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The raster files are the annual above ground growing season length time-series and the derived linear trends for the period 2000-2016. The data set addresses trends in the season length of land surface vegetation derived from remote sensing observed time series of vegetation indices. The vegetation index used in the indicator is the Plant Phenology Index (PPI, Jin and Eklundh, 2014). PPI is based on the MODIS Nadir BRDF-Adjusted Reflectance product (MODIS MCD43 NBAR. The product provides reflectance data for the MODIS “land” bands (1 - 7) adjusted using a bi-directional reflectance distribution function. This function models values as if they were collected from a nadir-view to remove so called cross-track illumination effects. The Plant Phenology Index (PPI) is a new vegetation index optimized for efficient monitoring of vegetation phenology. It is derived from radiative transfer solution using reflectance in visible-red (RED) and near-infrared (NIR) spectral domains. PPI is defined to have a linear relationship to the canopy green leaf area index (LAI) and its temporal pattern is strongly similar to the temporal pattern of gross primary productivity (GPP) estimated by flux towers at ground reference stations. PPI is less affected by presence of snow compared to commonly used vegetation indices such as Normalized Difference Vegetation Index (NDVI) or Enhanced Vegetation Index (EVI). The product is distributed with 500 m pixel size (MODIS Sinusoidal Grid) with 8-days compositing period.
The datasets below correspond to a new version of the Effective Mesh Density (seff) 2016 dataset with improved input data, for the years 2009, 2012 and 2015. This time-series uses the Copernicus Imperviousness and the TomTom TeleAtlas datasets as fragmenting geometries. The Effective Mesh Density (seff) is a measure of the degree to which movement between different parts of the landscape is interrupted by a Fragmentation Geometry (FG). FGs are defined as the presence of impervious surfaces and traffic infrastructure, including medium sized roads. The more FGs fragment the landscape, the higher the effective mesh density hence the higher the fragmentation. An important consequence of landscape fragmentation is the increased isolation of ecosystem patches that breaks the structural connections and decreases resilience and ability of habitats to provide various ecosystem services. Fragmentation also influences human communities, agriculture, recreation and overall quality of life. Monitoring how fragmentation decreases landscape quality and changes the visual perception of landscapes provides information for policy measures that aim at improving ecosystem condition and restoration as well as maintaining the attractiveness of landscapes for recreational activities. The geographic coverage of the datasets is EEA39.
This dataset refers to the Richness index of Species and Habitats of Conservation Concern indicator. This indicator has been developed to be used as a sub-indicator for contributing to the identification of the High Nature Value (HNV) Forest Areas as it will be integrated with other sub-indicators of horizontal structure, management and naturalness to generate the final composite indicator. It is composed itself of three sub-indicators: “Forest Non-bird species”, “Forest bird species” and “Forest habitats”. All the three sub-indicators build on distribution data from the reporting of habitat and species conservation status under Article 17 of the Habitats Directive and Article 12 of the Birds directive which describe their distribution at 10km grid resolution. The forest species and the forest habitats proposed to be used for the HNV forest area identification were selected based on expert judgement (ETC/BD) and raster files reporting the count of forest species and habitats were created. At this stage, no weight is applied based on Habitat and Species prioritization, conservation status or endemism. The sub-indicators were then normalized for each European forest type and successively combined not assigning any specific weight to a particular sub-indicator. The values for this indicator, present in this dataset, ranges between 0 and 1. The values close to 1 mean high presence of habitats and species related to forest, whereas the lower richness are closer to 0. It covers the forested areas of the EU27 Member States except for Cyprus (data from Croatia will be reported starting from the next update regarding the period 2013-2018).
