The Biodiversity data centre (BDC) provides access to data and information on species, habitat types and sites of interest in Europe and to related products for biodiversity indicators and assessments. Priority is given to policy-relevant data and information for European and national institutions, professionals, researchers and the public.
Vegetation productivity indicates the spatial distribution and change of the vegetation cover - a key characteristic of ecosystem condition.
Vegetation productivity in Europe on average has a regional pattern of increase and decline. Increase was observed most in South Eastern Europe, over croplands and wetlands in the Steppic region and grasslands and sparsely vegetated lands and in the Black Sea and Anatolian regions. Decline happened most over croplands and grasslands in the Atlantic region as well as over wetlands in the Alpine region.
Climate has important influence on vegetation productivity in Europe. Strongest is the influence of precipitation increase, especially in the South Eastern regions. Decreasing number of frost days increased productivity in the Pannonian region but decreased productivity in the Atlantic region.
Climatic variations are important drivers of vegetation productivity, but land use changes are even stronger. Productivity was most increased by agricultural land management and converting other lands to agriculture, whereas largest decrease was caused by sprawling urban areas.
The chart shows significant trends of vegetation productivity, expressed in % change. The % change values were derived from the fitted linear trend line.
The map shows the temporal development of the distribution of warm-favouring (Lusitanian) fish species and of cool-favouring (Boreal) fish species by statistical area in yearly intervals, 1967 to 2018.
Recognition and understanding of the term 'biodiversity' has increased in the European Union. 71 % of interviewed EU citizens have heard of biodiversity and over 41 % of these know what it means.
At least eight out of ten Europeans consider the various effects of biodiversity loss to be serious for humans and for nature and agree that it is important to halt its loss. The biggest perceived threats to biodiversity are pollution of air, soil and water, man-made disasters and climate change.
Just under a third of respondents are aware of the Natura 2000 network, including 19 % who say they have heard about it but do not know what it is. However, the overwhelming majority agree that nature protection areas are very important in protecting endangered animals and plants or safeguarding nature's role in providing food, clean air and water.
Most Europeans are not willing to trade damage or destruction of protected areas for economic development.
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.
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.
The chart shows the area of land take and recultivation for the last Corine Land Cover observation period 2012-2018 for each EEA39 country. Both land take and recultivation are expressed in m2/km2 of the country area for the sake of comparability.
Soil plays a crucial role in nature’s cycles, including the nutrient cycle, which involves how much soil organic matter — i.e. carbon, nitrogen and phosphorus — is taken up and stored in soil. Organic compounds, such as leaves and root tips, are broken down to simpler compounds by organisms living in soil before they can be used by plants. Some soil bacteria convert atmospheric nitrogen into mineral nitrogen, which is essential for plant growth. Fertilisers introduce nitrogen and phosphates to induce plant growth but not all amounts are taken up by plants. The excess can enter rivers and lakes and affect life in these water ecosystems.
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 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.
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.
Soil plays a crucial role in nature’s cycles, including the nutrient cycle, which involves how much soil organic matter — i.e. carbon, nitrogen and phosphorus — is taken up and stored in soil. Organic compounds, such as leaves and root tips, are broken down to simpler compounds by organisms living in soil before they can be used by plants. Some soil bacteria convert atmospheric nitrogen into mineral nitrogen, which is essential for plant growth. Fertilisers introduce nitrogen and phosphates to induce plant growth but not all amounts are taken up by plants. The excess can enter rivers and lakes and affect life in these water ecosystems.
Vegetation productivity indicates the spatial distribution and change of the vegetation cover - a key characteristic of ecosystem condition.
Vegetation productivity in Europe on average has a regional pattern of increase and decline. Increase was observed most in South Eastern Europe, over croplands and wetlands in the Steppic region and grasslands and sparsely vegetated lands and in the Black Sea and Anatolian regions. Decline happened most over croplands and grasslands in the Atlantic region as well as over wetlands in the Alpine region.
Climate has important influence on vegetation productivity in Europe. Strongest is the influence of precipitation increase, especially in the South Eastern regions. Decreasing number of frost days increased productivity in the Pannonian region but decreased productivity in the Atlantic region.
Climatic variations are important drivers of vegetation productivity, but land use changes are even stronger. Productivity was most increased by agricultural land management and converting other lands to agriculture, whereas largest decrease was caused by sprawling urban areas.
Recognition and understanding of the term 'biodiversity' has increased in the European Union. 71 % of interviewed EU citizens have heard of biodiversity and over 41 % of these know what it means.
At least eight out of ten Europeans consider the various effects of biodiversity loss to be serious for humans and for nature and agree that it is important to halt its loss. The biggest perceived threats to biodiversity are pollution of air, soil and water, man-made disasters and climate change.
Just under a third of respondents are aware of the Natura 2000 network, including 19 % who say they have heard about it but do not know what it is. However, the overwhelming majority agree that nature protection areas are very important in protecting endangered animals and plants or safeguarding nature's role in providing food, clean air and water.
Most Europeans are not willing to trade damage or destruction of protected areas for economic development.
Despite a reduction in the last decade (land take was over 1000km2/year between 2000-2006), land take in EU28 still amounted to 539km2/year between 2012-2018.
The net land take concept combines land take with land return to non-artificial land categories (re-cultivation). While some land was re-cultivated in the EU-28 in the period 2000-2018, 11 times more land was taken.
Between 2000 and 2018, 78 % of land take in the EU-28 affected agricultural areas, i.e. arable lands and pastures, and mosaic farmlands.
From 2000 to 2018, land take consumed 0.6 % of all arable lands and permanent crops, 0.5 % of all pastures and mosaic farmlands, and 0.3 % of all grasslands into urban areas.
In proportion to their area, Cyprus, the Netherlands and Albania saw the largest amount of land take between 2000 and 2018.
The re-cultivation of land increased from 2012 to 2018, led by Luxembourg, the Netherlands, the United Kingdom and Belgium.
The main drivers of land take during 2000-2018 were industrial and commercial land use as well as extension of residential areas and construction sites.
The map shows the temporal development of the distribution of warm-favouring (Lusitanian) fish species and of cool-favouring (Boreal) fish species by statistical area in yearly intervals, 1967 to 2018.
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.
The chart shows the area of land take and recultivation for the last Corine Land Cover observation period 2012-2018 for each EEA39 country. Both land take and recultivation are expressed in m2/km2 of the country area for the sake of comparability.
The chart shows significant trends of vegetation productivity, expressed in % change. The % change values were derived from the fitted linear trend line.
Cumulative number of new non-indigenous species (NIS) aggregated per 6 years in all European Seas, 1949-2017, per functional group (Invertebrates, vertebrates and primary producers, (EU + non EU)
Vegetation productivity indicates the spatial distribution and change of the vegetation cover - a key characteristic of ecosystem condition.
Vegetation productivity in Europe on average has a ...
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