en: satellite hazard-mapping avalanche snow natural-hazard

en: SPOT6 Avalanche Outlines 16 January 2019

media_type: SHP

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

satellite

display_name: satellite

snow

display_name: snow

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: SPOT6 Avalanche outlines 16 January 2019


maintainer: Frank Techel

en: long-term-trends avalanche-accident avalanche-statistics avalanche-fatalities avalanche-accident-statistics

state: active

type: dataset

en: Data avalanche fatalities 1969/1970 - 2014/2015

media_type: text/plain

en: The data comprises avalanche fatality data from the Alpine regions in the seven countries Austria, France, Germany, Italy, Liechtenstein, Slovenia, and Switzerland.

format: TXT

mimetype: text/plain

en:
During the last 45 years, about 100 people lost their lives in avalanches in the European Alps each year. Avalanche fatalities in settlements and on transportation corridors have considerably decreased since the 1970s. In contrast, the number of avalanche fatalities during recreational activities away from avalanche-secured terrain doubled between the 1960s and 1980s and has remained relatively stable since, despite a continuing strong increase in winter backcountry recreational activities.

Data complementing Figure 2 in:
_"Avalanche fatalities in the European Alps: long-term trends and statistics"_, by Techel, F., Jarry, F., Kronthaler, G., Mitterer, S., Nairz, P., Pavšek, M., Valt, M., and Darms, G.

Data description:
please refer to section 2 (Data and Methods) in the mentioned publication

avalanche-accident

display_name: avalanche-accident

avalanche-accident-statistics

display_name: avalanche-accident-statistics

avalanche-fatalities

display_name: avalanche-fatalities

avalanche-statistics

display_name: avalanche-statistics

long-term-trends

display_name: long-term-trends

education

fr: Education, science

de: Bildung, Wissenschaft

en: Education and science

it: Formazione e scienza

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

it: EnviDat: il portale dei dati ambientali

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Avalanche fatalities in the European Alps (1969/1970 - 2014/2015)


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

en: Scan from 27 December 2018, 1 mm resolution

media_type: ascii x, y, z

format: SERVICE

en: Scan from 27 December 2018, 10 cm resolution

en: Scan from 21 December 2018, 10 cm resolution

en: Scan from 11 January 2019, 10 cm resolution

en: Scan from 2 January 2019, 10 cm resolution

en: Scan from 21 December 2018, 1 mm resolution

en: Scan from 11 January 2019, 1 mm resolution

en: Scan from 2 January 2019, 1 mm resolution

en: Scan from 4 January 2019, 1 mm resolution

en: Scan from 4 January 2019, 10 cm resolution

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

Notes:

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

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

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

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

antarctica

display_name: antarctica

depth-of-snowfall

display_name: depth-of-snowfall

gridded-data

display_name: gridded-data

terrestiral-laserscanning

display_name: terrestiral-laserscanning

terrestrial-laser-scans

display_name: terrestrial-laser-scans

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


snow-depth-mapping

maintainer: Mauro Marty

title_for_slug: snow-depth-mapping

en: avalanche dsm aerial-imagery snow-hydrology snow-depth snow-water-equivalent image-matching

en: Snow depth map 20160420

media_type: image/tiff

format: TIFF

mimetype: image/tiff

en: Snow depth map 20100416

en: Snow depth map generated from ADS 80 imagery (CIR). GSD of imagery = 0.25 m

en: Snow depth map 20130415

media_type: application/pdf

format: PDF

mimetype: application/pdf

en: Snow depth map 20160309

en: Snow depth map 20120320

en: Snow depth map 20150415

en: Snow depth map 20140417

en: Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.18 m

en: Snow depth map 20160126

en:
Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.16 m
Because of very diffuse light conditions at time of image acquisition the quality of the resulting snow depth values are in some regions quite poor.

en:
The available datasets are snow depth maps with a spatial resolution of 2m generated from image matching of ADS 80/100 data. Image acquisition took place at peak of winter (time when the thickest snowpack is expected). The snow depth maps are the difference of a summer DSM from the winter DSM of the corresponding date . The summer DSM used is a product of image matching of ADS 80 data from summer 2013. In the available products buildings, vegetation and outliers were masked (set to NoData).
For the elimination of buildings the TLM layer (swisstopo) was used, because this layer might not represent exactly the
state of infrastructure at time of image acquisition, it is possible that mainly in dense settlement some buildings were not successfully masked. For the relevant area above treeline the masking of buildings showed good results. Vegetation got masked for a height above ground > 1m and was detected in a combination of summer and winter data sets. As Outliers were considered unrealistic snow depths caused by a failure of the image matching algorithm. Snow depths > 15m and smaller than < -15m were classified as outliers. Negative snow depth were kept, because of an uncertainty in image orientation accuracy. It is expected that in regions with negative snow depth also positive snow depth are underestimated by the same amount, which means that an estimation of snow volume should be carried out summing up the absolute values of snow depth (also the negative ones). For volume estimation in small regions the user has to take into account, that orientation accuracy of the images is roughly around 1-2 GSD (30cm), which propagates directly to the snow depth product. Areas which are not covered by snow got assigned a value of 0 as snow depth.

The work is published in:

Bühler, Y.; Marty, M.; Egli, L.; Veitinger, J.; Jonas, T.; Thee, P.; Ginzler, C., (2015). Snow depth mapping in high-alpine catchments using digital photogrammetry. Cryosphere, 9 (1), 229-243. doi: 10.5194/tc-9-229-2015

aerial-imagery

display_name: aerial-imagery

dsm

display_name: dsm

image-matching

display_name: image-matching

snow-depth

display_name: snow-depth

snow-hydrology

display_name: snow-hydrology

snow-water-equivalent

display_name: snow-water-equivalent

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: EnviDat Support

en: decomposition yellowstone fungi wolf detritus biogeochemical-cycling carrion soil-biodiversity bacteria carcass

en:
Site	ID for different carcasses across the Yellowstone Landscape
Location	Sampling location, either under carcass or control plot
Species	Indicating whether carcass was from bison or elk
Gender	Gener of carcass; F = female, M = male, ? = unknown
Lat	Latitude
Long	Longitude
DSK	Days since kill; in days
Soil.N	Soil N concentration (%)
Soil.C	Soil C concentration (%)
Veg.C	Vegetation C concentration (%); dominant grasses sampled
Veg.N	Vegetation N concentration (%); dominant grasses sampled
Veg.CtoN	Vegetation C to N ratio; dominant grasses sampled
Soil.resp	Soil respiration (g m-2 h-1)
soil.temp	Soil temperature (°C)
Soil.moist	Soil moisture (volumentric; %)
CRR	Cotton rotting rate
bacteria.richness	Bacteria richness
bacteria.diversity	Bacteria diverstiy
fungi.richness	Fungi richness
fungi.diversity	Fungi diversity
bacteria.abundance	Bacteria abundance
fungi.abundance	Fungi abundance

format: XLS

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

Paper Citation:

Risch, A.C., Frossard, A., Schütz, M., Frey, B., Morris, A.W., Bump, J.K. (accepted) Effects of elk and bison carcasses on soil microbial communities and ecosystem functions in Yellowstone, USA. (accepted). Functional Ecology
doi: ...

Methods
Study area and study sites
This study was conducted in YNP’s Northern Range (NR), located in north-western Wyoming and south-western Montana, USA (~44.9163° N, 110.4169° W). The NR expands over ~1000 km2 and features long cold winters and short dry summers. Grasslands and shrublands dominate the NR that is the home of large migratory herds of bison (winter counts 2017: ~3919 individuals; Geremia, Wallen, & White, 2017) and elk (~5349 individuals) as well as their main predators, approximately five packs of wolves with a total of 33 individuals (Smith et al., 2017). As part of a long-term research program within YNP, wolf predation has been studied since their reintroduction in 1995.
For our study, we received ground-truthed coordinates of bison and elk carcasses from winter 2016/17 (November 2016 through April 2017) from the YNP Wolf Project. Between June 20 and July 1, 2017, we visited 24 carcasses in total. At five sites, we could not sample as the carcasses were no longer found. In total we located remains (hairmats, rumen content, bones, teeth) of 19 adult male and female carcasses (7 bison, 12 elk; Supplementary Table 1). Live body weights of adult bison and elk are approximately 730 kg (male bison), 450 kg (female bison), 330 kg (male elk), and 235 kg (female elk, Meagher, 1973; Quimby & Johnson, 1951).
The kills and subsequent consumption happened between 34 and 173 days prior to our sampling (hereafter “days since kill”, DSK), for which we accounted in our statistics. Note that wolves and other scavengers consumed the soft tissue of the carcasses quickly, hence, there is close to no soft tissue left for decomposition as compared to an intact body left on the soil surface. The 19 carcass sites covered the extent of YNP’s NR, with both bison and elk carcasses showing similar distributions; elevation ranged from 1703 to 2884 m a.s.l. (Supplementary Fig 1 & Supplementary Table 1). The carcasses were all located in grassland or sage-brush shrubland, with or without sparsely scattered trees, and both bison and elk carcasses showed the same distribution of DSK. At each study site, we selected a reference plot (hereafter “control”) that was of comparable size, slope aspect and vegetation to the carcass location (hereafter “carcass”). The control was at least 10 m away (Danell, Berteaux, & Brathen, 2002; Melis et al., 2007) from the carcass itself to ensure the absence of potential direct and indirect carcass effects (paired design; (Bump, Webster, et al., 2009; Bump, Peterson, et al., 2009).