Forest management involves various degrees of human intervention to safeguard the forest ecosystem and its functions as well as the exploitation of forest resources. While the objectives of management vary widely and include the protection of resources in protected forests and nature reserves, the primary objective is mostly the production of wood products. Although sustained yield forestry continues to be widely practised, there is an increasing trend towards the management of forests as ecological systems with multiple economic benefits and environmental values, ensuring that benefits meet present as well as future generations’ needs. In order to assess forest management intensity in Europe an indicator based on three data sources has been developed: a) Fast track ecosystem capital accounts (forest growth & harvest – disaggregated to 1km grid), b) Potential forest management (gradient of intensity of intervention with the natural processes in a forest) c) Forest fragmentation (forest ecosystem network connected by forest bridges – GUIDOS Morphological Spatial Pattern Analysis). Each input dataset has been assessed separately in a first step in terms of pressures on forest ecosystems which are the result of the specific management, use or respectively state of the forest patch. The overall management related pressure is then derived by crossing the relative pressures by each input and evaluating the constellation of the input representative factors. This updated version of the management related forest pressures is based on the first assessment done in framework of the ETC-SIA report "Land use and land management related pressures on agricultural and forest ecosystems" (ETC-SIA, Task 1.8.4.3 Ecosystem pressures).
The natural assemblage species indicator dataset is a forest dataset that measures the congruency between the potential and current tree species distribution. The natural assemblage indicator is considered one of the key indicator for the identification of High Nature Value forest area in Europe. The reference year for this data set is 2006 and the spatial coverage is including the 28 EU Member States, Liechtenstein, Norway, Switzerland, and Turkey. The methodological approach is based on two data sources: (1) EUNIS woodland, forest and other wooded land habitats, predicted potential distribution of habitat suitability –EEA- as potential distribution; (2) Relative probability of presence of forest tree species (RPP) of European Atlas of Forest Tree Species –JRC- as current distribution. The dataset values express, in the fuzzy values between 0 and 1, the percentage of tree species vegetation agreed with potentially dominant tree species by pixels. This measure is independent of the current forest coverage. The values close to 1 mean high percentage of native tree species (natural) whereas values close to 0 are an approximation of a low level of naturalness, being a high percentage of non-native species.
These data sets show the European forest area in 2012 and in 2015 at 100m spatial resolution, covering EEA39 countries. They are based on Copernicus HRL forest products at 20m spatial resolution and comply with the FAO forest definition (i.e. minimum mapping unit of 0.5 ha, minimum coverage of 10% and excluding land that is predominantly under agricultural or urban land use). After the selection of those pixels identified as forest by the HRL forest products and also compliant with FAO criteria, the forest area dataset at 100m was computed as a Boolean product (i.e. forest / non-forest). The value 1 (forest area) correspond to the pixels where forest is the major coverage; otherwise the pixel value is 0 (non-forest area).
The delineation of European mountain areas was carried out by using digital elevation models, considering different criteria combination of thresholds of altitude, climate, and topography variables (IP2008 8.2.7 Regional and territorial development of mountain areas, ETC/LUSI - EEA). This dataset was created in 2008, covers the full European continent and is a reference layer for the EEA Report No 6/2010 on Europe's ecological backbone: recognising the true value of our mountain.
The present 100m raster datasets are the CORINE Land Cover status layers for 2000, 2006, 2012 and 2018, modified for the purpose of consistent statistical analysis in the land cover change accounting system at EEA. CORINE Land Cover (CLC) data are produced from 1986 for European (EEA member or cooperating) countries. Altogether five mapping inventories were implemented in this period, producing five status layers (CLC1990, CLC2000, CLC2006, CLC2012, CLC2018) and four CLC-Change (CLCC) layers for the corresponding periods (1990-2000, 2000-2006, 2006-2012, 2012-2018). Pan-European CLC and CLCC data are available as vector and raster products. Due to the technical characteristics of CLC and CLCC data, the evolution in CLC update methodology and in quality of input data, time-series statistics derived directly from historical CLC data includes several inconsistencies. In order to create a statistically solid basis for CLC-based time series analysis, a harmonization methodology was elaborated.
This database contain policies and measures (PaMs) reported by EU Member States following European Commission Implementing Decision (EU) 2018/1522 of 11 October 2018 laying down a common format for national air pollution control programmes under Directive (EU) 2016/2284 of the European Parliament and of the Council on the reduction of national emissions of certain atmospheric pollutants.
Potential quiet areas in Europe, based upon Quietness Suitability Index (QSI) and Natura 2000 protected areas
The dataset combines the Copernicus land service portfolio and marine bathymetry and seabed information with the non-spatial EUNIS habitat classification for a better biological characterization of ecosystems across Europe. As such it represents probabilities of EUNIS habitat presence for each MAES ecosystem type.
