en: built-up-classification urban-compactness basic-services urban-morphology planning-outcome forest-persistence spatial-modelling nature-conservation forest-gain topography urban-sprawl land-use-and-cover-change

en: Native forest cover persistence from 2005 to 2015

format: GeoTIFF

en: Increase in clean water provision service

en: Permanent housing density increase

en: Population density per pixel (change per year)

en: Native forest cover gains from 2005 to 2015

en: Variables description

en: Table of content. Variables description, source, and other important information

en: Urban persistence: Built-up 2005 = Built-up 2015

en: Increase in Normalized Difference Vegetation Index

en: isolated

en: New built-up pixels classified as isolated

en: Increase in sanitation service

en: Ecological-Economic Zoning (EEZ)

format: SHP

en: Descricao dos dados em portiuguês

en: Portugues description

en:
Basic education in % per census sector (change per
year)

en: Human Development Index (HDI) per municipality (change per year)

en: Master plan Ubatuba (zones)

en: Increase in waste collection service

en: Topographic Index Position (TPI)

en: Master plan Ihabela (zones)

en: slope

en: Slope °

en: infill

en: New built-up pixels classified as infill

en: Mean income in Reais (R$) per census sector (change per year)

en: Axial

en: New built-up pixels classified as axial

en:
The present dataset is part of the published scientific paper entitled “The role of spatial planning in land change: An assessment of urban planning and nature conservation efficiency at the southeastern coast of Brazil” (Pierri Daunt, Inostroza and Hersperger, 2021). In this work, we evaluated the conformance of stated spatial planning goals and the outcomes in terms of urban compactness, basic services and housing provision, and nature conservation for different land-use strategies. We evaluate the 2005 Ecological-Economic Zoning (EEZ) and two municipal master plans from 2006 in a coastal region in São Paulo State, Brazil. We used Partial Least Squares Path Modelling (PLS-PM) to explain the relationship between the plan strategies and land-use change ten years after implementation in terms of urban compactness, basic services and housing increase, and nature conservation.

We acquired the data for the explanatory variables from different sources listed on Table 1. Since the model is spatially explicit, all input data were transformed to a 30 m resolution raster. Regarding the evaluated spatial plans, we acquired the zones limits from the São Paulo State Environmental Planning Division (CPLA-SP), Ilhabela and Ubatuba municipality.

1)	Land use and cover data: Urban persistence, Urban axial, Urban infill, Urban Isolates, Forest cover persistence, Forest cover gain, NDVI increase

We acquired two Landsat Collection 1 Higher-Level Surface Reflectance images distributed by the U.S. Geological Survey (USGS), covering the entire study area (paths 76 and 77, row 220, WRS-2 reference system, https://earthexplorer.usgs.gov/). We classified one image acquired by the Landsat 5 Thematic Mapper (TM) sensor on 2005-05-150, and one image from the Landsat 8 Operational Land Imager (OLI) sensor from 2015-08-15. We collected 100 samples for forest cover, 100 samples for built-up cover and 100 samples for other classes. We then classified these three classes of land cover at each image date using the Support Vector Machine (SVM) supervised algorithm (Hsu et al., 2003), using ENVI 5.0 software.

Land-use and land-cover changes from 2005 to 2015 were quantified using map algebra, by mathematically adding them together in pairs (10*LULC2015 + LULC2005). We reclassified the LULC data into forest gain (conversion of any 2005 LULC to forest cover in 2015); forest persistence (2005 forested pixels that remained forested in 2015); new built-up area (conversion of any 2005 LULC to built-up in 2015); and urban maintenance (2005 built-up pixels that remained built-up in 2015).

To describe the spatial configuration of the urban expansion, we classified the new built-up areas into axial, infill and isolated, following Inostroza et al. (2013) (For details, please refer to Supplementary Material I at the original publication).

The NDVI was obtained from the same source used for the LULC data. With the Google Engine platform, we used an annual average for the best pixels (without clouds) for 2005 and 2015, and we calculated the changes between dates. We used increases of > 0.2 NDVI to represent an improvement in forest quality.

2)	Federal Census data organization: Urban Basic Services and Housing indicator, socioeconomic and population:

The data used to infer the values of basic services provision, socioeconomic and population drivers was derived from the Brazilian National Census data (IBGE, 2000 and 2010). Population density, permanent housing unit density, mean income, basic education, and the percentage of houses receiving waste collection, sanitation and water provision services, called basic services in the context of this study, were calculated per 30 m pixel. The Human Development Index is only available at the municipality level. We attributed the HDI for the vector file with the municipality border, and we rasterized (30 m resolution) this file in QGIS. Annual rates of change were then calculated to allow comparability between LULC periods. To infer the BSH, we used only areas with an increase in permanent housing density and basic services provision (See Supplementary Material I at the original publication).

3)	Topographic drivers

To infer the values of the topographic driver, we used the slope data and the Topographic Index Position (TPI) based on the digital elevation model from SRTM (30 m resolution) produced by ALOS (freely available at eorc.jaxa.jp/ALOS/en/about/about_index.htm), and both variables were considered constant from 2005 to 2015 (See Supplementary Material I at the original publication).

basic-services

display_name: basic-services

built-up-classification

display_name: built-up-classification

forest-gain

display_name: forest-gain

forest-persistence

display_name: forest-persistence

land-use-and-cover-change

display_name: land-use-and-cover-change

nature-conservation

display_name: nature-conservation

planning-outcome

display_name: planning-outcome

spatial-modelling

display_name: spatial-modelling

topography

display_name: topography

urban-compactness

display_name: urban-compactness

urban-morphology

display_name: urban-morphology

envidat

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en: Spatially explicit data to evaluate spatial planning outcomes in a coastal region in São Paulo State, Brazil


maintainer: EnviDat Support

en: gridded-data terrestiral-laserscanning antarctica terrestrial-laser-scans depth-of-snowfall

state: active

type: dataset

en: Scan from 11 January 2019, 10 cm resolution, version 2

media_type: application/zip

format: ZIP

mimetype: application/zip

en: Scan from 27 December 2018, 1 mm resolution

format: SERVICE

en: Deprecated: Scan from 11 January 2019, 10 cm resolution, version 1

en: This resource is deprecated. It contains erroneous data.

en: Scan from 2 January 2019, 1 mm resolution

en: Scan from 4 January 2019, 10 cm resolution

en: Scan from 4 January 2019, 1 mm resolution

en: Scan from 21 December 2018, 10 cm resolution

en: Scan from 21 December 2018, 1 mm resolution

en: Scan from 27 December 2018, 10 cm resolution

en: Scan from 2 January 2019, 10 cm resolution

en: Scan from 11 January 2019, 1 mm resolution

en:
Surface topography maps (spatial extent: 400 m x 400 m) obtained at approximately 300 m from the top of the Hammarryggen Ice Rise in Dronning Maud Land, East Antarctica, using a Riegl VZ-6000 Terrestrial Laser Scanner (TLS). Scans were obtained on 5 days in the 2018-2019 Austral summer: on December 21 and 27, and January 2, 4 and 11. By using reflectors installed on bamboo poles, scans were registered with respect to the reflectors, such that the difference between two successive scans reveals the spatial patterns of erosion and deposition of snow. On each scan day, we used multiple scan positions to create one combined point cloud. After applying an octree filter on the point cloud, a 3D surface was obtained. For each day, the dataset contains a 1 mm and a 10 cm octree filter resolution file, only including points in a 400 m x 400 m area centered around the scan positions.

Notes:

* All files in the dataset are in the same coordinate system. However, this coordinate system is arbitrary (i.e., not related to any global coordinate system).

* From the installed reflectors, 4 reflectors could be used over the full period. The scan accuracy is generally higher within the area enclosed by the reflectors.

* The scans from January 2 were found to have exhibited small tilt during the scan and are of lesser accuracy.

* By walking along fixed corridors, disturbance of the snow was limited.

antarctica

display_name: antarctica

depth-of-snowfall

display_name: depth-of-snowfall

gridded-data

display_name: gridded-data

terrestiral-laserscanning

display_name: terrestiral-laserscanning

terrestrial-laser-scans

display_name: terrestrial-laser-scans

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

fr: EnviDat est le portail de données du WSL; il offre un accès uniformisé et réglementé aux données de surveillance et de recherche en matière d’environnement. Ce portail est capable d’héberger et de publier des ensembles de données précieuses et uniques, qui sont partagées aux quatre coins de la planète. Ce faisant, EnviDat favorise la recherche et contribue à l’avancement des sciences environnementales.

de: EnviDat ist das Datenportal der WSL für einen einheitlichen und verwalteten Zugang zu Umweltdaten aus Monitoring- und Forschungsprojekten. Auf dem Portal können Datensätze gehostet und veröffentlicht werden. Durch die weltweite Bereitstellung einzigartiger und wertvoller Datensätze leistet EnviDat einen Beitrag zur Forschung im Bereich der Umweltwissenschaften.

it: EnviDat è il portale dati del WSL che fornisce un accesso unificato e gestito ai dati di monitoraggio ambientale e di ricerca. Il portale ha la capacità di ospitare e pubblicare serie di dati preziosi e unici che vengono condivisi in tutto il mondo. Così facendo, EnviDat promuove la ricerca e contribuisce al progresso della scienza ambientale.

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Terrestrial laser scans on Hammarryggen Ice Rise, Dronning Maud Land, East Antarctica


en: chemistry atmosphere snow-atmosphere-interactions laboratory-study ice

en: Near Ambient Pressure partial Auger-Meitner electron yield X-ray absorption spectra and photon flux data. X-ray energy in eV in column 1, analyzer electron count intensity in arb. units in column 2.

en: Processed data as shown in Figure 4 of publication. X-ray energy in eV in column one, electron count intensity in arb. units in column 2.

en:
The table lists all NEXAFS raw data with the following columns:

* name:						raw file name
* xRayEkinRange_eV: 		Smallest and largest X-ray photon energy in eV
* scientaPassEnergy_eV: 	Pass energy of electron analyzer in eV
* scientaDwellTime_ms: 		Dwell time of electron analyzer in ms
* regionName:				O K-edge for all data
* scientaAcquisitionMode: 	Fixed energy (snapshot) mode
* scientaEkinRange_eV: 		Smallest and largest energy of the kinetic energy window used for detection in this work
* scientaEkinStep_eV		Not defined in fixed energy mode
* scientaLensMode:			Tune table
* scientaRegionIterations: 	Iterations (sweeps)
* scientaSequenceIterations: Iterations (sweeps)
* sampleTemp_dC: 			Temperature as monitored at the sample holder in °C
* cellPressure_mbar: 		Total pressure in measurement cell in mbar
* iceTemp_dC: 				Temperature of ice sample calculated based on vapour pressure (assuming total pressure equals partial pressure of water) in °C
* smplX_mm: 				positon of measurement spot, x dimension in mm
* smplY_mm: 				positon of measurement spot, y dimension in mm
* smplZ_mm: 				positon of measurement spot, z dimension in mm
* sealingTemp:				temperature at base of sample manipulator
* lastModifiedDatestr: 		time and date when data aquisition was finished
* sample: 					sample description

en: Data are compiled that have been used to demonstrate the impact of high water partial pressure on X-ray absorption spectra of ice.

