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An average (mean) of this percentage for all educational facilities was calculated per city.\n\nThe sources that underpin the dataset are: Urban Audit spatial boundaries 2018 (French cities based on the 2020 Urban Audit spatial boundaries): (https://ec.europa.eu/eurostat/web/gisco/geodata/statistical-units/urban-audit), the Eurostat data on location of educational facilities (https://gisco-services.ec.europa.eu/pub/education/), and the data contained in the EEA 'Urban tree cover' dashboard (https://www.eea.europa.eu/data-and-maps/dashboards/urban-tree-cover), which was developed based on:\n\n* Urban Atlas - Street Tree Layer 2018 (vector), Feb. 2021 (https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/205691b3-7ae9-41dd-abf1-1fbf60d72c8c)\n* High Resolution Layer: Tree Cover Density 2018 (raster 100m), Sep. 2020 (https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/c7bf34ea-755c-4dbd-85b6-4efc5fd302a2)\n* Urban Atlas 2018 (vector), Jul. 2021 (https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/fb4dffa1-6ceb-4cc0-8372-1ed354c285e6)","langeng":"The points representing the educational facilities located within the spatial extent of the Urban Audit cities were selected. 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An average (mean) of this percentage for all educational facilities was calculated per city.\n\nThe sources that underpin the dataset are: Urban Audit spatial boundaries 2018 (French cities based on the 2020 Urban Audit spatial boundaries): (https://ec.europa.eu/eurostat/web/gisco/geodata/statistical-units/urban-audit), the Eurostat data on location of educational facilities (https://gisco-services.ec.europa.eu/pub/education/), and the data contained in the EEA 'Urban tree cover' dashboard (https://www.eea.europa.eu/data-and-maps/dashboards/urban-tree-cover), which was developed based on:\n\n* Urban Atlas - Street Tree Layer 2018 (vector), Feb. 2021 (https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/205691b3-7ae9-41dd-abf1-1fbf60d72c8c)\n* High Resolution Layer: Tree Cover Density 2018 (raster 100m), Sep. 2020 (https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/c7bf34ea-755c-4dbd-85b6-4efc5fd302a2)\n* Urban Atlas 2018 (vector), Jul. 2021 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The European Climate and Health Observatory platform provides easy access to a wide range of relevant publications, tools, websites and other resources related to climate change and human health.","langeng":"This metadata record refers to the vector dataset presenting, at EEA38 member country level for 2020, the ratio of the average urban tree cover between urban administrative units containing population with the highest highest unemployment rate (top 20% or 5th quintile) and the administrative units containing population with the lowest highest unemployment rate (bottom 20% or 1st quintile) in a given country. 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Due to intellectual property rights applied to the \"Percentage of unemployed people in working age population (2020), Nov. 2021\" this dataset is not available for public download.\n\nThe above applies without prejudice of the License CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Copyright holder: European Environment Agency (EEA).","langeng":"This data is a derived product based on the \"Percentage of unemployed people in working age population (2020), Nov. 2021\" (https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/018a767a-9b66-401d-9d46-898648a928b6). Due to intellectual property rights applied to the \"Percentage of unemployed people in working age population (2020), Nov. 2021\" this dataset is not available for public download.\n\nThe above applies without prejudice of the License CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/). 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The mean percentage of the administrative unit area that is classified as urban tree cover in the 5th quintile of percentage of unemployment rate was divided by the mean percentage of the administrative unit area that is classified as urban tree cover in the 1st quintile of unemployment rate to obtain the ratio.\n\nThe sources that underpin the dataset are: \"Percentage of unemployed people in working age population (2020), Nov. 2021\" (https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/018a767a-9b66-401d-9d46-898648a928b6) and the data contained in the EEA 'Urban tree cover’ dashboard (https://www.eea.europa.eu/data-and-maps/dashboards/urban-tree-cover), based on the Copernicus services: \"Urban Atlas 2018 (vector), Jul. 2021\", \"Urban Atlas - Street Tree Layer 2018 (vector), Feb. 2021\" and \"High Resolution Layer: Tree Cover Density 2018 (raster 100m), Sep. 2020\"","langeng":"The administrative units within Functional Urban Areas in each country were classified into 5 quintiles considering the unemployment rate. 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The data sources behind the dataset are ‘Percentage of unemployed people in working age population (2020), Nov. 2021' and the data contained in the EEA 'Urban green Infrastructure, 2018' dashboard based on data accessible from this SDI entry: \"Green Infrastructure Elements in the urban and commuting zones in 2018 - version 1, May 2021\".\n\nTemporal coverage: 2018/2020 (green space data is from 2018; the socio-economic data is from 2020, gap filled).