maintainer: Roksolana Petrovska

en: shade-tolerance mortality-rate lar regeneration dominance acer fagus

en: Mortality of regeneration

en: Acer platanoides, Acer pseudoplatanus and Fagus sylvatica living and dead juveniles (0-500 cm in height) grown in the deep shade of a prime beech-dominated forest.

en: The R-script of survival analysis for accelerated failure time model

en:
 One individual per species, vitality class (low and high) and height class (eight classes: 0–10, 11–20, 21–35, 36–60, 61–90, 91–130, 131–200 and 201–500 cm) was randomly selected and harvested in each of the six plots. This resulted in a sample of 82, 80 and 89 living individuals of A. platanoides, A. pseudoplatanus and F. sylvatica, respectively.

 Additionally stems of dead Acer spp. and F. sylvatica trees that had died within the last three years (2015–2018) were randomly harvested, matching the height classes of the harvested living trees wherever possible. In total, 179 dead young trees (60 A. platanoides, 72 A. pseudoplatanus and 47 F. sylvatica) were collected.

 Variables:
* species_code: a_pla - Acer platanoides, a_pse - Acer pseudoplatanus, f_syl - Fagus sylvatica
* species: as above
* dummy: 0 - living individual, 1 - dead individual
* LAR_cm2_g: leaf area ratio or ratio of leaf area to total plant biomass, [cm2/g]
* tree_age: in years
* avg_ring_micron: average width of the last 5 rings in tree life excluding the last ring
* dry_mass_g: aboveground and belowground biomass
* DLI: direct light index (measured only under living individuals)
* BLI: diffuse light index (measured only under living individuals)
* GLI: global light index

acer

display_name: acer

dominance

display_name: dominance

fagus

display_name: fagus

lar

display_name: lar

mortality-rate

display_name: mortality-rate

regeneration

display_name: regeneration

shade-tolerance

display_name: shade-tolerance

en: Mortality of regeneration: Acer spp. and Fagus sylvatica


maintainer: Henning Löwe

en: grain-shape angle-of-repose discrete-element-modelling fast-sintering granular-snow

state: active

type: dataset

en: Angle of repose and 3D CT images

en:
5 Snow types were examined and glass beads as a cohesion-less reference, named as follows:

RG: rounded grains (sieved with 0.7 - 1.4 mm)

FC: faceted grains (1.4 - 2 mm)

FCS: small faceted grains (0.7 - 1.4 mm)

IB: ice beads (4 mm)

IBS: small ice beads (2 - 2.2 mm)

GB: glass beads(2 mm)

format: SERVICE

media_type: text/plain

format: TXT

mimetype: text/plain

en:
Angle of repose experiments were performed with different snow types at temperatures between -2 and -40°C. They were used to examine granular snow dynamics on the grain-scale with focus on the role of grain shape and cohesion. The angle of repose was observed by sieving snow onto a round, freestanding base until a stationary heap was formed.

This dataset consists of 1) the images of the experimental heaps that were taken to determine the angle of repose, 2) one binary 3D micro computed tomography image of each snow type. The CT images were taken with the SLF micro-CT40 to characterize snow properties and grain shape.

The experiments with natural snow types (rounded and faceted grains) and spherical model snow allowed for an examination of the differences in granular properties between natural grain shapes and spherical particles in view of Discrete Element Modelling. With the chosen temperatures, the effect of sintering could be observed that increases the angle of repose with increasing temperature.

angle-of-repose

display_name: angle-of-repose

discrete-element-modelling

display_name: discrete-element-modelling

fast-sintering

display_name: fast-sintering

grain-shape

display_name: grain-shape

granular-snow

display_name: granular-snow

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

envidat

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.

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.

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: Angle of repose experiments with natural and spherical snow


maintainer: Elisabeth Hafner

en: snow hazard-mapping natural-hazard avalanche satellite

en: SPOT6 Avalanche Outlines 16 January 2019

en: Shapefile of the manually mapped avalanche outlines. Please only use this data together with the example key document provided with this dataset and within the limitations of the dataset described in the publications.

format: SHP

en:
Outlines of 6'041 avalanches mapped from SPOT6 satellite data over the Swiss Alps on 16 January 2019. The dataset was acquired following a period with very high avalanche danger. The outlines have different attributes described in the data example key (ExampleKey_AvalMapping16012019.pdf)

The generation of the data is described in:

Bühler, Y., E. D. Hafner, B. Zweifel, M. Zesiger, and H. Heisig (2019), Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps, The Cryosphere, 13(12), 3225-3238, doi:10.5194/tc-13-3225-2019.

The data was comprehensivly validated in a subset area in

Hafner, E.D.; Techel, F.; Leinss, S.; Bühler, Y., 2021: Mapping avalanches with satellites - evaluation of performance and completeness. Cryosphere, 15, 2: 983-1004. doi: 10.5194/tc-15-983-2021

avalanche

display_name: avalanche

hazard-mapping

display_name: hazard-mapping

natural-hazard

display_name: natural-hazard

satellite

display_name: satellite

snow

display_name: snow

en: SPOT6 Avalanche outlines 16 January 2019


maintainer: Axel Volkwein

en: natural-hazards field-test mass-movements trajectory field-tests field-data experiments experiment rockfall

media_type: application/zip

en: These data contain the point clouds obtained from laser scanning the test slope and the six test blocks used for the experiments. The coordinates of the slope are given in CH-1903 format. The coordinates of the block point clouds are kept in a lokal x/y/z coordinate system.

format: ZIP

mimetype: application/zip

en: Overview of all tests

en:
This table gives an overview on all tests performed including
- block used
- start & end position
- number of jumps
- etc.
-

en: Block trajectories recorded from local positioning system

en: This file contains more than 80 rockfall trajectories projected onto the terrain, i.e. the positions of the single blocks over time during the single tests that were recorded by the local positioning system with about 10Hz. It contains the detected positions as well as a smoothened path (see Volkwein et al., Geosciences, 2018). The table also contains the derived velocities and accelerations along the slope (attention: these accelerations are the once that occur during an impact).

en: Jumps and impacts

en: This table contains data of more than 2200 of each jumps and impacts of the test blocks on a grassy slope. It gives there durations in combination with acting accerlartions and the rotational speed of the blocks. The data were retrieved using the block internal accelerometer and rotation sensors. The combination of these data with the measurement from the local positioning system allows the deduction of jump lengths, slope gradient etc.

en:
In summer 2014, 6 rock blocks between 20 and 80kg have been thrown in total 111 times down a slope at the Swiss Oberalppass close to the village Tschamut. The slope was mainly covered by grass and its lower part was flat and large enough to provide natural runouts of the single trajectories.

An extensive measurement program has been set up to measure the block trajectories: With surveyor's instruments the slope and the six used rock blocks were scanned and the start and end positions of each test were recorded. During the single events two cameras filmed the trajectories. A special sensor device located within the blocks recorded the acting accelerations and rotational speeds over time. Further, the device emitted a Wifi signal that got detected from eight receivers around the slope. Based on this signal the block position has been recorded over time.

The dataset contains all data that were gathered through above field campaign.

experiment

display_name: experiment

experiments

display_name: experiments

field-data

display_name: field-data

field-test

display_name: field-test

field-tests

display_name: field-tests

mass-movements

display_name: mass-movements

natural-hazards

display_name: natural-hazards

rockfall

display_name: rockfall

trajectory

display_name: trajectory

en: Repetitive trajectory testing in Tschamut 2014


maintainer: Anita C. Risch

en: herbivory weed-management non-native-plant native-plant grassland

en: Correction: Data file native and non-native plant interactions in Australian grasslands

en:
taxa tab:	list and overview of all the recorded species

* family:                 family of the plant

* genus:                genus of the plant

* species:                plant species name

* origin:                indicates whether the plant is native to Australia or not (non-native)

* common name:	        common name of the plant species

* functional group:	        functional group of the plant species: forb, graminoid, fern, tree

* functional group detailed:	gives more detail about the type of graminoid and forb: legume, forb (non-leguminous),
                        grass, sedge, rush, fern, tree

* lifeform:                annual or perennial lifeform (species which can have both of these lifeforms where
                        considered as annuals)

plot tab:	list of species found in each plot and their cover (%)

* sampling.session:        provides information about the sampling time:
                        May2020 = data that was collected in May 2020
                        Nov2020 = data that was collected in November 2020

* farm.name:	            name of the farms where the sampling took place:
                        SF = farm of the Southern group
                        NF = farm of the Northern group
                        numbers 1-3 = randomly allocated to the Southern group of farms
                        numbers 4-6 = randomly allocated to the Northern group of farms

* site.type:	                KG: Kangaroo grass dominated site
                        ALG: ALG+KG site (Kangarooo grass and African lovegrass co-dominated site)

* plot.no:                plot number: 9-12

* scientific.name:	        scientific name of the plant species found in a certain plot

* functional group:	        indicates whether the species found is a tree, forb, graminoid or fern

* functional group detailed:	indicates whether the species found is a tree, legume, forb (non-leguminous),
                        grass, sedge, rush, fern

* lifeform:                specifies the lifeform of the species: annual or perennial lifeform
                        (species which can have both of these lifeforms where considered as annuals)

corrections tab:	Detailed description of corrections made in plot.data tab

format: XLS

en:
This dataset contains all data, on which the following publication below is based.

Paper Citation:

_Schlierenzauer, C., Risch, A.C., Schütz, M., Firn, J. 2021. Non-native Eragrostis curvula reduces plant species diversity in pastures of South-eastern Australia even when native Themeda triandra remains co-dominant. Plants 10, 596._

Please cite this paper together with the citation for the datafile.