atmosphere

display_name: atmosphere

chemistry

display_name: chemistry

ice

display_name: ice

laboratory-study

display_name: laboratory-study

snow-atmosphere-interactions

display_name: snow-atmosphere-interactions

envidat

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en: An ice oxygen K-edge NEXAFS spectroscopy data set on gas-phase processing


maintainer: Sebastian Schlögl

en: atmospheric-turbulence turbulent-sensible-heat-fluxes atmosphere stable-boundary-layer snow

en: Turbulence data WFJ07

en: lower sensor

en: Turbulence data AA00

en: sensor in 1 m height

en: upper sensor

en: Turbulence data WFJ11

en: Turbulence data GR00

en: sensor in 2 m height

en: sensor in 25 m height

en: Turbulence data PM07

en: NWW

en: sensor in 0.2 m height

en: SEE

en:
We used five different atmospheric turbulence datasets from four test sites, with these sites showing differences in their topographical characteristics. We chose one typical alpine test site with high topographical complexity (Weissfluhjoch, Davos, Switzerland) and three test sites consisting of one glacier site (Plaine Morte, Crans-Montana, Switzerland) and two polar sites (Greenland and Antarctica) representing a quasi-ideal site with homogeneous surface and quasi infinite fetch in all directions.
The turbulent sensible heat flux was calculated using the eddy-covariance method. Note that the sonic temperature fluctuations have been converted into virtual temperature fluctuations.
Three-dimensional wind velocity and air temperature were processed using a linear detrending (Rannik and Vesala, 1999) and a planar fit approach (Massmann and Lee, 2002) to rotate the coordinate system. Air temperature, relative humidity and air pressure from weather stations were used to calculate air properties, which are required for the data processing. The weather stations are located in the immediate vicinity of the turbulence tower and are affected by the same air masses. Turbulence data were averaged to 30-min intervals, whilst changing to a 15-min time interval marginally affects the heat fluxes at the Weissfluhjoch test site (Mott et al., 2011). Note that we define a negative sensible heat flux as being directed towards the snow surface and a positive sensible heat flux as being directed upwards.
The selected datasets and corresponding test sites are briefly introduced below:

Weissfluhjoch 2007 (WFJ07): A vertical set-up of two three-dimensional ultrasonic anemometers (CSAT3, Campbell Scientific, Inc.) was used at the traditional field site Weissfluhjoch (2540 m asl.) to measure three-dimensional wind velocity and air temperature at a frequency of 20 Hz. The sensors were mounted 3 m and 5 m above the ground and provided reliable data for 50 days between 11 February 2007 and 24 April 2007. Further information on the field campaign can be found in Stössel et al. (2010) and Mott et al. (2011).

Weissfluhjoch 2011-13 (WFJ11): Three-dimensional wind velocity and air temperature were recorded at 5 m above the ground at a frequency of 10 Hz with a three-dimensional ultrasonic anemometer (CSAT3). The analysis was conducted for data obtained between February and March in the years 2011-13.

Plaine Morte 2007 (PM07): Two three-dimensional ultrasonic anemometers (CSAT3) were installed on a horizontal boom facing opposite directions (west-north-west vs. east-south-east) at 3.75 m above the ground to measure air temperature and three-dimensional wind velocity at 20 Hz. The data were collected at the almost flat field site on the Plaine Morte glacier (2750 m asl.) near Crans-Montana, Switzerland from February to April 2007. High quality meteorological data were additionally recorded and used to force the model. A detailed description about the set-up at the Plaine Morte glacier can be found in Huwald et al. (2009) and Bou-Zeid et al. (2010).

Greenland 2000 (GR00): High-frequency three-dimensional ultrasonic anemometer measurements (CSAT3) were recorded at 50 Hz at the Summit Camp (72.3 °N, 38.8 °W, 3208 m asl.) located on the northern dome of the Greenland ice sheet. Data were collected at 1 m and 2 m above the snow surface during summer in 2000 and 2001. Additionally, meteorological measurements were obtained for the post processing and used to force the model. More information about the field campaign can be found in Cullen et al. (2007, 2014).

Antarctica 2000 (AA00): A set-up of three vertical three-dimensional ultrasonic anemometers (DA-600, Kaijo Denki) were installed at Mizuho Station (70°42' S, 44°20' E, 2230 m asl.) in Eastern Antarctica at 0.2, 1 and 25 m and recorded turbulence data at a frequency of 100 Hz from October to November 2000. Longwave and shortwave radiation, relative humidity, air and snow surface temperature were additionally measured and used to force the model. More information about the field campaign can be found in Nishimura and Nemoto (2005).

atmospheric-turbulence

display_name: atmospheric-turbulence

snow

display_name: snow

stable-boundary-layer

display_name: stable-boundary-layer

turbulent-sensible-heat-fluxes

display_name: turbulent-sensible-heat-fluxes

en: How do stability corrections perform in the stable boundary layer over snow?


maintainer: Mathias Bavay

en: wind-direction soil-temperature snow-depth relative-humidity air-pressure shortwave-radiation treeline wind-speed air-temperature stillberg dew-point-temperature precipitation-sum

en: The Davos Stillberg meteorological data (Stillberg_meteo_v1.1.zip) consists of timeseries from five meteorological stations operated at the Stillberg treeline ecological research site. Metadata are provided in the folder 'Stillberg_meteo_metadata' and the data in the folder 'Stillberg_meteo_data' with subfolders for the three quality levels 'raw_standardized', ‘raw_edited’, and ‘filtered’.

en: The Davos Stillberg meteorological data (Stillberg_meteo_v1.zip) consists of timeseries from five meteorological stations operated at the Stillberg treeline ecological research site. Metadata are provided in the folder 'Stillberg_meteo_metadata' and the data in the folder 'Stillberg_meteo_data' with subfolders for the three quality levels 'raw_standardized', ‘raw_edited’, and ‘filtered’.

en:
 Background information
The Stillberg ecological treeline research site is located in the transition zone between the relatively humid climate of the Northern Alps and the continental climate of the Central Alps. In 1975, 92,000 seedlings of the high-elevation conifer species *Larix decidua* Mill. (European larch), *Pinus cembra* L. (Cembran pine), and *Pinus mugo* ssp. *uncinata* (DC.) Domin (mountain pine) were systematically planted across an area of 5 hectares along an elevation gradient of about 150 metres, with the aim to develop ecologically, technically, and economically sustainable afforestation techniques at the treeline to reduce the risk of snow avalanches. In the course of time, additional research aspects gained importance, such as the ecology of the treeline ecotone under global change. Alongside the ecological long-term monitoring of the afforestation, several meteorological stations have recorded local meteorological conditions at the Stillberg research site. Here, we provide the Davos Stillberg meteorological timeseries of five stations from 1975 (01-01-1975), the year of the afforestation establishment, until the end of the year 2022 (31-12-2022).

 Station description
The five meteorological stations were all installed at the same location (46°46′25.015″N 9°52′01.792″E) at 2090 m a.s.l., in the lower part of the afforestation area. In general, the five stations were operated sequentially (Stillberg_meteo_metadata_stations_v1.csv). However, there are some overlapping time periods when more than one station was operated in parallel. The stations have recorded environmental parameters, such as air and soil temperature, dew point temperature, air pressure, relative humidity, wind direction and velocity, radiation, precipitation, and snow depth (Stillberg_meteo_metadata_parameters_v1.csv). The meteorological measurements were recorded hourly from 1975 until 1996 and have been recorded in 10-minute intervals since 1997.

 Data description
We processed the Davos Stillberg meteorological timeseries with the MeteoIO meteorological data pre-processing library (Bavay & Egger, 2014). Data files are provided for each station and quality level separately and named according to the station (see ‘Stillberg_meteo_metadata_stations_v1.csv’). From the raw data in their original formats, we generated three data quality levels: raw standardized (folder ‘raw_standardized’), edited (folder ‘raw_edited’) and filtered (folder ‘filtered’). The processing level is indicated in the headers of the data files. The whole processing protocol is described in a set of human-readable configuration files that are used by MeteoIO to generate the required data quality levels. This improves long-term reproducibility (Bavay et al., 2022), as the data could be regenerated in the future, even using a completely different software, to account for additional data points or to introduce new data corrections. The first quality level (raw standardized) is generated by parsing the original data files and interpreting them in order to convert all data points to a common format and meteorological parameter naming scheme, while excluding unreadable or duplicated data lines. The generated data files are derivatives of CSV files, with a standardised header that contains the metadata that are necessary to interpret and use the data (use metadata) and to populate a data index (search metadata). The latter is a textual implementation of the Attribute Convention for Data Discovery (ACDD) metadata standard (Attribute Convention for Data Discovery 1-3, 2022). The second quality level (edited) builds on the raw data by performing low-level data editing, such as removing some data periods that are known to be unusable (often based on maintenance records or anecdotal evidence) or applying undocumented calibration factors (for example, when there seems to be an obvious offset on a measured parameter for a period between two documented maintenance operations). The third quality level is generated by applying statistical filters on the data (per station and per meteorological parameter) to exclude presumably wrong values. We did not perform gap filling, as no single strategy could be relied upon that would work best for all possible data usage scenarios.

air-pressure

display_name: air-pressure

air-temperature

display_name: air-temperature

dew-point-temperature

display_name: dew-point-temperature

precipitation-sum

display_name: precipitation-sum

relative-humidity

display_name: relative-humidity

shortwave-radiation

display_name: shortwave-radiation

snow-depth

display_name: snow-depth

soil-temperature

display_name: soil-temperature

stillberg

display_name: stillberg

treeline

display_name: treeline

wind-direction

display_name: wind-direction

wind-speed

display_name: wind-speed

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en: Long-term meteorological station Stillberg, Davos, Switzerland at 2090 m a.s.l.


digitizing-historical-plague

maintainer: Christian Ginzler

title_for_slug: digitizing-historical-plague

en: bubonic-plague palaeo-epidemiology plague-inventory black-death plague-mapping plague-outbreaks historical-plague yersinia-pestis

en: We present newly digitized data on 6,929 plague outbreaks that occurred between 1347 and 1900 AD across Europe. The data base on an inventory initially published 1976. For georeferencing the information of Tele Atlas 2009 was used. The coordinates are in the reference systems ETRS89 and WGS84.

black-death

display_name: black-death

bubonic-plague

display_name: bubonic-plague

historical-plague

display_name: historical-plague

palaeo-epidemiology

display_name: palaeo-epidemiology

plague-inventory

display_name: plague-inventory

plague-mapping

display_name: plague-mapping

plague-outbreaks

display_name: plague-outbreaks

yersinia-pestis

display_name: yersinia-pestis

en: Digitizing historical plague


en: century climate-layers air-temperature free-climate-data climate timeseries maximum-temperature precipitation minimum-temperature

en: CHELSA Technical Documentation

media_type: application/pdf

en: As the CHELSA project is continuously expanded, certain new parameters, time scales etc. are not included anymore in the original publication (Karger et al. 2017). We are therefore provide this document as a guideline of the current climatic parameters available and will expand it as new parameters or time periods will become available. Please refer to the version history for changes made in the document.

format: PDF

mimetype: application/pdf

en: Data Access

en:
CHELSAcruts is a delta change monthly climate dataset for the years 1901-2016 for mean monthly maximum temperatures, mean monthly minimum temperatures, and monthly precipitation sum. Here we use the delta change method by B-spline interpolation of anomalies (deltas) of the CRU TS 4.01 dataset. Anomalies were interpolated between all CRU TS grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2 (Karger et al. 2017, Scientific Data). This method has the assumption that climate only varies on the scale of the coarser (CRU TS) dataset, and the spatial pattern (from CHELSA) is consistent over time. This is certainly a rather crude assumption, and for time periods for which more accurate data is available CHELSAcruts should be avoided if possible (e.g. use CHELSA V1.2 for 1979-2015). Different to CHELSA V1.2, CHELSAcruts does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages.

