en: High quality meteorological data are needed for long-term forest ecosystem research, particularly in the light of global change. The long-term data series published here comprises almost 20 years of measurements for two meteorological stations in Othmarsingen in Switzerland where one station is located within a natural deciduous forest stand (OTB) with European beech (_Fagus sylvatica_; 120-140 yrs) and lime trees (_Tilia sp._; 120-140 yrs) as dominant tree species. A second station is situated in the very vicinity outside of the forest (field station, OTF). The meteorological time series are presented in hourly time resolution of air temperature, relative humidity, precipitation, photosynthetically active radiation (PAR) and wind speed. Othmarsingen is part of the Long-term Forest Ecosystem Research Programme (LWF) established and maintained by the Swiss Federal Research Institute WSL.

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

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: Othmarsingen, Switzerland: Long-term forest meteorological data from the Long-term Forest Ecosystem Research Programme (LWF), from 1996 onwards

en:
Resource Title: Field station data (OTF)
    Size: 11582801 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/70a7c0f0-0ab9-4d31-b80c-c656c9fca7d9/resource/5b4e810a-ce77-47dc-9e23-ba691ade9192/download/otffield.csv
    Description: Data file.

en:
Resource Title: Site photo of the field station (OTF)
    Size: 11868194 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/70a7c0f0-0ab9-4d31-b80c-c656c9fca7d9/resource/74f4dcb2-e18d-40bb-bb18-8383f907a285/download/otf.jpg
    Description: Site photo of the field station from below (viewing upwards the slope).

en:
Resource Title: Forest station data (OTB)
    Size: 10501901 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/70a7c0f0-0ab9-4d31-b80c-c656c9fca7d9/resource/f5f71a78-9574-4bcc-a326-4d455b6821a4/download/otbforest.csv
    Description: Data file.

en:
Resource Title: Site photo of the forest station (OTB)
    Size: 319164 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/70a7c0f0-0ab9-4d31-b80c-c656c9fca7d9/resource/b1937555-6a89-4452-93f9-3cf418382fb6/download/otb.jpg
    Description: Site photo of the forest station from above (viewing downwards the slope).

en:
Resource Title: Site aerial photo (Othmarsingen)
    Size: 7687492 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/70a7c0f0-0ab9-4d31-b80c-c656c9fca7d9/resource/d74e4b24-bd75-4ce2-8bb5-4c04b77d8099/download/otfront.jpg
    Description: Site aerial photo from both Othmarsingen stations. Source: Google. 2016. Site aerial photo from both Othmarsingen stations. Retrieved November 11, 2016, from https://www.google.ch/maps

en:
Resource Title: Data plot of the field station (OTF)
    Size: 302026 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/70a7c0f0-0ab9-4d31-b80c-c656c9fca7d9/resource/663df714-b0fb-45d3-b83f-3e80755a0a1a/download/otfplot.pdf
    Description: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

en:
Resource Title: Data plot of the forest station (OTB)
    Size: 255873 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/70a7c0f0-0ab9-4d31-b80c-c656c9fca7d9/resource/f01faaab-8cee-4b07-b95c-1498f5b09285/download/otbplot.pdf
    Description: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

long-term

display_name: long-term


$oid: OBSOLETE

en:
This data set includes GPS-derived snow water equivalent (SWE), snow depth (HS) and liquid water content (LWC) data for three entire snow-covered seasons (2015-2016, 2016-2017, 2017-2018) at the study plot Weissfluhjoch 2540 m a.s.l. (Davos, Switzerland). The procedure to derive these snow properties is described in Koch et al. (2019). The novel approach is based on a combination of GPS signal attenuation and time delay.
The dataset also includes corresponding validation data for SWE and HS measured at Weissfluhjoch, and some additional meteorological data used for interpretation of the snow cover evolution.
Please refer to the Read-me file for further details on the data.
These data are the basis of the following publication:
- Koch, F., Henkel, P., Appel, F., Schmid, L., Bach, H., Lamm, M., Prasch, M., Schweizer, J., and Mauser, W., 2019. Retrieval of snow water equivalent, liquid water content and snow height of dry and wet snow by combining GPS signal attenuation and time delay. Water Resources Research, 55(5), 4465-4487. https://doi.org/10.1029/2018WR024431

en: snow-cover snow liquid-water-content snow-depth gps

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.

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

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: GPS-derived data of SWE, HS and LWC and corresponding validation data

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

en:
Resource Title: Dataset with GPS-derived snow cover properties and validation data
    Size: 90714 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/cd57ac77-a276-4783-88c9-f52f51552d2c/resource/01f4a113-7ad0-4993-9efa-f30bbe0f0474/download/gps-derived_snow_properties.csv
    Description: This file includes the data as described in the Read-me file.

format: CSV

media_type: text/csv

en:
Resource Title: Read-me
    Size: 109310 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/cd57ac77-a276-4783-88c9-f52f51552d2c/resource/d491aadc-0a43-4caa-8b32-09c988ec4841/download/readme.pdf
    Description: The read-me file contains the description of the data provided.

