Environmental Data Platform


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Results: 339 items found
4DMED_stations
The 4DMED-Hydrology project aims at achieving this objective by developing and validating a novel and advanced set of EO-based products that together with additional information (in-situ data, model results) may provide an accurate reconstruction of the Mediterranean Land “Earth system”, its land-atmosphere interactions and relevant processes (also human activities) based on the latest advances in EO technology (4DMed-Hydrology dataset). Further information can be found on the official website: <https://www.4dmed-hydrology.org/>.

Maps

4DMED,4dmed_stations,hydrology,Morocco,Tunisia,Europe,France,Italy,Spain,Turkey

4DMED stations Po areas
4DMED-Hydrology project aims at achieving this objective by developing and validating a novel and advanced set of EO-based products that together with additional information (in-situ data, model results) may provide an accurate reconstruction of the Mediterranean Land “Earth system”, its land-atmosphere interactions and relevant processes (also human activities) based on the latest advances in EO technology (4DMed-Hydrology dataset). Further information can be found in the official website: <https://www.4dmed-hydrology.org/>. t provided

Maps

4DMED,hydrology,Po_areas,station,Italy

Abschnitte des Straßennetzes - Tratti della rete stradale
Abschnitte des Straßennetzes - Tratti della rete stradale

Maps

Abschnitte des Stra�ennetzes,features,Rete stradale,Reti di trasporto,ROAD_STREET_VW,Stra�ennetz,Tratti della rete stradale,Verkehrsnetze,Italy

ADO Hydrological boundary
The overall objective of the Alpine Drought Observatory - ADO project is to create an online drought monitoring platform and develop policy implementation guidelines for proactive drought management in the Alpine regions. The ADO project consortium includes 11 institutions from 6 Alpine countries with a wide range of expertise, covering meteorological and hydrological monitoring, specific knowledge on modeling, drought risk and impact assessment, as well as water governance in the different sectors. Further information about the ADO project can be found here: https://www.alpine-space.eu/projects/ado/en/about.

Maps

ADO,ADO_boundaries,ADO_region,Europe

Agordino - Valle del Cordevole: Land use/Land cover
This layer shows the land use land cover for the Transalp study area Agordino-Valle del Cordevole.

Maps

land cover,landuse,Veneto,Italy

Air temperature - Venosta Valley
Daily averaged air temperature maps [C] maps for the Venosta Valley (South Tyrol,Italy) produced with the GEOtop hydrological model.

OpenEO

Land use,Land cover,collection,temperature,geotop,model

Alpinespace Eusalp boundary
EUSALP is a European strategy for the Alpine territory joining human passions, natural resources and economic assets, linking cities, plains, valleys and mountains to find solutions to challenges we can solve only together. We coordinate planning, integrate the best practices in the fields of economy, education, environment, accessibility and mobility, and commit as institutions to create sustainable solutions for the benefits of the citizens. By bringing governing closer to the people, EUSALP is proving that the European culture of cooperation lives. (source https://www.alpine-region.eu/mission-statement)

Maps

alpinespace_eusalp_boundary,Europe

Alpine space-Eusalp intersection
The Alpine Drought Observatory - ADO project is interested in discharge (only for stations with a catchment area > 1000 Km2 and currently active), groundwater (only for stations for major groundwater bodies), and major lake levels (only for major water bodies (surface > 5 km2)) data. The overall objective of the Alpine Drought Observatory - ADO project is to create an online drought monitoring platform and develop policy implementation guidelines for proactive drought management in the Alpine regions. The ADO project consortium includes 11 institutions from 6 Alpine countries with a wide range of expertise, covering meteorological and hydrological monitoring, specific knowledge on modeling, drought risk and impact assessment, as well as water governance in the different sectors. Further information about the ADO project can be found here: https://www.alpine-space.eu/projects/ado/en/about.

Maps

ADO,alp,eusalp,Europe

Alpinespace Eusalp NUTS2
No abstract provided

Maps

alpinespace_eusalp_NUTS2,features,Global

Alps boundary
Alps border polygon

Maps

Alps_boundary_3035,features,Global

ALPS: Glaciers outline 2015-2020
High-resolution outline of glaciers from both Sentinel-1 and Sentinel-2 satellites over South Tyrol (years 2015 to 2020, included).

Maps

features,st_glaciers_outlines_pol_s4_20152020_eurac,Austria,Italy,Switzerland

Annual Mean Value photovoltaic energy - CDTE modul
Annual Mean Value of photovoltaic energy produced for a Cadmium-Tellurid module.

Maps

GeoTIFF,irradiation,photovoltaic,solar,WCS,Italy

Annual Mean Value photovoltaic energy - PCSI modul
Annual Mean Value of photovoltaic energy produced for a Polykristallines-Silizium modul.

Maps

GeoTIFF,irradiation,solar,WCS,Italy

Archivio Tirolese -Argento Vivo
Archivio Tirolese per la documentazione e l"arte fotografica di Lienz (TAP): - Collezione Lisl Gaggl-Meirer (Paesaggio, montagna; Tirolo Orientale; 1970-1990) - Collezione Klebelsberg, Istituto di Geologia, Università di Innsbruck (Paesaggio, montagna, militari; Dolomiti; 1907-1910) - Collezione Hans Peter Falkner (Città, Lienz; ca. 1965-1985) - Collezione Foto Baptist (Paesaggio, montagna; Tirolo Orientale; 1965-1975)

Maps

archive,archivio,features,foto,picture,Italy

Auf den spuren der tracks
Auf den spuren der tracks in Bletterback Park

Maps

features,park,track,Italy

Backscatter Sentinel-1 Track015
Sentinel-1A backscatter timeseries. Sigma0 for VH and VV polarization calculated using SNAP.

OpenEO

Land use,Land cover,collection,S-1,WLF,sigma,backscatter,intensity,Sentinel-1A

Backscatter Sentinel-1 Track044
Sentinel-1A backscatter timeseries. Sigma0 for VH and VV polarization calculated using SNAP.

OpenEO

Land use,Land cover,collection,S-1,WLF,sigma,backscatter,intensity,Sentinel-1A

Backscatter Sentinel-1 Track066
Sentinel-1A backscatter timeseries. Sigma0 for VH and VV polarization calculated using SNAP.

OpenEO

Land use,Land cover,collection,S-1,WLF,sigma,backscatter,intensity,Sentinel-1A

Backscatter Sentinel-1 Track088
Sentinel-1A backscatter timeseries. Sigma0 for VH and VV polarization calculated using SNAP.

OpenEO

Land use,Land cover,collection,S-1,WLF,sigma,backscatter,intensity,Sentinel-1A

Backscatter Sentinel-1 Track095
Sentinel-1A backscatter timeseries. Sigma0 for VH and VV polarization calculated using SNAP.

OpenEO

Land use,Land cover,collection,S-1,WLF,sigma,backscatter,intensity,Sentinel-1A

Backscatter Sentinel-1 Track117
Sentinel-1A backscatter timeseries. Sigma0 for VH and VV polarization calculated using SNAP.

OpenEO

Land use,Land cover,collection,S-1,WLF,sigma,backscatter,intensity,Sentinel-1A

Backscatter Sentinel-1 Track139
Sentinel-1A backscatter timeseries. Sigma0 for VH and VV polarization calculated using SNAP.

OpenEO

Land use,Land cover,collection,S-1,WLF,sigma,backscatter,intensity,Sentinel-1A

Backscatter Sentinel-1 Track168
Sentinel-1A backscatter timeseries. Sigma0 for VH and VV polarization calculated using SNAP.

OpenEO

Land use,Land cover,collection,S-1,WLF,sigma,backscatter,intensity,Sentinel-1A

bdi_settl_extents_hamlets_pol
No abstract provided

Maps

bdi_settl_extents_hamlets_pol,features,Burundi

bdi_settl_extents_small_settlement_area_pol
No abstract provided

Maps

bdi_settl_extents_small_settlement_area_pol,features,Burundi

Biotop Bletterbach
Biotope area of the Bletterbach geological Park

Maps

Biotop_Bletterbach,features,Italy

bletterbachshclucht tracks
Bletterbachshclucht Parck tracks

Maps

features,park,track,Italy

Bolzano: Forest Protective Function
Surfaces with potential forest auto- and hetero-protective function.

Maps

features,forestprotectivefunction_polygon,Italy

Bolzano: Hydrological Risk Map
Hydrological risk maps of the province of Bolzano.

Maps

features,st_hazard_plan_water_2013_apb_pol_pp,Italy

Bolzano: Landslide Hazard Level Map
Gravitational mass movement hazard level maps of the province of Bolzano.

Maps

features,urbanplan-hazardzoneplan-landslides_polygon,Italy

BOZEN AREA: Hexagonal municipality tessellation (~250m)
Tessellation onto regular hexagonal cells of the area of Bolzano at a resolution of ~250m.

Maps

bozen,features,st_tessellation_250m_bolzano_area_pol,tessellation,Italy

BOZEN AREA: tessellated OSM drivable roads (~250m)
Drivable roads from OpenStreetMap over the area of Bozen (South Tyrol) onto an hexagonal tessellation of ~250m.

Maps

drive,features,osm,st_tran_rds_ln_s4_osm_pp_drive_250tess_bz,tessellated,Italy

BOZEN: Hexagonal municipality tessellation (~250m)
Tessellation onto regular hexagonal cells of the municipality of Bolzano at a resolution of ~250m.

Maps

bozen,features,st_tessellation_250m_municipality_bolzano_pol,tessellation,Italy

BOZEN: Voronoi diagram N.1
Partition of the the municipality of Bolzano into Voronoi regions for proper aggregation of sensitive data (created by APB Osservatorio del Lavoro).

Maps

apb,features,st_tess_voronoi_pol_s4_apb,Italy

BURUNDI: 2008 Population Census by Communes
From the third general population and housing census of Burundi made by ISTEEBU Institute in 2008.

Maps

bdi_pop_adm2_isteebu_2019_pol,features,Burundi

BURUNDI: 2021 Population estimates by Communes
Population estimation by UNFPA with Institut de Statistiques et d"Etudes Economiques du Burundi (ISTEEBU). Burundi administrative level 0-2 2021 sex and age disaggregated projections from 2008 population census statistics

Maps

distribution,Population,Burundi

BURUNDI: Admin Level 0 (International) Boundaries
The dataset represents the international boundaries of Burundi.

Maps

bdi_adm_adm0_igebu_ocha_itos_2017_utm35s,features,Burundi

BURUNDI: Admin Level 1 Boundaries
The dataset represents the provinces of Burundi.

Maps

bdi_adm_adm1_igebu_ocha_2017_utm35s,features,Burundi

BURUNDI: Admin Level 2 Boundaries
Burundi Level 2 administrative boundaries.

Maps

bdi_adm2_test,features,Global

BURUNDI: Admin Level 2 Boundaries
The dataset represents the communes of Burundi.

Maps

bdi_adm_adm2_igebu_ocha_2017_utm35s,features,Burundi

BURUNDI: Admin Level 3 Boundaries
The dataset represents the collines of Burundi.

Maps

burundi,collines,Burundi

Burundi: Buildings count (100m)
his layer shows gridded buildings with 100m resolution for Burundi. It was extracted from Gridded maps of building patterns throughout sub-Saharan Africa, version 2.0. This raster contains counts of buildings that fall within a grid cell. Each buildings was counted in the grid cell that contained the centroid of its building footprint

Maps

bdi_bldgs_buildings_Worldpop_v2_0_count_ras_100m,GeoTIFF,WCS,Global

BURUNDI: buildings taxonomy per commune
Taxonomy of buildings in Burundi, per each commune (IDOM).

Maps

buildings,burundi,taxonomy,Burundi

BURUNDI: buildings taxonomy per province
Taxonomy of buildings in Burundi, per each province (IDOM).

Maps

buildings,burundi,taxonomy,Burundi

BURUNDI: Cropland
Land classified as cropland over Burundi (from Copernicus Land Cover product).

Maps

bdi_lc100m_v3_2019_cropland_copernicus_utm35s,GeoTIFF,WCS,Burundi

Burundi - Dams (Aquastat)
Dam locations in Burundi extracted from Aquastat Dam database for Africa. AQUASTAT gathers detailed information about dams in each country, especially on location, height, reservoir capacity, surface area and main purpose. http://www.fao.org/aquastat/en/databases/dams

Maps

bdi_energy_dams_aquastat_pp,features,Burundi

BURUNDI: Gridded Population estimates (2019 | 100m)
Gridded population estimates from WorldPop (10.5258/SOTON/WP00682) over Burundi calibrated to match the 2019 population projections by commune by ISTEEBU/UNFPA (https://data.humdata.org/dataset/burundi-administrative-level-0-2-population-statistics-2018).

