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Environmental Accounting Reference Layers - SDI

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Prod-ID: DAT-228-en
Created 13 Dec 2019 Published 10 Mar 2020 Last modified 22 Apr 2020
4 min read
Topics:
This is a collection of the geospatial Environmental Accounting Reference Layers of the Integrated Data Platform published on the website and registered in EEA`s Spatial Data Infrastructure (SDI). The link under "GIS data" points to the SDI thematic node on Environmental Accounting Reference Layers.

Metadata

information.png Reporting obligations (ROD)
  • Not applicable
Relation:
Parent data set:  Imperviousness change 2009-2012
Parent data set:  Nationally designated areas (CDDA)
Parent data set:  Copernicus aggregated Imperviousness change information 2009-2012
Parent data set:  Copernicus Land Monitoring Service - High Resolution Layers - Imperviousness
Parent data set:  Copernicus Land Monitoring Service - High Resolution Layers - Forests
Parent data set:  Copernicus Land Monitoring Service - Urban Atlas
Parent data set:  Copernicus Land Monitoring Service - Riparian Zones
Parent data set:  Copernicus Land Monitoring Service - Natura 2000
Parent data set:  Copernicus Land Monitoring Service - EU-DEM
Parent data set:  Copernicus Land Monitoring Service - EU-Hydro
Parent data set:  Landscape fragmentation indicator effective mesh density (seff) - major and medium anthrophogenic fragmentation (FGA2_S_2016)
Parent data set:  Natura 2000 data - the European network of protected sites
Parent data set:  Copernicus Land Monitoring Service - High Resolution Layers - Grassland
Parent data set:  Copernicus Land Monitoring Service - High Resolution Layers - Water and Wetness
Parent data set:  Bathing Water Directive - Status of bathing water
Parent data set:  WISE WFD Database
Parent data set:  WISE WFD Database (Water Framework Directive Database)
Parent data set:  Nationally designated areas (CDDA)
Parent data set:  Waterbase - Emissions
Parent data set:  Waterbase - Water Quantity
Parent data set:  Waterbase - Water Quality
Parent data set:  WISE EIONET spatial data sets
Parent data set:  WISE WFD reference spatial data sets
Parent data set:  Europe Seas
Parent data set:  EEA coastline for analysis
Parent data set:  Ecosystem types of Europe
Parent data set:  Waterbase - Emissions
Parent data set:  Waterbase - Water Quantity
Parent data set:  Waterbase - Water Quality
Parent data set:  Natura 2000 data - the European network of protected sites
Parent data set:  WISE EIONET spatial data sets
Parent data set:  WISE Water Framework Directive Database
Parent data set:  WISE WFD reference spatial data sets
Parent data set:  Nationally designated areas (CDDA)
Parent data set:  Corine Land Cover Accounting Layers
Parent data set:  European Mountain Areas
Parent data set:  European Forest Areas based on Copernicus data
Parent data set:  Management related pressures on forest ecosystems
Parent data set:  Richness of forest-related species and habitats indicator 2012 dataset
Parent data set:  Natural assemblage tree species indicator 2006 dataset
Parent data set:  High nature value (HNV) farmland
Parent data set:  Waterbase - UWWTD: Urban Waste Water Treatment Directive – reported data
Parent data set:  WISE WFD protected area spatial data sets
Parent data set:  Landscape fragmentation Effective Mesh Density time-series: major and medium anthropogenic fragmenting elements (FGA2-S)
Parent data set:  WISE Water Accounts Spatial Units dataset
Parent data set:  WISE Water Accounts database for Europe
Parent data set:  Vegetation productivity 2000-2016
Parent data set:  Vegetation growing season length 2000-2016
Parent data set:  Start of vegetation growing season 2000-2016
Derived data set:  Ecosystem types of Europe
Derived data set:  Start of vegetation growing season 2000-2016
Derived data set:  Vegetation growing season length 2000-2016
Derived data set:  Landscape fragmentation Effective Mesh Density time-series: major and medium anthropogenic fragmenting elements (FGA2-S)
Derived data set:  Vegetation productivity 2000-2016
Derived data set:  Land cover flows based on Corine Land Cover accounting layers (2000-2018)
information.png Rights:
EEA standard re-use policy: unless otherwise indicated, re-use of content on the EEA website for commercial or non-commercial purposes is permitted free of charge, provided that the source is acknowledged (https://www.eea.europa.eu/legal/copyright). Copyright holder: European Environment Agency (EEA).
information.png Methodology:

For exploring contextual details about the added datasets, please activate the information icon in the upper right corner. Legends of the added datasets can be explored by activating the legend icon.

https://www.eea.europa.eu/data-and-maps/explore-interactive-maps/integrated-data-platform-map-viewer

Web map services are produced in ArcGIS desktop and visualized in ArcGIS Online. Once a web map service is quality controlled, the service is registered in the Spatial Data Infrastructure and read into the IDP Web Map Viewer.

