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Results: 416 items found

2018-2019 Forest changes

This layer highlights strong changes in forest greenness occurring between 2018 and 2019. Since the most relevant changes for this period are constituted by damages caused by the Vaia storm, this layer contains mostly information about these damages. However, other strong changes may also be highlighted by this layer, such as new forest roads, building sites and other human interventions on forested areas.

Maps

GeoTIFF,Europe,Italy,South Tyrol

2019-2020 Forest changes

This layer captures the main areas where forest change occurred between 2019 and 2020. Most of these changes are likely caused by snow breaks that occurred during that winter (and late autumn 2019). Since these damages appear to be sparse and of varying intensity, their mapping can be problematic. Here, a conservative approach was taken, by slightly underestimating the changes.

Maps

GeoTIFF,Europe

2019-2020 Forest clearing

This layer shows the areas where a strong change in forest greenness (NDVI) occurred between the two reference years. These are mostly areas interested by salvage logging and other activities such as the building of new forest roads.

Maps

GeoTIFF,Europe,Italy,South Tyrol

2020-2021 Forest changes

This layer highlights forest changes occurred between 2020 and 2021. These are expected to be caused mostly by bark beetle attacks, but also changes due to other causes such as salvage logging are included (these are also included in the clearing layers). Since for 2021 the available images are fewer than usual, at some specific locations some “ghost” changes occur, and the composites should be used as a reference together with the change maps to correctly interpret these. This layer also includes pixels where change was observed for the previous years, where a further change was detected for 2021.

Maps

GeoTIFF,Europe,Italy,South Tyrol

2020-2021 Forest clearing

Maps

GeoTIFF,Europe,Italy,South Tyrol

2021-2022 Forest changes

This layer highlights forest changes occurred between 2021 and 2022. These are expected to be caused mostly by bark beetle attacks, but also changes due to other causes such as salvage logging are included (these are also included in the clearing layers). This layer also includes pixels where change was observed for the previous years, where a further change was detected for 2022.

Maps

GeoTIFF,Europe,Italy,South Tyrol

2021-2022 Forest clearing

Maps

GeoTIFF,Europe,Italy,South Tyrol

4dmed_stations

This layer displays stations collected in the 4DMED project with hydrological datasets.

Maps

4dmed,4dmed_stations,eodc,features,Global

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

NUTS level 2 layer for the countries in the EUSALP area.

Maps

alpinespace_eusalp_NUTS2,features,Europe

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 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: OSM intrinsic completeness by discrete classification

OpenStreetMap intrinsic complete analysis by discrete classification of its collines using terrain ruggedness and gridded population estimates as auxiliary predictors.

Maps

bdi_osm_discr_class,completeness,features,intrinsic,osm,Africa

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

Maps

bdi_topology_indicators_edges,features,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 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

Charging points installed by MOBSTER in South Tyrol

Layer to represent the charging points for e-vehicles installed thanks to the MOBSTER project in South Tyrol.

Maps

e-mobility,South Tyrol

Climate Classification - NUTS0

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

Maps

climate_class_nut0,features,Global

CO2H2O_KalternEddyCov

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,CO2 concentration - average,H2O concentration - average,KalternEddyCov,collection,SOS,Sensor Observation Service,MONALISA

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

daily_LaimburgProvince

SOS

Land use,Land cover,Air Humidity - 10 minute average,Air Humidity - daily average,Air Pressure - 10 minute average,Air Pressure - daily average,Air Temperature - 10 minute average,Air Temperature - daily average,Air Temperature - daily maximum,Air Temperature - daily minimum,Dew point - 10 minute average,Global Radiation - 10 minute average,Global Radiation - daily average,Precipitation - 5 minute sum,Precipitation - daily sum,Sunshine duration - 10 minute average,Wind Direction - 10 minute average,Wind Speed - 10 minute average,Wind Speed - daily average,LaimburgProvince,collection,SOS,Sensor Observation Service,MONALISA

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.