Maps

bdi_pop_worldpop_2020_rescaled_to_2019isteebuadm2_ras_,GeoTIFF,WCS,Burundi

BURUNDI: Gridded Population estimates (2021 | 100m)
Gridded population estimates from WorldPop (10.5258/SOTON/WP00682) over Burundi calibrated to match the 2021 population projections by commune by ISTEEBU/UNFPA (https://data.humdata.org/dataset/burundi-administrative-level-0-2-population-statistics-2018).

Maps

bdi_pop_worldpop100mrescaled_ras_pp,GeoTIFF,WCS,Burundi

BURUNDI: Health facilities accessibility
Accessibility to nearest health facility on drivable roads in Burundi.

Maps

bdi_health_facilities_access,features,Burundi

Burundi: Health sites
This layer contains the health sites in Burundi classified by type. The data source is the Burundi Ministry of Health.

Maps

bdi_heal_health_sites_pnt_fosa_gps_v4_minesante_utm35s,Burundi

BURUNDI: High resolution landslide susceptibility map (priority areas)
Landslide susceptibility map for the priority areas of Burundi that incorporates landslide release susceptibilities and potential runout paths.

Maps

bdi_haz_landslides_ras_s5_eurac_re_prio_areas,landslides,natural hazard,WCS,Burundi

BURUNDI: Land Cover
Land classification over Burundi (from Copernicus Land Cover product).

Maps

copernicus,GeoTIFF,WCS,Burundi

BURUNDI: Landslides susceptibility map (April)
The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included.

Maps

April,bdi_haz_landslides_ras_s4_eurac_re_april,burundi,GeoTIFF,landslides,WCS,Burundi

BURUNDI: Landslides susceptibility map (August)
The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included.

Maps

August,bdi_haz_landslides_ras_s4_eurac_re_august,burundi,GeoTIFF,landslides,WCS,Burundi

BURUNDI: Landslides susceptibility map (February)
The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included.

Maps

bdi_haz_landslides_ras_s4_eurac_re_february,burundi,February,GeoTIFF,landslides,WCS,Burundi

BURUNDI: Landslides susceptibility map (January)
The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included.

Maps

bdi_haz_landslides_ras_s4_eurac_re_january,burundi,GeoTIFF,January,landslides,WCS,Burundi

BURUNDI: Landslides susceptibility map (July)
The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included.

Maps

bdi_haz_landslides_ras_s4_eurac_re_july,burundi,GeoTIFF,July,landslides,WCS,Burundi

BURUNDI: Landslides susceptibility map (June)
The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included.

Maps

bdi_haz_landslides_ras_s4_eurac_re_june,burundi,GeoTIFF,June,landslides,WCS,Burundi

BURUNDI: Landslides susceptibility map (March)
The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included.

Maps

bdi_haz_landslides_ras_s4_eurac_re_march,burundi,GeoTIFF,landslides,March,WCS,Burundi

BURUNDI: Landslides susceptibility map (May)
The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included.

Maps

bdi_haz_landslides_ras_s4_eurac_re_may,burundi,GeoTIFF,landslides,May,WCS,Burundi

BURUNDI: Landslides susceptibility map (national scale)
National-scale unclassified landslide susceptibility map for Burundi.

Maps

burundi,landslides,natural hazards,Burundi

BURUNDI: Landslides susceptibility map (November)
The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included.

Maps

bdi_haz_landslides_ras_s4_eurac_re_november,burundi,GeoTIFF,landslides,November,WCS,Burundi

BURUNDI: Landslides susceptibility map (October)
The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included.

Maps

bdi_haz_landslides_ras_s4_eurac_re_october,burundi,GeoTIFF,landslides,October,WCS,Burundi

BURUNDI: Landslides susceptibility map (September)
The map represents the national landslide susceptibility for the respective month of the year where the rainfall dynamics are included.

Maps

bdi_haz_landslides_ras_s4_eurac_re_september,burundi,GeoTIFF,landslides,September,WCS,Burundi

BURUNDI: Level-2 Geological Units
Lithological units of Burundi (second step of aggregation).

Maps

bdi_haz_landslides_pol_s1_eurac_pp_geounits_l2,features,geological units,Burundi

BURUNDI: Level-3 Geological Units
Lithological units of Burundi (third step of aggregation).

Maps

bdi_haz_landslides_pol_s1_eurac_pp_geounits_l3,features,geological units,Burundi

BURUNDI: Level-4 Geological Units
Lithological units of Burundi (fourth step of aggregation).

Maps

bdi_haz_landslides_pol_s1_eurac_pp_geounits_l4,features,geological units,Burundi

BURUNDI: Multi-Hazard Average Annual Loss (commune level)
Average Annual Loss (AAL) measured in USDs, estimated for multiple hazards over the communes of Burundi.

Maps

aal,bdi_risk_aal_py_s4_eurac_comm,communes,features,loss,risk,Burundi

BURUNDI: Multi-Hazard Average Annual Loss (province level)
Average Annual Loss (AAL) measured in USDs, estimated for multiple hazards over the provinces of Burundi.

Maps

aal,bdi_risk_aal_py_s3_eurac_prov,features,loss,provinces,risk,Burundi

Burundi: named settlements
Geographic names of populated places in Burundi by NGA Geonet Names Server (NGA). Last updated: 12. July 2021.

Maps

burundi,settlements,Burundi

BURUNDI: OSM airports
Airports of Burundi (OSM).

Maps

features,hotosm_bdi_airports_points,Burundi

BURUNDI: OSM bridges
Bridges of Burundi (OSM). OSM Download from September 2020.

Maps

bdi_trans_roads_bridges_osm_ln_p,features,Burundi

BURUNDI: OSM drivable roads
Year-round drivable roads of Burundi from OpenStreetMap (OSM).

Maps

bdi_drive_roads,features,Burundi

BURUNDI: OSM education facilities
Education facilities of Burundi (OSM).

Maps

features,hotosm_bdi_education_facilities_points,Burundi

BURUNDI: OSM health facilities
Health facilities in Burundi (OSM).

Maps

features,hotosm_bdi_health_facilities_points,Burundi

BURUNDI: OSMnx roads
Overpass query: ["highway"!~"abandoned|construction|planned|platform|proposed|raceway"] ["service"!~"private"]

Maps

bdi_all_roads,features,Global

BURUNDI: OSM roads and footways
All roads and footways of Burundi from OpenStreetMap (OSM).

Maps

bdi_all_roads,features,Burundi

BURUNDI: OSM sea ports
Sea ports of Burundi (OSM).

Maps

features,hotosm_bdi_sea_ports_points,Burundi

BURUNDI: Population by Collines
100m population distribution of Burundi by Worldpop aggregated to colline level.

Maps

bdi_pop2020_worldpop_aggregated_collinesbcg2020,features,Burundi

BURUNDI: Power grid
Electricity transmission network of Burundi (World Bank+REGIDISO). https://energydata.info/dataset/burundi-electricity-transmission-network-2007

Maps

burundi_grid,features,Burundi

BURUNDI: Power plants
Power plants in Burundi with total installed generating capacity 10 mw from the Platts World Electric Power Plants Database (WEPP 2006). https://datacatalog.worldbank.org/dataset/burundi-power-plants

Maps

bdi_powerplants,features,Global

BURUNDI: Primary and secondary roads
This layer contains primary and secondary roads in Burundi catagorising the roads into three classes and providing information on surface and usability. The data originates from the Burundi Ministry of Health.

Maps

bdi_trans_roads_ln_dsnisv3_minesante,features,Burundi

BURUNDI: Priority Areas for landslide risk assessment
TBD

Maps

bdi_haz_landslides_pol_s4_eurac_pp_prio_areas,features,Burundi

Burundi: Protected Areas
This layer shows protected areas in Burundi according to the World Database of protected areas.

Maps

burundi,nature,protected,Burundi

BURUNDI: Roads topologic indicators
The layer contains the Betweenness Centrailty indicator computed on the edges of the OpenStreetMap (OSM) main roads (up to tertiary).

Maps

bdi_topology_indicators_main_edges,features,Burundi

BURUNDI: Roads topologic indicators
The layer contains two topologic indicators computed on the nodes (roads intersections and dead-ends) of the OpenStreetMap (OSM) drivable roads dataset: i) Betweenness Centrality, and ii) Current Flow Betweenness Centrality.

Maps

bdi_topology_indicators_edges,features,Burundi

BURUNDI: Roads topologic indicators by province
The layer contains two topologic indicators computed on the nodes (roads intersections and dead-ends) of the OpenStreetMap (OSM) drivable roads dataset: i) Betweenness Centrality, and ii) Current Flow Betweenness Centrality. The data has been computed separately on each province of Burundi, then merged on the same file.

Maps

bdi_topology_indicators_edges_adm1,features,Burundi

Burundi: Schools
This layer shows the location of schools in Burundi. The data originates from BCG.

Maps

bdi_edu_ecoles_v1_pnt_bcg,features,Burundi

Burundi: Settlement extents - build-up area
No abstract provided

Maps

built-up area,burundi,settlements,Burundi

Burundi: Settlements (OpenStreetMap)
This layer contains populated places extracted from OpenStreetMap 01 July 2021.

Maps

burundi,place,settlements,Burundi

BURUNDI: Terrain Ruggedness (7.5 arc-sec)
Terrain ruggedness (elevation standard deviation) over Burundi at 7.5 arc-sec (225 m) of spatial resolution. Cropped from the original GMTED2010 global topographic elevation model from USGS/NGA.

Maps

GeoTIFF,WCS,Burundi

Burundi: Touristic sites
This layers shows touristic sites in Burundi. The data was provided by BCG.

Maps

bdi_touristic_sites_pnt_bcg_iom,features,Burundi

BURUNDI: Vulnerability Indices (colline level)
Vulnerability indices over Burundi at colline level (where available).

Maps

burundi,collines,vulnerability,Burundi

BURUNDI: Vulnerability Indices (province level)
Vulnerability indices over Burundi for each province.

Maps

burundi,provinces,vulnerability,Burundi

CDD - NUTS level 0
Cooling Degree Days at country level (NUTS level 0) is a weather-based technical index designed to describe the need for the heating energy requirements of buildings. CDD is derived from meteorological observations of air temperature, interpolated to regular grids at 25 km resolution for Europe. Calculated gridded CDD is aggregated and subsequently presented on NUTS-0 level.

Maps

CDD,Nuts0,Europe

CDD - NUTS level 2
Cooling Degree Day (CDD) index is a weather-based technical index designed to describe the need for the heating energy requirements of buildings. HDD is derived from meteorological observations of air temperature, interpolated to regular grids at 25 km resolution for Europe. Calculated gridded HDD is aggregated and subsequently presented on NUTS-2 level.

Maps

cooling,features,Nuts2,Europe

CE_demo_cases
The layer shows the following information about Plus Energy Buildings (PEB) demo cases: 1) context, 2) key features, 3) owner, 4) Location, 5) Technologies integration, 6) Demo community and 7) partnership.

Maps

CE_demo_cases,features,Europe

Climate Classification - NUTS0
Climate classification in european countries. The climates were extracted by the Koppen-Geiger classification

Maps

climate_class_nut0,features,Global

Coherence Data over Sevilla Area
Interferometric Coherence Amplitudes for the Area of Sevilla using a mobile window 1x5 with Gamma filter

OpenEO

Land use,Land cover,collection,copernicus,sentinel-1,coherence,multi-temporal,Gamma,No Platform Information Available

confine
No abstract provided

Maps

confine,features,Global

confine0
No abstract provided

Maps

confine,features,Global

confine1
No abstract provided

Maps

confine,features,Global

Copernicus Surface Soil Moisture - 1km
The Soil Water Index quantifies the moisture condition at various depths in the soil. It is mainly driven by the precipitation via the process of infiltration. Soil moisture is a very heterogeneous variable and varies on small scales with soil properties and drainage patterns. Satellite measurements integrate over relative large-scale areas, with the presence of vegetation adding complexity to the interpretation.

OpenEO

Land use,Land cover,collection,surface soil moisture,ASCAT,Sentinel-1,ADO project,ADO,Sentinel-1 A/B; MetOp A/B

Corvara Landslide monitoring network
Monitoring network for Corvara Landslide managed by CSS - Eurac Research. The layer show the position of GPS, cameras and other sensors installed in the network.

