en:
The main datasets available are 4 nationwide **Digital Surface Models (DSMs)** and corresponding **Vegetation Height Models (VHMs)** with a spatial resolution of 1 m.
The Vegetation Height Models are calculated by subtraction of a Digital Terrain Model (swissAlti3d version 2017, swisstopo) from the generated DSMs. DSMs were derived by image matching of **scanned historical images** (panchromatic 8-bit) acquired with a RC10 frame camera, in the context of the swisswide image acquisition campaigns of swisstopo.
Nationwide products could be generated for the **four epochs**
 1979-1985 / 1985-1991 / 1990-1998 / 1998-2006.
Additional information about successful image matching vs. interpolation of pixels and about the year of image acquisition are available in the corresponding files (**matched_area_xxxx_xxxx_binay.tif** and **meta_acquisition_year_xxxx_xxxx.json**, respectively).
To separate vegetation from non-vegetation areas a mask derived from a combination of information of the Topographic landscape model - TLM (swisstopo) and a NDVI layer was used and is supplied as **"TLM_mask.tif"**
Due to the occurrence of unrealistic vegetation height values caused by the failure of the image matching process (mainly overexposed images and clouds or very steep rocky terrain), pixel values - 60 m were set to NoData and - 0 m to 0.
All datasets are available in the Reference System **LV95 (EPSG: 2056) LN02 (EPSG: 5728)** and share the common **extent definition** (xmin ymin xmax ymax): 2480000 1070000 2840000 1300000

en: photogrammetry dsm nfi vhm forest historical

en: Historical Vegetation Height Model NFI

envi

en: Environment

de:
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    Description: Description of available data

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Resource Title: 1998-2006 Vegetation Height Model
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de:
Resource Title: 1990-1998 Vegetation Height Model
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de:
Resource Title: 1985-1991 Vegetation Height Model
    Size: 219902325555 bytes
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    Description:

dsm

display_name: dsm

forest

display_name: forest

historical

display_name: historical

nfi

display_name: nfi

photogrammetry

display_name: photogrammetry

vhm

display_name: vhm


$oid: OBSOLETE

en: Climate data matching the scales at which organisms experience climatic conditions are often missing. Yet, such data on microclimatic conditions are required to better understand climate-change impacts on biodiversity and ecosystem functioning. Here we combine a national network of microclimate temperature measurements with a novel radiative transfer model to map monthly minimum, mean and maximum temperatures during the vegetation period at a 10 meter spatial resolution across Switzerland. The temperature measurements took place in 107 sampling plots distributed across different habitat types, with 62 plots in forests, 22 below trees outside forests, and 23 in open grasslands. In each plot we measured temperature in the topsoil (-5cm), as well as in the air at 5cm and 100cm height above ground. Spatial interpolation was achieved by using a hybrid approach based on linear mixed effects models with input from detailed radiation estimates that account for topographic and vegetation shading, as well as other predictor variables related to the macroclimate, topography and vegetation height. Our data reveals strong horizontal and vertical variability in microclimate temperature, particularly for maximum temperatures at 5 cm above the ground and within the topsoil. Compared to macroclimate conditions as measured by weather stations outside forests, diurnal air and topsoil temperature ranges inside forests were reduced by up to 3.0 and 7.8 °C, respectively, while below trees outside forests, e.g. in hedges and below solitary trees, this buffering effect was 1.8 and 7.2 °C. We also found that in open grasslands, maximum temperatures at 5 cm above ground are on average 3.4 °C warmer than that of macroclimate, suggesting that in such habitats heat exposure close to the ground is often underestimated when using macroclimatic data. After accounting for macroclimate effects, microclimate patterns were primarily driven by radiation, with particularly strong effects on maximum temperatures. Results from spatial block cross-validation revealed predictive accuracies as measured by RSME’s ranging from 1.18 to 3.43 °C, with minimum temperatures generally being predicted more accurately than maximum temperatures. The microclimate maps presented here enable a more biologically relevant perspective when analysing climate-species interactions, which is expected to lead to a better understanding of biotic and ecosystem responses to climate and land use change.

en: microclimate statistical-interpolation biodiversity radiation-transfer forest ecology climate-change

envidat

approval_status: approved

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

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

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

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

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

state: active

type: organization

wsl

capacity: public

de: EnviDat

en: Monthly topsoil and near surface microclimate temperature data for Switzerland

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

fr: Environnement

it: Ambiente

de:
Resource Title: topsoil.zip
    Size: 8375186227 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.431/topsoil.zip
    Description: Predicted microclimate topsoil temperatures at 5 cm below ground. 10 m spatial resolution raster files are provided for mean daily minimum (tmin), mean (tmean) and maximum (tmax) temperatures during each month from April (04) to October (10) over the period between 2012 to 2021.

format: ZIP

media_type: application/zip

de:
Resource Title: 100cm.zip
    Size: 8235599790 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.431/100cm.zip
    Description: Predicted microclimate air temperatures at 100 cm above ground. 10 m spatial resolution raster files are provided for mean daily minimum (tmin), mean (tmean) and maximum (tmax) temperatures during each month from April (04) to October (10) over the period between 2012 to 2021.

de:
Resource Title: 5cm.zip
    Size: 8836895212 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.431/5cm.zip
    Description: Predicted microclimate air temperatures at 5 cm above ground. 10 m spatial resolution raster files are provided for mean daily minimum (tmin), mean (tmean) and maximum (tmax) temperatures during each month from April (04) to October (10) over the period between 2012 to 2021.

biodiversity

display_name: biodiversity

climate-change

display_name: climate-change

ecology

display_name: ecology

microclimate

display_name: microclimate

radiation-transfer

display_name: radiation-transfer

statistical-interpolation

display_name: statistical-interpolation


en: We conducted two drone flights with the Wingtra and DJI Phantom 4 RTK drones in Davos Wolfgang Arelen, on 25.08.2021. The data was processed with the Agisoft Metashape Professional Software.The Wingtra point cloud was further processed to derive a ground classification in individual LASTools and Terrasolid workflows.

en: ground-classification uav drone-data photogrammetry dsm

en: Photogrammetric Drone Data and derived Ground Classification Wolfgang Arelen

drone-data

display_name: drone-data

ground-classification

display_name: ground-classification

uav

display_name: uav


en:
UAV-derived DSMs and orthoimages
Unmanned Aerial Vehicle  (UAV) surveys were conducted between 2015 and 2016 on the Sankt Annafirn, Findelen- and Griesgletscher, situated in the Swiss Alps. Three surveys at the Sankt Annafirn allowed for a full glacier coverage,  four  surveys at Griesgletscher allowed an almost full glacier coverage and seven surveys at Findelengletscher allowed for a partial coverage of the glacier tongue (see individual datasets for exact extent). For each survey, a high resolution orthoimage and a Digital Surface Model (DSM) was created.
 UAV surveys:
Prior flight, Ground Control Points (GCPs) were deployed on the glacier surface and measured with a differential GPS (Trimble R7 or Leica GPS 1200). They allowed precise georeferencing of the UAV-derived datasets. UAV flight plans were planned with the software *eMotion 2* and a SenseFly eBee was used as surveying platform. The images were then processed with the software Agisoft Photoscan Pro 1.1.6 .
The location and dates of each survey can be found in the table together with the number of flights performed (Nflights), the number of acquired images (Nimages), the number of GCPs set (NGCPs) and the surveyed area.
A folder for each dataset is available (see folder name in table), which contains:
- An orthoimage  *glacier_date_photoscan_oi_CH1903+_LV95_0.1m.tif*
- A Digital Surface Model  *glacier_date_photoscan_dsm_CH1903+_LV95_0.1m.tif*
- The Agisoft Photoscan automatic processing report   *glacier_date_photoscan_report.pdf*
where:
- *glacier* is the name of the surveyed glacier
- *date* is the date of the UAV image acquisition
- *photoscan* is the name of the photogrammetric software
- *oi* or *dsm* the type of dataset
- *CH1903+_LV95* is the coordinate system and datum of the dataset
- *0.1m* is the resolution of the dataset in meter
- *.tif* is the extention of the dataset

Details about the UAV surveys, the image processing and the accuracy of the UAV-derived products can be found in this publication below.
Paper Citation:
 - _Gindraux et al. 2017. Accuracy Assessment of Digital Surface Models from Unmanned Aerial Vehicles’Imagery on Glaciers, Remote Sensing, 9, 186, 1-15,  [doi: 10.3390/rs9020186](https://doi.org/10.3390/rs9020186)._
The folder UAV_flight_paths.zip contains all UAV flights performed on the Sankt Annafirn, Findelengletscher and Griesgletscher. The flights were planned with the software eMotion2 and have the .afp extention.