\n\nThe data is included in the European Climate and Health Observatory: https://climate-adapt.eea.europa.eu/observatory. The European Climate and Health Observatory platform provides easy access to a wide range of relevant publications, tools, websites and other resources related to climate change and human health.","langeng":"This vector dataset presents, at EEA38 member country level for 2020, the ratio of the average urban green space between urban administrative units containing population with the highest unemployment rate (top 20% or 5th quintile) and the administrative units containing population with the lowest unemployment rate (bottom 20% or 1st quintile) in a given country. The data sources behind the dataset are ‘Percentage of unemployed people in working age population (2020), Nov. 2021' and the data contained in the EEA 'Urban green Infrastructure, 2018' dashboard based on data accessible from this SDI entry: \"Green Infrastructure Elements in the urban and commuting zones in 2018 - version 1, May 2021\".\n\nTemporal coverage: 2018/2020 (green space data is from 2018; the socio-economic data is from 2020, gap filled).\n\nThe data is included in the European Climate and Health Observatory: https://climate-adapt.eea.europa.eu/observatory. The European Climate and Health Observatory platform provides easy access to a wide range of relevant publications, tools, websites and other resources related to climate change and human health."},"isHarvested":"false","th_eea-topics":[],"th_httpinspireeceuropaeumetadatacodelistSpatialScope-SpatialScopeNumber":"1","specificationConformance":[{"explanation":"See the referenced specification","title":"Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services","date":"2010-12-08","pass":""}],"logo":"/images/logos/d1bd08f0-16ac-47c3-b581-2e8db715530b.png","creationYearForResource":"2022","MD_LegalConstraintsOtherConstraintsObject":[{"langeng":"public access limited according to Article 13(1)(a) of the INSPIRE Directive","default":"public access limited according to Article 13(1)(a) of the INSPIRE Directive","link":"http://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/INSPIRE_Directive_Article13_1a"},{"default":"This data is a derived product based on the \"Percentage of population over 65 years (2020), Nov. 2021\" (https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/2b345477-33df-491f-be4d-743bcdfc7274). Due to intellectual property rights applied to the \"Percentage of population over 65 years (2020), Nov. 2021\" this dataset is not available for public download.\n\nThe above applies without prejudice of the License CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Copyright holder: European Environment Agency (EEA).","langeng":"This data is a derived product based on the \"Percentage of population over 65 years (2020), Nov. 2021\" (https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/2b345477-33df-491f-be4d-743bcdfc7274). Due to intellectual property rights applied to the \"Percentage of population over 65 years (2020), Nov. 2021\" this dataset is not available for public download.\n\nThe above applies without prejudice of the License CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/). 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The data sources behind the dataset are the 'EEA Percentage of population over 65 years (2020), Nov. 2021' and the data contained in the EEA 'Urban green Infrastructure, 2018' dashboard. \n\nThe temporal coverage of the data is: 2018/2020 (green space data is from 2018; the socio-economic data is from 2020, gap filled).\n\nThe data is included in the European Climate and Health Observatory: https://climate-adapt.eea.europa.eu/observatory. The European Climate and Health Observatory platform provides easy access to a wide range of relevant publications, tools, websites and other resources related to climate change and human health.\n\nThe EEA 'Urban green Infrastructure, 2018' dashboard has been developed based on: \"Green Infrastructure Elements in the urban and commuting zones in 2018 - version 1, May 2021\".","langeng":"This metadata refers to the vector dataset presenting, at EEA38 member country level for 2020, the ratio of the average urban green space between urban administrative units containing population with the highest percentage of people over 65 years old (top 20% or 5th quintile) and the administrative units containing population with the lowest proportion of people over 65 years old (bottom 20% or 1st quintile) in a given country. The data sources behind the dataset are the 'EEA Percentage of population over 65 years (2020), Nov. 2021' and the data contained in the EEA 'Urban green Infrastructure, 2018' dashboard. \n\nThe temporal coverage of the data is: 2018/2020 (green space data is from 2018; the socio-economic data is from 2020, gap filled).\n\nThe data is included in the European Climate and Health Observatory: https://climate-adapt.eea.europa.eu/observatory. The European Climate and Health Observatory platform provides easy access to a wide range of relevant publications, tools, websites and other resources related to climate change and human health.\n\nThe EEA 'Urban green Infrastructure, 2018' dashboard has been developed based on: \"Green Infrastructure Elements in the urban and commuting zones in 2018 - version 1, May 2021\"."