Study area
The study was conducted in the lowland grassy woodlands of the Bega Valley Region, which is located in the south-east corner of New South Wales, Australia. Embedded between the Pacific Ocean and the Australian Alps, the lowland grassy woodlands are mostly located on granitic substrates and reach elevations of roughly 500 m above sea level. Typically, these grassy woodlands receive less precipitation (mean annual precipitation between 700-1100 mm) compared to the more elevated areas that surround them (NSW Government - Office of Environment and Heritage 2017).
The vegetation is dominated by an open tree canopy layer consisting of Eucalyptus tereticornis Sm, Angophora floribunda Sm. (Sweet) and a range of other eucalypt species. Sometimes shrub or small trees are also present, whereas grasses and forbs form the ground-cover. In areas without intensive agricultural history, this layer is dominated by perennial, tussock grasses such as Themeda triandra Forssk, Microlaena stipoides R.Br (Weeping Grass), Eragrostis leptostachya Steud. (Paddock Lovegrass) and Echinopogon ovatus P.Beauv (Forest Hedgehog Grass). The remaining inter-tussock spaces are occupied by a diversity of growth-restricted grasses and herbaceous forbs (NSW - Department of Planing, Industries and Environment 2019; NSW Government - Office of Environment and Heritage 2017).
Clearing, pasture sowing, fertilizer application and livestock grazing resulted in a dramatic decrease in the extent of these natural woodlands, with less than five percent within conservation reserves and overall, with only about 20% of their original extent in New South Wales still existing (Tozer et al. 2010). The remaining areas outside of reserves are threatened by altered fire frequencies, habitat clearing, livestock grazing and especially by non-native plant invasion, particularly Eragrostis curvula (Schrad.) Nees. For this reason, the grassy woodlands are listed as an endangered ecological community in the NSW state legislation. Additionally, they are considered as critically endangered by the Commonwealth of Australia (Threatened Species Scientific Committee (TSSC) 2013).

Experimental design and sampling
The study was conducted on six farms and in each of them two sites were chosen, representing a paired design. One of the sites at each farm is dominated by native Themeda triandra, the other one co-dominated by non-native Eragrostis curvula and Themeda triandra. All farms are within a radius of approximately 10 km from the town Candelo. Three of the farms are located North (36°40’ to 36°42’ S and 149°38’ to 149°42’ E) and three of them are located South (36°51’ to 36°49’ S and 149°38’ to 149°42’ E) of Candelo.
Non-native herbivores (mainly cattle, sheep and rabbits) and native herbivorous marsupials (mainly kangaroos, wallabies and wombats) are present in the area of these sites. On each site, data was collected within four plots (each 1 x 1 m) in May and November 2020. All plant species found within a plot were recorded and their relative abundance was estimated.

References
NSW - Department of Planing, Industries and Environment. 2019. “Lowland Grassy Woodland in the South East Corner Bioregion - Endangered Ecological Community Listing.” https://www.environment.nsw.gov.au/topics/animals-and-plants/threatened-species/nsw-threatened-species-scientific-committee/determinations/final-determinations/2004-2007/lowland-grassy-woodland-south-east-corner-bioregion-endangered-ecological-community-l (February 18, 2021).
NSW Government - Office of Environment and Heritage. 2017. “Lowland Grassy Woodland in the South East Corner Bioregion - Profile.” https://www.environment.nsw.gov.au/threatenedSpeciesApp/profile.aspx?id=20070 (January 31, 2021).
Threatened Species Scientific Committee (TSSC). 2013. Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act) Conservation Advice for Lowland Grassy Woodland in the South East Corner Bioregion. http://www.environment.gov.au/biodiversity/threatened/communities/pubs/82-conservation-advice.pdf.
Tozer, Mark et al. 2010. “Native Vegetation of Southeast NSW: A Revised Classification and Map for the Coast and Eastern Tablelands.” Cunninghamia : a journal of plant ecology for eastern Australia 11(3): 359–406.

grassland

display_name: grassland

herbivory

display_name: herbivory

native-plant

display_name: native-plant

non-native-plant

display_name: non-native-plant

weed-management

display_name: weed-management

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: Native and no-native plant interactions in Australian grasslands


maintainer: Ionuț Iosifescu Enescu

en: map 1GB

media_type: image/tiff

format: TIFF

mimetype: image/tiff

en: map preview 0.5MB

mimetype: image/jpeg

en: Bulk Data Access

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

en: map 0.2GB

en: large map 10GB

en: map 0.5GB

en: huge map 100GB

en: map 2GB

en:
The attached data are some large GIS raster files (GeoTIFFs) made with Natural Earth data. Natural Earth is a free vector and raster map data @ naturalearthdata.com. The data used for creating these large files was the "Cross Blended Hypso with Shaded Relief and Water". Data was concatenated to achieve larger and larger files. Internal pyramids were created, in order that the files can be opened easily in a GIS software such as QGIS or by a (future) GIS data visualisation module integrated in EnviDat.

Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com

gis

display_name: gis

large-files

display_name: large-files

natural-earth

display_name: natural-earth

qgis

display_name: qgis

en: Large GIS raster data derived from Natural Earth Data (Cross Blended Hypso with Shaded Relief and Water)


maintainer: EnviDat Support

en: dsm drone-data uav wingtra photogrammetry

en: Data Access

en:
- DSM 10cm resolution
- Orthomosaic 3cm resolution
- Agisoft Metashape Processing Report
- original RGB images

All generated products are also available as a cloud optimized geotiff.

en: We conducted four drone flights in Davos Wolfgang Arelen, in 2020/21 and 2022 to obtain data for the generation of DSMs and orthomosaics at a high resolution. The data was processed with the Agisoft Metashape Professional Software.

drone-data

display_name: drone-data

dsm

display_name: dsm

photogrammetry

display_name: photogrammetry

uav

display_name: uav

wingtra

display_name: wingtra

en: Photogrammetric Drone Data Wolfgang Arelen


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

en: Axial

en: New built-up pixels classified as axial

format: GeoTIFF

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

en: Increase in sanitation service

en: Increase in waste collection service

en: isolated

en: New built-up pixels classified as isolated

en: Topographic Index Position (TPI)

en: infill

en: New built-up pixels classified as infill

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

en: slope

en: Slope °

en: Permanent housing density increase

en: Descricao dos dados em portiuguês

media_type: application/pdf

en: Portugues description

format: PDF

en: Native forest cover persistence from 2005 to 2015

en: Increase in clean water provision service

en: Master plan Ubatuba (zones)

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

en: Ecological-Economic Zoning (EEZ)

en: Population density per pixel (change per year)

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

en: Variables description

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

en: Increase in Normalized Difference Vegetation Index

en: Master plan Ihabela (zones)

en: Native forest cover gains from 2005 to 2015

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: Stephanie Mayer

en: snow-profiles snow-instability failure-layer snowpack-simulation rutschblock snowpack snow-stratigraphy

en: Observed snow profiles

en: Observed snow stratigraphy in form of one *.csv-file per snow profile from the DAV and SWISS data sets (in respective subfolders). Each file is named after the profile ID which allows identifying the corresponding simulated profile in the "simulated_profiles" folder. One row corresponds to one observed snow layer. The content of the columns is described in Table 2 of the ReadMe file.

en: SWISS data set: Properties of observed snow profiles and simulated failure layers

en: For each profile pair from the SWISS data set, this file contains one row with information on the observed snow stability and all extracted features describing the simulated failure layers. Please note that only those profile pairs which fulfilled the similarity criteria (column similarity_obs_sim = 1, 121 profiles) were used to validate the RF model. The content of the columns is explained in Table 1 of the ReadMe file.

en: Simulated snow profiles

en: Simulated snow stratigraphy in form of one *.csv-file per snow profile from the DAV and SWISS data sets (in respective subfolders). Each file is named after the profile ID which allows identifying the corresponding observed profile in the "observed_profiles" folder. One row corresponds to one simulated snow layer. The content of the columns 1-7 is described in Table 3 of the ReadMe file. The remaining columns (8-31) contain all features that were used in the development of the RF model and are described in Table 1 of the ReadMe file.

en: DAV data set: Properties of observed snow profiles and simulated failure layers

media_type: text/csv

en:
For each profile pair from the DAV data set, this file contains one row with information on the observed snow stability and all extracted features describing the simulated failure layers. The content of the columns is explained in Table 1 of the ReadMe file. Only those profile pairs which fulfilled predefined similarity criteria (column
"similarity_obs_sim" = 1) and which belonged to the stability classes "stable" and "unstable" (column "stability_class"
= 0 or 1) were used to train the RF model. Please note that for each of the two snow profiles with RB score 7 (profile
IDs 7228 and 9526), three additional layers were included in this data set to obtain a balanced set of training data (73
unstable and 73 stable profiles).

format: CSV

en: ReadMe

en: ReadMe file explaining data structure and content

en:
This data set includes information on all observed and simulated snow profiles that were used to train and validate the
random forest model described in Mayer et al. (2022). The RF model was trained to assess snow instability from simulated snow stratigraphy. The data set contains observed snow profiles from the region of Davos (DAV subset, 512 profiles) and from all over Switzerland (SWISS subset, 230 profiles). For each observed snow profile, there is a corresponding simulated profile which was obtained using meteorological input data for the numerical snow cover model SNOWPACK. The information on the observed snow profile contains a Rutschblock test result including the depth of the failure interface. As part of the study described in Mayer et al. (2022), each observed snow profile was manually compared to its simulated counterpart and the simulated layer corresponding to the Rutschblock failure layer was identified.