CHELSAcruts is licensed under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.

air-temperature

display_name: air-temperature

century

display_name: century

climate

display_name: climate

climate-layers

display_name: climate-layers

free-climate-data

display_name: free-climate-data

maximum-temperature

display_name: maximum-temperature

minimum-temperature

display_name: minimum-temperature

precipitation

display_name: precipitation

timeseries

display_name: timeseries

en: CHELSAcruts -  High resolution temperature and precipitation timeseries for the 20th century and beyond


maintainer: Thorsten Bartels-Rausch

en: metamorphism heterogeneous brine chemistry alpine arctic laboratory-study reaction antarctica ozone solution interface snow

en: Ozone data

en: Trend of ozone mixing ratio ( _O3mixingRatio_ppb_ with time ( _time_s_ ) as measured downstream of the snow sample during the experiments. Time "0" denotes the time when data acquisition with ozone analyser was started. And, ozone (_nOzone_molec_per_s_ ) loss for each experiment with duration of ozone exposure ( _duration_s_ ) of ozone exposure, as derived from the ozone trends shown above. Time "0" denotes the time when the carrier gas with ozone was passed via the snow sample.

en: data list

en: Lists file names, snow sample properties, and flow settings for each data-set.

en: Data Figure 1

en: Data of published Figure

en: Matlab Script to view and plot data

en: Matlab Data

en: All data as Matlab mat file

en: Acetone data

en:
intensity of the acetone-water-cluster and water-cluster (as derived by a chemical ionisation mass spectrometer) with time in the carrier gas downstream of packed bed flow tube holding the snow sample or the by-pass, respectively. Time "0" denotes the time when the mass spectrometer data acquisition was started. When the carrier
gas with acetone was passed via the packed bed flow tube, that is switched from by-pass to pass the snow, acetone concentration in the carrier gas rapidly dropped due to surface adsorption and slowly recovered as the adsorption sites saturate. From this, the surface area can be obtained.

en: Readme file

en: This file shows data, code, and plots.

en: Earth’s snow cover is very dynamic on diurnal time scales. The changes to the snow structure during this metamorphism have wide ranging impacts such as on avalanche formation and on the capacity of surface snow to exchange trace gases with the atmosphere. Here, we investigate the influence of dry metamorphism, which involves fluxes of water vapor, on the chemical reactivity of bromide in the snow. For this, the heterogeneous reactive loss of ozone at a concentration of 5-6E12 molecules cm-3 is investigated in artificial, shock-frozen snow samples doped with 6.2 uM sodium bromide and with varying metamorphism history. The oxidation of bromide in snow is one reaction initiating polar bromine releases and ozone depletions.

alpine

display_name: alpine

antarctica

display_name: antarctica

arctic

display_name: arctic

brine

display_name: brine

chemistry

display_name: chemistry

heterogeneous

display_name: heterogeneous

interface

display_name: interface

laboratory-study

display_name: laboratory-study

metamorphism

display_name: metamorphism

ozone

display_name: ozone

reaction

display_name: reaction

snow

display_name: snow

solution

display_name: solution

en: Data set on bromide oxidation by ozone in snow during metamorphism from laboratory study


maintainer: Francesco Comola

en: saltation snow cohesion sediment sand wind

en: Output of the model simulations

en: Model results on granular splash and saltation process of sand and snow particles

en:
The wind-driven saltation of sand and snow shapes dunes and ripples, generates dust emission, and erodes the surface of the Antarctic ice sheet. Here, we use a model based on the discrete element method to simulate grain-flow interactions and study the effect of particle cohesion on saltation dynamics.

The data contains the model output of granular splash simulations and saltation simulations. Granular splash, the main particle entrainment process in saltation, occurs upon impact of saltating particles with the granular bed. We performed Monte Carlo simulations of granular splash for loose sand grains and for cohesive ice grains. The analysis indicate that different values of cohesion have significant effects not on the number of splashed grains, on the ejection velocity, and the rebound velocity.

In our saltation simulations, we trigger particle movement with a single splash event at the inlet section section and let the system evolve until steady state. Our results show that saltation over cohesive surfaces is difficult to initiate but easy to sustain at low wind speed. The occurrence of transport thus depends on the history of the wind speed, a phenomenon known as hysteresis. We also show that saltation over cohesive surfaces presents higher mass fluxes but requires longer distances to saturate, which increases the size of the smallest stable surface ripples. Our model results have implications for large-scale aeolian processes on Earth and Titan, where sand grains are thought to be very cohesive.

cohesion

display_name: cohesion

saltation

display_name: saltation

sand

display_name: sand

sediment

display_name: sediment

wind

display_name: wind

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en: Saltation of cohesive granular materials


maintainer: Athanasios Nenes

en: cloud-droplets supersaturation cloud-modeling sigma-w raclets wolfgangpass

en: Predicted cloud droplet numbers (kappa=0.25) Weissfluhjoch

media_type: text/csv

en:
In-situ measurements of aerosol size distributions along with updraft velocities collected from a wind Doppler Lidar are coupled with a state-of-the art droplet activation parameterization (Morales and Nenes, 2014) to determine the cloud droplet number and maximum in-cloud supersaturation that can potentially form over the Davos Wolfgang station between February 24 and March 8 2019. For this, as a sensitivity test, a hygroscopicity parameter of 0.25 is assumed, which is the upper limit determined from the analysis carried out for Weissfluhjoch (https://www.envidat.ch/dataset/ccn-hygroscopicity-predicted-cloud-droplet-numbers-weissfluhjoch). The data file contains:

* Date and time (DD/MM/YYYY hh:mm:ss) in UTC
* The wind-lidar derived standard deviation of updraft velocities (sigma-w) in m/s, used to apply the parameterization employing a single characteristic velocity (Morales and Nenes, 2010)
* The maximum predicted cloud parcel supersaturation (Smax%)
* The total in-situ aerosol number concentration (Naer) in cmˉ³, measured by the SMPS
* The predicted cloud droplet numbers (Nd) in cmˉ³
* The activation fraction (%)

format: CSV

mimetype: text/csv

en: Predicted cloud droplet numbers (kappa=0.1) Davos Wolfgang

en:
In-situ measurements of aerosol size distributions along with updraft velocities collected from a wind Doppler Lidar are coupled with a state-of-the art droplet activation parameterization (Morales and Nenes, 2014) to determine the cloud droplet number and maximum in-cloud supersaturation that can potentially form over the Davos Wolfgang station between February 24 and March 8 2019. For this, as a sensitivity test, a hygroscopicity parameter of 0.1 is assumed, which is the lower limit determined from the analysis carried out for Weissfluhjoch (https://www.envidat.ch/dataset/ccn-hygroscopicity-predicted-cloud-droplet-numbers-weissfluhjoch). The data file contains:

* Date and time (DD/MM/YYYY hh:mm:ss) in UTC
* The wind-lidar derived standard deviation of updraft velocities (sigma-w) in m/s, used to apply the parameterization employing a single characteristic velocity (Morales and Nenes, 2010)
* The maximum predicted cloud parcel supersaturation (Smax%)
* The total in-situ aerosol number concentration (Naer) in cmˉ³, measured by the SMPS
* The predicted cloud droplet numbers (Nd) in cmˉ³
* The activation fraction (%)

en: Cloud droplet properties were predicted between February 24 and March 8 2019 for the measurement site Davos Wolfgang (1630 m a.s.l., LON: 9.853594, LAT: 46.835577). Droplet calculations are carried out with the physically based aerosol activation parameterization of Morales and Nenes (2014), employing the “characteristic velocity” approach of Morales and Nenes (2010). Aerosol size distribution observations required to predict the cloud droplet numbers and maximum in-cloud supersaturation are obtained from a Scanning Mobility Particle Size Spectrometer (SMPS) instrument deployed at Davos Wolfgang (https://www.envidat.ch/dataset/aerosol-data-davos-wolfgang). The required vertical velocity measurements are derived from the wind Doppler Lidar (https://www.envidat.ch/dataset/lidar-wind-profiler-data) deployed at the same station and are extracted from the first bin of the instrument, being 200 m above ground level. The hygroscopic properties of the particles measured at Davos Wolfgang could not be estimated, owing to a lack of concurrent CCN measurements. As a sensitivity test, droplet calculations are performed assuming two different values of the aerosol hygroscopicity parameter, 0.1 and 0.25, based on the analysis carried out for Weissfluhjoch. Additional information can be found in Section 2.3 [here](https://acp.copernicus.org/preprints/acp-2020-1036/).

cloud-droplets

display_name: cloud-droplets

cloud-modeling

display_name: cloud-modeling

raclets

display_name: raclets

sigma-w

display_name: sigma-w

supersaturation

display_name: supersaturation

wolfgangpass

display_name: wolfgangpass

en: Predicted cloud droplet numbers Davos Wolfgang


maintainer: Milojevic

en: low-flow extreme-values return-periods alpine-precipitation reservoir-inflow

en: Daily Luzzone Reservoir Inflow 01-01-2000 to 31-12-2019

en: Daily total water inflow to Luzzone reservoir. Natural inflow (LUZ_inflow_raw) is back-calculated based on daily water level in reservoir (removing hydropower inputs and spills). Linear interpolation between daily values is used to bring all inflow values into positive space (LUZ_inflow_intrp).

en: R codes for Extreme Values Analysis

en: Zip file containing R codes for running extreme value analyses for high precipitation and inflow events as well as code for determining return levels of low inflow and low precipitation events

en:
 Summary

* Dataset of daily inflow to Luzzone reservoir in Ticino, Switzerland

* R scripts used to generate return levels for low reservoir inflow, low precipitation, high inflow, and extreme high precipitation based on various methods from extreme value analysis

 Data
The dataset included here is the "natural" reservoir inflow for the Luzzone reservoir.
Additional analyses were conducted on daily total precipitation of 6 meteorological stations (abbreviations: TIOLI, TIOLV, COM, VRN, VLS, ZEV). These precipitation data are freely available for teaching and research from the MeteoSwiss IDAweb portal (https://www.meteoswiss.admin.ch/services-and-publications/service/weather-and-climate-products/data-portal-for-teaching-and-research.html).