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

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

de:
Resource Title: gries_20160913
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    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.48/uav-datasets-for-three-alpine-glaciers/gries_20160913.zip
    Description: This .zip folder contains:

 - An orthoimage  (oi) of 0.1m resolution

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

- The Agisoft Photoscan automatic processing report

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

de:
Resource Title: Bulk Data Access
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    URL: https://envicloud.wsl.ch/#/?prefix=doi/envidat.48
    Description: Link to the S3 bucket where all the data resources can be downloaded in bulk

de:
Resource Title: stanna_20150715
    Size: 813185979 bytes
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    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.48/uav-datasets-for-three-alpine-glaciers/stanna_20150715.zip
    Description: This .zip folder contains:

 - An orthoimage  (oi) of 0.1m resolution

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

- The Agisoft Photoscan automatic processing report

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

de:
Resource Title: UAV_flight_paths
    Size: 5116350 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.48/uav-datasets-for-three-alpine-glaciers/uav_flight_paths.zip
    Description: This folder contains all UAV flights paths that we created for the Sankt Annafirn, the Findelengletscher and Griesgletscher. They have been created with the software eMotion2, that was developped for the eBee platform from Sensefly.

de:
Resource Title: findelen_20150721
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    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.48/uav-datasets-for-three-alpine-glaciers/findelen_20150721.zip
    Description: This .zip folder contains:

 - An orthoimage  (oi) of 0.1m resolution

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

- The Agisoft Photoscan automatic processing report

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

de:
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    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.48/uav-datasets-for-three-alpine-glaciers/gries_20150926.zip
    Description: This .zip folder contains:

 - An orthoimage  (oi) of 0.1m resolution

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

- The Agisoft Photoscan automatic processing report

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

de:
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    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.48/uav-datasets-for-three-alpine-glaciers/findelen_20150920.zip
    Description: This .zip folder contains:

 - An orthoimage  (oi) of 0.1m resolution

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

- The Agisoft Photoscan automatic processing report

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

de:
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    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.48/uav-datasets-for-three-alpine-glaciers/findelen_20160922.zip
    Description: This .zip folder contains:

 - An orthoimage  (oi) of 0.1m resolution

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

- The Agisoft Photoscan automatic processing report

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

de:
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    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.48/uav-datasets-for-three-alpine-glaciers/stanna_20150415.zip
    Description: This .zip folder contains:

 - An orthoimage  (oi) of 0.1m resolution

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

- The Agisoft Photoscan automatic processing report

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

de:
Resource Title: stanna_20150928
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    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.48/uav-datasets-for-three-alpine-glaciers/stanna_20150928.zip
    Description: This .zip folder contains:

 - An orthoimage  (oi) of 0.1m resolution

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

- The Agisoft Photoscan automatic processing report

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

de:
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    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.48/uav-datasets-for-three-alpine-glaciers/findelen_20150409.zip
    Description: This .zip folder contains:

 - An orthoimage  (oi) of 0.1m resolution

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

- The Agisoft Photoscan automatic processing report

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

de:
Resource Title: gries_20160815
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    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.48/uav-datasets-for-three-alpine-glaciers/gries_20160815.zip
    Description: This .zip folder contains:

 - An orthoimage  (oi) of 0.1m resolution

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

- The Agisoft Photoscan automatic processing report

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

de:
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    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.48/uav-datasets-for-three-alpine-glaciers/findelen_20160702.zip
    Description: This .zip folder contains:

 - An orthoimage  (oi) of 0.1m resolution

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

- The Agisoft Photoscan automatic processing report

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

de:
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    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.48/uav-datasets-for-three-alpine-glaciers/findelen_20160830.zip
    Description: This .zip folder contains:

 - An orthoimage  (oi) of 0.1m resolution

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

- The Agisoft Photoscan automatic processing report

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

de:
Resource Title: datasets_overview
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    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.48/uav-datasets-for-three-alpine-glaciers/datasets_overview.pdf
    Description: This .pdf contains an orthoimage for each dataset as well as a Table which displays the number of flights made, image acquired, number of GCP set and the total area covered.

format: PDF

media_type: application/pdf

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    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.48/uav-datasets-for-three-alpine-glaciers/findelen_20160419.zip
    Description: This .zip folder contains:

 - An orthoimage  (oi) of 0.1m resolution

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

- The Agisoft Photoscan automatic processing report

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

de:
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    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.48/uav-datasets-for-three-alpine-glaciers/gries_20150803.zip
    Description: This .zip folder contains:

 - An orthoimage  (oi) of 0.1m resolution

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

- The Agisoft Photoscan automatic processing report

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

alps

display_name: alps

dem

display_name: dem

drone

display_name: drone

findelengletscher

display_name: findelengletscher

glacier

display_name: glacier

gletscher

display_name: gletscher

griesgletscher

display_name: griesgletscher

orthoimage

display_name: orthoimage

orthophoto

display_name: orthophoto

sankt-annafirn

display_name: sankt-annafirn


en: Although species range may be obtained using expert maps or modeling methods, expert data is often species-limited and statistical models need more technical expertise as well as many species observations. When unavailable, such information may be extracted from the Global Biodiversity Information facility (GBIF), the largest public data repository inventorying georeferenced species observations worldwide. However, retrieving GBIF records at large scale may be tedious if users are unaware of specific tools and functions that need to be employed. Here we present *gbif.range*, an R library that contains automated methods to generate species range maps from scratch using in-house ecoregions shapefiles and an easy-to-use GBIF download wrapper. Finally, this library also offers a set of additional very useful parameters and functions for large GBIF datasets (generate doi, extract GBIF taxonomy, records filtering...). [gbif.range R project](https://github.com/8Ginette8/gbif.range)

en: flags occurrence-records landsca species-observations range-maps ecoregions species-distribution gbif accepted-name conservation r-package taxonomy large-scale hard-limit filtering synonyms

envidat

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.

de: EnviDat: das Umwelt-Datenportal

de: EnviDat

en: gbif.range - An R package to generate species range maps based on ecoregions and a user-friendly GBIF wrapper

envi

de: Umwelt

de:
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    Format: application/zip
    URL: https://www.envidat.ch/dataset/f5f4a8fd-7172-4db0-9b41-832a94ea9cab/resource/3290357e-2e2f-4a10-a254-4689b5f91705/download/gbif.range.zip
    Description:

de:
Resource Title: wsl.gbif R package version 0.1 (deprecated)
    Size: 2704605 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f5f4a8fd-7172-4db0-9b41-832a94ea9cab/resource/402acab8-6657-452e-bde3-53b1cbb59dcd/download/wsl.gbif-main-version.2022-10-25.zip
    Description: Test version, not to be used.

de:
Resource Title: gbif.range R package version 0.2 (deprecated)
    Size: 75970559 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f5f4a8fd-7172-4db0-9b41-832a94ea9cab/resource/802ecb6b-f4c6-49f8-b25d-093bf1ff9d7c/download/gbif.range_r_package.zip
    Description:

de:
Resource Title: GitHub URL
    Size: 0
    Format: .range
    URL: https://github.com/8Ginette8/gbif.range
    Description:

accepted-name

display_name: accepted-name

conservation

display_name: conservation

ecoregions

display_name: ecoregions

filtering

display_name: filtering

flags

display_name: flags

gbif

display_name: gbif

hard-limit

display_name: hard-limit

landsca

display_name: landsca

large-scale

display_name: large-scale

occurrence-records

display_name: occurrence-records

r-package

display_name: r-package

range-maps

display_name: range-maps

species-distribution

display_name: species-distribution

species-observations

display_name: species-observations

synonyms

display_name: synonyms

taxonomy

display_name: taxonomy


en:
Data associated to the publication "Rapid field-based detection of a threatened and elusive species with environmental DNA and CRISPR-Dx"
-br-
 Authors:
-br-
Flurin Leugger, Martina Lüthi, Michel Schmidlin, Zacharias Kontarakis and Loïc Pellissier
-br-
 Contact:
-br-
flurinleugger@gmail.com
loic.pellissier@usys.ethz.ch
-br-
 Project description:
-br-
We collected eDNA samples in the Conkouati-Douli National Park in the Republic of Congo in 2023 in collaboration with Beauval Nature and HELP Congo. We analyzed the samples on site in the national park using CRISPR-Dx assays to detect the African manatee (*Trichechus senegalensis*). Additionally, we transported the remaining samples (buffer) to Switzerland and used a high-sensitivity protocol with CRISPR-Dx assays and a qPCR assay to verify the field-based detections. The qPCR of Hunter et al. (2018) detects the genus manatee (Trichechus). See publication for more details (doi will be added after publication).
-br-
 Folders:
-br-
 lateral_flow_tests_images\:
-br-
Images of the lateral flow tests used to analyze the eDNA extracts in the national park. File names correspond to the site names. At each site, we took two filter replicates. We ran 8 PCRs per filter/extract and pooled two PCRs together, resulting in 4 CRISPR-Dx assays per extract. The EVE number on top of the tests indicates the environmental extract number we use to track the extracts in our data base. It corresponds to the extract_number_field_based in the metadata and data folder. The negative extraction control with the strongest "band" was placed right to the eDNA extract on each image (labelled hand written with NC x.y).
-br-
 metadata\:
-br-
File containing overview of site names and extract number (field-based or lab-based extraction). The extract_number_field_based corresponds to the identically called column in CRISPR_field_based_classifications.csv file, as the extract_number_lab_based to the column in the file CRISPR_lab_based_classifications.csv file and the qPCR_Cp_2nd_derivative.csv file. The file includes the GPS coordinates of the sampling location (transect start point).
-br-
 data\:
-br-
Folder containing the files with Cp values (qPCR) and the CRISPR-Dx classifications. CRISPR_field_based_classifications.csv file contains the classifications per pool per extract (0: no detection, 1: detection) for the African manatee (Trichechus senegalensis).
CRISPR_lab_based_classifications.csv file contains the classifications per pool per extract (0: no detection, 1: detection).
qPCR_Cp_2nd_derivative.csv file contains the Cp value of the 8 replicates of each extract.

en: edna biodiversity-monitoring qpcr field-based-protocol crispr-dx threatened-species crispr-cas environmental-dna african-manatee

en: Field-based eDNA analysis to detect the threatened and elusive African manatee

de:
Resource Title: metadata.zip
    Size: 10066 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/4a99830f-a254-4179-a490-d7ca76c5ed56/resource/8501e9b9-e55f-49de-86ac-0754f15579dd/download/metadata.zip
    Description: File containing overview of site names and extract number (field-based or lab-based extraction). The extract_number_field_based corresponds to the identically called column in CRISPR_field_based_classifications.csv file in the data folder, as the extract_number_lab_based to the column in the file CRISPR_lab_based_classifications.csv file and the qPCR_Cp_2nd_derivative.csv file (also in the data folder). The file includes the GPS coordinates of the sampling location (transect start point).