Ecosystem functions and soil properties
We randomly collected 50 g of mineral soil from three locations on both control and carcass plots to a depth of 5 cm with sterile techniques and gently mixed the material to obtain a composite sample. Half the soil sample was immediately bagged in plastic bags (whirl packs), stored in a cooler with ice packs (~5 ºC), sieved (2-mm) and frozen within 4-6 hours of collection to assess soil microbial communities. For this purpose, we extracted total genomic DNA from 0.5 g soil using the PowerSoil DNA Isolation Kit (Qiagen, Hilden, Germany). DNA concentrations were measured using PicoGreen (Molecular Probes, Eugene, OR, USA). PCR amplifications of partial bacterial small-subunit ribosomal RNA genes (region V3–V4 of 16S rRNA) and fungal ribosomal internal transcribed spacers (region ITS2) were performed as described previously (Frey et al., 2016). Each sample consisting of 40 ng DNA was amplified in triplicate and pooled before purification with Agencourt AMPure XP beads (Beckman Colter, Berea, CA, USA) and quantified with the Qubit 2.0 fluorometric system (Life Technologies, Paisley, UK). Amplicons were sent to the Genome Quebec Innovation Center (Montreal, Canada) for barcoding using the Fluidigm Access Array technology and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, CA, USA).
Quality control of bacterial and fungal reads was performed using a customized pipeline (Supplementary Table 2; Frey et al., 2016). Paired-ends reads were matched with USEARCH (Edgar & Flyvbjerg, 2015), substitution errors were corrected using Bayeshammer (Nikolenko, Korobeynikov, & Alekseyev, 2013) and PCR primers were trimmed (allowing for 1 mismatch, read length >300 bp for 16S and >200 bp for ITS primers) using Cutadapt (M. Martin, 2011). Sequences were dereplicated and singleton reads removed prior to clustering into operational taxonomic units (OTUs) at 97% identity using USEARCH (Edgar, 2013). The remaining centroid sequences were tested for the presence of ribosomal signatures using Metaxa2 (Bengtsson-Palme et al., 2015) or ITSx (Bengtsson-Palme et al., 2013). Taxonomic assignments of the OTUs were obtained using Bayesian classifier (Wang, Garrity, Tiedje, & Cole, 2007) with a minimum bootstrap support of 60% implemented in mothur (Schloss et al., 2009) by querying the bacterial and fungal reads against the SILVA Release 128 (Quast et al., 2013) and UNITE 8.0 (Abarenkov et al., 2010) reference databases for 16S and ITS OTUs, respectively.
Abundances of the bacterial 16S rRNA gene and fungal ITS amplicon were determined by quantitative real-time PCR (qPCR) on an ABI7500 Fast Real-Time PCR system (Applied Biosystems, Foster City, CA, USA) as described previously (Frossard et al., 2018). The same primers (without barcodes) and cycling conditions as for the sequencing approach were used for the 16S and ITS qPCR. Three standard curves per target region were obtained using tenfold serial dilutions of plasmids generated from cloned targets (Frey, Niklaus, Kremer, Lüscher, & Zimmermann, 2011). Data were converted to represent mean copy number of targets per gram of soil (dry weight).
The other half of the soil sample was bagged in paper, dried to constant weight at 60°C, passed through a 2 mm sieve and analyzed for total C and N concentration with a CE Instruments NC 2100 soil analyzer (CE Elantech Inc., Lakewood NJ, USA). We also collected 20 mature and undamaged leaves of the dominant grass species growing on control and carcass sites, but taxa were not recorded. The plant material was dried at 60°C, finely ground till homogenized and also analyzed to obtain total C and N concentrations. Soil temperature (10 cm depth) was measured with a waterproof digital thermometer (Barnstead International, Dubuque IA, USA) at three locations each at the control and carcass site. Soil moisture (0 – 10 cm depth) was measured with time domain reflectometry (Field-Scout TDR-100; Spectrum Technologies, Plainfield IL, USA) at five randomly chosen points on control and carcass sites. We measured soil respiration at five randomly chosen points at both control and carcass sites with a PP-Systems SRC-1 soil respiration chamber (closed circuit) attached to a PP-Systems EGM-4 infrared gas analyzer (PP-Systems, Amesbury, MA, USA). For each measurement the soil chamber (15 cm high; 10 cm diameter) was tightly placed on the soil surface, after clipping plants to avoid measuring plant respiration or photosynthesis. Measurements were conducted over 120 s.
In addition, we assessed the decomposition rates of standardized OM using the cotton strip assay (Latter & Howson, 1977; Latter & Walton, 1988). Cotton cloth tensile strength loss (CTSL) is a measure of decomposition, and an index to express the combined effect of soil microclimatic, physical, chemical and biological properties on decomposition while accounting for OM quality (Latter & Walton, 1988; Risch, Jurgensen, & Frank, 2007; Withington & Sanford Jr., 2007). We placed five 20 cm wide x 13 cm long sheets of 100% unbleached cotton cloth (American Type SM 1/18’’, Warp: 34/1, Weft: 20/1, Weave plain, 29.5 picks/cm warp, 22 picks/cm weft, 237 g/m2; Daniel Jenny & Co., Switzerland;) at each carcass and control site vertically into the soil by making slits with a flat spade to a depth of 12 cm. We inserted each cloth with the spade, and then pushed the slit closed to assure tight contact with the soil. The cloths were retrieved after 18 to 27 days. After retrieval, the cloths were air-dried, remaining soil gently removed by hand, and 1.5 cm wide strips were cut at the 3.5-5.0 cm (top) and the 9-10.5 cm (bottom) soil depth. The strips were equilibrated at 50 % relative humidity and 20°C for 48 hours (climate chamber) prior to strength testing (Scanpro Awetron TH-1 tensile strength tester; AB Lorentzen and Wettre, Kista, Sweden). Cotton rotting rate (CRR) = (CTScontrol - CTSfinal/CTSfinal)1/3 * (365/t), where CTScontrol is the cotton tensile strength of a control cloth and CTSfinal the cotton tensile strength of the incubated sample, t is the incubation period in days. Control cloths were inserted into the ground and immediately retrieved to account for tensile strength loss associated with cloth insertion. We averaged the CRR of top and bottom strips for further analyses as no difference was found between the two. All sampling and cloth insertion took place between June 20 and July 1, 2017, cloths were retrieved between July 17 and 20, 2017. Soil respiration, average CRR, vegetation N concentration and vegetation C:N ratio are defined as ecosystem functions, soil C and N concentration, soil temperature and moisture as soil abiotic properties, and bacterial and fungal richness (number of taxa), diversity (Shannon) and abundance as soil biotic properties.

Statistical analyses
Univariate analyses for ecosystem functions, soil biotic and abiotic properties
We tested whether individual ecosystem functions, soil biotic and abiotic properties differed between carcass and control (“Location”), bison and elk (“Species”) and days since kill (“DSK”). For this purpose, we used linear mixed effect models (LMM, “nlme” package v 3.1 – 131.1 in R v 3.4.4; Pinheiro, Bates, DebRoy, & Sarkar, 2018; R Core Team, 2019) with Location, Species, Location x Species and DSK as fixed effects. Site was included as random effect to account for the paired design. We developed a separate model for all dependent variables. All but bacterial richness, fungal richness, fungal diversity and vegetation N concentration were natural-log transformed to meet model assumptions. For each LMM, we calculated contrasts to assess the specific comparisons we were interested in with the “lsmeans” package v 2.27-62 (Lenth & Love, 2018): 1) carcass vs control, 2) carcass bison vs control bison, and 3) carcass elk vs control elk. We also tested whether we had differences between bison and elk carcasses or the sites where bison and elk were killed and included contrasts 4) carcass bison vs carcass elk and 5) control bison vs control elk.
We calculated the log response ratio (LRR = ln[carcass/control]) to obtain carcass effects for all variables for both species separately. LRR < 0 indicates higher value at control compared to carcass, LRR > 0 indicates higher values at carcass compared control. We used LRRs for visualization and to assess spatial patterns in carcass effects across YNP. For this purpose we calculated the Moran’s I statistic for each ecosystem function, soil biotic and abiotic property based on a latitude-longitude matrix with the “moran.test” function in the “spdep” package version 1.1-3 (Bivand et al., 2019).
Multivariate analyses
Rare OTUs, defined as OTUs with a low abundance of reads, were retained in multivariate methods because they only marginally influence these analyses (Gobet, Quince, & Ramette, 2010). Bray–Curtis dissimilarity matrices were generated based on square-root-transformed matrices. We used Principal Coordinate Analyses (PCoA) to assess how soil bacterial and fungal communities differed between control and carcass of bison and elk (“vegan” package v 2.5-4, Oksanen et al., 2019). We then extracted PCoA axes scores 1 and 2 and used LMM (“nlme” package) with Location, Species, Location x Species and DSK as fixed effects. Site was, again, included as random effect. We again calculated the contrasts as described above using the “lsmeans” package. We also assessed how ecosystem functions, and soil abiotic and biotic properties were related to the soil bacteria and fungi community structure associated with bison and elk control and carcasses using the “envfit” function in the “vegan” package (Oksanen et al., 2019).
Indicator species analyses were performed using the multipatt function implemented in the “indicspecies” package version 1.7.6 with 100000 permutations (De Caceres & Jansen, 2016). This step allowed to identify OTUs that led to changes in multivariate patterns between control and carcass of both bison and elk separately (De Cáceres, Legendre, & Moretti, 2010). The multipatt function uses a point biserial correlation coefficient statistical test. Indicator OTUs were defined as bacterial and fungal OTUs with more than 50 sequences, i.e., removing rare taxa and taxa with low abundances containing little indicator information (Rime et al., 2015) and that were significantly correlated with Location (p < 0.05, correlation coefficient > 0.3). A heatmap of these OTUs were generated with the vegan and ggplot2 packages. The indicator analyses were performed in R version 3.3.3 (R Core Team, 2017).