Maps

camera,features,monitoring_network,Italy

Daily meteorological records - Climate Data Base
The Climate Database (CDB) contains meteorological time series of daily temperature (maximum, minimum and mean) and daily total precipitation for more than 250 station sites in Trentino – South Tyrol region. The data were collected from the regional meteorological networks of Bolzano and Trento Provinces and include open access records from several close sites in Austria. The spanned period is 1950 – 2021. Note that mean temperature is defined by averaging minimum and maximum values in all cases. The CDB was built in the framework of the Use-Case number 8 of the DPS4ESLAB project. Citation: Crespi, A. (2020). Daily meteorological records - Climate Data Base (Version 1.0) [Data set]. Eurac Research. https://doi.org/10.48784/B1NP-2628

Database

climatology,meteorological observations,temperature,precipitation,cdb,Trentino - Alto Adige,Meteorological geographical features

DinAlpConnect Project area
This data set represents the considered area between the Alps and Dinaric mountains to analyze the situation of ecological connectivity. Link to map: https://maps.eurac.edu/maps/1140/view File: DinAlpConnect_Project_area.shp

Maps

DinAlpConnect_Project_area,features,Global

Dinaric Alps: Continuum Suitability Index
This indicator evaluates the landscape permeability for a variety of species on land from 0 to 10, based on land use, population pressure, protection status, fragmentation and topography. File name: DinaricAlps_CSI.tif

Maps

Dinaric Alps,DinaricAlps_CSI,GeoTIFF,WCS,Global

Dinaric Alps: Ecological stepping stones (SACA1)
Stepping stones are representing areas with a high ecological value, important for ecological linkages. They are calculated by the Continuum Suitability Index (CSI) and the Strategic Connectivity Areas, considering small and less important Ecological Conservation Areas. File: DinaricAlps_SACA1_Ecological_Stepping_Stones.shp

Maps

Dinaric Alps,DinaricAlps_SACA1_Ecological_Stepping_Stones,features,Global

Dinaric Alps: Environmental protection indicator
This indicator evaluates the landscape permeability for a variety of species on land from 0 to 10, based on the protection status of protected areas. File name: DinaricAlps_ENV.tif

Maps

Dinaric Alps,DinaricAlps_ENV,GeoTIFF,WCS,Europe

Dinaric Alps: Fragmentation indicator
This indicator evaluates the landscape permeability for a variety of species on land from 0 to 10, based on the effective mesh size and the effective mesh density. File name: DinaricAlps_FRA.tif

Maps

Dinaric Alps,DinaricAlps_FRA,fragmentation,GeoTIFF,WCS,Europe

Dinaric Alps: Land cover indicator
This indicator evaluates the landscape permeability for a variety of species on land from 0 to 10, based on land cover classes. File name: DinaricAlps_LAN.tif

Maps

Dinaric Alps,DinaricAlps_LAN,GeoTIFF,WCS,Europe

Dinaric Alps: Motorway barriers
This layer is showing motorway barriers with potential ecological linkages in the Dinaric Alps. File Name: DinaricAlps_SACA2_motorway_barriers.shp

Maps

Dinaric Alps,DinaricAlps_SACA2_motorway_barriers,features,Europe

Dinaric Alps: Population indicator
This indicator evaluates the landscape permeability for a variety of species on land from 0 to 10, based on population density data. File name: DinaricAlps_POP.tif

Maps

Dinaric Alps,DinaricAlps_POP,GeoTIFF,population density,WCS,Europe

DinaricAlps: Regional ecological corridors (SACA2)
This layer shows the designed width of ecological corridors, that connect Ecological Conservation Areas. An approximate width of 2km was designed by truncating the normalized cost-weighted distances of the corridors at 40.000 km. File name: DinaricAlps_SACA2_Reg_ecological_corridors.shp

Maps

Dinaric Alps,DinaricAlps_SACA2_Reg_ecological_corridors,features,Europe

Dinaric Alps: Topography indicator
This indicator evaluates the landscape permeability for a variety of species on land from 0 to 10, based on altitude and slope conditions. File name: DinaricAlps_TOP

Maps

Dinaric Alps,DinaricAlps_TOP,GeoTIFF,WCS,Europe

Easttyrol: Vaia storm damage areas
No abstract provided

Maps

2021-04-20_transalp_aut-study-area_vaia_storm-damage-areas,features,Austria

Ecological Conservation Areas (SACA1) Dinaric Alps
DinaricAlps_SACA1_Ecological_Conservation_Areas.shp

Maps

DinaricAlps_SACA1_Ecological_Conservation_Areas,features,Europe

Ecological Linkages (SACA2)
Ecological linkages are least cost paths, connecting the most important Ecological Conservation Areas (SACA1). They are part of the ecological intervention areas (SACA2). File name: DinaricAlps_SACA2_Regional_ecological_linkages_LCP_Assessment.shp

Maps

Dinaric Alps,DinaricAlps_SACA2_Regional_ecological_linkages_LCP_Assessment,features,Global

Ecological Restoration Areas / Barriers (SACA3) Dinaric Alps
File: DinaricAlps_SACA3_Ecological_Barriers.shp

Maps

DinaricAlps_SACA3_Ecological_Barriers,features,Global

Electricity Prices for Households
Electricity prices components for household consumers -annual data (from 2007 onwards). Annual values taken from the Eurostat dataset with a national level resolution.

Maps

Electricty,Household,price,Europe

Energy Cultures Drivers
The information provided intends to support a profiling exercise of users’ domestic energy use and how these variations are translated into different domestic energy-intensity practices across EU territories. This layer includes descriptive information on the energy demand dynamics at household level by means of taking into account the cultural-climatic aspects which characterise the EU climatic areas as part of this research.

Maps

energy,energy_culutral_drivers,features,households,Europe

Environmental Parameters
This layer contents specific IEQ data sets provided which are rereferred to the closest possible locations of the Cultural-E demo cases. Specific locations and coordinates are included for each one in the IEQ related files that have been made available for downloading. Each geo-referred location contains IEQ related information and graphics, in the specific: 1. Reference year -.epw file-. 2. Climatic statistical data -.txt file-. Weather data plots, elaborated with Climate consultant and merged in a unique document -.pdf file-. 4. Weather data summary elaborated -.xls file-. Relevant information is provided in relation to: data source, used tool, implemented comfort tool, weather stations spec, software download links. This aims to illustrate the accuracy and "data fairness" of the data provided.

Maps

features,households,IEQ,weather,Global

EU climate classification (Köppen-Geiger)
The most frequently used climate classification map is that of Wladimir Köppen, presented in its latest version 1961 by Rudolf Geiger. A huge number of climate studies and subsequent publications adopted this or a former release of the Köppen-Geiger map. While the climate classification concept has been widely applied to a broad range of topics in climate and climate change research as well as in physical geography, hydrology, agriculture, biology and educational aspects, a well-documented update of the world climate classification map is still missing. Based on recent data sets from the Climatic Research Unit (CRU) of the University of East Anglia and the Global Precipitation Climatology Centre (GPCC) at the German Weather Service, we present here a new digital Köppen-Geiger world map on climate classification for the second half of the 20th century.

Maps

GeoTIFF,KG_climate_class_clip,WCS,Global

Eurac Snow Cloud Removal Modis
Snow maps with clouds removed by spatial and temporal filters

OpenEO

Land use,Land cover,collection,No Keywords Available,Aqua, Terra

Evapotranspiration - Venosta valley
Daily evapotranspiration [mm/day] maps for the Venosta Valley (South Tyrol,Italy) produced with the GEOtop hydrological model.

OpenEO

Land use,Land cover,collection,evapotranspiration,geotop,model

Factor available water capacity
USDA soil property map: available water capacity. - Resolution: 500m - Geographical Coverage: Alpine space - Input data: LUCAS 2009 Topsoil- Model: Multivariate Additive Regression Splines (MARS) - Year: 2015. For further information visit the website European soil data centre (ESDAC).

OpenEO

Land use,Land cover,collection,soil map,available water capacity,ESDAC,LUCAS,topsoil,ADO project,ADO,N/A

Factor distance to water
Distance [m] calculated at each location to the nearest lakes, water reservoirs and rivers. Rivers were filtered to Strahler order &amp;gt; 3.

OpenEO

Land use,Land cover,collection,Distance to large water bodies,ADO project,ADO,N/A

Factor elevation
ADO_elevation is a digital surface model (DSM). For further information visit website copernicus land monitoring service: https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1-0-and-derived-products/eu-dem-v1.0?tab=metadata

OpenEO

Land use,Land cover,collection,Digital Elevation Model,Elevation,Copernicus Land,Copernicus,Alps,ADO project,ADO,N/A

Factor humus content
Organic Carbon Content In Topsoils In Europe (OCTOP) - ESDAC makes available the Maps of Organic carbon content (%) in the surface horizon of soils in Europe. Resolution: 1 km - Year: 2004. The result is available as a map and an explaining booklet: The Map of Organic Carbon Content In Topsoils In Europe: Version 1.2 September - 2003 (S.P.I.04.72).

OpenEO

Land use,Land cover,collection,soil Organic Carbon,Carbon content,ESDAC,topsoil,SOC,ADO project,ADO,N/A

Factor landscape diversity
Shannon eveness index provides information on area composition and richness ranging from 0 to 1. It is calculated considering 9 Corine Land Cover classes of numeric matrices using a moving window algorithm of 5 pixels side and dividing this result by its maximum.

OpenEO

Land use,Land cover,collection,landscape diversity,Shannon eveness index,ADO project,ADO,N/A

Factor presence of irrigation infrastructure
Permanently irrigated agricultural land is based on the corine land cover 2018 (CLC) from Copernicus. It has been extracted the permanent irrigated class, which is the class 12 in the CLC raster. The output is a binary raster, whereas 1 corresponds for permanent irrigated land and 0 corresponds to not permanent irrigated land.

OpenEO

Land use,Land cover,collection,permanent irrigated land,presence of irrigation infrastructure,ADO project,ADO,N/A

Factor slope
Slope derived from EU-DEM version 1.0. year: 2000. For further information visit: https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1-0-and-derived-products/slope?tab=metadata

OpenEO

Land use,Land cover,collection,Slope,Copernicus,Land,Elevation,Digital Elevation Model,Copernicus Land,Pan-European,ADO project,ADO,N/A

Factor soil texture
USDA soil textural classes derived from clay, silt and sand maps. - Resolution: 500m - Geographical Coverage: Alpine space - Input data: LUCAS 2009 Topsoil- Model: Multivariate Additive Regression Splines (MARS) - Year: 2015- Soil texture is classified into 12 classes: 1: Clay, 2: Silty-Clay, 3: Silty Clay-Loam, 4: Sandy Clay, 5: Sandy Clay-Loam, 6: Clay-Loam, 7: Silt, 8: Silt-Loam, 9: Loam, 10: Sand, 11: Loam Sand, 12: Sandy Loam. For further information visit the website European soil data centre (ESDAC).

OpenEO

Land use,Land cover,collection,soil texture,soil textural classes,ESDAC,LUCAS,topsoil,ADO project,ADO,N/A

Farmhouses in South Tyrol
Layer to represent the position of eight case study buildings of exemplary energy efficient interventions in historic buildings. All buildings are retrofitted farm houses located in South Tyrol province.

Maps

energy refurbishment,historic buildings,Italy

Farmhouses in South Tyrol tour
Layer to represent the "tour" to eight case study buildings of the HiBERatlas (a best-practice database of exemplary energy efficient interventions in historic buildings). All buildings are retrofitted farm houses located in South Tyrol province.

Maps

historic buildings,virtual tour,Italy

Forest fires of July 2021 in Sardinia - One week after
On 24 July 2021, a large fire broke out on the Italian island of Sardinia. With strong winds, high temperatures, and dry vegetation, the blaze spread rapidly. In this image, taken about one week after, it is evident the extension area of the fire. credit: produced from ESA remote sensing data

Maps

fire,GeoTIFF,WCS,Italy

Forest fires of July 2021 in Sardinia - Two days before
On 24 July 2021, a large fire broke out on the Italian island of Sardinia. With strong winds, high temperatures, and dry vegetation, the blaze spread rapidly. In this image, taken about two days before of the event, is it possible to see the destroyed vegetated area. credit: produced from ESA remote sensing data

Maps

GeoTIFF,WCS,Italy

Gas Prices for Household
Layer about Gas prices components for household consumers - annual data, derived by Eurostat datasets at country level.

Maps

features,gas,price,Europe

Gross Domestic Product (GDP)
Layer about Gross Domestic Product prices components for household consumers - annual data, derived by Eurostat datasets at country level.

Maps

features,GDP,Europe

HDD - NUTS2
Heating degree day (HDD) index is a weather-based technical index designed to describe the need for the heating energy requirements of buildings. HDD is derived from meteorological observations of air temperature, interpolated to regular grids at 25 km resolution for Europe. Calculated gridded HDD is aggregated and subsequently presented on NUTS-2 level.