format: PDF

media_type: application/pdf

gps

display_name: gps

liquid-water-content

display_name: liquid-water-content

snow

display_name: snow

snow-cover

display_name: snow-cover

snow-depth

display_name: snow-depth


en:
This dataset contains open vector data for railways, forests and power lines, as well an open digital elevation model (DEM) for a small area around a sample forest range in Europe (Germany, Upper Bavaria, Kochel Forest Range, some 70 km south of München, at the edge of Bavarian Alps).
The purpose of this dataset is to provide a documented sample dataset in order to demonstrate geospatial preprocessing at FOSS4G2019 based on open data and software. This sample has been produced based on several existing open data sources (detailed below), therefore documenting the sources for obtaining some data needed for computations related to forest accessibility and wood harvesting. For example, they can be used with the open methodology and QGIS plugin [Seilaplan]( https://doi.org/10.16904/envidat.software.1) for optimising the geometric layout cable roads or with additional open software for computing the forest accessibility for wood harvesting.
The vector data (railways, forests and power lines) was extracted from OpenStreetMap (data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org). The railways and forests were downloaded and extracted on 18.05.2019 using the open sources QGIS (https://www.qgis.org) with the QuickOSM plugin, while the power lines were downloaded a couple of days later on 23.05.2019.
Additional notes for vector data:
Please note that OpenStreeMap data extracts such as forests, roads and railways (except power lines) can also be downloaded in a GIS friendly format (Shapefile) from http://download.geofabrik.de/ or using the QGIS built-in download function for OpenStreetMap data. The most efficient way to retrieve specific OSM tags (such as power=line) is to use the QuickOSM plugin for QGIS (using the Overpass API -  https://wiki.openstreetmap.org/wiki/Overpass_API) or directly using overpass turbo (https://overpass-turbo.eu/). Finally, the digitised perimeter of the sample forest range is also made available for reproducibility purposes, although any  perimeter or area can be digitised freely using the QGIS editing toolbar.
The DEM was originally adapted and modified also with QGIS (https://www.qgis.org) based on the elevation data available from two different sources, by reprojecting and downsampling datasets to 25m then selecting, for each individual raster cell, the elevation value that was closer to the average. These two different elevation sources are:
- Copernicus Land Monitoring Service - EU-DEM v.1.1 (TILE ID E40N20, downloaded from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1; this original DEM was produced by the Copernicus Land Monitoring Service “with funding by the European Union” based on SRTM and ASTER GDEM)
- Digitales Geländemodell 50 m Gitterweite (https://opendata.bayern.de/detailansicht/datensatz/digitales-gelaendemodell-50-m-gitterweite/), produced by the Bayerische Vermessungsverwaltung – www.geodaten.bayern.de –and downloaded from http://www.geodaten.bayern.de/opendata/DGM50/dgm50_epsg4258.tif
This methodology was chosen as a way of performing a basic quality check, by comparing the EU-DEM v.1.1 derived from globally available DEM data (such as SRTM) with more authoritative data for the randomly selected region, since using authoritative data is preferred (if open and available). For other sample regions, where authoritative open data is not available, such comparisons cannot longer be performed.
Additional notes DEM: a very good DEM open data source for Germany is the open data set collected and resampled by Sonny (sonnyy7@gmail.com) and made available on the Austrian Open Data Portal http://data.opendataportal.at/dataset/dtm-germany. In order to simplify end-to-end reproducibility of the paper planned for FOSS4G2019, we use and distribute an adapted (reprojected and resampled to 25 meters) sample of the above mentioned dataset for the selected forest range.
This sample dataset is accompanied by software in Python, as a Jupiter Notebook that generates harmonized output rasters with the same extent from the input data. The extent is given by the polygon vector dataset (Perimeter). These output rasters, such as obstacles, aspect, slope, forest cover, can serve as input data for later computations related to forest accessibility and wood harvesting questions. The obstacles output is obtained by transforming line vector datasets (railway lines, high voltage power lines) to raster. Aspect and slope are both derived from the sample digital elevation model.

en: accessible-forest seilaplan qgis forest openstreetmap geoprocessing

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

en:
Resource Title: DEM (for the area of the sample forest range)
    Size: 1695063 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/d28614a0-0825-4040-bc1b-e0455b1e4df6/resource/1bd80d45-a8fd-44dc-b153-7abfa6345d1c/download/eudem_ogdbayern_25m_25832.tif
    Description: EPSG:25832

format: TIFF

media_type: image/tiff

en:
Resource Title: Preview
    Size: 391085 bytes
    Format: image/png
    URL: https://www.envidat.ch/dataset/d28614a0-0825-4040-bc1b-e0455b1e4df6/resource/729c975c-df90-4af2-979a-af44069dc02f/download/preview.png
    Description:

format: PNG

media_type: image/png

en:
Resource Title: Digitised sample perimeter
    Size: 2083 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/d28614a0-0825-4040-bc1b-e0455b1e4df6/resource/710a065d-d50e-4a3d-a993-2a65197cef7b/download/digitized_sample_perimeter.zip
    Description: EPSG:25832

format: ZIP

media_type: application/zip

en:
Resource Title: Juypter Notebook for Data Preparation
    Size: 29318 bytes
    Format: application/x-ipynb+json
    URL: https://www.envidat.ch/dataset/d28614a0-0825-4040-bc1b-e0455b1e4df6/resource/e0c2a4f8-ee8e-4ca9-b05f-0adf2401c879/download/data_preparation_foss4g2019.ipynb
    Description:

en:
Resource Title: Railways vector data (close to the sample forest range)
    Size: 50776 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/d28614a0-0825-4040-bc1b-e0455b1e4df6/resource/a787f798-0c3d-4cd3-a9d1-c93f51176465/download/osm_railways_export_25832.zip
    Description: EPSG:25832

en:
Resource Title: Forest vector data (for sample forest range)
    Size: 288311 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/d28614a0-0825-4040-bc1b-e0455b1e4df6/resource/16db97c5-5546-4f80-9cb9-e562637b589c/download/osm_forest_export_25832.zip
    Description: EPSG:25832

en:
Resource Title: Juypter Notebook for Data Preparation (online)
    Size: 0
    Format: application/x-ipynb+json
    URL: https://nbviewer.jupyter.org/urls/www.envidat.ch/dataset/d28614a0-0825-4040-bc1b-e0455b1e4df6/resource/e0c2a4f8-ee8e-4ca9-b05f-0adf2401c879/download/data_preparation_foss4g2019.ipynb
    Description:

en:
Resource Title: Power lines vector data (close to the area of the sample forest range)
    Size: 66908 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/d28614a0-0825-4040-bc1b-e0455b1e4df6/resource/c567f81c-26c9-4122-8fbd-045c55fb73c0/download/osm_powerlines_export_25832.zip
    Description: EPSG:25832

accessible-forest

display_name: accessible-forest

forest

display_name: forest

geoprocessing

display_name: geoprocessing

openstreetmap

display_name: openstreetmap

qgis

display_name: qgis

seilaplan

display_name: seilaplan


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

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

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

en:
Resource Title: Further details on the data repository
    Size: 17153511 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/7e6a34bc-3c2c-45f2-8e3b-1b58f1ba5074/resource/ee5c2d0d-e229-42e3-9144-37e92827f614/download/furtherdetailsschanis.pdf
    Description: Meta data documentation.

en:
Resource Title: Site aerial photo (Schänis)
    Size: 7026512 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/7e6a34bc-3c2c-45f2-8e3b-1b58f1ba5074/resource/9a6bd255-998b-44c0-9ab2-cc501f9bb78b/download/scfront.jpg
    Description: Site aerial photo from both Schänis stations. Source: Google. 2016. Site aerial photo from both Schänis stations. Retrieved November 11, 2016, from https://www.google.ch/maps

format: jpeg

en:
Resource Title: Forest station data (SCB)
    Size: 10593777 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/7e6a34bc-3c2c-45f2-8e3b-1b58f1ba5074/resource/f5242372-a0bd-42fa-a3be-038395f57134/download/scbforest.csv
    Description: Data file.

en:
Resource Title: Data plot of the field station (SCF)
    Size: 304254 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/7e6a34bc-3c2c-45f2-8e3b-1b58f1ba5074/resource/43a2b0ef-6611-4bf2-9d60-60577413515d/download/scfplot.pdf
    Description: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

en:
Resource Title: Field station data (SCF)
    Size: 10669678 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/7e6a34bc-3c2c-45f2-8e3b-1b58f1ba5074/resource/9bd7e72f-3b65-4250-9f24-f945c4476a0e/download/scffield.csv
    Description: Data file.

en:
Resource Title: Site photo of the forest station (SCB)
    Size: 388600 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/7e6a34bc-3c2c-45f2-8e3b-1b58f1ba5074/resource/00b2b4fa-a6b1-40b7-b1b7-f5c35c91b1a2/download/scb.jpg
    Description: Site photo of the forest station from below (viewing upwards the slope).

en:
Resource Title: Data plot of the forest station (SCB)
    Size: 253856 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/7e6a34bc-3c2c-45f2-8e3b-1b58f1ba5074/resource/7a59ef22-0dea-4b48-b92e-8aaca534665c/download/scbplot.pdf
    Description: Data overview for air temperature, relative humidity, precipitation, PAR, wind speed.

en:
Resource Title: Site photo of the field station (SCF)
    Size: 8615493 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/7e6a34bc-3c2c-45f2-8e3b-1b58f1ba5074/resource/68696b24-8c46-48ad-8bb2-c15468b466ce/download/scf.jpg
    Description: Site photo of the field station from above (viewing downwards the slope).

climate-change

display_name: climate-change

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed


data-broedlin-cnp

en: Mircocosm experiment to identify the individual patterns and controls of C, N, and P mobilization in soils under beech forests. Organic and mineral horizons sampled along a nutrient availability gradient in Germany were exposed to either permanent moist conditions or to dry spells in microcosms and quantified the release of inorganic and organic C, N, and P.

en: stoichiometry dissolved-organic-matter drying-rewetting nitrogen phosphorus forest soil carbon mineralization

en: Data Broedlin CNP

title_for_slug: data-broedlin-cnp

envi

en: Environment

en:
Resource Title: Data Broedlin-CNP-Release
    Size: 1249259 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/9dd9a649-405d-413a-ad44-d859ea084bba/resource/2ec747a1-3a2b-4743-b89f-db13a372c49f/download/broedlin_et_al_cnp_rawdata_v2.xlsx
    Description: Raw data of C-N-P mobilization experiment from Oi, Oe/Oa, and A horizons of beech forest soils along a nutrient availability gradient.