PostgreSQL

meteo,precipitation,temperature,database,ground station,processed data

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,Dinaric Alps,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 Conservation Areas (SACA1)

This data set shows important Ecological Conservation Areas (SACA1), bigger than 100ha, in the Dinaric Alps. These areas are expected to have a high biological value and that ecological connectivity is functioning well. Strategic Connectivity Areas (SACAs) derive from the Continuum Suitability Index. This approach is a way to display via GIS the most important sites for the overall ecological network on a macro-regional level (SACA1). Here, the most important ones were selected by expert evaluation. Filename: DinaricAlps_SACA1_Ecological_Conservation_Areas.shp

Maps

DinaricAlps_SACA1_Ecological_Conservation_Areas,features,Europe,Albania,Austria,Bosnia and Herzegovina,Croatia,Greece,Italy,Montenegro,Slovenia

Dinaric Alps: Ecological Restoration Areas / Barriers (SACA3)

Ecological Restoration Areas represent important barriers and have a low continuum suitability index (CSI). Ecological Restoration Areas (SACA3) are those ones, where ecological movements are not possible at the current stage and where it is necessary to implement restoration measures. These areas are currently the main barriers. For the calculation of these areas, all areas with a CSI of 1-4 were selected. File: DinaricAlps_SACA3_Ecological_Barriers.shp

Maps

Dinaric Alps,DinaricAlps_SACA3_Ecological_Barriers,features,Europe

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

DrSchär mais field monitoring timeseries

Timeseries of the DrSchär field monitoring in Este (PD) during summer 2022. Dataset of the soil temperature and soil humidity collected from different type of sensors distributed among the mais field. In the field were installed different types of sensors as many TDR sensors and one capacitive sensor; they collect temperature and humidity of the soil. The data from the sensor were collected by two networks: a Zigbee-based and one LoraWAN-based network.

InfluxDB

Agricultural and aquaculture facilities,Environmental monitoring facilities,water,humidity,soil moisture,temperature,soil,lorawan,zigbee,tdr,mais,capacitive,sensor,irrigation,agriculture

Easttyrol: Vaia storm damage areas

No abstract provided

Maps

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

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

EDCOV_KalternEddyCov

SOS

Land use,Land cover,E_QC,Ecosystem respiration,Evapotranspiration,Gross primary production,LE_f,Latent heat - filled,Net CO2 flux - quality checked,Net ecosystem exchange,Sensible heat - filled,Sensible heat - quality checked,KalternEddyCov,collection,SOS,Sensor Observation Service,MONALISA

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

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 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

Fragsburg_rgb_flight1_3035

UAV orthophoto of apple orchard maintained by Laimburg Research Centre near Fragsburg for precision mapping of Apple Proliferation

Maps

Fragsburg_rgb_flight1_3035,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

Harmful pollutants and microclimatic parameters from autonomous low-cost sensors deployed in the city center of Bolzano, Italy

O3, NO2, PM1, PM2.5, PM10, temperature, relative humidity, atmospheric pressure and solar radiation low-cost sensors have been co-located with the certified air quality monitoring station of Bolzano - Italy (UTM 46°49′44″ N, 11°34′24″ E, 262 m a.s.l.) managed by the local environmental agency (APPA). The filed campaign is aimed at comparing the low-cost sensors" timeseries to high-resolution instruments, which comply with the protocol for standardized acquisition released by the European Environmental Agency. The core unit of the low-cost sensors acquisition system is a Raspberry Pi 4, which couples to the sensors via the I2C bus. Specifically, the low-cost sensors involved in the campaign are: O3 (Alphasense OX-A431), NO2 (Alphasense NO2-A431) and PM1, 2.5, 10 (Alphasense OPC-N3), Tin/RHin (Sensirion SHT31), Tout/RHout (Galtech PM15PS), atmospheric pressure (Bosch Sensortech BMP388), and SR (Apogee SP 420 Smart). Citation: Tondini, S. (2022). Harmful pollutants and microclimatic parameters from autonomous low-cost sensors deployed in the city center of Bolzano, Italy [Data set]. Eurac Research. https://doi.org/10.48784/MYPZ-EV45

InfluxDB

Environmental monitoring facilities,air,pollution,relative humidity,temperature,solar radiation,O3,NO2,PM,atmospheric pressure,low cost sensor,air,pollution,radiations,research,urban environment, urban stress

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

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

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

Maps

IEQ,IndoorEnvironmentalQuality,Thermalcomfort,Thermalfeeling,Europe

Meteo_Algund2

SOS

Land use,Land cover,Air Humidity - 5 minute average,Air Temperature - 5 minute average,Irrigation (on/off),Irrigation - 5 minute sum,Precipitation - 5 minute sum,Wind Speed - 5 minute average,Algund2,collection,SOS,Sensor Observation Service,MONALISA

Meteo_Girlan1Lamm

SOS

Land use,Land cover,Air Humidity - 5 minute average,Air Temperature - 5 minute average,Irrigation (on/off),Irrigation - 5 minute sum,Precipitation - 5 minute sum,Wind Speed - 5 minute average,Girlan1Lamm,collection,SOS,Sensor Observation Service,MONALISA