Maps

HDD,NUT,temp,Europe

HDD - NUTS level 0
Heating degree day (HDD) index is a weather-based technical index designed to describe the need for the heating energy requirements of buildings. HDD is derived from meteorological observations of air temperature, interpolated to regular grids at 25 km resolution for Europe. Calculated gridded HDD is aggregated and subsequently presented on NUTS-0 level.

Maps

energy,HDD,Nuts0,Europe

Household Cooking Practices
This Layer shows the share of fuels in the final energy consumption in the residential sector for coocking. The Frequency is annual.

Maps

cooking,energy,households,Europe

Households Disposable Income
The Layer shows the households disposable income at the nation level for the household. The frequency is annual. The Unit of measure is GINI. GINI is a measure of statistical dispersion intended to represent the income inequality or wealth inequality within a nation or any other group of people. The Gini coefficient measures the inequality among values of a frequency distribution (for example, levels of income). A Gini coefficient of zero expresses perfect equality, where all values are the same (for example, where everyone has the same income). A Gini coefficient of one (or 100%) expresses maximal inequality among values (e.g., for a large number of people where only one person has all the income or consumption and all others have none, the Gini coefficient will be nearly one).

Maps

GINI,households,income,Europe

Households Electricity consumption
Layer about Electricity Consumption in Households at nation level. The frequency of data is annual. The dataset is taken from Eurostat dataset.

Maps

Electricty,features,households,Europe

Households Gas Consumption
Layer about Gas Consumption in Households at nation level. The frequency of data is annual. The dataset is taken from Eurostat dataset.

Maps

features,gas,houshold,Europe

Household Space Cooling
The Layers shows the share of final energy consumption in the residential sector for space cooling. The frequency of data is annual.

Maps

cooling,houshold,Space,Europe

Household Space Heating
The Layers shows the share of fuels in the final energy consumption in the residential sector for space heating. The frequency of data is annual.

Maps

heating,Household,Space,Europe

Hydrological stations
Hydrological stations collected in ADO project

Maps

features,metadata_disc,Europe

In der Bletterbachschl track
In Der Bletterback park track

Maps

features,track,Italy

Industry, trade and education buildings in Europe
Layer to represent the position of eight case study buildings of exemplary energy efficient interventions in historic buildings. The case studies represent historic buildings with a particular use, such as for industry, trade or education.

Maps

energy refurbishment,historic buildings,Europe

Industry, trade and education buildings in Europe tour
Layer to represent the "tour" to eight case study buildings of exemplary energy efficient interventions in historic buildings. The case studies represent historic buildings with a particular use, such as for industry, trade or education.

Maps

historic buildings,virtual tour,Europe

Land Surface Temperature - 231m 8 day mean
The Land Surface Temperature (LST) is based on MODIS satellite data. The LST is based on 8 day MOD11A2 (v006) LST products. The spatial resolution is 231 m after regridding from the original 1000 m resolution. The LST is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetatated areas are masked using the MODIS land cover product layer MCD12Q1 FAO-Land Cover Classification System 1 (LCCS1). The final product is regridded to the LAEA Projection (EPSG:3035). The Land Surface Temperature is expressed in degree Celsius.

OpenEO

Land use,Land cover,collection,land surface temperature,lst,modis,ADO project,ADO,Terra

LIA Sentinel-1 Track015_Indexed
Sentinel-1A Local Incidence Angle. This LIA layer belongs to the respective Backscatter Sentinel-1 collection. It contains only one time step, since the LIA is stable over time.

OpenEO

Land use,Land cover,collection,S-1,Local Incidence Angle,LIA,Sentinel-1A

LIA Sentinel-1 Track044
Sentinel-1A Local Incidence Angle. This LIA layer belongs to the respective Backscatter Sentinel-1 collection. It contains only one time step, since the LIA is stable over time.

OpenEO

Land use,Land cover,collection,S-1,Local Incidence Angle,LIA,Sentinel-1A

LIA Sentinel-1 Track066
Sentinel-1A Local Incidence Angle. This LIA layer belongs to the respective Backscatter Sentinel-1 collection. It contains only one time step, since the LIA is stable over time.

OpenEO

Land use,Land cover,collection,S-1,Local Incidence Angle,LIA,Sentinel-1A

LIA Sentinel-1 Track088
Sentinel-1A Local Incidence Angle. This LIA layer belongs to the respective Backscatter Sentinel-1 collection. It contains only one time step, since the LIA is stable over time.

OpenEO

Land use,Land cover,collection,S-1,Local Incidence Angle,LIA,Sentinel-1A

LIA Sentinel-1 Track095
Sentinel-1A Local Incidence Angle. This LIA layer belongs to the respective Backscatter Sentinel-1 collection. It contains only one time step, since the LIA is stable over time.

OpenEO

Land use,Land cover,collection,S-1,Local Incidence Angle,LIA,Sentinel-1A

LIA Sentinel-1 Track117
Sentinel-1A Local Incidence Angle. This LIA layer belongs to the respective Backscatter Sentinel-1 collection. It contains only one time step, since the LIA is stable over time.

OpenEO

Land use,Land cover,collection,S-1,Local Incidence Angle,LIA,Sentinel-1A

LIA Sentinel-1 Track139
Sentinel-1A Local Incidence Angle. This LIA layer belongs to the respective Backscatter Sentinel-1 collection. It contains only one time step, since the LIA is stable over time.

OpenEO

Land use,Land cover,collection,S-1,Local Incidence Angle,LIA,Sentinel-1A

LIA Sentinel-1 Track168
Sentinel-1A Local Incidence Angle. This LIA layer belongs to the respective Backscatter Sentinel-1 collection. It contains only one time step, since the LIA is stable over time.

OpenEO

Land use,Land cover,collection,S-1,Local Incidence Angle,LIA,Sentinel-1A

Lighting and Appliances
This layer describes the energy consumption of lights and appliances on National Level for the households. The frequency of data is annual and it is connected with the Nuts0 Level.

Maps

Appliances,consumption,energy,Lighting,Europe

Locali convenzionati
This layer shows 90 % of the locations of bars/restaurants where the Eurac lunchcard can be used. As of: 01.09.2021.

Maps

lunch,restaurant,Italy

local_policies
The information provided in this layer gives an overview of the legislation and requirements in each country and shows how they impact the spread of PEB concepts, with the help of a practical example. Different policies and related boundary conditions in each country have a great influence on the successful implementation of plus energy concepts.Therefore, national funding schemes and local policies are analysed in regard to support renewable energy generation in buildings and favour the connection with the electric grid and other district buildings (e.g. direct delivery of power to neighbour buildings, grid feed-in) as well as the local energy market (e.g. energy prices, feed-in tariff) and foreseen developments and environmental aspects.

Maps

boundary_conditions,local_policies,PEB,Europe

Mean Snow Cover Duration 2041-2070 RCP8.5
Mean Snow Cover Duration according to climate predictions of the RCP 8.5 scenario from 2041 to 2070

Maps

GeoTIFF,scd_2041_2070_rcp85_noglacier_16bit_3035,WCS,Global

MECHANICALLY VENTILATED buildings in SUMMER
In this layer, the user can find a prediction of the occupants" thermal feeling (TF) according to specific scenarios recommended in the Standard EN 16798-1, and values of Operative Temperature referring to the four Indoor Environmental Quality categories.

Maps

IEQ,IndoorEnvironmentalQuality,Thermalcomfort,Thermalfeeling,Europe

MECHANICALLY VENTILATED buildings in WINTER
In this layer, the user can find a prediction of the occupants" thermal feeling (TF) according to specific scenarios recommended in the Standard EN 16798-1, and values of Operative Temperature referring to the four Indoor Environmental Quality categories.

Maps

IEQ,IndoorEnvironmentalQuality,Thermalcomfort,Thermalfeeling,Europe

Meteo stations information
Layer with informations about meteorological stations for Trentino-Alto Adige region, Austria and Switzerland. The layer describe the Climate-Database of Eurac Research that contains meteorological time series of daily temperature (maximum, minimum and mean) and daily total precipitation for more than 250 station sites.

Maps

climate,metadata,meteo,Italy

MOD16 Evapotranspiration - 500 m
Operational MODIS ET product over the Alps

OpenEO

Land use,Land cover,collection,evapotranspiration,energy balance,MOD16,ADO project,ADO,Aqua, Terra

MODIS SNOW ALPS DataCubes
SNOW map derived from daily MODIS observations (Aqua and Terra) for the entire Alps arc. The input data are the atmospherically-corrected reflectances of MODIS MOD09GQ, MOD09GA for tile h19v04 and h18v04. The map has two bands: - SNOW MAP: Snow cover classification map with four classes [0 = NO DATA - missing or corrupt data of one or more input bands; 1 = SNOW - pixel covered by snow; 2 = NO_SNOW - pixel not covered by snow; 3 = CLOUD - pixel covered by clouds], QUALITY FLAG: Snow cover quality map [NO DATA - missing or corrupt data of one or more input bands; QUALITY_INDEX - higher values indicate higher likeliness of correct classification].

OpenEO

Land use,Land cover,collection,SNOW,MODIS,Aqua, Terra

MODIS SNOW map over the ALPS
The collection contains binary snow cover maps covering the entire Alpine Arc. The maps have a daily frequency and a ground resolution of 250 m. They are derived from daily MODIS observations (Aqua and Terra) for the entire Alps arc. The input data are the atmospherically-corrected reflectances of MODIS MOD09GQ, MOD09GA for tile h19v04 and h18v04. The map has two bands: - SNOW MAP: Snow cover classification map with four classes [0 = NO DATA - missing or corrupt data of one or more input bands; 1 = SNOW - pixel covered by snow; 2 = NO_SNOW - pixel not covered by snow; 3 = CLOUD - pixel covered by clouds], QUALITY FLAG: Snow cover quality map [NO DATA - missing or corrupt data of one or more input bands; QUALITY_INDEX - higher values indicate higher likeliness of correct classification].

OpenEO

Land use,Land cover,collection,SNOW,MODIS,snow map,Alps,Aqua, Terra

MONALISA - SOS timeseries
Stations and timeseries of the MONALISA-SOS service. Environmental timeseries collected in the SouthTyrol province by the MONALISA project partners. Query the features and click the link to view last values collected

Maps

features,timeseries_sos,Italy

Monthly climatologies - Climate Data Base
The dataset contains the 1981 – 2010 monthly climatologies of mean, minimum and maximum temperature and total precipitation for more than 250 locations in Trentino – South Tyrol. They were derived from the observation records of the regional meteorological network after checking all series for quality and homogeneity. Climatologies (or normals) are the mean monthly values computed over a 30-years reference interval and they represent the mean local climatic conditions. Citation: Crespi, A. (2020). Monthly climatologies - Climate Data Base (Version 1.0) [Data set]. Eurac Research. https://doi.org/10.48784/EGX7-RZ63

Database

climatology,meteorological observations,temperature,precipitation,cdb,Trentino - Alto Adige,Meteorological geographical features

MOSAIC_2022_08_09_DOY_START_NAN0
this is the bla

Maps

GeoTIFF,MOSAIC_2022_08_09_DOY_START_NAN,WCS,Italy

Municipality labels
Layer to display municipalities labels in Trento and Bolzano provinces

Maps

comuni_TN_BZ,features,label,municipality,Global

NATURALLY VENTILATED buildings in SUMMER
In this layer, the user can find a prediction of the occupants" thermal feeling (TF) according to specific scenarios recommended in the Standard EN 16798-1, and values of Operative Temperature referring to the four Indoor Environmental Quality categories.

Maps

IEQ,IndoorEnvironmentalQuality,Thermalcomfort,Thermalfeeling,Europe

NATURALLY VENTILATED buildings in WINTER
In this layer, the user can find a prediction of the occupants" thermal feeling (TF) according to specific scenarios recommended in the Standard EN 16798-1, and values of Operative Temperature referring to the four Indoor Environmental Quality categories.

Maps

IEQ,IndoorEnvironmentalQuality,Thermalcomfort,Thermalfeeling,Europe

Normalized Difference Vegetation Index - 231m 8 day Maximum Value Composite
The Normalized Difference Vegetation Index (NDVI) is based on MODIS satellite data. The NDVI is based on 8 day maximum value composite MOD09Q1 (v006) reflectance products. The spatial resolution is 231 m. The NDVI is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetatated areas are masked using the MODIS land cover product layer MCD12Q1 FAO-Land Cover Classification System 1 (LCCS1). The final product is regridded to the LAEA Projection (EPSG:3035). The NDVI is calculated using the formula NDVI = (NIR - Red) / (NIR + Red). The NDVI expresses the vitality of vegetation. The data is provided as 8 day measures. The time series is starting from 2001. The NDVI values range from -1 - 1, whereas high values correspond to healthy vegetation.