 Codes
R scripts used to determine return levels of the data set are included for both extreme high events and low events. The scripts include the following methods for calculating return levels:

* GEV (Generalized Extreme Value)

* GPD and GPDd (Generalized Pareto Distribution including declustered version)

* eGPD (extended Generalized Pareto Distribution)

* MEV (Metastatistical Extreme Value)

alpine-precipitation

display_name: alpine-precipitation

extreme-values

display_name: extreme-values

low-flow

display_name: low-flow

reservoir-inflow

display_name: reservoir-inflow

return-periods

display_name: return-periods

en: Data and Code on Extreme Inflow and Lowflow Analysis for Alpine Reservoirs


sensitivity-of-modeled-snow-instability

maintainer: Bettina Richter

title_for_slug: sensitivity-of-modeled-snow-instability

en: snowpack numerical-avalanche-forecasting global-sensitivity-analysis snow-instability snow-cover-modeling

en: Manually observed snow profiles

en: Manually observed snow profiles at Weissfluhjoch field site for winter season 2016-2017, including layer depth, hand hardness, grain size and grain shape for each layer.

en: Reference run

en: Reference run for the sensitivity analysis at Weissfluhjoch field site for winter season 2016-2017. Meteorological data from the automatic weather stations are used. No biases were introduced to the input data.

en: Case WL

en: Extracted output data for case ALL. Here biases were introduced to meteorological input data during the period of weak layer formation. Data include quasi random biases, which were introduced, extracted output of simulated snow profiles and sobol indices for each output variable.

en: Case ALL

en: Extracted output data for case ALL. Here biases were introduced to meteorological input data during the whole simulation period. Data include quasi random biases, which were introduced, extracted output of simulated snow profiles and sobol indices for each output variable.

en: Case SL

en: Extracted output data for case SL. Here biases were introduced to meteorological input data during the period of slab formation. Data include quasi random biases, which were introduced, extracted output of simulated snow profiles and sobol indices for each output variable.

en: We investigated the sensitivity of modeled snow instability to meteorological input data using SNOWPACK. We therefore used input data from the automatic weather station at the Weissfluhjoch field site for the year 2016-2017. We investigated three scenarios and performed 14'000 simulations for each scenario. The dataset contains extracted output data from modeled SNOWPACK simulations, including setup files to reproduce the simulations. For further information read the README file.

global-sensitivity-analysis

display_name: global-sensitivity-analysis

numerical-avalanche-forecasting

display_name: numerical-avalanche-forecasting

snow-cover-modeling

display_name: snow-cover-modeling

snow-instability

display_name: snow-instability

snowpack

display_name: snowpack

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Sensitivity of modeled snow instability


en: swiss-geodata digital-elevation-model seilaplan open-source geoinformation-system qgis-plugin digital-planning wms-data cable-yarding

en: View video on youtube!

en: WMS-Layer als Hintergrundkarten laden

en:
In order to digitally plan a cable line using the QGIS plugin ‘Seilaplan’, maps with various background information are helpful. In this tutorial we show you how to obtain maps that are helpful for cable line planning, for example a national map of Switzerland at different scales, the NFI vegetation height model or the NFI forest mix rate. For this we explain what WMS datasets are and how to integrate them into QGIS. No download of large data is needed for this, only a good internet connection. Please note that the tutorial language is German!

Link for the integration of WMS data: https://wms.geo.admin.ch/

Link to the description on the Swisstopo website:
https://www.geo.admin.ch/en/geo-services/geo-services/portrayal-services-web-mapping/web-map-services-wms.html

Link to the Seilaplan website: https://seilaplan.wsl.ch

**************************

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung sind verschiedene Hintergrundkarten hilfreich. In diesem Tutorialvideo zeigen wir, was WMS Daten sind und wie man diese in QGIS einbinden kann. Dafür müssen die Daten nicht heruntergeladen werden. Es braucht lediglich eine gute Internetverbindung. Für die Seillinienplanung hilfreiche Karten sind bspw. die Landeskarte der Schweiz in verschiedenen Massstäben, das Vegetationshöhenmodell LFI oder der Waldmischungsgrad LFI.

Link zur Einbindung der WMS Daten: https://wms.geo.admin.ch/

Link zur Beschreibung auf der Swisstopo Webseite: https://www.geo.admin.ch/de/geo-dienstleistungen/geodienste/darstellungsdienste-webmapping-webgis-anwendungen/web-map-services-wms.html

Link zur Seilaplan-Website: https://seilaplan.wsl.ch

cable-yarding

display_name: cable-yarding

digital-elevation-model

display_name: digital-elevation-model

digital-planning

display_name: digital-planning

geoinformation-system

display_name: geoinformation-system

open-source

display_name: open-source

qgis-plugin

display_name: qgis-plugin

seilaplan

display_name: seilaplan

swiss-geodata

display_name: swiss-geodata

wms-data

display_name: wms-data

envidat

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en: Seilaplan Tutorial: Load WMS layers as background maps


en: stillberg long-term-experiment treeline mountain-forest afforestation

en: Documentation

en: This data documentation is outdated and has been replaced with an updated version (see description).

mimetype: application/msword

en: Dataset V1

en: This dataset is outdated and has been replaced with an updated version (see description).

format: GDB

en: Site photo

en: This site photo is outdated and has been replaced with an updated version (see description).

en:
 Important
This EnviDat entry is outdated. The most recent, usable version of the data can be found under the new EnviDat entry "Long-term afforestation experiment at the Alpine treeline site Stillberg, Switzerland." The entry can be found under this link <https://www.envidat.ch/dataset/afforestation-stillberg> and with this DOI 10.16904/envidat.397.

afforestation

display_name: afforestation

long-term-experiment

display_name: long-term-experiment

mountain-forest

display_name: mountain-forest

stillberg

display_name: stillberg

treeline

display_name: treeline

en: Long-term treeline research dataset at Stillberg, Davos


maintainer: Michael Lehning

en: snow-distribution snow-cover alpine-precipitation snow-hydrology snow alpine3d alpine3d-simulations

en: ADS datasets and simulation inputs

en: The file contains the ADS datasets of snow depth and input data used for simulations.

en:
Dataset (Model input, snow distribution and validation) for the precipitation scaling paper, which should be cited along with the data set citation.

This data is useful for distributed hydrological modelling or other tasks that involve the study of snow distribution and precipitation in the high Alpine.

The format of the data is for Alpine3D (models.slf.ch) model runs but other models could be used, too.

Please cite:

_Vögeli, C., Lehning, M., Wever, N., Bavay M., 2016: Scaling Precipitation Input to Spatially Distributed Hydrological Models by Measured Snow Distribution., Front. Earth Sci. 4: 108. doi: 10.3389/feart.2016.00108._

Dataset is provided as a single zip file.
The archive contains two directories, the valuable distributed snow depth maps for the landscape Davos and the simulation input.
The archive also contains the file: "ReadMeMetadataDataSetPrecipitationScaling" which explains the data structure.

alpine-precipitation

display_name: alpine-precipitation

snow

display_name: snow

snow-cover

display_name: snow-cover

snow-distribution

display_name: snow-distribution

snow-hydrology

display_name: snow-hydrology

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Precipitation Scaling Data Set (Vögeli et al., Frontiers)


en: groundwater stable-water-isotopes alptal runoff streamflow

en: Deuterium groundwater and baseflow October 2016

en:
This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

Date: Date (year-month-day)
Location: Well number or outlet
X: X coordinate (CH1903 LV03)
Y: Y coordinate (CH1903 LV03)
riparian: riparian wells (i.e. from the riparian-like cluster as presented in Kiewiet et al. (2019)) are marked a one, all other wells are marked a zero
d.D (per mil): Deuterium composition (per mil)

en: Deuterium streamwater event A

en:
This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date and time of sample collection (UTC; year-month-day, hours:minutes)
Q d.D: Deuterium composition (per mil) of streamwater sampled at the catchment outlet (697234,210472); the first sample is used as pre-event streamflow sample

en: Deuterium groundwater and baseflow August 2017

en: Precipitation and discharge event A

en:
This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
With any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date (year-month-day) and time (hours:minutes:seconds) in UTC
Q: Discharge in mm per hour at catchment outlet (697234,210472)
P: Rainfall in mm per hour averaged for two rain gauges (697494,210675) and (698004, 210680)

en: Deuterium rainfall extra event

en:
This data is collected at the Studibach catchment (Alptal, Switzerland) by Leonie Kiewiet (University of Zürich)
For any questions about the data, please contact leonie.kiewiet@geo.uzh.ch or leoniekiewiet@gmail.com

datetime: Date (year-month-day) and time (hours:minutes:seconds) in UTC
P d.D: Deuterium composition of the rainfall samples (per mil)
X: X coordinate (CH1903 LV03)
Y: Y coordinate (CH1903 LV03)

en: Deuterium groundwater and baseflow October 2017

en: Precipitation and discharge event C

en: Deuterium rainfall event A

en: Deuterium rainfall event B

en: Precipitation and discharge event B

en: Deuterium rainfall event C

en: Precipitation and discharge extra event

en: Deuterium streamwater extra event

en: Deuterium streamwater event B

en: Deuterium streamwater event C

en:
This dataset includes discharge and rainfall measurements and deuterium compositions of streamflow, rainfall and groundwater, for four rainfall events and three baseflow snapshot campaigns in the Studibach (Alptal, Switzerland). More specifically, we present the following data:

- Specific discharge at the catchment outlet at 5-minute resolution (mm per hour);
- Rainfall at 5-minute resolution (mm per hour);
- Rainfall deuterium composition (‰);
- Stormflow deuterium composition (‰);
- Groundwater and baseflow deuterium compositions (‰).

For the files containing rainfall and discharge timeseries (QP), and rainfall and streamwater deuterium compositions ("Deuterium_Rainfall" and "Deuterium_Streamwater"), we added the corresponding event identifier (A, B, C or extra) to the file names. For the files containing the groundwater and baseflow deuterium values ("Deuterium_Snapshot") we added the sample collection date to the file name. We included the X and Y coordinates for each data point (coordinate system: CH1903 LV3) as well as the date and time (UTC).

More information on the data collection and preparation can be found in Kiewiet et al. (in review). A detailed description of the baseflow snapshot campaigns can also be found in Kiewiet et al., 2019.

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en: Discharge, rainfall, and deuterium compositions of streamwater, rainwater and groundwater, for four rainfall events in the Studibach, Alptal, Switzerland


maintainer: Yohann Chauvier

en: point-process-model plant-species target-group independent-dataset random-stratified-sampling cluster background-data covariate-correction survey-effort environmental-stratification

en: Non-copyright data, scripts, diversity outputs

en:
Aim: While species distribution models (SDMs) are standard tools to predict species distributions, they can suffer from observation and sampling biases, particularly presence-only SDMs that often rely on species observations from non-standardized sampling efforts. To address this issue, sampling background points with a target-group strategy is commonly used, although more robust strategies and refinements could be implemented. Here, we exploited a dataset of plant species from the European Alps to propose and demonstrate efficient ways to correct for observer and sampling bias in presence-only models.

Innovation: Recent methods correct for observer bias by using covariates related to accessibility in model calibrations (classic bias covariate correction, Classic-BCC). However, depending on how species are sampled, accessibility covariates may not sufficiently capture observer bias. Here, we introduced BCCs more directly related to sampling effort, as well as a novel corrective method based on stratified resampling of the observational dataset before model calibration (environmental bias correction, EBC). We compared, individually and jointly, the effect of EBC and different BCC strategies, when modelling the distributions of 1’900 plant species. We evaluated model performance with spatial block split-sampling and independent test data, and assessed the accuracy of plant diversity predictions across the European Alps.

Main conclusions: Implementing EBC with BCC showed best results for every evaluation method. Particularly, adding the observation density of a target group as bias covariate (Target-BCC) displayed most realistic modelled species distributions, with a clear positive correlation (r≃0.5) found between predicted and expert-based species richness. Although EBC must be carefully implemented in a species-specific manner, such limitations may be addressed via automated diagnostics included in a provided R function. Implementing EBC and bias covariate correction together may allow future studies to address efficiently observer bias in presence-only models, and overcome the standard need of an independent test dataset for model evaluation.

background-data

display_name: background-data

cluster

display_name: cluster

covariate-correction

display_name: covariate-correction

environmental-stratification

display_name: environmental-stratification

independent-dataset

display_name: independent-dataset

plant-species

display_name: plant-species

point-process-model

display_name: point-process-model

random-stratified-sampling

display_name: random-stratified-sampling

survey-effort

display_name: survey-effort

target-group

display_name: target-group

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

en: Novel methods to correct for observer and sampling bias in presence-only species distribution models


en: dem gletscher dsm griesgletscher drone sankt-annafirn glacier uav orthoimage orthophoto alps findelengletscher

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Findelengletscher on the 21th of July 2015.

