WMS This is a description of your Web Map Server. WFS WMS GEOSERVER

NONE NONE text/xml image/png application/atom+xml application/json;type=utfgrid application/pdf application/rss+xml application/vnd.google-earth.kml+xml application/vnd.google-earth.kml+xml;mode=networklink application/vnd.google-earth.kmz image/geotiff image/geotiff8 image/gif image/jpeg image/png; mode=8bit image/svg+xml image/tiff image/tiff8 image/vnd.jpeg-png text/html; subtype=openlayers text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml; subtype=gml/3.1.1 text/html application/json XML INIMAGE BLANK JSON This is a description of your Web Map Server. EPSG:4326 EPSG:3785 EPSG:3857 EPSG:900913 EPSG:32647 EPSG:32736 CRS:84 18.908178833967074 27.897681642582587 53.253480608528164 57.06166901105508 geonode:BFor2018_eld Percentage of area occupied by riparian forests and woodland (3.1.1. Broad-leaved forest, 3.1.2. Coniferous forest, 3.1.3. Mixed forest, 3.2.1. and 3.2.4. Transitional woodland shrub) in a buffer strip of 25 meters at eldership level. Data expressed in percent. Land use data from Corine Land Cover 2018 and water courses from Lithuanian cadastre. Classes were carried out according to Quantile interval. Waste EPSG:3035 CRS:84 20.56415915718929 27.21931886282636 53.60463616097619 56.817110500282716 other other text/xml other other other other geonode:CStorage Carbon sequestration calculated using Net primary production (NPP) (average 2000-2013) per eldership (average). NPP data is derived from MODIS/055/MOD17A3 satelite (500 m resolution) Data in kg C/m². Classes were carried out according to Quantile interval. Carbon EPSG:3035 CRS:84 20.56415915718929 27.21931886282636 53.60463616097619 56.817110500282716 other other text/xml other other other other geonode:Climate_Regulation Climate regulation calculated using as indicator net primary production (NPP)(average 2000-2013) by forests and woodlands (3.1.1. Broad-leaved forest, 3.1.2. Coniferous forest, 3.1.3. Mixed forest and 3.2.4. Transitional woodland shrub) per eldership (average). NPP data is derived from MODIS/055/MOD17A3 satelite (500 m resolution) and Corine land cover 2018 data from (https://land.copernicus.eu/pan-european/corine-land-cover/clc2018). Data in kg C/m² . Classes were carried out according to Quantile interval. Climate_Regulation EPSG:3035 CRS:84 20.564159157189266 27.219318862826327 53.60463616097616 56.81711050028268 other other text/xml other other other other geonode:CropC Total average (2014-2018) of cereal and grain production (grain crops, cereals, winter cereals, winter wheat, winter triticale, winter rye, winter barley, spring cereals, spring wheat, spring triticale, spring rye, oats, buckwheat, mixed cereals, grain maize, dried pulses, peas, beans, vetches, mixed dried pulses, lupines, mustard, hemp and sunflower). Data in tons per hectare (t/ha). Cereals and grain data are from the Lithuanian statistical portal (2018). Classes were carried out according to Quantile interval. Grain Cereal EPSG:3035 CRS:84 20.56415915718929 27.21931886282636 53.60463616097619 56.817110500282716 other other text/xml other other other other geonode:CropFV Total Average (2013-2017) of field vegetables (all brassicas, carrots, onions, beetroot). Data in tons per hectare (t/ha). Field vegetables data is from the Lithuanian statistical portal (2018). Classes were carried out according to Quantile interval. Vegetables EPSG:3035 CRS:84 20.56415915718929 27.21931886282636 53.60463616097619 56.817110500282716 other other text/xml other other other other geonode:CropGV Total Average (2013-2017) of vegetables produced in greenhouses (tomatoes and cucumbers). Data in tons per hectare (t/ha). Vegetables under glass data are from the Lithuanian statistical portal (2018). Classes were carried out according to Quantile interval. Vegetables EPSG:3035 CRS:84 20.56415915718929 27.21931886282636 53.60463616097619 56.817110500282716 other other text/xml other other other other geonode:Crop_Dem Crop demand calculated from the average (2000-2017) bread and grain, vegetables, potatoes, fruits and berries consumption per capita at eldership level. The average of Bread and grain, vegetables, potatoes, fruits and berries consumption per capita was multiplied by the number of residents in the different elderships in 2011. Crop consumption per capita and Census 2011 data from Lithuanian Statistical Department (2018). Data in kg/eldership. Classes were carried out according to Quantile interval. CropC EPSG:3035 CRS:84 20.564159157189266 27.219318862826327 53.60463616097616 56.81711050028268 other other text/xml other other other other geonode:Crop_mun Crop demand calculated from the average (2000-2017) bread and grain, vegetables, potatoes, fruits and berries consumption per capita at municipal level. The average of Bread and grain, vegetables, potatoes, fruits and berries consumption per capita was multiplied by the number of residents in the different municipalities (average 20015-2018). Crop consumption per capita and Census 2011 data from Lithuanian Statistical Department (2018). Data in kg/eldership. Classes were carried out according to Quantile interval. CropC EPSG:3035 CRS:84 20.56415915718929 27.21931886282636 53.60463616097619 56.817110500282716 other other text/xml other other other other geonode:Cult_Nat Cultural and natural supply service was calculated by using the point and area data of natural and cultural objects. Data sample included botanical and geological objects, forthills, natural monuments (points), nature reservations, parks, cultural heritage sites (areas). The data was gathered from Department of Cultural Heritage, piliakalniai.lt, Cadastre of protected areas in Lithuania mapped in Google Maps based on data provided in State service for protected areas website.The kernel density estimation was applied to point data, results were reclassified (jenks), overlayed with area data. The zonal statistics was applied to calculate the mean of cultural/natural supply density per eldership. Cultural_Natural Cultural EPSG:3035 CRS:84 20.56415915718929 27.21931886282636 53.60463616097619 56.817110500282716 other other text/xml other other other other geonode:FDFinal Build up areas (1.1.1. Continuous urban fabric, 1.1.2. Discontinuous urban fabric, 1.2.1. Industrial and commercial units, 1.2.2. Road and rail networks and associated land, 1.2.3. Port areas, 1.2.4. Airports, Construction sites and 1.4.2. Sport and leisure facilities) in floodplain and coastal risky areas at eldership level. Floodplain and coastal risk areas have the probability to be flooded one time per thousand years (returning period). Floodplain and coastal risky areas data from from Lithuanian Environmental Protection Agency and land use data from Corine Land Cover 2018. Flood_area_protection EPSG:3035 CRS:84 20.576225320881125 27.21931886282636 53.650964178221074 56.817110500282716 other other text/xml other other other other geonode:Fish_consumption Wild seafood demand was calculated from the average (2009-2019) of fish consumption per capita at eldership level. The average fish consumption was multiplied by the number of residents in the different elderships in 2013. Fish consumption per capita and Census 2013 data extracted from Lithuanian Official Statistics Portal, 2013 and Official Lithuanian Statistics Department 2019. Data in kg/eldership. Classes were carried out according to Natural