},"isHarvested":"false","th_eea-topics":[],"th_httpinspireeceuropaeumetadatacodelistSpatialScope-SpatialScopeNumber":"1","specificationConformance":[{"link":"","explanation":"See the referenced specification","title":"Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services","date":"2010-12-08","pass":""}],"logo":"/images/logos/d1bd08f0-16ac-47c3-b581-2e8db715530b.png","creationYearForResource":"2022","MD_LegalConstraintsOtherConstraintsObject":[{"langeng":"public access limited according to Article 13(1)(a) of the INSPIRE Directive","default":"public access limited according to Article 13(1)(a) of the INSPIRE Directive","link":"http://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/INSPIRE_Directive_Article13_1a"},{"default":"This data is a derived product based on the \"Percentage of population over 65 years (2020), Nov. 2021\" (https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/2b345477-33df-491f-be4d-743bcdfc7274). Due to intellectual property rights applied to the \"Percentage of population over 65 years (2020), Nov. 2021\" this dataset is not available for public download.\n\nThe above applies without prejudice of the License CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Copyright holder: European Environment Agency (EEA).","langeng":"This data is a derived product based on the \"Percentage of population over 65 years (2020), Nov. 2021\" (https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/2b345477-33df-491f-be4d-743bcdfc7274). Due to intellectual property rights applied to the \"Percentage of population over 65 years (2020), Nov. 2021\" this dataset is not available for public download.\n\nThe above applies without prejudice of the License CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Copyright holder: European Environment Agency (EEA)."}],"op5":["1","0","2"],"lineageObject":{"default":"The administrative units within Functional Urban Areas in each country were classified into 5 quintiles considering the percentage of people over 65 years old. For each quintile, the average (mean) percentage of the administrative unit area that is classified as urban green space was calculated. The mean percentage of the administrative unit area that is classified as urban green space in the 5th quintile of percentage of people over 65 was divided by the mean percentage of the administrative unit area that is classified as urban green space in the 1st quintile of percentage of people over 65 to obtain the ratio.\n\nThe sources that underpin the dataset are: \"Percentage of population over 65 years (2020), Nov. 2021\"(https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/2b345477-33df-491f-be4d-743bcdfc7274) and the data contained in the EEA 'Urban green Infrastructure, 2018' dashboard (https://www.eea.europa.eu/data-and-maps/dashboards/urban-green-infrastructure-2018).","langeng":"The administrative units within Functional Urban Areas in each country were classified into 5 quintiles considering the percentage of people over 65 years old. For each quintile, the average (mean) percentage of the administrative unit area that is classified as urban green space was calculated. The mean percentage of the administrative unit area that is classified as urban green space in the 5th quintile of percentage of people over 65 was divided by the mean percentage of the administrative unit area that is classified as urban green space in the 1st quintile of percentage of people over 65 to obtain the ratio.\n\nThe sources that underpin the dataset are: \"Percentage of population over 65 years (2020), Nov. 2021\"(https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/2b345477-33df-491f-be4d-743bcdfc7274) and the data contained in the EEA 'Urban green Infrastructure, 2018' dashboard (https://www.eea.europa.eu/data-and-maps/dashboards/urban-green-infrastructure-2018)."},"op0":["1","0","2"],"mainLanguage":"eng","isOpenData":"false","docType":"metadata","documentStandard":"iso19115-3.2018","th_httpinspireeceuropaeutheme-theme_tree":{"default":["Human health and safety","Population distribution — 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European Journal of Soil Science. 66(1): 121-134 . doi: 10.1111/ejss.12193\n\nAbout ESDAC:\nPanagos P., Van Liedekerke M., Jones A., Montanarella L., “European Soil Data Centre: Response to European policy support and public data requirements”; (2012) Land Use Policy, 29 (2), pp. 329-338. doi:10.1016/j.landusepol.2011.07.003\nEuropean Soil Data Centre (ESDAC), esdac.jrc.ec.europa.eu, European Commission, Joint Research Centre","langeng":"For information about the sources used and the generation of the maps: \nD. de Brogniez, C. Ballabio, A. Stevens, R. J. A. Jones, L. Montanarella and B. van Wesemael (2014). A map of the topsoil organic carbon content of Europe generated by a generalized additive model. 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The TCD raster product provides information on the proportional crown coverage per pixel at 10m spatial resolution and ranges from 0% (all non-tree covered areas) to 100%, whereby Tree Cover Density is defined as the \"vertical projection of tree crowns to a horizontal earth’s surface“. The production of the High Resolution Forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme.\n\nThe HRL 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:\n1. Tree cover density (TCD) (level of tree cover density in a range from 0-100%)\n2. Dominant leaf type (DLT) (broadleaved or coniferous majority)\n3. 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: 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 (now only available on demand), 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 100 m product trees under agricultural use and urban context from the support layer are removed.