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In this analysis, being forests the main objective of this study, the conditions of forests are disaggregated into the classes of forests obtained by combining the information described in this chapter of the guide.
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- Broad-leave forest (311) - https://land.copernicus.eu/content/corine-land-cover-nomenclature-guidelines/html/index-clc-311.html
- Coniferous forest (312) - https://land.copernicus.eu/content/corine-land-cover-nomenclature-guidelines/html/index-clc-312.html
- Mixed forest (313) - https://land.copernicus.eu/content/corine-land-cover-nomenclature-guidelines/html/index-clc-313.html
- Transitional woodland & shrub (324) - https://land.copernicus.eu/content/corine-land-cover-nomenclature-guidelines/html/index-clc-324.html
Bioregion zones
Such categories of forest were also combined with the information on the European bioregion zones, used to define 11 bioregions over:
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The Scandinavian Alpine zone was differentiated from the rest of the Alpine bioregion for its characteristics and covers the forest in the Scandinavian mountains on the border between Norway and Sweden.
The combination of landcover forests and bioregions results in 44 forest types, following the definition of Maes, J., Bruzón, A.G., Barredo, J.I. et al. Accounting for forest conditions in Europe based on an international statistical standard. Nat Commun 14, 3723 (2023).- https://doi.org/10.1038/s41467-023-39434-0
The following datasets were used to obtain this information:
- Forest landcovers from Corine year 2000 (vector/ https://land.copernicus.eu/en/products/corine-land-cover/clc-2000), and
- Corine accounting adjusted year 2000 (raster/ https://www.eea.europa.eu/en/datahub/datahubitem-view/a55d9224-a326-4cb1-9b9c-3a324520341a?activeAccordion=1069872%2C1069948);
- European bioregion zones (https://sdi.eea.europa.eu/data/def7ac06-7d3f-4da5-880c-a76a73953cfc).
Forest ecosystem condition variables
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| Ecosystem Typology Group | Ecosystem Typology Class | Typology class description | Variable | Variable description and link to dataset or DOI | Spatial Resolution | Spatial Extent | Temporal resolution/ coverage |
|---|---|---|---|---|---|---|---|
A. Abiotic ecosystem characteristics | A.1 Physical state | Physical descriptors of the abiotic components of the ecosystem (e.g. Soil structure, impervious surface, water availability) | A1.1 Normalized Difference Water Index | GEE Landsat 8 Collection 1 Tier 1 32-Day NDWI Composite These Landsat 8 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the computed top-of-atmosphere (TOA) reflectance. See Chander et al. (2009) for details on the TOA computation. | 30m | Global | 32-day 2013-2022 |
A.2 Chemical state | Chemical composition of the abiotic ecosystem compartments (e.g. Soil nutrient concentration, air and water quality) | A2.1 Soil Organic Carbon | 2003 OCTOP: Topsoil Organic Carbon Content for Europe - Organic carbon content in the first 30 cm of soil | 1km | Europe | 2003 | |
2014 LUCAS: Topsoil Soil Organic Carbon (LUCAS) for EU25 - Organic carbon content in the first 30 cm of soil | 500m | Europe | 2014 | ||||
B. Biotic ecosystem characteristics | B.1 Compositional state | Composition/diversity of the ecological communities at a given location and time (e.g. Presence/abundance of key species, species richness, genetic diversity, presence of threatened species, diversity/abundance of relevant species groups) | B1.1 Threatened Forest Bird Species Diversity | Population trend of bird species: datasets from Article 12, Birds Directive 2009/147/EC reporting (2008-2012) | 5km? | Europe | 2000 and 2008 |
B.2 Structural state | Aggregate properties (e.g. mass, density) of the whole ecosystem or its main biotic components (e.g. Total biomass, canopy coverage, annual maximum NDVI, Vegetation density, habitat structure, food chain and trophic levels) | B2.1 Above-ground biomass | ESA’s Climate Change Initiative Biomass ESA’s Climate Change Initiative Biomass project provides global maps of above-ground biomass (Mg ha-1), with these being capable of supporting quantification of biomass change. | 100m | Global | 2010, 2017-2020 | |
B2.2 Leaf Area Index | Copernicus leaf area index The LAI quantifies the thickness of the vegetation cover and it's recognized as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). | 300m | Global | 2014 to present | |||
| 1km | Global | 1999 to present | |||||
