next
previous
items
European Union flag
Home
Topics
Analysis and data
Countries
Newsroom
About us

GIS Map Application

Environmental Accounting Reference Layers - maps

GIS Map Application Published 21 Nov 2019 Last modified 08 Feb 2023
1 min read
The map viewer of the Integrated Data Platform visualizes spatial datasets by web map services. Those spatial datasets are selected which are frequently used in assessments. 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.
 

More information

 -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.

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.

 

Related content

Based on data

Data High nature value (HNV) farmland The concept of High Nature Value (HNV) farmland describes the link between extensive farming systems and their use of semi-natural land and the conservation of high biodiversity in agricultural landscapes. The HNV farmland data set supports analysis of the distribution and extent of farmland that holds a special biodiversity value and enables an analysis of spatial patterns and time trends. The data set presented here shows a 2017 update of the time series for the years 2000 and 2012 on the basis of the Corine Land Cover (CLC) accounting layers 2000 and 2012. This recalculation of the HNV farmland time series based on the CLC accounting layers ensures full coherence of the data points and enables a correct calculation of changes between HNV 2000 and HNV 2012. Please note that the JRC/EEA HNV farmland methodology is currently being revised and an updated time series for the years 2000 to 2018 will be published by the end of 2022.
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 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 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.

Briefings

Briefing Land cover accounts — an approach to geospatial environmental accounting Land use and land use change are fundamental for sustainable resource use and the delivery of ecosystem services, including the provision of food, nutrient cycling and climate change mitigation through carbon sequestration. Land resources are part of our shared natural capital and must be well managed to maintain a healthy environment and human well-being (EEA, 2019b). As such, only if land use and its impacts are properly addressed is progress towards sustainable development in Europe possible. Land-use related policies require the development of harmonised datasets, transparent methodologies and easily interpretable statistics. Land accounts fit the bill, describing how land resource stocks change over time. This briefing describes the use of the EEA’s Integrated Data Platform and CORINE Land cover data for transparent, repeatable and efficient land accounting.

Permalinks

Geographic coverage

Temporal coverage

For references, please go to https://www.eea.europa.eu/data-and-maps/explore-interactive-maps/integrated-data-platform-map-viewer or scan the QR code.

PDF generated on 08 Sep 2024, 10:51 PM

EEA Page URL QR

Topics

Document Actions
The EEA is an agency of the European Union

Engineered by: EEA Web Team

Software updated on 26 September 2023 08:13 from version 23.8.18

Software version: EEA Plone KGS 23.9.14

Code for developers

Systems Status

Legal notice

Creative commons license

CMS login