\nNEW 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.\n\nThis dataset is provided as 10 meter rasters (fully conformant with the EEA reference grid) in 100 x 100 km tiles grouped according to the EEA38 countries and the United Kingdom.\n\nYou can find more information about the product here: https://land.copernicus.eu/en/products/high-resolution-layer-tree-cover-density/tree-cover-density-2018.","langeng":"This metadata refers to the HRL Forest 2018 primary status layer Tree Cover Density (TCD). The TCD raster product provides information on the proportional crown coverage per pixel at 10m spatial resolution and ranges from 0% (all non-tree covered areas) to 100%, whereby Tree Cover Density is defined as the \"vertical projection of tree crowns to a horizontal earth’s surface“. The production of the High Resolution Forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme.\n\nThe HRL 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:\n1. Tree cover density (TCD) (level of tree cover density in a range from 0-100%)\n2. Dominant leaf type (DLT) (broadleaved or coniferous majority)\n3. 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: 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 (now only available on demand), 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 100 m product trees under agricultural use and urban context from the support layer are removed.\nNEW 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.\n\nThis dataset is provided as 10 meter rasters (fully conformant with the EEA reference grid) in 100 x 100 km tiles grouped according to the EEA38 countries and the United Kingdom.\n\nYou can find more information about the product here: https://land.copernicus.eu/en/products/high-resolution-layer-tree-cover-density/tree-cover-density-2018."},"isHarvested":"false","th_eea-topics":[{"langeng":"Land use","default":"Land use","link":"https://www.eea.europa.eu/themes#term23"}],"th_httpinspireeceuropaeumetadatacodelistSpatialScope-SpatialScopeNumber":"1","specificationConformance":[{"link":"","explanation":"See the referenced specification","title":"Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services","date":"2010-12-08","pass":""}],"logo":"/images/logos/d1bd08f0-16ac-47c3-b581-2e8db715530b.png","creationYearForResource":"2020","MD_LegalConstraintsOtherConstraintsObject":[{"langeng":"no limitations to public access","default":"no limitations to public access","link":"http://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/noLimitations"},{"default":"The Copernicus component is governed by Regulation (EU) No 2021/696 of the European Parliament and of the Council of 28 April 2021 establishing the Union Space Programme and the European Union Agency for the Space Programme and repealing Regulations (EU) No 912/2010, (EU) No 1285/2013 and (EU) No 377/2014 and Decision No 541/2014/EU. Within the Copernicus component, a portfolio of land monitoring activities has been delegated by the European Union to the European Environment Agency (EEA) and the DG Joint Research Centre of the European Commission.\n\n The Copernicus land monitoring products and services are made available on a principle of full, open and free access, as established by the Commission Delegated Regulation (EU) No 1159/2013 of 12 July 2013.\n\n Free, full and open access to the products and services of the Copernicus Land Monitoring Service is made on the conditions that:\n\n 1. When distributing or communicating Copernicus Land Monitoring Service products and services (data, software scripts, web services, user and methodological documentation and similar) to the public, users shall inform the public of the source of these products and services.\n\n 2. Where the Copernicus Land Monitoring Service products and services have been adapted or modified by the user, the user shall clearly state this.\n\n 3. Users shall make sure not to convey the impression to the public that the user's activities are officially endorsed by the European Union.","langeng":"The Copernicus component is governed by Regulation (EU) No 2021/696 of the European Parliament and of the Council of 28 April 2021 establishing the Union Space Programme and the European Union Agency for the Space Programme and repealing Regulations (EU) No 912/2010, (EU) No 1285/2013 and (EU) No 377/2014 and Decision No 541/2014/EU. Within the Copernicus component, a portfolio of land monitoring activities has been delegated by the European Union to the European Environment Agency (EEA) and the DG Joint Research Centre of the European Commission.\n\n The Copernicus land monitoring products and services are made available on a principle of full, open and free access, as established by the Commission Delegated Regulation (EU) No 1159/2013 of 12 July 2013.\n\n Free, full and open access to the products and services of the Copernicus Land Monitoring Service is made on the conditions that:\n\n 1. When distributing or communicating Copernicus Land Monitoring Service products and services (data, software scripts, web services, user and methodological documentation and similar) to the public, users shall inform the public of the source of these products and services.\n\n 2. Where the Copernicus Land Monitoring Service products and services have been adapted or modified by the user, the user shall clearly state this.\n\n 3. Users shall make sure not to convey the impression to the public that the user's activities are officially endorsed by the European Union."