OpenEO

Land use,Land cover,collection,normalized difference vegetation index,ndvi,modis,ADO project,ADO,Terra

Nuts2 alpinespace-Eusalp intersection
The Alpine Drought Observatory - ADO project is interested in discharge (only for stations with a catchment area > 1000 Km2 and currently active), groundwater (only for stations for major groundwater bodies), and major lake levels (only for major water bodies (surface > 5 km2)) data. The overall objective of the Alpine Drought Observatory - ADO project is to create an online drought monitoring platform and develop policy implementation guidelines for proactive drought management in the Alpine regions. The ADO project consortium includes 11 institutions from 6 Alpine countries with a wide range of expertise, covering meteorological and hydrological monitoring, specific knowledge on modeling, drought risk and impact assessment, as well as water governance in the different sectors. Further information about the ADO project can be found here: https://www.alpine-space.eu/projects/ado/en/about.

Maps

nuts2_alpinespaceEusalp_intersection,Europe

NUTS2_Alps
Nuts features level-2 for alpine region. The features represent provinces for this area.

Maps

features,NUTS2_Alps,Europe

nuts2_simplified
NUTS region, level 2 for the EUSALP area. The border are simplified respect to the original data source to get a lighter version.

Maps

features,nuts2_simplified,Europe

OBM_v02
No abstract provided

Maps

features,OBM_v02,Global

openEO Reference data S2_32619_10m_L1C
The Copernicus Sentinel-2 dataset that is to be used as a reference dataset for testing and validation on various backends in the openEO Project.

OpenEO

Land use,Land cover,collection,openEO,Sentinel-2,Sentinel-2A

openEO Reference data S2_32619_20m_L1C
The Copernicus Sentinel-2 dataset that is to be used as a reference dataset for testing and validation on various backends in the openEO Project.

OpenEO

Land use,Land cover,collection,openEO,Sentinel-2,Sentinel-2A

openEO Reference data S2_32619_60m_L1C
The Copernicus Sentinel-2 dataset that is to be used as a reference dataset for testing and validation on various backends in the openEO Project.

OpenEO

Land use,Land cover,collection,openEO,Sentinel-2,Sentinel-2A

openEO Reference data S2_32632_10m_L1C_D22
The Copernicus Sentinel-2 dataset that is to be used as a reference dataset for testing and validation on various backends in the openEO Project.

OpenEO

Land use,Land cover,collection,openEO,Sentinel-2,Sentinel-2B

openEO Reference data S2_32632_10m_L2A_D22
The Copernicus Sentinel-2 dataset that is to be used as a reference dataset for testing and validation on various backends in the openEO Project.

OpenEO

Land use,Land cover,collection,openEO,Sentinel-2,Sentinel-2B

openEO Reference data S2_32632_20m_L1C_D22
The Copernicus Sentinel-2 dataset that is to be used as a reference dataset for testing and validation on various backends in the openEO Project.

OpenEO

Land use,Land cover,collection,openEO,Sentinel-2,Sentinel-2B

openEO Reference data S2_32632_20m_L2A_D22
The Copernicus Sentinel-2 dataset that is to be used as a reference dataset for testing and validation on various backends in the openEO Project.

OpenEO

Land use,Land cover,collection,openEO,Sentinel-2,Sentinel-2B

openEO Reference data S2_32632_60m_L1C_D22
The Copernicus Sentinel-2 dataset that is to be used as a reference dataset for testing and validation on various backends in the openEO Project.

OpenEO

Land use,Land cover,collection,openEO,Sentinel-2,Sentinel-2B

openEO Reference data S2_32632_60m_L2A_D22
The Copernicus Sentinel-2 dataset that is to be used as a reference dataset for testing and validation on various backends in the openEO Project.

OpenEO

Land use,Land cover,collection,openEO,Sentinel-2,Sentinel-2B

openEO Reference data S2_32636_10m_L2A
The Copernicus Sentinel-2 dataset that is to be used as a reference dataset for testing and validation on various backends in the openEO Project.

OpenEO

Land use,Land cover,collection,openEO,Sentinel-2,Sentinel-2A

openEO Reference data S2_32636_10m_L2A
The Copernicus Sentinel-2 dataset that is to be used as a reference dataset for testing and validation on various backends in the openEO Project.

OpenEO

Land use,Land cover,collection,openEO,Sentinel-2,Sentinel-2A

openEO Reference data S2_32636_10m_L2A
The Copernicus Sentinel-2 dataset that is to be used as a reference dataset for testing and validation on various backends in the openEO Project.

OpenEO

Land use,Land cover,collection,openEO,Sentinel-2,Sentinel-2A

openEO Reference data S2_32636_10m_L2A
The Copernicus Sentinel-2 dataset that is to be used as a reference dataset for testing and validation on various backends in the openEO Project.

OpenEO

Land use,Land cover,collection,openEO,Sentinel-2,Sentinel-2B

openEO Reference data S2_32636_10m_L2A
The Copernicus Sentinel-2 dataset that is to be used as a reference dataset for testing and validation on various backends in the openEO Project.

OpenEO

Land use,Land cover,collection,openEO,Sentinel-2,Sentinel-2B

openEO Reference data S2_32636_20m_L2A
The Copernicus Sentinel-2 dataset that is to be used as a reference dataset for testing and validation on various backends in the openEO Project.

OpenEO

Land use,Land cover,collection,openEO,Sentinel-2,Sentinel-2A

openEO Reference data S2_32636_20m_L2A
The Copernicus Sentinel-2 dataset that is to be used as a reference dataset for testing and validation on various backends in the openEO Project.

OpenEO

Land use,Land cover,collection,openEO,Sentinel-2,Sentinel-2B

openEO Reference data S2_32636_20m_L2A
The Copernicus Sentinel-2 dataset that is to be used as a reference dataset for testing and validation on various backends in the openEO Project.

OpenEO

Land use,Land cover,collection,openEO,Sentinel-2,Sentinel-2B

openEO Reference data S2_32636_20m_L2A
The Copernicus Sentinel-2 dataset that is to be used as a reference dataset for testing and validation on various backends in the openEO Project.

OpenEO

Land use,Land cover,collection,openEO,Sentinel-2,Sentinel-2A

openEO Reference data S2_32636_20m_L2A
The Copernicus Sentinel-2 dataset that is to be used as a reference dataset for testing and validation on various backends in the openEO Project.

OpenEO

Land use,Land cover,collection,openEO,Sentinel-2,Sentinel-2A

openEO Reference data S2_32636_60m_L2A
The Copernicus Sentinel-2 dataset that is to be used as a reference dataset for testing and validation on various backends in the openEO Project.

OpenEO

Land use,Land cover,collection,openEO,Sentinel-2,Sentinel-2A

openEO Reference data S2_32636_60m_L2A
The Copernicus Sentinel-2 dataset that is to be used as a reference dataset for testing and validation on various backends in the openEO Project.

OpenEO

Land use,Land cover,collection,openEO,Sentinel-2,Sentinel-2B

openEO Reference data S2_32636_60m_L2A
The Copernicus Sentinel-2 dataset that is to be used as a reference dataset for testing and validation on various backends in the openEO Project.

OpenEO

Land use,Land cover,collection,openEO,Sentinel-2,Sentinel-2A

openEO Reference data S2_32636_60m_L2A
The Copernicus Sentinel-2 dataset that is to be used as a reference dataset for testing and validation on various backends in the openEO Project.

OpenEO

Land use,Land cover,collection,openEO,Sentinel-2,Sentinel-2B

openEO Reference data S2_32636_60m_L2A
The Copernicus Sentinel-2 dataset that is to be used as a reference dataset for testing and validation on various backends in the openEO Project.

OpenEO

Land use,Land cover,collection,openEO,Sentinel-2,Sentinel-2A

OpenEO service
The Eurac Research backend provides EO data available for processing using OGC WC(P)S

OpenEO

Alps_boundary_3035,features,Global

Ortler_Alpen_Specialkarte_Meurer-Freytag
Historical Map (1:50.000) of the Ortler Alps made by Julius Meurer (1838-1923), Gustav Freytag (?-1938) in 1884.

Maps

GeoTIFF,Ortler_Alpen_Specialkarte_Meurer-Freytag,WCS,Europe,Austria,Italy

PAR_domes2000
Offering (timeseries group) of the Sensor Observation Service - SOS - collected within the MONALISA project belonging to a network of 31 measuring stations in the Bolzano province. The offering groups timeseries from a specific sensor installed in the station site. Timeseries are identified with the observed parameter name that are listed in the keywords list. The naming convention of the title define the offering identification name: “Offering”_”Station name and altitude”. The complete list of the timeseries provided by the web service, is available in json format in the API: http://monalisasos.eurac.edu/sos/api/v1/timeseries/ . Further information can be found on the project website: http://monalisasos.eurac.edu/sos/. To browse and/or download the timeseries data a map viewer is available: http://monalisasos.eurac.edu/sos/static/client/helgoland/index.html#/map

SOS

Land use,Land cover,Photosynthetically Active Radiation - down average,Photosynthetically Active Radiation - up average,domes2000,collection

Past Avalanche Events
Southtyrol: past avalanche events.

Maps

avalanche,events,hazard,Italy

Path simulation
Simulation for the START project of the path of 3 person in the Park site. In the future this simulation will be replaced by a near real time updated layer. You can see on the map different position at different time using the arrow of the timeline tool on the map.

Maps

features,path,person,simulation,Italy

PEB_guidelines
This layer provides exemplary national initiatives and guidelines materials for the design and experimentation for high standards of energy efficient buildings such as PEB, ZEB and NZEB. The different national initiatives referenced intends to represent the 4 EU climates which are object of study of CULTURAL-E.

Maps

energy,features,households,PEB_guidelines,Global

Population Density
The ratio between the annual average population and the land area. The land area concept (excluding inland waters) should be used wherever available; if not available then the total area, including inland waters (area of lakes and rivers) is used. The frequency is annual.

Maps

Density,Population,Europe

Precipitation Anomalies - ERA5_QM REL_RR-1
Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2022).

OpenEO

Land use,Land cover,collection,RR anomalies,relative precipitation anomalies,precipitation anomalies,ERA5,ADO project,ADO,N/A

Precipitation Anomalies - ERA5_QM REL_RR-12
Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2022).

OpenEO

Land use,Land cover,collection,RR anomalies,relative precipitation anomalies,precipitation anomalies,ERA5,ADO project,ADO,N/A

Precipitation Anomalies - ERA5_QM REL_RR-2
Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2022).

OpenEO

Land use,Land cover,collection,RR anomalies,relative precipitation anomalies,precipitation anomalies,ERA5,ADO project,ADO,N/A

Precipitation Anomalies - ERA5_QM REL_RR-3
Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2022).

OpenEO

Land use,Land cover,collection,RR anomalies,relative precipitation anomalies,precipitation anomalies,ERA5,ADO project,ADO,N/A

Precipitation Anomalies - ERA5_QM REL_RR-6
Relative precipitation anomalies are based on downscaled ERA5 reanalysis data (downscaling is performed using quantile mapping method) and calculated for different time scales (1, 2, 3, 6, 12 months). The values represent the % of normal precipitation, where normal is defined as the long-term average (1981-2022).

OpenEO

Land use,Land cover,collection,RR anomalies,relative precipitation anomalies,precipitation anomalies,ERA5,ADO project,ADO,N/A

Prosnow mosaic
Prosnow project mosaic to get the last Sentinel-2 image cloud free available. The images cover 10 ski areas in the Alps region and are updated every day.

Maps

mosaic,sentinel,snow,Italy

punti_progetto0
No abstract provided

Maps

features,punti_progetto,Global

punti_progetto_new
No abstract provided

Maps

features,punti_progetto_new,Global

RandomForestClassifier_batch_FT_M6789_WTE_CORINE_con_DEM_N5000_32TPS_32TQS_32TPT_32TQT_32TPS
Downscaled landcover classification map using WTE classes generated using the pipeline developed in the AI4EBV project, using Random Forest, for year 2018.

Maps

AI4EBV,land cover,WTE,Europe

RandomForestClassifier_batch_FT_M6789_WTE_CORINE_con_DEM_N5000_32TPS_32TQS_32TPT_32TQT_32TPT
Downscaled landcover classification map using WTE classes generated using the pipeline developed in the AI4EBV project, using Random Forest, for year 2018.

Maps

AI4EBV,land cover,WTE,Europe

RandomForestClassifier_batch_FT_M6789_WTE_CORINE_con_DEM_N5000_32TPS_32TQS_32TPT_32TQT_32TQS
Downscaled landcover classification map using WTE classes generated using the pipeline developed in the AI4EBV project, using Random Forest, for year 2018.