References
Abarenkov, K., Henrik Nilsson, R., Larsson, K.-H., Alexander, I. J., Eberhardt, U., Erland, S., … Kõljalg, U. (2010). The UNITE database for molecular identification of fungi – recent updates and future perspectives. New Phytologist, 186(2), 281–285. doi:10.1111/j.1469-8137.2009.03160.x
Bengtsson-Palme, J., Hartmann, M., Eriksson, K. M., Pal, C., Thorell, K., Larsson, D. G. J., & Nilsson, R. H. (2015). metaxa2: improved identification and taxonomic classification of small and large subunit rRNA in metagenomic data. Molecular Ecology Resources, 15(6), 1403–1414. doi:10.1111/1755-0998.12399
Bengtsson-Palme, J., Ryberg, M., Hartmann, M., Branco, S., Wang, Z., Godhe, A., … Nilsson, R. H. (2013). Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods in Ecology and Evolution, 4(10), 914–919. doi:10.1111/2041-210X.12073
Bivand, R., Altman, M., Anselin, L., Assuncao, R., Berke, O., Blanchet, G., … Yu, D. (2019). spdep: Spatial dependence, weighthing schemes, statistics. R package version 1.1-3.
Bump, J. K., Peterson, R. O., & Vucetich, J. A. (2009). Wolves modulate soil nutrient heterogeneity and foliar nitrogen by configuring the distribution of ungulate carcasses. Ecology, 90(11), 3159–3167.
Bump, J. K., Webster, C. R., Vucetich, J. A., Peterson, R. O., Shields, J. M., & Powers, M. D. (2009). Ungulate carcasses perforate ecological filters and create biogeochemical hotspots in forest herbaceous layers allowing trees a competitive advantage. Ecosystems, 12(6), 996–1007. doi:10.1007/s10021-009-9274-0
Danell, K., Berteaux, D., & Brathen, K. A. (2002). Effect of muskox carcasses on nitrogen concentration in tundra vegetation. Arctic, 55(4), 389392.
De Caceres, M., & Jansen, F. (2016). indicspecies: relationship between species and groups of species. R package version 1.7.6.
De Cáceres, M., Legendre, P., & Moretti, M. (2010). Improving indicator species analysis by combining groups of sites. Oikos, 119(10), 1674–1684. doi:10.1111/j.1600-0706.2010.18334.x
Edgar, R. C. (2013). UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods, 10, 996.
Edgar, R. C., & Flyvbjerg, H. (2015). Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics, 31(21), 3476–3482. doi:10.1093/bioinformatics/btv401
Frey, B., Niklaus, P. A., Kremer, J., Lüscher, P., & Zimmermann, S. (2011). Heavy-machinery traffic impacts methane emissions as well as methanogen abundance and community structure in oxic forest soils. Applied and Environmental Microbiology, 77(17), 6060–6068. doi:10.1128/AEM.05206-11
Frey, B., Rime, T., Phillips, M., Stierli, B., Hajdas, I., Widmer, F., & Hartmann, M. (2016). Microbial diversity in European alpine permafrost and active layers. FEMS Microbial Ecology, 92(3), fiw018.
Frossard, A., Donhauser, J., Mestrot, A., Gygax, S., Bååth, E., & Frey, B. (2018). Long- and short-term effects of mercury pollution on the soil microbiome. Soil Biology and Biochemistry, 120, 191–199. doi:https://doi.org/10.1016/j.soilbio.2018.01.028
Geremia, C., Wallen, R., & White, P. J. (2017). Status report of the Yellowstone bison population, September 2017. Yellowstone National Park, Mammoth, WY, USA: National Park Service, Yellowstone Center for Resources.
Gobet, A., Quince, C., & Ramette, A. (2010). Multivariate cutoff level analysis (MultiCoLA) of large community data sets. Nucleic Acids Research, 38(15), e155–e155. doi:10.1093/nar/gkq545
Latter, P., & Howson, G. (1977). The use of cotton strips to indicate cellulose decomposition in the field. Pedobiologia, (17), 145–155.
Latter, P., & Walton, D. (1988). The cotton strip assay for cellulose decomposition studies in soil: history of the assay and development. In Cotton strip assay: an index for decomposition in soils (pp. 7–9). ITE Symposium, Institute of Terrestrial Ecology, Natural Environment Research Council, UK.
Lenth, R., & Love, J. (2018). lsmeans: least-squares means. R package version 2.27-62.
Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.Journal, 17(1), 10–12.
Meagher, M. M. (1973). The bison of Yellowstone National Park. NPS Scientific Monograph (Vol. 1). National Park Service, Yellowstone Center for Resources.
Melis, C., Selva, N., Teurlings, I., Skarpe, C., Linnell, J. D. C., & Andersen, R. (2007). Soil and vegetation nutrient response to bison carcasses in Białowieża Primeval Forest, Poland. Ecological Research, 22(5), 807–813. doi:10.1007/s11284-006-0321-4
Nikolenko, S. I., Korobeynikov, A. I., & Alekseyev, M. A. (2013). BayesHammer: Bayesian clustering for error correction in single-cell sequencing. BMC Genomics, 14(1), S7. doi:10.1186/1471-2164-14-S1-S7
Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., … Wagner, H. H. (2019). vegan: community ecology package. R package version 2.5-4.
Pinheiro, J., Bates, D., DebRoy, S., & Sarkar, D. (2018). nlme: Linear and nonlinear mixed effect models. R package version 3.1-131.1.
Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., … Glöckner, F. O. (2013). The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research, 41(Database issue), D590–D596. doi:10.1093/nar/gks1219
Quimby, D. C., & Johnson, D. E. (1951). Weights and measurements of Rocky Mountain elk. Journal of Wildlife Management, 15, 57–62.
R Core Team. (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Zurich, Switzerland.
R Core Team. (2019). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
Rime, T., Hartmann, M., Brunner, I., Widmer, F., Zeyer, J., & Frey, B. (2015). Vertical distribution of the soil microbiota along a successional gradient in a glacier forefield. Molecular Ecology, 24(5), 1091–1108. doi:10.1111/mec.13051
Risch, A. C., Jurgensen, M. F., & Frank, D. A. (2007). Effects of grazing and soil micro-climate on decomposition rates in a spatio-temporally heterogeneous grassland. Plant and Soil, 298(1–2), 191–201. doi:10.1007/s11104-007-9354-x
Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., … Weber, C. F. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology, 75(23), 7537–7541. doi:10.1128/AEM.01541-09
Smith, D., Stahler, D., Cassidy, K., Stahler, E., Metz, M., Cassidy, B., … Cato, E. (2018). Yellowstone National Park wolf project annual report 2017. Yellowstone National Park, Mammoth, WY, USA: National Park Service, Yellowstone Center of Resources.
Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. (2007). Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology, 73(16), 5261–5267. doi:10.1128/AEM.00062-07
Withington, C., & Sanford Jr., R. (2007). Decomposition rates of buried substances increase with altitude in a forest-alpine tundra ecotone. Soil Biology and Biochemistry, (39), 68–75.

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

bacteria

display_name: bacteria

biogeochemical-cycling

display_name: biogeochemical-cycling

carcass

display_name: carcass

carrion

display_name: carrion

decomposition

display_name: decomposition

detritus

display_name: detritus

fungi

display_name: fungi

soil-biodiversity

display_name: soil-biodiversity

wolf

display_name: wolf

yellowstone

display_name: yellowstone

education

en: Education and science

en: Elk and bison carcasses in Yellowstone, USA


maintainer: Athanasios Nenes

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

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

media_type: text/csv

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

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

format: CSV

mimetype: text/csv

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

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

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

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

cloud-droplets

display_name: cloud-droplets

cloud-modeling

display_name: cloud-modeling

raclets

display_name: raclets

sigma-w

display_name: sigma-w

supersaturation

display_name: supersaturation

wolfgangpass

display_name: wolfgangpass

en: Predicted cloud droplet numbers Davos Wolfgang


en: precipitation relative-humidity swiss-forest-lab wind-speed meteo par long-term meteorology climate-change temperature

en: Forest station data (SCB)

media_type: CSV

en: Data file.

en: Site photo of the field station (SCF)

media_type: JPEG

en: Site photo of the field station from above (viewing downwards the slope).

en: Site aerial photo (Schänis)

en: Site aerial photo from both Schänis stations. Source: Google. 2016. Site aerial photo from both Schänis stations. Retrieved November 11, 2016, from https://www.google.ch/maps

en: Site photo of the forest station (SCB)

en: Site photo of the forest station from below (viewing upwards the slope).

en: Further details on the data repository

media_type: PDF

en: Meta data documentation.

en: Field station data (SCF)

en: Data plot of the forest station (SCB)

en: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

en: Data plot of the field station (SCF)

en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Schänis in Switzerland where one station is located within a natural mixed forest stand (SCB) with European beech (_Fagus sylvatica_; 130-150 yrs), European silver fir (_Abies alba_; 130-150 yrs) and European ash (_Fraxinus excelsior_; 130-150 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, SCF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Schänis is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed

en: Schänis, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1998 onwards


maintainer: Adam Winstral

en: snow-depth snow-simulation snow jules-investigation-model jim

en: File/data descriptor

en: Station forcing data

media_type: application/zip

en: All forcing data need to run JIM at HS and SWE stations. These are data downscaled directly from COSMO NWP data (WY14, WY15) and further enhanced by additional station observations of snow depth (HS) and air temperature (Ta) in WY17. Further information can be found in README file. Note that in WY14 and WY15, shortwave radiation was not split into direct and diffuse (not necessary for JIM) and that the default method in JIM for splitting precipitation phase was used. In WY 17, direct and diffuse were combined as input to JIM and JIM was forced with pre-processed liquid and solid precipitation phases.