The web application offers three main functions to the user for data exploration:

  1. Search for a web map service: the search uses 1) keywords (i.e. tags such as land use), or 2) topic names (such as biodiversity). The keywords are also searched in the abstract of the related spatial dataset, which is harvested from EEA`s Spatial Data Infrastructure (SDI). Hence, all datasets can be found which have e.g. Corine Land Cover in their abstract.

  2. The user is able to visualise and explore the web maps identified by the search function. Furthermore, selected web maps can be overlaid for further exploration of commonalities between spatial datasets.

  3. Once the wished services are found and explored by overlays and zoom functions, the user can find all relevant semantic information of the spatial datasets when activating the info button. These semantic information are combinations of technical information coming from the SDI and other semantics harvested from the content management system.

information.png Units:

"

information.png Data sources

Geospatial Environmental Accounting

information.png Owners:
European Environment Agency (EEA)

Additional information

The layers can be explored in the Environmental Accounting Reference Layer - map viewer of the Integrated Data Platform, which visualizes the spatial datasets by web map services.

https://www.eea.europa.eu/data-and-maps/explore-interactive-maps/integrated-data-platform-map-viewer

The web map viewer enables spatial overlays so that the datasets can be interactively explored. Through exploring the datasets their potentials in environmental assessments can be better understood.

 