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Findelengletscher on the 2nd of July 2016.

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Findelengletscher on the 22th of September 2016.

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Griesgletscher on the 26th of September 2015.

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Findelengletscher on the 09th of April 2015.

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Sankt Annafirn on the 15th of April 2015.

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Griesgletscher on the 13th of September 2016.

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Findelengletscher on the 30th of August 2016.

en: This folder contains all UAV flights paths that we created for the Sankt Annafirn, the Findelengletscher and Griesgletscher. They have been created with the software eMotion2, that was developped for the eBee platform from Sensefly.

en: This .pdf contains an orthoimage for each dataset as well as a Table which displays the number of flights made, image acquired, number of GCP set and the total area covered.

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Findelengletscher on the 15th of September 2015.

en: Bulk Data Access

en: Link to the S3 bucket where all the data resources can be downloaded in bulk

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Sankt Annafirn on the 15th of July 2015.

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Findelengletscher on the 19th of April 2016.

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Sankt Annafirn on the 28th of September 2015.

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Griesgletscher on the 15th of August 2015.

en:
This .zip folder contains:

 - An orthoimage (oi) of 0.1m resolution

- A Digital Surface Model (dsm) of 0.1m resolution

- The Agisoft Photoscan automatic processing report

for the UAV field campaign conducted on the Griesgletscher on the 3rd of August 2015.

en:
 UAV-derived DSMs and orthoimages

Unmanned Aerial Vehicle (UAV) surveys were conducted between 2015 and 2016 on the Sankt Annafirn, Findelen- and Griesgletscher, situated in the Swiss Alps. Three surveys at the Sankt Annafirn allowed for a full glacier coverage, four surveys at Griesgletscher allowed an almost full glacier coverage and seven surveys at Findelengletscher allowed for a partial coverage of the glacier tongue (see individual datasets for exact extent). For each survey, a high resolution orthoimage and a Digital Surface Model (DSM) was created.

 UAV surveys:

Prior flight, Ground Control Points (GCPs) were deployed on the glacier surface and measured with a differential GPS (Trimble R7 or Leica GPS 1200). They allowed precise georeferencing of the UAV-derived datasets. UAV flight plans were planned with the software *eMotion 2* and a SenseFly eBee was used as surveying platform. The images were then processed with the software Agisoft Photoscan Pro 1.1.6 .

The location and dates of each survey can be found in the table together with the number of flights performed (Nflights), the number of acquired images (Nimages), the number of GCPs set (NGCPs) and the surveyed area.

A folder for each dataset is available (see folder name in table), which contains:

- An orthoimage *glacier_date_photoscan_oi_CH1903+_LV95_0.1m.tif*

- A Digital Surface Model *glacier_date_photoscan_dsm_CH1903+_LV95_0.1m.tif*

- The Agisoft Photoscan automatic processing report  *glacier_date_photoscan_report.pdf*

where:

- *glacier* is the name of the surveyed glacier

- *date* is the date of the UAV image acquisition

- *photoscan* is the name of the photogrammetric software

- *oi* or *dsm* the type of dataset

- *CH1903+_LV95* is the coordinate system and datum of the dataset

- *0.1m* is the resolution of the dataset in meter

- *.tif* is the extention of the dataset

Details about the UAV surveys, the image processing and the accuracy of the UAV-derived products can be found in this publication below.

Paper Citation:
 > _Gindraux et al. 2017. Accuracy Assessment of Digital Surface Models from Unmanned Aerial Vehicles’Imagery on Glaciers, Remote Sensing, 9, 186, 1-15, [doi: 10.3390/rs9020186](https://doi.org/10.3390/rs9020186)._

The folder UAV_flight_paths.zip contains all UAV flights performed on the Sankt Annafirn, Findelengletscher and Griesgletscher. The flights were planned with the software eMotion2 and have the .afp extention.

alps

display_name: alps

dem

display_name: dem

drone

display_name: drone

dsm

display_name: dsm

findelengletscher

display_name: findelengletscher

glacier

display_name: glacier

gletscher

display_name: gletscher

griesgletscher

display_name: griesgletscher

orthoimage

display_name: orthoimage

orthophoto

display_name: orthophoto

sankt-annafirn

display_name: sankt-annafirn

uav

display_name: uav

en: UAV-derived Digital Surface Models and orthoimages for three alpine glaciers


verkehrsmessstellen

regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

tran

fr: Transports

de: Verkehr

en: Transport

it: Trasporti

title_for_slug: verkehrsmessstellen

de: messstellen messstellennetz vde verkehrsdaten verkehrsdatenerfassung zahlstellen

label: geocat.ch permalink

fr: Map (Preview)

de: Map (Preview) Stadtplan

en: Map (Preview)

it: Map (Preview)

de: Link zum Thema im Internet-Stadtplan der Stadt Bern

de: WMS Verkehrsmessstellen

de: Web Map Services WMS

format: WMS

de: WFS Verkehrsmessstellen

de: Web Feature Services WFS

format: WFS

de: Alle bestehenden und geplanten, permanenten und periodischen Messstellen für den motorisierten Individualverkehr, Velo- und Fussverkehr auf städtischen, kantonalen und eidgenössischen Strassen

spatial: Stadt Bern

geoinformation-der-stadt-bern

fr: L’Office du cadastre de la ville de Berne

de: Geoinformation Stadt Bern

en: The Land Surveying Office of the City of Bern

it: L'ufficio del catasto nella Città di Berna

fr: L’Office du cadastre (Geoinformation Stadt Bern) est l’organisme compétent pour la collecte, l’accès et la diffusion d’informations géographiques dans la ville de Berne.

de: Geoinformation Stadt Bern (ehemals Vermessungsamt) ist die Kompetenzstelle für die Beschaffung, den Zugang und die Verbreitung von Geoinformationen in der Stadt Bern.

en: The Surveying Office (Geoinformation Stadt Bern) is the centre of competence in the City of Bern for the procurement of, access to and dissemination of geographic information.

it: L'ufficio del catasto (Geoinformation Stadt Bern) è il centro di competenza per il reperimento, l'accesso e la diffusione di geoinformazioni nella Città di Berna.

political_level: commune

value: Geoinformation Stadt Bern - Geocat Harvester

de: Verkehrsmessstellen


bff-qualitatsstufe-ii-ur

agri

fr: Agriculture, pêche, sylviculture et alimentation

de: Landwirtschaft, Fischerei, Forstwirtschaft und Nahrungsmittel

en: Agriculture, fisheries, forestry and food

it: Agricoltura, pesca, silvicoltura e prodotti alimentari

title_for_slug: bff-qualitatsstufe-ii-ur

fr: estivage geodonnees-de-base

de: sommerung geobasisdaten

en: summering official-geodata

it: estivazione geodati-di-base

label: Web GIS Client des Kanton Uri. Die Daten können direkt via dem Portal in den unterschiedlichsten Datenformaten heruntergeladen werden.

fr: WMS-Dienst des Kanton Uri

de: WMS-Dienst des Kanton Uri

en: WMS-Dienst des Kanton Uri

it: WMS-Dienst des Kanton Uri

fr: WFS-Dienst des Kanton Uri

de: WFS-Dienst des Kanton Uri

en: WFS-Dienst des Kanton Uri

it: WFS-Dienst des Kanton Uri

fr: In diesem Layer sind Flächen enthalten, welche die Anforderungen an die Qualitätsstufe II erfüllen und die erforderliche botanische Qualität oder Strukturen zur Förderung der Biodiversität aufweisen. Die gelb eingefärbte Fläche ist die effektive Fläche, die über Q II verfügt. Gelb schraffiert ist die ganze Biodiversitätsförderfläche BFF mit dem Q II-Anteil in Prozenten. Die Bewirtschafter melden die Flächen an, wenn sie vermuten, dass eine BFF I die Kriterien für die Qualitätsstufe II erfüllen könnte. Eine Fachperson überprüft anschliessend die Fläche. Die minimale Vertragsdauer ist 8 Jahre.

spatial: Canton of Uri (UR)

geoinformation-kanton-uri

de: Lisag AG, Geoinformation Kanton Uri

de: Die Lisag AG bewirtschaftet das geografische Informationssystem des Kantons Uri (GIS Uri), ist die verantwortliche Stelle für den ÖREB-Kataster Uri und zudem zuständig für die Durchführung der amtlichen Vermessung auf Urner Kantonsgebiet.

political_level: canton

value: Uri-Kt - Geocat Harvester

fr: BFF Qualitätsstufe II (UR)

de: BFF Qualitätsstufe II (UR)

en: BFF Qualitätsstufe II (UR)

it: BFF Qualitätsstufe II (UR)


regi

de: Regionen und Städte

envi

fr: Environnement

de: Umwelt

en: Environment

it: Ambiente

de: baum bauminventar baumkataster stadtbaum

de: Map (Preview) Stadtplan

de: WMS Baumkataster

de: WFS Baumkataster

de: Durch Stadtgrün bewirtschaftete Bäume, Symbole für Laubbaum, Nadelbaum und Obstbaum, mit den Informationen über Gattung, Art, deutscher Name, Pflanzjahr und Standort.

de: Baumkataster


nutzungsflachen-inkl-bff-i-ur

title_for_slug: nutzungsflachen-inkl-bff-i-ur

url: https://geo.ur.ch/?layers=Nutzungsflächen inkl. BFF I

fr: Der Geobasisdatensatz 153.1 Nutzungsflächen (inkl. Biodiversitätsförderflächen Qualitätsstufe I) beinhaltet die landwirtschaftlich genutzten Flächen gemäss landwirtschaftlicher Begriffsverordnung (LBV), Direktzahlungsverordnung (DZV) und der daraus abgeleiteten Definitionen (inkl. Codierung) der Nutzungen durch den Bund und die Kantone. Die Biodiversitätsförderflächen (BFF) Qualitätsstufe I sind zusätzlich auch noch als separater Layer verfügbar.

fr: Nutzungsflächen inkl. BFF I (UR)

de: Nutzungsflächen inkl. BFF I (UR)

en: Nutzungsflächen inkl. BFF I (UR)

it: Nutzungsflächen inkl. BFF I (UR)


gewasserschutzbereiche-projektiert-ur

title_for_slug: gewasserschutzbereiche-projektiert-ur

fr: Die Kantone bezeichnen zum Schutz des Grundwassers und als Grundlage für die zu treffenden Schutzmassnahmen die besonders gefährdeten und die übrigen Bereiche. Die besonders gefährdeten Bereiche umfassen den Gewässerschutzbereich Au, den Gewässerschutzbereich Ao, den Zuströmbereich Zu und den Zuströmbereich Zo. Die Gewässerschutzbereiche Au und Zu dienen dem planerischen Schutz der unterirdischen Gewässer. Die Bereiche Ao und Zo bezwecken den Schutz von Oberflächengewässern. Die übrigen Bereiche (üB) umfassen den Rest des Kantons.

