A gridded dataset of European Forest Types to support forest monitoring, modelling and reporting

Giannetti, Francesca; Zorzi, Ilaria; Linser, Stefanie; Neumann, Mathias; Cheval, Sorin; Collalti, Alessio; Vangi, Elia; Grieco, Elisa; Morichetti, Mauro; D’Amico, Giovanni; Tudose, Nicu Constantin; Ludvig, Alice; Passarino, Livia; Scriva, Jessica; Giambastiani, Yamuna; Fattoretto, Irene; Secchi, Giuliano; Travaglini, Davide; Chirici, Gherardo; Corona, Piermaria; Marchetti, Marco; Barbati, Anna

doi:10.5194/essd-2026-112

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https://doi.org/10.5194/essd-2026-112

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18 Mar 2026

| 18 Mar 2026

Status: this preprint is currently under review for the journal ESSD.

A gridded dataset of European Forest Types to support forest monitoring, modelling and reporting

Francesca Giannetti, Ilaria Zorzi, Stefanie Linser, Mathias Neumann, Sorin Cheval, Alessio Collalti, Elia Vangi, Elisa Grieco, Mauro Morichetti, Giovanni D’Amico, Nicu Constantin Tudose, Alice Ludvig, Livia Passarino, Jessica Scriva, Yamuna Giambastiani, Irene Fattoretto, Giuliano Secchi, Davide Travaglini, Gherardo Chirici, Piermaria Corona, Marco Marchetti, and Anna Barbati

Abstract. A standardized system of nomenclature for forest types is essential for effectively monitoring and understanding the impacts of climate change on diverse ecosystems in Europe and beyond. A comprehensive classification system, such as the European Forest Types (EFTs) scheme, is essential for assessing baseline conditions, tracking changes, and guiding conservation decisions. A unified forest type nomenclature supports international collaboration, enables researchers and policymakers to accurately compare data across regions and time periods, and enhances the development of targeted conservation strategies and adaptive management practices aimed at preserving biodiversity and ecosystem services. This classification breaks down forested areas in Europe into a handful of ecologically homogeneous units, thus facilitating the analysis of data related to forest conditions and management practices across a wide range of climatic and edaphic conditions. The current lack of an EFTs map for Europe prompted its processing, using a shared rule-based expert system algorithm. Utilizing a dataset featuring 39 "relative probability of presence (RPP) maps” of tree species and various forest masks, the algorithm identified 14 EFT categories. This initiative filled a critical gap in spatial monitoring, providing the first consistent pan-European EFT maps gridded dataset. The availability of standardized and comprehensive spatial data on forest types enhances our capacity to understand, manage, and conserve forest ecosystems effectively. Such data support biodiversity conservation and ensure the sustained provision of essential ecosystem services, highlighting the critical role of forest types in maintaining ecological balance and supporting human well-being.

How to cite. Giannetti, F., Zorzi, I., Linser, S., Neumann, M., Cheval, S., Collalti, A., Vangi, E., Grieco, E., Morichetti, M., D’Amico, G., Tudose, N. C., Ludvig, A., Passarino, L., Scriva, J., Giambastiani, Y., Fattoretto, I., Secchi, G., Travaglini, D., Chirici, G., Corona, P., Marchetti, M., and Barbati, A.: A gridded dataset of European Forest Types to support forest monitoring, modelling and reporting, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2026-112, in review, 2026.

Received: 09 Feb 2026 – Discussion started: 18 Mar 2026

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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RC1:
'Comment on essd-2026-112', Anonymous Referee #1, 29 Apr 2026
This study presents the high-resolution (100 m) gridded dataset of European Forest Types (EFTs) covering 39 countries, following the EEA classification system. The authors leverage an expert-system approach by integrating tree species Relative Probability of Presence (RPP) data with environmental masks. While the methodology builds on established classification rules, the manuscript suffers from critical weaknesses regarding data scaling, validation independence, and the handling of mixed or non-zonal forest categories.
The core input data (JRC RPP maps) has a native resolution of 1 km, yet the output product is presented at 100 m. The manuscript does not provide a scientifically rigorous justification for this downscaling. Simply resampling a 1-km probability layer to 100 m does not add ecological information; it merely creates a smoother visual. This discrepancy raises concerns about "false precision," where users might assume the map captures stand-level heterogeneity that the underlying data simply cannot support.

The validation relies heavily on the ICP BIOSOIL dataset, which is relatively sparse compared to the vast heterogeneity of European forests. To be truly "fatal," the absence of validation against independent, plot-level National Forest Inventory (NFI) data—which is the gold standard for forest type reporting in Europe—severely limits the credibility of the accuracy assessment. Without NFI-based cross-validation, the reported accuracy levels may be overly optimistic.

The EFT classification rules (e.g., defining a forest as "Beech forest") are based on basal area or crown cover dominance. However, the input RPP data represents the relative probability of occurrence. A species might have a high probability of being present in a pixel without being the dominant species in terms of biomass or structure. The paper fails to bridge this conceptual gap, leading to potential systematic misclassification in mixed-species regions.