Meteo_Gries2NeufeldGreifensteinerweg

SOS

Land use,Land cover,Air Humidity - 5 minute average,Air Temperature - 5 minute average,Irrigation (on/off),Irrigation - 5 minute sum,Precipitation - 5 minute sum,Wind Speed - 5 minute average,Gries2NeufeldGreifensteinerweg,collection,SOS,Sensor Observation Service,MONALISA

Meteo_Lana6

SOS

Land use,Land cover,Air Humidity - 5 minute average,Air Temperature - 5 minute average,Irrigation (on/off),Irrigation - 5 minute sum,Precipitation - 5 minute sum,Wind Speed - 5 minute average,Lana6,collection,SOS,Sensor Observation Service,MONALISA

Meteo_Nals2OberauCarli

SOS

Land use,Land cover,Air Humidity - 5 minute average,Air Temperature - 5 minute average,Irrigation (on/off),Irrigation - 5 minute sum,Precipitation - 5 minute sum,Wind Speed - 5 minute average,Nals2OberauCarli,collection,SOS,Sensor Observation Service,MONALISA

Meteo_NeumarktStiermoos

SOS

Land use,Land cover,Air Humidity - 5 minute average,Air Temperature - 5 minute average,Irrigation (on/off),Irrigation - 5 minute sum,Precipitation - 5 minute sum,Wind Speed - 5 minute average,NeumarktStiermoos,collection,SOS,Sensor Observation Service,MONALISA

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

Meteo_Terlan3

SOS

Land use,Land cover,Air Humidity - 5 minute average,Air Temperature - 5 minute average,Irrigation (on/off),Irrigation - 5 minute sum,Precipitation - 5 minute sum,Wind Speed - 5 minute average,Terlan3,collection,SOS,Sensor Observation Service,MONALISA

Meteo_Terlan3a

SOS

Land use,Land cover,Air Humidity - 5 minute average,Air Temperature - 5 minute average,Irrigation (on/off),Irrigation - 5 minute sum,Precipitation - 5 minute sum,Wind Speed - 5 minute average,Terlan3a,collection,SOS,Sensor Observation Service,MONALISA

Meteo_Tramin13er

SOS

Land use,Land cover,Air Humidity - 5 minute average,Air Temperature - 5 minute average,Irrigation (on/off),Irrigation - 5 minute sum,Precipitation - 5 minute sum,Wind Speed - 5 minute average,Tramin13er,collection,SOS,Sensor Observation Service,MONALISA

Meteo_Unterrain2FuchsangerEppan

SOS

Land use,Land cover,Air Humidity - 5 minute average,Air Temperature - 5 minute average,Irrigation (on/off),Irrigation - 5 minute sum,Precipitation - 5 minute sum,Wind Speed - 5 minute average,Unterrain2FuchsangerEppan,collection,SOS,Sensor Observation Service,MONALISA

min10_LaimburgProvince

SOS

min5_LaimburgProvince

SOS

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. The CDB was built in the framework of the Use-Case number 8 of the DPS4ESLAB project.

PostgreSQL

meteo,precipitation,temperature,database,ground station,climatology,processed data

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

Maps

IEQ,IndoorEnvironmentalQuality,Thermalcomfort,Thermalfeeling,Europe

NATURALLY VENTILATED buildings in WINTER

Maps

IEQ,IndoorEnvironmentalQuality,Thermalcomfort,Thermalfeeling,Europe

NDVI_domef1500

SOS

Land use,Land cover,Normalized Difference Vegetation Index - Index1,Normalized Difference Vegetation Index - Index2,Normalized Difference Vegetation Index - average,Normalized Difference Vegetation Index - down near infrared,Normalized Difference Vegetation Index - down red,Normalized Difference Vegetation Index - up near infrared,Normalized Difference Vegetation Index - up red,Photochemical Reflectance Index - Index1,Photochemical Reflectance Index - Index2,Photochemical Reflectance Index - average,Photochemical Reflectance Index - down near infrared,Photochemical Reflectance Index - down red,Photochemical Reflectance Index - up near infrared,Photochemical Reflectance Index - up red,domef1500,collection,SOS,Sensor Observation Service,MONALISA