}],"op5":["1","0"],"lineageObject":{"default":"Hierarchical spatio-temporal classification of time features derived from Sentinel-2A+B time series (Level-2A data) using a Random Forest (RF) classifier with 200 trees. The selected time window ranges from 01-03-2018 to 31-10-2018 and covers the whole reference year 2018. In total, 59 statistical time features have been calculated using more than 138,000 samples, automatically collected from the LUCAS 2018 database and various CLMS products plus additional manual sampling. First, a binary Tree Cover Mask (TCM) with 2 classes (all non-tree covered areas / tree cover) has been generated. Various post-processing steps have been applied in order to improve the quality of the mask (e.g. correction of omission errors caused by the topographic overcorrection within the native Sentinel-2 Level-2A input data). Subsequently, Tree Cover Density values have been classified within the confines of the Tree Cover Mask by a multiple linear regression algorithm using more than 33,000 samples. The final product will be accompanied by a series of quality layers (Confidence Layer, Data Score, Layer, Parent Scene Identification Layer).\n\nQuality assurance follows the ISO 9001:2015 standards for Quality Management and comprises of dedicated procedures of quality checks (QA breakpoints) during implementation of the production chain, in order to keep persistent control over the various stages of production, assure fitness-for-purpose of the end-products and that all quality requirements are fulfilled. 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The data quality elements considered are: (i) Completeness, (ii) Logical Consistency, (iii) positional accuracy, (iv) Thematic Accuracy, (v) Temporal quality and (vi) Usability.\n\nGeometric accuracy (positioning scale): Less than one pixel (10m) according to ortho-rectified satellite image base (Sentinel-2 Level-2A) delivered by ESA.\nThematic target accuracy: 90% producer and user accuracy.\nPreliminary validation results TCD (30% density threshold): 96.43% overall accuracy with a 95% confidence level applied (no tree cover: 98.62% producer accuracy, 95.92% user accuracy; tree cover: 92.52% producer accuracy; 97.40% user accuracy).\nThematic accuracy has been assessed using a stratified random sampling approach with 9,695 points (area weighted), visually interpreted using VHR_IMAGE_2018 data and Sentinel-2 time series data, complemented by additional data sources like virtual globes (e.g. Google Earth Pro).","langeng":"Hierarchical spatio-temporal classification of time features derived from Sentinel-2A+B time series (Level-2A data) using a Random Forest (RF) classifier with 200 trees. 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The data quality elements considered are: (i) Completeness, (ii) Logical Consistency, (iii) positional accuracy, (iv) Thematic Accuracy, (v) Temporal quality and (vi) Usability.\n\nGeometric accuracy (positioning scale): Less than one pixel (10m) according to ortho-rectified satellite image base (Sentinel-2 Level-2A) delivered by ESA.\nThematic target accuracy: 90% producer and user accuracy.\nPreliminary validation results TCD (30% density threshold): 96.43% overall accuracy with a 95% confidence level applied (no tree cover: 98.62% producer accuracy, 95.92% user accuracy; tree cover: 92.52% producer accuracy; 97.40% user accuracy).\nThematic accuracy has been assessed using a stratified random sampling approach with 9,695 points (area weighted), visually interpreted using VHR_IMAGE_2018 data and Sentinel-2 time series data, complemented by additional data sources like virtual globes (e.g. Google Earth Pro).\n\nThe TCD at 100m spatial resolution is derived through spatial aggregation from the 10m status layer according to the following rules:\n1) The spatially consistent EEA 100m grid is overlaid to the 10m Tree Cover Density product. \n2) For each 100m grid cell the arithmetic mean density of all underlying 10m pixels (with density values from 0-100) is calculated. The thereof resulting mean values from the aggregation (floating point data type) are rounded and finally converted to integer values (e.g. raw values in the range from 33.5 to 34.4 are converted to a density value of 34).\n3) The class 255 = outside area is predefined by the 100m boundary layer in European and national projection and remains unchanged.\nAs a consequence of this aggregation, small patches in the 100m x 100m grid will be generalised (i.e. exaggerated) according to the above aggregation rules.","langeng":"Hierarchical spatio-temporal classification of time features derived from Sentinel-2A+B time series (Level-2A data) using a Random Forest (RF) classifier with 200 trees. The selected time window ranges from 01-03-2018 to 31-10-2018 and covers the whole reference year 2018. In total, 59 statistical time features have been calculated using more than 138,000 samples, automatically collected from the LUCAS 2018 database and various CLMS products plus additional manual sampling. First, a binary Tree Cover Mask (TCM) with 2 classes (all non-tree covered areas / tree cover) has been generated. Various post-processing steps have been applied in order to improve the quality of the mask (e.g. correction of omission errors caused by the topographic overcorrection within the native Sentinel-2 Level-2A input data). Subsequently, Tree Cover Density values have been classified within the confines of the Tree Cover Mask by a multiple linear regression algorithm using more than 33,000 samples. The final product will be accompanied by a series of quality layers (Confidence Layer, Data Score, Layer, Parent Scene Identification Layer).