format: ZIP

mimetype: application/zip

en: DEM

en: 1-km resolution DEM used for downscaling gridded NWP data to stations and for distributing BDE evaluations at HS-stations to SWE-stations

en: HS and SWE observations

en: Automated (HS) and manual (SWE) data used to evaluate model simulations

en: These data were used to drive and evaluate Jules Investigation Model (JIM) snow simulations. The data provided are the forcing data used for the "deterministic" runs as described in Winstral et al., 2019. The bias-detecting ensemble (Winstral et al., 2019) used observed snow depths (HS) to detect biases in these deterministic simulations related to precipitation and energy inputs to JIM. Simulations that included the BDE evaluations substantially improved JIM simulations.

jim

display_name: jim

jules-investigation-model

display_name: jules-investigation-model

snow

display_name: snow

snow-depth

display_name: snow-depth

snow-simulation

display_name: snow-simulation

education

en: Education and science

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: Meteorological data used to develop and validate the bias-detecting ensemble (BDE)


en: rutschblock-test avalanche-observations extended-column-test switzerland norway avalanche-danger

en: ECT data (Norway, Switzerland)

en: avalanche occurrence data (Norway, Sitzerland) - avalanche sizes per day and warning region

en: avalanche occurrence data (Norway, Sitzerland) - largest avalanche size per day and warning region

en: Rutschblock data (Switzerland)

en:
Observational data used to quantitatively describe the key elements describing avalanche danger: snowpack stability, the frequency distribution of snowpack stability, and avalanche size.
The data set consists of
- Rutschblock test results (Switzerland)
- Extended Column Test results (Switzerland, Norway)
- Avalanche occurrence data (Switzerland, Norway).
The data were extracted from the respective operational databases of the national avalanche warning services in Switzerland (WSL Institute for Snow and Avalanche Research SLF Davos, Switzerland) and Norway (The Norwegian Water Resources and Energy Directorate NVE).
For further information regarding the data, please refer to the publication or contact the author.

avalanche-danger

display_name: avalanche-danger

avalanche-observations

display_name: avalanche-observations

extended-column-test

display_name: extended-column-test

norway

display_name: norway

rutschblock-test

display_name: rutschblock-test

switzerland

display_name: switzerland

en: Stability tests, avalanche observations, Switzerland, Norway


en:
This dataset contains the statistics on the number of avalanche fatalities per **calendar year** in Switzerland. The data collection commences with the beginning of the year 1937. After the completion of a hydrological year, which is the standard way avalanche fatalities are summarized in Switzerland and ends on the 30th of September, the new data is appended to the existing dataset.
If you require annual statistics per hydrological year, please download the data from here: [https://www.envidat.ch/dataset/avalanche-fatalities-switzerland-1936]

The following information is contained (by column and column title):
 - year
 - number of fatalities in the backcountry (=tour)
 - number of fatalities in terrain close to ski areas (=offpiste, away from open and secured ski runs)
 - number of fatalities on transportation corridors including ski runs, roads, railway lines (=transportation.corridors)
 - number of fatalities in or around buildings or in settlements (= buildings)
 - sum (of all four categories)

The definitions for these four categories, as described in the guidelines to the avalanche accident database are:

tour:
activities include back-country ski, snowboard or snow-shoe touring

offpiste:
access from ski area, generally from the top of a skilift with short hiking distances

transportation.corridors (Techel et al., 2016):
people travelling or recreating on open or temporarily closed transportation corridors (e.g. a road user or a skier on a ski run) and people working on open or closed transportation corridors (e.g. maintenance crews on roads, professional rescue teams)

buildings (Techel et al., 2016):
people inside or just outside buildings, and workers on high alpine building sites

avalanche-accident-statistics

display_name: avalanche-accident-statistics

avalanche-fatalities

display_name: avalanche-fatalities

fatalities-in-or-around-buildings

display_name: fatalities-in-or-around-buildings

fatalities-in-settlements

display_name: fatalities-in-settlements

fatalities-in-the-backcountry

display_name: fatalities-in-the-backcountry

fatalities-on-transportation-corridors

display_name: fatalities-on-transportation-corridors

en: Number of avalanche fatalities per calendar year in Switzerland since 1937


en: liverwort bryophyte-life-strategy functional-taxonomic-groups madeira-island moss climate-change lichen climate-change-impact endemic elevational-gradient bryophyte biodiversity disturbance photobiont functional-taxonomic-group lichen-growthform endemic-species lichen-growth-form

en: MadCrypto – Bryophyte and macrolichen diversity in laurel forests of Madeira

en:
This dataset includes species lists of bryophytes and macrolichens (presence/absence) sampled on the forest floor and on trees in disturbed and undisturbed plots along elevation gradients in the laurel forests of Madeira island. It also contains species specific information (bryophytes: red list status, endemic status, taxonomic group, life strategy; macrolichens: photobiont type, growth form) as well as plot information (Plot_ID, sampling date, coordinates, elevation a.s.l. (m), disturbance type, sampled host tree species).

The dataset was used for the paper
Boch S, Martins A, Ruas S, Fontinha S, Carvalho P, Reis F, Bergamini A, Sim-Sim M (2019) Bryophyte and macrolichen diversity show contrasting elevation relationships and are negatively affected by disturbances in laurel forests of Madeira island. Journal of Vegetation Science.

en:
This dataset includes species lists of bryophytes and macrolichens (presence/absence) sampled on the forest floor and on trees in disturbed and undisturbed plots along elevation gradients in the laurel forests of Madeira island. It also contains species specific information (bryophytes: red list status, endemic status, taxonomic group, life strategy; macrolichens: photobiont type, growth form) as well as plot information (Plot_ID, sampling date, coordinates, elevation a.s.l. (m), disturbance type, sampled host tree species).

The dataset was used for the paper
Boch S, Martins A, Ruas S, Fontinha S, Carvalho P, Reis F, Bergamini A, Sim-Sim M (2019) Bryophyte and macrolichen diversity show contrasting elevation relationships and are negatively affected by disturbances in laurel forests of Madeira island. Journal of Vegetation Science 30: 1122–1133.

The excel file contains 5 sheets:
1) Plot information
2) Bryophyte data with species specific information, separated per substrate
3) Macrolichen data with species specific information, separated per substrate
4) Bryophyte data with species specific information (plot level data). Species lists of the two substrates were merged; if one substrate in a plot hosted a species identified only to genus level (genus spec.) but the second investigated substrate hosted an identified species of the same genus, we removed the genus spec. entry in the particular plot. The species list of sheet 2 and 4 might therefore differ slightly.
5) Macrolichen data with species specific information (plot level data). Species lists of the two substrates were merged; if one substrate in a plot hosted a species identified only to genus level (genus spec.) but the second investigated substrate hosted an identified species of the same genus, we removed the genus spec. entry in the particular plot. The species list of sheet 3 and 5 might therefore differ slightly.

biodiversity

display_name: biodiversity

bryophyte

display_name: bryophyte

bryophyte-life-strategy

display_name: bryophyte-life-strategy

climate-change

display_name: climate-change

climate-change-impact

display_name: climate-change-impact

disturbance

display_name: disturbance

elevational-gradient

display_name: elevational-gradient

endemic

display_name: endemic

endemic-species

display_name: endemic-species

functional-taxonomic-group

display_name: functional-taxonomic-group

functional-taxonomic-groups

display_name: functional-taxonomic-groups

lichen

display_name: lichen

lichen-growth-form

display_name: lichen-growth-form

lichen-growthform

display_name: lichen-growthform

liverwort

display_name: liverwort

madeira-island

display_name: madeira-island

moss

display_name: moss

photobiont

display_name: photobiont


maintainer: Yohann Chauvier

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

media_type: ZIP

en: Non-copyright data, scripts, diversity outputs

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

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

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

background-data

display_name: background-data

cluster

display_name: cluster

covariate-correction

display_name: covariate-correction

environmental-stratification

display_name: environmental-stratification

independent-dataset

display_name: independent-dataset

plant-species

display_name: plant-species

point-process-model

display_name: point-process-model

random-stratified-sampling

display_name: random-stratified-sampling

survey-effort

display_name: survey-effort

target-group

display_name: target-group

education

en: Education and science

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


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

en: Field observations of snow instabilities

en: Read-me file

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

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, in review, 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

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.


biogas-aus-hofdunger-in-der-schweiz

title_for_slug: biogas-aus-hofdunger-in-der-schweiz

en: switzerland manure bioenergy review biogas

en: Biogas aus Hofdünger in der Schweiz

en: Ziel dieses Whitepapers ist es, Entscheidungsträgern, Verwaltungen und Stakeholdern die aktuellsten Forschungsergebnisse zur Verfügung zu stellen, um die optimale Nutzung von Bioenergie aus Hofdünger in der Schweizer Energiewende zu fördern. Zu diesem Zweck werden die Ergebnisse des Schweizer Kompetenzzentrums für Bioenergieforschung - SCCER BIOSWEET - zusammengefasst und in einem breiteren Kontext dargestellt. Wenn nichts anderes erwähnt wird, beziehen sich die Ergebnisse auf die Schweiz und im Falle der Ressourcen auf die heimischen Biomassepotenziale.

bioenergy

display_name: bioenergy

biogas

display_name: biogas

manure

display_name: manure

review

display_name: review

switzerland

display_name: switzerland


maintainer: Axel Volkwein

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

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.

en: Jumps and impacts

media_type: ascii, tab separated, also as excel table

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: Block trajectories recorded from local positioning system

media_type: ascii, tab separated, also as Excel table

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: 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:
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


en: snow-density antarctica microstructure snow-density-profile snowmicropen

en: Manual snow pits and other snow density measurements

media_type: .txt

en: List of SnowMicroPen measurements

en: List of all SnowMicroPen measurements, including date and location, and processing status. This list has been generated by the python code (see other resources in this dataset).

media_type: ipynb

en: Jupyter notebook for processing the SnowMicroPen files.