NDVI_domef2000

SOS

Land use,Land cover,Normalized Difference Vegetation Index - Index1,Normalized Difference Vegetation Index - Index2,Normalized Difference Vegetation Index - average,Normalized Difference Vegetation Index - down near infrared,Normalized Difference Vegetation Index - down red,Normalized Difference Vegetation Index - up near infrared,Normalized Difference Vegetation Index - up red,Photochemical Reflectance Index - Index1,Photochemical Reflectance Index - Index2,Photochemical Reflectance Index - average,Photochemical Reflectance Index - down near infrared,Photochemical Reflectance Index - down red,Photochemical Reflectance Index - up near infrared,Photochemical Reflectance Index - up red,domef2000,collection,SOS,Sensor Observation Service,MONALISA

NDVI_KalternEddyCov

SOS

Land use,Land cover,Normalized Difference Vegetation Index - average,Normalized Difference Vegetation Index - down near infrared,Normalized Difference Vegetation Index - down red,Normalized Difference Vegetation Index - up near infrared,Normalized Difference Vegetation Index - up red,Photochemical Reflectance Index - Gamon,Photochemical Reflectance Index - down near infrared,Photochemical Reflectance Index - down red,Photochemical Reflectance Index - up near infrared,Photochemical Reflectance Index - up red,KalternEddyCov,collection,SOS,Sensor Observation Service,MONALISA

NDVI_nemef1500

SOS

Land use,Land cover,Normalized Difference Vegetation Index - Index1,Normalized Difference Vegetation Index - Index2,Normalized Difference Vegetation Index - average,Normalized Difference Vegetation Index - down near infrared,Normalized Difference Vegetation Index - down red,Normalized Difference Vegetation Index - up near infrared,Normalized Difference Vegetation Index - up red,Photochemical Reflectance Index - Index1,Photochemical Reflectance Index - Index2,Photochemical Reflectance Index - average,Photochemical Reflectance Index - down near infrared,Photochemical Reflectance Index - down red,Photochemical Reflectance Index - up near infrared,Photochemical Reflectance Index - up red,nemef1500,collection,SOS,Sensor Observation Service,MONALISA

NDVI_nemef2000

SOS

Land use,Land cover,Normalized Difference Vegetation Index - Index1,Normalized Difference Vegetation Index - Index2,Normalized Difference Vegetation Index - average,Normalized Difference Vegetation Index - down near infrared,Normalized Difference Vegetation Index - down red,Normalized Difference Vegetation Index - up near infrared,Normalized Difference Vegetation Index - up red,Photochemical Reflectance Index - Index1,Photochemical Reflectance Index - Index2,Photochemical Reflectance Index - average,Photochemical Reflectance Index - down near infrared,Photochemical Reflectance Index - down red,Photochemical Reflectance Index - up near infrared,Photochemical Reflectance Index - up red,nemef2000,collection,SOS,Sensor Observation Service,MONALISA

NDVI_vimef2000

SOS

Land use,Land cover,Normalized Difference Vegetation Index - Index1,Normalized Difference Vegetation Index - Index2,Normalized Difference Vegetation Index - average,Normalized Difference Vegetation Index - down near infrared,Normalized Difference Vegetation Index - down red,Normalized Difference Vegetation Index - up near infrared,Normalized Difference Vegetation Index - up red,Photochemical Reflectance Index - Index1,Photochemical Reflectance Index - Index2,Photochemical Reflectance Index - average,Photochemical Reflectance Index - down near infrared,Photochemical Reflectance Index - down red,Photochemical Reflectance Index - up near infrared,Photochemical Reflectance Index - up red,vimef2000,collection,SOS,Sensor Observation Service,MONALISA

NDVI_vimes1500

SOS

Land use,Land cover,Normalized Difference Vegetation Index - Index1,Normalized Difference Vegetation Index - Index2,Normalized Difference Vegetation Index - average,Normalized Difference Vegetation Index - down near infrared,Normalized Difference Vegetation Index - down red,Normalized Difference Vegetation Index - up near infrared,Normalized Difference Vegetation Index - up red,Photochemical Reflectance Index - Index1,Photochemical Reflectance Index - Index2,Photochemical Reflectance Index - average,Photochemical Reflectance Index - down near infrared,Photochemical Reflectance Index - down red,Photochemical Reflectance Index - up near infrared,Photochemical Reflectance Index - up red,vimes1500,collection,SOS,Sensor Observation Service,MONALISA

Nuts2 alpinespace-Eusalp intersection

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

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_domef1500

SOS

Land use,Land cover,Photosynthetically Active Radiation - down average,Photosynthetically Active Radiation - up average,domef1500,collection,SOS,Sensor Observation Service,MONALISA