format: XLS

en:
Resource Title: Data Broedlin-CNP
    Size: 1189635 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/9dd9a649-405d-413a-ad44-d859ea084bba/resource/c4fce049-b79d-495f-a44d-94f1d59b913a/download/broedlin_cnp_rawdata.xlsx
    Description:

carbon

display_name: carbon

dissolved-organic-matter

display_name: dissolved-organic-matter

drying-rewetting

display_name: drying-rewetting

mineralization

display_name: mineralization

nitrogen

display_name: nitrogen

phosphorus

display_name: phosphorus

soil

display_name: soil

stoichiometry

display_name: stoichiometry


en:
Naturally, large parts of the Swiss Plateau are characterised by wetlands
and meandering rivers. That this is no longer the case today is the result
of centuries of efforts to obtain dry land. But how did this process take
place? What were the relevant actors and what were their motivations? And
what can be said about the ecological consequences of this development?
In a research project on the history of wetlands in Switzerland since 1700,
we conducted (a) a historical analysis of the development of land use in
wetlands and the actors involved, (b) a historical-cartographic
reconstruction of wetland extent since 1850 and (c) an evaluation of
ecological effects of changes in wetlands on various organisms groups.
The series of GIS layers on wetland history stem from the second part of
the project. The area reconstruction is based on digitized and homogenized
signatures from national map series, as they have been available since
about 1850. Details about the digitalization process and the homogenization
procedures applied ("Rekonstruktionen") are included in Stuber & Bürgi
2019.
Book Citation:
 - Stuber M, Bürgi M (2019) Vom «eroberten Land» zum Renaturierungsprojekt. Geschichte der Feuchtgebiete in der Schweiz seit 1700. "Bristol Schriftenreihe", Band 59. Haupt Verlag, Bern, Stuttgart, Wien. 262 Seiten.

en: historical-carthography wetlands ecology environmental--policy switzerland environmental---history

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: History of wetlands in Switzerland since 1850

ecology

display_name: ecology

environmental---history

display_name: environmental---history

environmental--policy

display_name: environmental--policy

historical-carthography

display_name: historical-carthography

switzerland

display_name: switzerland

wetlands

display_name: wetlands


automatic-classification-of-avalanches

en: This dataset contains the classification and localization results obtained during the automatic classification of avalanches during the winter season 2017.

en: localization avalanche monitoring machine-learning seismic

envidat

en: EnviDat: the Environmental Data Portal

en: Automatic Classification of Avalanches

title_for_slug: automatic-classification-of-avalanches

avalanche

display_name: avalanche

localization

display_name: localization

machine-learning

display_name: machine-learning

monitoring

display_name: monitoring

seismic

display_name: seismic


en:
Daily discharge and the related hydro-meteorological variables precipitation, snowmelt, and soil moisture are provided for current (1981-2017) and for future climate conditions (1981-2100) for 307 medium-sized catchments in Switzerland. The catchments have a median catchment area of 117 km². The 307 catchments together form a set representative of the climatological conditions and runoff characteristics in Switzerland. The four variables were simulated at a daily resolution using the hydrological model PREVAH. PREVAH is a conceptual process-based model that was run in this study in its fully distributed version on a 500 m grid (Viviroli et al. 2009a). For the calibration, runoff time series from 140 mesoscale catchments covering the different runoff regimes were used. The model calibration was conducted over the period 1993-1997. Verification was performed on the period 1983-2005 using (i) volumetric deviation (Viviroli et al. 2007) and (ii) benchmark efficiency (Schäfli et al 2007) as objective functions. The calibration and validation procedures are described in detail in Köplin et al. (2010). The parameters for each model grid cell were derived by regionalizing the parameters obtained for the 140 catchments with a procedure based on ordinary kriging (Viviroli et al. 2009b, Köplin et al. 2010).
The calibrated and validated model was then driven with transient meteorological data (precipitation, temperature, radiation, and wind) representing both reference (1981-2017) and future climate conditions (2018-2099). The data were derived from the CH2018 climate scenarios (NCCS 2018) provided by the Swiss National Centre for Climate Services (NCCS). They were obtained from climate experiments produced with different climate modeling chains, consisting of a global and a regional circulation model each, within EUROCORDEX  for three representative concentration pathways (RCP) emission scenarios. Downscaled output of ten climate model chains derived by quantile mapping were considered. The focus was on the chains of the EUR-11 domain with a horizontal resolution of 0.11 degrees (roughly 12.5 km). The climate model chains (GCM, RCM, RCP, and grid resolution) used are listed below:
- ICHEC-EC-EARTH	DMI-HIRHAM5	2.6	EUR-11
- ICHEC-EC-EARTH	DMI-HIRHAM5	4.5	EUR-11
- ICHEC-EC-EARTH	DMI-HIRHAM5	8.5	EUR-11
- ICHEC-EC-EARTH	SMHI-RCA4	2.6	EUR-11
- ICHEC-EC-EARTH	SMHI-RCA4	4.5	EUR-11
- ICHEC-EC-EARTH	SMHI-RCA4	8.5	EUR-11
- MOHC-HadGEM2-ES	SMHI-RCA4	4.5	EUR-11
- MOHC-HadGEM2-ES	SMHI-RCA4	8.5	EUR-11
- MPI-M-MPI-ESM-LR	SMHI-RCA4	4.5	EUR-11
- MPI-M-MPI-ESM-LR	SMHI-RCA4	8.5	EUR-11
*References*:
-	Köplin, N., D. Viviroli, B. Schädler, and R. Weingartner (2010), _How does climate change affect mesoscale catchments in Switzerland? - A framework for a comprehensive assessment_, Advances in Geosciences, 27, 111-119, doi:10.5194/adgeo-27-111-2010.
-	National Centre for Climate Services (2018), CH2018 - _Climate Scenarios for Switzerland_, Tech. rep., NCCS, Zurich.
-	Schäfli, B., and H. V. Gupta (2007), _Do Nash values have value?_, Hydrological Processes, 21, 2075-2080, doi:10.1002/hyp.6825.
-	Viviroli, D., J. Gurtz, and M. Zappa (2007), _The hydrological modelling system PREVAH.  Part II - Physical model description_, Geographica Bernensia, 40, 1-89.
-	Viviroli, D., M. Zappa, J. Gurtz, and R. Weingartner (2009a), _An introduction to the hydrological modelling system PREVAH and its pre- and post-processing-tools_, Environmental Modelling & Software, 24, 1209-1222, doi:10.1016/j.envsoft.2009.04.001.
-	Viviroli, D., H. Mittelbach, J. Gurtz, and R. Weingartner (2009b), _Continuous simulation  for flood estimation in ungauged mesoscale catchments of Switzerland-Part II: Parameter regionalisation and flood estimation results_, Journal of Hydrology, 377 (1), 208-225, doi:10.1016/j.jhydrol.2009.08.022.