jenks interval. Marine Fish_consumption EPSG:3035 CRS:84 20.56415915718929 27.21931886282636 53.60463616097619 56.817110500282716 other other text/xml other other other other geonode:Flood_Demand_ Area for flood protection (1.1.1. Continuous urban fabric, 1.1.2. Discontinuous urban fabric, 1.2.1. Industrial and commercial units, 1.2.2. Road and rail networks and associated land, 1.2.3. Port areas, 1.2.4. Airports, 1.3.1. Mineral extraction sites, 1.3.2. Dump sites, 1.3.3. Construction sites and 1.4.2. Sport and leisure facilities) in floodplain risk areas. Floodplain risk areas have the probability to be affected one time per thousand years (returning period). Floodplain and coastal risk areas from Lithuanian Environmental Protection Agency and land use from Corine Land Cover 2018. Demand EPSG:3035 CRS:84 20.60563216488474 27.08824711125419 53.70340383148196 56.75295655935475 other other other other text/xml other other geonode:Flood_Supply_Ratio Ratio between area between flood protection area supply in flood plain and coastal risky areas (1.4.1. Green urban areas, 2.2.2. Fruit trees and berry plantations 2.3.1. Pastures; 2.4.2. Complex cultivation patterns; 2.4.3. Land principally occupied by agriculture, with significant areas of natural vegetation; 3.1.1. Broad-leaved forest, 3.1.2. Coniferous forest, 3.1.3. Mixed forest, 3.2.1. Natural grassland, 3.2.2. Moors and heathland, 3.2.4. Transitional woodland shrub, 3.3.1. Beaches, dunes, and sand plains, 3.3.3. Sparsely vegetated areas, 4.1.1. Inland marshes and 4.1.2. Peatbogs) and the total flooded area at Eldership level. Floodplain risk areas have the probability to be affected one time per thousand years (returning period). Floodplain and coastal risk areas from Lithuanian Environmental Protection Agency and land use from Corine Land Cover 2018. Flood_area_protection EPSG:3035 CRS:84 20.56415915718929 27.21931886282636 53.60463616097619 56.817110500282716 other other text/xml other other other other geonode:Flood_dem00 Flood Regulation Demand 2000 flood MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Flood_dem06 Flood Regulation Demand 2006 flood MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Flood_dem12 Flood Regulation Demand 2012 flood MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Flood_dem18 Flood Regulation Demand 2018 flood MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Flood_dem90 Flood Regulation Demand 1990 flood MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Flood_sup_00 Flood Regulation Supply, 2000 MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Flood_sup_06 Flood Regulation Supply 2006 flood MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Flood_sup_12 Flood Regulation Supply 2012 MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Flood_sup_18 Flood Regulation Supply 2018 MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Flood_sup_22 Flood Regulation Supply 2022 flood MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Flood_sup_90 Flood Regulation Supply, 1990 flood MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other qqq:Forest_2021 Layers produced under the work carried out within the project “System of Environmental-Economic Accounting – Ecosystem Accounting in Lithuania” (SEEAL), funded by the Lithuanian Research Council (Contract: S-PD-24-18). GeoTIFF WCS Forest_2021 EPSG:3035 CRS:84 20.565366009693403 27.211175667174263 53.6070679325125 56.81744657787916 other other text/xml other other other other qqq:Forest_2024 Layers produced under the work carried out within the project “System of Environmental-Economic Accounting – Ecosystem Accounting in Lithuania” (SEEAL), funded by the Lithuanian Research Council (Contract: S-PD-24-18). Forest_2024 WCS GeoTIFF EPSG:3035 CRS:84 20.56536565140028 27.211175045837166 53.60706655509093 56.81744517973784 other other text/xml other other other other geonode:Fruits_veg Total Average (2013-2017) of fruits and berries (orchards and berries plantations, apples, blackcurrant and strawberries). Data in tons per hectare (t/ha). Crop production (fruits and berries) data are from the Lithuanian statistical portal (2018). Classes were carried out according to Quantile interval. Berries Fruits_veg EPSG:3035 CRS:84 20.56415915718929 27.21931886282636 53.60463616097619 56.817110500282716 other other text/xml other other other other qqq:GWRc_Ka_1989_reclass No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2021_reclass No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2050_s0ssp1_reclass0 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2050_s0ssp1_reclass1 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2050_s0ssp2_reclass No abstract provided GeoTIFF WCS GWRc_Ka_2050_s0ssp2_reclass EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2050_s0ssp2_reclass0 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2050_s0ssp2_reclass1 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2050_s0ssp5_reclass No abstract provided GeoTIFF WCS GWRc_Ka_2050_s0ssp5_reclass EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2050_s0ssp5_reclass0 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2050_s0ssp5_reclass1 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2050_s1ssp1_reclass No abstract provided GeoTIFF WCS GWRc_Ka_2050_s1ssp1_reclass EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2050_s1ssp1_reclass0 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other other other text/xml other other qqq:GWRc_Ka_2050_s1ssp1_reclass1 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2050_s1ssp2_reclass No abstract provided GeoTIFF WCS GWRc_Ka_2050_s1ssp2_reclass EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2050_s1ssp2_reclass0 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2050_s1ssp2_reclass1 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2050_s1ssp5_reclass No abstract provided GeoTIFF WCS GWRc_Ka_2050_s1ssp5_reclass EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2050_s1ssp5_reclass0 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2050_s1ssp5_reclass1 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2050_s2ssp1_reclass No abstract provided GWRc_Ka_2050_s2ssp1_reclass GeoTIFF WCS EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2050_s2ssp1_reclass0 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2050_s2ssp1_reclass1 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2050_s2ssp2_reclass No abstract provided GeoTIFF WCS GWRc_Ka_2050_s2ssp2_reclass EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2050_s2ssp2_reclass0 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2050_s2ssp2_reclass1 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2050_s2ssp5_reclass No abstract provided GeoTIFF WCS GWRc_Ka_2050_s2ssp5_reclass EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2050_s2ssp5_reclass0 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Ka_2050_s2ssp5_reclass1 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:GWRc_Kl_1989_reclass No abstract provided GeoTIFF WCS GWRc_Kl_1989_reclass EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:GWRc_Kl_1989_reclass0 No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:GWRc_Kl_1989_reclass1 No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:GWRc_Kl_2020_reclass No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:GWRc_Kl_2050_s0ssp1_reclass No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:GWRc_Kl_2050_s0ssp2_reclass