Related content

Based on external data

Data Start of vegetation growing season 2000-2016 The raster files are the time series of the start of the vegetation growing season (day of the year) and the derived linear trends (in day / year). The start of the growing season time-series is based on the time series of the Plant Phenology Index (PPI) derived from the MODIS BRDF-Adjusted Reflectance product (MODIS MCD43 NBAR). The PPI index is optimized for efficient monitoring of vegetation phenology and is derived from the source MODIS data using radiative transfer solutions applied to the reflectance in visible-red and near infrared spectral domains. The start of season indicator is based on calculating the start of the vegetation growing season from the annual PPI temporal curve using the TIMESAT software for each year between and including 2000 and 2016.
Data WISE Water Accounts Spatial Units dataset The Water Accounts Spatial Units dataset is an extraction of the European catchments and Rivers network system (ECRINS), aggregation catchments and reference layers - version 1, Jun. 2012. It contains 117 river basins extracted from the ECRINS functional river basin districts (EcrAgg). The Ecrins river basin districts are delineated according to the hydrological thresholds and do not necessarily follow administrative boundaries. The main purpose of the data set is to display the EEA water accounts outputs at the river basin level.
Data WISE Water Accounts database for Europe The WISE Water Accounts database contains monthly water accounts for the years 1990-2015 for 117 European river basins extracted from the ECRINS functional river basin districts. The water accounts data can be downloaded in two different formats: a spreadsheet that contains each accounting variable in a separate worksheet, and a database that contains all the variables of the asset and flow accounts to facilitate an integrated analysis. The WISE Water Accounts Spatial Units can also be downloaded from the EEA web site. Extensive clarifications on the development of the European water accounts can be found in the following reports published by the European Commission (DG ENV): "Guidance document on the application of water balances for supporting the implementation of the Water Framework Directive" (http://ec.europa.eu/environment/water/blueprint/balances.htm) and "Water ecosystem accounts reports" (http://ec.europa.eu/environment/water/blueprint/balances.htm)
Data Vegetation growing season length 2000-2016 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.
Data Vegetation productivity 2000-2016 The raster files are the above ground vegetation productivity time-series and the derived linear trend for the period 2000-2016.The data set addresses trends in land surface productivity 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.
Data Landscape fragmentation Effective Mesh Density time-series: major and medium anthropogenic fragmenting elements (FGA2-S) 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.
Data Richness of forest-related species and habitats indicator 2012 dataset 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).
Data Management related pressures on forest ecosystems 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).
Data Natural assemblage tree species indicator 2006 dataset 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.
Data European Forest Areas based on Copernicus data 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).
Data European Mountain Areas 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.
Data Corine Land Cover Accounting Layers 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.
Data Landscape fragmentation indicator effective mesh density (seff) - major and medium anthrophogenic fragmentation (FGA2_S_2016) The raster file is the basis of the indicator for assessing landscape fragmentation due to urban and transport expansion. The computation is based on the method of Effective Mesh Density (seff). The Effective Mesh Density (seff) is a measure of landscape fragmentation, i.e. 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. The Effective Mesh Density gives the effective number of meshes (or landscape patches) per 1000 km2, in other words, the density of the meshes. The more FGs fragment the landscape, the higher the effective mesh density. The seff values are reported within the 1km sq regular LEAC grid.
Data Ecosystem types of Europe 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.
Data EEA coastline for analysis 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.
Data Copernicus Land Monitoring Service - High Resolution Layers - Grassland 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).
Data Copernicus Land Monitoring Service - High Resolution Layers - Water and Wetness 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.
Data Copernicus Land Monitoring Service - Urban Atlas 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.
Data Copernicus Land Monitoring Service - High Resolution Layers - Forests The high resolution forest product consists of 3 types of (status) products, and additional change products. The status products are available for 2012, 2015, and 2018 reference years: Tree cover density (TCD) (level of tree cover density in a range from 0-100%) Dominant leaf type (DLT) (broadleaved or coniferous majority) A Forest type product (FTY). The forest type product allows to get as close as possible to the FAO forest definition. In its original (10m (2018) / 20m (2012, 2015)) resolution it consists of two products: 1) a dominant leaf type product that has a MMU of 0.5 ha, as well as a 10% tree cover density threshold applied, and 2) a support layer that maps, based on the dominant leaf type product, trees under agricultural use and in urban context (derived from CLC and imperviousness 2009 data). For the final 100m product trees under agricultural use and urban context from the support layer are removed. NEW for 2018: the 10m 2018 reference year FTY product now also has the agricultural/urban trees removed. In the past this was done only for the 100m product, now it is consistently applied for both the 10m and the 100m FTY products. Change layers The forest change products are new, and comprise a) TCDC: a simple tree cover density change product for 2012-2015 (% increase or decrease of real TCD changes). For the 2015-2018 mapping, the change layer is instead only masking loss and new tree cover. b) experimental complex dominant leaf type change product (DLTC), mapping different types of dominant leaf type changes (not yet available online).
Data Copernicus Land Monitoring Service - High Resolution Layers - Imperviousness The imperviousness products capture the percentage and change of soil sealing. Sealed/Impervious areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. The imperviousness HRL captures the spatial distribution of artificially sealed areas, including the level of sealing of the soil per area unit. The level of sealed soil (imperviousness degree 1-100%) is produced using a semi-automated classification, based on calibrated NDVI. Imperviousness data is available for the reference years 2006, 2009, 2012, 2015, and 2018, and contains two types of products: 1. Status layers Imperviousness Density (IMD) The percentage of sealed area is mapped for each status layer for any of the 5 reference years (e.g. degree of Imperviousness 2012). The status layers are available in 10m spatial resolution (2018), 20m spatial resolution (2006-2015), and as aggregated 100m products. Impervious Built-up (IBU) This product shows built-up areas, the part of the sealed surfaces where buildings can be found. Built-up areas are a sub-group of the sealed areas. It refers to areas where above-ground building constructions can be found. In contrast to the Imperviousness characterized by a continuous range of imperviousness measurements, built-up in the HRL 2018 is a binary product, expressed as built-up or non-built-up areas. This product is new for the 2018 mapping campaign and is available in 10 meter resolution, as well as a 100 meter aggregated version called Share of Built-up (SBU) 2. Change layers Two types of change products are available for each of the 3-year periods between the 5 reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018), and in addition, for the period 2006-2012 (that is in line with the 6-year period between two Corine Land Cover products): a) A simple layer mapping the percentage of sealing increase or decrease for those pixels that show real sealing change in the period covered. This product is available in 20m and 100m pixel size. b) A classified change product that maps the most relevant categories of sealing change (unchanged no sealing, new cover, loss of cover, unchanged sealed, increased sealing, decreased sealing). This product is available in 20m pixel size only.
Data Copernicus aggregated Imperviousness change information 2009-2012 Copernicus aggregated Imperviousness change information 2009-2012 for EEA Indicator on Imperviousness & Imperviousness change
Data Copernicus Land Monitoring Service - EU-Hydro EU-Hydro is a dataset for all EEA39 countries providing photo-interpreted river network, consistent of surface interpretation of water bodies (lakes and wide rivers), and a drainage model (also called Drainage Network), derived from EU-DEM, with catchments and drainage lines and nodes. The EU-Hydro dataset is distributed in separate files (river network and drainage network) for each of the 35 major basins of the EEA39 area. The production of EU-Hydro and the derived layers was coordinated by the European Environment Agency in the frame of the EU Copernicus programme.
Data Copernicus Land Monitoring Service - EU-DEM EU-DEM is a digital surface model (DSM) of EEA member and cooperating countries representing the first surface as illuminated by the sensors. It is a hybrid product based on SRTM and ASTER GDEM data fused by a weighted averaging approach.
Data Copernicus Land Monitoring Service - Natura 2000 Natura 2000 (an EU-wide network of nature protection areas established under the 1992 Habitats Directive) is the centrepiece of EU nature & biodiversity policy. The aim of the network is to assure the long-term survival of Europe's most valuable and threatened species and habitats. A selection of Natura 2000 grassland-rich sites (5 grassland habitats types 6210, 6240, 6250, 6510 and 6520, including a 2km buffer and covering approx. 160.000 km2) sites was mapped in order to assess their actual area, their condition and their development over time. The sites were analysed for the 2006 and 2012 reference years and a change analysis was performed. The mapping also included a 2 km buffer zone where an analysis of pressures and threats was conducted.
Data Copernicus Land Monitoring Service - Riparian Zones Riparian zones represent transitional areas occurring between land and freshwater ecosystems, characterised by distinctive hydrology, soil and biotic conditions and strongly influenced by the stream water. They provide a wide range of riparian functions (e.g. chemical filtration, flood control, bank stabilization, aquatic life and riparian wildlife support, etc.) and ecosystem services. Three complementary product groups provide detailed information on the state and characteristics of riparian zones across the EEA member and cooperating countries: Land Cover / Land Use (LCLU), Delineation of Riparian Zones (DRZ), Green Linear Elements (GLE).
Data
Draft
High nature value (HNV) farmland The concept of High Nature Value (HNV) farmland ties together biodiversity to the continuation of farming on certain types of land and the maintenance of specific farming systems. The general goal of the data set is to enhance the European map of HNV farmland 2000 that shows the estimated distribution and presence likelihood of HNV farmland across the whole European territory. The HNV farmland map aims to gain a better insight into the distribution and extent of farmland that holds a special biodiversity value, and to develop a more effective tool for carrying out further analyses on spatial and time trends. A first version of the European HNV map developed by JRC/EEA in 2008 was based on CORINE land cover 2000 and biodiversity-related data sets (Paracchini, M. L.; Petersen, J.-E.; Hoogeveen, Y.; Bamps, C.; Burfield, I. and van Swaay, C., 2008. High Nature Value Farmland in Europe. An estimate of the distribution patterns on the basis of land cover and biodiversity data. JRC Scientific and Technical Reports. European Communities, Luxembourg). A further update of HNV farmland in Europe was carried out in 2012 to update the HNV farmland dataset based on the CLC data 2006 and to include countries previously not part of the European HNV farmland assessment. The update done in 2017 includes a recalculation of years 2000 and 2012 on the basis of Corine Land Cover accounting layer. The main focus of the 2017 exercise was to update the HNV farmland dataset based on the CLC 2012 accounting layer and to recalculate HNV 2000 based on the CLC 2000 accounting layer in order to maintain coherence for the calculation of a time series and changes between HNV 2000 and HNV 2012.