en: discharge hydrology snowmelt soil-moisture precipitation climate-change

en: Simulated future discharge and climatological variables for medium-sized catchments in Switzerland

en:
Resource Title: Daily simulated data for the future period
    Size: 1010416415 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/6c1c0a28-a23f-4777-88b2-982841adb9b8/resource/be17bcca-3f34-414f-960e-bcbdd855ba81/download/climate_simulations.zip
    Description: Daily simulated discharge, precipitation, snowmelt, and soil moisture for the period 1981-2100 in mm for the 307 catchments.

en:
Resource Title: Daily simulated data for the reference period
    Size: 31447784 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/6c1c0a28-a23f-4777-88b2-982841adb9b8/resource/a67b4308-25ca-47ea-9aed-f529b8dc7f4d/download/reference.zip
    Description: Daily simulated discharge, precipitation, snowmelt, and soil moisture for the reference period 1981-2017 in mm.

en:
Resource Title: Shapefile of 307 catchments in Switzerland
    Size: 103266 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/6c1c0a28-a23f-4777-88b2-982841adb9b8/resource/05c1a038-40c2-423a-8ad9-f11b43689cf1/download/catchment_shapes.zip
    Description: Shapefile of the 307 medium-sized catchments in Switzerland. The "gridcode" provides the ID of the catchment used in the datafiles.

climate-change

display_name: climate-change

discharge

display_name: discharge

hydrology

display_name: hydrology

precipitation

display_name: precipitation

snowmelt

display_name: snowmelt

soil-moisture

display_name: soil-moisture


en: Data obtained in the free-air CO2 enrichment (FACE) experiment at Hofstetten, NW Switzerland, between 2009 and 2016. This dataset contains analyses of the soil solution throughout the experiment, especially for nitrate, as well as different analyses done at the end of the experiment: ammonium and nitrate captured by ion-exchange resin bags and extracted from soil cores, gross N mineralisation and nitrification measured by isotope dilution.

en: picea-abies face soil forest nitrogen co2

en: Nitrogen availability under trees exposed to CO2 enrichment (FACE)

envi

en: Environment

en:
Resource Title: Water analyses
    Size: 65553 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/6be8b9ed-d122-4f25-bdc1-26efe41a0ce0/resource/50749920-cf85-4140-8802-54beffcf490e/download/water-analyses.csv
    Description: Analyses of water, especially soil solution

en:
Resource Title: Gross N mineralisation and nitrification
    Size: 1183 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/6be8b9ed-d122-4f25-bdc1-26efe41a0ce0/resource/f6a9e85f-187b-4a55-9f57-e243d14906ab/download/mineralisation-nitrification.csv
    Description: Gross rates of N mineralisation and nitrification (15N dilution)

en:
Resource Title: Resin bags
    Size: 1237 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/6be8b9ed-d122-4f25-bdc1-26efe41a0ce0/resource/594288d3-95ff-4ad6-914e-d3e9c1d0ea42/download/resin-bags.csv
    Description: Ammonium, nitrate and their 15N in resin bags

en:
Resource Title: Collection of soil solution
    Size: 33840 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/6be8b9ed-d122-4f25-bdc1-26efe41a0ce0/resource/3cfe06e1-c280-4b18-903a-321ceef6044a/download/soil-solution.csv
    Description: Soil solution collected by suction cups