No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:GWRc_Kl_2050_s0ssp5_reclass No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:GWRc_Kl_2050_s1ssp1_reclass No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:GWRc_Kl_2050_s1ssp2_reclass No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:GWRc_Kl_2050_s1ssp5_reclass No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:GWRc_Kl_2050_s2ssp1_reclass No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:GWRc_Kl_2050_s2ssp2_reclass No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:GWRc_Kl_2050_s2ssp5_reclass No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:GWRc_Vl_1989_reclass No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other qqq:GWRc_Vl_2020_reclass No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other qqq:GWRc_Vl_2050_s0ssp1_reclass No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other qqq:GWRc_Vl_2050_s0ssp2_reclass No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other qqq:GWRc_Vl_2050_s0ssp5_reclass No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other qqq:GWRc_Vl_2050_s1ssp1_reclass No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other qqq:GWRc_Vl_2050_s1ssp2_reclass No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other qqq:GWRc_Vl_2050_s1ssp5_reclass No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other qqq:GWRc_Vl_2050_s2ssp1_reclass No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other qqq:GWRc_Vl_2050_s2ssp2_reclass No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other qqq:GWRc_Vl_2050_s2ssp5_reclass No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other geonode:Grass_Supp Grassland supply in ha (2.3.1. pastures and 3.2.1. natural grassland} per municipality in Lithuania. Pastures and grassland data are from corine land cover 2018 (Copernicus). Data in ha per eldership. Classes were carried out according to Quantile interval. Data in ha per municipality. Grassland_Supply EPSG:4669 CRS:84 20.9540670000083 26.8356224999899 53.896668999488 56.4503709995068 other other text/xml other other other other geonode:Grass_Supp0 Grassland supply in ha (2.3.1. pastures and 3.2.1. natural grassland} per municipality in Lithuania. Pastures and grassland data are from corine land cover 2012 (Copernicus). Data in ha per eldership. Classes were carried out according to Quantile interval. Data in ha per municipality. Grassland_Supply EPSG:3035 CRS:84 20.56415915718929 27.21931886282636 53.60463616097619 56.817110500282716 other other text/xml other other other other geonode:Grassland_Supply Grassland supply in ha (2.3.1. pastures and 3.2.1. natural grassland} per municipality in Lithuania. Pastures and grassland data are from corine land cover 2012 (Copernicus). Data in ha per municipality. Classes were carried out according to Quantile interval. Data in ha per municipality. Grassland_Supply EPSG:4669 CRS:84 20.9540670000083 26.8356224999899 53.896668999488 56.4503709995068 other other text/xml other other other other geonode:Grnd_h2o_dem00 No abstract provided MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Grnd_h2o_dem06 No abstract provided MALPES Groundwater EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Grnd_h2o_dem12 No abstract provided MALPES Groundwater EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Grnd_h2o_dem18 No abstract provided MALPES Groundwater EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Grnd_h2o_dem22 No abstract provided MALPES Groundwater EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Grnd_h2o_dem90_22 No abstract provided MALPES Groundwater EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Grnd_sup_00 Groundwater Recharge Supply 2000 MALPES Groundwater EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Grnd_sup_06 Groundwater Recharge Supply 2006 MALPES Groundwater EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Grnd_sup_12 Groundwater Supply 2012 MALPES Groundwater EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Grnd_sup_18 Groundwater Recharge Supply 2018 MALPES Groundwater EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Grnd_sup_22 Groundwater Recharge Supply 2022 MALPES Groundwater EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Grnd_sup_90 Groundwater recharge supply for 1990 MALPES Groundwater EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:H2O_dem_00 No abstract provided Water demand in NRPs for 2000 MALPES EPSG:3035 CRS:84 20.671610833910012 26.582910458949772 53.84094996715071 56.54370642952675 other other text/xml other other other other geonode:H2O_dem_06 Water demand in NRPs for 2006 MALPES EPSG:3035 CRS:84 20.671610833910012 26.582910458949772 53.84094996715071 56.54370642952675 other other text/xml other other other other geonode:H2O_dem_12 Water demand in NRPs for 2012 MALPES EPSG:3035 CRS:84 20.671610833910012 26.582910458949772 53.84094996715071 56.54370642952675 other other text/xml other other other other geonode:H2O_dem_18 Water demand in NRPs for 2018 MALPES EPSG:3035 CRS:84 20.671610833910012 26.582910458949772 53.84094996715071 56.54370642952675 other other text/xml other other other other geonode:H2O_dem_22 Water demand in NRPs for 2022 MALPES EPSG:3035 CRS:84 20.671610833910012 26.582910458949772 53.84094996715071 56.54370642952675 other other text/xml other other other other geonode:H2O_dem_90 Water demand in Protected Areas 1990 MALPES EPSG:3035 CRS:84 20.671610833910012 26.582910458949772 53.84094996715071 56.54370642952675 other other text/xml other other other other geonode:H2O_surf_sup00 Surface water supply in NRPs for 2000 MALPES EPSG:3035 CRS:84 20.67161083395586 26.582910458932616 53.840949966272454 56.54370642867354 other other text/xml other other other other geonode:H2O_surf_sup06 Surface water supply in national and regional parks for 2006 MALPES EPSG:3035 CRS:84 20.67161083395586 26.582910458932616 53.840949966272454 56.54370642867354 other other text/xml other other other other geonode:H2O_surf_sup12 Surface water supply in national and regional parks for 2012 MALPES EPSG:3035 CRS:84 20.67161083395586 26.582910458932616 53.840949966272454 56.54370642867354 other other text/xml other other other other geonode:H2O_surf_sup18 Surface water supply in national and regional parks for 2018 MALPES EPSG:3035 CRS:84 20.67161083395586 26.582910458932616 53.840949966272454 56.54370642867354 other other text/xml other other other other geonode:H2O_surf_sup22 Surface water supply in national and regional parks for 2022 MALPES EPSG:3035 CRS:84 20.67161083395586 26.582910458932616 53.840949966272454 56.54370642867354 other other text/xml other other other other geonode:H2O_surf_sup90 Surface water supply in NRPs for 1990 MALPES EPSG:3035 CRS:84 20.67161083395586 26.582910458932616 53.840949966272454 56.54370642867354 other other text/xml other other other other geonode:Her_dem00 Heritage Demand 2000-2006 MALPES Heritage EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Her_dem06 Heritage Demand 2006-2012 MALPES Heritage EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Her_dem12_18 Heritage Demand 2012-2018 MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Her_dem18_22 Heritage Demand 2018-2022 MALPES Heritage EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Her_dem90 Heritage Demand 1990-2000 MALPES Heritage EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Her_sup90_00 