EEA templates and data, for map production in accordance with EEA Guidelines.
The CORINE Land Cover (CLC) inventory was initiated in 1985 (reference year 1990) to standardize data collection on land in Europe to support environmental policy development. Updates were produced in 2000, 2006, 2012 and 2018. Change layers were produced for 2000, 2006, 2012 and 2018.
Directive 2009/28/EC on the promotion and use of energy from renewable sources commits the EU to achieving a 20 % share of renewable energy sources (RES) in its gross final energy consumption by 2020 and a 10 % share of renewable energy in transport energy consumption by the same year. Article 22 of the directive requires Member States to report on national progress in the promotion and use of energy from renewable sources, biennially, starting with 31 December 2011. It specifies that the national reports shall contain, inter alia, the estimated net GHG emission savings due to the use of energy from renewable sources. Information regarding the specific methodologies used by the countries for producing the above estimates of net GHG emission savings is not always available. To that end, the EEA and its European Topic Centre for Air Pollution and Climate Change Mitigation (ETC/ACM) produce each year a consistent set of estimated co-benefits, at the Member State and EU level, concerning the impact on fossil fuels and GHG emissions due to the increase in RES consumption since 2005. For 2017, these estimated co-benefits are based on the EEA 2017 RES share proxies and the EEA 2017 proxies on primary and final energy consumption.
The EEA coastline dataset has been created for detailed analysis (e.g.:1/100000) for geographical Europe. The coastline is a hybrid product obtained from satellite imagery from two projects: EUHYDRO (Pan-European hydrographic and drainage database) and GSHHG (A Global Self-consistent, Hierarchical, High-resolution Geography Database), as well as some manual amendments to meet requirements from EU Nature Directives, Water Framework Directive and Marine Strategy Framework Directive. In 2015, several corrections were made in the Kalogeroi Islands and two other Greek little islets, as well as in the peninsula of Porkkala. In this revision from 2017, two big lagoons have been removed from Baltic region, because, according to HELCOM, are freshwater lagoons.
The European Red Lists of species is a review of the conservation status of more than 10 000 European species (mammals, birds, reptiles, amphibians, freshwater and marine fishes, butterflies, dragonflies, freshwater molluscs, selected groups of beetles, terrestrial molluscs, vascular plants including medicinal plants, bees, grasshoppers, locusts and crickets, lycopods and ferns), according to IUCN regional Red Listing guidelines applied to the EU28 and to the Pan-European level. New in the 2017 December version of the database is the updated European Red List of saproxylic beetles.
The main High Resolution Grassland product is a binary grassland/non-grassland product in 10m (2018) / 20m (2015) pixels size, that includes the full spectrum of grassland use intensity (from natural to managed grasslands).
The combined Water and Wetness product is a thematic product showing the occurrence of water and wet surfaces over the period from 2009 to 2018. These layers are based on multi-temporal and multi-seasonal optical high-resolution satellite imagery. In addition, these layers are also based on radar information (Sentinel-1 data) with a geometric resolution of 10m on a pan-European basis. A multitude of optical and SAR imagery is used, covering a prolonged time series of 7 years, which aim at capturing the intra-annual dynamics as much as possible within a given area and lead to one image composite per season (each season covered by 3 months) and year during the observation period.
The European Urban Atlas provides reliable, inter-comparable, high-resolution land use maps for over 300 Large Urban Zones and their surroundings (more than 100.000 inhabitants as defined by the Urban Audit) for the 2006 reference year in EU member states and for about 800 Functional Urban Area (FUA) and their surroundings (more than 50.000 inhabitants) for the 2012 and 2018 reference year in EEA39. Two additional layers were produced starting from the 2012 reference year: 1) Street Tree Layer within selected FUAs (depending on availability and suitability of satellite imagery) and 2) Building Heights for core urban areas of selected cities in EEA39. The first change layers were produced in 2012.
For references, please go to https://www.eea.europa.eu/data-and-maps/data or scan the QR code.
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