Derived dataset

Data Land cover flows based on Corine Land Cover accounting layers (2000-2018) The raster datasets describe land cover flows between 2000-2018, 2000-2006, 2006-2012 and 2012-2018 for the EEA39 region. Land Cover Flows summarize and interpret the 44x43=1892 possible one-to-one changes between the 44 CORINE land cover classes. The changes are grouped to so called flows of land cover and are classified according to major land use processes. The nomenclature of flows is organized on 3 hierarchical levels. See lineage on the nomenclature. The classification of land cover flows results from the feasibility studies and subsequent revisions after discussion with experts in agri-environment and forestry. Basically, the classification of land cover flows distinguishes change between broad land cover classes and changes internal to these classes. Analysis of land cover flows supplies a rapid vision of land use change processes taking place and they shed light on the drivers of various land use change processes such as e.g. urbanization.
Data Start of vegetation growing season 2000-2016 The raster files are the time series of the start of the vegetation growing season (day of the year) and the derived linear trends (in day / year). The start of the growing season time-series is based on the time series of the Plant Phenology Index (PPI) derived from the MODIS BRDF-Adjusted Reflectance product (MODIS MCD43 NBAR). The PPI index is optimized for efficient monitoring of vegetation phenology and is derived from the source MODIS data using radiative transfer solutions applied to the reflectance in visible-red and near infrared spectral domains. The start of season indicator is based on calculating the start of the vegetation growing season from the annual PPI temporal curve using the TIMESAT software for each year between and including 2000 and 2016.
Data Vegetation growing season length 2000-2016 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.
Data Vegetation productivity 2000-2016 The raster files are the above ground vegetation productivity time-series and the derived linear trend for the period 2000-2016.The data set addresses trends in land surface productivity 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.
Data Landscape fragmentation Effective Mesh Density time-series: major and medium anthropogenic fragmenting elements (FGA2-S) 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.
Data Ecosystem types of Europe 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.

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For references, please go to https://www.eea.europa.eu/data-and-maps/data/reference-layers-of-the-idp or scan the QR code.

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