Maps

AI4EBV,land cover,WTE,Europe

RandomForestClassifier_batch_FT_M6789_WTE_CORINE_con_DEM_N5000_32TPS_32TQS_32TPT_32TQT_32TQT
Downscaled landcover classification map using WTE classes generated using the pipeline developed in the AI4EBV project, using Random Forest, for year 2018.

Maps

AI4EBV,land cover,WTE,Europe

Rund um dolomiten tracks
Rund um dolomiten tracks in the Bletterback park

Maps

features,track,Italy

SAO Sentinel-2 L2A Reference data S2_ST_BRDF_10m_L2A
The Copernicus Sentinel-2 L2A Dataset for South Tyrol Region

OpenEO

Land use,Land cover,collection,SAO,Sentinel-2,L2A,Sentinel-2A

SAO Sentinel-2 L2A Reference data S2_ST_BRDF_20m_L2A
The Copernicus Sentinel-2 L2A Dataset for South Tyrol Region

OpenEO

Land use,Land cover,collection,SAO,Sentinel-2,L2A,Sentinel-2A

SAO Sentinel-2 L2A Reference data S2_ST_BRDF_20m_L2A_SCL
The Copernicus Sentinel-2 L2A Scene Classification Dataset for South Tyrol Region

OpenEO

Land use,Land cover,collection,SAO,Sentinel-2,L2A,Sentinel-2A

SAO Sentinel-2 L2A Reference data S2_ST_BRDF_60m_L2A
The Copernicus Sentinel-2 L2A Dataset for South Tyrol Region

OpenEO

Land use,Land cover,collection,SAO,Sentinel-2,L2A,Sentinel-2A

SAO Sentinel-2 L2A Reference data S2_ST_BRDF_60m_L2A_SCL
The Copernicus Sentinel-2 L2A Scene Classification Dataset for South Tyrol Region

OpenEO

Land use,Land cover,collection,SAO,Sentinel-2,L2A,Sentinel-2A

scd_20001001_20190930_16bit_3035
No abstract provided

Maps

GeoTIFF,scd_20001001_20190930_16bit_3035,WCS,Global

scd_2041_2070_rcp26_noglacier_16bit_3035
No abstract provided

Maps

GeoTIFF,scd_2041_2070_rcp26_noglacier_16bit_3035,WCS,Global

scd_2071_2100_rcp26_noglacier_16bit_3035
No abstract provided

Maps

GeoTIFF,scd_2071_2100_rcp26_noglacier_16bit_3035,WCS,Global

scd_2071_2100_rcp85_noglacier_16bit_3035
No abstract provided

Maps

GeoTIFF,scd_2071_2100_rcp85_noglacier_16bit_3035,WCS,Global

Sentinel-2 Cloudless Data S2_Cloudless_32631_10m_L1C
The Copernicus Sentinel-2 Cloudless dataset.

OpenEO

Land use,Land cover,collection,Cloudless,Sentinel-2,Sentinel-2A, Sentinel-2B

Sentinel-2 Cloudless Data S2_Cloudless_32632_10m_L1C
The Copernicus Sentinel-2 Cloudless dataset.

OpenEO

Land use,Land cover,collection,Cloudless,Sentinel-2,Sentinel-2A, Sentinel-2B

Sentinel-2 Cloudless Data S2_Cloudless_32633_10m_L1C
The Copernicus Sentinel-2 Cloudless dataset.

OpenEO

Land use,Land cover,collection,Cloudless,Sentinel-2,Sentinel-2A, Sentinel-2B

sentinel2_grid
This layer display the uodate-date for each tile of the Sentinel-2 rgb mosaic. It is daily updated for each tile if a new good (lexx 30% cloud coverage) image is downloaded.

Maps

features,sentinel2_grid,update,Europe

Sentinel-2 MOSAIC-2019
Sentinel-2 mosaic clouds free. Year 2019

Maps

GeoTIFF,S2_MOSAIC_2019_ST_VIS_stretch,WCS,Global

Sentinel-2 RGB last image available
This layer is a mosaic of rgb tiles from SENTINEL-2 mission, updated every day with the last images available with less than 30% of cloud coverage. Have look to this map for information regarding update dates: https://maps.eurac.edu/maps/118/view

Maps

color,mosaic,rgb,sentinel,Europe

Sentinel-2 RGB mosaic before Vaja Storm
RGB cloudless image to view the situation of the vegetation before Vaja storm.

Maps

GeoTIFF,Sentinel2_20180926_8bit_rgb,WCS,Italy

Sentinel-2 rgb mosaic no clouds for 2018
This picture is a mosaic with Sntinel-2 images without clouds to estimate the forest coverage in South Tyrol.

Maps

GeoTIFF,rgb,sentinel,Sentinel2_20181015-20181018_8bit_rgb,WCS,Italy

Snow depth - Venosta Valley
Daily snow depth [mm] maps for the Venosta Valley (South Tyrol,Italy) produced with the GEOtop hydrological model.

OpenEO

Land use,Land cover,collection,snow,depth,geotop,model

Soil Moisture Anomalies - ERA5_QM
ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 4 to 7 decades. Currently data is available from 1950, split into Climate Data Store entries for 1950-1978 (preliminary back extension) and from 1979 onwards (final release plus timely updates, this page). ERA5 replaces the ERA-Interim reanalysis. ERA5-Land offers "land" variables with an enhanced resolution, compared to ERA5. Albeit, at the time of processing with a higher latency. Therefore, ERA5 was downscaled to the 9 km ERA5-Land grid using a quantile mapping approach. The soil moisture anomalies are based on the original ERA5 fields "Volumetric soil water layer 1 - 4", representing the following depths: layer 1 (0-7cm), layer 2 (7-28cm), layer 3 (28-100 cm), layer 4 (100-289 cm). Anomalies were calculated based on the period 1981-2010 as a reference. Contains modified Copernicus Climate Change Service information [1980-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1980-current year]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. Citation: Greifeneder, F. (2022). Soil Moisture Anomalies - ERA5_QM (Version v1) [Data set]. Institute for Earth Observation. https://doi.org/10.48784/ea665ca2-0ceb-11ed-86c5-02000a08f4e5.

OpenEO

Land use,Land cover,collection,Soil moisture,soil moisture anomalies,ERA5,ERA5-Land,Copernicus,ADO project,ADO,N/A

Solar energy in alpine historic buildings
Layer to represent the position of seven case study buildings of exemplary energy efficient interventions in historic buildings. In all buildings, a photovoltaic or solar thermal system was integrated as one of the renovation measures.

Maps

energy refurbishment,historic buildings,Europe

Solar energy in alpine historic buildings tour
Layer to represent the "tour" to seven case study buildings of exemplary energy efficient interventions in historic buildings. In all buildings, a photovoltaic or solar thermal system was integrated as one of the renovation measures.

Maps

historic buildings,virtual tour,Europe

Solar Irradiation - Monthly Mean Annual Average
Monthly Mean Annual Average of Solar Irradiation in kW/h.

Maps

GeoTIFF,irradiation,solar,WCS,Italy

Southtyrol: Administrative Units and Municipalities
This layer shows the municipality boundaries for Southtyrol, the Autonomous Province of Bolzano/Bozen.

Maps

administrative,boundaries,municipality,Italy

SOUTH TYROL: Hexagonal tessellation (~250m)
Tessellation onto regular hexagonal cells of South Tyrol (IT) at a resolution of ~250m.

Maps

features,st_tessellation_250m_pol,suedtirol,tessellation,Austria,Italy,Switzerland

SOUTH TYROL: impacts of flood events (ED30)
Impacts of flood events in South Tyrol (IT) taken from the ED30 database.

Maps

ed30,features,st_hzd_evnt_hydro_ed30_apb_pnt_all,Italy

South Tyrol Land Use Land Cover (Level 1)
This layer shows the level 1 land use land cover classes for the Province of South Tyrol.

Maps

land cover,landuse,South Tyrol,Italy

SOUTH TYROL: Population Density (Ago 2015, 02PM, 100m)
Dynamic population density model for South Tyrol (IT) at 100m of spatial resolution at 2:00 PM (Aug 2015 run).

Maps

August,GeoTIFF,Population,ST_pop_100m_Aug2015_1400_WD_AllAgeGroups_ras,WCS,Italy

SOUTH TYROL: Population Density (Aug 2015, 02AM, 100m)
Dynamic population density model for South Tyrol (IT) at 100m of spatial resolution at 2:00 AM (Aug 2015 run).

Maps

August,GeoTIFF,Population,ST_pop_100m_Aug2015_0200_WD_AllAgeGroups_ras,WCS,Italy

SOUTH TYROL: Population Density (Feb 2015, 02PM, 100m)
Dynamic population density model for South Tyrol (IT) at 100m of spatial resolution at 2:00 PM (Feb 2015 run).

Maps

February,GeoTIFF,Population,ST_pop_100m_Feb2015_1400_WD_AllAgeGroups_ras,WCS,Italy

SOUTH TYROL: Population flow comparison with traffic counts
Absolute difference between traffic counts in the 5-9 AM time interval (2021 averages) and the commuting population flow model output.

Maps

commuting,features,flow,st_traffic_vs_flow_250tess_2021,traffic,validation,Italy

Southtyrol Settlements
No abstract provided

Maps

settlements,Southtyrol,Italy

SOUTH TYROL: tessellated OSM drivable roads (~250m)
Drivable roads from OpenStreetMap over South Tyrol (IT) onto an hexagonal tessellation of ~250m.

Maps

drive,features,south tyrol,st_tran_rds_ln_s4_osm_pp_drive_250tess,tessellation,Italy

SOUTH TYROL: tessellated OSM drivable roads with traffic simulation (~250m)
Drivable roads from OpenStreetMap over South Tyrol (IT) onto an hexagonal tessellation of ~250m, where the weight of each edge is reduced by an amount thatis proportional to the simulation of home->work traffic of South Tyrol.

Maps

features,st_tran_rds_ln_s4_osm_pp_drive_250tess_dyn,Italy

SOUTH TYROL: Tessellated population day/night (~250m)
Multi-temporal aggregated population data over South Tyrol onto an hexagonal tessellation of ~250m. Day-time, night-time and commuting time are available.

Maps

day,features,night,Population,st_pop_pol_s3_250m_daynight,Italy

SOUTH TYROL: Traffic counts per hour [2021]
2021 yearly averages of traffic counts per each hour of the day, over South Tyrol.

Maps

features,hour,st_trans_traffic_counts_hourly_average_2021_apb_pnt,traffic,Italy

SOUTH TYROL: Traffic Report (current situation)
Accumulated records of the traffic situation over the roads of South Tyrol (data taken from South Tyrol geo-portal at https://geoservices2.civis.bz.it/geoserver/pczs-Traffic/wfs).

Maps

features,southtyrol_tran_traffic_report_pt_s4_pa_pp,traffic,Italy

SOUTH TYROL: Traffic Report (mountain roads and passes)
Accumulated records of traffic events over mountain roads and passes in South Tyrol (data taken from South Tyrol geo-portal at https://geoservices2.civis.bz.it/geoserver/pczs-Traffic/wfs).

Maps

features,mountain,southtyrol_tran_traffic_report_pt_s4_pa_pp_mroads_and_passes,traffic,Italy

SOUTH TYROL: Traffic Report (neighbouring countries)
Accumulated records of traffic events related to border areas in South Tyrol (data taken from South Tyrol geo-portal at https://geoservices2.civis.bz.it/geoserver/pczs-Traffic/wfs).

Maps

border,features,southtyrol_tran_traffic_report_pt_s4_pa_pp_neigh_countries,traffic,Italy

SOUTH TYROL: Traffic Report (public transports)
Accumulated records of traffic events related to public transports in South Tyrol (data taken from South Tyrol geo-portal at https://geoservices2.civis.bz.it/geoserver/pczs-Traffic/wfs).

Maps

public,southtyrol_tran_traffic_report_pt_s4_pa_pp_public_transports,traffic,Italy

SOUTH TYROL: Traffic Report (road works and locks)
Accumulated records of works and locks over the roads of South Tyrol (data taken from South Tyrol geo-portal at https://geoservices2.civis.bz.it/geoserver/pczs-Traffic/wfs).

Maps

features,locks,southtyrol_tran_traffic_report_pt_s4_pa_pp_works_and_locks,traffic,works,Italy

SSEBop Evapotranspiration - 1 km
Operational FEWS NET ET product over the Alps

OpenEO

Land use,Land cover,collection,evapotranspiration,ssebop,energy balance,MOD16,ADO project,ADO,Aqua, Terra

Standardised Precipitation-Evapotranspiration Index - ERA5_QM
The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year]. Citation: Slovenian Environment Agency, & Central Institution for Meteorology and Geodynamics. (2022). Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-2 (Version 1.0) [Data set]. Eurac Research. https://doi.org/10.48784/50A0BE8-09CF-11ED-8E5D-02000A08F4E5.

