User Tools

Site Tools


Sidebar





This project is co-financed by the European Regional Development Fund through the Interreg Alpine Space programme

This project is co-financed by the European Regional Development Fund through the Interreg Alpine Space programme

This project is co-financed by the European Regional Development Fund through the Interreg Alpine Space programme

wiki:smartaltitude_webgis_layers

Smart Altitude - WebGIS Layers

Content TreeDatasetDescriptionUnitsFormatDateNote
Ski Resorts → Key Performance Indicators (KPI)Ski resorts KPIs are measurable values that demonstrates how effectively the ski resort is achieving key business objectives. For more information see Description ski resort KPIs as output of the Winter Energy Management Tool (Wi-EMT)Unit: 1-5 as weighted average of scores regarding following parameters: For four Living Labs in the Alpine Space
Overall Ski-Resort KPI (OV)This value is designed as average of scores from the 8 following KPIs.
Energy Efficiency (E_EF)
Energy Economy (E_EC)
Sustainability (S)
Energy Management (EM)
Smart Grid (SG)
Adaptation to Climate Change (ACC)
Self Evaluation (SE)
Future Outlook (FO)
Ski Resorts → Open Ski Map Through the Open Ski Map ski related datasets can be found, which are implemented in the Smart Altitude WebGIS. Downloaded 09.08.2019
Pistes Line Vectors
Aerialways Line Vectors
Ski Resorts Polygons
Pistes length per LAU Polygons
Pistes length per resort Polygons
Ski Resorts → Smart Altitude Study Sites The WebGIS offers different border layers concerning the Alpine Space. Polygons
Alpine Space Area
Alpine Convention Area
Smart Altitude – Case Study Areas (Ski Resort LAU Communities)
Renewable Energy Potential → Solar → Global Solar Atlas (GSA Version 2.0) The Global Solar Atlas offers long-term yearly/monthly averaged daily totals of several datasets for solar resources and the photovoltaic power potential. The data presented here is only suitable for preliminary analysis. Raster, 1x1km
Photovoltaic Electricity Output (PVOUT) Amount of energy, converted by a PV system into electricity that is expected to be generated according to the geographical conditions of a site and the configuration of a PV system. [kWh/kWp]
Global Horizontal Irradiation (GHI) Sum of direct and diffuse components of solar radiation. [kWh/m2 ]
Diffuse Horizontal Irradiation (DIF) Solar radiation component that is scattered by the atmosphere. [kWh/m²]
Global Irradiation for Optimally Tilted Surface (GTI) Sum of direct and diffuse solar radiation falling on a tilted surface of fixed-mounted PV modules. Compared to the horizontal surface, the tilted surface also receives a small amount of ground-reflected solar radiation. [kWh/m²]
Optimum Tilt to Maximize Yearly Yield (OPTA) Optimum inclination of an inclined and fixed PV module for a specific azimuth (orientation), for which the PV modules receive the highest amount of solar radiation per year. [°]
Direct Normal Irradiation (DNI) Solar radiation component that directly reaches the surface. [kWh/m²]
Air Temperature at 2m AGL (TEMP) Air temperature determines the temperature of PV cells and modules. It has a direct impact on PV energy conversion efficiency and resulting energy losses. [°C]
Terrain Elevation ASL (ELE) Represents terrain elevation (altitude) relative to the sea level. Only data for the land area is shown. [m]Raster, 90x90m
Renewable Energy Potential → Solar → Global Solar Atlas (GSA Version 2.0) → Median per LAUThe Global Solar Atlas offers long-term yearly/monthly averaged daily totals of several datasets for solar resources and the photovoltaic power potential. The data presented here is only suitable for preliminary analysis. Polygon
LAU median: Photovoltaic Electricity Output (PVOUT) Amount of energy, converted by a PV system into electricity that is expected to be generated according to the geographical conditions of a site and the configuration of a PV system. [kWh/kWp]
LAU median: Global Horizontal Irradiation (GHI) Sum of direct and diffuse components of solar radiation. [kWh/m2 ]