The data are provided in the following form: one file each per observed and simulated snow profile (2x512 files DAV, 2x230 files SWISS), two files (1 file DAV, 1 file SWISS) containing the observed information on snow instability, the allocation between observed and simulated failure layer, and all features extracted from the simulated weak layers that were used to develop the RF model.

failure-layer

display_name: failure-layer

rutschblock

display_name: rutschblock

snow-instability

display_name: snow-instability

snow-profiles

display_name: snow-profiles

snow-stratigraphy

display_name: snow-stratigraphy

snowpack

display_name: snowpack

snowpack-simulation

display_name: snowpack-simulation

educ

en: Education, culture and sport

en: EnviDat: the Environmental Data Portal

en: Observed and simulated snow profile data


maintainer: Jana von Freyberg

en: stable-water-isotopes snowpack-outflow snowmelt-rate solutes snowmelt-lysimeter

en: The dataset contains volumes, solutes and stable water isotopes of snowpack outflow at daily resolution. 10-minute data are provided of the measurements taken by three snowmelt lysimeter systems at spatially-distributed locations.

en:
This data contain volumes, solutes and isotopes of snowpack outflow measured by a snowmelt lysimeter system at three locations in the southern Alp catchment, situated Central Switzerland. The river Alp is a snow-dominated catchment situated in Central Switzerland characterized by an elevation range from 840 to 1898 m a.s.l.
The dataset provides solutes (major anions and cations, trace metals) and stable water isotopes and water fluxes (snowpack outflow volumes) at daily intervals from several sampling locations. Additionally, the data measured by the snowmelt lysimeter system are provided in 10-minute resolution.

snowmelt-lysimeter

display_name: snowmelt-lysimeter

snowmelt-rate

display_name: snowmelt-rate

snowpack-outflow

display_name: snowpack-outflow

solutes

display_name: solutes

stable-water-isotopes

display_name: stable-water-isotopes

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: Daily data of the volumes, solutes and isotopes in snowpack outflow measured at three locations in the southern Alp catchment


swiss-fluxnet-site-davos

title_for_slug: swiss-fluxnet-site-davos

en: swiss-forest-lab forest swiss-fluxnet icos flux meteorological-measurements greenhouse-gas eddy-covariance

en: Site description

en: Data access

en: The flux and meteo data of the Swiss FluxNet site Davos are openly available. Near real-time flux and meteo data are uploaded daily to the ICOS Carbon Portal.

en: Processed data

en: Processed flux and meteo data are available from the European Fluxes Database Cluster.

en: Fluxnet2015 dataset

en: Processed flux and meteo data are also part of the Fluxnet2015 dataset.

en:
The Swiss FluxNet Site Davos is a managed subalpine evergreen forest, located on the Seehorn mountain near Davos in the Swiss Alps. The site is dominated by Norway spruce. The tower is owned by the Federal Office for the Environment (FOEN).
Ecosystem flux measurements of CO2, H2O (since 1997) as well as CH4 and N2O (since 2016) are performed with the eddy covariance method.
In addition to Swiss FluxNet, the site is part of the National Air Pollution Monitoring Network (NABEL), the Long term Forest Ecosystem Research (LWF), the biological drought and growth indicator network (TreeNet) and of ICOS Switzerland (Integrated Carbon Observation System). Since November 2019, the site is an ICOS Class 1 Ecosystem station.

Measurements

- Ecosystem flux measurements of CO2, H2O vapour (since 1997) as well a CH4 and N2O (since 2016) are performed with the eddy-covariance method. This method is based on measurements of trace gas mixing ratios, using infrared gas analyzers (for CO2, H2O vapor) and laser spectrometers (for CH4 and N2O), combined with wind speed and wind direction measurements, using 3D sonic anemometers. To resolve the short-term turbulent fluctuations in the atmosphere, very fast measurements are needed: we measure at 10-20 Hz, i.e., 10-20 times per second. To assess the energy budget of each ecosystem, also radiation sensors and soil climate profiles are installed at the site.
- Sub-canopy eddy fluxes (CO2, H2O, since 2023 also CH4).
- Continuous profile concentration and forest floor flux measurement of CO2, H2O, CH4, N2O.
- Auxiliary micrometeorology and soil climate measurements.

Data availability

Near real-time flux and meteo data uploaded daily to the ICOS Carbon Portal. Processed flux and meteo data are also available from the European Fluxes Database Cluster and part of Fluxnet2015 dataset.

Data policy

ICOS data license:
[https://www.icos-cp.eu/data-services/about-data-portal/data-license](https://www.icos-cp.eu/data-services/about-data-portal/data-license)

Detailed site info:
[https://www.swissfluxnet.ethz.ch/index.php/sites/ch-dav-davos/site-info-ch-dav/](https://www.swissfluxnet.ethz.ch/index.php/sites/ch-dav-davos/site-info-ch-dav/)

eddy-covariance

display_name: eddy-covariance

flux

display_name: flux

forest

display_name: forest

greenhouse-gas

display_name: greenhouse-gas

icos

display_name: icos

meteorological-measurements

display_name: meteorological-measurements

swiss-fluxnet

display_name: swiss-fluxnet

swiss-forest-lab

display_name: swiss-forest-lab

en: Swiss FluxNet Site Davos


data-set-on-snow-instability

title_for_slug: data-set-on-snow-instability

en: slab-tensile-support snow-avalanche weak-layer crack-propagation avalanche-forcasting slab snow-stability-evaluation failure-initiation rutschblock propgatoin-saw-test

en: Read-me file on snow instability data

mimetype: application/pdf

en: data set on snow instability

mimetype: text/csv

en:
These data on snow instability include three data subsets that were analyzed and the results published by Reuter and Schweizer (2018) who suggest a novel framework on how to describe snow instability by failure initiation, crack propagation and slab tensile support.

Please refer to the Read-me file for further details on the data.

These data are the basis of the following publication:
Reuter, B. and Schweizer, J., 2018. Describing snow instability by failure initiation, crack propagation and slab tensile support. Geophys. Res. Lett., 45, doi: 10.1029/2018GL078069.

avalanche-forcasting

display_name: avalanche-forcasting

crack-propagation

display_name: crack-propagation

failure-initiation

display_name: failure-initiation

propgatoin-saw-test

display_name: propgatoin-saw-test

rutschblock

display_name: rutschblock

slab

display_name: slab

slab-tensile-support

display_name: slab-tensile-support

snow-avalanche

display_name: snow-avalanche

snow-stability-evaluation

display_name: snow-stability-evaluation

weak-layer

display_name: weak-layer

educ

en: Education, culture and sport

envidat

en: EnviDat: the Environmental Data Portal

en: Data set on snow instability


maintainer: Océane Hames

en: arctic-sea-ice snow-deposition mosaic openfoam snow-transport

en: snowBedFoam 1.0.

en:
OpenFOAM-2.3.0. scripts containing the implemented Eulerian-Lagrangian snow transport model.

The code file provided here is not updated. The most recent version of the script can be found on the WSL/SLF GitLab repository: https://gitlabext.wsl.ch/atmospheric-models/snowbedfoam-v1-0.git

en: Paper Draft

en: This is the initial draft of the paper where the snowBedFoam 1.0. results are tested against Terrestrial Laser Scan (TLS) measurements of snow dynamics on Arctic sea ice (MOSAiC expedition). It highlights the numerical limitations of our model as well as the measurement uncertainties, which may occur in such extreme environments.

en: MOSAiC Met City preliminary (Level 2) wind data

en:
Extract from the preliminary MOSAiC atmospheric data (Level 2) for the time period 6th to 13th of November, 2019.
This data was used to set up the fluid forcing parameters in the snowBedFoam simulations on MOSAiC sea ice. It is being published in this repository with the kind permission from Matthew Shupe and his team.
The final processed and quality controlled version of this dataset (Level 3) has not been published yet. A complete reference will be added in this description once it is released.

en:
snowBedFoam 1.0. is a snow transport solver implemented in the Computational Fluid Dynamics (CFD) software OpenFOAM. It is adapted from the standard multi-phase flow solver DPMFoam for application in snow-influenced environments.

To simulate aeolian snow transport, snowBedFoam 1.0. handles coupled Eulerian–Lagrangian phases, which involve a finite number of particles (snow) spread in a continuous phase (air). The snow erosion and deposition are modelled through physically-based equations such as the ones employed in the well-established LES-Lagrangian Stochastic Model (Comola and Lehning, 2017 ; Sharma et al., 2018). This modelling approach is computationally intensive and thus adapted to simulate snow movement and distribution on small scale terrain.

snowBedFoam 1.0. was tested against topographical data collected on Arctic sea ice during the MOSAiC expedition (Clemens-Sewall, 2021) . Together with atmospheric data from the MOSAiC Met City (Shupe et al., 2021) used for the fluid forcing, the model was able to accurately simulate the zones of erosion and deposition of snow along a complex ice ridge structure.

The core code of snowBedFoam 1.0. is also accessible on the WSL/SLF GitLab repository (more details in the Resources section).

arctic-sea-ice

display_name: arctic-sea-ice

mosaic

display_name: mosaic

openfoam

display_name: openfoam

snow-deposition

display_name: snow-deposition

snow-transport

display_name: snow-transport

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: snowBedFoam: an OpenFOAM Eulerian-Lagrangian solver for modelling snow transport


maintainer: Robert Pazúr

en: sentinel-2 grassland phenology agriculture machine-learning habitat remote-sensing vegetation-indices switzerland cropland

en: (current version) Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.6 (v1.5)

en: Assuming that sealed surfaces have little or no phenological response we calculated a mask of unsealed surfaces based on the 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.6.

en: Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.7 (v1.0)

en: !!! Please check out the newer version in this repository !!! Assuming that sealed surfaces have little or no phenological response we calculated a mask of unsealed surfaces based on the 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.7.

en: (current version) Cropland and grassland map of Switzerland (v.1.5)

en: GEE (Google Earth Engine) script (JavaScript) to derived the output map  (v1.5)

en: The code used in Google Earth Engine to derive the cropland, grassland and shrubland map of Switzerland. Compare to the previous version the newer version includes the shadow masking procedure which might be important in the mountainous areas.

en: Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.6  (v1.0)

en:
!!! Please check out the newer version in the repository !!!
Mask of unsealed surfaces of Switzerland based on 3 years (2017-2019) Sentinel-2 NDVI 95th percentile threshold value: 0.6

en: GEE (Google Earth Engine) script (JavaScript) to derived the output map  (v1.0)

en: !!! Please check out the newer version in the repository !!! The code used in Google Earth Engine to derive the cropland, grassland and shrubland map of Switzerland

en: (current version) Cropland, grassland, and shrubland map of Switzerland (v.1.5)

en: Cropland, grassland, and shrubland map of Switzerland (v1.0)

en: !!! Please check out the newer version in this repository !!! (1) cropland; (2) grassland; (3) shrubland; other LC classes are masked out from the map

en: (current version) Mask of unsealed surfaces of Switzerland based on 3 years Sentinel-2 NDVI 95th percentile threshold value: 0.7 (v1.5)

en: Cropland and grassland map of Switzerland  (v.1.0)

en: !!! Please check out the newer version in this repository !!! (1) cropland; (2) grassland; other LC classes are masked out from the map

en: We developed a map of cropland and grassland allocation for Switzerland based on several indices dominantly derived from Sentinel-2 satellite imagery captured over multiple growing seasons. The classification model was trained based on parcel-based data derived from landholder reporting. The mapping was conducted on Google Earth Engine platform using random forest classifier. Areas of high vegetation, shrubland, sealed surface and non-vegetated areas were masked out from the country-wide map. The resulting map has high accuracy in lowlands as well as mountainous areas.

agriculture

display_name: agriculture

cropland

display_name: cropland

grassland

display_name: grassland

habitat

display_name: habitat

machine-learning

display_name: machine-learning

phenology

display_name: phenology

remote-sensing

display_name: remote-sensing

switzerland

display_name: switzerland

vegetation-indices

display_name: vegetation-indices

en: Cropland and grassland map of Switzerland based on Sentinel-2 data


en: avalanche-forecasting snow-instability danger-level avalanche snow avalanche-danger

en: Read-me file

en: This read-me file contains the descriptions of the variables.

en: Field observations of snow instabilities

en:
This data set includes 589 snow profile observations including a rutschblock test, observations of signs of instability and an assessment of the local avalanche danger level, mainly recorded in the region of Davos (eastern Swiss Alps) during the winter seasons 2001-2002 to 2018-2019.