en: SnowMicroPen pts files and processed txt files

en: Zip archive containing the acquired *.pts files (binary files recorded by the SnowMicroPen), and the converted *.txt files using the SMP.ipynb jupyter notebook (see the python code in the other resources in this dataset)

en: Documentation SnowMicroPen measurements

media_type: .zip

en: This *.zip archive contains all available documentation of the SnowMicroPen measurements, including the mapping of the SMP measurements in the terrestrial laser scan field at PEA (see documentation for 20161220) and at the Hammarryggen Ice Rise (see documentation for 20190104 and 20190111).

en: Python code exported from the Jupyter notebook for processing the SnowMicroPen files

media_type: py

en: SnowMicroPen (SMP) measurements and manual snowpits from Dronning Maud Land, East Antarctica. Measurements were taken in the vicinity of the Belgium Princess Elisabeth Station (PEA), in a transect towards the coast, and on the Lokeryggen and Hammarryggen Ice Rises near the coast. Measurements were taken during 3 individual campaigns in the 2016-2017, 2018-2019 and 2019-2020 field seasons.

antarctica

display_name: antarctica

microstructure

display_name: microstructure

snow-density

display_name: snow-density

snow-density-profile

display_name: snow-density-profile

snowmicropen

display_name: snowmicropen

education

en: Education and science

en: SnowMicroPen measurements and manual snowpits from Dronning Maud Land, East Antarctica


en: online-questionnaire-survey perception-of-ecosystem-services visual-attractiveness adults-and-teenagers nfi-photos

en: Questionnaire French

media_type: .pdf

en: SPSS-Dataset

en: Codebook to SPSS-file

en: Dataset in csv-format

en: Questionnaire Italian

en: Questionnaire German

en: This questionnaire survey was conducted as an online survey and aimed at investigating the relationship between physical forest characteristics, visual attractiveness of forest and the perception of ecological and cultural ecosystem services in urban forests. Each participant was shown 6 photos out of a pool of 50 photos taken from the Swiss National Forest Inventory (NFI) database. Physical forest characteristics were derived from the photos. The study was conducted as part of the "WaMos meets LFI" (WML) project.

adults-and-teenagers

display_name: adults-and-teenagers

nfi-photos

display_name: nfi-photos

online-questionnaire-survey

display_name: online-questionnaire-survey

perception-of-ecosystem-services

display_name: perception-of-ecosystem-services

visual-attractiveness

display_name: visual-attractiveness

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: Relationship between physical forest characteristics, visual attractiveness and perception of ecosystem services in urban forests


maintainer: Lisa Hülsmann

en: empirical-mortality-models tree-growth tree-mortality forest-reserves inventory-data

en: Calibration data

en:
A set of three consecutive inventories was used to generate records for the calibration of mortality models based on trees that were alive in the first and second inventory and either dead or alive in the third inventory.

 Meta data
Reserve: Name of the forest reserve

Plot: Permanent plot number

 Tree data
Species: Latin species name (or species group, cf. _Quercus_ spp.)

y: Status of the tree with 0 = alive and 1 = dead

dt: Length of the mortality period (years)

DBH: Diameter at breast height (mm) of the second inventory

relBAI: Annual relative basal area increment (-) calculated based on the first and the second DBH measurement as the compound annual growth rate of the trees basal area

 Stand data
_All stand variables were calculated for the second inventory._

mDBH: Mean diameter at breast height (mm)

iqrDBH: Interquartile range of diameter at breast height (mm)

BA: Basal area per hectare (m2 ha-1)

N: Number of trees per hectare (ha-1)

 Climate data
_Both climate variables were calculated for the period between the second and the third inventory._

P: Mean annual precipitation sum (mm)

mT: Mean annual air temperature (°C)

en:
The dataset comprises > 90 000 records from inventories in 54 strict forest reserves in [Switzerland](https://www.wsl.ch/de/wald/biodiversitaet-naturschutz-urwald/naturwaldreservate.html) and [Lower Saxony / Germany](http://naturwaelder.de/) along a considerable environmental gradient. It was used to develop parsimonious, species-specific mortality models for 18 European tree species based on tree size and growth as well as additional covariates on stand structure and climate.

 Inventory data
Measurements had been conducted repeatedly on up to 14 permanent plots per reserve for up to 60 years with re-measurement intervals of 4 - 27 years. The permanent plots vary in size between 0.03 and 3.47 ha. The inventories provide diameter measurements at breast height (DBH) and information on the species and status (alive or dead) of trees with DBH ≥ 4 cm for Switzerland and ≥ 7 cm for Germany.

 Data selection
We excluded three permanent plots where at least 80 % of the trees died during an interval of 10 years, and mortality could be clearly assigned to a disturbance agent. Mortality in the remaining stands was rather low, with a mean annual mortality rate of 1.5 % and strong variation between plots from 0 to 6.5 % (assessed for trees of all species with DBH ≥ 7 cm).
We only used data from permanent plots with at least 20 trees per species to obtain reliable plot-level mortality rates even for species with low mortality rates (about 5 % during 10 years), and selected tree species occurring on at least 10 plots to cover sufficient ecological gradients. This led to a dataset of 197 permanent plots and 18 tree or shrub species: _Abies alba_ Mill., _Acer campestre_ L., _Acer pseudoplatanus_ L., _Alnus incana_ Moench., _Betula pendula_ Roth, _Carpinus betulus_ L., _Cornus mas_ L., _Corylus avellana_ L., _Fagus sylvatica_ L., _Fraxinus excelsior_ L., _Picea abies_ (L.) Karst, _Pinus mugo_ Turra, _Pinus sylvestris_ L., _Quercus pubescens_ Willd., _Quercus_ spp. (_Q. petraea_ Liebl. and _Q. robur_ L.; not properly differentiated in the Swiss inventories), _Sorbus aria_ Crantz, _Tilia cordata_ Mill. and _Ulmus glabra_ Huds..

 Predictors of tree mortality
We considered tree size and growth as key indicators for mortality risk. Radial stem growth between the first and second inventory and DBH at the second inventory were used to predict tree status (alive or dead) at the third inventory. To this end, the annual relative basal area increment (relBAI) was calculated as the compound annual growth rate of the trees basal area. Additional covariates on stand structure and climate comprise mean annual precipitation sum (P), mean annual air temperature (mT), the mean and the interquartile range of DBH (mDBH, iqrDBH), basal area (BA) and the number of trees (N) per hectare.

 Further information
For further information, refer to Hülsmann _et al_. (in press) How to kill a tree – Empirical mortality models for eighteen species and their performance in a dynamic forest model. _Ecological Applications_.

empirical-mortality-models

display_name: empirical-mortality-models

forest-reserves

display_name: forest-reserves

inventory-data

display_name: inventory-data

tree-growth

display_name: tree-growth

tree-mortality

display_name: tree-mortality

en: Calibration data for empirical mortality models of 18 European tree species


label: geocat.ch permalink

name: Geoinformationszentrum (GIS) Stadt Luzern

stadt-luzern

de: Stadt Luzern

de:
Die Stadt Luzern veröffentlicht hier schrittweise OGD-Datensätze der städtischen Dienstabteilungen. Mehr über «Open Government Data» bei der Stadt Luzern ist über den Link <www.ogd.stadtluzern.ch> zu erfahren.

Die Geometadaten zu den einzelnen OGD-Daten sind unter "Zusätzliche Informationen" als Link aufgeführt.
Zu beachten sind weitere Bestimmungen zum Datenschutz und Nutzungsbestimmungen gemäss OGD-Internetauftritt der Stadt Luzern.

political_level: commune

value: Stadt Luzern - Geocat Harvester


forstliche-standortkarte

agriculture

fr: Agriculture, sylviculture

de: Land- und Forstwirtschaft

en: Agriculture, forestry

it: Agricoltura, selvicoltura

geography

fr: Géographie

de: Geographie

en: Geography

it: Geografia

territory

fr: Territoire et environnement

de: Raum und Umwelt

en: Territory and environment

it: Territorio e ambiente

title_for_slug: forstliche-standortkarte

fr: geodonnees faune flore bois sylviculture

de: geodaten fauna flora wald forstwirtschaft

en: geodata fauna flora-biology wood forestry

it: geodati fauna flora-biologia bosco forestazione

label: Geobasisdatenkatalog

label: ThurGIS Shop

label: Open Government Data

spatial: Kanton Thurgau (TG)

de: Forstliche Standortkarte

format: WMS

format: WFS

de: Unterteilung der Wälder in Standorteinheiten (= potenzielle natürliche Vegetation) aufgrund von Bodeneigenschaften und typischen Pflanzen. Attribute sind die Befahrbarkeit des Bodens (5 Klassen) und der empfohlene Laubholzanteil in % (10 Klassen). Die Standortkarte wurde in den Jahren 1994-2002 kartiert und auf Basis des damaligen "Übersichtsplanes" digitalisiert, weshalb die Lagegenauigkeit auf der heutigen Amtlichen Vermessung nicht sehr gut ist.

kanton-thurgau

fr: Canton de Thurgovie

de: Kanton Thurgau

en: Canton of Thurgau

it: Cantone di Turgovia

de: Der Kanton Thurgau veröffentlicht hier schrittweise OGD-Datensätze der kantonalen Verwaltung.

political_level: canton

value: TG-Kt - Geocat Harvester


maintainer: EnviDat

en: metadata-convention csv research-data-management nead meteorological-data-format

en: NEAD syntax v0.1 (EBNF)

media_type: text/plain

en: MeteoIO (future reference implementation)

media_type: text/html

format: HTML

mimetype: text/html

en: PyNEAD (future reference implementation)

en: NEAD on GitHub

media_type: HTML

en:
Acknowledgement: The NEAD format includes NetCDF metadata and is proudly inspired by both SMET and NetCDF formats. NEAD is designed as a long-term data preservation and exchange format.

The NEAD specifications were presented at the "WMO Data Conference 2020 - Earth System Data Exchange in the 21st Century" (Virtual Conference).