Category 14 is ecologically and economically vital but notoriously difficult to map. The current methodology relies on tree species lists that often overlap with native ranges or historical naturalizations. If the RPP data cannot distinguish between a natural stand and a plantation (e.g., Pseudotsuga menziesii native conifers in certain contexts), the 4.2% coverage reported for Category 14 may be significantly under- or over-estimated. A more robust spatial "masking" for plantations is needed.

The Mediterranean forest types (Categories 8 and 9) are characterized by high horizontal and vertical complexity. The expert system approach used here appears too rigid for these "soft" ecotones. The manuscript lacks a sensitivity analysis on how the 14 rules perform in transition zones where multiple species have similar RPP values, likely leading to high "pixel-flipping" instability.

Mire/Peatland forests (Cat 11) and Floodplain forests (Cat 12) are determined by edaphic/hydrological conditions, not just species presence. While the authors mention "environmental masks," the resolution and accuracy of these masks (e.g., soil moisture, peatland maps) are not sufficiently detailed. If the mask is inaccurate, the model will default to a zonal forest type (like Beech or Spruce), leading to a significant loss of rare and protected habitat data.

For a dataset intended for "modelling and reporting," providing a single categorical map is insufficient. Users need an Uncertainty Map (e.g., Shannon Entropy or probability margins between the first and second most likely EFT). Without this, the dataset acts as a "black box," where a pixel classified as "Category 1" with 51% certainty is treated the same as one with 99% certainty.

The Copernicus Forest Type (FTY) product already provides a 10m/20m Broadleaved/Coniferous split. This study must demonstrate that its 100m EFT product is consistent with the established Copernicus products. If the aggregated EFTs (e.g., all needle-leaf categories) show major spatial disagreements with the Copernicus HRL, the validity of the expert system rules or the RPP inputs must be questioned. A formal "agreement analysis" with Copernicus is missing.
Citation: https://doi.org/10.5194/essd-2026-112-RC1
RC2:
'Comment on essd-2026-112', Anonymous Referee #2, 18 May 2026
This study presents a pan-European gridded dataset with 14 European Forest Types (EFTs), generated by integrating multiple existing datasets and a rule-based expert system to identify forest types across Europe. The topic is important and timely, and I appreciate the significant effort invested in producing such a large-scale and harmonized map product. However, the manuscript currently suffers from issues related to readability and the clarity of several claims. More importantly, the paper lacks a sufficiently rigorous validation of the generated maps, as well as a deeper discussion of uncertainties and limitations. Given that the main contribution is the map product itself, a more concrete quantitative evaluation is necessary to support the reliability and usefulness of the dataset.
Major comments
The readability of the introduction could be improved. Some sentences are difficult to follow, for example around line 65 (“break down data ... to a higher level of information”), which appears semantically unclear. There are also phrases: “such as for example...” and “it is important to highlighted that...”. Consider revising the text for clarity and conciseness.

In Section 2, when introducing the EFT classes, the authors should provide the actual forest type names (e.g., Boreal forest) rather than only referring to “Type 1–14”. This would make the manuscript more self-contained and easier to follow.

Since the Copernicus Forest Types map is used in generating the final product, it should also be properly introduced in the data section. Currently, this dataset is missing from the dataset description.

It is unclear how the final product can reliably be considered a 100 m resolution map, as there are various layers with different resolution involved in the map generation pipeline.

Figure 2 is not properly referred to or discussed in the main text. In addition, the labels are difficult to read, and the figure lacks a clear legend or explanation of the colored layers.

The manuscript lacks a thorough and high-quality validation of the final map product. In the results section, the new map is mainly compared with the FOREST EUROPE:
If FOREST EUROPE is used as the main reference dataset, it should be properly introduced, including information about its generation process, intended use, and known accuracy, so that the comparison can be better interpreted.

The current comparison appears more like a map consistency check rather than a proper accuracy assessment. Ideally, map validation should rely on high-quality independent reference data like NFI rather than comparison with another existing map product.

The readability and organization of the results section could also be improved.

In the discussion section, the authors mention the usability of the map as one of the key points. This part could be strengthened by including more concrete application examples or user scenarios.

Minor comments
A comma appears to be missing around line 50: “ecosystem classification systems, those linked …”

Table 1 is mentioned later in the manuscript than Table 2.
Citation: https://doi.org/10.5194/essd-2026-112-RC2

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https://doi.org/10.5194/essd-2026-112-supplement

Data sets

European Forest Types gridded dataset Francesca Giannetti, Ilaria Zorzi, Stefanie Linser, Mathias Neumann, Sorin Cheval, Alessio Collalti, Elia Vangi, Elisa Grieco, Mauro Morichetti, Giovanni D’Amico, Nicu Constantin Tudose, Alice Ludvig, Livia Passarino, Jessica Scriva, Yamuna Giambastiani, Irene Fattoretto, Giuliano Secchi, Davide Travaglini, Gherardo Chirici, Piermaria Corona, Marco Marchetti, Anna Barbati https://doi.org/10.5281/zenodo.18496150

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This study provides the first consistent map of forest types across Europe. By combining tree species information and forest maps using a transparent method, we identified major forest types across the continent. The dataset supports better forest monitoring, conservation planning, and policy decisions to protect biodiversity and ecosystem services.


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