\n\nQuality assurance follows the ISO 9001:2015 standards for Quality Management and comprises of dedicated procedures of quality checks (QA breakpoints) during implementation of the production chain, in order to keep persistent control over the various stages of production, assure fitness-for-purpose of the end-products and that all quality requirements are fulfilled. Priority has been given to the target thematic accuracy to be achieved by each product, as well as to the issues of product consistency (spatial, thematic, temporal) and homogeneity. Quality Assessment: The quality assessment has been performed according to INSPIRE Data Specifications. The data quality elements considered are: (i) Completeness, (ii) Logical Consistency, (iii) positional accuracy, (iv) Thematic Accuracy, (v) Temporal quality and (vi) Usability.\n\nGeometric accuracy (positioning scale): Less than one pixel (10m) according to ortho-rectified satellite image base (Sentinel-2 Level-2A) delivered by ESA.\nThematic target accuracy: 90% producer and user accuracy.\nPreliminary validation results TCD (30% density threshold): 96.43% overall accuracy with a 95% confidence level applied (no tree cover: 98.62% producer accuracy, 95.92% user accuracy; tree cover: 92.52% producer accuracy; 97.40% user accuracy).\nThematic accuracy has been assessed using a stratified random sampling approach with 9,695 points (area weighted), visually interpreted using VHR_IMAGE_2018 data and Sentinel-2 time series data, complemented by additional data sources like virtual globes (e.g. Google Earth Pro).\n\nThe TCD at 100m spatial resolution is derived through spatial aggregation from the 10m status layer according to the following rules:\n1) The spatially consistent EEA 100m grid is overlaid to the 10m Tree Cover Density product. \n2) For each 100m grid cell the arithmetic mean density of all underlying 10m pixels (with density values from 0-100) is calculated. 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Within the Copernicus component, a portfolio of land monitoring activities has been delegated by the European Union to the European Environment Agency (EEA) and the DG Joint Research Centre of the European Commission.\n\n The Copernicus land monitoring products and services are made available on a principle of full, open and free access, as established by the Commission Delegated Regulation (EU) No 1159/2013 of 12 July 2013.\n\n Free, full and open access to the products and services of the Copernicus Land Monitoring Service is made on the conditions that:\n\n 1. When distributing or communicating Copernicus Land Monitoring Service products and services (data, software scripts, web services, user and methodological documentation and similar) to the public, users shall inform the public of the source of these products and services.\n\n 2. 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The high resolution forest change products comprise a simple tree cover density change product for 2012-2015 (% increase or decrease of real tree cover density changes). \nThe production of the high resolution forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme."},"isHarvested":"false","th_eea-topics":[{"langeng":"Biodiversity","default":"Biodiversity","link":"https://www.eea.europa.eu/themes#term4"},{"langeng":"Forests and forestry","default":"Forests and forestry","link":"https://www.eea.europa.eu/themes#term20"},{"langeng":"Land use","default":"Land use","link":"https://www.eea.europa.eu/themes#term23"}],"th_httpinspireeceuropaeumetadatacodelistSpatialScope-SpatialScopeNumber":"1","specificationConformance":[{"link":"","explanation":"See the referenced specification","title":"Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services","date":"2010-12-08","pass":""}],"logo":"/images/logos/d1bd08f0-16ac-47c3-b581-2e8db715530b.png","creationYearForResource":"2018","MD_LegalConstraintsOtherConstraintsObject":[{"langeng":"no limitations to public access","default":"no limitations to public access","link":"http://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/noLimitations"},{"default":"The Copernicus component is governed by Regulation (EU) No 2021/696 of the European Parliament and of the Council of 28 April 2021 establishing the Union Space Programme and the European Union Agency for the Space Programme and repealing Regulations (EU) No 912/2010, (EU) No 1285/2013 and (EU) No 377/2014 and Decision No 541/2014/EU. Within the Copernicus component, a portfolio of land monitoring activities has been delegated by the European Union to the European Environment Agency (EEA) and the DG Joint Research Centre of the European Commission.\n\n The Copernicus land monitoring products and services are made available on a principle of full, open and free access, as established by the Commission Delegated Regulation (EU) No 1159/2013 of 12 July 2013.\n\n Free, full and open access to the products and services of the Copernicus Land Monitoring Service is made on the conditions that:\n\n 1. When distributing or communicating Copernicus Land Monitoring Service products and services (data, software scripts, web services, user and methodological documentation and similar) to the public, users shall inform the public of the source of these products and services.