Heritage Supply 1990-2000 MALPES Heritage EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Her_sup_00_06 Heritage Supply 2000-2006 MALPES Heritage EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Her_sup_06_12 Heritage Supply 2006-2012 MALPES Heritage EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Her_sup_12_18 Heritage Supply 2012-2018 MALPES Heritage EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Her_sup_18_22 Heritage Supply 2018-2022 MALPES Heritage EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other qqq:LU_2050_Kaunas_S1_SSP1 LU_2050_Kaunas_S1_SSP1 WCS GeoTIFF EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 qqq:LU_2050_klaipeda_S2_SSP5 LU_2050_klaipeda_S2_SSP5 WCS GeoTIFF EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 geonode:Livestock_demand Average (2005-2016) number of livestock (Cattle, dairy cows, pigs, sheep, goats and horses) per municipality. Livestock data is from the Lithuanian statistical portal (2018). Classes were carried out according to Quantile interval. Livestock_demand Demand EPSG:4669 CRS:84 20.9540670000083 26.8356224999899 53.896668999488 56.4503709995068 other other text/xml other other other other qqq:Maintenance_of_Nursery_conditions_2021 Layers produced under the work carried out within the project “System of Environmental-Economic Accounting – Ecosystem Accounting in Lithuania” (SEEAL), funded by the Lithuanian Research Council (Contract: S-PD-24-18). GeoTIFF WCS Maintenance_of_Nursery_conditions_2021 EPSG:3035 CRS:84 18.909654496798456 21.246346912275214 55.274543999136746 56.224980544107595 other other text/xml other other other other qqq:Maintenance_of_Nursery_conditions_2024 Layers produced under the work carried out within the project “System of Environmental-Economic Accounting – Ecosystem Accounting in Lithuania” (SEEAL), funded by the Lithuanian Research Council (Contract: S-PD-24-18). GeoTIFF WCS Maintenance_of_Nursery_conditions_2024 EPSG:3035 CRS:84 18.909654496798456 21.246346912275214 55.274543999136746 56.224980544107595 other other text/xml other other other other qqq:Marine_2021 Layers produced under the work carried out within the project “System of Environmental-Economic Accounting – Ecosystem Accounting in Lithuania” (SEEAL), funded by the Lithuanian Research Council (Contract: S-PD-24-18). GeoTIFF WCS Marine_2021 EPSG:3035 CRS:84 18.908977968454384 21.24740873150685 55.2741886313395 56.22502005362124 other other text/xml other other other other qqq:Marine_2024 Layers produced under the work carried out within the project “System of Environmental-Economic Accounting – Ecosystem Accounting in Lithuania” (SEEAL), funded by the Lithuanian Research Council (Contract: S-PD-24-18). GeoTIFF WCS Marine_2024 EPSG:3035 CRS:84 18.908178833967074 21.25079758324748 55.27050584297915 56.231252857858635 other other text/xml other other other other geonode:Med_wastes_drvchC Percentage of area occupied by riparian forests and woodland (3.1.1. Broad-leaved forest, 3.1.2. Coniferous forest, 3.1.3. Mixed forest, 3.2.1. and 3.2.4. Transitional woodland shrub) in a buffer strip of 25 meters at eldership level. Data expressed in percent. Land use data from Corine 1990 and 2018, and water courses from Lithuanian cadastre. Data in %. Drivers of Change MedWastes_drich EPSG:3035 CRS:84 20.56415915718929 27.219318862826317 53.60463616097619 56.81711050028262 other other text/xml other other other other geonode:Mun Grassland supply in ha (2.3.1. pastures and 3.2.1. natural grassland} per municipality in Lithuania. Pastures and grassland data are from corine land cover 2018 (Copernicus). Data in ha per municipality. Classes were carried out according to Quantile interval. Data in ha per municipality. Grassland_Supply EPSG:3035 CRS:84 20.56415915718929 27.21931886282636 53.60463616097619 56.817110500282716 other other text/xml other other other other qqq:Nutrient_Regulation_2021 Layers produced under the work carried out within the project “System of Environmental-Economic Accounting – Ecosystem Accounting in Lithuania” (SEEAL), funded by the Lithuanian Research Council (Contract: S-PD-24-18). GeoTIFF Nutrient_Regulation_2021 WCS EPSG:3035 CRS:84 18.909654496798456 21.246346912275214 55.274543999136746 56.224980544107595 other other text/xml other other other other qqq:Nutrient_Regulation_2024 Layers produced under the work carried out within the project “System of Environmental-Economic Accounting – Ecosystem Accounting in Lithuania” (SEEAL), funded by the Lithuanian Research Council (Contract: S-PD-24-18). GeoTIFF WCS Nutrient_Regulation_2024 EPSG:3035 CRS:84 18.909654496798456 21.246346912275214 55.274543999136746 56.224980544107595 other other text/xml other other other other geonode:Polination Proportion of arable crops demanding insect pollination (2.1.1. Non-irrigated arable land, 2.2.2. Fruit trees and berry plantations, 2.4.2. Complex cultivation patterns and 2.4.3. Land principally occupied by agriculture, with significant areas of natural vegetation). Land use data from Corine Land Cover 2018. Classes were carried out according to Quantile interval. Pollination Demand EPSG:3035 CRS:84 20.56415915718929 27.21931886282636 53.60463616097619 56.817110500282716 other other text/xml other other other other geonode:Potential Wild seafood potential was calculated from the modeled probability of occurrence of prey items of two important fish species in Lithuania: cod and flounder. Prey item composition was extracted from Šiaulys et al. (2012) and the occurrence of prey items extracted from Gogina et al. (2016). To map the probable occurrence of prey items the Maxent model Phillips et al. (2006) was applied for each species individually and then averaged together in the raster calculator tool of ArcGis. The environmental descriptors used in Maxent were extracted from BALANCE MODEL (Al-Hamdani and Reker, 2007; Bendtsen et al., 2007) and IOW Topographic maps from Seifert et al. (2011). Classes were carried out according to Natural jenks interval. Fisheries Marine EPSG:3035 CRS:84 18.911916133914293 21.246952891492306 55.274250995066005 56.22396212247646 other other text/xml other other other other geonode:R_supply Recreational opportunities provided by nature at a local level. Data in 20 m resolution. Outdoor recreation composed of naturalness (hemeroby index, CORINE land cover 2018), protection status (grouped into 3 classes), distance to water bodies and coast proximity to natural areas (water courses from Lithuanian cadastre), scenic spots (nature heritage objects, SSPA), and landscape diversity (visual aesthetic potential of landscape from National landscape management plan) of the area. Recreation Outdoor EPSG:3035 CRS:84 20.564159157189298 27.219239104355776 53.60465097350672 56.81719343808251 other other text/xml other other other other geonode:Rec_dem06 Recreation Demand 2006 Recreation MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Rec_dem12 Recreation Demand 2012 Recreation MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Rec_dem120 Recreation Demand 2018 Recreation MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Rec_dem22 Recreation Demand 2022 Recreation MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Rec_dem90 Recreation Demand 1990 recreation MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Rec_sup00 Recreation Supply 2000 Recreation MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Rec_sup06 