en:
Resource Title: Soil C and N
    Size: 1298 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/6be8b9ed-d122-4f25-bdc1-26efe41a0ce0/resource/dc99385c-0ab8-4c7f-88cd-cf53ba0aea41/download/soil-c-n.csv
    Description: Analyses of C, N and their stable isotopes in the soil, and soil pH

en:
Resource Title: Collection points
    Size: 453 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/6be8b9ed-d122-4f25-bdc1-26efe41a0ce0/resource/1e7dfaf0-d504-4bab-ae3b-8f96181b4e84/download/points.csv
    Description: Collection points under the trees

face

display_name: face

forest

display_name: forest

nitrogen

display_name: nitrogen

picea-abies

display_name: picea-abies

soil

display_name: soil


en: The dataset provides simulated 1) precipitation, 2) discharge, 3) soil moisture, and 4) low-flow simulations for 307 medium-sized catchments in Switzerland for the period 1981-2018. The data were simulated using the hydrological model PREVAH in its gridded-version. The simulated time series are provided at daily resolution. A detailed description of the modeling approach can be found in Brunner et al. 2019 submitted to NHESS.

en: low-flow prevah soil-moisture precipitation hydrological-simulations discharge drought-2018

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: Hydro-meteorological simulations for the period 1981-2018 for Switzerland

en:
Resource Title: Low-flow storage
    Size: 53492028 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/62573c81-105f-4830-8ad1-6e745b13ae2a/resource/a1338c95-09df-4bce-8d9c-7b059ae2f6a1/download/low_flow.txt
    Description: Simulated low-flow storage for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm.

format: TXT

media_type: text/plain

en:
Resource Title: Discharge
    Size: 53492028 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/62573c81-105f-4830-8ad1-6e745b13ae2a/resource/4c11fec4-0846-48a0-8dfe-9f7f5a4b574f/download/discharge.txt
    Description: Simulated discharge for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm/d.

en:
Resource Title: Soil moisture
    Size: 53492028 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/62573c81-105f-4830-8ad1-6e745b13ae2a/resource/0ad6127b-f7d6-4356-9ac4-283166385578/download/soil_moisture.txt
    Description: Simulated plant-available soil moisture for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm.

en:
Resource Title: Precipitation
    Size: 53492028 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/62573c81-105f-4830-8ad1-6e745b13ae2a/resource/0f551cfd-804d-4f3a-80d9-c33562ea8b45/download/precipitation.txt
    Description: Simulated precipitation for the 307 medium-sized catchments. The first column provides the date in the format YYYYMMDD. One column corresponds to one catchment. Unit: mm/d.

en:
Resource Title: Shapefile of 307 catchments in Switzerland
    Size: 103266 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/62573c81-105f-4830-8ad1-6e745b13ae2a/resource/7ebed8a1-98ce-4833-b1dd-64246bd07e5f/download/catchment_shapes.zip
    Description: Shapefile of 307 catchments for which simulations of precipitation, discharge, soil moisture, and low flow are provided. The OBJECTID entry corresponds to the data columns in the textfiles of the simulated data provided.

hydrological-simulations

display_name: hydrological-simulations

low-flow

display_name: low-flow

prevah

display_name: prevah


en:
The files contain the datasets used to produce Figures 2, 3, and 4 of the manuscript ([doi: 10.1002/2016GL071822](http://dx.doi.org/10.1002/2016GL071822)).
 Manuscript Abstract:
Despite being the main sediment entrainment mechanism in aeolian transport, granular splash is still poorly understood. We provide a deeper insight into the dynamics of sand and snow ejection with a stochastic model derived from the energy and momentum conservation laws. Our analysis highlights that the ejection regime of uniform sand is inherently different from that of heterogeneous sand. Moreover, we show that cohesive snow presents a mixed ejection regime, statistically controlled either by energy or momentum conservation depending on the impact velocity. The proposed formulation can provide a solid base for granular splash simulations in saltation models, leading to more reliable assessments of aeolian transport on Earth and Mars.

en: mars model snow sediment-transport erosion

en: Energy- and momentum-conserving model of splash entrainment in sand and snow saltation

envi

en: Environment

en:
Resource Title:
    Size: 3587 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/467b2213-b72a-4578-8c2e-f1181b6a7742/resource/1f30b883-fc62-4bbf-a8f5-88f716008097/download/figure2.txt
    Description: Datasets of Figure 2

en:
Resource Title:
    Size: 2904 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/467b2213-b72a-4578-8c2e-f1181b6a7742/resource/709f4330-9a35-4685-aac8-347067a5e483/download/figure4b.txt
    Description: Datasets of Figure 4b

en:
Resource Title:
    Size: 3626 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/467b2213-b72a-4578-8c2e-f1181b6a7742/resource/6ee21796-08ad-4069-a4cb-da6c35ffede7/download/figure4a.txt
    Description: Datasets of Figure 4a

en:
Resource Title:
    Size: 1434 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/467b2213-b72a-4578-8c2e-f1181b6a7742/resource/8ab9bafb-62f5-4420-80da-a2a96d22be74/download/figure3a.txt
    Description: Datasets of Figure 3a

en:
Resource Title:
    Size: 2190 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/467b2213-b72a-4578-8c2e-f1181b6a7742/resource/82479779-1b20-486f-b4bc-414c6024c25d/download/figure3b.txt
    Description: Datasets of Figure 3b