These data were analyzed and results published by Schweizer et al. (2021). They characterized the avalanche danger levels based on signs of instability (whumpfs, shooting cracks, recent avalanches), snow stability class and new snow height.

The data are provided in a csv file (589 records); the variables are described in the corresponding read-me file.

These data are the basis of the following publication: Schweizer, J., Mitterer, C., Reuter, B., and Techel, F.: Avalanche danger level characteristics from field observations of snow instability, Cryosphere, 15, 3293-3315, https://doi.org/10.5194/tc-15-3293-2021, 2021.

 Acknowlegements
Many of the data were recorded by SLF observers and staff members, among those Roland Meister, Stephan Harvey, Lukas Dürr, Marcia Phillips, Christine Pielmeier and Thomas Stucki. Their contribution is gratefully acknowledged.

avalanche

display_name: avalanche

avalanche-danger

display_name: avalanche-danger

avalanche-forecasting

display_name: avalanche-forecasting

danger-level

display_name: danger-level

snow

display_name: snow

snow-instability

display_name: snow-instability

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.


maintainer: Leo Gallus Bont

en: accessible-forest openstreetmap forest qgis geoprocessing seilaplan

en: Forest vector data (for sample forest range)

en: DEM (for the area of the sample forest range)

en: Juypter Notebook for Data Preparation

en: Power lines vector data (close to the area of the sample forest range)

en: Digitised sample perimeter

en: Preview

media_type: image/png

format: PNG

mimetype: image/png

en: Railways vector data (close to the sample forest range)

en: Juypter Notebook for Data Preparation (online)

en:
This dataset contains open vector data for railways, forests and power lines, as well an open digital elevation model (DEM) for a small area around a sample forest range in Europe (Germany, Upper Bavaria, Kochel Forest Range, some 70 km south of München, at the edge of Bavarian Alps).
The purpose of this dataset is to provide a documented sample dataset in order to demonstrate geospatial preprocessing at FOSS4G2019 based on open data and software. This sample has been produced based on several existing open data sources (detailed below), therefore documenting the sources for obtaining some data needed for computations related to forest accessibility and wood harvesting. For example, they can be used with the open methodology and QGIS plugin [Seilaplan]( https://doi.org/10.16904/envidat.software.1) for optimising the geometric layout cable roads or with additional open software for computing the forest accessibility for wood harvesting.
The vector data (railways, forests and power lines) was extracted from OpenStreetMap (data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org). The railways and forests were downloaded and extracted on 18.05.2019 using the open sources QGIS (https://www.qgis.org) with the QuickOSM plugin, while the power lines were downloaded a couple of days later on 23.05.2019.

Additional notes for vector data:
Please note that OpenStreeMap data extracts such as forests, roads and railways (except power lines) can also be downloaded in a GIS friendly format (Shapefile) from http://download.geofabrik.de/ or using the QGIS built-in download function for OpenStreetMap data. The most efficient way to retrieve specific OSM tags (such as power=line) is to use the QuickOSM plugin for QGIS (using the Overpass API - https://wiki.openstreetmap.org/wiki/Overpass_API) or directly using overpass turbo (https://overpass-turbo.eu/). Finally, the digitised perimeter of the sample forest range is also made available for reproducibility purposes, although any perimeter or area can be digitised freely using the QGIS editing toolbar.

The DEM was originally adapted and modified also with QGIS (https://www.qgis.org) based on the elevation data available from two different sources, by reprojecting and downsampling datasets to 25m then selecting, for each individual raster cell, the elevation value that was closer to the average. These two different elevation sources are:

- Copernicus Land Monitoring Service - EU-DEM v.1.1 (TILE ID E40N20, downloaded from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1; this original DEM was produced by the Copernicus Land Monitoring Service “with funding by the European Union” based on SRTM and ASTER GDEM)
- Digitales Geländemodell 50 m Gitterweite (https://opendata.bayern.de/detailansicht/datensatz/digitales-gelaendemodell-50-m-gitterweite/), produced by the Bayerische Vermessungsverwaltung – www.geodaten.bayern.de –and downloaded from http://www.geodaten.bayern.de/opendata/DGM50/dgm50_epsg4258.tif

This methodology was chosen as a way of performing a basic quality check, by comparing the EU-DEM v.1.1 derived from globally available DEM data (such as SRTM) with more authoritative data for the randomly selected region, since using authoritative data is preferred (if open and available). For other sample regions, where authoritative open data is not available, such comparisons cannot longer be performed.

Additional notes DEM: a very good DEM open data source for Germany is the open data set collected and resampled by Sonny (sonnyy7@gmail.com) and made available on the Austrian Open Data Portal http://data.opendataportal.at/dataset/dtm-germany. In order to simplify end-to-end reproducibility of the paper planned for FOSS4G2019, we use and distribute an adapted (reprojected and resampled to 25 meters) sample of the above mentioned dataset for the selected forest range.

This sample dataset is accompanied by software in Python, as a Jupiter Notebook that generates harmonized output rasters with the same extent from the input data. The extent is given by the polygon vector dataset (Perimeter). These output rasters, such as obstacles, aspect, slope, forest cover, can serve as input data for later computations related to forest accessibility and wood harvesting questions. The obstacles output is obtained by transforming line vector datasets (railway lines, high voltage power lines) to raster. Aspect and slope are both derived from the sample digital elevation model.

accessible-forest

display_name: accessible-forest

forest

display_name: forest

geoprocessing

display_name: geoprocessing

openstreetmap

display_name: openstreetmap

qgis

display_name: qgis

seilaplan

display_name: seilaplan

en: Sample Geodata and Software for Demonstrating Geospatial Preprocessing for Forest Accessibility and Wood Harvesting at FOSS4G2019


maintainer: Frank Techel

en: avalanche-size machine-learning snowpack-model avalanche natural-avalanches

en: Data set AV3 was used for validation of the avalanche-day classifier (Sections 2.1.3 and 3.3(1) in Mayer et al.).

en: Data set DL was used for validation of the avalanche-day classifier and the avalanche-size estimator. The data set contains the quality-checked danger levels merged with the respective SNOWPACK simulations (Sections 2.3 and 3.3(2) in Mayer et al.).

en: Data set AV2 used for training the avalanche-size estimator (Sections 2.1.2 and 3.2 in Mayer et al.)

en: Data set AV1 was used for training the avalanche-day classifier (Sections 2.1.1, 2.2, and 3.1 in Mayer et al.).

en:
The data set contained in this repository was used in the analysis by Mayer et al. (2023):
Mayer, S. I., Techel, F., Schweizer, J., and van Herwijnen, A.: Prediction of natural dry-snow avalanche activity using physics-based snowpack simulations, EGUsphere, https://doi.org/10.5194/egusphere-2023-646, 2023.

avalanche-size

display_name: avalanche-size

machine-learning

display_name: machine-learning

natural-avalanches

display_name: natural-avalanches

snowpack-model

display_name: snowpack-model

en: Data-set for prediction of natural dry-snow avalanche activity and avalanche size using physics-based snowpack simulations


maintainer: Irmi Seidl

en: community-energy renewable-energy switzerland energy-cooperatives cooperatives

en: Raw date (SPSS)

en: Raw data in sav. format (SPSS). For comprehension of the data and variable structure, please consult the modified questionnaire with variable names.

en: Raw data (.csv)

en: Raw data in csv. format. For comprehension of the data and variable structure, please consult the codebook and the modified questionnaire with variable names.

en: Questionnaire with variable names

en: Modified questionnaire with variable names for each item. Should be used together with the data files (.csv or .sav) to reconstruct the data and variable structures.

en: Codebook

en: Description of variable attributes and valid n

en:
 Topic of Survey
The data at hand on energy cooperatives in Switzerland were collected in 2016 as part of the project "Collective financing of renewable energy projects in Switzerland and Germany" of the National Research Programme 71 "Managing Energy Consumption". The cooperatives were surveyed on their organizational structure, their activities in electricity and heat generation, their finances, the political context and their assessments of the future.

 Survey Method
The survey was targeted at all energy cooperatives in Switzerland (this is the basic population). The Swiss Commercial Register was searched for cooperatives and specific keywords in order to determine this basic population and collect addresses. This search in May 2016 resulted in a total of 304 energy cooperatives, to which a questionnaire was sent in July 2016. A pre-test with 8 persons had been carried out before the questionnaire was sent out. The questionnaire was provided in German and French. It was sent by mail and an attached letter referred to a link for the digital version if preferred. The online version was designed with the software "Sawtooth". After three weeks, a first, and after six weeks a second reminder letter was sent to those cooperatives that had not yet completed the questionnaire. The returned hardcopy questionnaires were manually entered into the database and then combined with the electronic data from the online survey. In the course of the survey, the total population was reduced from 304 to 289: in 4 cases the survey was not deliverable, 4 cooperatives had dissolved, 6 were not actually energy cooperatives, 1 case had recently changed its legal form. With a response rate of 47%, the final data set comprises 136 responses (from 77 digital and 59 hardcopy questionnaires). However, not all 136 of the returned questionnaires were filled out completely. We checked for answers that seemed contradictory or incomprehensible. If an error could be clearly identified and the correct answer derived, the answer was adjusted, otherwise the answer was replaced by "missing data".