Recreation Supply 2006 MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Rec_sup12 Recreation Supply 2012 Recreation MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Rec_sup18 Recreation Supply 2018 Recreation MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Rec_sup22 No abstract provided Recreation MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Rec_sup90 Recreation Supply 1990 Recreation MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Recreation Recreation demand in Lithuania at eldership level. Kernel density was applied to the residential buildings distribution in Lithuania. Subsequently, zonal statistics method was applied in order to calculate the mean building density per eldership. Residential buildings distribution was used as a proxy to estimate recreation demand. Residential buildings data from Lithuanian cadastre. Data in Km/Km2. Classes were carried out according to Quantile interval. Recreation EPSG:3035 CRS:84 20.56415915718929 27.21931886282636 53.60463616097619 56.817110500282716 other other text/xml other other other other geonode:Red_dem00 Recreation Demand 2000 recreation MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:SW Weathering was assessed using a multi-criteria model. Different types of information were used to map this service such as lithology, time of glacial retreat, topography (slope inclination, topographic wetness index, roughness index), soil (minerology, depth to rock, texture, available water capacity, pH, organic matter, organic carbon and cation exchange capacity), climate (average June temperature, average temperature range January-June and average annual precipitation) and land use (Corine land cover 2018). Data was reclassified and ranked according to the least to the most favorable conditions to weathering, using a likert scale. Variables weight in the model were assessed using the Analytic Hierarchical Process developed by Saaty (1997). With the exception of cation exchange capacity (used for validation), all variables were incorporated in the model. Variables were overlaid using weight sum approach. Model had an acceptable validation (r2=0.49). Weathering EPSG:3035 CRS:84 20.578378414711946 27.20712173202475 53.617012417881945 56.81462872199997 other other text/xml other other other other geonode:Sand_Potential The potential of mineral (sand) extraction was mapped by matching the sediment substrate found in the known wxtraction sites against the seabed sediment distribution in the Lithuanian EEZ. The location of the official sand extraction sites and seabed sediment map was retrieved from the EMODnet Geology “Marine Minerals” and “Seabed Substrates” data products. Marine Sand_Potential EPSG:3035 CRS:84 18.920755900534214 21.227008950475646 55.31347557330219 56.15749691281974 other other text/xml other other other other geonode:Sand_supply In the Lithuanian EEZ, there are two official locations where mineral (sand) is extracted from the seabed. The two areas are located in the northern and southern part of the EEZ at a depth of 30 and 40 meters. The location of the extraction sites was obtained through Emodnet Geology (2019) which acquire the data from the Lithuanian Geological Survey. Marine Sand_supply EPSG:3035 CRS:84 20.433570220919105 21.11624825626888 55.42865698856786 56.03082607107045 other other text/xml other other other other geonode:Shape Average (2012-2016) stand productivity per municipality. Stand productivity is the ratio of wood production (m3) per stand dimension in hectares (ha). Data in m3 per ha. Classes were carried out according to Quantile interval. Timber EPSG:4669 CRS:84 20.9540670000083 26.8356224999899 53.896668999488 56.4503709995068 other other text/xml other other other other geonode:Shape0 Average (2012-2016) ground and surface water consumption for non-drinkable purposes (Industry, energy, agricultural and fisheries) at municipal level. Data in m3. Classes were carried out according to Quantile interval. Terrestrial Territorial Waters Demand EPSG:3035 CRS:84 20.56415915718929 27.21931886282636 53.60463616097619 56.817110500282716 other other other other other other text/xml geonode:Soil_dem_00 Soil Erosion Demand 2000 MALPES erosion EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Soil_dem_06 Soil Erosion Demand 2006 MALPES erosion EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Soil_dem_12 Soil Erosion Demand 2012 MALPES erosion EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Soil_dem_18 Soil Erosion Demand 2018 MALPES erosion EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Soil_dem_22 Soil Erosion Demand 2022 MALPES erosion EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Soil_dem_90 Soil Erosion Demand 1990 MALPES erosion EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Soil_er_00 Soil Erosion Regulation Supply 2000 MALPES erosion EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Soil_er_06 Soil Erosion Regulation Supply 2006 MALPES erosion EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Soil_er_12 Soil Erosion Regulation Supply 2012 MALPES EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Soil_er_18 Soil Erosion Regulation Supply 2018 MALPES erosion EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Soil_er_22 Soil Erosion Regulation Supply 2022 MALPES erosion EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Soil_er_90 Soil Erosion Regulation Supply 1990 MALPES erosion EPSG:3035 CRS:84 20.695080087753965 26.58291045893246 53.840949966272476 56.54153775510771 other other text/xml other other other other geonode:Solar_Supply Solar energy supply was assessed using different variables. Solar radiation (kJ m-2 day-1) (1970-2000) from word climate database, digital elevation model from Copernicus (to calculate aspect and slope) and insolation data. Solar energy data was re-sampled to 25 m resolution. Subsequently, all the variables reclassified from the least/low (1) to the most/high (5) favorable conditions/values according to natural breaks (jenks) interval. The final map was produced using a weight sum approach. It was attributed a weight of 50% to solar radiation and 25% to slope and aspect. The final results were validated with insolation data from 14 meteorological stations (r2=0.38). Solar_Supply EPSG:3035 CRS:84 20.380665508131813 27.897681642582587 53.253480608528164 57.06166901105508 other other text/xml other other other other geonode:Timber_demand_Geonode Number of companies operating (2018) according to the forest area for exploitation (2018) per municipality. The timber demand index calculated is a modified version of the formulas proposed by Kroll et al.