fr: Gewässerschutzbereiche projektiert (UR)

de: Gewässerschutzbereiche projektiert (UR)

en: Gewässerschutzbereiche projektiert (UR)

it: Gewässerschutzbereiche projektiert (UR)


label: Link zum Fachportal

fr: Map (Preview) Aperçu map.geo.admin.ch

de: Map (Preview) Vorschau map.geo.admin.ch

en: Map (Preview)  map.geo.admin.ch

it: Map (Preview) Previsione map.geo.admin.ch

fr: Aperçu map.geo.admin.ch

de: Vorschau map.geo.admin.ch

en: Preview map.geo.admin.ch

it: Previsione map.geo.admin.ch

fr: Service WMS-IFDG, couche "Renouée du Japon"

de: WMS-BGDI Dienst, Layer "Japanischer Staudenknöterich"

en: WMS-FSDI service, layer "Reynoutria japonica"

it: Servizio WMS-IFDG, strato "Poligono del Giappone"

fr: Service WMTS-IFDG, couche , Layer "Renouée du Japon"

de: WMTS-BGDI Dienst, Layer "Japanischer Staudenknöterich"

en: WMTS-FSDI service, layer "Reynoutria japonica"

it: Servizio WMTS-IFDG, strato "Poligono del Giappone"

format: WMTS

fr: Lien vers la distribution des données

de: Link für Datenbezug

en: Link for download

it: Link per le fonti dei dati

format: HTML

fr: Le set « plantes exotiques envahissantes » contient les cartes de distribution potentielle pour 56 plantes exotiques envahissantes en Suisse et répertoriées dans la Liste Noire et la Watch-List, ou présentes dans les pays voisins et ayant le potentiel de coloniser la Suisse. Les cartes sont le résultat de modélisation de l'Université de Lausanne et prédisent leur potentiel de propagation de ces espèces en Suisse. Les modèles sont basés respectivement sur les données d'occurrence du GBIF (Global Biodiversity Information Facility) et associées aux données climatiques de Worldclim (résolution de 1km), et sur les données d'occurrence d'infoflora associées aux données climatiques du WSL (résolution de 100m). Les cartes à 100m utilisent également d'autres variables telles que l'utilisation du sol et de la géologie.

de: Das Set Invasive gebietsfremde Pflanzen enthält die Karten der potentiellen Verbreitung von 56 in der Schweiz vorkommenden invasiven gebietsfremden Arten sowie solchen aus Nachbarländern mit dem Potential sich in die Schweiz auszudehnen. Die Karten sind das Ergebnis einer Modellierung der Uni Lausanne zur Vorhersage der Verbreitung von invasiven Pflanzen, welche in der Schwarzen Liste resp. der Watchliste aufgeführt sind. Das Modell basiert auf den verfügbaren Daten von GBIF (Global Biodiversity Information Facility), verknüpft mit aktuellen Klimadaten von Worldclim (1km Auflösung) sowie den verfügbaren Daten von InfoFlora verknüpft mit Klimadaten der WSL (100 m Auflösung). Die Karten mit 100 m Auflösung beinhalten weitere Variablen wie Landnutzung und Geologie.

en: The set of invasive alien plants shows the maps of the potential areas of 56 alien plants species present in Switzerland as well as some invasive species present in neighboring countries with the potential to reach Switzerland. The maps are the result of model-based tool to anticipate the spread of invasive alien plants listed on the Swiss Black and Watch list. The model is using occurrence data from GBIF (Global Biodiversity Information Facility), current climate data from Worldclim(1 km resolution) and occurrence data from InfoFlora combined with climate data from the WSL (100 m resolution).The maps with 100 m resolution utilize additional necessary variables including landuse and geology.

it: Il set Piante esotiche invasive contiene le carte di distribuzione potenziale di 56 specie di piante invasive non indigene presenti in Svizzera nonché di quelle dei Paesi vicini che hanno il potenziale di diffondersi in Svizzera. Le carte sono il frutto di una modellizzazione operata dall'Università di Losanna per prevedere la distribuzione delle piante invasive riportate nella lista nera, rispettivamente nella lista di osservazione. Il modello si basa sui dati disponibili della GBIF (Global Biodiversity Information Facility) combinati con dati climatici aggiornati di Worldclim (risoluzione 1km) nonché sui dati disponibili di InfoFlora combinati con dati climatici di WSL (risoluzione 100 m). Le carte con una risoluzione di 100 m contengono altre variabili, come lo sfruttamento del terreno e la geologia.

spatial: Schweiz

name: Bundesamt für Umwelt / Abteilung Biodiversität und Landschaft

bundesamt-fur-umwelt-bafu

fr: Office fédéral de l'environnement OFEV

de: Bundesamt für Umwelt BAFU

en: Federal Office for the Environment FOEN

it: Ufficio federale dell'ambiente UFAM

fr: L’autorité compétente pour les questions environnementales. Il est à ce titre chargé de gérer les ressources naturelles selon les principes du développement durable et de protéger l’homme contre les dangers naturels et l’environnement contre les pollutions.

de: Die Fachbehörde für die Umwelt. Es ist zuständig für die nachhaltige Nutzung der natürlichen Ressourcen sowie für den Schutz des Menschen vor Naturgefahren und den Schutz der Umwelt vor übermässigen Belastungen.

en: The federal environmental authority. It is responsible for ensuring that natural resources are used sustainably, that the public is protected against natural hazards, and that the environment is protected from unacceptable adverse impacts.

it: Il servizio specializzato in materia ambientale, è competente per quanto riguarda l'utilizzazione sostenibile delle risorse naturali, la protezione delle persone dai pericoli naturali e dell'ambiente da un inquinamento eccessivo.

political_level: confederation

value: BAFU - Geocat Harvester

fr: Plantes exotiques envahissantes - Carte de distribution potentiel Renouée du Japon

de: Invasive gebietsfremde Pflanzen - Potentialkarte Japanischer Staudenknöterich

en: Invasive alien plants - map of the potential area Reynoutria japonica

it: Piante esotiche invasive - carte di distribuzione potenziale Poligono del Giappone


gewasserschutzbereiche-differenz-ur

regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

envi

de: Umwelt

title_for_slug: gewasserschutzbereiche-differenz-ur

fr: WMS-Dienst des Kanton Uri

de: WMS-Dienst des Kanton Uri

fr: WFS-Dienst des Kanton Uri

de: WFS-Dienst des Kanton Uri

de: Die Kantone bezeichnen zum Schutz des Grundwassers und als Grundlage für die zu treffenden Schutzmassnahmen die besonders gefährdeten und die übrigen Bereiche. Die besonders gefährdeten Bereiche umfassen den Gewässerschutzbereich Au, den Gewässerschutzbereich Ao, den Zuströmbereich Zu und den Zuströmbereich Zo. Die Gewässerschutzbereiche Au und Zu dienen dem planerischen Schutz der unterirdischen Gewässer. Die Bereiche Ao und Zo bezwecken den Schutz von Oberflächengewässern. Die übrigen Bereiche (üB) umfassen den Rest des Kantons.

geoinformation-kanton-uri

de: Lisag AG, Geoinformation Kanton Uri

de: Die Lisag AG bewirtschaftet das geografische Informationssystem des Kantons Uri (GIS Uri), ist die verantwortliche Stelle für den ÖREB-Kataster Uri und zudem zuständig für die Durchführung der amtlichen Vermessung auf Urner Kantonsgebiet.

fr: Gewässerschutzbereiche Differenz (UR)

de: Gewässerschutzbereiche Differenz (UR)

en: Gewässerschutzbereiche Differenz (UR)

it: Gewässerschutzbereiche Differenz (UR)


envi

fr: Environnement

fr: affectation-primaire planification-de-lutilisation-du-sol plan-de-zones geodonnees-de-base

de: grundnutzung nutzungsplanung zonenplan geobasisdaten

en: primary-use land-use-planning zoning-plan official-geodata

it: utilizzazione-di-base pianificazione-dellutilizzazione piano-delle-zone geodati-di-base

en: WMS-Dienst des Kanton Uri

it: WMS-Dienst des Kanton Uri

fr: Die Nutzungsplanung ordnet die räumlichen Sachverhalte in genereller Weise: die Nutzung des Bodens, die Erschliessung, den Schutz usw., indem er eigentümerverbindlich und parzellenscharf Art und Mass der Nutzung festlegt (Art. 14 RPG).

fr: Gemeindliche Nutzungsplanung Differenz (UR)


gletscherstande

title_for_slug: gletscherstande

en: WFS-Dienst des Kanton Uri

it: WFS-Dienst des Kanton Uri

fr: Die abgebildeten Gletscherstände wurden in der Karte "Gletscherstände im Kanton Uri" im Buch "Geologie des Kantons Uri" (Hrsg.: Naturforschende Gesellschaft Uri) 2011 veröffentlicht. Die Karte enthält eine praktisch vollständige Bestandesaufnahme aller Moränenwälle im Kanton. Erfasst wurden nicht nur die Moränen des letzten Gletscherhochstandes um 1850 (Kleine Eiszeit), sondern auch Moänenablagerungen von Gletschervorstössen während verschiedener Kältephasen in den letzten 16'000 Jahren. Die unterschiedlichen Farben der eingezeichneten Moränen geben einen Hinweis auf das Alter der Ablagerungen und der gleichzeitige Gletschervorstösse in den einzelnen Tälern. Sämtliche Aufnahmen basieren auf Feldkartierungen in den Jahren 1976 bis 2010.

fr: Gletscherstände

de: Gletscherstände

en: Gletscherstände

it: Gletscherstände


grundwasserschutzareale-ur

envi

en: Environment

it: Ambiente

title_for_slug: grundwasserschutzareale-ur

de: grundwasserschutzareal geobasisdaten

fr: Grundwasserschutzareale sind speziell ausgeschiedene Gebiete, in welchen der Schutz des unterirdischen Gewässers im Hinblick auf eine künftige Grundwasserbewirtschaftung (Nutzung oder Anreicherung) vorsorglich sichergestellt werden soll. In diesen Arealen dürfen keine Bauten und Anlagen erstellt werden, welche die künftige Bewirtschaftung beeinträchtigen können.

fr: Grundwasserschutzareale (UR)

de: Grundwasserschutzareale (UR)

en: Grundwasserschutzareale (UR)

it: Grundwasserschutzareale (UR)


gewasserschutzbereiche-ur

title_for_slug: gewasserschutzbereiche-ur

fr: Die Kantone bezeichnen zum Schutz des Grundwassers und als Grundlage für die zu treffenden Schutzmassnahmen die besonders gefährdeten und die übrigen Bereiche. Die besonders gefährdeten Bereiche umfassen den Gewässerschutzbereich Au, den Gewässerschutzbereich Ao, den Zuströmbereich Zu und den Zuströmbereich Zo. Die Gewässerschutzbereiche Au und Zu dienen dem planerischen Schutz der unterirdischen Gewässer. Die Bereiche Ao und Zo bezwecken den Schutz von Oberflächengewässern. Die übrigen Bereiche (üB) umfassen den Rest des Kantons.

fr: Gewässerschutzbereiche (UR)

de: Gewässerschutzbereiche (UR)

en: Gewässerschutzbereiche (UR)

it: Gewässerschutzbereiche (UR)


grundwasserschutzareale-projektiert-ur

title_for_slug: grundwasserschutzareale-projektiert-ur

de: Grundwasserschutzareale sind speziell ausgeschiedene Gebiete, in welchen der Schutz des unterirdischen Gewässers im Hinblick auf eine künftige Grundwasserbewirtschaftung (Nutzung oder Anreicherung) vorsorglich sichergestellt werden soll. In diesen Arealen dürfen keine Bauten und Anlagen erstellt werden, welche die künftige Bewirtschaftung beeinträchtigen können.


