudinal gradient, would that not also impact the sample-based estimates of accuracy and area because differences in area of the sampled pixels would need to be accounted for?
L361: Specifically, the “moderate overestimation” would be 3.6% according to Table 2.
L405: Figure 6a reports results in terms of number of sample units. Such an analysis ignores the sampling design and the necessary “weighting” of sample observations to produce estimates due to different sampling intensity in different strata. I would not advocate for re-analysis, but I do believe it is necessary to flag the analysis and alert readers that this particular set of results is not using the sample design information.
L455 uses “R-squared” and L442 uses “r^2”. It would be good to be consistent with notation.
L535: “potentials” should be “potential”
Citation: https://doi.org/10.5194/essd-2025-351-RC2 -
RC3: 'Comment on essd-2025-351', Anonymous Referee #3, 16 Sep 2025
reply
The study presented a harmonized and globally consistent map of forest cover at 10 meter spatial resolution. The proposed method is innovative and dataset is of high accuracy. However, there are still some problems that deserve to solve before publications.
(1) In the introduction part, the author should mention more details about data and methods used for forest mapping, as well as the deficiencies of the existing research.
(2) In the method part and figure 1, the authors emphasized more on the extraction and excluding rules for the forest mapping. I wonder how to conduct these complex rules.
(3) Multisource data may have different acquisition time and the tree cover may have some phenological features. Did the authors consider these?
(4) The consistency check and normal distribution test should be conducted for the sample construction.
(5) Some of the figures and tables are in poor quality and the authors should improve them. Some comparisons with existing products and deeper analysis of these difference are also necessary.
(6) There are still some grammatical and lingual problems, and authors should make a thorough revision.
Citation: https://doi.org/10.5194/essd-2025-351-RC3 -
RC4: 'Comment on essd-2025-351', Anonymous Referee #4, 23 Sep 2025
reply
Comment on "GFC2020: A Global Map of Forest Land Use for year 2020 to Support the EU Deforestation Regulation" by Bourgoin et al., ESSD
General comments:
This manuscript follows the definition of forest from EU Deforestation Regulation and constructed an updated forest cover dataset at 10-m resolution. It provides a good example of how to choose appropriate datasets, merge data products from multiple sources for dataset production and perform accuracy assessment. This paper is already well written and I can barely find any obvious errors. I have the following doubts and suggestions, mostly related to writing.
Specific comments:
Some of my major concerns are already conveyed through other reviewers so I here only have one major comment. Since authors emphasized the data processing, production and evaluation to be a general framework for producing other categorical maps in high resolution, I expect to see some further discussions about the potential usage of this framework in mapping other categorical variables. Maybe one example in 2-3 sentences will add more merits to the discussion section of this article.
Technical correction:
Line 16: EO refers to both remote sensing based and local experimental based approaches for measurement at different scales, which is quite a big concept. Here I believe the major datasets you used are RS based. I suggest changing it to RS based observations, or EO on a global scale.
Line 33: It will be good to emphasize in your abstract that your definition of forest follows European Union Deforestation Regulation (EUDR), from the very beginning of your article.
Line 118: Why do you need weight? Isn't the maximum extent to be the Union of all input products?
Line 120: Just want to confirm with the authors how you analyze historical tree cover loss?
Line 127: Should move references about expert judgement from Line 139 (see Bourgoin et al. 2024b; Colditz et al. 2024).) to here.
Line 140: "WRI, IIASA" Need full names.
Line 191: "we create a global cropland extent by combining" here combining means the union of the area extent from all data products or another approach?
Line 270: "labelled 'other wooded land' " Is the label information available together with the data on GEE? I did not find this information.
Line 302: "GAUL" shall add the full name "Global Administrative Unit Layers".
Figure 5: It might be good to tell readers the central coordinates of each subplot.
Table 3: Missing (CI) after overall accuracy.
Line 429: "generally align will with" shall be "generally align well with"
Citation: https://doi.org/10.5194/essd-2025-351-RC4
Data sets
Global Forest Cover 2020 Clement Bourgoin et al. https://forobs.jrc.ec.europa.eu/GFC
Validation dataset for the global map of forest cover 2020 – version 2 René Colditz et al. https://data.jrc.ec.europa.eu/dataset/8fbace34-a2fe-47b9-ad82-3e9226b7a9a6
Model code and software
Joint Research Centre – Global Forest Cover for year 2020, version 2. Code source Clément Bourgoin https://doi.org/10.6084/m9.figshare.29315528.v1
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,136 | 52 | 13 | 1,201 | 17 | 14 |
- HTML: 1,136
- PDF: 52
- XML: 13
- Total: 1,201
- BibTeX: 17
- EndNote: 14
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
In line 81-90 authors correctly clarify the challenge that ‘tree cover’ as observable characteristic of land differs from ‘forest’ as a land use category in policy designs and instruments. Yet, in subsequent text it seems that ‘tree cover’ is equated with ‘forest’.
Line 103-109 claims that the GFC2020 maps ‘align with’ EUDR and FAO forest definitions. The text acknowledges the challenges in this claim especially where tree crops are involved that are the primary concern of EUDR regulations, but don’t follow up on these concerns.
Specifically, a recent publication (van Noordwijk et al. 2025) suggested multiple types of evidence for an ‘agroforestry’ (and thus non-forest) status of land in an institutional interpretation of the EUDR and FAO forest definitions, regardless of tree cover. It would be appropriate if authors comment on these evidence categories and the way they Cn (or should) be taken into account if the target is to create an EUDR-policy relevant map of 2020 global forest cover.
The procedure described in lines 200-203 can deal with part of the ‘agroforestry’ area, but certainly not all.
Where the paper employs standard concepts of ‘producer’ and ‘user’ accuracy, van Noordwijk et al. (2025) used (and pleaded for wider use of) a more specific ‘user accuracy’ in the context of EUDR. The most relevant use of the maps in EUDR context is to evaluate whether or not tree crops (incl coffee, cocoa, rubber) marketed after 2025 were derived from land deforested before or after 2020. A ‘real user’ accuracy would assess the likelihood that plots known to already produce any of these commodities before 2020 is correctly classified as ‘non-forest’. A number of studies, incl van Noordwijk et al. (2025), but also studies for Cameroon, Ivory coats and Peru that are on their wway to publication, have found erroneous classification of such points to be around 60%.
Before recommending the use of the current GFC2020 map for EUDR implementation, this issue may need to be addressed (or at least acknowledged).
van Noordwijk, M., Dewi, S., Minang, P.A., Harrison, R.D., Leimona, B., Ekadinata, A., Burgers, P., Slingerland, M., Sassen, M., Watson, C. and Sayer, J., 2025. Beyond imperfect maps: Evidence for EUDR‐compliant agroforestry. People and Nature 7:1713–1723. https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1002/pan3.70088