de:
Resource Title: Readme.txt
    Size: 2387 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4a99830f-a254-4179-a490-d7ca76c5ed56/resource/ad00caf8-bff2-4b98-be8b-122329cee39e/download/readme.txt
    Description: Description of project and folders and files

format: TXT

media_type: text/plain

de:
Resource Title: data.zip
    Size: 2126 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/4a99830f-a254-4179-a490-d7ca76c5ed56/resource/0b379b83-6480-4aca-b189-ff2435deb297/download/data.zip
    Description: Folder containing the data files with Cp values (qPCR) and the CRISPR-Dx classifications. CRISPR_field_based_classifications.csv file contains the classifications per pool per extract (0: no detection, 1: detection) for the African manatee (Trichechus senegalensis).
CRISPR_lab_based_classifications.csv file contains the classifications per pool per extract (0: no detection, 1: detection).
qPCR_Cp_2nd_derivative.csv file contains the Cp value of the 8 replicates of each extract.

de:
Resource Title: lateral_flow_tests_images.zip
    Size: 7430379 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/4a99830f-a254-4179-a490-d7ca76c5ed56/resource/196f5458-7ced-49e3-a693-357ee2816f5a/download/lateral_flow_tests_images.zip
    Description: Images of the lateral flow tests used to analyze the eDNA extracts in the national park. File names correspond to the site names. At each site, we took two filter replicates. We ran 8 PCRs per filter/extract and pooled two PCRs together, resulting in 4 CRISPR-Dx assays per extract. The EVE number on top of the tests indicates the environmental extract number we use to track the extracts in our data base. It corresponds to the extract_number_field_based in the metadata and data folder. The negative extraction control with the strongest "band" was placed right to the eDNA extract on each image (labelled hand written with NC x.y).

african-manatee

display_name: african-manatee

biodiversity-monitoring

display_name: biodiversity-monitoring

crispr-cas

display_name: crispr-cas

crispr-dx

display_name: crispr-dx

edna

display_name: edna

environmental-dna

display_name: environmental-dna

field-based-protocol

display_name: field-based-protocol

qpcr

display_name: qpcr

threatened-species

display_name: threatened-species


en:
General description
This data set was created for the case study on CRISPR-Dx assays to monitor amphibians with eDNA (in the Canton of Aargau, Switzerland). It encompasses a the environmental DNA (eDNA) sampling metadata, the average CRISPR-Dx assay readout per extract, the summarized traditional monitoring data and the permits received for sampling and data usage.
**Corresponding author**: Flurin Leugger (flurinleugger@gmail.com)
 Description of the zip folders
- **CRISRP_5km**:  CRISPR-Dx fluorescence readout and summary per extract and eDNA metadata. The coordinates are provided at 5km resolution (randomly sampled points within 5km buffer around precise location) to meet the agreement with Canton of Aargau.
- **traditional_monitoring**: Summarized traditional monitoring data per location, provided by Canton of Aargau
- **CRISPR**: CRISPR-Dx fluorescence readout and summary per extract and eDNA metadata. This folder contains the exact sampling coordinates and thus cannot be shared publicly to meet the agreement with Canton of Aargau. It can only be shared upon reasonable request.
- The permits are provided on request by the authors and are not made available publicly together with the data.

en: biodiversity-monitoring monitoring environmental-dna amphibians biodiversity ampliscanning crispr

en: CRISPR-Dx assays and environmental DNA to monitor amphibians

envi

en: Environment

de:
Resource Title: CRISPR_5km.zip
    Size: 17504 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/5925a83c-d99d-4d91-b5f5-eb57f208f5fa/resource/714061a7-e8b3-4d6b-b20c-b525b48199df/download/crispr_5km.zip
    Description: Data of CRISPR-Dx assays fluorescence readout (summarized), incl. sampling, extraction and amplification metadata

Coordinates in the CRISPeD_EnviDat_fw_CHE_AG_2022.csv are randomized per location within a buffer of 5km to meet the agreement with Canton of Aargau.

de:
Resource Title: traditional_data.zip
    Size: 1366 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/5925a83c-d99d-4d91-b5f5-eb57f208f5fa/resource/d7522117-ac24-4bd6-9a97-50e215e5c983/download/traditional_data.zip
    Description: Summarized traditional monitoring data per site, provided by Canton of Aargau

de:
Resource Title: metadata_amphibians.txt
    Size: 1129 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/5925a83c-d99d-4d91-b5f5-eb57f208f5fa/resource/406e0e26-7a44-4690-ac50-36f61a19aae9/download/metadata_amphibians.txt
    Description: Metadata for amphibian data in EnviDat

de:
Resource Title: CRISPR.zip
    Size: 17384 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/5925a83c-d99d-4d91-b5f5-eb57f208f5fa/resource/2024621c-2069-43f3-a7b6-4bfb301e9f78/download/crispr.zip
    Description: Data of CRISPR-Dx assays fluorescence readout (summarized), incl. sampling, extraction and amplification metadata

amphibians

display_name: amphibians

ampliscanning

display_name: ampliscanning

biodiversity

display_name: biodiversity

crispr

display_name: crispr

monitoring

display_name: monitoring


en:
The dataset contains the input and output files from the publication by Hirschberg et al. (2022).
The input files are the climate forcing time series generated with the AWE-GEN model. The output files include the hydrological outputs, which is the same for scenarios 1-6 considered in Hirschberg et al. (2022), and the sediment-related outputs, whereas the transport-limited scenario 6 is included in the output of scenario 1.
The input file includes:
- time _D_ (h)
- precipitation _Pr_ (mm/h)
- atmospheric temperature _Ta_ (°C)
- incoming shortwave radiation _Rsw_ (W/m^2)
- cloudiness _N_ (-)
The output files include:
- hydrological outputs (accroding to time in input and normalized by basin area)
    - total discharge _Q_ (mm/h)
    - surface discharge _Qs_ (mm/h)
    - subsurface discharge _Qss_ (mm/h)
    - soil water storage _Vw_ (mm)
    - snow depth _snow_ (mm SWE)
    - snow depth change _snowacc_ (mm/h SWE)
    - potential evapotranspiration _PET_ (mm/h)
    - actual evapotranspiration _AET_ (mm/h)
- sediment outputs (accroding to time in input and normalized by basin area)
    - hillslope landslide magnitude _ls_ (mm/h)
    - channel sediment storage _sc_ (mm)
    - hillslope sediment storage _sh_ (mm)
    - total sediment discharge _so_ (mm/h)
    - transport-limited total sediment discharge _sopot_ (mm/h)
    - sediment discharge by debris flows _dfs_ (mm/h)
    - transport-limited sediment discharge by debris flows _dfspot_

en: sediment-transport sedcas awe-gen modelling illgraben geomorphology debris-flow

envidat

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

en: EnviDat: the Environmental Data Portal

en: Data for Numerical Investigation of Sediment Yield Underestimation in Supply-Limited Mountain Basins with Short Records

awe-gen

display_name: awe-gen

debris-flow

display_name: debris-flow

geomorphology

display_name: geomorphology

illgraben

display_name: illgraben

modelling

display_name: modelling

sedcas

display_name: sedcas

sediment-transport

display_name: sediment-transport


en:
This repository contains extensive data for the European Alps:
   - Observations of ~3,500 plant species
   - Climate (1-km), soil (100-m) and land cover predictors (1km); current and future scenarios (28 CMIP6-GCMs, 2 land cover change and 3 dispersal scenarios i.e., unlimited, no and realistic vegetation dispersal)
   - Flora migration rates (categorical) and ecological preferences (continuous indicator values)
   - Regional maps of barriers to migration and water bodies at 100-m resolution
   - Sampling effort, distance to roads and cities predictors at 100-m resolution
   - Present and future abundances over the study region at 1-km resolution (~2,000 species)
   - Present and future multifaceted and uniqueness of the European Alps' Flora at  1-km resolution
   - Present and future conservation recommendations at 1-km resolution (26 current and future strategies)
   - Phylogenetic data and functional traits of ~2,000 plants (raw data and classification trees)
   - All scripts, data and plots used for the analyses, including a singularity container (mini-linux) to run them

en: functional-diversity taxonomic-diversity species-distribution-models beta-diversity phylogenetic-diversity migclim plants land-use-change zonation conservation european-alps global-change

en: Present and future multifaceted plant diversity, uniqueness and conservation in the European Alps

de:
Resource Title: Data Access
    Size: 85899345920 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.371%2F
    Description:

de:
Resource Title: Updated SDM outputs
    Size: 10242439935 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/7b947fcf-73a0-46a7-be03-804797c9c3d7/resource/4838348a-6050-4774-83f1-cab5efe7b244/download/sdm_outputs_updated.zip
    Description: Refined scenarios of plants distribution in the European Alps

beta-diversity

display_name: beta-diversity

conservation

display_name: conservation

european-alps

display_name: european-alps

functional-diversity

display_name: functional-diversity

global-change

display_name: global-change

land-use-change

display_name: land-use-change

migclim

display_name: migclim

phylogenetic-diversity

display_name: phylogenetic-diversity

plants

display_name: plants

species-distribution-models

display_name: species-distribution-models

taxonomic-diversity

display_name: taxonomic-diversity

zonation

display_name: zonation


en: The dataset contains for three variables (snow water equivalent, surface water input and liquid precipitation) 50 realizations of current and future climate periods for two time horizons (mid end end of century), two emission senarions (RCP 4.5 and 8.5) and 10 climate model chains (all EUR11 chains within CH2018). To quantify natural climate variability for projections of snow conditions and resulting rain-on-snow (ROS) flood events, a weather generator was applied to simulate inherently consistent climate variables for multiple realizations of current and future climates at 100 m spatial and hourly temporal resolution over a 12 x 12 km high-altitude study area in the Swiss Alps. The output of the weather generator was used as input for subsequent simulations with an energy balance snow model. The data was extracted in 2021 from original model output.

