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This dashboard provides information on the need and implementation of conservation measures for species and habitats
This dashboard provides information about pressures and threats acting on habitats and species, sorted by their level of importance.
This dashboard provides information on the number of habitats and species reported by each Member State.
This dashboard provides statistics on data quality and completeness of the Member states’ reports
This dashboard provides numbers of Annex V species being taken in the wild or exploited.
This dashboard provides information on the proportion of area of habitat types in good and not good condition. This information relates to the parameter ‘structure and functions of a habitat type’.
This dashboard provides information on the numbers and surface area of Natura 2000 sites and associated legal measures in place per Member State. For official count of number of sites and their areas and EU area statistics please refer to the Natura 2000 barometer.
These dashboards show a summary of data reported by Member States in the frame of the 6-yearly progress report under the Habitats Directive for the period 2013-2018, grouped in various themes
This dashboard provides information on the share of habitats and species that occurs inside the Natura 2000 network as well as on their trends inside the Natura 2000 network
This dashboard provides information on conservation status and trends of habitats and species at the level of Member States.
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.
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.
For visualisation purposes, the initial 100 m spatial resolution Corine Land Cover dataset was re-sampled to a 10 km2 grid. The observation periods can be visualised by activating the 'layers' icon and selecting the respective periods.
This web map application uses the new version of the Effective Mesh Density (seff) 2016 dataset with improved input data, for the years 2009, 2012 and 2015. This new dataset uses the Copernicus Imperviousness and the TomTom TeleAtlas data sets as fragmenting geometries. The application shows the change in effective mesh density (seff), i.e. the number of landscape elements between 2009 and 2012 and between 2012 and 2015.
Timelines for achieving good environmental staus as reported by members states
For references, please go to https://www.eea.europa.eu/themes/biodiversity/dm or scan the QR code.
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Software updated on 26 September 2023 08:13 from version 23.8.18
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