B2.3 Tree cover density | Copernicus HRL tree cover density layer It offers high-resolution information on the percentage of tree cover in a given area | 100m | Europe | 2012, 2015, 2018 | |||
B.3 Functional state | Summary statistics (e.g. frequency, intensity) of the biological, chemical, and physical interactions between the main ecosystem compartments (e.g. Primary productivity, community age, distribution frequency, decomposition processes) | B3.1 Net Primary Production | Dry Matter Productivity and Net Primary Production The ecosystem Net Primary Production (NPP) represents the net growth rate of the vegetation(excluding autotrophic respiration), expressed in kilograms of production per hectare per day (gC/m²/day). It is a proportion of Gross Primary Production and it's directly related to ecosystem Dry Matter Productivity, expressed in kgNPP/ha/day. | 300m | Global | 2014 to 2020 | |
| 1km | Global | 1999 to 2020 | |||||
B3.2 Fraction of Photosynthetically active radiation | https://docs.terrascope.be/DataProducts/Sentinel-2/references/VITO_S2_ATBD_S2_NDVI_BIOPAR_V200.pdf The FPAR (Fraction of Photosynthetically active radiation) quantifies the fraction of the solar radiation absorbed by live leaves for photosynthesis activity. Then, it refers only to the green and alive elements of the canopy. The FAPAR depends on the canopy structure, vegetation element optical properties, atmospheric conditions, and angular configuration. To overcome this latter dependency, a daily integrated FAPAR value is assessed. | 10m | Europe | ||||
B3.3 Burned severity | MOSEV: A global burn severity database from MODIS (2000-2020) - Global Patterns and Dynamics of Burned Area and Burn Severity (developed from MOSEV data) | 500m | global | Annual 2000-2020 | |||
B3.4 Drought Severity | https://crudata.uea.ac.uk/cru/data/drought/#global Self-calibrating Palmer Drought Severity Index (scPDSI): The scPDSI metric was introduced by Wells et al. (2004), who gave detailed information about its calculation. The scPDSI is a variant of the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from a time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. | 0,5 degree | Global | Annual / 1998 to 2018 | |||
B3.4 Drought Severity (2nd variable - alternative) | Combined drought indicator (v3) - A combination of spatial patterns of precipitation, soil moisture, and greenness vegetation anomalies, the CDI identifies areas at risk of agricultural drought. | 5 km | Europe | 10 day observations 2012-2023 | |||
B3.5 Normalized Difference Vegetation Index | https://land.copernicus.eu/global/products/ndvi The Normalized Difference Vegetation Index (NDVI) is an indicator of the greenness of the biomes. | 300m | Global | 3 day observations 2020 to present | |||
| 1km | Global | 3 day observations 1998 to 2020 | |||||
B3.6 Green index | Might be added later in the project | - | - | - | |||
B3.7 Fire occurrence | Might be added later in the project | - | - | - | |||
C. Landscape level characteristics | C.1 Landscape and seascape at coarse scale | Metrics describing mosaics of ecosystem types at coarse (such as landscape and seascape) spatial scale (e.g. Landscape diversity, connectivity, fragmentation, ecosystem type mosaics) | C1.1 Forest Connectivity | Generated using GUIDOS toolbox and Corine landcover daatset - Methodology | 100m | Europe | 2000, 2006, 2012, 2018 |
C1.2 Landscape Naturalness | Generated using GUIDOS toolbox and Corine landcover daatset - Methodology | 100m | Europe | 2000, 2006, 2012, 2018 | |||
C1.1 Forest Fragmentation | Relative Magnitude of Fragmentation (RMF) Global remote-sensing data product (i.e. the 27-year annual ESA CCI land cover maps which can be categorized as an EBV ‘Ecosystem Distribution’) | 300m | Global | Annual 1992-2020 |
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There are two methodologies adopted to define the Condition Index Accounts, both follows follow the theoretical framework developed in the scientific publication publication Maes, J., Bruzón, A.G., Barredo, J.I. et al. Accounting for forest conditions condition in Europe based on an international statistical standard. Nat Commun 14, 3723 (2023), here in after referred to as the Nature Communication method. The differences in their implementation (and their reasons) are fully documented in this guide.
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- being primary forest, alias forests where the signs of human impacts, if any, are strongly blurred due to decades without forest management or
- being protected areas (form from before the year 2000) and highly tree-covered throughout the time series (areas with a tree-covered density loss equal to or higher than 5% are excluded).