en: uncertainty snow-water-equivalent rain-on-snow climate-change-impact cryosphere natural-climate-variability

envidat

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.

de: EnviDat: das Umwelt-Datenportal

en: Multiple realizations of daily snow water equivalent, surface water input and liquid precipitation projections for mid- and late-century

climate-change-impact

display_name: climate-change-impact

cryosphere

display_name: cryosphere

natural-climate-variability

display_name: natural-climate-variability

rain-on-snow

display_name: rain-on-snow

snow-water-equivalent

display_name: snow-water-equivalent

uncertainty

display_name: uncertainty


en:
This dataset consist of simulated hourly power production from an Enercon E82 Turbine at 100 m hub-height. It describes the hourly power output a 1MW turbine would produce in each 0.01° grid cell for the years 2016 and 2017.
100 m wind speed data was taken from the COSMO-1 model (Consortium for Small-scale Modeling 2017), which has a 0.01° horizontal resolution. The domain covered is the whole of Switzerland, with the exclusion of lakes. As such, the number of 0.01◦ pixels within Switzerland amounts to 48657.
Conversion to power output was done based on the power curve of the Enercon E82 Turbine. As power output is lower at altitude due to lower air density, we corrected for this effect as described in (Kruyt et al. 2017).
Please cite the following paper in connection with the dataset:
Paper Citation:
 - _Bert Kruyt, Jérôme Dujardin, and Michael Lehning: Improvement of wind power assessment in complex terrain: The case of COSMO-1 in the Swiss Alps, Front. Energy Res., [doi:10.3389/fenrg.2018.00102] (https://doi.org/10.3389/fenrg.2018.00102)_

de: EnviDat

en: Dataset on Cosmo-1 based Energy Potential in Swiss Alps

envi

de: Umwelt

de:
Resource Title: Data Source
    Size: 3144305336 bytes
    Format: application/x-r-data
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.51/dataset-on-wind-fields-and-energy-potential-in-swiss-alps/unit_pow_16_17.rdata
    Description: Hourly Gridded Wind Power Time Series

de:
Resource Title: Description
    Size: 42299 bytes
    Format: application/pdf
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.51/dataset-on-wind-fields-and-energy-potential-in-swiss-alps/envidat_cosmo-desc.pdf
    Description: Full Data Description

de:
Resource Title: Coordinates of Grid Points
    Size: 255151 bytes
    Format: application/x-rds
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.51/dataset-on-wind-fields-and-energy-potential-in-swiss-alps/gridch.rds
    Description: Coordinates of Grid Points

mountain-wind-potential

display_name: mountain-wind-potential

topography

display_name: topography

wind-energy

display_name: wind-energy


en:
The data archive contains site specific geographical data such as DEM and orthophoto as well as the deposition points of manually induced rockfall by releasing differently shaped boulders with 46, 200, 800 and 2670 kg of mass.
Additionally available are all the reconstructed data sets for all trajectories with videogrammetry installed comprising StoneNode data streams for rocks equipped with a sensor. The data set consists of:
 Resources (individual zip-archives)
ExperimentalRuns:  Archive with all available StoneNode data streams and its respective figure (.mat files)
Input: Archive containing folders
* GNSS: 182 Deposition points of all different weight and shape classes, shape files for release point, cliff and scree line,
* UAS: UAS generated pre- and post-experimental digital surface models and orthophoto of the four most important experimental days and
* VG_Coord: Reconstruction input: Videogrammetry based coordinate list along side with the corresponding sensor/video times
EOTA: Point cloud of cubic EOTA(111) and platy EOTA(221) rock as input for RAMMS::ROCKFALL or other suitable rockfall simulation codes incorporating complex shape files.
Output: Reconstruced trajectory information for all 82 reconstructed trajectories
Video: available video streams for all runs
 Further information
Preceeding publications concering the deployed sensors and the reconstruction methods are found in the subsequent references:
A. Caviezel et al., Design and Evaluation of a Low-Power Sensor Device for Induced Rockfall Experiments, IEEE Transactions on Instrumentation and Measurement, 2018, 67, 767-779, http://ieeexplore.ieee.org/document/8122020/
P. Niklaus et al., StoneNode: A low-power sensor device for induced rockfall experiments, 2017 IEEE Sensors Applications Symposium (SAS), 2017, 1-6, http://ieeexplore.ieee.org/document/7894081/
Caviezel, A., Demmel, S. E., Ringenbach, A., Bühler, Y., Lu, G., Christen, M., Dinneen, C. E., Eberhard, L. A., von Rickenbach, D., and Bartelt, P.: Reconstruction of four-dimensional rockfall trajectories using remote sensing and rock-based accelerometers and gyroscopes, Earth Surf. Dynam., 7, 199–210, https://doi.org/10.5194/esurf-7-199-2019, 2019

en: dem rockfall-experiments stonenode natural-hazard earth-surface-dynamics rockfall-runout stonenodedata induced-rockfall rockfall

en: Induced Rockfall Dataset #2 (Chant Sura Experimental Campaign), Flüelapass, Grisons, Switzerland

de:
Resource Title: EOTA
    Size: 419 bytes
    Format: application/x-7z-compressed
    URL: https://www.envidat.ch/dataset/71e65311-b0dd-48df-acfc-791c99cb0662/resource/eaa94cfe-a85f-4c26-ad2a-2f53b9e4af56/download/eota.7z
    Description: Cubic EOTA(111) and platy EOTA(221) point cloud.

de:
Resource Title: Video Archive
    Size: 5368709120 bytes
    Format: application/x-7z-compressed
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.174/caviezel_et_al_2020/Video.7z
    Description:

de:
Resource Title: Output Archive
    Size: 4736837 bytes
    Format: application/x-7z-compressed
    URL: https://www.envidat.ch/dataset/71e65311-b0dd-48df-acfc-791c99cb0662/resource/a2e218f8-cffe-4c6a-8515-01a0ff704524/download/output.7z
    Description: Data under embargo: Trajectory reconstruction tables

de:
Resource Title: Input Archive
    Size: 11811160064 bytes
    Format: application/x-7z-compressed
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.174/caviezel_et_al_2020/Input.7z
    Description:

de:
Resource Title: Experimental Runs Archive
    Size: 538660719 bytes
    Format: application/x-7z-compressed
    URL: https://www.envidat.ch/dataset/71e65311-b0dd-48df-acfc-791c99cb0662/resource/d9c77b83-fd15-414c-a257-461e6d2b2545/download/experimentalruns.7z
    Description: Compressed archive with the StoneNode experimental runs and the respective figures. Matlab format.

dem

display_name: dem

earth-surface-dynamics

display_name: earth-surface-dynamics

induced-rockfall

display_name: induced-rockfall

natural-hazard

display_name: natural-hazard

rockfall

display_name: rockfall

rockfall-experiments

display_name: rockfall-experiments

rockfall-runout

display_name: rockfall-runout

stonenode

display_name: stonenode

stonenodedata

display_name: stonenodedata


en:
This dataset includes all data and simulation files presented in the publication: Bender et al. 2020. Changes in climatology, snow cover and ground temperatures at high alpine locations, DOI: 10.3389/feart.2020.00100.
This includes:
* meteorological forcing,
* climate change timeries and
* simulation files together with
* snow depth
* ground temperature
Please refer to the following publication for further details which should be cited when using this dataset:
Bender et al. 2020. Changes in climatology, snow cover and ground temperatures at high alpine locations, DOI: 10.3389/feart.2020.00100.

en: depth-of-snowfall swiss-alps climate-change-impact meteo-station ground-temperature

en: Changes in climatology, snow cover and ground temperatures at high alpine locations in Switzerland (Bender et al. 2020)

envi

en: Environment

de:
Resource Title: GenPSUm
    Size: 14668578 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/10658b8c-a7ff-4433-94f5-ebdf16d5304f/resource/a747974a-30ee-46d8-95fa-8b3bd4123d79/download/genpsum-20200405t154030z-001.zip
    Description: GenPSUm contains Files to get the complete precipitation set:

1. MeteoData.ini: Ini file for MeteoIO to get the met. input data. It has to be run two times, to get prec. Data from Imis 	stations and from ANETZ regressions.

2 GenPSUM.ini: Inifile for Snowpack (in research mode), to get winter precipitation calculated by snowpack out of the snow hight measurements.
	In:_sno contain the soil profils for the station
	In_met contains the meterological input data. It's from the data base and MeteoData.ini is used to get it.

3. The folder R_programms contains the R sripts to generate the precipitation data set, it reads in IMIS, ANETZ and SNOWPACK precipi and makes one complet data set out of it.  (it contains the function- script plus a script to use the function)

de:
Resource Title: R-Script
    Size: 81604064 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/10658b8c-a7ff-4433-94f5-ebdf16d5304f/resource/d5ad28b3-5a3c-4fbf-be06-f3c4c2f91902/download/rscripts-20200405t154017z-001.zip
    Description: The Folder R- Script contains, the R files to create the Climate Change input and the Data for the WFJ- Modeling.

de:
Resource Title: SNOWPACK climate change simulation files
    Size: 1104566162 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.144/gtmodeling_snowpack.zip
    Description: This folder contains SNOWPACK INI-files for snowpack, soil profiles, and input data for climate change modeling.

de:
Resource Title: Readme.txt
    Size: 2048 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/10658b8c-a7ff-4433-94f5-ebdf16d5304f/resource/2c74496f-2c9a-4f87-9ebc-095bcece4623/download/readme.txt
    Description:

de:
Resource Title: Precipitation datasets
    Size: 39685479 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/10658b8c-a7ff-4433-94f5-ebdf16d5304f/resource/4d951298-4a0f-4d8f-bbdd-2915f7b33666/download/precipitationdatasets-20200405t154024z-001.zip
    Description: This folder contains 16 ideal precipitation data sets for IMIS stations.