LAU median: Diffuse Horizontal Irradiation (DIF) Solar radiation component that is scattered by the atmosphere. [kWh/m²]
LAU median: Global Irradiation for Optimally Tilted Surface (GTI) Sum of direct and diffuse solar radiation falling on a tilted surface of fixed-mounted PV modules. Compared to the horizontal surface, the tilted surface also receives a small amount of ground-reflected solar radiation. [kWh/m²]
LAU median: Optimum Tilt to Maximize Yearly Yield (OPTA) Optimum inclination of an inclined and fixed PV module for a specific azimuth (orientation), for which the PV modules receive the highest amount of solar radiation per year. [°]
LAU median: Direct Normal Irradiation (DNI) Solar radiation component that directly reaches the surface. [kWh/m²]
LAU median: Air Temperature at 2m AGL (TEMP) Air temperature determines the temperature of PV cells and modules. It has a direct impact on PV energy conversion efficiency and resulting energy losses. [°C]
Renewable Energy Potential → Solar → Hotmaps Project The EU project Hotmaps collected data on energy potential of renewable sources and re-elaborated them on national level, in order to build datasets for all EU28 countries at NUTS3 level. Several renewable sources have been considered. All Hotmaps data must be interpreted as indicators. Raster, 100x100m
Solar Energy Potential Data on annual global radiation on globally inclined surfaces were retrieved from the PVGIS as a 1km x 1km raster layer and clipped by considering the building footprint with a resolution of 100m x 100m from Copernicus Services. [kWh/m²] Downloaded 10.08.2019
Renewable Energy Potential → Wind → Global Wind Atlas (GWA Version 3.0) → Wind Energy Layers The Global Wind Atlas is a free, web-based application developed to help policymakers, planners, and investors to identify high-wind areas for wind power generation, and then perform preliminary calculations. A high value indicates a high potential for renewable energy from wind. Raster, 1x1km Simulation period 2008-2017
Capacity Factor IEC Class 1 High wind
Capacity Factor IEC Class 2 Medium wind
Capacity Factor IEC Class 3 Low wind
Renewable Energy Potential → Wind → Global Wind Atlas (GWA Version 3.0) → Wind Energy Layers → Median per LAUThe Global Wind Atlas is a free, web-based application developed to help policymakers, planners, and investors to identify high-wind areas for wind power generation, and then perform preliminary calculations. A high value indicates a high potential for renewable energy from wind. Polygon Simulation period 2008-2017
LAU median: Capacity Factor IEC Class 1 High wind
LAU median: Capacity Factor IEC Class 2 Medium wind
LAU median: Capacity Factor IEC Class 3 Low wind
Renewable Energy Potential → Wind → Global Wind Atlas (GWA Version 3.0) → Wind-Speed at a height of Mean wind speed at a specific height is a measure of the wind resource. Higher values normally indicate better wind resources, but mean wind power density gives a more accurate indication of the available wind resource. Raster, 1x1kmSimulation period 2008-2017
10m [m/s]
50m [m/s]
100m [m/s]
Renewable Energy Potential → Wind → Global Wind Atlas (GWA Version 3.0) → Wind-Speed at a height of → Median per LAUMean wind speed at a specific height is a measure of the wind resource. Higher values normally indicate better wind resources, but mean wind power density gives a more accurate indication of the available wind resource. PolygonSimulation period 2008-2017
LAU median: 10m [m/s]
LAU median: 50m [m/s]
LAU median: 100m [m/s]
Renewable Energy Potential → Wind → Global Wind Atlas (GWA Version 3.0) → Power-Density at a height of Mean wind power density at a specific height is a measure of the wind resource. Higher values indicate better wind resources. Raster, 1x1kmSimulation period 2008-2017
10m [W/m²]
50m [W/m²]
100m [W/m²]