-----------------------

Summary:
The Non-Binary Environmental Data Archive (NEAD) format is being developed as a generic and intuitive format that combines the self-documenting features of NetCDF with human readable and writeable features of CSV. It is designed for exchange and preservation of time series data in environmental data repositories.

License: The NEAD specifications are released to the public domain under a Creative Commons CC0 "No Rights Reserved" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions.

csv

display_name: csv

metadata-convention

display_name: metadata-convention

meteorological-data-format

display_name: meteorological-data-format

nead

display_name: nead

research-data-management

display_name: research-data-management

education

en: Education and science

en: Non-Binary Environmental Archive Data (NEAD) format


friedensrichteramter

title_for_slug: friedensrichteramter

de: geodaten offentliche-verwaltungseinrichtung interessenkonflikt verwaltungsgrenze verwaltung

label: Permalink opendata.swiss

de: Friedensrichterämter

de: Open Government Data

de: Dieser Datensatz zeigt die Standorte der Friedensrichterämter und dessen Zuständigkeitsgebiete.


forstreviere

title_for_slug: forstreviere

de: Forstreviere

de: Parzellenscharfe, eindeutige Zuweisung des Wald-Eigentums zu einem Forstrevier, gemäss Waldgesetzgebung TG. Deckt sich häufig mit den Gemeindegrenzen.


zonenplane

title_for_slug: zonenplane

de: baulinie eigentumsbeschrankungoreb grundeigentum nutzungsplanung raumplanung zonenplan

spatial: Kanton Luzern

protocol: https

de: Datenshop

de: Produktansicht Geodatenshop

de: Zonenpläne regeln die zulässigen Nutzungen im Gebiet einer Gemeinde.

fr: Géoinformation du canton de Lucerne

de: Geoinformation Kanton Luzern

en: Geoinformation Canton of Lucerne

it: Geoinformazione del Cantone di Lucerna

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

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

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

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

value: LU-Kt - Geocat Harvester

de: Zonenpläne


stutzmauern

construction

fr: Construction, logement

de: Bau- und Wohnungswesen

en: Construction and housing

it: Costruzione e abitazione

geography

de: Geographie

title_for_slug: stutzmauern

de: mauerwerk mauerwerk

spatial: Stadt Luzern

media_type: JSON

de: Stützmauern als json-Datei

format: JSON

media_type: DXF

de: Stützmauern als dxf-Datei

format: DXF

media_type: GPKG

de: Stützmauern als Geopackage (.gpkg)

format: GPKG

de: Der Datensatz umfasst die Lage von Stützmauern inklusive verschiedener Attribute wie Baujahr, Konstruktion usw.

stadt-luzern

de: Stadt Luzern

de:
Die Stadt Luzern veröffentlicht hier schrittweise OGD-Datensätze der städtischen Dienstabteilungen. Mehr über «Open Government Data» bei der Stadt Luzern ist über den Link <www.ogd.stadtluzern.ch> zu erfahren.

Die Geometadaten zu den einzelnen OGD-Daten sind unter "Zusätzliche Informationen" als Link aufgeführt.
Zu beachten sind weitere Bestimmungen zum Datenschutz und Nutzungsbestimmungen gemäss OGD-Internetauftritt der Stadt Luzern.

de: Stützmauern


kantonales-schutzinventar-ksi

title_for_slug: kantonales-schutzinventar-ksi

fr: denkmalinventar baudenkmal

de: denkmalinventar baudenkmal

en: denkmalinventar baudenkmal

it: denkmalinventar baudenkmal

spatial: Kanton Schwyz (SZ)

fr: Kantonales Inventar geschützter Bauten

de: Kantonales Inventar geschützter Bauten

en: Kantonales Inventar geschützter Bauten

it: Kantonales Inventar geschützter Bauten

fr: Kantonales Inventar geschützter und schützenswerter Bauten

amt-geoinformation-sz

de: Amt für Geoinformation Kanton Schwyz

de: Das Amt für Geoinformation verwaltet und veröffentlicht die Geodaten des Kantons Schwyz als OGC-Geodienste und als Datei-Download. Alle aktuell verfügbaren Geodaten, welche dem OGD unterliegen, sind hier aufgeführt.

value: SZ-Kt Geoinformation - Geocat Harvester

fr: Kantonales Schutzinventar KSI

de: Kantonales Schutzinventar KSI

en: Kantonales Schutzinventar KSI

it: Kantonales Schutzinventar KSI


elemente-mit-landschaftsqualitat

agriculture

de: Land- und Forstwirtschaft

en: Agriculture, forestry

geography

en: Geography

title_for_slug: elemente-mit-landschaftsqualitat

fr: geodonnees gestion-agricole exploitation-agricole exploitation-agricole agriculture politique-de-preservation-des-paysages

de: geodaten agrarmanagement landwirtschaftliche-bewirtschaftung landwirtschaftliche-nutzung landwirtschaft landschaftspflege

en: geodata agricultural-management agricultural-exploitation agricultural-exploitation agriculture landscape-conservation-policy

it: geodati gestione-agricola gestione-agricola sfruttamento-agricolo agricoltura politica-di-conservazione-del-paesaggio

de: Elemente mit Landschaftsqualität

de: Das Modell beschreibt die Ebenen Landschaftsräume, Projektperimeter und Vertragsobjekte. Die Erfassung von Vertragsobjekten ist optional. Im Kanton Thurgau werden keine Vertragsobjekte LQ im GIS erfasst.

kanton-thurgau

de: Kanton Thurgau

de: Der Kanton Thurgau veröffentlicht hier schrittweise OGD-Datensätze der kantonalen Verwaltung.


geography

de: Geographie

territory

de: Raum und Umwelt

en: Territory and environment

fr: geodonnees loi-sur-la-peche peche peche

de: geodaten fischereirecht fischerei fischerei

en: geodata fishing-law fishery fishery

it: geodati legislazione-sulla-pesca pesca pesca

de: Fischereiverbote

de: Einschränkungen der Fischerei am Bodensee-Obersee sowie Boden-Untersee. Enthält Fischereiregelungen an Häfen, Uferanlagen und Schifflandestegen.

kanton-thurgau

en: Canton of Thurgau

de: Fischereirechtliche Einteilung der Gewässer


erdwarme

culture

fr: Culture, médias, société de l'information, sport

de: Kultur, Medien, Informationsgesellschaft, Sport

en: Culture, media, information society, sport

it: Cultura, media, società dell'informazione, sport

energy

fr: Energie

de: Energie

en: Energy

it: Energia

title_for_slug: erdwarme

fr: geodonnees energie energie-geothermique

de: geodaten energie erdwarme

en: geodata energy geothermal-energy

it: geodati energia energia-geotermica

de: Erdwärme

de: Das Kantonsgebiet ist in drei Zonen aufgeteilt, welche sich im Wesentlichen nach der Grundwasserkarte und dem planerischen Gewässerschutz richten. In der Zone 1 sind Erdwärmesonden grundsätzlich mit gewässerschutzrechtlichen Standardauflagen zulässig. Im Nahbereich von Quellfassungen in der Zone 1 können auch spezifische Auflagen, z.B. Quellenmonitoring, verfügt werden. Die Zone 2 betrifft vor allem Randgebiete von Grundwasservorkommen, geringmächtige und/oder schwach durchlässige Grundwasserleiter, Gebiete mit artesisch gespanntem Grundwasser sowie der Nahbereich von Grundwasser- und Quellfassungen mit Grundwasserschutzzonen. Erdwärmesonden können in der Zone 2 in der Regel mit zusätzlichen Auflagen zum Schutz des Grundwassers genehmigt werden (z.B. Abdichtungsmassnahmen, Grundwasserüberwachung, geologische Begleitung, Spülbohrung). Die Zone 3 umfasst im Wesentlichen für die Trinkwasserversorgung nutzbare Grundwasservorkommen, Grundwasserschutzzonen und unterirdische Bauten, wobei Erdwärmesonden grundsätzlich nicht zulässig sind. Bereits ausgeführte Erdwärmesondenbohrungen können in der Kartenebene "Erdwärmesondenbohrungen" eingesehen werden.


fischschongebiete

title_for_slug: fischschongebiete

fr: geodonnees loi-sur-la-peche reserve-de-peche peche peche

de: geodaten fischereirecht fischereischutzgebiet fischerei fischerei

en: geodata fishing-law fishing-preserve fishery fishery

it: geodati legislazione-sulla-pesca riserva-di-pesca pesca pesca

de: Fischschongebiete im Boden-Obersee sowie Boden-Untersee Kanton Thurgau. Enthält räumliche Fischereiverbote gemäss Verordnung über die Fischerei im Boden-Obersee, gemäss Anordnungen nach §38 Abs 2 der Unterseefischereiordnung oder aufgrund bestehender privater Fischenzen.

de: Fischschongebiete


forstliche-bestandeskarte

agriculture

fr: Agriculture, sylviculture

de: Land- und Forstwirtschaft

it: Agricoltura, selvicoltura

geography

fr: Géographie

title_for_slug: forstliche-bestandeskarte

fr: geodonnees faune flore bois sylviculture ressource-forestiere

de: geodaten fauna flora wald forstwirtschaft waldbestand

en: geodata fauna flora-biology wood forestry forest-resource

it: geodati fauna flora-biologia bosco forestazione risorse-forestali

de: Fortliche Bestandeskarte

de: Darstellung der Waldstrukturen; Unterteilung in Entwicklungsstufen (Jungwuchs bis Altholz); Codierung der Altersklassen, der Baumartenmischung und der Hauptbaumarten (häufigste, dominierende Baumarten pro Bestand).