*Updated documentation 24 August 2020:*

1. Datasets have been filtered for unphysical values using a MeteoIO min/max tool, however spikes still exist that pass this filter yet appear to be outliers, no further manual filtering was applied.
2. Data prior to 2009 can have large data gaps in certain stations. This is documented in the accompanying publication and was not used for simulations.
3. GUT2 was erroneously filtered during summer periods. This does not affect published snow modelling results but summer (Jun-Oct) values of 8.25 should not be used and filtered prior to use. This affects 4759 summer values or 1.2% of the time-series.

climate-change-impact

display_name: climate-change-impact

depth-of-snowfall

display_name: depth-of-snowfall

ground-temperature

display_name: ground-temperature

meteo-station

display_name: meteo-station

swiss-alps

display_name: swiss-alps


en:
Environmental pressures on species can cascade within food webs and even extend beyond individual ecosystems to interconnected systems at large spatial scales. To facilitate the exploration of these dynamics, we constructed trophiCH: a data-based national trophic meta-food web (henceforth the metaweb), that includes vertebrates, invertebrates, and vascular plants within Switzerland's national boundaries, and drawing from literature published between 1862 and 2024. Our comprehensive dataset catalogues 1,112,073 trophic interactions involving 23,151 species and 125 feeding guilds (e.g., detritivores, fungivores, etc). Interactions were primarily documented at the species level from the literature. Additional species-level interactions were inferred by resolving coarser taxonomic records (e.g., using “species A feeds on genus B” to infer interactions between species A and species within genus B) using taxonomic and habitat co-occurrence information.
We provide seven datasets: 1) the metaweb, 2) the taxa checklist, 3) the data source meta-dataset, 4) the list of generalist basal and predator families and polylectic species with citations and 5) a dataset with citations for the inferences of missing predators, 6) a dataset with citations for the parallel inference of diets from similar species and 7) a list of other existing metawebs. The empirical dataset is 00_empirical_metaweb.csv. To reproduce the final metaweb (01_final_metaweb.csv), start the R Project file: trophich.Rproj. We provide five scripts, accompanying functions, and the raw data required to run these scripts to reproduce the taxonomic expansion and validation of the datasets. In the first script (01_inferring_interactions.R), we infer interactions using genus and family level interactions and for basal feeding groups (see Methods: Taxonomic expansion). In the second script (02_inferring_interactions_special_cases.R), we infer further interactions for a few special cases with detailed explanations. In the third script (03_metaweb_comparisons.Rmd), we provide the statistical comparisons between our metaweb and other empirical metawebs as an R Markdown document. We additionally provide a a Python Jupyter Notebook document, outlining the error validation of the data extraction process (04_error_validation.ipynb and an accompanying .html file). Finally, we provide a script to reproduce figure 1 from the associated data paper (05_metaweb_summary_figure_1.R).
This is a first step towards a comprehensive food web for Switzerland, but data gaps remain (see Reji Chacko et al 2024, Fig.1C). We encourage addition of new datasets to this metaweb and welcome any collaborations to contruibute to future versions of this dataset. Please contact: merin.rejichacko@gmail.com.

en: food-webs switzerland metaweb vascular-plants trophic-networks vertebrates invertebarates

envidat

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

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

en: EnviDat: the Environmental Data Portal

en: trophiCH v1 - a food web for Switzerland

de:
Resource Title: trophiCH 1.0
    Size: 33123041 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/4f9547a1-3018-46f1-a399-09d65a3223f3/resource/6d7f57cc-a98f-4513-9c65-c0230a15bba7/download/trophich.zip
    Description: Most current version of the dataset, after revision round 1 at Scientific Data.

de:
Resource Title: trophich 0.1
    Size: 34142639 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/4f9547a1-3018-46f1-a399-09d65a3223f3/resource/fe802436-f927-4243-b199-0829a3e0d21b/download/trophich.zip
    Description: Deprecated version from before the first round of review at Scientific Data

food-webs

display_name: food-webs

invertebarates

display_name: invertebarates

metaweb

display_name: metaweb

switzerland

display_name: switzerland

trophic-networks

display_name: trophic-networks

vascular-plants

display_name: vascular-plants

vertebrates

display_name: vertebrates


en:
The WFJ_DailySMP dataset contains daily SnowMicroPen (SMP) measurements conducted throughout the winter seasons 2015-2025 (ongoing) at the Weissfluhjoch research site, Davos, Switzerland. The measurement program DailySMP started in winter 2015/2016 during which the measurement protocol was established as part of the RHOSSA campaign, described in the related publication.
Since then, measurements have been repeated every winter as part of the standard snow monitoring program by the PhD students at SLF. All published SMP files have undergone manual quality control by an expert, including a supervised detection of the snow surface, which is included in the dataset.
In addition to the raw data, we provide a rudimentary post-processing workflow in which the derived SSA and density profiles are adjusted by the total snow height obtained from the adjacent IMIS snow station (https://www.slf.ch/en/avalanche-bulletin-and-snow-situation/measured-values/description-of-automated-stations/) and their evolution is plotted over time. Please refer to the README for detailed information on how to get started.

en: snow-measurements snow-density weisfluhjoch microstructure snow-stratigraphy wfj-research-site weissfluhjoch snowmicropen snow snow-ssa

envidat

de: EnviDat: das Umwelt-Datenportal

de: EnviDat

en: WFJ_DailySMP - High-Resolution Snow Stratigraphy at Weissfluhjoch -- Davos 2015-ongoing

envi

de: Umwelt

microstructure

display_name: microstructure

snow

display_name: snow

snow-density

display_name: snow-density

snow-measurements

display_name: snow-measurements

snow-ssa

display_name: snow-ssa

snow-stratigraphy

display_name: snow-stratigraphy

snowmicropen

display_name: snowmicropen

weisfluhjoch

display_name: weisfluhjoch

weissfluhjoch

display_name: weissfluhjoch

wfj-research-site

display_name: wfj-research-site


en: The data was collected with a Wingtra Gen II drone and a Sony RX1R II sensor. In total, 10 flights were conducted at different dates, both in summer and winter. A DSM, an orthophoto, a snow depth raster and the original drone images from every flight are available at a high resolution (10cm and 3cm, respectively).

en: snow-depth-mapping orthophoto dsm snow-distribution drone-data photogrammetry snow uav

en: Photogrammetric Drone Data Dorfberg

drone-data

display_name: drone-data

dsm

display_name: dsm

orthophoto

display_name: orthophoto

photogrammetry

display_name: photogrammetry

snow-depth-mapping

display_name: snow-depth-mapping

snow-distribution

display_name: snow-distribution

uav

display_name: uav


en:
We developed a monitoring system using low-cost lidar (Livox Avia) and optical sensors, to measure snow depth variations in an avalanche release area at a high spatiotemporal scale (centimeter to low decimeter spatial resolution and hourly temporal resolution). A detailed description of the setup and a first data analysis is published in:
[Ruttner-Jansen, P., Voordendag, A., Hartmann, T., Glaus, J., Wieser, A., & Bühler, Y. (2024). Monitoring snow depth variations in an avalanche release area using low cost lidar and optical sensors. EGUsphere, 2024, 1-20.](https://nhess.copernicus.org/articles/25/1315/2025/)
This dataset contains hourly 3D lidar data of the snow surface at the test site Braemabuel in Davos, Switzerland from the winter season 2023-2024 (23.11.2023 - 14.04.2024). Braema1 is installed at 2191 m a.s.l. in the slope, looking up into the avalache release area, and Braema2 is installed at 2255 m a.s.l at the ridge, looking down. Both scans together cover an area of about 15000 m².
The data is in format ".las" and contains the points in XYZ [m], in the scanner own coordinate system
- scalar fields: intensity, return number, timestamp, tag, label
-- tag: binary code from Livox (see [Livox Avia Manual](https://terra-1-g.djicdn.com/65c028cd298f4669a7f0e40e50ba1131/Download/Avia/Livox%20Avia%20User%20Manual%20202204.pdf))
-- label: marking outliers (filtered by distance and SOR to get snow particles in the air)

en: lidar snow low-cost avalanche monitoring wind-drifted-snow

en: Snow depth mapping by lidar station Braemabuel

de:
Resource Title: Braema1
    Size: 22441204122 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.581/braema1_23_24_v2.zip
    Description:

de:
Resource Title: Braema2
    Size: 20937965568 bytes
    Format: application/zip
    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.581/braema2_23_24.zip
    Description:

avalanche

display_name: avalanche

lidar

display_name: lidar

low-cost

display_name: low-cost

monitoring

display_name: monitoring

wind-drifted-snow

display_name: wind-drifted-snow


en: Site description of the Seehornwald Davos (CH-DAV) research site. The material provided offers information on the biomass and growth of trees in CH-DAV, including supporting materials such as maps, geolocations and analyses. CH-DAV is part of networks such as ICOS, LWF, TreeNet, ICPForests and is dedicated to forest monitoring and research.