archaologie-funderwartungsgebiete

soci

fr: Population et société

de: Bevölkerung und Gesellschaft

en: Population and society

it: Popolazione e società

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

title_for_slug: archaologie-funderwartungsgebiete

fr: Der Datensatz zeigt Gebiete, wo mit hoher Wahrscheinlichkeit archäologische Funde zu erwarten sind.

fr: Archäologie: Funderwartungsgebiete

de: Archäologie: Funderwartungsgebiete

en: Archäologie: Funderwartungsgebiete

it: Archäologie: Funderwartungsgebiete


gk-prozess-lawine

regi

fr: Régions et villes

de: Regionen und Städte

title_for_slug: gk-prozess-lawine

fr: WMS-Dienst des Kanton Uri

fr: Eine Lawine ergibt sich aus plötzlicher, schneller Talwärtsbewegung von Schnee als gleitende, fliessende, rollende Masse oder als aufgewirbelte Schneewolke.

fr: GK Prozess Lawine

de: GK Prozess Lawine

en: GK Prozess Lawine

it: GK Prozess Lawine


primarschulkreise

envi

fr: Environnement

de: Umwelt

title_for_slug: primarschulkreise

de: WMS-Dienst des Kanton Uri

fr: WFS-Dienst des Kanton Uri

fr: Einzugsgebiete und Schulhausstandorte der Primarschulen im Kanton Uri

fr: Primarschulkreise

de: Primarschulkreise

en: Primarschulkreise

it: Primarschulkreise


zustand-fremdwasser

ener

fr: Énergie

de: Energie

en: Energy

it: Energia

title_for_slug: zustand-fremdwasser

de: WFS-Dienst des Kanton Uri

fr: Der Zustandsbericht Fremdwasser gibt eine Übersicht über den Anfall von Fremdwasser und sogenanntem Reinabwasser. Der Bericht umfasst: Einleitungen von Bächen, Drainagen aus Meliorationsgebieten, Sickerleitungen von Gebäuden und Anlagen, unverschmutzten Kühlwässern, Brunnen / Eindringen von Grundwasser in undichte Kanalisationen / Anschlüsse von Quellfassungsüberläufen / Rückleitungen von Grundwasser aus Wärmepumpenanlagen / Einleitungen von See- und Bachwasser (Neben der Bezeichnung der genauen Einleitstellen werden auch die Mengen angegeben). Dieser Layer ist Teil des GEP (ID 129), wurde aber noch nicht gemäss dem minimalen Geodatenmodell (MGDM) des Bundes umgesetzt!

geoinformation-kanton-uri

de: Lisag AG, Geoinformation Kanton Uri

fr: Zustand Fremdwasser

de: Zustand Fremdwasser

en: Zustand Fremdwasser

it: Zustand Fremdwasser


de: larmempfindlichkeitsstufe geobasisdaten

fr: Gemäss Lärmschutz-Verordnung (LSV, SR 814.41) sind für alle Nutzungszonen Lärmempfindlichkeitsstufen zu erfassen. Es werden die Empfindlichkeitsstufen keine ES, ES I, ES II, ES III und ES IV unterschieden. Ausserdem können Teile von Nutzungszonen der ES I und der ES II der nächst höheren Stufe zugeordnet werden, wenn sie mit Lärm vorbelastet sind.

geoinformation-kanton-uri

de: Die Lisag AG bewirtschaftet das geografische Informationssystem des Kantons Uri (GIS Uri), ist die verantwortliche Stelle für den ÖREB-Kataster Uri und zudem zuständig für die Durchführung der amtlichen Vermessung auf Urner Kantonsgebiet.


tran

fr: Transports

de: Verkehr

en: Transport

it: Trasporti

de: netzstruktur strasse strassenverkehr weg

protocol: https

de: Datenshop

de: Produktansicht Geodatenshop

de: Gesamtes Strassen- und Wegnetz (Kantons-, Gemeinde-, Güter und Privatstrassen) des Kanton Luzerns mit Achsgeometrien und Strassenkategorien.

spatial: Kanton Luzern

fr: Géoinformation du canton de Lucerne

de: Geoinformation Kanton Luzern

en: Geoinformation Canton of Lucerne

it: Geoinformazione del Cantone di Lucerna

fr: La géoinformation du canton de Lucerne est responsable de la collecte, du traitement et de la gestion des données à référence spatiale. Elle dirige, coordonne et surveille les travaux de la géoinformation et de la mensuration officielle.

en: Geoinformation of the Canton of Lucerne is responsible for the collection, processing and management of spatial data. It manages, coordinates and monitors the work of geoinformation and of the cadastral surveying.

de: Die Geoinformation des Kantons Luzern ist für die Erhebung, Verarbeitung und Verwaltung von raumbezogenen Daten zuständig. Sie leitet, koordiniert und überwacht die Arbeiten der Geoinformation und der amtlichen Vermessung.

it: La Geoinformation del Cantone di Lucerna è responsabile della raccolta, dell'elaborazione e della gestione dei dati territoriali. Gestisce, coordina e monitora il lavoro della geoinformazione e della misurazione ufficiale.

value: LU-Kt - Geocat Harvester

de: Strassen- und Wegnetz


larmempfindlichkeitsstufen-differenz-ur

regi

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

envi

de: Umwelt

title_for_slug: larmempfindlichkeitsstufen-differenz-ur

de: Gemäss Lärmschutz-Verordnung (LSV, SR 814.41) sind für alle Nutzungszonen Lärmempfindlichkeitsstufen zu erfassen. Es werden die Empfindlichkeitsstufen keine ES, ES I, ES II, ES III und ES IV unterschieden. Ausserdem können Teile von Nutzungszonen der ES I und der ES II der nächst höheren Stufe zugeordnet werden, wenn sie mit Lärm vorbelastet sind.


de: real-time pv elektrizitat strom smart-meter solar

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (ov2)

license: CC BY

de: fgb

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (fgb)

format: octet-stream

de: parquet

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (parquet)

de: gpx

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (gpx)

de: json

media_type: application/json

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (json)

format: JSON

de: kml

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (kml)

format: KML

de: csv

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (csv)

de: shp

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (shp)

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (n3)

de: geojson

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (geojson)

de: turtle

media_type: text/turtle

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (turtle)

format: RDF Turtle

de: jsonld

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (jsonld)

format: JSON-LD

de: xls

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (xls)

format: XLS

de: rdfxml

media_type: application/rdf+xml

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (rdfxml)

format: RDF XML

de: jsonl

de: Aktuelle Stromproduktion der Solaranlagen der St.Galler Stadtwerke (Pilotprojekt Kraftwerk St.Gallen) (jsonl)

de: Der Datensatz zeigt die Echtzeitdaten der Solaranlagen der St.Galler Stadtwerke (sgsw), welche im Rahmen des Pilotprojekts "Kraftwerk St.Gallen" mit Stromzähler ausgerüstet wurden. Nur ein Teil der Solaranlagen, vorwiegend die Kleineren, wurden mit Smart-Me Zählern ausgerüstet. Der Datensatz wird alle 5 Minuten aktualisiert.Alle Solaranlagen der St.Galler Stadtwerke finden Sie in folgendem Datensatz:https://daten.stadt.sg.ch/explore/dataset/solaranlagen-der-stgaller-stadtwerke

elektrizitat

display_name: elektrizitat

pv

display_name: pv

real-time

display_name: real-time

smart-meter

display_name: smart-meter

solar

display_name: solar

stadt-st-gallen

fr: Avec une population de 80 000 habitants, Saint-Gall, huitième ville de Suisse, est la métropole économique, politique, sociale et culturelle de la Suisse orientale. Ses 1400 ans d'histoire se mêlent à la créativité urbaine, à une économie innovante et à un paysage préservé.

en: With a population of 80,000, St.Gallen, Switzerland’s eighth largest city, is the economic, political, social and cultural metropolis of eastern Switzerland. Its 1400 years of history blend with urban creativity, an innovative economy and an unspoilt landscape.

de: St.Gallen, die achtgrösste Stadt der Schweiz, ist mit 80'000 Einwohnern die wirtschaftliche, politische, soziale und kulturelle Metropole der Ostschweiz. Ihre 1400-jährige Geschichte verbindet sich mit urbaner Kreativität, einer innovativen Wirtschaft und einer intakten Landschaft.

it: Con una popolazione di 80.000 abitanti, San Gallo, l'ottava città più grande della Svizzera, è la metropoli economica, politica, sociale e culturale della Svizzera orientale. I suoi 1400 anni di storia si fondono con la creatività urbana, un'economia innovativa e un paesaggio incontaminato.


de: betreibung finanzen verlust

de: csv

de: Ausgestellte Verlustscheine Stadt St.Gallen (2005-2018) (csv)

de: Ausgestellte Verlustscheine Stadt St.Gallen (2005-2018) (rdfxml)

de: fgb

de: Ausgestellte Verlustscheine Stadt St.Gallen (2005-2018) (fgb)

de: Ausgestellte Verlustscheine Stadt St.Gallen (2005-2018) (turtle)

de: Ausgestellte Verlustscheine Stadt St.Gallen (2005-2018) (xls)

de: Ausgestellte Verlustscheine Stadt St.Gallen (2005-2018) (n3)

de: json

de: Ausgestellte Verlustscheine Stadt St.Gallen (2005-2018) (json)

de: Ausgestellte Verlustscheine Stadt St.Gallen (2005-2018) (jsonld)

de: parquet

de: Ausgestellte Verlustscheine Stadt St.Gallen (2005-2018) (parquet)

de: Ausgestellte Verlustscheine Stadt St.Gallen (2005-2018) (jsonl)

de: Anzahl ausgestellter Verlustscheine der Stadt St.Gallen (Anzahl und in Mio., Jahre 2005-2018)

betreibung

display_name: betreibung

finanzen

display_name: finanzen

verlust

display_name: verlust

econ

fr: Économie et finances

de: Wirtschaft und Finanzen

en: Economy and finance

it: Economia e finanze

fr: Ville de Saint-Gall

de: Stadt St.Gallen

en: City of St.Gallen

it: Città di San Gallo

value: DCAT Harvester Stadt St. Gallen


de: sharing freefloating scooter tier

de: gpx

de: Verfügbare E-Scooter in der Stadt St.Gallen (gpx)

de: geojson

de: Verfügbare E-Scooter in der Stadt St.Gallen (geojson)

de: csv

de: Verfügbare E-Scooter in der Stadt St.Gallen (csv)

de: rdfxml

de: Verfügbare E-Scooter in der Stadt St.Gallen (rdfxml)

de: Verfügbare E-Scooter in der Stadt St.Gallen (ov2)

de: xls

de: Verfügbare E-Scooter in der Stadt St.Gallen (xls)

de: Verfügbare E-Scooter in der Stadt St.Gallen (json)

de: Verfügbare E-Scooter in der Stadt St.Gallen (fgb)

de: Verfügbare E-Scooter in der Stadt St.Gallen (parquet)

de: jsonl

de: Verfügbare E-Scooter in der Stadt St.Gallen (jsonl)

de: shp

de: Verfügbare E-Scooter in der Stadt St.Gallen (shp)

de: kml

de: Verfügbare E-Scooter in der Stadt St.Gallen (kml)

de: turtle

de: Verfügbare E-Scooter in der Stadt St.Gallen (turtle)

de: jsonld

de: Verfügbare E-Scooter in der Stadt St.Gallen (jsonld)

de: Verfügbare E-Scooter in der Stadt St.Gallen (n3)

de: Der Datensatz zeigt die verfügbaren E-Scooter in der Stadt St.Gallen. Die Daten stammen von der API der Shared Mobility Plattform des BFEs und beziehen sich auf sämtliche freefloating Angebote in der Stadt St.Gallen. Zurzeit sind in der Stadt St.Gallen lediglich die E-Scooter von Tier verfügbar.Weitere Informationen zum Datensatz unter: https://opendata.swiss/de/dataset/standorte-und-verfugbarkeit-von-shared-mobility-angebotenBeschreibung der API und der Daten unter: https://www.sharedmobility.ch/info und https://github.com/NABSA/gbfs/blob/v2.0/gbfs.md

freefloating

display_name: freefloating

scooter

display_name: scooter

sharing

display_name: sharing

tier

display_name: tier

tran

de: Verkehr

stadt-st-gallen

de: St.Gallen, die achtgrösste Stadt der Schweiz, ist mit 80'000 Einwohnern die wirtschaftliche, politische, soziale und kulturelle Metropole der Ostschweiz. Ihre 1400-jährige Geschichte verbindet sich mit urbaner Kreativität, einer innovativen Wirtschaft und einer intakten Landschaft.