OpenEO

Land use,Land cover,collection,SPEI,standardised precipitation-evapotranspiration index,surface water balance anomalies,ERA5,ADO project,ADO,N/A

Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-12
The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year]. Citation: Slovenian Environment Agency, & Central Institution for Meteorology and Geodynamics. (2022). Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-12 (Version 1.0) [Data set]. Eurac Research. https://doi.org/10.48784/1653510a-534a-11ec-a1e6-02000a08f41d.

OpenEO

Land use,Land cover,collection,SPEI,standardised precipitation-evapotranspiration index,surface water balance anomalies,ERA5,ADO project,ADO,N/A

Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-3
The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping.Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year]. Citation: Slovenian Environment Agency, & Central Institution for Meteorology and Geodynamics. (2022). Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-3 (Version 1.0) [Data set]. Eurac Research. https://doi.org/10.48784/16A63578-534A-11EC-B14F-02000A08F41D.

OpenEO

Land use,Land cover,collection,SPEI,standardised precipitation-evapotranspiration index,surface water balance anomalies,ERA5,ADO project,ADO,N/A

Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-6
The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. Citation: Slovenian Environment Agency, & Central Institution for Meteorology and Geodynamics. (2022). Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-6 (Version 1.0) [Data set]. Eurac Research. https://doi.org/10.48784/16C49734-534A-11EC-BE9B-02000A08F41D.

OpenEO

Land use,Land cover,collection,SPEI,standardised precipitation-evapotranspiration index,surface water balance anomalies,ERA5,ADO project,ADO,N/A

Standardised Precipitation Index - ERA5_QM SPI-1
The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year]. Citation: Slovenian Environment Agency, & Central Institution for Meteorology and Geodynamics. (2022). Standardised Precipitation Index - ERA5_QM SPI-1 (Version 1.0) [Data set]. Eurac Research. https://doi.org/10.48784/15abe686-534a-11ec-b9ef-02000a08f41d.

OpenEO

Land use,Land cover,collection,SPI,standardised precipitation index,precipitation anomalies,ADO project,ADO,N/A

Standardised Precipitation Index - ERA5_QM SPI-12
The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year]. Citation: Slovenian Environment Agency, & Central Institution for Meteorology and Geodynamics. (2022). Standardised Precipitation Index - ERA5_QM SPI-12 (Version 1.0) [Data set]. Eurac Research. https://doi.org/10.48784/15943c3e-534a-11ec-8013-02000a08f41d.

OpenEO

Land use,Land cover,collection,SPI,standardised precipitation index,precipitation anomalies,ADO project,ADO,N/A

Standardised Precipitation Index - ERA5_QM SPI-2
The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year]. Citation: Slovenian Environment Agency, & Central Institution for Meteorology and Geodynamics. (2022). Standardised Precipitation Index - ERA5_QM SPI-2 (Version 1.0) [Data set]. Eurac Research. https://doi.org/10.48784/15c8945c-534a-11ec-a1d1-02000a08f41d.

OpenEO

Land use,Land cover,collection,SPI,standardised precipitation index,precipitation anomalies,ADO project,ADO,N/A

Standardised Precipitation Index - ERA5_QM SPI-3
The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year]. Citation: Slovenian Environment Agency, & Central Institution for Meteorology and Geodynamics. (2022). Standardised Precipitation Index - ERA5_QM SPI-3 (Version 1.0) [Data set]. Eurac Research. https://doi.org/10.48784/15e38a8c-534a-11ec-9aa9-02000a08f41d.

OpenEO

Land use,Land cover,collection,SPI,standardised precipitation index,precipitation anomalies,ADO project,ADO,N/A

Standardised Precipitation Index - ERA5_QM SPI-6
The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. SPI is used on different time scales (1, 2, 3, 6, 12 months). The value of the SPI index around 0 represents the normal expected conditions regarding the amount of precipitation in the selected time scale compared to the long-term average (1981-2020). Value 1 represents approximately one standard deviation of precipitation amount during wet conditions and -1 denotes about one standard deviation of precipitation amount during dry conditions. Drought is usually defined as period when SPI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year]. Citation: Slovenian Environment Agency, & Central Institution for Meteorology and Geodynamics. (2022). Standardised Precipitation Index - ERA5_QM SPI-6 (Version 1.0) [Data set]. Eurac Research. https://doi.org/10.48784/16006b70-534a-11ec-809b-02000a08f41d.

OpenEO

Land use,Land cover,collection,SPI,standardised precipitation index,precipitation anomalies,ADO project,ADO,N/A

Standardised Snow Pack Index - ERA5_QM SSPI-10
The Standardized Snow Pack Index (SSPI) represents a standardized measure of what a certain value of snow water equivalent (SWE) averaged over the selected time period means in relation to the expected value for this period. SSPI is computed the same way as the SPI (using gamma distribution), except for being based on daily SWE timeseries instead of daily precipitation. It is calculated using the average SWE over a period of 10 and 30 days. The value of the SSPI index around 0 represents the normal expected conditions for the average SWE in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus, while the value of -1 is about one standard deviation of the deficit. SWE data used as input for the calculation of SSPI are derived using a modified version of the deterministic snow model SNOWGRID-CL, with downscaled ERA5 data used as model input data. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. Citation: Slovenian Environment Agency, & Central Institution for Meteorology and Geodynamics. (2022). Standardised Snow Pack Index - ERA5_QM SSPI-10 (Version 1.0) [Data set]. Eurac Research. https://doi.org/10.48784/0ca021a6-7942-11ec-a314-02000a08f41d.

OpenEO

Land use,Land cover,collection,SSPI,standardised snow pack index,ERA5,SNOWGRID,ADO project,ADO,N/A

Standardised Snow Pack Index - ERA5_QM SSPI-30
The Standardized Snow Pack Index (SSPI) represents a standardized measure of what a certain value of snow water equivalent (SWE) averaged over the selected time period means in relation to the expected value for this period. SSPI is computed the same way as the SPI (using gamma distribution), except for being based on daily SWE timeseries instead of daily precipitation. It is calculated using the average SWE over a period of 10 and 30 days. The value of the SSPI index around 0 represents the normal expected conditions for the average SWE in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus, while the value of -1 is about one standard deviation of the deficit. SWE data used as input for the calculation of SSPI are derived using a modified version of the deterministic snow model SNOWGRID-CL, with downscaled ERA5 data used as model input data. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. Citation: Slovenian Environment Agency, & Central Institution for Meteorology and Geodynamics. (2022). Standardised Snow Pack Index - ERA5_QM SSPI-30 (Version 1.0) [Data set]. Eurac Research. https://doi.org/10.48784/bd079696-7942-11ec-b64b-02000a08f41d.

OpenEO

Land use,Land cover,collection,SSPI,standardised snow pack index,ERA5,SNOWGRID,ADO project,ADO,N/A

Standardisied Precipitation-Evapotranspiration Index - ERA5_QM SPEI-1
The Standardized Precipitation-Evapotranspiration Index (SPEI) represents a standardized measure of what a certain value of surface water balance (precipitation minus potential evapotranspiration) over the selected time period means in relation to expected value of surface water balance for this period. SPEI is calculated on different time scales (1, 2, 3, 6, 12 months). The value of the SPEI index around 0 represents the normal expected conditions for the surface water balance in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus in the surface water balance, while the value of -1 is about one standard deviation of the deficit. Drought is usually defined as period when SPEI values fall below -1. Input precipitation data is downscaled from ERA5 reanalysis using quantile mapping. Contains modified Copernicus Climate Change Service information [1978-current year]; Contains modified Copernicus Atmosphere Monitoring Service information [1978-current year]. Citation: Slovenian Environment Agency, & Central Institution for Meteorology and Geodynamics. (2022). Standardised Precipitation-Evapotranspiration Index - ERA5_QM SPEI-1 (Version 1.0) [Data set]. Eurac Research. https://doi.org/10.48784/166E51EE-534A-11EC-9143-02000A08F41D.

OpenEO

Land use,Land cover,collection,SPEI,standardised precipitation-evapotranspiration index,surface water balance anomalies,ERA5,N/A

ST_GRIDDED_TIME_SERIES_PRECIPITATION
The product contains the gridded climatologies of monthly total precipitation for Trentino – South Tyrol for the period 1981–2010. The dataset was obtained by interpolating on a 250-m resolution grid the observed monthly climatologies of more than 200 station sites of the regional meteorological network and some extra-regional sites close to the borders. All observation data used for deriving the gridded fields were prior checked for quality and homogeneity and they are stored in the Climate Database: https://edp-portal.eurac.edu/cdb_doc/. The climatologies refer to the averages over a reference 30-year period. Further details can be found in the published paper (Crespi et al., 2021; https://doi.org/10.5194/essd-13-2801-2021). The dataset is also available in PANGAEA repository (Crespi et al., 2020; https://doi.org/10.1594/PANGAEA.924502).

OpenEO

Land use,Land cover,collection,Precipitation,Daily,High-resolution

ST_GRIDDED_TIME_SERIES_TEMPERATURE
The product contains the gridded daily series of mean temperature at 250-m spatial resolution for the region Trentino – South Tyrol. The dataset currently spans the period 1980 – 2020, but it is expected to be regularly updated. It was obtained by applying an anomaly-based interpolation to the observations of more than 200 station sites of the regional meteorological network and some extra-regional sites close to the borders. All station series used for deriving the gridded fields were prior checked for quality and homogeneity and they are stored in the Climate Database: https://edp-portal.eurac.edu/cdb_doc/. Mean temperature was here defined as the daily average of maximum and minimum temperature. Further details can be found in the published paper (Crespi et al., 2021; https://doi.org/10.5194/essd-13-2801-2021). The dataset is also available in PANGAEA repository (Crespi et al., 2020; https://doi.org/10.1594/PANGAEA.924502).

OpenEO

Land use,Land cover,collection,Temperature,Daily,High-resolution

Süd-Ago-Ost: Betweenness centrality on OSM drivable roads
Betweenness centrality topologic indicator calculated on the OSM drivable roads over the trans-national area composed of Alto Adige / Südtirol, Agordino (Belluno, Italy), and Osttirol (Lienz districit, Austria).

Maps

betweenness centrality,drive,features,osm,sudagoost_tran_rds_ln_s4_osm_pp_drive_betwcentr,Austria,Italy

Süd-Ago-Ost: Hospitals accessibility on OSM drivable roads
Hospital accessibility indicator calculated on the OSM drivable roads over the trans-national area composed of Alto Adige / Südtirol, Agordino (Belluno, Italy), and Osttirol (Lienz districit, Austria).

Maps

accessibilty,features,osm,sudagoost_tran_rds_ln_s4_osm_pp_drive_hospacc,Austria,Italy

Süd-Ago-Ost: OSM drivable roads
Drivable roads from OpenStreetMap over the trans-national area composed of Alto Adige / Südtirol, Agordino (Belluno, Italy), and Osttirol (Lienz districit, Austria).

Maps

drive,features,osm,sudagoost_tran_rds_ln_s4_osm_pp_drive,Austria,Italy

Süd-Ago-Ost: OSM main roads
Main roads from OpenStreetMap over the trans-national area composed of Alto Adige / Südtirol, Agordino (Belluno, Italy), and Osttirol (Lienz districit, Austria).

Maps

features,main,osm,sudagoost_tran_rds_ln_s3_osm_pp_main,Austria,Italy

Suitable areas for e-bike chargers in Canton Ticino
Layer to represent the most suitable locations for installing charging infrastructure for e-bikes in Canton Ticino (CH).

Maps

e-mobility,Switzerland

Suitable areas for e-bike chargers in South Tyrol
Layer to represent the most suitable locations for installing charging infrastructure for e-bikes in South Tyrol province.

Maps

e-mobility,Italy

Suitable areas for e-bike chargers in Verbano-Cusio-Ossola
Layer to represent the most suitable locations for installing charging infrastructure for e-bikes in Verbano-Cusio-Ossola province.

Maps

e-mobility,Italy

Suitable areas for e-car chargers in Canton Ticino
Layer to represent the most suitable locations for installing charging infrastructure for e-cars in Canton Ticino (CH).

Maps

e-mobility,Switzerland

Suitable areas for e-car chargers in South Tyrol
Layer to represent the most suitable locations for installing charging infrastructure for e-cars in South Tyrol province.

Maps

e-mobility,Italy

Suitable areas for e-car chargers in Verbano-Cusio-Ossola
Layer to represent the most suitable locations for installing charging infrastructure for e-cars in Verbano-Cusio-Ossola province.