en: forest-biomass dendrometer biomass davos forest climate-change atmospheric-conditions dbh water-relations icp-forests lwf picea-abies eddy-covariance-measurements stem-growth carbon-sequestration forest-ecosystems automated-monitoring seehornwald flux carbon-cycle tree-stem forest-area metadata ecosystem-functioning carbon-dioxide sap-flow ilter eddy-covariance twd carbon-storage icos treenet open-data forest-canopy forest-function subalpine co2 forest-monitoring forest-dynamics climate-growth forest-characteristics ecophysiology

en: Site description Davos Seehornwald CH-DAV

de:
Resource Title: Readme
    Size: 2476 bytes
    Format: application/rtf
    URL: https://www.envidat.ch/dataset/4d332ffa-d5f5-413c-ba1c-5f73ceebbed6/resource/7f13f634-76b5-4d56-aa5a-61140240f616/download/readme.rtf
    Description:

format: rtf

de:
Resource Title: CH-DAV_Site_2024.zip
    Size: 34214528 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/4d332ffa-d5f5-413c-ba1c-5f73ceebbed6/resource/73875879-bec0-4e15-a805-e6cd9d4fe596/download/ch-dav_site_2024.zip
    Description: Collection of files with information about the research site Seehornwald Davos CH-DAV

de:
Resource Title: CH-DAV_Site_2024_V2.zip
    Size: 27267396 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/4d332ffa-d5f5-413c-ba1c-5f73ceebbed6/resource/41e3d87c-7907-4a01-bf5e-36a0d4457c40/download/ch-dav_site_2024_v2.zip
    Description: Collection of files with information about the research site Seehornwald Davos CH-DAV

atmospheric-conditions

display_name: atmospheric-conditions

automated-monitoring

display_name: automated-monitoring

biomass

display_name: biomass

carbon-cycle

display_name: carbon-cycle

carbon-dioxide

display_name: carbon-dioxide

carbon-sequestration

display_name: carbon-sequestration

carbon-storage

display_name: carbon-storage

climate-change

display_name: climate-change

climate-growth

display_name: climate-growth

davos

display_name: davos

dbh

display_name: dbh

dendrometer

display_name: dendrometer

ecophysiology

display_name: ecophysiology

ecosystem-functioning

display_name: ecosystem-functioning

eddy-covariance

display_name: eddy-covariance

eddy-covariance-measurements

display_name: eddy-covariance-measurements

flux

display_name: flux

forest

display_name: forest

forest-area

display_name: forest-area

forest-biomass

display_name: forest-biomass

forest-canopy

display_name: forest-canopy

forest-characteristics

display_name: forest-characteristics

forest-dynamics

display_name: forest-dynamics

forest-ecosystems

display_name: forest-ecosystems

forest-function

display_name: forest-function

forest-monitoring

display_name: forest-monitoring

icos

display_name: icos

icp-forests

display_name: icp-forests

ilter

display_name: ilter

lwf

display_name: lwf

metadata

display_name: metadata

open-data

display_name: open-data

picea-abies

display_name: picea-abies


en: The data results from a forest visitor survey that was conducted in June 2022 in the Waldlabor on Hönggerberg in Zurich. This questionnaire’s aim was to investigate how visitors perceived the different local forest management forms and to gain insights into the influence of visitors’ forest preferences, reasons for the visit and performed activities. The visual perceived forest attractiveness was collected based on six different survey locations featuring different forest management forms respectively different stages. This study was supported by the Waldlabor Zürich.

en: forest-recreation coppice-with-standards forest-management forest-attractiveness forest-characteristics

en: The influence of different forest management forms on the visual perceived attractiveness of forests for recreational use

envi

en: Environment

de:
Resource Title: Questionnaire German
    Size: 214415 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/76529e56-b3ba-43a5-9433-e2eb76b57718/resource/52eb9892-e63b-4daf-be96-f70c08d6d460/download/fragebogen.pdf
    Description:

de:
Resource Title: SPSS-Dataset
    Size: 108035 bytes
    Format: application/x-spss-sav
    URL: https://www.envidat.ch/dataset/76529e56-b3ba-43a5-9433-e2eb76b57718/resource/fec0b2ad-27cb-4fe4-afcd-02cb20a06d89/download/spss_datensatz_bewirtschaftungsformen-waldlabor.sav
    Description: The survey's dataset collected in the Waldlabor Hönggerberg, Zurich in SPSS-format.

de:
Resource Title: Codebook to SPSS-file
    Size: 130582 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/76529e56-b3ba-43a5-9433-e2eb76b57718/resource/ad1e39f1-6797-4f57-9d33-713f544f7ffe/download/codebook_variablen.pdf
    Description:

de:
Resource Title: Dataset in Excel-format
    Size: 148438 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/76529e56-b3ba-43a5-9433-e2eb76b57718/resource/d7be3d25-5199-49b6-8de0-7bfd62e3821e/download/spss_datensatz_bewirtschaftungsformen-waldlabor.xlsx
    Description: The survey's dataset collected in the Waldlabor Hönggerberg, Zurich in Excel-format.

format: XLS

coppice-with-standards

display_name: coppice-with-standards

forest-attractiveness

display_name: forest-attractiveness

forest-management

display_name: forest-management

forest-recreation

display_name: forest-recreation


en:
Starter kit for the TreeMig framework that allows for an easy application of the forest landscape model TreeMig for simulating forest dynamics in space under changing environmental and land use conditions.
The starter kit consists of a set of climate, land-use, topography and soil property input data for Switzerland, a script for installing the framework and running the GUI of the framework with the link to the input data, a tutorial covering a range of simulation examples for Switzerland and a transect through Europe, and an associated example simulation environment containing the model executables and other model inputs.

en: forest-dynamics spatial-modelling species-interaction forest-model gui land-use bioclimatic-variables forest treemig r-package species-migration

envidat

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

en: EnviDat: the Environmental Data Portal

en: Starter Kit for the TreeMig framework v1 for easy simulation of spatio-temporal forest dynamics

de:
Resource Title: TreeMig_Tutorial_v2024_12_04.pdf
    Size: 6400462 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/d4c15251-917c-4102-b172-256fecd39161/download/treemig_tutorial_v2024_12_04.pdf
    Description:

de:
Resource Title: TreeMig Simulation Environment and Input Data
    Size: 1612103870 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/5eef16d1-80af-442b-9da2-f64b4fa94671/download/tmenvandinputdata.zip
    Description: The zip file consists of a R-script to provide the framework, an example simulation environment and a set of input data.
- The R-script installAndRunTreeMigWrapperAndGUI.R installs the R-package, links to the input data and starts the graphical user interface.
- The gridded input datasets in TMInputData are specifically tailored to Switzerland and have the same coordinate reference system and a congruent 200m spatial resolution. They consist of climate, topographic, land use and soil data.  For details see TMInputDataDetails.pdf.
- TMEnvironment is an example simulation environment, i.e. a set of hierarchically structured folders.  For the first example simulation of the tutorial it contains the
   - the TreeMig executables for MacOS, Linux and Windows
   - simulation control files that define the setup and input files of the simulation (in folder C)
   - species parameters (in folder P)
   - bioclimatic data  (in folder E)
   - folder R for results
   - folder Plots for plots of the simulation results.
   - This environment can be also used for the subsequent simulations of the tutorial.
- Some additional R-scripts demonstrate more complex applications described in the tutorial, e.g. coupling to another model.

de:
Resource Title: readme_descriptioninputdata.pdf
    Size: 269312 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/3db8ca57-2838-4b26-82a9-9770b75b7096/download/readme_descriptioninputdata.pdf
    Description: Where the environmental and land use input data come from and how they were processed

de:
Resource Title: TreeMig_Tutorial_v2024_12.pdf
    Size: 3399819 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/5188a2e1-470c-4ed7-83c6-26dc72bd18ab/download/treemig_tutorial_v2024_12.pdf
    Description: Tutorial for using the TreeMig framework; updated

de:
Resource Title: TreeMig Simulation Environment and Input Data
    Size: 1602386904 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/c2ae78fd-418a-4cdb-a511-0914eb46224f/download/tmenvandinputdata.zip
    Description: The zip file consists of a R-script to provide the framework, an example simulation environment and a set of input data.
- The R-script installAndRunTreeMigWrapperAndGUI.R installs the R-package, links to the input data and starts the graphical user interface.
- The gridded input datasets in TMInputData are specifically tailored to Switzerland and have the same coordinate reference system and a congruent 200m spatial resolution. They consist of climate, topographic, land use and soil data.  For details see TMInputDataDetails.pdf.
- TMEnvironment is an example simulation environment, i.e. a set of hierarchically structured folders.  For the first example simulation of the tutorial it contains the
   - the TreeMig executables for MacOS, Linux and Windows
   - simulation control files that define the setup and input files of the simulation (in folder C)
   - species parameters (in folder P)
   - bioclimatic data  (in folder E)
   - folder R for results
   - folder Plots for plots of the simulation results.
   - This environment can be also used for the subsequent simulations of the tutorial.
- Some additional R-scripts demonstrate more complex applications described in the tutorial, e.g. coupling to another model.