PAR_domef2000

SOS

Land use,Land cover,Photosynthetically Active Radiation - down average,Photosynthetically Active Radiation - up average,domef2000,collection,SOS,Sensor Observation Service,MONALISA

PAR_domes2000

SOS

Land use,Land cover,Photosynthetically Active Radiation - down average,Photosynthetically Active Radiation - up average,domes2000,collection,SOS,Sensor Observation Service,MONALISA

PAR_KalternEddyCov

SOS

Land use,Land cover,Photosynthetically Active Radiation,KalternEddyCov,collection,SOS,Sensor Observation Service,MONALISA

PAR_nemef1500

SOS

Land use,Land cover,Photosynthetically Active Radiation - down average,Photosynthetically Active Radiation - up average,nemef1500,collection,SOS,Sensor Observation Service,MONALISA

PAR_nemef2000

SOS

Land use,Land cover,Photosynthetically Active Radiation - down average,Photosynthetically Active Radiation - up average,nemef2000,collection,SOS,Sensor Observation Service,MONALISA

PAR_nemes1500

SOS

Land use,Land cover,Photosynthetically Active Radiation - down average,Photosynthetically Active Radiation - up average,nemes1500,collection,SOS,Sensor Observation Service,MONALISA

PAR_nemes2000

SOS

Land use,Land cover,Photosynthetically Active Radiation - down average,Photosynthetically Active Radiation - up average,nemes2000,collection,SOS,Sensor Observation Service,MONALISA

PAR_nepas2000

SOS

Land use,Land cover,Photosynthetically Active Radiation - down average,Photosynthetically Active Radiation - up average,nepas2000,collection,SOS,Sensor Observation Service,MONALISA

PAR_vimef1500

SOS

Land use,Land cover,Photosynthetically Active Radiation - down average,Photosynthetically Active Radiation - up average,vimef1500,collection,SOS,Sensor Observation Service,MONALISA

PAR_vimef2000

SOS

Land use,Land cover,Photosynthetically Active Radiation - down average,Photosynthetically Active Radiation - up average,vimef2000,collection,SOS,Sensor Observation Service,MONALISA

PAR_vimes1500

SOS

Land use,Land cover,Photosynthetically Active Radiation - down average,Photosynthetically Active Radiation - up average,vimes1500,collection,SOS,Sensor Observation Service,MONALISA

PAR_vimes2000

SOS

Land use,Land cover,Photosynthetically Active Radiation - down average,Photosynthetically Active Radiation - up average,vimes2000,collection,SOS,Sensor Observation Service,MONALISA

PAR_vipas2000

SOS

Land use,Land cover,Photosynthetically Active Radiation - down average,Photosynthetically Active Radiation - up average,vipas2000,collection,SOS,Sensor Observation Service,MONALISA

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

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

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

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

OpenEO

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

PREC_KalternEddyCov

SOS

Land use,Land cover,Precipitation - 30 minute sum,KalternEddyCov,collection,SOS,Sensor Observation Service,MONALISA

PRI_KalternEddyCov

SOS

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

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

SENTINEL2_MOSAIC_2018_06_09_CIR

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2018. Displayed as color-infrared (CIR) representation combining the nir, green and blue bands.

Maps

GeoTIFF,SENTINEL2_MOSAIC_2018_06_09_CIR,WCS,Global

SENTINEL2_MOSAIC_2018_06_09_RGB

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2018. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

GeoTIFF,SENTINEL2_MOSAIC_2018_06_09_RGB,WCS,Global

Sentinel-2 MOSAIC-2019

Sentinel-2 mosaic clouds free. Year 2019

Maps

GeoTIFF,S2_MOSAIC_2019_ST_VIS_stretch,WCS,Global

SENTINEL2_MOSAIC_2019_06_09_CIR

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2019. Displayed as color-infrared (CIR) representation combining the nir, green and blue bands.

Maps

GeoTIFF,SENTINEL2_MOSAIC_2019_06_09_CIR,WCS,Global

SENTINEL2_MOSAIC_2019_06_09_RGB

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2019. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

GeoTIFF,SENTINEL2_MOSAIC_2019_06_09_RGB,WCS,Global

SENTINEL2_MOSAIC_2020_06_09_CIR

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2020. Displayed as color-infrared (CIR) representation combining the nir, green and blue bands.

Maps

GeoTIFF,SENTINEL2_MOSAIC_2020_06_09_CIR,WCS,Global

SENTINEL2_MOSAIC_2020_06_09_RGB

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2020. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

GeoTIFF,SENTINEL2_MOSAIC_2020_06_09_RGB,WCS,Global

SENTINEL2_MOSAIC_2021_06_09_CIR

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2021. Displayed as color-infrared (CIR) representation combining the nir, green and blue bands.