de: Forstliche Bestandeskarte


deutschkurse

geography

de: Geographie

it: Geografia

population

fr: Population

de: Bevölkerung

en: Population

it: Popolazione

title_for_slug: deutschkurse

fr: geodonnees discours formation-continue migration-humaine culture-aspects-sociaux societe-humaine population

de: geodaten sprache weiterbildung bevolkerungsmigration kultur gesellschaft bevolkerung

en: geodata speech continuing-education human-migration culture-society society human-population

it: geodati discorso formazione-continua migrazione-umana cultura societa popolazione-umana

label:
Open Government
Data

de: Deutschkurse

de: Angebote von Deutschkursen im Kanton Thurgau

kanton-thurgau

fr: Canton de Thurgovie

de: Kanton Thurgau

de: Der Kanton Thurgau veröffentlicht hier schrittweise OGD-Datensätze der kantonalen Verwaltung.


aed-standorte

health

fr: Santé

de: Gesundheit

en: Health

it: Sanità

title_for_slug: aed-standorte

fr: sante defibrillateur

de: gesundheit defibrillator

spatial: Kanton Bern (reduziert)

fr: Lien vers le téléchargement de données (Zip)

de: Link zum Daten-Download (Zip)

fr: Emplacement et accessibilité des DEA dans le canton de Berne, disponibles 24 heures sur 24 et disponibles temporairement, lesquels sont enregistrés sur le site [firstresponder.be](https://www.firstresponder.be/français).

de: Standorte und Erreichbarkeit der dauerhaft und zeitlich begrenzt zugänglichen Defibrilatoren im Kanton Bern, welche bei [firstresponder.be](https://www.firstresponder.be) registriert sind.

fr: Office de l'information géographique du canton de Berne

de: Amt für Geoinformation des Kantons Bern

fr: L'Office de la géoinformation est le centre de compétence pour l'information spatiale dans le canton de Berne. Les géodonnées cantonales peuvent être utilisées gratuitement, sous réserve des conditions d'utilisation. Le Géoportail du canton de Berne est le point d'entrée central pour les cartes interactives, les géodonnées et les géoservices de l'administration cantonale.

de: Das Amt für Geoinformation ist das Kompetenzzentrum für räumliche Informationen im Kanton Bern. Die kantonalen Geodaten sind unter Einhaltung der Nutzungsbedingungen kostenlos nutzbar. Das Geoportal des Kantons Bern ist der zentrale Einstiegspunkt für interaktive Karten, Geodaten und Geodienste der kantonalen Verwaltung.

value: Bern-Kt - Geocat Harvester

fr: Sites DEA

de: AED-Standorte


forstkreise

agriculture

de: Land- und Forstwirtschaft

territory

fr: Territoire et environnement

de: Raum und Umwelt

it: Territorio e ambiente

title_for_slug: forstkreise

fr: geodonnees faune flore bois sylviculture

de: geodaten fauna flora wald forstwirtschaft

en: geodata fauna flora-biology wood forestry

it: geodati fauna flora-biologia bosco forestazione

de: Forstkreise

de: Verwaltungseinheit, dem Forstamt unterstellt; besteht aus mehreren Forstrevieren; wird geleitet durch einen Kreisforstingenieur.

kanton-thurgau

it: Cantone di Turgovia


national-economy

fr: Economie nationale

de: Volkswirtschaft

en: National economy

it: Economia

geography

de: Geographie

work

fr: Travail, rémunération

de: Arbeit, Erwerb

en: Work and income

it: Lavoro e reddito

fr: geodonnees peche peche autorite-de-controle

de: geodaten fischerei fischerei aufsichtsbehorde

en: geodata fishery fishery controlling-authority

it: geodati pesca pesca autorita-di-controllo

de: Fischereiaufsichtskreise

de: Aufsichtskreise bzw. Zuständigkeitsgebiete der Fischereiaufseher Kanton Thurgau inkl. Enklave Häggenschwil (St.Gallen)


geography

fr: Géographie

en: Geography

fr: relief carte pente modele-terrestre-numerique exposition

de: relief karte hang digitales-gelandemodell exposition

spatial: Kanton Bern

fr: Lien vers le téléchargement de données (zonées)

de: Link zum Daten-Download (zoniert)

fr: L'exposition, l'inclinaison des pentes et le relief d'une résolution de 1 m ont été déduits sur la base des données [MNT LiDAR 50 cm (géoproduit LDTM50CM)](https://www.agi.dij.be.ch/fr/start/geoportal/geodaten/detail.html?type=geoproduct&code=LDTM50CM). Les données couvrent la surface du canton de Berne au-dessous de 2000 m. Les données supplémentaires sur les trajectoires de vol et sur les densités de points sont disponibles dans le géoproduit [LBEZUS: Modèle altimétrique numér. LiDAR données supplémentaires](https://www.agi.dij.be.ch/fr/start/geoportal/geodaten/detail.html?type=geoproduct&code=LBEZUS).

de: Auf Basis der [DTM-LiDAR-Daten 50cm (Geoprodukt LDTM50CM)](https://www.agi.dij.be.ch/de/start/geoportal/geodaten/detail.html?type=geoproduct&code=LDTM50CM) wurden die Exposition, die Hangneigung und das Relief in der Auflösung 1m abgeleitet. Die Daten decken die Fläche des Kantons Bern unterhalb von 2000m ab. Die Zusatzdaten mit Angaben zu den Flugwegen und zu den Punktdichten sind enthalten im Geoprodukt [LBEZUS: Digitales Höhenmodell LiDAR Zusatzdaten](https://www.agi.dij.be.ch/de/start/geoportal/geodaten/detail.html?type=geoproduct&code=LBEZUS).

name: Amt für Wald und Naturgefahren - Fachdienste und Ressourcen

fr: MNT LiDAR déduc. de l'exposition, pente, relief 1m

de: DTM LiDAR Ableitung Exposition, Neigung, Relief 1m


fr: reseau-de-mesure eau-de-surface eau-substance ressource-en-eau eau-propre-a-la-consommation extraction-deau eaux-geographie eaux-interieures

de: messstellennetz oberflachengewasser wasser wasserressourcen gebrauchswasser wasserentnahme gewasser binnengewasser

fr: Une concession cantonale est nécessaire pour exploiter une installation fixe, tandis qu'une autorisation communale suffit pour des prélèvements temporaires, effectués par le biais d'une installation mobile. La carte indique les cours d'eau dans lesquels il est possible de prélever de l'eau. Elle représente les classes suivantes: - type 1: cours d'eau sans limite de prélèvement, - type 2: cours d'eau dans lesquels le prélèvement est autorisé si le débit effectif est supérieur au débit de dotation (resp. le niveau du lac est supérieur du niveau de prélèvement), - type 3: cours d'eau dans lesquels la commune peut autoriser le prélèvement d'eau à condition que le débit de dotation, déterminé de cas en cas par l'OED, soit respecté. - type 4: Fermeture temporaire dune section de cours deau pour les prélèvements. Pour tous les autres cours d'eau, les communes ne sont pas habilitées à octroyer des autorisations de pompage.

de: Jede Wasserentnahme aus Oberflächengewässern benötigt eine Konzession (Kanton) oder bei zeitlich beschränkten und mobilen Anlagen eine Bewilligung (Gemeinden). Die Karte zeigt an, aus welchen Bächen, Flüssen und Seen grundsätzlich Wasser entnommen werden kann. Dargestellt sind: - Typ 1: Gewässer aus denen die Entnahme ohne Einschränkung möglich ist. - Typ 2: Gewässer aus denen die Entnahme möglich ist, sofern die Abflussmenge über der Dotierwassermenge bzw. der Seepegel über der Entnahmegrenze liegt. - Typ 3: Gewässer aus denen die Entnahme möglich ist, sofern die Abflussmenge über der Dotierwassermenge liegt, die aber keine Dotierwassermarken aufweisen. Fallweise vom Amt für Wasser und Abfall (AWA) festzulegen. - Typ 4: Temporäre Sperrung eines Gewässerabschnitts für die Entnahme. Bei allen übrigen Gewässern dürfen die Gemeinden keine Bewilligungen für Wasserentnahmen erteilen.

de: Amt für Geoinformation des Kantons Bern

de: Das Amt für Geoinformation ist das Kompetenzzentrum für räumliche Informationen im Kanton Bern. Die kantonalen Geodaten sind unter Einhaltung der Nutzungsbedingungen kostenlos nutzbar. Das Geoportal des Kantons Bern ist der zentrale Einstiegspunkt für interaktive Karten, Geodaten und Geodienste der kantonalen Verwaltung.

fr: Prélèvements d’eau dans les eaux de surface


religionslandkarte

culture

fr: Culture, médias, société de l'information, sport

de: Kultur, Medien, Informationsgesellschaft, Sport

population

fr: Population

de: Bevölkerung

title_for_slug: religionslandkarte

fr: anglicane alevisme eglise synagogue lieu-de-culte reformee-evangelique temple religion vajrayana tradition-religieuse eglise-nationale catholique-romaine sikhisme christianisme lutherienne bouddhisme islam hindouisme theravada mahayana chiite bahaisme eglise-de-la-migration sunnite diversite-des-religions mosquee eglise-libre edifices-religieux orthodoxe judaisme catholique-chretienne

de: anglikanisch alevitentum kirche synagoge kultstatte evangelisch-reformiert tempel religion vajrayana religiose-tradition landeskirche romisch-katholisch sikh christentum lutherisch buddhismus islam hinduismus theravada mahayana schiitisch bahai migrationskirche sunnitisch religionsvielfalt moschee freikirche religiose-gebaude orthodox judentum christkatholisch

spatial: Kanton Bern (erweitert)

fr: Lien vers le téléchargement de données (Zip)

de: Link zum Daten-Download (Zip)

fr: Plusieurs communautés religieuses coexistent dans le canton de Berne. La carte numérique des religions renseigne sur leurs lieux de rassemblement, les coordonnées utiles et d’autres aspects encore. Elle présente les communautés religieuses qui organisent des rencontres régulières pour des célébrations et des rituels propres à leur tradition ainsi que pour des échanges personnels. Les communautés qui ne disposent pas de lieu fixe, les groupements mettant l’accent sur une activité d’utilité publique et d’autres groupes ayant diverses convictions à connotation religieuse ne figurent pas sur cette carte.