de:
Resource Title: TreeMig_Tutorial_2025-05-02.pdf
    Size: 2107648 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/a1dec65a-30ba-4688-90ac-50df5db73d4c/download/treemig_tutorial_2025-05-02.pdf
    Description: Tutorial for using the TreeMig framework; updated

de:
Resource Title: TreeMig Tutorial
    Size: 2046576 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/8e6ac324-ab2b-40bc-a12f-70960c8f4bd1/download/treemig_tutorial.pdf
    Description: Tutorial for using the TreeMig framework

bioclimatic-variables

display_name: bioclimatic-variables

forest

display_name: forest

forest-model

display_name: forest-model

gui

display_name: gui

land-use

display_name: land-use

r-package

display_name: r-package

spatial-modelling

display_name: spatial-modelling

species-interaction

display_name: species-interaction

species-migration

display_name: species-migration

treemig

display_name: treemig


en:
This dataset contains all processed data, simulation outputs, and R/Python scripts used to assess the robustness of regional multi-habitat food webs in Switzerland under various non-random species extinction scenarios. It accompanies the manuscript submitted to Communications Biology, and available as a preprint here:
Reji Chacko, M,  Albouy, C., Altermatt, F., et al. Decreases in the robustness of regional food webs to sequential species extinctions following habitat loss, 04 December 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-5225132/v1]
Please note that the most up-to-date version of the simulations code is available here: https://github.com/mrejichacko/casCHades

en: food-webs trophich network-analysis metaweb habitat-fragmentation robustness trophic-networks

envidat

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.

de: EnviDat: das Umwelt-Datenportal

de: EnviDat

en: Simulation data and analyses of regional food web robustness under habitat loss

envi

de: Umwelt

food-webs

display_name: food-webs

habitat-fragmentation

display_name: habitat-fragmentation

metaweb

display_name: metaweb

network-analysis

display_name: network-analysis

robustness

display_name: robustness

trophic-networks

display_name: trophic-networks

trophich

display_name: trophich


en:
Description: Fish environmental DNA data set collected in 2023 in the Calanques National Park
The eDNA samples were collected in 2023 in two locations (Moyades, M−FPA and “Impérial du large”, I-LPA), during the winter (January- February) season, with the sampling dates depending on the weather conditions. For the M−FPA, samples were collected between Moyades island and Riou island, while for the I-LPA, they were collected on the south side of the “Impérial du large” island. To account for the existing bathymetry, in the M−FPA the samples were taken at two sampling sites with depths of 20 and 40 m, while in the I-LPA they were taken at two sites with depths of 20 and 80 m.
At each site, in-situ filtration of seawater was performed using a double-head submersible pump (Subspace, Geneva, Switzerland; nominal flow of ca. 1 L/min) strapped to an underwater scooter with 2 VigiDNA 0.20 µm filtration capsules (SPYGEN, le Bourget du Lac, France), along with disposable sterile tubing. The samples were collected along two horizontal transects (up to 400 m in length) during each closed-circuit rebreather dive, enabling the filtration of a water volume of 15 L/filter per depth, as close as possible to the substrate. Two filter replicates were collected by two divers at each sampling site, except in two cases where bad weather conditions or logistical issues meant that only one replicate was sampled.
After the filtration, the remaining seawater was emptied from the capsule back on the boat and replaced by a 80 mL CL1 conservation buffer (SPYGEN, le Bourget du Lac, France). To prevent any contamination, a strict protocol was followed during the entire process, requiring disposable gloves and single-use filtration equipment. Finally, the samples were stored at room temperature. We followed a strict contamination control protocol in both field and laboratory stages. Each water sample processing included the use of disposable gloves and single-use filtration equipment to avoid any risk of contamination. Libraries were prepared with ligation using the MetaFast protocol (Fasteris).
Data content:
	* rawdata/: contains the raw reads for each individual sample. One archive contains the paired-end reads specified by the _R1 or _R2 suffix as well as individually tagged PCR replicates (if available) together with an archive containing all extraction and PCR blank samples of the library. Reads have been demultiplexed using cutadapt but not trimmed, individual demultiplexing tags and primers remain present in the sequences.
	* taxadata/: contains the table with all detected taxonomy for each sample after bioinformatic processing (see Polanco et al. 2020 for details; https://doi.org/10.1002/edn3.140) and associated field metadata.
	* metadata/: contains two metadata files, one related to the data collected in the field for each filter, and the second related to the sequencing process in the lab (including the tag sequence, library name, and marker information for each sample)

en: mediterraneaen fish-composition marine teleo edna marseille

en: Environmental DNA Marine France Calanques 2023

edna

display_name: edna

fish-composition

display_name: fish-composition

marine

display_name: marine

marseille

display_name: marseille

mediterraneaen

display_name: mediterraneaen

teleo

display_name: teleo


lwf-alptal-long-term-research-site

en:
![alt text](https://www.envidat.ch/dataset/49729a45-f5bf-4bc0-afdd-77123894d3bb/resource/aa505753-198c-49dc-a33e-c4c8e4fcb611/download/lwf_alptal.jpg "LWF Alptal")
LWF plot Alptal
- Community: Alpthal / canton SZ
- Date of installation: 31 May 1995
- Size of the plot: 0.6 ha
- Altitude: 1149-1170 m
- Mean slope: 23%
- Geology (in German): Nordpenninikum; obere Kreide-unteres Eozän, Wägitaler Flysch
- Soil types (WSL) : Mollic Gleysols, Gleyic Cambisols
- Woodland association after EK72: 49: Equiseto-Abietetum
- Main tree species: Picea abies
- Management system: high forest
- Silvicultural system: selection
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 39.3 cm
- Number of trees BHD -= 12 cm (2011): 321
- Maximum tree age: Picea abies 180-230 yr
More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/alptal.html

en: lwf infrastructure swiss-forest-lab meteorological-data forest plot meteorological-measurements

en: LWF Alptal long-term research site

title_for_slug: lwf-alptal-long-term-research-site

de:
Resource Title: LWF plot Alptal
    Size: 155754 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/49729a45-f5bf-4bc0-afdd-77123894d3bb/resource/aa505753-198c-49dc-a33e-c4c8e4fcb611/download/lwf_alptal.jpg
    Description: Photo: Flurin Sutter, 2013

format: jpeg

de:
Resource Title: Alpthal Meteorological Data LWF
    Size: 1 bytes
    Format: No Info
    URL: https://doi.org/10.16904/envidat.94
    Description:

de:
Resource Title: Site_image
    Size: 123357 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/49729a45-f5bf-4bc0-afdd-77123894d3bb/resource/c76f9779-0388-416b-b41c-91f099d526fc/download/alptal.jpg
    Description: LWF plot Alptal Photo: P.Waldner

forest

display_name: forest

infrastructure

display_name: infrastructure

lwf

display_name: lwf

meteorological-data

display_name: meteorological-data

meteorological-measurements

display_name: meteorological-measurements

plot

display_name: plot

swiss-forest-lab

display_name: swiss-forest-lab


en:
This dataset accompanies the publication "How Flat is Flat? Investigating the spatial variability of snow surface temperature and roughness on landfast sea ice using UAVs in McMurdo Sound, Antarctica".
The dataset consists of 1) processed orthomosaic of RGB values, snow topography (digital elevation model), and surface temperature, 2) topography-dependent irradiance map, and 3) point data of measured radiation and atmospheric parameters in the McMurdo Sound, October-December 2022.  The data was collected as part of the New Zealand Marsden Fund Research Grant 21-VUW-103 "Can Snow Change the Fate of Antarctic Sea Ice?". Unprocessed data associated with this field campaign are published in 10.16904/envidat.633.
For the airborne measurements, we used a DJI Matrice 30T, a multi-rotor (quadcopter) UAV with self-heating batteries, suitable for operations in polar conditions (temperature range from -20 to +50 °C). The UAV has a wide-angle camera (12 megapixels) for RGB images and a thermal camera (Uncooled Vox Microbolometer, long-wave infrared spectrum 8-14 µm, 1.31 megapixels) for TIR images with an accuracy of ±2 °C or ± 2 %.
GNSS data includes high-precision location information (latitude, longitude, ellipsoidal height, receiver clock offset, and timestamps formatted in decimal hours and day-of-year notation).
WE calculated the distribution of received solar irradiance over the sea-ice surface using a terrain-corrected radiative transfer model and measured shortwave radiation components, accounting for surface roughness, aspect, slope, and solar position during the flight, while applying terrain shading and measured diffuse radiation.
The broadband radiation station data consists of shortwave (Sw) and longwave (Lw) radiation measurements (Kipp & Zonen CMP22 pyranometers and CGR4 pyrgeometers).
Surface temperature readings from cold, sediment, and hot targets were recorded using Apogee SI-121-SS sensors.
This dataset also contains automated weather station (AWS) data (air and snow surface temperatures, relative humidity, wind direction, and wind speed).
For detailed information, please use the README file.

en: snow-roughness irra drone snow uav sea-ice antarctica snow-temperature

en: Snow On Antarctic Sea Ice - McMurdoSound 2022 - UAV Retrievals of Snow Topography and Snow Surface Temperature