Maps

e-mobility,Italy

Temperature Condition Index - 231 m 8 days
The Temperature Condition Index (TCI) is based on the Land Surface Temperature (LST) MODIS satellite data. The LST is based on 8 day MOD11A2 (v006) LST products. The spatial resolution is 231 m after regridding from the original 1000 m resolution. The LST is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetated areas are masked using the most recent Corine Land Cover product version for the according year. The final product is regridded to the LAEA Projection (EPSG:3035). The TCI is calculated using the formula TCIi = (LSTmax,i - LSTi)/(LSTmax,i - LSTmin,i) * 100. The TCI expresses anomalies of the LST. The data is provided as 8 day measures. The time series is starting from 2001. The TCI values range from 0-100, whereas high values correspond to optimal vegetation conditions and low values indicate unfavorable vegetation conditions.

OpenEO

Land use,Land cover,collection,temperature condition index,tci,modis,ADO project,ADO,Terra

Test layer alternative fuel pag.10
iMONITRAF layer, Alternative fuel pag 10 of the document

Maps

altern_fuel_10,features,Europe

TRANSALP Study Area
Extent of the cross-border study area for the TRANSALP project, which includes South Tyrol (IT), Valle Agordino (Veneto, IT), and East Tyrol (AT).

Maps

extent,features,study,transalp_test_site_extent_pol_pp,Austria,Italy

TRANSALP Study Area Agordino - Valle del Cordevole
This layer shows the geographic extent of the TRANSALP study area Agordino - Valle del Cordevole.

Maps

extent,Veneto,Italy

TRANSALP Study Area Agordino - Valle del Cordevole (IT)
This layer shows the spatial extent of the Transalp study area Agordino - Valle del Cordevole.

Maps

extent,study area,Veneto,Global

TRANSALP Study Area: Betweenness centrality on tessellated OSM drivable roads
Betweenness centrality topologic indicator calculated on the OSM drivable roads over the trans-national area of South Tyrol (IT), Agordino (Veneto, IT) and East Tyrol (AU). Roads have been projected onto a 250m regular hexagonal tessellation before analysis.

Maps

betweenness centrality,drive,features,osm,tessellation,transalp_tran_rds_ln_s4_osm_pp_drive_250tess_betwcentr,Austria,Italy

TRANSALP Study Area: CORINE Land Cover 2018
The Copernicus "CORINE Land Cover" dataset of 2018, clipped over the TRANSALP project cross-border test area.

Maps

features,transalp_landuse_corine_pol_pp_2018,Global

TRANSALP Study Area East Tyrol
This layer shows the spatial extent of the TRANSALP study area East Tyrol.

Maps

East tyrol,study area,Austria

TRANSALP Study Area: Hexagonal tessellation (~250m)
Tessellation onto regular hexagonal cells of the TransAlp project"s study area, which comprises South Tyrol (IT), Valle Agordina (Veneto) and East Tyrol (AU), at a resolution of ~250m.

Maps

features,tessellation,transalp,transalp_tesselation_250m_cross_border_studyarea,Austria,Italy

TRANSALP Study Area: Hospitals accessibility on tessellated OSM drivable roads
Hospital accessibility indicator calculated on the tessellated OSM drivable roads over the trans-national area covering South Tyrol (IT), Agordino (Veneto, IT), and East Tyrol (AU).

Maps

accessibilty,features,hospitals,tessellation,Austria,Italy

TRANSALP Study Area South Tyrol
This layer shows the geographic extent of the TRANSALP study area South Tyrol.

Maps

extent,South Tyrol,Italy

TRANSALP Study Area: tessellated OSM drivable roads (~250m)
Drivable roads from OpenStreetMap over TransAlp project"s study area (South Tyrol, East Tyrol, Valle Agordina) onto an hexagonal tessellation of ~250m.

Maps

drive,features,tessellation,transalp,transalp_tran_rds_ln_s4_osm_pp_drive_250tess,Austria,Italy

TRANSALP Study Area: Tessellated population (~250m)
Aggregated population data over the TransAlp project"s study area, which comprises South Tyrol (IT), Valle Agordina (Veneto) and East Tyrol (AU), onto an hexagonal tessellation of ~250m.

Maps

features,Population,tessellation,transalp_study_area_tesselation_population_pol,Austria,Italy

TVO: OSM drivable roads
OpenStreetMap drivable roads over Trentino Alto-Adige, Veneto and Osttirol (Austria).

Maps

drive,osm,tvo_tran_rds_ln_s3_osm_pp_drive,Austria,Italy

Tyrol: Roads Network
Lineares Referenzsystem der Verkehrsinfrastrukturen von Tirol - beinhaltet Hochrangiges Strassen- und Bahnnetz bis hin zu den Fuss- und Wanderwegen. Originaldatensatz wird in der Graphenintegrations-Plattform Tirol gewartet.

Maps

features,pa,roads,tirol_trans_roads_paths_ln_s5_pp,Austria

UAV DataCubes
High resolution - hyperspectral maps as part of the data time series produced by MONALISA project. 16 Hyperspectral bands and Orthomosaic and Digital Surface models

OpenEO

Land use,Land cover,collection,UAV,Multispectral,Suface models,UAV octocopter

UAV DataCubes 20150507
HR Digital surface models, HR Land monitoring, HR Multi-spectral imaging

OpenEO

Land use,Land cover,collection,UAV,Multispectral,Suface models,Soleon

UAV DataCubes 20150821
HR Digital surface models, HR Land monitoring, HR Multi-spectral imaging

OpenEO

Land use,Land cover,collection,UAV,Multispectral,Suface models,Soleon

UAV DataCubes 20150909
HR Digital surface models, HR Land monitoring, HR Multi-spectral imaging

OpenEO

Land use,Land cover,collection,UAV,Multispectral,Suface models,Soleon

Urban monitoring
Low-cost cloud-connected position-enriched sensors for mobile monitoring of several environmental parameters have been tested in the city of Bolzano (Italy), proving their suitability in identifying the spatial variability of the local climate in relation to the urban morphology, and for highlighting the presence of urban heat island. An exploratory field campaign has been carried out in May 2021 to monitor the diurnal evolution of the microclimate conditions (Tair/RH fields). Data have been acquired performing three sessions during daytime on weekdays: at morning (i.e. 08:30-10:30), noon (i.e. 12:00-14:00), and afternoon (i.e. 16:00-18:00). The measurements have been carried out in 8 days, chosen for the stationary weather conditions (i.e. clear sky and absence of wind). The selected pathway has a length of 9 km, starting and ending at NOI Techpark, crosses the city center and reaches the northern part of the city. It is specifically designed to monitor areas of Bolzano characterized by different land use, urban morphology, and human activities.

Maps

features,humidity,sensor,solar,temperature,urban,Italy

Vegetation Condition Index - 231 m 8 days
The Vegetation Condition Index (VCI) is based on the Normalized Difference Vegetation Index (NDVI) derived from MODIS satellite data. The NDVI is based on 8 day maximum value composite MOD09Q1 (v006) reflectance products. The spatial resolution is 231 m. The NDVI is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetated areas are masked using the most recent Corine Land Cover product version for the according year. The final product is regridded to the LAEA Projection (EPSG:3035). The VCI is calculated using the formula VCIi = (NDVIi - NDVImin,i)/(NDVImax,i - NDVImin,i) * 100. The VCI expresses anomalies of the NDVI. The data is provided as 8 day measures. The time series is starting from 2001. The VCI values range from 0-100, whereas high values correspond to healthy vegetation and low values indicate stressed vegetation. Citation: Zellner, P. (2022). Vegetation Condition Index - 231 m 8 days (Version 1.0) [Data set]. Eurac Research. https://doi.org/10.48784/16367c6a-534a-11ec-b0a3-02000a08f41d.

OpenEO

Land use,Land cover,collection,vegetation condition index,vci,modis,ADO project,ADO,Terra

Vegetation Health Index - 231 m 8 days
The Vegetation Health Index (VHI) is based on a combination of products extracted from vegetation signals, namely the Normalized Difference Vegetation Index (NDVI) and the land surface temperature, both derived from MODIS satellite data. The NDVI is based on 8 day maximum value composite MOD09Q1 (v006) reflectance and the land surface temperature (LST) on 8 day MOD11A2 (v006) LST products. The spatial resolution is 231 m, therefore the original 1000 m resolution of the MOD11A2 LST is downscaled to 231 m of the MOD09Q1 reflectance. Both products are masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetated areas are masked using the most recent Corine Land Cover product version for the according year. The final product is regridded to the LAEA Projection (EPSG:3035). The VHI relies on a strong inverse correlation between NDVI and land surface temperature, since increasing land temperatures are assumed to act negatively on vegetation vigour and consequently to cause stress. The data is provided as 8 day measures. The time series is starting from 2001. The VHI values range from 0-100, whereas high values correspond to healthy vegetation and low values indicate stressed vegetation. Citation: Zellner, P. (2022). Vegetation Health Index - 231 m 8 days (Version 1.0) [Data set]. Eurac Research. https://doi.org/10.48784/161b3496-534a-11ec-b78a-02000a08f41d.

OpenEO

Land use,Land cover,collection,vegetation health index,vhi,modisi,ADO project,ADO,Terra

Veneto: Roads Network
Rete stradale derivata da DataBase strati prioritario in scala 1:10.000 (Regione Veneto,Sezione Pianificazione Territoriale Strategica e Cartografia)

Maps

features,pa,roads,veneto_tran_rds_ln_s4_pa_pp,Italy

Water Heating
The Layer of share of final energy consumption in the residential sector for water heating. The frequency of data is annual.

Maps

consumption,energy,heating,water,Europe

World_Land_Cover_Himalayas_2015_v1
Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

ecosystem,landcover,Asia,China,Nepal

World_Land_Cover_Himalayas_2016_v1
Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

ecosystem,landcover,Global

World_Land_Cover_Himalayas_2017_v1
Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

ecosystem,landcover,Global

World_Land_Cover_Himalayas_2018_v1
Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

ecosystem,landcover,Global

World_Land_Cover_Himalayas_2019_v1
Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

ecosystem,landcover,Global

World_Land_Cover_Himalayas_2020_v1
Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

ecosystem,landcover,Global

World_Land_Cover_Province_2015_v1
Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

ecosystem,landcover,Europe,Austria,Italy

World_Land_Cover_Province_2016_v1
Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

ecosystem,landcover,Global

World_Land_Cover_Province_2017_v1
Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

ecosystem,landcover,Global

World_Land_Cover_Province_2018_v1
Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

ecosystem,landcover,Global

World_Land_Cover_Province_2019_v1
Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

ecosystem,landcover,Global

World_Land_Cover_Province_2020_v1
Downscaled land cover component of the World Terrestrial Ecosystem maps of the AI4EBV project.

Maps

ecosystem,landcover,Global

World_Terrestrial_Ecosystems_Himalayas_2015_v1
The World Terrestrial Ecosystems map of the AI4EBV project.

Maps

ecosystem,landcover,Asia,China,Nepal

World_Terrestrial_Ecosystems_Himalayas_2016_v1
No abstract provided

Maps

ecosystem,landcover,Global

World_Terrestrial_Ecosystems_Himalayas_2017_v1
No abstract provided

Maps

ecosystem,landcover,Global

World_Terrestrial_Ecosystems_Himalayas_2018_v1
No abstract provided

Maps

ecosystem,landcover,Global

World Terrestrial Ecosystems Himalayas 2019
The World Terrestrial Ecosystems map of the AI4EBV project.

Maps

ecosystem,landcover,Global

World_Terrestrial_Ecosystems_Himalayas_2020_v1
No abstract provided

Maps

ecosystem,landcover,Global

World_Terrestrial_Ecosystems_Province_2015_v1
The World Terrestrial Ecosystems map of the AI4EBV project.

Maps

ecosystem,landcover,Europe,Austria,Italy

World_Terrestrial_Ecosystems_Province_2016_v1
No abstract provided

Maps

ecosystem,landcover,Global

World_Terrestrial_Ecosystems_Province_2017_v1
No abstract provided

Maps

ecosystem,landcover,Global

World_Terrestrial_Ecosystems_Province_2018_v1
No abstract provided

Maps

ecosystem,landcover,Global

World_Terrestrial_Ecosystems_Province_2019_v1
The World Terrestrial Ecosystems map of the AI4EBV project.

Maps

ecosystem,landcover,Global

World_Terrestrial_Ecosystems_Province_2020_v1
No abstract provided

Maps

ecosystem,landcover,Global

WUR Reference data
openEO useCase Dataset for WUR

OpenEO

Land use,Land cover,collection,No Keywords Available,No Platform Information Available