de: Im Kanton Bern sind verschiedene Religionsgemeinschaften beheimatet. Die digitale Religionslandkarte beinhaltet Informationen zu den Versammlungsorten, Kontaktdaten und weiteren Merkmalen der Religionsgemeinschaften im Kanton Bern. Aufgeführt werden Religionsgemeinschaften, die sich regelmässig zu Feiern und Ritualen ihrer Tradition und zu persönlichem Austausch treffen. Auf der Karte nicht berücksichtigt sind Gemeinschaften ohne festen Standort, Gruppierungen mit gemeinnützigem Fokus und weitere Gruppen verschiedener quasireligiöser Weltanschauungen.

name: Beauftragter für kirchliche und religiöse Angelegenheiten

fr: Carte des religions

de: Religionslandkarte


de: energiespeicherung energieversorgung erdgas energiepolitik erdwarme gasversorgung grundwassertrager elektrizitatsversorgung energie kataster grundwasserentnahme hochspannungsleitung energietrager grundwasser grundwasserschutz

fr: Service d'image

de: Darstellungsdienst

fr: Le service représente les données de base du canton de Berne selon la géocatégorie Q conforme à eCH-0166. Il comprend le cadastre des zones de desserte, les conduites de gaz, les plans directeurs communaux de l'énergie, les sondes géothermiques ainsi que l'utilisation des eaux souterraines.

de: Der Dienst stellt Geobasisdatensätze des Kantons Bern der Geokategorie Q entsprechend eCH-0166 dar. Er enthält die Erdwärmesonden, die Gasleitungen, die Grundwassernutzung, den Kataster der Netzgebiete sowie die kommunalen Richtpläne Energie sowie .

fr: Office de l'information géographique du canton de Berne

fr: L'Office de la géoinformation est le centre de compétence pour l'information spatiale dans le canton de Berne. Les géodonnées cantonales peuvent être utilisées gratuitement, sous réserve des conditions d'utilisation. Le Géoportail du canton de Berne est le point d'entrée central pour les cartes interactives, les géodonnées et les géoservices de l'administration cantonale.

fr: Approvisionnement, élimination et communication dans le canton de Berne

de: Versorgung, Entsorgung und Kommunikation im Kanton Bern


waldinformation

agriculture

fr: Agriculture, sylviculture

de: Land- und Forstwirtschaft

en: Agriculture, forestry

geography

fr: Géographie

de: Geographie

territory

fr: Territoire et environnement

de: Raum und Umwelt

title_for_slug: waldinformation

fr: Lien vers le téléchargement de données (zonées)

de: Link zum Daten-Download (zoniert)

fr: L'ensemble du territoire du canton de Berne, jusqu'à une altitude de 2000 m, a été saisi au moyen de télédétection par laser à haute résolution (LiDAR) et les résultats mis en valeur. De ces mesures ont découlé, entre autres, les présentes couches d'information, qui décrivent graphiquement la nature des forêts du canton de Berne.

de: Der gesamte Kanton Bern wurde bis auf eine Höhe von 2000 m ü. M. mittels hochaufgelöstem Laserscanning (LiDAR) erfasst und ausgewertet. Dabei entstanden unter anderem die vorliegenden Grundlagenebenen, welche die Beschaffenheit der Wälder des Kanton Bern grafisch darstellen.

fr: Information sur la fôret

de: Waldinformation


fr: geophysique trou-perce reseau-de-mesure eau-de-surface climat bioclimatologie vent temperature-du-sol eaux-geographie temperature-de-surface-du-sol energie-eolienne pergelisol climatologie geoelectrique sismologie brouillard

de: geophysik bohrloch messstellennetz oberflachengewasser klima bioklimatologie wind bodentemperatur gewasser bodenoberflachentemperatur windenergie permafrost klimatologie geoelektrik seismik nebel

fr: Le service représente les données de base du canton de Berne selon la géocatégorie K conforme à eCH-0166. Il comprend actuellement les données BernClim de phénologie végétale, durée de l'enneigement, fréquence du brouillard, la carte d'analyse relative au modèle climatique cantonal, la carte indicative de planification (CIP) relative au modèle climatique cantonal, les scénarios du modèle climatique cantonalet les données sur le pergélisol et la vitesse du vent.

de: Der Dienst stellt Geobasisdatensätze des Kantons Bern der Geokategorie K gemäss eCH-0166 dar. Er enthält aktuell die Analysekarte zum kantonalen Klimamodell, die BernClim-Daten Pflanzenphänologie, Schneedauer, Nebelhäufigkeit, die Planungshinweiskarte zum kantonalen Klimamodell (PHK), Szenarien des kantonalen Klimamodells sowie die Datensätze Permafrost und Windgeschwindigkeit.

de: Amt für Geoinformation des Kantons Bern

de: Das Amt für Geoinformation ist das Kompetenzzentrum für räumliche Informationen im Kanton Bern. Die kantonalen Geodaten sind unter Einhaltung der Nutzungsbedingungen kostenlos nutzbar. Das Geoportal des Kantons Bern ist der zentrale Einstiegspunkt für interaktive Karten, Geodaten und Geodienste der kantonalen Verwaltung.

fr: Thèmes d'atmosphère, air et climat du canton de Berne

de: Atmosphären-, Luft- und Klimathemen des Kantons Bern


geschutzte-geologische-objekte

territory

en: Territory and environment

title_for_slug: geschutzte-geologische-objekte

de: naturschutz naturdenkmal geologie

fr: Lien vers le téléchargement de données (Zip)

de: Link zum Daten-Download (Zip)

fr: Thème du cadastre RDPPF L'ordonnance classe le jeu de géodonnées de base 'Objets géologiques protégés' (OCGéo ID 84-BE) dans le cadastre des restrictions de droit public à la propriété foncière (cadastre RDPPF). Le catalogue de données des objets géologiques protégés du canton de Berne (GGO) comprend des monuments naturels tels que des blocs erratiques, des blocs exotiques, des affleurements géologiques, un moulin glaciaire ainsi qu’une fissure cristalline.

de: ÖREB-Katasterthema Der Geobasisdatensatz 'Geschützte geologische Objekte' (KGeoIV ID 84-BE) ist gemäss der Verordnung über den Kataster der öffentlich-rechtlichen Eigentumsbeschränkungen (ÖREBKV) Bestandteil des ÖREB-Katasters. Der Datensatz der kantonal geschützten geologischen Objekte (GGO) umfasst Naturdenkmäler wie Findlinge, exotische Blöcke, geologische Aufschlüsse, eine Gletschermühle sowie eine Mineralkluft.

name: Amt für Landwirtschaft und Natur - Naturförderung ANF

fr: Objets géologiques protégés

de: Geschützte geologische Objekte


fr: sauvegarde-de-lenvironnement affaissement-du-sol type-de-sol eboulement decharge zone-a-risque relief prevention-des-incendies-de-foret traceur gaz-naturel risque-naturel eau-substance radon roche ecoulement-des-crues avalanche atmosphere-du-sol protection-contre-les-crues sol service-dincendie incendie-de-foret decomposition tassement transport sciences-naturelles degat-des-eaux geologie inondation structure-regionale protection-de-la-foret processus-geomorphologique cadastre index geomorphologie prevention-des-avalanches bois protection-de-la-montagne crue danger eaux-souterraines mesure-de-protection avalanche gestion-forestiere erosion

de: umweltschutz bodensenkung bodenart erdrutsch deponie gefahrenzone relief waldbrandverhutung tracer erdgas naturgefahr wasser radon fels hochwasserabfluss lawine bodenluft hochwasserschutz boden feuerwehr waldbrand abbau bodensetzung transport naturwissenschaft wasserschaden geologie uberschwemmung raumstruktur waldschutz geomorphologischer-prozess kataster index geomorphologie lawinenschutz wald bergschutz hochwasser gefahren grundwasser schutzmassnahme schneerutsch forstwirtschaft erosion

fr: Service d'image

de: Darstellungsdienst

fr: Le service représente les données de base du canton de Berne selon la géocatégorie F conforme à eCH-0166. Il comprend actuellement le cadastre des événements liés aux dangers naturels, la carte des gisements de matières premières EDT, la carte indicative des forêts protectrices, la carte du toit du rocher, les cartes et les indices des dangers naturels, les concentrations de radon à l'intérieur des bâtiments, les constructions paravalanches, les données relatives à des points au sol, les géotopes d'importance régionale, l'indication de sols anthropiques, les lignes de connexion du traceur, les sites définis pour les panneaux d'interdiction de faire du feu, les sondages géologiques ainsi que la typologie des ensembles naturels.

de: Der Dienst stellt Geobasisdatensätze des Kantons Bern der Geokategorie F gemäss eCH-0166 dar. Er enthält aktuell die Bodenpunktdaten, die Felsreliefkarte, Feuerverbotsschilder-Standorte (Waldbrandmanagement), die geologische Rohstoffkarte ADT, die geologischen Sondierungen, die Geotope von regionaler Bedeutung, die Hinweise auf anthropogene Böden, die Lawinenverbauungen, den Naturgefahren-Hinweiskataster, die Naturgefahrenkarten und -hinweise, die Naturraumgliederung, die Radonkonzentrationen in Innenräumen, die Schutzwaldhinweiskarte sowie die Tracer-Verbindungslinien.

fr: Géologie, sols et dangers naturels du canton de Berne

de: Geologie, Böden und naturbedingte Risiken des Kantons Bern