(2012): (WP X NC) / FAE where, WP is the wood production (average 2013-2017), NC is ithe number of companies, and FAE the forest area allowed for exploitation at each municipality. Data in m3 per ha. Classes were carried out according to Quantile interval. Timber Demand EPSG:4669 CRS:84 20.9540670000083 26.8356224999899 53.896668999488 56.4503709995068 other other text/xml other other other other qqq:Urban_2021 Layers produced under the work carried out within the project “System of Environmental-Economic Accounting – Ecosystem Accounting in Lithuania” (SEEAL), funded by the Lithuanian Research Council (Contract: S-PD-24-18). Urban_2021 GeoTIFF WCS EPSG:3035 CRS:84 20.59040449380016 26.660567652857218 53.758403029041396 56.58751108634498 other other text/xml other other other other qqq:Urban_2024 Urban Ecosystem Condition final Index for 2024. This layer was prepared within the project “System of Environmental-Economic Accounting – Ecosystem Accounting in Lithuania” (SEEAL), funded by the Lithuanian Research Council (Contract: S-PD-24-18). GeoTIFF WCS Urban_2024 EPSG:3035 CRS:84 20.59040449380016 26.660567652857218 53.758403029041396 56.58751108634498 other other text/xml other other other other qqq:Vilnius_RF_class_120m_1989 Vilnius_RF_class_120m_1989 WCS GeoTIFF EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 geonode:WEld Wind energy was measured using the annual average wind velocity (1971-2000) data from Worldclim (1km resolution). Using Zonal statistics, the index calculated was extracted at eldership level (average). Classes were carried out according to natural breaks interval. Annual average wind velocity data from: http://worldclim.org/version2 Energy Wind EPSG:3035 CRS:84 20.56415915718929 27.219318862826317 53.60463616097619 56.81711050028262 other other text/xml other other other other geonode:WHD_ Average (2012-2016) ground and surface water consumption in households according to the urban fabric (1.1.1. Continuous urban fabric and 1.1.2. Discontinuous urban fabric from Corine land cover 2018) area and the population per municipality in 2018. The index of household water demand was calculated according to the formula of Kroll et al. (2012): (WD_pop X POP)/A_urban =W/A_urban Where, WDpop is the household consumption in m3 per municipality, POP the population of the municipality and A_urban, the urban fabric area at each municipality. Classes were carried out according to Quantile interval. Territorial Waters EPSG:3035 CRS:84 20.56415915718929 27.21931886282636 53.60463616097619 56.817110500282716 other other text/xml other other other other qqq:Wildseafood_2021 Layers produced under the work carried out within the project “System of Environmental-Economic Accounting – Ecosystem Accounting in Lithuania” (SEEAL), funded by the Lithuanian Research Council (Contract: S-PD-24-18). GeoTIFF WCS Wildseafood_2021 EPSG:3035 CRS:84 18.909654496798456 21.246346912275214 55.274543999136746 56.224980544107595 other other text/xml other other other other geonode:gwsGeotiff Groundwater recharge capacity was assessed with different types of data such as lithology, geomorphology, topography (slope inclination, topographic position index, topographic wetness index, roughness index, curvature index, drainage density, linement density) soil (texture, depth, permeability), average annual precipitation and land use (Corine land cover). Data was reclassified and ranked according to the least to the most favorable conditions to weathering, using a likert scale. Subsequently a fuzzy overlay was applied and the final map was validating using soil clay content data. Data is showed is log-normal transformed. The model had an acceptable validation (r2=0.52). Groundwater EPSG:3035 CRS:84 20.570526813655107 27.21895270475151 53.60499744459495 56.81653027186075 other other text/xml other other other other geonode:habitat_quality_1990 Habitat Quality was assessed to 1990 (habitat_quality_1990) land use using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model. Habitat Quality evaluates the capacity of a habitat to supply biodiversity. The results range from 0 (non-suitable habitat) to 1 (maximum suitability). We estimated the impacts on Habitat Quality of various threats, namely motorways, primary roads, secondary roads, tertiary roads, residential, railways, agriculture areas and buildings and other artificial areas or imperviousness soils. We evaluated (i) the vulnerability of each land-use class to the threats; (ii) the impact of each threat; and (iii) the maximum distance of influence of each threat (assigned by 33 experts - scientists and practitioners - from different fields (e.g., Geographers, Biologists, Ecologists and Environmental planners). Terrestrial EPSG:3035 CRS:84 20.564975313605196 27.218718118768447 53.604528234462684 56.81676463271404 other other text/xml other other other other geonode:habitat_quality_2018 Habitat Quality was assessed to 2018 (habitat_quality_2018) land use using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model. Habitat Quality evaluates the capacity of a habitat to supply biodiversity. The results range from 0 (non-suitable habitat) to 1 (maximum suitability). We estimated the impacts on Habitat Quality of various threats, namely motorways, primary roads, secondary roads, tertiary roads, residential, railways, agriculture areas and buildings and other artificial areas or imperviousness soils. We evaluated (i) the vulnerability of each land-use class to the threats; (ii) the impact of each threat; and (iii) the maximum distance of influence of each threat (assigned by 33 experts - scientists and practitioners - from different fields (e.g., Geographers, Biologists, Ecologists and Environmental planners). Drivers of Change EPSG:3035 CRS:84 20.564975313605867 27.218718118769136 53.6045282344626 56.816764632714 other other text/xml other other other other geonode:habitat_quality_A1 Habitat Quality was assessed to A1 scenario (habitat_quality_A1) land use using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model. Habitat Quality evaluates the capacity of a habitat to supply biodiversity. The results range from 0 (non-suitable habitat) to 1 (maximum suitability). We estimated the impacts on Habitat Quality of various threats, namely motorways, primary roads, secondary roads, tertiary roads, residential, railways, agriculture areas and buildings and other artificial areas or imperviousness soils. We evaluated (i) the vulnerability of each land-use class to the threats; (ii) the impact of each threat; and (iii) the maximum distance of influence of each threat (assigned by 33 experts - scientists and practitioners - from different fields (e.g., Geographers, Biologists, Ecologists and Environmental planners). Drivers of Change Scenario EPSG:3035 CRS:84 20.564975313605867 27.218718118769136 53.6045282344626 56.816764632714 other other text/xml other other other other geonode:habitat_quality_A2 Habitat Quality was assessed to A2 scenario (habitat_quality_A2) land use using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model. Habitat Quality evaluates the capacity of a habitat to supply biodiversity. The results range from 0 (non-suitable habitat) to 1 (maximum suitability). We estimated the impacts on Habitat Quality of various threats, namely motorways, primary roads, secondary roads, tertiary roads, residential, railways, agriculture areas and buildings and other artificial areas or imperviousness soils. We evaluated (i) the vulnerability of each land-use class to the threats; (ii) the impact of each threat; and (iii) the maximum distance of influence of each threat (assigned by 33 experts - scientists and practitioners - from different fields (e.g., Geographers, Biologists, Ecologists and Environmental planners). Drivers of Change Scenario EPSG:3035 CRS:84 20.564975313605867 27.218718118769136 53.6045282344626 56.816764632714 other other text/xml other other other other geonode:habitat_quality_A3 Habitat Quality was assessed to A3 scenario (habitat_quality_A3) land use using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model. Habitat Quality evaluates the capacity of a habitat to supply biodiversity. The