erosion

display_name: erosion

mars

display_name: mars

model

display_name: model

sediment-transport

display_name: sediment-transport

snow

display_name: snow


snow-depth-mapping

en:
The available datasets are snow depth maps with a spatial resolution of 2m generated from image matching of ADS 80/100 data. Image acquisition took place at peak of winter (time when the thickest snowpack is expected). The snow depth maps are the difference of a summer DSM from the winter DSM of the corresponding date . The summer DSM used is a product of image matching of ADS 80 data from summer 2013. In the available products buildings, vegetation and outliers were masked (set to NoData).
For the elimination of buildings the TLM layer (swisstopo) was used, because this layer might not represent exactly the
state of infrastructure at time of image acquisition, it is possible that mainly in dense settlement some buildings were not successfully masked. For the relevant area above treeline the masking of buildings showed good results. Vegetation got masked for a height above ground - 1m and was detected in a combination of summer and winter data sets. As Outliers were considered unrealistic snow depths caused by a failure of the image matching algorithm. Snow depths - 15m and smaller than - -15m were classified as outliers. Negative snow depth were kept, because of an uncertainty in image orientation accuracy. It is expected that in regions with negative snow depth also positive snow depth are underestimated by the same amount, which means that an estimation of snow volume should be carried out summing up the absolute values of snow depth (also the negative ones). For volume estimation in small regions the user has to take into account, that orientation accuracy of the images is roughly around 1-2 GSD (30cm), which propagates directly to the snow depth product. Areas which are not covered by snow got assigned a value of 0 as snow depth.
The work is published in:
Bühler, Y.; Marty, M.; Egli, L.; Veitinger, J.; Jonas, T.; Thee, P.; Ginzler, C., (2015). Snow depth mapping in high-alpine catchments using digital photogrammetry. Cryosphere, 9 (1), 229-243. doi: 10.5194/tc-9-229-2015

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

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: Snow Depth Mapping

title_for_slug: snow-depth-mapping

en:
Resource Title: Snow depth mapping in high-alpine catchments using digital photogrammetry
    Size: 8122296 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/3b711e29-cea1-48e8-8de6-a008850c4f94/download/snow-depth-mapping-in-high-alpine-catchments.pdf
    Description:

en:
Resource Title: Snow depth map 20130415
    Size: 412272526 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/da3ca703-c629-45c0-8dac-e78645b0fa5e/download/snowmap_2013_ads_80_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 80 imagery (CIR). GSD of imagery = 0.25 m

en:
Resource Title: Snow depth map 20160309
    Size: 392871502 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/ceae68a6-28f9-43f4-8ff5-9715588ade57/download/snowmap_20160309_ads_100_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.16 m

en:
Resource Title: Snow depth map 20120320
    Size: 412272526 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/aff41055-8e6e-4287-b52d-2c2fb61633c2/download/snowmap_2012_ads_80_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 80 imagery (CIR). GSD of imagery = 0.25 m

en:
Resource Title: Snow depth map 20160126
    Size: 392871502 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/9229984f-b417-45e5-8dbe-031f295d0406/download/snowmap_20160126_ads_100_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.16 m
Because of very diffuse light conditions at time of image acquisition the quality of the resulting snow depth values are in some regions quite poor.

en:
Resource Title: Snow depth map 20140417
    Size: 392871502 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/5cf5d3a5-555d-429e-9819-6c93ccce6dfc/download/snowmap_2014_ads_100_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.18 m

en:
Resource Title: Snow depth map 20160420
    Size: 392871502 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/bc035833-3193-4394-af92-f8d195d5e034/download/snowmap_20160420_ads_100_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.16 m

en:
Resource Title: Snow depth map 20100416
    Size: 412324171 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/4ba96052-9e3e-4b7a-9968-2dcc58fc96c0/download/snowmap_2010_ads_80_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 80 imagery (CIR). GSD of imagery = 0.25 m

en:
Resource Title: Snow depth map 20150415
    Size: 392871502 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/32acdacc-98dd-4fa7-87e8-449278bd24f0/resource/578e95ce-ffa0-4fc6-8109-d9653b2c622a/download/snowmap_2015_ads_100_extent_maskiert_nosnow0_noausreisser.tif
    Description: Snow depth map generated from ADS 100 imagery (CIR). GSD of imagery = 0.16 m

aerial-imagery

display_name: aerial-imagery

avalanche

display_name: avalanche

dsm

display_name: dsm

image-matching

display_name: image-matching

snow-depth

display_name: snow-depth

snow-hydrology

display_name: snow-hydrology

snow-water-equivalent

display_name: snow-water-equivalent


en:
Throughfall, litterflow and soil solution were sampled during one whole year under five Oriental beech trees in a mixed Hyrcanian beech forest. The amounts of Ca2+, Mg2+, K+ and Na+ in these fluxes were calculated based on their concentrations and the sampled volumes, and subsequently compared with the respective fluxes in the rainfall and soil solution of an adjacent forest gap.
In addition six soil profiles, one close to every single tree and one in the forest gap, were analyzed for pH, CaCO3, organic matter and texture.