Renewable Energy Potential → Wind → Global Wind Atlas (GWA Version 3.0) → Power-Density at a height of → Median per LAUMean wind power density at a specific height is a measure of the wind resource. Higher values indicate better wind resources. PolygonSimulation period 2008-2017
LAU median: 10m [W/m²]
LAU median: 50m [W/m²]
LAU median: 100m [W/m²]
Renewable Energy Potential → Wind → Global Wind Atlas (GWA Version 3.0) → Air-Density at a height of Mean air density at a specific height is a measure of the wind resource. Raster, 1x1kmSimulation period 2008-2017
10m [kg/m³]
50m [kg/m³]
100m [kg/m³]
Renewable Energy Potential → Wind → Global Wind Atlas (GWA Version 3.0) → Air-Density at a height of → Median per LAUMean air density at a specific height is a measure of the wind resource. PolygonSimulation period 2008-2017
LAU median: 10m [kg/m³]
LAU median: 50m [kg/m³]
LAU median: 100m [kg/m³]
Renewable Energy Potential → Wind → Hotmaps Project This data shows the total energy potential of wind in the EU28 at NUTS3 level. The original dataset is the Wind Global Atlas, which was aggregated at NUTS3 level, through the CORINE Land Cover and by excluding urban areas, bird connectivity corridors, mountain peaks over 2500m and protected areas from the Natura 2000 framework. Raster, 300x300m
Power Density Potential at 50m height [W/m²] Downloaded 10.08.2019
Renewable Energy Potential → Geothermal → Thermomap Project / Hotmaps Project Data on very shallow geothermal energy potential (up to 10 metres) were retrieved from the project Thermomap as a vector layer and presented in the Hotmaps project without further eleboration. PolygonDownloaded 10.08.2019
Thermomap (Geothermal Energy Potential) [W/m K]
Average per LAU: Geothermal energy potential [W/m K]
Renewable Energy Potential → Geothermal → GEOELEC Project RasterDownloaded 15.10.2020
Basal Heat Flow The here presented layer showes the basal heat flow for whole Europe.[mW/m²]
Surface Heat Flow The here presented layer showes the surface heat flow for whole Europe.[mW/m²]
Theoretical Potential 2030 The theoretical potential for 2030 is the maximum possible (theoretical) technical potential.[MW/km²]
Economic Potential 2030 The economic potential for 2030 is calculated from the realistic underground technical potential, accepting only those subvolumes where the levelized cost of energy is less than a given threshold.[MW/km²]
Theoretical Potential 2050 The theoretical potential for 2050 is the maximum possible (theoretical) technical potential.[MW/km²]
Economic Potential 2050 The economic potential for 2050 is calculated from the realistic underground technical potential, accepting only those subvolumes where the levelized cost of energy is less than a given threshold.[MW/km²]
Renewable Energy Potential → Biomass → Hotmaps Project Data concerning biomass developed within the EU project Hotmaps. Polygon
Energy Potential from Agricultural Residues Considered agricultural residues are crop, cereals, maize, oilseed rape and sunflower, sugar beet, rice, olives, citrus and grape. [PJ]
Energy Potential from Forest Residues Energy potential have been spatialized by using the CORINE Land Cover. Forest biomass includes two categories of residues: Fuelwood and roundwood.[PJ]
Energy Potential from Livestock Effluents Considered livestock effluents for the energy generation are solid and liquid manure from breeding of cattle, pigs and poultry. [PJ]
Renewable Energy Potential → Biomass → Global Forest Watch Project Presents data concerning biomass potential from the open data portal Global Forest. [Mg ha-1]
Aboveground Biomass Raster, 30x30m
Aboveground Biomass per LAU Polygon (LAU)
Renewable Energy Potential → Biomass → AlpES Project The calculated indicator represents the mapping of biomass production supply and was created within the AlpES project. For more information see Biomass production from grassland
Grassland Biomass Ecosystem Service [t DM ha-1 y-1] Polygon (LAU)
Renewable Energy Potential → Hydro The dataset contains all potential hydropower plant locations for micro to large hydropower plants based on the GMTED2010 breakline dataset (elevation) and runoff data from the Global Runoff Data Centre. Coverage time 2010