 Anonymization
Participating cooperatives have been assured that their information will be kept confidential and will only be made public anonymously. For this reason, the data have been anonymized in in order to prevent any identification of individual cooperatives.

 How to Use the Data
* The data are available in CSV and SPSS (sav.) format.
* A codebook and a modified version of the used questionnaire are provided to illustrate the data and variable structure. In the questionnaire, the variable names are assigned to the corresponding questions. In the codebook, further information on these variables (valid n, answer categories) can be found. This information (of the codebook) is already integrated in the SPSS file.

 Current Embargo on Data
These data are currently under embargo and will only be released when the project is completed (not before 2020).

#Additional Information
* The used questionnaire is provided in German and French.
* Descriptive results of the survey were published in a WSL report: https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:18943

community-energy

display_name: community-energy

cooperatives

display_name: cooperatives

energy-cooperatives

display_name: energy-cooperatives

renewable-energy

display_name: renewable-energy

switzerland

display_name: switzerland

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: Energy Cooperatives in Switzerland: Survey Results // Energiegenossenschaften in der Schweiz: Befragungsergebnisse


maintainer: Joan Casanelles Abella

en: dna-metabarcoding cavity-nesting-bees hylaeus-communis paris zurich trap-nest osmia-bicornis urban-ecology tartu antwerp osmia-cornuta poznan plant-pollinator-interactions chelostoma-florisomne

en: Larval food composition

en: Explanation data variables

en: Urbanization poses threats and opportunities for the biodiversity of wild bees. A main gap relates to the food preferences of wild bees in urban ecosystems, which usually harbour large numbers of plant species, particularly at the larval stage. This data sets describes the larval food of four wild bee species (i.e. Chelostoma florisomne, Hylaeus communis, Osmica bicornis and Osmia cornuta) and three genera (i.e. Chelostoma sp., Hylaeus sp, and Osmia sp.) common in urban areas in five different European cities (i.e. Antwerp, Paris, Poznan, Tartu and Zurich). This data results from a European-level study aimed at understanding the effects of urbanization on biodiversity across different cities and citiscapes, and a Swiss project aimed at understanding the effects of urban ecosystems in wild bee feeding behaviour. Wild bees were sampled using standardized trap-nests in 80 sites (32 in Zurich and 12 in each of the remaining cities), selected following a double gradient of available habitat at local and landscape scales. Larval pollen was obtained from the bee nests and identified using DNA metabarconding. The data provides the plant composition at the species or genus level of the different bee nests of the studied species in the studied sites of the five European cities. For Hylaeus communis, this is the first study in reporting larval food composition.

antwerp

display_name: antwerp

cavity-nesting-bees

display_name: cavity-nesting-bees

chelostoma-florisomne

display_name: chelostoma-florisomne

dna-metabarcoding

display_name: dna-metabarcoding

hylaeus-communis

display_name: hylaeus-communis

osmia-bicornis

display_name: osmia-bicornis

osmia-cornuta

display_name: osmia-cornuta

paris

display_name: paris

plant-pollinator-interactions

display_name: plant-pollinator-interactions

poznan

display_name: poznan

tartu

display_name: tartu

trap-nest

display_name: trap-nest

urban-ecology

display_name: urban-ecology

zurich

display_name: zurich

en: Larval food composition of four wild bee species in five European cities


en: sintering viscous-deformation snow snow-failure snow-mechanics acoustic-emissions snow-avalanche fiber-bundle-model

en: fiber-bundle-model-for-snow-failure

en: Data underlying Figures in publication "Modelling Snow Failure Behavior and Concurrent Acoustic Emissions Signatures with a Fiber Bundle Model" submitted to "Geophysical Research Letters"

en:
This dataset contains modeled and experimental results for laboratory snow failure experiments and the concurrent acoustic emissions signatures for different loading rates. For modelling the snow failure we used a fiber bundle model that includes sintering and viscous deformation.

The data underlay the figures in the publication "Modelling Snow Failure Behavior and Concurrent Acoustic Emissions Signatures with a Fiber Bundle Model" submitted for publication to "Geophysical Research Letters".

acoustic-emissions

display_name: acoustic-emissions

fiber-bundle-model

display_name: fiber-bundle-model

sintering

display_name: sintering

snow

display_name: snow

snow-avalanche

display_name: snow-avalanche

snow-failure

display_name: snow-failure

snow-mechanics

display_name: snow-mechanics

viscous-deformation

display_name: viscous-deformation

en: Fiber Bundle Model for snow failure and concurrent Acoustic Emissions


en: swisstopo open-source seilaplan cable-yarding geoinformation-software swiss-geodata qgis-plugin digital-planning digital-elevation-model

en: View video on youtube!

en: Herunterladen des DHM von swisstopo Website

en:
In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. As an alternative to using the ‘Swiss Geo Downloader’ plugin, the DTM can be obtained directly from Swisstopo. In this tutorial we explain step by step how to download the necessary DTM from the Swisstopo Website, and how to use it in QGIS for the digital planning of a cable line using the plugin ‘Seilaplan’. Please note that the tutorial language is German!

Link to the elevation model on the swisstopo website: https://www.swisstopo.admin.ch/de/geodata/height/alti3d.html#technische_details

Link to the rope map website: https://seilaplan.wsl.ch

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

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. Als Alternative zum Swiss Geo Downloader erklären wir in diesem Tutorial Schritt für Schritt, wie man das nötige Höhenmodell von der Swisstopo Webseite herunterladen und in QGIS zur Seillinienplanung verwenden kann.

Link zum Höhenmodell auf der swisstopo Webseite: https://www.swisstopo.admin.ch/de/geodata/height/alti3d.html#technische_details

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-software

display_name: geoinformation-software

open-source

display_name: open-source

qgis-plugin

display_name: qgis-plugin

seilaplan

display_name: seilaplan

swiss-geodata

display_name: swiss-geodata

swisstopo

display_name: swisstopo

en: Seilaplan Tutorial: DTM download from swisstopo website


regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

it: Regioni e città

envi

fr: Environnement

de: Umwelt

en: Environment

it: Ambiente

de: bgdi-bundesgeodaten-infrastruktur optisch sentinel-2 oberflachenreflexion satellitenbild orthofotografie

label: geocat.ch permalink

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: Lien vers les données à télécharger

de: Link für Datenbezug

en: Link for data access

it: Link per l'accesso ai dati

format: HTML

fr: RESTful API

de: RESTful API

fr: RESTful API de geo.admin.ch

de: RESTful API von geo.admin.ch

format: API

fr: Les données satellitaires optiques (Sentinel-2) représentent les réflectances de la surface terrestre pour les quatre canaux Rouge, Vert, Bleu et Infrarouge proche avec une résolution spatiale de dix mètres. La Suisse est entièrement capturée environ tous les trois jours, cependant, l'utilisabilité des données dépend fortement des conditions météorologiques car le capteur d'imagerie ne peut pas voir à travers les nuages. En plus de la géolocalisation déjà appliquée aux données, une coenregistrement optimisée pour la Suisse est utilisée pour une précision de localisation sub-pixel ainsi qu'une rectification orthographique améliorée, et les données sont fournies dans une projection uniforme. Des couches de qualité optimisées avec des masques pour les nuages ( et leures ombres) et les ombres topographiques sont également fournies. Contient des données Copernicus Sentinel modifiées

de: Optische Satellitendaten (Sentinel-2) welche die Reflektanzen der Landoberfläche für die vier Kanäle Rot, Grün, Blau und nahes Infrarot in einer räumlichen Auflösung von zehn Metern darstellen. Die Schweiz wird ca. alle drei Tage vollständig aufgenommen, allerdings ist die Brauchbarkeit der Daten stark von den meteorologischen Bedingungen abhängig, da der Aufnahmesensor nicht durch Wolken hindurchsehen kann. Zusätzlich zu der bereits angewendeten Verortung der Daten wird eine auf die Schweiz optimierte Ko-Registrierung der Daten für eine sub-Pixel Lagegenauigkeit sowie eine verbesserte Orthorektifizierung angewandt und die Daten werden in einer einheitlichen Projektion geliefert. Ebenso mitgeliefert werden optimierte Qualitätslayer mit Masken für Wolken(-schatten) und topographischen Schattenwurf. Enthält geänderte Copernicus Sentinel-Daten.

en: Optical satellite data (Sentinel-2) which show the land surface reflectances of the four channels red, green, blue and near infrared in a spatial resolution of ten metres. Switzerland is fully imaged approximately every three days, but the usefulness of the data is depending heavily on meteorological conditions, as the imaging sensor cannot see through clouds. In addition to the already applied registration of the data, a co-registration of the data optimised for Switzerland is applied for sub-pixel positional accuracy and the data is delivered in a uniform projection. Also included are optimised quality layers with masks for clouds ( and cloud shadows) and topographic shadows. Contains modified Copernicus Sentinel data.

it: I dati satellitari ottici (Sentinel-2) rappresentano le riflettanze della superficie terrestre per i quattro canali Rosso, Verde, Blu e Infrarosso vicino con una risoluzione spaziale di dieci metri. La Svizzera viene completamente rilevata ogni tre giorni circa, tuttavia l'utilità dei dati dipende fortemente dalle condizioni meteorologiche poiché il sensore di acquisizione non può vedere attraverso le nuvole. Oltre alla geolocalizzazione già applicata ai dati, viene utilizzata una co-registrazione ottimizzata per la Svizzera per una precisione di posizionamento sub-pixel, così come una migliorata ortorettificazione, e i dati sono forniti in una proiezione uniforme. Sono inoltre forniti layer di qualità ottimizzati con maschere per le nuvole (e le loro ombre) e le ombre topografiche. Contiene dati modificati di Copernicus Sentinel

spatial: Schweiz

name: Bundesamt für Landestopografie swisstopo

fr: Office fédéral de topographie swisstopo

en: Federal Office of Topography swisstopo

it: Ufficio federale di topografia swisstopo

fr: Le centre de compétence de la Confédération suisse pour les informations géographiques, c'est-à-dire pour la description, la représentation et l’archivage de données à référence spatiale.