\n\n 2. Where the Copernicus Land Monitoring Service products and services have been adapted or modified by the user, the user shall clearly state this.\n\n 3. Users shall make sure not to convey the impression to the public that the user's activities are officially endorsed by the European Union.","langeng":"The Copernicus component is governed by Regulation (EU) No 2021/696 of the European Parliament and of the Council of 28 April 2021 establishing the Union Space Programme and the European Union Agency for the Space Programme and repealing Regulations (EU) No 912/2010, (EU) No 1285/2013 and (EU) No 377/2014 and Decision No 541/2014/EU. Within the Copernicus component, a portfolio of land monitoring activities has been delegated by the European Union to the European Environment Agency (EEA) and the DG Joint Research Centre of the European Commission.\n\n The Copernicus land monitoring products and services are made available on a principle of full, open and free access, as established by the Commission Delegated Regulation (EU) No 1159/2013 of 12 July 2013.\n\n Free, full and open access to the products and services of the Copernicus Land Monitoring Service is made on the conditions that:\n\n 1. When distributing or communicating Copernicus Land Monitoring Service products and services (data, software scripts, web services, user and methodological documentation and similar) to the public, users shall inform the public of the source of these products and services.\n\n 2. Where the Copernicus Land Monitoring Service products and services have been adapted or modified by the user, the user shall clearly state this.\n\n 3. Users shall make sure not to convey the impression to the public that the user's activities are officially endorsed by the European Union."}],"op5":["1","0"],"lineageObject":{"default":"Semi-automatic classification of pre-processed multitemporal High Resolution (HR) satellite image data (IRS-p6, ResourceSat-2, SPOT-4, SPOT-5) with reference year 2012 (+/- 1 year), using supervised and unsupervised elements, leading to scene-based binary masks of tree cover. Subsequently, interactive manual corrections of the derived tree cover mask have been performed and integrated to a seamless mosaic. Thereafter a monotemporal, regression-based thematic classification of Tree Cover Density values has been performed on HR_IMAGE_2012 and selected Landsat 8 data. Finally, TCD results have been mosaicked to an area wide pan-European TCD dataset, absolutely and relatively calibrated (if necessary) to the HRL Forest 2015 reference and intersected with the derived tree cover mask. Geometric accuracy (positioning scale): Less than one pixel according to ortho-rectified satellite image base delivered by ESA. Thematic target accuracy: >90% Overall Accuracy. \nQuality assurance follows the ISO9000 standards for Quality Management and comprises of dedicated procedures of ongoing quality checks (QA breakpoints) during implementation of the production chain, in order to keep persistent control over the various stages of production, assure fitness-for-purpose of the end-products and that all quality requirements are fulfilled. Priority has been given to the target thematic accuracies to be achieved by each product, as well as to the issues of product consistency (spatial, thematic, temporal) and homogeneity. Quality Assessment: The quality assessment has been performed according to INSPIRE Data Specifications. The data quality elements considered are:\n(i) Completeness,\n(ii) Logical Consistency,\n(iii) Thematic Accuracy,\n(iv) Temporal quality and\n(v) Usability.\nEach of them (excl. the Thematic Accuracy hereafter) forms a section in the QA/QC Procedures.","langeng":"Semi-automatic classification of pre-processed multitemporal High Resolution (HR) satellite image data (IRS-p6, ResourceSat-2, SPOT-4, SPOT-5) with reference year 2012 (+/- 1 year), using supervised and unsupervised elements, leading to scene-based binary masks of tree cover. Subsequently, interactive manual corrections of the derived tree cover mask have been performed and integrated to a seamless mosaic. Thereafter a monotemporal, regression-based thematic classification of Tree Cover Density values has been performed on HR_IMAGE_2012 and selected Landsat 8 data. Finally, TCD results have been mosaicked to an area wide pan-European TCD dataset, absolutely and relatively calibrated (if necessary) to the HRL Forest 2015 reference and intersected with the derived tree cover mask. Geometric accuracy (positioning scale): Less than one pixel according to ortho-rectified satellite image base delivered by ESA. Thematic target accuracy: >90% Overall Accuracy. \nQuality assurance follows the ISO9000 standards for Quality Management and comprises of dedicated procedures of ongoing quality checks (QA breakpoints) during implementation of the production chain, in order to keep persistent control over the various stages of production, assure fitness-for-purpose of the end-products and that all quality requirements are fulfilled. Priority has been given to the target thematic accuracies to be achieved by each product, as well as to the issues of product consistency (spatial, thematic, temporal) and homogeneity. Quality Assessment: The quality assessment has been performed according to INSPIRE Data Specifications. The data quality elements considered are:\n(i) Completeness,\n(ii) Logical Consistency,\n(iii) Thematic Accuracy,\n(iv) Temporal quality and\n(v) Usability.\nEach of them (excl. the Thematic Accuracy hereafter) forms a section in the QA/QC Procedures."