Hydropower Potential Locations Point Feature
Total Hydropower Potential per LAU The Total Hydropower Potential per LAU showes the sum values of all hydropower potential locations on municipality level. Polygon (LAU)
Energy Infrastructure → Power Plants – Global Power Plant Database Database covers approximately 30000 power plants around the world. Beside thermal plants also renewable plants are included.
Power Plants, incl. Renewables
Energy Infrastructure
Charging PointsOpen Charge Map is a non-commercial, non-profit, electric vehicle data service hosted and supported by a community of businesses, charities, developers and interested parties around the world. The aim is to work with the community to develop and provide a high quality, public, free, open database of charging equipment locations globally Point Feature
Land Use and Land Cover Land use and land cover mapping for all EEA39 countries (2018). 25 ha minimum mapping unit, 100m minimum mapping width, 100m positional accuracy, >85% thematic accuracy. Raster, 100x100m
CORINE Land Cover (CLC) CORINE Land Cover (CLC) was specified to standardize data collection on land cover in Europe to support environmental policy development. downloaded 08.08.2019
European Forest Areas This dataset shows the European forest area for 2012 and 2015 covering EEA39 countries. 1 (forest), 0 (non-forest) downloaded 11.08.2019
Political-Geographical Boundaries → Administrative Units Euro Boundary Map provides a European geographic database for administrative and statistical regions. EBM offers the combination of detailed European administrative units and linkages to the corresponding LAU and NUTS codes. PolygonsData delivery: Mai 2019
Admin Unit/Boundary Level 1 National borders/ states
Admin Unit/Boundary Level 2 Federal states
Admin Unit/Boundary Level 3
Admin Unit/Boundary Level 4
LAU – Local Administrative Units Municipality level
Political-Geographical Boundaries → Statistical Units The database includes relations between the European-wide unique identifiers of administrative units and their corresponding statistical codes and units (NUTS) maintained and published by Eurostat.
NUTS 1 – Major socio-economic regions
NUTS 2 – Basic regions for the application of regional policies
NUTS 3 – Small regions for specific diagnoses
Political-Geographical Boundaries The WebGIS offers different border layers concerning the Alpine Space. Polygon
Alpine Space Area
Alpine Convention Area
Political-Geographical Boundaries → Protected Areas → World Database on Protected Areas (WDPA) Protected Planet is a source of information on protected areas, updated monthly with submissions from governments, non-governmental organizations, landowners and communities. Downloaded 12.08.2019
WDPA Areas Polygon
WDPA Points (Addition only for Slovenia) Point Feature
Political-Geographical Boundaries → Protected Areas
Natura 2000 Natura 2000 is the key instrument to protect biodiversity. It is an ecological network of protected areas, set up to ensure the survival of Europe's most valuable species and habitats. PolygonDownloaded 08.08.2019
Nationally Designated Areas (CDDA) The dataset contains data on nationally designated areas and corresponding protected site for spatial features in EEA member and collaborating countries. PolygonDownloaded 08.08.2019
Political-Geographical Boundaries → Protected Areas → Protected Areas – IGF curated Polygon
Biosphere Reserves
National Parks
Natural Parks
World Heritage Sites
Digital Elevation Model & Hillshade DEM and Hillshade are resampled (250m, 1000m) from EU-DEM, which is a 1:100 000 scale digital elevation model providing height data for 40 European countries and territories. It describes the distribution of terrain, not including vegetation and man-made structures.
Digital Elevation Model (DEM) [m]
Hillshade [°]
Combined DEM & Hillshade


wiki/smartaltitude_webgis_layers.txt · Last modified: 2020/11/20 11:20 by apolderman