de: Das Kompetenzzentrum der Schweizerischen Eidgenossenschaft für Geoinformation, d.h. für die Beschreibung, Darstellung und Archivierung von raumbezogenen Geodaten.

en: The centre of competence for the Swiss Confederation responsible for geographical reference data, for instance the description, representation and archiving of geographic spatial data.

it: Il centro d’eccellenza della Confederazione Elvetica per geoinformazione, cioè per la descrizione, rappresentazione e archiviazione dei dati georeferenziati (geodati).

political_level: confederation

url: URL Detailbeschreibung

value: Swisstopo - Geocat Harvester

fr: swissEO S2-SR: Données satellitaires optiques (Sentinel-2)

de: swissEO S2-SR: Optische Satellitendaten (Sentinel-2)

en: swissEO S2-SR: Optical satellite data (Sentinel-2)

it: swissEO S2-SR: Dati satellitari ottici (Sentinel-2)


seilbahnen-und-skilifte

soci

fr: Population et société

de: Bevölkerung und Gesellschaft

en: Population and society

it: Popolazione e società

tran

fr: Transports

de: Verkehr

en: Transport

it: Trasporti

title_for_slug: seilbahnen-und-skilifte

de: offentlicher-verkehr seilbahn skilift

fr: WFS für Open-Government-Data des Kantons Zürich (OGD ZH WFS)

format: WFS

fr: Gemäss der kantonalen Geoinformationsverordnung (KGeoIV), Anhang 2, ist das AFM für das Erheben, Nachführen und Verwalten der Geobasisdaten zu den „Plänen und Bewilligungen der nicht eidgenössisch konzessionierten Seilbahnen und Skilifte“ zuständig (Identifikator: 116-ZH). Dargestellt werden alle Kleinskilifte, Skilifte und Seilbahnen die kantonal bewilligt sind oder eine eidgenössische Konzession haben.

format: WMS

spatial: Kanton Zürich

geoinformation-kanton-zuerich

fr: Département de Géoinformation du Canton de Zurich

de: Geoinformation Kanton Zürich

en: Department of Geoinformation of the Canton of Zurich

it: Dipartimento della geoinformazione del Cantone di Zurigo

fr:
Le département de géomatique est le centre de compétence du system d'information géographique du canton de Zurich.
Nous coordinons l'aquisition des géodonnées d'intérêt commun sur le territoire cantonal et les mettons à disposition des
secteurs public et privé et des particuliers.

en:
The department of geoinformation is the geospatial competency center of the Canton of Zurich.
We coordinate the acquisition of geodata within the cantonal territory and provide public, private, and government sectors with data of outstanding quality.

de: Die Abteilung Geoinformation ist das Kompetenzzentrum für räumliche Informationen im Kanton Zürich. Wir versorgen Gesellschaft, Wirtschaft und öffentliche Verwaltung mit qualitativ hochstehenden Geodaten und Informationen über den Kanton Zürich.

it: La divisione della geoinformazione è il centro d'eccellenza del Cantone di Zurigo per dati georeferenziati. Mettiamo a disposizione dati geografici e informazioni di alta qualità per l'economia, la società e la pubblica amministrazione su tutto il cantone.

political_level: canton

value: ZH - Geocat Harvester

fr: Seilbahnen und Skilifte

de: Seilbahnen und Skilifte

en: Seilbahnen und Skilifte

it: Seilbahnen und Skilifte


en: snowpack snow avalanche snow-stability-evaluation avalanche-forecast

en: SNOWPACK simulations

en: This dataset contains all SNOWPACK simulations, including input files, setup and output files.

en: README

en: README file explaining data structure and content

en: Extracted data

en: Extracted data contain validation and results published in Richter et al. (2019).

en: Stability tests

en: Stability tests including csv-files with CT, ECT and PST experiments for each year and each field site.

en: Manually observed snow profiles

en: Manually observed snow profiles as csv-files containing depth [cm], thickness [cm], density [kg m-3], hand hardness, first grain type, second grain type, first grain size [mm], and second grain size [mm] of each layer.

en:
To validate the critical crack length as implemented in the snow cover model SNOWPACK, PST experiments were conducted for three winter seasons (2015-2017) at two field site above Davos, Switzerland. This dataset contains manually observed snow profiles and stability tests. Furthermore, corresponding SNOWPACK simulations are included.
These data were analyzed and results were published in Richter et al. (2019).

Please refer to the Readme file for further details on the data.

These data are the basis of the following publication:
Richter, B., Schweizer, J., Rotach, M. W., and van Herwijnen, A.: Validating modeled critical crack length for crack propagation in the snow cover model SNOWPACK, The Cryosphere, 13, 3353–3366, https://doi.org/10.5194/tc-13-3353-2019, 2019.

avalanche

display_name: avalanche

avalanche-forecast

display_name: avalanche-forecast

snow-stability-evaluation

display_name: snow-stability-evaluation

snowpack

display_name: snowpack

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

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.

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.

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.

en: Validating and improving the critical crack length in SNOWPACK


ruhezonen-fur-wildtiere-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

regi

fr: Régions et villes

de: Regionen und Städte

en: Regions and cities

envi

fr: Environnement

de: Umwelt

en: Environment

title_for_slug: ruhezonen-fur-wildtiere-ur

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: Gemäss Art. 28 KJSV scheidet der Regierungsrat Schutzzonen aus, um das Wild in seinen empfindlichen Lebensräumen zu schützen. Der Schutz gilt vom 1. Dezember oder dem Vorliegen einer geschlossenen Schneedecke bis 30. April. Im Kanton Uri wurden die letzten Wildruhezonen im Jahr 2008 ausgeschieden.

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.

value: Uri-Kt - Geocat Harvester

fr: Ruhezonen für Wildtiere (UR)

de: Ruhezonen für Wildtiere (UR)

en: Ruhezonen für Wildtiere (UR)

it: Ruhezonen für Wildtiere (UR)


zustand-fremdwasser

ener

fr: Énergie

de: Energie

en: Energy

it: Energia

regi

it: Regioni e città

envi

it: Ambiente

title_for_slug: zustand-fremdwasser

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!

fr: Zustand Fremdwasser

de: Zustand Fremdwasser

en: Zustand Fremdwasser

it: Zustand Fremdwasser


zustand-einzugsgebiete

title_for_slug: zustand-einzugsgebiete

fr: Der Zustandsbericht Einzugsgebiete beinhaltet die Darstellung der Oberfläche des Siedlungsgebiets wie z.B.: Befestigungsarten / Gebiete mit Regenwasser-Versickerung, -Retention / Gebiete mit direkter Einleitung des Regenwassers in ein Gewässer / Gebiete mit direkter Regenwasser-Ableitung ins Umgelände / Charakterisierung der Verschmutzung der befestigten Oberflächen/ Geländeneigung. Dieser Layer ist Teil des GEP (ID 129), wurde aber noch nicht gemäss dem minimalen Geodatenmodell (MGDM) des Bundes umgesetzt!

fr: Zustand Einzugsgebiete

de: Zustand Einzugsgebiete

en: Zustand Einzugsgebiete

it: Zustand Einzugsgebiete


schwarzplan

title_for_slug: schwarzplan

de: av grundkarte hintergrundkarte

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 Schwarzplan

de: Web Map Services WMS

de: WMTS Schwarzplan

de: Web Map Tile Services WMTS

format: WMTS

de: Die Gebäudeflächen aus der Bodenbedeckung der Amtlichen Vermessung schwarz eingefärbt bilden eine einfache Grundkarte "Schwarzplan".

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: Schwarzplan


zustand-gefahrenbereich

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

regi

de: Regionen und Städte

envi

de: Umwelt

title_for_slug: zustand-gefahrenbereich

de: WMS-Dienst des Kanton Uri

de: WFS-Dienst des Kanton Uri

fr: Der Bericht gibt Auskunft über die Gefahrenbereiche im Siedlungsgebiet (Industrie und Gewerbe, Verkehrsanlagen, Umschlagplätze usw.), die bei Schadenfällen die Entwässerungsanlagen belasten und die Gewässer und den Kläranlagenbetrieb gefährden können. 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 Gefahrenbereich

de: Zustand Gefahrenbereich

en: Zustand Gefahrenbereich

it: Zustand Gefahrenbereich


kantonale-planungszonen-ur

regi

fr: Régions et villes

title_for_slug: kantonale-planungszonen-ur

fr: Zurzeit hat der Kanton keine Planungszone nach Art. 56 ff. PBG erlassen.