antarctica

display_name: antarctica

drone

display_name: drone

irra

display_name: irra

sea-ice

display_name: sea-ice

snow

display_name: snow

snow-roughness

display_name: snow-roughness

snow-temperature

display_name: snow-temperature

uav

display_name: uav


en:
In Memory of Dr. Konrad (Koni) Steffen
-br /-
-br /-
Update October 2022: The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).
Starting October 3, 2022, the access to the latest versions of the "ready to use" L1 data has been migrated to GEUS. Future data versions will be available at:  [https://doi.org/10.22008/FK2/VVXGUT](https://doi.org/10.22008/FK2/VVXGUT)
 Background
Starting with a single station in 1991, the Greenland Climate Network (commonly known as GC-Net) is a set of Automatic Weather Stations (AWS) set up and managed by the late Prof. Dr. Konrad (Koni) Steffen, and spanning the Greenland Ice Sheet (GrIS). This first station was "Swiss Camp" or the "ETH-CU" camp (GC-Net station 01) which was used as a field science and education site by Koni for years. The GC-Net was expanded with multiple NASA, NOAA, and NSF grants throughout the years, and then supported by WSL in the later years. These data (see "C-file" below) were previously hosted by the Cooperative Institute for Research in Environmental Sciences (CIRES) in Boulder, Colorado.
 Overview
Provided in this dataset are the 16 longest running stations in the network, which are spread over a significant area of the GrIS and the majority of the unique climatic zones. From the South Dome high point in the South, to the Western Jakobshavn ablation region in the west, to the Petermann glacier in the North across east of the Northeast Greenland Ice Stream to the east, GC-Net is the longest running climatological record of Greenland.
 The standard GC-Net station consists of:
* Air temperature measurements at 2 heights above the surface
* Temperature and humidity measurements at 2 heights above the surface
* Wind speed and direction measured at 2 heights above the surface
* Sonic distance sounder measurements for 2 snow height and distance of instruments to surface
* Incoming shortwave radiation measurement
* Reflected shortwave radiation measurement
* Net broadband radiation (long- and short-wave) measurement
* Air pressure measurement
Data have often been repatriated in near-real time using one of either the GOES geostationary satellite or the ARGOS polar orbiting satellite transmission system. The stations were visited typically every 1-2 years for maintenance and service, and to download full uncorrupted data directly from the dataloggers. GC-Net stations were visited by Twin Otter equipped with snow skids to land directly on the open-ice at the AWS locations, or by helicopter near the west coast. The AWSs operate on solar and battery power and occasionally lost power during the dark and cold winter months, particularly when the batteries were aging.
 Dataset
This dataset consists of 2 main data levels; Level 0 and Level 1.
Level 0 is the raw data from the dataloggers, historical processing codes, satellite transmissions, and Koni’s personal data archive. Level 0 data (.zip) directories contain subdirectories:
* “C file” - contains the historical processed datafile for each station.
* “Campbell logger files” - contains the raw csv datafiles from the stations’ Campbell Scientific dataloggers since the CR1000 era (~2007-2008 for most stations).
* “Photos” - contains photographs of the station when available marked by year.
Level 1 is the appended, calibrated, cleaned, and quality flagged data. The full processing scheme is open-source and publicly available on the following GitHub repository (please also check GitHub for the latest L1 data):
[GC-Net L1 data on GitHub](https://github.com/GEUS-Glaciology-and-Climate/GC-Net-level-1-data-processing "GC-Net-level-1-data-processing")
Level 1 data is provided in the newly described csv-compatible [NEAD format](https://www.envidat.ch//metadata/nead "NEAD format").
-br /-
 Additional Details
Dataset description publication will be forthcoming.
The Geological Survey of Denmark and Greenland (GEUS) has been imperative in the reprocessing and continuity mission of GC-Net. Multiple GC-Net stations have been replaced with updated and upgraded AWS hardware at the same coordinates by GEUS. This effort will ensure continuity of the GC-Net dataset into the future.

en: air-temperature net-radiation automatic-weather-station aws climate greenland

en: Greenland Climate Network (GC-Net) Data

envi

en: Environment

de:
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    Description: Stations parameters from static bucket

format: JSON

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    Description: The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).

Starting October 3, 2022, the access to the latest versions of the "ready to use" L1 data has been migrated to GEUS, (doi:10.22008/FK2/VVXGUT). Future versions will be available from GEUS, while the initial versions of L0 and L1 data have been archived.

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de:
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    Description: The GC-Net is kindly continued by the Geological Survey of Denmark and Greenland (GEUS).

Starting October 3, 2022, the access to the latest versions of the "ready to use" L1 data has been migrated to GEUS, (doi:10.22008/FK2/VVXGUT). Future versions will be available from GEUS, while the initial version of L1 data has been archived.

format: xhtml+xml

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air-temperature

display_name: air-temperature

automatic-weather-station

display_name: automatic-weather-station

aws

display_name: aws

climate

display_name: climate

greenland

display_name: greenland

net-radiation

display_name: net-radiation


en:
Background information
Climate change-induced range expansion of treeline populations depends on their successful recruitment, which requires dispersal of viable seeds followed by successful establishment of individual propagules. The Global Treeline Range Expansion Experiment (G-TREE) is a global initiative involving researchers from Europe, North America, Australia and New Zealand (Brown et al., 2013). At 15 alpine and Arctic treeline sites worldwide the mechanisms determining the elevational and latitudinal distribution of tree populations are studied using a standardized experimental approach. In summer 2013, a multifactorial seedling recruitment experiment has been established at the Stillberg ecological treeline research site. The aim of this experiment, is to quantify the effect of multiple abiotic and biotic drivers on emergence, survival, and growth of *Larix decidua* and *Picea abies* seedlings in replicated plots along an elevation gradient with three sites below (1930 m a.s.l.), at (2090 m a.s.l.), and above treeline (2410 m a.s.l.; Frei et al., 2018). All plots have been surveyed annually to count seedlings and to measure their total height. Additional environmental factors, such as soil temperature, have been recorded.
 Experimental design
The Stillberg research area is located in the Eastern Swiss Alps near Davos, Switzerland. The site has been used for several long-term monitoring as well as experimental studies for the last four decades. Our G-TREE experiment consists of a lowest site located in a subalpine Larch-Spruce forest (*Larici-Picetum*) dominated by *Larix decidua* and *Picea abies* (1930 m a.s.l.), a transition zone site dominated by alpine shrubs (2100 m a.s.l.), and an uppermost site in an alpine meadow with some dwarf shrubs (2390 m a.s.l.). The three experimental sites were set up following the standard protocol of the global G-TREE initiative (Brown et al., 2013). In a split-plot design, 20 plots (224 cm × 45 cm) were established at each site, which were randomly assigned to the 2 × 2 treatment combinations of the main factors seeding and scarification (i.e. seeding and scarification, seeding only, scarification only, and full control), resulting in five replications per main treatment combination. Each plot was divided into 16 split-plots (22.5 cm × 28 cm), to which treatment combinations of four additional two-level factors species (larch and spruce), provenance (low- and high-elevation), herbivore exclosure (with and without exclosure), and seeding year (2013, 2014) were randomly assigned, which resulted in a total of 960 split-plots (Details see Frei et al. 2018).
 Data description
All plots have been surveyed annually to count seedlings and to measure their total height. Seedling height was assessed with a hand ruler as the total length from the original emerging point to the apical meristem (Details see Frei et al. 2018). Additionally, soil temperature at each site, has been continuously recorded since 2013. Here, we present data from eight years (2013–2021).

en: stillberg swiss-alps treeline g-tree forest climate-change seedling-recruitment picea-abies larix-decidua

envidat

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

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.

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

de: EnviDat

en: G-TREE: Global Treeline Range Expansion Experiment Davos, Switzerland

envi

de: Umwelt

climate-change

display_name: climate-change

forest

display_name: forest

g-tree

display_name: g-tree

larix-decidua

display_name: larix-decidua

picea-abies

display_name: picea-abies

seedling-recruitment

display_name: seedling-recruitment

stillberg

display_name: stillberg

swiss-alps

display_name: swiss-alps

treeline

display_name: treeline


en:
This dataset groups numerical simulation outputs and validation measurement data produced in the context of the following [publication]: _not published yet_
The snow transport model [snowBedFoam] was used to analyse snow deposition around a specific type of Alpine PV structures named HELIOPLANT®.
The results of a sensitivity analysis of multiple key parameters that govern the spatial organisation of these structures are provided here. Measurements of snow distribution taken from the test-site of the [Gondosolar] project, are provided too.

To reproduce the results of the aforementioned publication, follow the instructions on this [repository].
[publication]: https://
[snowBedFoam]: https://www.doi.org/10.16904/envidat.223
[Gondosolar]: https://www.gondosolar.ch
[repository]: https://github.com/frischwood/snowbed-helio.git

en: photovoltaic preferential-deposition renewable-energy alpine snow-transport

en: Drifting and blowing snow distribution around structures for Alpine PV applications

de:
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de:
Resource Title: simulations.zip
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    URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.589/simulations.zip
    Description: This file contains the snowBedFoam simulations carried out to produce the results presented in: _article not published yet_.

## Content

### 1. /simulations
Groups all simulations folders organised by parameter (azimuth, groupsize, height, interspace) and by type of experiment (single_ref, group_ref).

### 2. /simulations/[parameter]/
For each parameter multiple simulations were carried out. Each simulation folder follows the same naming systematic:

`helioplant_n#_h#_ARL#_BL#_az#_windDir#`
- n: number of Helioplant units
- h: height-above-surface
- sp: regular distance between the units
- ARL: additional refinement level
- BL: boundary layer first cell height
- windDir: wind direction
- stag: staggered arangement (suffix)

Here is an example of a folder content for a specific parameter
(simulations/azimuth/):
- `helioplant_n9_h07_sp10_ARL45_BL0015_az30_windDir5`: following the naming systematic this folder corresponds to a simulation with 9 aligned Helioplant units with 0.7m of height-above-surface, an inter-unit distance of 10m an azimuth of 30° and a wind direction of 5°.

### 3. /simulations/[parameter]/[helioplant_n#_h#_ARL#_BL#_az#_windDir#]
Each simulation folder contains multiple sub-folders (0/, 100/, 300/, constant/ etc.) and files that define the experiment. For each simulation, the files used in the processing [scripts] are the following:
- 0/Cx: contains the x coordinates of sampling grid cells and of the Helioplants structures.
- 0/Cy: contains the y coordinates of sampling grid cells and of the Helioplants structures.
- 300/kinematicCloud-massCheckPatterns: contains the snow mass distribution values [kg/m2] for each grid cell.

[scripts]: https://github.com/frischwood/snowbed-helio.git

alpine

display_name: alpine

photovoltaic

display_name: photovoltaic

preferential-deposition

display_name: preferential-deposition

renewable-energy

display_name: renewable-energy

snow-transport

display_name: snow-transport