veloplan

title_for_slug: veloplan

de: steigung velo velorouten bike strassen

de: xls

de: Veloplan Stadt St.Gallen (xls)

de: rdfxml

de: Veloplan Stadt St.Gallen (rdfxml)

de: gpx

de: Veloplan Stadt St.Gallen (gpx)

de: Veloplan Stadt St.Gallen (ov2)

de: parquet

de: Veloplan Stadt St.Gallen (parquet)

de: Veloplan Stadt St.Gallen (jsonl)

de: Veloplan Stadt St.Gallen (turtle)

de: csv

de: Veloplan Stadt St.Gallen (csv)

de: Veloplan Stadt St.Gallen (shp)

de: json

de: Veloplan Stadt St.Gallen (json)

de: Veloplan Stadt St.Gallen (jsonld)

de: fgb

de: Veloplan Stadt St.Gallen (fgb)

de: Veloplan Stadt St.Gallen (kml)

de: geojson

de: Veloplan Stadt St.Gallen (geojson)


de: medienmitteilung newsfeed news real-time

de: Newsfeed Medienmitteilungen Stadtverwaltung St.Gallen (n3)

de: jsonl

de: Newsfeed Medienmitteilungen Stadtverwaltung St.Gallen (jsonl)

de: xls

de: Newsfeed Medienmitteilungen Stadtverwaltung St.Gallen (xls)

de: rdfxml

de: Newsfeed Medienmitteilungen Stadtverwaltung St.Gallen (rdfxml)

de: turtle

de: Newsfeed Medienmitteilungen Stadtverwaltung St.Gallen (turtle)

de: Newsfeed Medienmitteilungen Stadtverwaltung St.Gallen (fgb)

de: Newsfeed Medienmitteilungen Stadtverwaltung St.Gallen (parquet)

de: Newsfeed Medienmitteilungen Stadtverwaltung St.Gallen (csv)

de: Newsfeed Medienmitteilungen Stadtverwaltung St.Gallen (json)

de: jsonld

de: Newsfeed Medienmitteilungen Stadtverwaltung St.Gallen (jsonld)

de: Der Datensatz bildet eine Schnittstelle zum Newsfeed der Medienmitteilungen der Stadtverwaltung. Der Link zum Newsfeed befindet sich unter "Referenzen"

medienmitteilung

display_name: medienmitteilung

news

display_name: news

newsfeed

display_name: newsfeed

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

stadt-st-gallen

de: Stadt St.Gallen

de: St.Gallen, die achtgrösste Stadt der Schweiz, ist mit 80'000 Einwohnern die wirtschaftliche, politische, soziale und kulturelle Metropole der Ostschweiz. Ihre 1400-jährige Geschichte verbindet sich mit urbaner Kreativität, einer innovativen Wirtschaft und einer intakten Landschaft.


de: schulden armut betreibung pfandung

de: csv

de: Pfändungen Stadt St.Gallen (2005-2018) (csv)

de: Pfändungen Stadt St.Gallen (2005-2018) (xls)

de: Pfändungen Stadt St.Gallen (2005-2018) (jsonld)

de: Pfändungen Stadt St.Gallen (2005-2018) (n3)

de: jsonl

de: Pfändungen Stadt St.Gallen (2005-2018) (jsonl)

de: Pfändungen Stadt St.Gallen (2005-2018) (rdfxml)

de: fgb

de: Pfändungen Stadt St.Gallen (2005-2018) (fgb)

de: json

de: Pfändungen Stadt St.Gallen (2005-2018) (json)

de: parquet

de: Pfändungen Stadt St.Gallen (2005-2018) (parquet)

de: Pfändungen Stadt St.Gallen (2005-2018) (turtle)

de: Anzahl Pfändungen der Stadt St.Gallen (Jahre 2005-2018)

armut

display_name: armut

pfandung

display_name: pfandung

schulden

display_name: schulden

econ

de: Wirtschaft und Finanzen

de: Pfändungen Stadt St.Gallen (2005-2018)


de: funktionen budget rechnung haushalt

de: Budget 2021 der Stadt St.Gallen nach Funktionaler Gliederung (English Data) (n3)

en: Budget 2020 of the City of St.Gallen in Functional Structure (English Data) (n3)

de: turtle

de: Budget 2021 der Stadt St.Gallen nach Funktionaler Gliederung (English Data) (turtle)

en: Budget 2020 of the City of St.Gallen in Functional Structure (English Data) (turtle)

de: jsonld

de: Budget 2021 der Stadt St.Gallen nach Funktionaler Gliederung (English Data) (jsonld)

en: Budget 2020 of the City of St.Gallen in Functional Structure (English Data) (jsonld)

de: xls

de: Budget 2021 der Stadt St.Gallen nach Funktionaler Gliederung (English Data) (xls)

en: Budget 2020 of the City of St.Gallen in Functional Structure (English Data) (xls)

de: csv

de: Budget 2021 der Stadt St.Gallen nach Funktionaler Gliederung (English Data) (csv)

en: Budget 2020 of the City of St.Gallen in Functional Structure (English Data) (csv)

de: Budget 2021 der Stadt St.Gallen nach Funktionaler Gliederung (English Data) (json)

en: Budget 2020 of the City of St.Gallen in Functional Structure (English Data) (json)

de: Budget 2021 der Stadt St.Gallen nach Funktionaler Gliederung (English Data) (fgb)

en: Budget 2020 of the City of St.Gallen in Functional Structure (English Data) (fgb)

de: Budget 2021 der Stadt St.Gallen nach Funktionaler Gliederung (English Data) (jsonl)

en: Budget 2020 of the City of St.Gallen in Functional Structure (English Data) (jsonl)

de: rdfxml

de: Budget 2021 der Stadt St.Gallen nach Funktionaler Gliederung (English Data) (rdfxml)

en: Budget 2020 of the City of St.Gallen in Functional Structure (English Data) (rdfxml)

de: Budget 2021 der Stadt St.Gallen nach Funktionaler Gliederung (English Data) (parquet)

en: Budget 2020 of the City of St.Gallen in Functional Structure (English Data) (parquet)

de: Der Datensatz zeigt das Budget 2021 der Stadt St.Gallen nach Funktionaler Gliederung. Ebenfalls enthalten sind die Rechnung 2019, das Budget 2020, sowie die Abweichung zum Budget 2021. Eine klickbare Visualisierung des Budgets finden Sie im Tab "Klickbares Budget".

en: The dataset shows the Budget 2021 of the city of St.Gallen in functional structure. It also contains the Invoice 2019, the Budget 2020 and the deviation to the Budget 2020. You find a clickable version of the Budget in the Tab "Klickbares Budget"

budget

display_name: budget

funktionen

display_name: funktionen

haushalt

display_name: haushalt

rechnung

display_name: rechnung

stadt-st-gallen

de: Stadt St.Gallen

de: St.Gallen, die achtgrösste Stadt der Schweiz, ist mit 80'000 Einwohnern die wirtschaftliche, politische, soziale und kulturelle Metropole der Ostschweiz. Ihre 1400-jährige Geschichte verbindet sich mit urbaner Kreativität, einer innovativen Wirtschaft und einer intakten Landschaft.


deponieanlieferungen-tufentobel

title_for_slug: deponieanlieferungen-tufentobel

de: deponie-typ inertstoffe aushub grungut raumungs-schnee schlacke

de: Deponieanlieferungen Tüfentobel (csv)

de: Deponieanlieferungen Tüfentobel (xls)

de: turtle

de: fgb

de: Deponieanlieferungen Tüfentobel (fgb)

de: Deponieanlieferungen Tüfentobel (n3)

de: json

de: Deponieanlieferungen Tüfentobel (json)

de: parquet

de: jsonl

de: Deponieanlieferungen Tüfentobel (jsonl)

de: Diese Daten von der Entsorgung St.Gallen zeigen die Nutzung der Deponie Tüfentobel auf. Darin ist ersichtlich wie viel, von welcher Sorte Abfall entsorgt wird.

aushub

display_name: aushub

deponie-typ

display_name: deponie-typ

grungut

display_name: grungut

inertstoffe

display_name: inertstoffe

raumungs-schnee

display_name: raumungs-schnee

schlacke

display_name: schlacke

envi

de: Umwelt

en: Environment

it: Ambiente


de: verkehr standortforderung sensor langsamverkehr fussganger passanten

de: csv

de: Passantenfrequenz St.Galler Innenstadt (Multergasse) (csv)

de: Passantenfrequenz St.Galler Innenstadt (Multergasse) (jsonl)

de: Passantenfrequenz St.Galler Innenstadt (Multergasse) (n3)

de: rdfxml

de: Passantenfrequenz St.Galler Innenstadt (Multergasse) (rdfxml)

de: jsonld

de: Passantenfrequenz St.Galler Innenstadt (Multergasse) (jsonld)

de: Passantenfrequenz St.Galler Innenstadt (Multergasse) (fgb)

de: Passantenfrequenz St.Galler Innenstadt (Multergasse) (turtle)

de: xls

de: Passantenfrequenz St.Galler Innenstadt (Multergasse) (xls)

de: Passantenfrequenz St.Galler Innenstadt (Multergasse) (json)

de: Passantenfrequenz St.Galler Innenstadt (Multergasse) (parquet)

de: In der Multergasse, der zentralen Einkaufsstrasse der Stadt St.Gallen, befindet sich seit 2020 ein Sensor der Firma hystreet, welcher die Frequenz des Passantenaufkommens erfasst. Der Datensatz zeigt das Passantenaufkommen auf Tagesbasis. Weitere, stündliche Visualisierungsmöglichkeiten finden Sie unter https://hystreet.com/locations/298

fussganger

display_name: fussganger

langsamverkehr

display_name: langsamverkehr

passanten

display_name: passanten

sensor

display_name: sensor

standortforderung

display_name: standortforderung

verkehr

display_name: verkehr

tran

de: Verkehr

en: Transport