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: Kantonale Planungszonen (UR)

de: Kantonale Planungszonen (UR)

en: Kantonale Planungszonen (UR)

it: Kantonale Planungszonen (UR)


korporationsstrassen-und-wege

tran

fr: Transports

de: Verkehr

en: Transport

it: Trasporti

title_for_slug: korporationsstrassen-und-wege

fr: WMS-Dienst des Kanton Uri

fr: WFS-Dienst des Kanton Uri

fr: Strassen unter Zuständigkeit der Korporation.

fr: Korporationsstrassen und -wege

de: Korporationsstrassen und -wege

en: Korporationsstrassen und -wege

it: Korporationsstrassen und -wege


grundwasserschutzzonen-ur

envi

fr: Environnement

title_for_slug: grundwasserschutzzonen-ur

de: grundwasserschutzareal geobasisdaten

fr: Grundwasserschutzzonen dienen dazu, Trinkwassergewinnungsanlagen und das Grundwasser unmittelbar vor seiner Nutzung als Trinkwasser vor Beeinträchtigungen zu schützen. Sie sind um die im öffentlichen Interesse liegenden Grundwasserfassungen auszuscheiden, d.h. um alle Fassungen, deren Wasser den Anforderungen der Lebensmittelgesetzgebung entsprechen muss, sowie um Grundwasser-Anreicherungsanlagen. Die Grundwasserschutzzonen sind das wichtigste Instrument des nutzungsorientierten, planerischen Grundwasserschutzes. Grundwasserschutzzonen werden gegliedert in: Zone S1 (Fassungsbereich), Zone S2 (Engere Schutzzone), Zone S3 (Weitere Schutzzone)

fr: Grundwasserschutzzonen (UR)

de: Grundwasserschutzzonen (UR)

en: Grundwasserschutzzonen (UR)

it: Grundwasserschutzzonen (UR)


energietrager-heizung

educ

en: Education, culture and sport

it: Istruzione, cultura e sport

ener

fr: Énergie

de: Energie

title_for_slug: energietrager-heizung

fr: Der Datensatz beinhaltet die Daten aus dem eidgenössischen Gebäude- und Wohnungsregister (GWR). Das GWR wurde anhand von Daten der Volkszählung 2000 zu statistischen Zwecken erstellt.Das GWR hat sich nicht zuletzt dank der am 1. Juli 2017 in Kraft getretenen Totalrevision der Verordnung (VGWR) als massgebendes nationales Informationssystem für Gebäude etabliert.

fr: Energieträger Heizung

de: Energieträger Heizung

en: Energieträger Heizung

it: Energieträger Heizung


ov-erschliessungsqualitat-kanton-uri

regi

en: Regions and cities

title_for_slug: ov-erschliessungsqualitat-kanton-uri

it: WMS-Dienst des Kanton Uri

it: WFS-Dienst des Kanton Uri

fr: Die ÖV-Güteklassen sind ein wichtiger Indikator für die Beurteilung der Erschliessung mit dem ÖV. Das Bundesamt für Raumentwicklung hat basierend auf der Methodik zur Definition der Erschliessungsgüte des Verbands Schweizerischer Strassenfachleute (VSS) eine Methode zur Berechnung der ÖV-Güteklassen (A-D) entwickelt, um die Erschliessungsqualität der verschiedenen Standorte schweizweit vergleichen zu können. Für den Kanton Uri sind die ÖV-Güteklassen des ARE jedoch nicht hinreichend. Der Kanton Uri hat deshalb die Methodik um die ÖV-Güteklassen E-G ergänzt. Um die beiden Erfassungsmethoden zu unterscheiden, wird bei der kantonalen Methode der Begriff "ÖV-Erschliessungsqualität" verwendet. Link zum PDF Technischer Bericht (Methodik): https://webgis.lisag.ch/PDF/OeV_Erschliessungsqualitaet/32007_1_dl_bericht_150618_RK_red.pdf

fr: ÖV-Erschliessungsqualität Kanton Uri

de: ÖV-Erschliessungsqualität Kanton Uri

en: ÖV-Erschliessungsqualität Kanton Uri

it: ÖV-Erschliessungsqualität Kanton Uri


baulanddatenbank

regi

it: Regioni e città

envi

en: Environment

title_for_slug: baulanddatenbank

en: WMS-Dienst des Kanton Uri

en: WFS-Dienst des Kanton Uri

fr: Die Baulanddatenbank beinhaltet die bebaubaren Flächen im ganzen Kanton Uri. Die Informationen basieren auf Auswertungen der kommunalen Nutzungsplanungen gemäss der Methode Raum+. Sämtliche Inhalte der Baulanddatenbank sind unverbindlich und jegliche Haftung wird abgelehnt.

fr: Baulanddatenbank

de: Baulanddatenbank

en: Baulanddatenbank

it: Baulanddatenbank


fledermaus-inventar

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

regi

fr: Régions et villes

envi

it: Ambiente

title_for_slug: fledermaus-inventar

fr: Dieses Inventar dient dazu, einen Überblick über dachstockbewohnende Fledermäuse in ausgewählten öffentlichen Gebäuden des Kantons zu bekommen. Einige Fledermausarten nutzen ungestörte Dachstöcke. Es sind dies vor allem seltene Arten wie das Grosse Mausohr, das Braune und das Alpenlangohr sowie die Kleine Hufeisennase. Letztere ist im Kanton Uri jedoch nicht nachgewiesen. Durch die Angaben können besonders wertvolle Quartiere und Wochenstuben geschützt und der Schutz der Fledermäuse kann bei Sanierungsmassnahmen der Gebäude beachtet werden.

fr: Fledermaus Inventar

de: Fledermaus Inventar

en: Fledermaus Inventar

it: Fledermaus Inventar


konzessionen-erdsonden

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

ener

en: Energy

it: Energia

regi

de: Regionen und Städte

envi

de: Umwelt

title_for_slug: konzessionen-erdsonden

fr: WMS-Dienst des Kanton Uri

fr: WFS-Dienst des Kanton Uri

fr: Hier sind die vom Kanton Uri verliehenen Konzessionen zur Wärmenutzung mittels Erdsonden als Punkte aufgeschaltet. Die Wärme aus Erdsonden wird in den meisten Fällen zu Heizzwecken (mit Wärmepumpen) genutzt. Es gibt aber auch andere Anwendungen wie zum Beispiel zur Kühlung (Klimatisierung, Prozesskühlung). Ergänzende Informationen für die Wärmenutzung aus Boden und Untergrund liefert das Wärmenutzungskonzept Kanton Uri 2017 (auf www.ur.ch) sowie die folgenden Layer auf www.geo.ur.ch: Konzessionen Grundwasserwärmepumpen, Zulässigkeit Erdsonden, Zulässigkeit Grundwasserwärmepumpen, Grundwasserleiter und Isohypsen.

fr: Konzessionen Erdsonden

de: Konzessionen Erdsonden

en: Konzessionen Erdsonden

it: Konzessionen Erdsonden


bff-qualitatsstufe-i

title_for_slug: bff-qualitatsstufe-i

de: WMS-Dienst des Kanton Uri

de: WFS-Dienst des Kanton Uri

fr: In diesem Datensatz sind sämtliche BFF der Qualitätsstufe I abgebildet. Die früheren Beiträge für den ökologischen Ausgleich, die biologische Qualität und die Vernetzung wurden im Rahmen der Agrarpolitik 2014-2017 zu den Biodiversitätsbeiträgen fusioniert. Mit diesen Beiträgen fördert der Bund die Arten- und Lebensraumvielfalt. Der Anteil an Biodiversitätsförderflächen muss mindestens 7 Prozent der landwirtschaftlichen Nutzfläche ohne Spezialkulturen und 3,5 Prozent der mit Spezialkulturen belegten landwirtschaftlichen Nutzfläche betragen. Der Anteil der Hochstamm-Feldobstbäume und der einheimischen, standortgerechten Einzelbäume und Alleen darf maximal die Hälfte der verlangten Biodiversitätsförderfläche betragen.

geoinformation-kanton-uri

de: Lisag AG, Geoinformation Kanton Uri

fr: BFF Qualitätsstufe I

de: BFF Qualitätsstufe I

en: BFF Qualitätsstufe I

it: BFF Qualitätsstufe I


schutzbautenkataster-bevolkerungsschutz

gove

fr: Gouvernement et secteur public

de: Regierung und öffentlicher Sektor

en: Government and public sector

it: Governo e settore pubblico

title_for_slug: schutzbautenkataster-bevolkerungsschutz

fr: abri geodonnees-de-base

de: schutzraum geobasisdaten

en: shelter official-geodata

it: rifugio geodati-di-base

fr: Der Schutzbautenkataster bezeichnet Gebäude in welchen ein vollwertiger Schutzraum oder eine Schutzanlage gemäss den Technischen Weisungen des Bundesamts für Bevölkerungsschutz (BABS) integriert ist.

name: Amt für Bevölkerungsschutz und Militär Kt. Uri

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.


bff-im-sommerungsgebiet

title_for_slug: bff-im-sommerungsgebiet

fr: Die Biodiversitätsförderflächen (BFF) im Sömmerungsgebiet beinhalten die "Artenreichen Grün- und Streueflächen im Sömmerungsgebiet", die Teil des Geobasisdatensatzes Biodiversitätsförderflächen Qualitätsstufe II (ID 153.3) sind. In den Jahren 2013-2016 wurde der Qualitätsanteil sowie der Abzug der unproduktiven Flächenanteile für die einzelnen Teilflächen einer Alp im Feld beurteilt. Die Kontrolle der beurteilten Flächen im Feld erfolgt alle acht Jahre. Laufende Nachführungen der Amtlichen Vermessung (z.B. neue Strassen, Gebäude, Bachläufe usw.) werden jedoch jährlich im November aktualisiert.

fr: BFF im Sömmerungsgebiet

de: BFF im Sömmerungsgebiet

en: BFF im Sömmerungsgebiet

it: BFF im Sömmerungsgebiet


kreisel-ur

tran

fr: Transports

de: Verkehr

en: Transport

it: Trasporti

title_for_slug: kreisel-ur

url: https://geo.ur.ch/?layers=Kreisel UR

fr: Realisierte und geplante Kreisel im Kanton Uri gemäss den Informationen vom Amt für Tiefbau.

fr: Kreisel UR

de: Kreisel UR

en: Kreisel UR

it: Kreisel UR


alpen

agri

fr: Agriculture, pêche, sylviculture et alimentation

regi

fr: Régions et villes

title_for_slug: alpen

fr: Der Datensatz beinhaltet die bewirtschafteten Alpen, wobei die Grenzen der einzelnen Alpen (= Alpperimeter) rot abgebildet und die nicht beweidbaren Teilgebiete gelb schraffiert dargestellt sind. Grundsätzlich basieren die Grenzen und Flächen auf digitalisierten Plan-Einzeichnungen (Massstab 1:5'000) der verschiedenen Allmendaufseher. Bei denjenigen Alpen, wo im Geodatensatz "BFF im Sömmerungsgebiet" beurteilte Flächen vorhanden sind, wurden die Flächen in Feldbegehungen überprüft (sofern die betroffenen Flächen nicht über 2'200 m liegen, bzw. über 2'400 m im Urserental).