results range from 0 (non-suitable habitat) to 1 (maximum suitability). We estimated the impacts on Habitat Quality of various threats, namely motorways, primary roads, secondary roads, tertiary roads, residential, railways, agriculture areas and buildings and other artificial areas or imperviousness soils. We evaluated (i) the vulnerability of each land-use class to the threats; (ii) the impact of each threat; and (iii) the maximum distance of influence of each threat (assigned by 33 experts - scientists and practitioners - from different fields (e.g., Geographers, Biologists, Ecologists and Environmental planners). Drivers of Change Scenario EPSG:3035 CRS:84 20.564975313605867 27.218718118769136 53.6045282344626 56.816764632714 other other other other text/xml other other qqq:ka_FloodRegRes_1989_reclass No abstract provided ka_FloodRegRes_1989_reclass GeoTIFF WCS EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_FloodRegRes_1989_reclass0 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_FloodRegRes_1989_reclass1 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_FloodRegRes_2021_reclass No abstract provided GeoTIFF WCS ka_FloodRegRes_2021_reclass EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_FloodRegRes_2021_reclass0 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_FloodRegRes_2021_reclass1 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_FloodRegRes_2050_s0ssp1_reclass No abstract provided GeoTIFF WCS ka_FloodRegRes_2050_s0ssp1_reclass EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_FloodRegRes_2050_s0ssp1_reclass0 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_FloodRegRes_2050_s0ssp1_reclass1 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_cropProdES_1989 No abstract provided GeoTIFF WCS ka_cropProdES_1989 EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_cropProdES_2021 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_cropProdES_2050_s0_ssp1 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_cropProdES_2050_s0_ssp2 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_cropProdES_2050_s0_ssp5 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_cropProdES_2050_s1_ssp1 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_cropProdES_2050_s1_ssp2 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_cropProdES_2050_s1_ssp5 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_cropProdES_2050_s2_ssp1 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_cropProdES_2050_s2_ssp2 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_cropProdES_2050_s2_ssp5 No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_microClimRegES_1989_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_microClimRegES_2021_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_microClimRegES_2050_s0_ssp1_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_microClimRegES_2050_s0_ssp2_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_microClimRegES_2050_s0_ssp5_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_microClimRegES_2050_s1_ssp1_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_microClimRegES_2050_s1_ssp2_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_microClimRegES_2050_s1_ssp5_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_microClimRegES_2050_s2_ssp1_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_microClimRegES_2050_s2_ssp2_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:ka_microClimRegES_2050_s2_ssp5_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 23.387053572809744 24.244745305377663 54.71725436317598 55.20312737774521 other other text/xml other other other other qqq:kl_cropProdES_1989 No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_cropProdES_2020 No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_cropProdES_2050_s0_ssp1 No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_cropProdES_2050_s0_ssp2 No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_cropProdES_2050_s0_ssp5 No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_cropProdES_2050_s1_ssp1 No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_cropProdES_2050_s1_ssp2 No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_cropProdES_2050_s1_ssp5 No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_cropProdES_2050_s2_ssp1 No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_cropProdES_2050_s2_ssp2 No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_cropProdES_2050_s2_ssp5 No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_habQ_classes_1989 No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_habQ_classes_2020 No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_habQ_classes_2050_s0ssp1 No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_habQ_classes_2050_s0ssp2 No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_habQ_classes_2050_s0ssp5 No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_habQ_classes_2050_s1ssp1 No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_habQ_classes_2050_s1ssp2 No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_microClimRegES_1989 No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_microClimRegES_2020 No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_microClimRegES_2050_s0_ssp1_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_microClimRegES_2050_s0_ssp2 No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_microClimRegES_2050_s0_ssp5_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_microClimRegES_2050_s1_ssp1_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_microClimRegES_2050_s1_ssp2_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_microClimRegES_2050_s1_ssp5_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_microClimRegES_2050_s2_ssp1_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_microClimRegES_2050_s2_ssp2_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other qqq:kl_microClimRegES_2050_s2_ssp5_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 21.054105639241413 21.888699440819586 55.46222743081368 55.908276862708476 other other text/xml other other other other geonode:laq_lt_FIN1 Landscape aesthetic quality (LAQ) of Lithuania (national scale) was calculated by assessing three indicators: a) landscape diversity, b) landscape naturalness, c) landscape uniqueness. The framework was originally described by Hermes et al., 2018a. It was reviewed and improved for the current study, adjusting it to available datasets and geographical location of Lithuania. Each pixel represents the 100 x 100 m cell with low (red) to high (green) score for aesthetic values. The framework for the LAQ is based on subjective and objective criteria. For more detailed description of methodology please consult the scientific article "Mapping and assessment of landscape aesthetic quality in Lithuania" by Kalinauskas, M., Mikša, K., Inácio, M., Gomes, E., & Pereira, P. (2021). https://doi.org/10.1016/j.jenvman.2021.112239 Cultural EPSG:3035 CRS:84 20.564240974533405 27.216482112370258 53.60532369080917 56.81733557561602 other other other other text/xml other other geonode:supply_clip Wild seafood supply was calculated from the cumulative average of fishing effort, fishing intensity and fishing ship density in the Lithuanian Exclusive Economic Zone. Fishing intensity and fishing ship density data was extracted from HELCOM (2018) and fishing effort from ICES (2015). Fishing intensity was averaged for the available years and