Maps

GeoTIFF,SENTINEL2_MOSAIC_2021_06_09_CIR,WCS,Global

SENTINEL2_MOSAIC_2021_06_09_RGB

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2021. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

GeoTIFF,SENTINEL2_MOSAIC_2021_06_09_RGB,WCS,Italy

SENTINEL2_MOSAIC_2022_06_09_CIR

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2022. Displayed as color-infrared (CIR) representation combining the nir, green and blue bands.

Maps

GeoTIFF,SENTINEL2_MOSAIC_2022_06_09_CIR,WCS,Global

SENTINEL2_MOSAIC_2022_06_09_RGB

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within June and September 2022. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

GeoTIFF,rgb,SENTINEL2_MOSAIC_2022_06_09_RGB,south tyrol,WCS,Europe,Italy,South Tyrol

SENTINEL2_MOSAIC_2022_08_09_CIR

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within August and September 2022. Displayed as color-infrared (CIR) representation combining the nir, green and blue bands.

Maps

GeoTIFF,SENTINEL2_MOSAIC_2022_08_09_CIR,WCS,Global

SENTINEL2_MOSAIC_2022_08_09_RGB

Cloudfree satellite mosaic composed of Sentinel-2 images acquired within August and September 2022. Displayed as true color (RGB) representation combining the red, green and blue bands.

Maps

GeoTIFF,SENTINEL2_MOSAIC_2022_08_09_RGB,WCS,Italy

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

SH_KalternEddyCov

SOS

Land use,Land cover,Soil Heat Flux 1,Soil Heat Flux 2,KalternEddyCov,collection,SOS,Sensor Observation Service,MONALISA

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.

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: impacts of landslide events (IFFI)

Impacts of landslides events in South Tyrol (IT) taken from the IFFI (Inventory of Landslide Phenomena in Italy) database [last update 18 Nov 2022].

Maps

events,features,IFFI,landslides,south tyrol,st_hzd_evnt_landslides_pt_s4_p_iffi,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

SOUTH TYROL / VAIA: Landslide probability maps

Polygonized time-series of landslide susceptibility (%) over South Tyrol.

Maps

landslides,st_hzd_pred_landslides_pol_s4_p_polygonized,susceptibility,Italy

ST_Algund2

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Water Content - 20cm,Soil Water Content - 40cm,Algund2,collection,SOS,Sensor Observation Service,MONALISA

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].

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

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].

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

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].

OpenEO

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

Standardised Precipitation Index - ERA5_QM SPI-12

OpenEO

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

Standardised Precipitation Index - ERA5_QM SPI-2

OpenEO

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

Standardised Precipitation Index - ERA5_QM SPI-3

OpenEO

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

Standardised Precipitation Index - ERA5_QM SPI-6

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].

OpenEO

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

Standardised Snow Pack Index - ERA5_QM SSPI-30

OpenEO

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

Standardisied Precipitation-Evapotranspiration Index - ERA5_QM SPEI-1

OpenEO

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

ST_domef1500

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 2cm,Soil Temperature - 5cm,Soil Water Content - 20cm,Soil Water Content - 2cm,Soil Water Content - 5cm,domef1500,collection,SOS,Sensor Observation Service,MONALISA

ST_domef2000

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 2cm,Soil Temperature - 5cm,Soil Water Content - 20cm,Soil Water Content - 2cm,Soil Water Content - 5cm,domef2000,collection,SOS,Sensor Observation Service,MONALISA

ST_domes1500

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 2cm,Soil Temperature - 5cm,Soil Water Content - 20cm,Soil Water Content - 2cm,Soil Water Content - 5cm,domes1500,collection,SOS,Sensor Observation Service,MONALISA

ST_domes2000

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 2cm,Soil Temperature - 5cm,Soil Water Content - 20cm,Soil Water Content - 2cm,Soil Water Content - 5cm,domes2000,collection,SOS,Sensor Observation Service,MONALISA

ST_dopas2000

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 2cm,Soil Temperature - 5cm,Soil Water Content - 20cm,Soil Water Content - 2cm,Soil Water Content - 5cm,dopas2000,collection,SOS,Sensor Observation Service,MONALISA

ST_Eppanberg

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Water Content - 20cm,Soil Water Content - 40cm,Eppanberg,collection,SOS,Sensor Observation Service,MONALISA