},"publisherOrgForResourceObject":{"default":"European Environment Agency","langeng":"European Environment Agency"},"op0":["1","0","2"],"mainLanguage":"eng","isOpenData":"false","docType":"metadata","documentStandard":"iso19115-3.2018","th_httpinspireeceuropaeutheme-theme_tree":{"default":["Land 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Within the Copernicus component, a portfolio of land monitoring activities has been delegated by the European Union to the European Environment Agency (EEA) and the DG Joint Research Centre of the European Commission.\n\n The Copernicus land monitoring products and services are made available on a principle of full, open and free access, as established by the Commission Delegated Regulation (EU) No 1159/2013 of 12 July 2013.\n\n Free, full and open access to the products and services of the Copernicus Land Monitoring Service is made on the conditions that:\n\n 1. When distributing or communicating Copernicus Land Monitoring Service products and services (data, software scripts, web services, user and methodological documentation and similar) to the public, users shall inform the public of the source of these products and services.\n\n 2. 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Priority has been given to the target thematic accuracy to be achieved by each product, as well as to the issues of product consistency (spatial, thematic, temporal) and homogeneity. Quality Assessment: The quality assessment has been performed according to INSPIRE Data Specifications. The data quality elements considered are: (i) Completeness, (ii) Logical Consistency, (iii) positional accuracy, (iv) Thematic Accuracy, (v) Temporal quality and (vi) Usability.\n\nGeometric accuracy (positioning scale): Less than one pixel (10m) according to ortho-rectified satellite image base (Sentinel-2 Level-2A) delivered by ESA.\n\nThematic target accuracy: 90% producer and user accuracy.\nFinal validation results TCCM 1518 at pan-European level: 97.99% overall accuracy with a 95% confidence level applied (unchanged areas with no tree cover: 98.45% producer accuracy and 98.47% user accuracy; new tree cover: 58.74% producer accuracy and 86.73% user accuracy; loss of tree cover: 65.08% producer accuracy and 87.28% user accuracy; unchanged areas with tree cover: 98.02% producer accuracy and 97.93% user accuracy).\nThematic accuracy has been assessed using a stratified random sampling approach with 9,695 points (area weighted), visually interpreted using VHR_IMAGE_2018, VHR_IMAGE_2015 and Sentinel-2 time series data, complemented by additional data sources like virtual globes (e.g. Google Earth Pro).","langeng":"The Tree Cover Change Mask (TCCM) 2015-2018 is a change product based on the binary Tree Cover Masks (TCMs) of the primary status layers Dominant Leaf Type 2015 at 20m spatial resolution and Dominant Leaf Type 2018 at 10m spatial resolution. First, the 2018 product has been aggregated to 20m to enable a map-to-map comparison. The therof derived pixel-based change map is categorised into loss and gain strata and subsequently reclassified using the Reference Database for Change Calibration to improve the TCM 2018 and to detect omission and commission errors in the TCM 2015. Subsequently, a 1 pixel boundary filter has been applied in order to mitigate geometric imprecisions between the input layers 2015 and 2018, caused by the different satellite input data characteristics. Remaining change areas are filtered according to the specified Minimum Mapping Unit (MMU) of 1 ha. Finally, a manual enhancement has been performed within identified regional clusters of remaining issues, considering both: loss and gain of tree cover. The product covers the whole EEA39 area and is provided in European projection. National products might show a broken MMU due to reprojection.\n\nQuality assurance follows the ISO 9001:2015 standards for Quality Management and comprises of dedicated procedures of quality checks (QA breakpoints) during implementation of the production chain, in order to keep persistent control over the various stages of production, assure fitness-for-purpose of the end-products and that all quality requirements are fulfilled. Priority has been given to the target thematic accuracy to be achieved by each product, as well as to the issues of product consistency (spatial, thematic, temporal) and homogeneity. Quality Assessment: The quality assessment has been performed according to INSPIRE Data Specifications. The data quality elements considered are: (i) Completeness, (ii) Logical Consistency, (iii) positional accuracy, (iv) Thematic Accuracy, (v) Temporal quality and (vi) Usability.\n\nGeometric accuracy (positioning scale): Less than one pixel (10m) according to ortho-rectified satellite image base (Sentinel-2 Level-2A) delivered by ESA.\n\nThematic target accuracy: 90% producer and user accuracy.\nFinal validation results TCCM 1518 at pan-European level: 97.99% overall accuracy with a 95% confidence level applied (unchanged areas with no tree cover: 98.45% producer accuracy and 98.47% user accuracy; new tree cover: 58.74% producer accuracy and 86.73% user accuracy; loss of tree cover: 65.08% producer accuracy and 87.28% user accuracy; unchanged areas with tree cover: 98.02% producer accuracy and 97.93% user accuracy).\nThematic accuracy has been assessed using a stratified random sampling approach with 9,695 points (area weighted), visually interpreted using VHR_IMAGE_2018, 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