fishing gear types (2006-2016), rasterized and resampled to 20 m resolution using ArcGis. Fishing effort was averaged for the available years and fishing gear types (2009-2013), rasterized and resampled to 20 m resolution using ArcGis. Fishing ship density was averaged for the available years (2006-2016) and resampled to 20 m resolution using ArcGis. Classes were carried out according to Natural jenks interval. Fisheries Marine EPSG:3035 CRS:84 18.91782753055086 21.235676298650816 55.27531960747564 56.18334037841623 other other text/xml other other other other qqq:vl_cropProdES_1989 No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other geonode:vl_cropProdES_1989_vl_cropProdES_1989 No abstract provided features vl_cropProdES_1989_vl_cropProdES_1989 EPSG:3346 CRS:84 24.39463607225975 25.78284648306086 54.37931953631601 55.052071036532915 other other other other text/xml other other qqq:vl_cropProdES_2020 No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other qqq:vl_cropProdES_2050_s0_ssp1 No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other qqq:vl_cropProdES_2050_s0_ssp2 No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other qqq:vl_cropProdES_2050_s0_ssp5 No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other qqq:vl_cropProdES_2050_s1_ssp1 No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other qqq:vl_cropProdES_2050_s1_ssp2 No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other qqq:vl_cropProdES_2050_s1_ssp5 No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other qqq:vl_cropProdES_2050_s2_ssp1 No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other geonode:vl_cropProdES_2050_s2_ssp1_vl_cropProdES_2050 No abstract provided features vl_cropProdES_2050_s2_ssp1_vl_cropProdES_2050_s2_ssp1 EPSG:3346 CRS:84 24.402472719956545 25.782858450378214 54.377702895579155 55.05231401145504 other other other other text/xml other other qqq:vl_cropProdES_2050_s2_ssp2 No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other geonode:vl_cropProdES_2050_s2_ssp2_vl_cropProdES_2050 No abstract provided features vl_cropProdES_2050_s2_ssp2_vl_cropProdES_2050_s2_ssp2 EPSG:3346 CRS:84 24.394752360656966 25.789876748014223 54.37429486229473 55.05565074146811 other other other other text/xml other other qqq:vl_cropProdES_2050_s2_ssp5 No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other geonode:vl_cropProdES_2050_s2_ssp5_vl_cropProdES_2050 No abstract provided features vl_cropProdES_2050_s2_ssp5_vl_cropProdES_2050_s2_ssp5 EPSG:3346 CRS:84 24.40154893627441 25.782858450378214 54.377702895579155 55.05231716270963 other other other other text/xml other other qqq:vl_habQ_classes_2050_s1ssp2 No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.3925807272834 25.783616021492026 54.37742279338098 55.0526169003455 other other text/xml other other other other qqq:vl_habQ_classes_2050_s1ssp5 No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.3925807272834 25.783616021492026 54.37742279338098 55.0526169003455 other other text/xml other other other other qqq:vl_habQ_classes_2050_s2ssp1 No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.3925807272834 25.783616021492026 54.37742279338098 55.0526169003455 other other text/xml other other other other qqq:vl_habQ_classes_2050_s2ssp2 No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.3925807272834 25.783616021492026 54.37742279338098 55.0526169003455 other other text/xml other other other other qqq:vl_habQ_classes_2050_s2ssp5 No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.3925807272834 25.783616021492026 54.37742279338098 55.0526169003455 other other text/xml other other other other qqq:vl_microClimRegES_1989 No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other geonode:vl_microClimRegES_1989_out No abstract provided features vl_microClimRegES_1989_out EPSG:3346 CRS:84 24.392773882376243 25.781055219426875 54.37782284944186 55.05237884091213 other other other other text/xml other other qqq:vl_microClimRegES_2020 No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other geonode:vl_microClimRegES_2020_out No abstract provided features vl_microClimRegES_2020_out EPSG:3346 CRS:84 24.392773882376243 25.782892729128836 54.37779659137091 55.05237884091213 other other other other text/xml other other qqq:vl_microClimRegES_2050_s0_ssp1_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other geonode:vl_microClimRegES_2050_s0_ssp1_out No abstract provided features vl_microClimRegES_2050_s0_ssp1_out EPSG:3346 CRS:84 24.392773882376243 25.782892729128836 54.37779659137091 55.05237884091213 other other other other text/xml other other qqq:vl_microClimRegES_2050_s0_ssp2_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other geonode:vl_microClimRegES_2050_s0_ssp2_out No abstract provided features vl_microClimRegES_2050_s0_ssp2_out EPSG:3346 CRS:84 24.392773882376243 25.782892729128836 54.37779659137091 55.05237884091213 other other other other text/xml other other qqq:vl_microClimRegES_2050_s0_ssp5_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other geonode:vl_microClimRegES_2050_s0_ssp5_out No abstract provided features vl_microClimRegES_2050_s0_ssp5_out EPSG:3346 CRS:84 24.392773882376243 25.782892729128836 54.37779659137091 55.05237884091213 other other other other text/xml other other qqq:vl_microClimRegES_2050_s1_ssp1_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other geonode:vl_microClimRegES_2050_s1_ssp1_out No abstract provided features vl_microClimRegES_2050_s1_ssp1_out EPSG:3346 CRS:84 24.392773882376243 25.782892729128836 54.37779659137091 55.05237884091213 other other other other text/xml other other qqq:vl_microClimRegES_2050_s1_ssp2_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other geonode:vl_microClimRegES_2050_s1_ssp2_out No abstract provided features vl_microClimRegES_2050_s1_ssp2_out EPSG:3346 CRS:84 24.392773882376243 25.782892729128836 54.37779659137091 55.05237884091213 text/xml other other other other other other qqq:vl_microClimRegES_2050_s1_ssp5_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other geonode:vl_microClimRegES_2050_s1_ssp5_out No abstract provided features vl_microClimRegES_2050_s1_ssp5_out EPSG:3346 CRS:84 24.392773882376243 25.782892729128836 54.37779659137091 55.05237884091213 text/xml other other other other other other qqq:vl_microClimRegES_2050_s2_ssp1_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other geonode:vl_microClimRegES_2050_s2_ssp1_out No abstract provided features vl_microClimRegES_2050_s2_ssp1_out EPSG:3346 CRS:84 24.392773882376243 25.782892729128836 54.37779659137091 55.05237884091213 other other other other text/xml other other qqq:vl_microClimRegES_2050_s2_ssp2_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other geonode:vl_microClimRegES_2050_s2_ssp2_out No abstract provided features vl_microClimRegES_2050_s2_ssp2_out EPSG:3346 CRS:84 24.392773882376243 25.782892729128836 54.37779659137091 55.05237884091213 other other other other other other text/xml qqq:vl_microClimRegES_2050_s2_ssp5_out No abstract provided Ecosystem Services EPSG:3346 CRS:84 24.392772982699096 25.783339546750224 54.377696187556445 55.05261626830767 other other text/xml other other other other geonode:vl_microClimRegES_2050_s2_ssp5_out No abstract provided features vl_microClimRegES_2050_s2_ssp5_out EPSG:3346 CRS:84 24.392773882376243 25.782892729128836 54.37779659137091 55.05237884091213 other other text/xml other other other other