ST_Eppanberg_matrix

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Temperature - 60cm,Soil Water Potential - 20cm,Soil Water Potential - 40cm,Soil Water Potential - 60cm,Eppanberg,collection,SOS,Sensor Observation Service,MONALISA

ST_Girlan1Lamm

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Water Content - 20cm,Soil Water Content - 40cm,Girlan1Lamm,collection,SOS,Sensor Observation Service,MONALISA

ST_GRIDDED_TIME_SERIES_PRECIPITATION

The product contains the gridded daily series of mean precipitation at 250-m spatial resolution for the region Trentino – South Tyrol. 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

ST_Gries2NeufeldGreifensteinerweg

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Water Content - 20cm,Soil Water Content - 40cm,Gries2NeufeldGreifensteinerweg,collection,SOS,Sensor Observation Service,MONALISA

ST_Kaltern

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Water Content - 20cm,Soil Water Content - 40cm,Kaltern,collection,SOS,Sensor Observation Service,MONALISA

ST_Kaltern_matrix

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Temperature - 60cm,Soil Water Potential - 20cm,Soil Water Potential - 40cm,Soil Water Potential - 60cm,Kaltern,collection,SOS,Sensor Observation Service,MONALISA

ST_Laimburg

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Water Content - 20cm,Soil Water Content - 40cm,Laimburg,collection,SOS,Sensor Observation Service,MONALISA

ST_Lana6

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Water Content - 20cm,Soil Water Content - 40cm,Lana6,collection,SOS,Sensor Observation Service,MONALISA

ST_nemef1500

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 2cm,Soil Temperature - 5cm,Soil Water Content - 20cm,Soil Water Content - 2cm,Soil Water Content - 5cm,nemef1500,collection,SOS,Sensor Observation Service,MONALISA

ST_nemef2000

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 2cm,Soil Temperature - 5cm,Soil Water Content - 20cm,Soil Water Content - 2cm,Soil Water Content - 5cm,nemef2000,collection,SOS,Sensor Observation Service,MONALISA

ST_nemes1500

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 2cm,Soil Temperature - 5cm,Soil Water Content - 20cm,Soil Water Content - 2cm,Soil Water Content - 5cm,nemes1500,collection,SOS,Sensor Observation Service,MONALISA

ST_nemes2000

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 2cm,Soil Temperature - 5cm,Soil Water Content - 20cm,Soil Water Content - 2cm,Soil Water Content - 5cm,nemes2000,collection,SOS,Sensor Observation Service,MONALISA

ST_nepas2000

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 2cm,Soil Temperature - 5cm,Soil Water Content - 20cm,Soil Water Content - 2cm,Soil Water Content - 5cm,nepas2000,collection,SOS,Sensor Observation Service,MONALISA

ST_NeumarktStiermoos

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Water Content - 20cm,Soil Water Content - 40cm,NeumarktStiermoos,collection,SOS,Sensor Observation Service,MONALISA

ST_StPaulsFeld

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Water Content - 20cm,Soil Water Content - 40cm,StPaulsFeld,collection,SOS,Sensor Observation Service,MONALISA

ST_Terlan3a

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Water Content - 20cm,Soil Water Content - 40cm,Terlan3a,collection,SOS,Sensor Observation Service,MONALISA

ST_Tramin13er

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Water Content - 20cm,Soil Water Content - 40cm,Tramin13er,collection,SOS,Sensor Observation Service,MONALISA

ST_Unterrain2FuchsangerEppan

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Water Content - 20cm,Soil Water Content - 40cm,Unterrain2FuchsangerEppan,collection,SOS,Sensor Observation Service,MONALISA

ST_vimef1500

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 2cm,Soil Temperature - 5cm,Soil Water Content - 20cm,Soil Water Content - 2cm,Soil Water Content - 5cm,vimef1500,collection,SOS,Sensor Observation Service,MONALISA

ST_vimef2000

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 2cm,Soil Temperature - 5cm,Soil Water Content - 20cm,Soil Water Content - 2cm,Soil Water Content - 5cm,vimef2000,collection,SOS,Sensor Observation Service,MONALISA

ST_vimes1500

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 2cm,Soil Temperature - 5cm,Soil Water Content - 20cm,Soil Water Content - 2cm,Soil Water Content - 5cm,vimes1500,collection,SOS,Sensor Observation Service,MONALISA

ST_vimes2000

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 2cm,Soil Temperature - 5cm,Soil Water Content - 20cm,Soil Water Content - 2cm,Soil Water Content - 5cm,vimes2000,collection,SOS,Sensor Observation Service,MONALISA

ST_vipas2000

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 2cm,Soil Temperature - 5cm,Soil Water Content - 20cm,Soil Water Content - 2cm,Soil Water Content - 5cm,vipas2000,collection,SOS,Sensor Observation Service,MONALISA

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 in Canton Ticino for e-bike chargers

Layer to represent the most suitable locations for installing charging infrastructure for e-bikes in Canton Ticino (Switzerland).