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- Primary forests from EPFD v2.0 - https://www.nature.com/articles/s41597-021-00988-7
- Protected Areas from IUCN data - https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA
- Copernicus's High-Resolution Layer Tree Cover Density - https://land.copernicus.eu/en/products/high-resolution-layer-tree-cover-density
Results are obtained by combining the following inputs by using, as consistently as possible, the year 2000 as the reference year, except for the information on the Tree-cover density, whose earliest observation is in the year 2012.
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- the polygons of primary forests,
- the points of primary forests and buffer to 200m radius.
Primary forests forest extents match roughly with the Maes et al. figures (Supplementary Table 8)
Protected Area in the PeopleEA approach:
The Protected Areas that have been protected areas from before the year 2000 and which don't exhibit tree-cover loss higher than 5% were considered for this analysis.
The categories of protected areas considered were:
- Strict nature reserves Nature Reserves 1a,
- Wilderness or wildlands 1b, and
- National parks 2.
In the Maes approach, depending on the bioregion analysed, different categories of Protected Areas were selected.
The selection of the Categories 1a, 1b & 2 was the combination that matched more closely the Maes et al. figures (Supplementary Table 8)
Healthy reference areas roughly match, but there are significant differences (>5%) for some European forest categories.
The deviations by forest type and by bioregion are detailed in the tables above (ATBD and Maes et al. 2023 Supplementary tables).
Reference values: upper and lower thresholds
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- Spatial Resolution describes the size of the smallest feature that can be detected by a satellite sensor or displayed in a satellite image. It is usually presented as a single value representing the length of one side of a square,
- Temporal resolution refers to the total amount of years in the dataset,
- Temporal frequency considers the availability of temporal observation over the same period. While all variable variables are annualized, annual averages obtained from highly frequent observation are more accurate and thus considered better input overall.
- Dataset quality represents the proximity of the first and latest year in the dataset, respectively, to the to reference year (2000) and to the present.
- When variables are ranked the same in one criterion, they are assigned the average of the positions they would represent. For example, if there are three variables which should be ranked 7, they will be assigned 6 since it is the average of the positions these variables represent (5, 6 and 7).
Indicators weighted using
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an arithmetic average (PeopleEA index)
Overall condition index =
(Net Difference Water Index x 0.24) + (Soil Organic Carbon x 0.05) + (Threatened Forest Bird Species Diversity x 0.08)+
(Above-ground Biomass x 0.15) + (Net Primary Production x 0.19) + (Burned severity x 0.17) + (Forest Connectivity x 0.12)
Indicators weighted using a
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geometric average (Euclidean distance)
The formulation of the resulting Forest Condition Index is the following:
Forest Condition Index = sqrt(
(NDWI^2 x 0.27) + (SOC^2 x 0.07) + (TBFSD^2 x 0.10) + (AGB^2 x 0.18) + (NPP^2 x 0.22) + (FC^2 x 0.16) )
For areas where Soil Organic Carbon is missing, because of dataset coverage, the index is calculated by redistributing the weights:
Forest Condition Index = sqrt(
(NDWI^2 x 0.31) + (TBFSD^2 x 0.11) + (AGB^2 x 0.19) + (NPP^2 x 0.24) + (FC^2 x 0.15) )
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- the Net Difference Water Index,
- Soil Organic Carbon,
- Threatened forest bird species diversity,
- Tree cover density (Canopy cover),
- Net Difference Vegetation Index (Forest productivity),
- Forest connectivity and
- Landscape naturalness.
To obtain a unique assessment of the forest condition, the index uses the weighted average of indicator accounts. The weights for indicator accounts are assigned based on their capacity to represent the ecosystem structure (intrinsic relevance), the ecosystem services provided (instrumental relevance), the changes in forest condition derived from external factors (directionality meaning) and the anthropogenic repercussion (sensibility to human influence) as well as on their adequacy to the SEEA EA framework (framework adequacy). Indicators are ranked between 1 (lowest position) and 7 (highest position) for five conceptual criteria proposed to select ecosystem condition variables (SEEAEAWhiteCover2021, CzuczEtAl2021 . Better positioned indicators have a larger weight in the index, and the relative sum of the indicator positions results in their index weight. Positions and weights attributed to indicator accounts are detailed in the table below, which corresponds to Maes et al. figures (Supplementary Table 9).
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