fr: Alpen

de: Alpen

en: Alpen

it: Alpen


werkschulkreise

regi

en: Regions and cities

envi

fr: Environnement

title_for_slug: werkschulkreise

fr: WMS-Dienst des Kanton Uri

it: WMS-Dienst des Kanton Uri

fr: WFS-Dienst des Kanton Uri

fr: Einzugsgebiet und Schulhausstandorte der Werkschulen im Kanton Uri.

fr: Werkschulkreise

de: Werkschulkreise

en: Werkschulkreise

it: Werkschulkreise


gewasserraum-ur

envi

en: Environment

title_for_slug: gewasserraum-ur

fr: alignement geodonnees-de-base

de: baulinie geobasisdaten

en: building-line official-geodata

it: linea-di-edificazione geodati-di-base

it: WFS-Dienst des Kanton Uri

fr: Gewässerräume gemäss Art. 36a GSchG werden gestützt auf Art. 12 Abs. 2 KUG in der gemeindlichen Nutzungsplanung ausgeschieden. Aus den dort festgelegten Gewässerraumzonen (Grundnutzung sowie überlagerte Nutzung) wird der Gewässerraum gemäss MGDM ID=190 abgeleitet. Übergangsweise werden auch die Gewässerbaulinien (aus der Nutzungsplanung) zum Gewässerraum hinzugezählt.

fr: Gewässerraum (UR)

de: Gewässerraum (UR)

en: Gewässerraum (UR)

it: Gewässerraum (UR)


agri

de: Landwirtschaft, Fischerei, Forstwirtschaft und Nahrungsmittel

it: Agricoltura, pesca, silvicoltura e prodotti alimentari

regi

de: Regionen und Städte

it: Regioni e città

en: WMS-Dienst des Kanton Uri

en: WFS-Dienst des Kanton Uri

fr: Unter dem Begriff "Waldstandort" ist die Gesamtheit der Umwelteinflüsse an einer bestimmten Stelle im Wald zu verstehen, denen Pflanzen und somit auch die Bäume ausgesetzt sind (Klima, Lage, Exposition, Wasser- und Nährstoffverfügbarkeit, Bodenaufbau etc.). Die Waldstandortskarte dient dem Forstdienst und den Waldeigentümern als Grundlage für die Waldbewirtschaftung: Sie liefert Hinweise für den Waldbau sowie Angaben zur natürlichen Baumartenzusammensetzung und zur Schutzwürdigkeit der Lebensräume. Die Waldstandortkatierung ist nicht für den gesamten Wald des Kantons Uri vorhanden. Die Kartierung der Waldgesellschaften wird aber laufend ergänzt.

fr: Waldstandorte

de: Waldstandorte

en: Waldstandorte

it: Waldstandorte


waldentwicklungsplan

agri

en: Agriculture, fisheries, forestry and food

envi

de: Umwelt

it: Ambiente

title_for_slug: waldentwicklungsplan

de: WMS-Dienst des Kanton Uri

fr: Der Waldentwicklungsplan (WEP) Uri ist als Sachplan Wald das forstliche Planungsinstrument auf kantonaler Ebene. Er dient der Sicherstellung der öffentlichen Interessen am Wald und ist ein Führungsinstrument des kantonalen Forstdienstes. Planungseinheit ist das gesamte Waldareal des Kantons. Er gibt Aufschluss über die an die Wälder gestellten Ansprüche und umschreibt die Entwicklungsabsichten und Bewirtschaftungsgrundsätze für die kommenden Jahre.

fr: Waldentwicklungsplan

de: Waldentwicklungsplan

en: Waldentwicklungsplan

it: Waldentwicklungsplan


gk-prozess-permanente-rutschung

title_for_slug: gk-prozess-permanente-rutschung

fr: carte-des-dangers glissement-de-terrain

de: gefahrenkarte rutschung

en: hazard-map slide

it: carta-dei-pericoli franamento

url: https://geo.ur.ch/?layers=GK Prozess Permanente Rutschung

de: WFS-Dienst des Kanton Uri

fr: Als Rutschung wird eine hangabwärts gerichtete, gleitende Bewegung von Hangteilen aus Fels- und/oder Lockergesteinsmassen an Hängen als Ergebnis eines Scherbruchs an der Grenze der bewegten Masse.

geoinformation-kanton-uri

de: Lisag AG, Geoinformation Kanton Uri

fr: GK Prozess Permanente Rutschung

de: GK Prozess Permanente Rutschung

en: GK Prozess Permanente Rutschung

it: GK Prozess Permanente Rutschung


gk-synoptische-gefahrenkarte

regi

fr: Régions et villes

title_for_slug: gk-synoptische-gefahrenkarte

fr: In der synoptischen Gefahrenkarte werden die Gefahrenstufen aller im Untersuchungsperimeter auftretenden Naturgefahrenprozesse überlagert und jeweils die höchste Stufe abgebildet.

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: GK Synoptische Gefahrenkarte

de: GK Synoptische Gefahrenkarte

en: GK Synoptische Gefahrenkarte

it: GK Synoptische Gefahrenkarte


gk-prozess-einsturz

envi

fr: Environnement

title_for_slug: gk-prozess-einsturz

fr: WMS-Dienst des Kanton Uri

fr: Gefahrengebiete mit Absenkungs- und Einsturzgefahr infolge Dolinen und unterirdischen Hohlräumen fallen unter diesen Prozess.

fr: GK Prozess Einsturz

de: GK Prozess Einsturz

en: GK Prozess Einsturz

it: GK Prozess Einsturz


gk-prozess-sturz-steinschlag

title_for_slug: gk-prozess-sturz-steinschlag

fr: carte-des-dangers chute-de-pierres

de: gefahrenkarte steinschlag

en: hazard-map rockfall

it: carta-dei-pericoli caduta-di-sassi

url: https://geo.ur.ch/?layers=GK Prozess Sturz Steinschlag

fr: WFS-Dienst des Kanton Uri

fr: Als Sturzprozesse werden schnelle Massenbewegungen bezeichnet, bei welchen das bewegte Material den grössten Teil des Weges in der Luft zurücklegt (fallend, springend, teilweise auch rollend).

fr: GK Prozess Sturz Steinschlag

de: GK Prozess Sturz Steinschlag

en: GK Prozess Sturz Steinschlag

it: GK Prozess Sturz Steinschlag


kindergartenschulkreise

title_for_slug: kindergartenschulkreise

fr: Einzugsgebiete und Standorte der Kindergärten im Kanton Uri

fr: Kindergartenschulkreise

de: Kindergartenschulkreise

en: Kindergartenschulkreise

it: Kindergartenschulkreise


agenda-der-victoria-hall-der-stadt-genf

title_for_slug: Agenda der Victoria Hall der Stadt Genf

fr: geneve agenda victoria-hall openagenda

de: openagenda agenda victoria-hall genf

en: geneva agenda openagenda victoria-hall

it: openagenda agenda geneva victoria-hall

fr: Agenda du Victoria Hall de la Ville de Genève

de: Agenda der Victoria Hall der Stadt Genf

en: Agenda of the Victoria Hall of the City of Geneva

it: Agenda della Victoria Hall della Città di Ginevra

fr: Agenda du Victoria Hall de Genève

en: Events calendar of the Victoria Hall in Geneva

de: Terminkalender der Victoria Hall in Genf

it: Calendario degli eventi del Victoria Hall di Ginevra

agenda

display_name: agenda

geneva

display_name: geneva

geneve

display_name: geneve

genf

display_name: genf

openagenda

display_name: openagenda

victoria-hall

display_name: victoria-hall

it: Ordine del giorno della Sala della Vittoria della Città di Ginevra

educ

fr: Éducation, culture et sport

de: Bildung, Kultur und Sport

en: Education, culture and sport

it: Istruzione, cultura e sport

ville-geneve

fr: Ville de Genève

de: Stadt Genf

en: City of Geneva

it: Città di Ginevra

fr: Conformément à sa politique des données, la Ville de Genève s’engage résolument dans une démarche d’ouverture de ses données publiques, faisant de celle-ci la norme partout où c’est pertinent (« open data par défaut »). S’inscrivant dans le cadre de la « Stratégie en matière de libre accès aux données publiques en Suisse », elle met à la disposition du public ses données en privilégiant des formats ouverts (open source) et lisibles de manière automatisée en vue d’une réutilisation. La Ville de Genève privilégie une approche basée sur la transparence et l’efficacité de l’action de l’administration, la participation citoyenne et l’innovation.

en: In compliance with its data policy, the City of Geneva is firmly committed to an open approach to its public data, making this the norm in all relevant areas ("open data by default"). As part of the "Strategy for free access to public data in Switzerland", the City of Geneva provides its data publicly in open formats (open source) which can be read automatically with the purpose of being re-used. The City of Geneva favours an approach based on transparency and efficiency of the administration's actions, citizen participation and innovation.

de: Die Stadt Genf setzt sich in Übereinstimmung mit ihrer Datenpolitik für einen offenen Umgang mit öffentlichen Daten ein, der in allen relevanten Bereichen zur Norm wird ("offene Daten per Default"). Im Rahmen der "Strategie für den freien Zugang zu öffentlichen Daten in der Schweiz" stellt die Stadt Genf ihre Daten öffentlich in offenen Formaten (Open Source) zur Verfügung, die automatisch gelesen werden können, um sie weiterzuverwenden. Die Stadt Genf setzt auf Transparenz und Effizienz der Verwaltung, Bürgerbeteiligung und Innovation.

it: Conformemente alla sua politica sui dati, la Città di Ginevra si impegna fermamente in un percorso di apertura dei suoi dati pubblici, rendendola la norma ovunque sia pertinente ("open data by default"). Nell'ambito della "Strategia per il libero accesso ai dati pubblici in Svizzera", la Città di Ginevra mette a disposizione del pubblico i propri dati privilegiando formati aperti (open source) che possono essere letti automaticamente per consentire il riutilizzo. La Città di Ginevra promuove un approccio basato sulla trasparenza e sull'efficienza delle azioni amministrative, sulla partecipazione dei cittadini e sull'innovazione.