Maps

e-mobility,Switzerland

Suitable areas in Canton Ticino for e-car chargers

Layer to represent the most suitable locations for installing charging infrastructure for e-cars in Canton Ticino (Switzerland).

Maps

e-mobility,Switzerland

Suitable areas in South Tyrol for e-bike chargers

Layer to represent the most suitable locations for installing charging infrastructure for e-bikes in South Tyrol.

Maps

e-mobility,Italy

Suitable areas in South Tyrol for e-car chargers

Layer to represent the most suitable locations for installing charging infrastructure for e-cars in South Tyrol.

Maps

e-mobility,Italy

Suitable areas in Verbano-Cusio-Ossola for e-bike chargers

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 in Verbano-Cusio-Ossola for e-car chargers

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

Maps

e-mobility,Italy

SWC_Algund2

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Water Content - 20cm,Soil Water Content - 40cm,Algund2,collection,SOS,Sensor Observation Service,MONALISA

SWC_domef1500

SOS

SWC_domef2000

SOS

SWC_domes1500

SOS

SWC_domes2000

SOS

SWC_dopas2000

SOS

SWC_Eppanberg

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Water Content - 20cm,Soil Water Content - 40cm,Eppanberg,collection,SOS,Sensor Observation Service,MONALISA

SWC_Girlan1Lamm

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Water Content - 20cm,Soil Water Content - 40cm,Girlan1Lamm,collection,SOS,Sensor Observation Service,MONALISA

SWC_Gries2NeufeldGreifensteinerweg

SOS

SWC_Kaltern

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Water Content - 20cm,Soil Water Content - 40cm,Kaltern,collection,SOS,Sensor Observation Service,MONALISA

SWC_Laimburg

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Water Content - 20cm,Soil Water Content - 40cm,Laimburg,collection,SOS,Sensor Observation Service,MONALISA

SWC_Lana6

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Water Content - 20cm,Soil Water Content - 40cm,Lana6,collection,SOS,Sensor Observation Service,MONALISA

SWC_nemef1500

SOS

SWC_nemef2000

SOS

SWC_nemes1500

SOS

SWC_nemes2000

SOS

SWC_nepas2000

SOS

SWC_NeumarktStiermoos

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Water Content - 20cm,Soil Water Content - 40cm,NeumarktStiermoos,collection,SOS,Sensor Observation Service,MONALISA

SWC_StPaulsFeld

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Water Content - 20cm,Soil Water Content - 40cm,StPaulsFeld,collection,SOS,Sensor Observation Service,MONALISA

SWC_Terlan3

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Water Content - 20cm,Soil Water Content - 40cm,Terlan3,collection,SOS,Sensor Observation Service,MONALISA

SWC_Terlan3a

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Water Content - 20cm,Soil Water Content - 40cm,Terlan3a,collection,SOS,Sensor Observation Service,MONALISA

SWC_Tramin13er

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Water Content - 20cm,Soil Water Content - 40cm,Tramin13er,collection,SOS,Sensor Observation Service,MONALISA

SWC_Unterrain2FuchsangerEppan

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Water Content - 20cm,Soil Water Content - 40cm,Unterrain2FuchsangerEppan,collection,SOS,Sensor Observation Service,MONALISA

SWC_vimef1500

SOS

SWC_vimef2000

SOS

SWC_vimes1500

SOS

SWC_vimes2000

SOS

SWC_vipas2000

SOS

SWP_Eppanberg

SOS

SWP_Kaltern

SOS

Land use,Land cover,Soil Temperature - 20cm,Soil Temperature - 40cm,Soil Temperature - 60cm,Soil Water Potential - 20cm,Soil Water Potential - 40cm,Soil Water Potential - 60cm,Kaltern,collection,SOS,Sensor Observation Service,MONALISA

TEMPHUM_KalternEddyCov

SOS

Land use,Land cover,Air Temperature - 30 minute average,Relative Air Humidity - 30 minute average,KalternEddyCov,collection,SOS,Sensor Observation Service,MONALISA

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,Austria,Italy

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.

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.

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