en: fagus-orientalis-lipsky biogeochemical-cycling potassium calcium magnesium

envidat

en: EnviDat: the Environmental Data Portal

en: Base cation dynamics in an Oriental beech forest

envi

en: Environment

biogeochemical-cycling

display_name: biogeochemical-cycling

calcium

display_name: calcium

fagus-orientalis-lipsky

display_name: fagus-orientalis-lipsky

magnesium

display_name: magnesium

potassium

display_name: potassium


seilaplan

en:
Cable-based technologies have been a backbone for harvesting on steep slopes. The layout of a single cable road is challenging because one must identify intermediate support locations and heights that guarantee structural safety and operational efficiency while minimizing set-up and dismantling costs. Seilaplan optimizes the layout of a cable road by
Seilaplan stands for Cable Road Layout Planner. Seilaplan is able to calculate the optimal rope line layout (position and height of the supports) between defined start and end coordinates on the basis of a digital elevation model (DEM).
The program is designed for Central European conditions and is designed on the basis of a fixed suspension rope anchored at both ends. For the calculation of the properties of the load path curve an iterative method is used, which was described by Zweifel (1960) and was developed especially for standing skylines. When testing the feasibility of the cable line, care is taken that 1) the maximum permissible stresses in the skyline are not exceeded, 2) there is a minimum distance between the load path and the ground and 3) when using a gravitational system, there is a minimum inclination in the load path. The solution is selected which has a minimum number of supports in the first priority and minimizes the support height in the second priority.
The newly developed method calculates the load path curve and the forces occurring in it more accurately than tools available on the market to date (status 2019) and is able to determine the optimum position and height of the intermediate supports.
The reason for the more accurate results of the new tool is the assumption that the skyline is anchored at both end points. Forest cable yarders used in Europe have a skyline that is fixed at both ends. The behaviour of fixed-anchored suspension ropes is very difficult to describe mathematically and cannot be solved analytically. For this reason, simplified linearized assumptions have so far been used in the forestry sector, which corresponds to the behaviour of a weight-tensioned suspension rope and is known as the Pestal method (1961). Weight-tensioned suspension ropes are used for passenger transport.
For the calculation of the load path curve we use an iterative method, which was described by Zweifel (1960) and developed especially for fixed anchored suspension ropes. This makes mathematics much more demanding, but leads to more accurate and realistic results. Since there are no current models which describe the installation costs with adequate accuracy, the solution sought is the one which has a minimum number of supports in the first priority and minimises the support height in the second priority (Figure 2). The presented method is the first one, which starts from a fixed anchored supporting rope and identifies the mathematically optimal column layout at the same time. In contrast to methods that assume a weight-tensioned suspension rope, this approach achieves more realistic solutions with longer spans and lower support heights, which ultimately leads to lower installation costs. Background information on rope mechanics and calculation methods is documented in Bont and Heinimann (2012).
License: GNU, General Public License, Version 2 or newer.
Literature:
Bont, L., & Heinimann, H. R. (2012). Optimum geometric layout of a single cable road. European journal of forest research, 131(5), 1439-1448.

en: qgis cable-yarding catenary-load-path software steep-terrain-harvesting python cable-planning

en: Seilaplan

title_for_slug: seilaplan

cable-planning

display_name: cable-planning

cable-yarding

display_name: cable-yarding

catenary-load-path

display_name: catenary-load-path

python

display_name: python

qgis

display_name: qgis

software

display_name: software

steep-terrain-harvesting

display_name: steep-terrain-harvesting


vegetation-height-model-sentinel-nfi

en: Countrywide vegetation height models (VHM) were generated for Switzerland based on Copernicus Sentinel-2 imagery and the digital terrain model (DTM) swissALTI3D from the Swiss Federal Office of Topography swisstopo. A Convolutional Neural Network (CNN) model was trained to estimate the maximum vegetation height at the spatial resolution of the Sentinel-2 pixel of 10 m. Vegetation heights from the spatially higher-resolved VHM Lidar NFI were used as reference data for the CNN training. Within the framework of the Swiss National Forest Inventory (NFI), the VHMs were modelled annually based on available Sentinel-2 imagery from May – September of the respective year. Further details on the creation of the VHM Sentinel NFI can be found in the paper Jiang et al. (2023, https://doi.org/10.1016/j.srs.2023.100099). Contains modified Copernicus Sentinel data.

en: nfi vegetation-height switzerland cnn forest remote-sensing sentinel-2

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: Vegetation Height Model Sentinel NFI

title_for_slug: vegetation-height-model-sentinel-nfi

en:
Resource Title: VHM_Sentinel_NFI_2018
    Size: 791849627 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/3b1cae17-fc7a-4722-95da-92d3be869273/resource/6f9a20f7-78bf-4cc5-b734-c52d63bbfd0a/download/landesforstinventar-vegetationshoehenmodell_sentinel_2018_2056.tif
    Description:

en:
Resource Title: VHM_Sentinel_NFI_2021
    Size: 793778198 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/3b1cae17-fc7a-4722-95da-92d3be869273/resource/8fc7f306-c788-4b1c-9a17-8dfe946c9681/download/landesforstinventar-vegetationshoehenmodell_sentinel_2021_2056.tif
    Description:

en:
Resource Title: VHM_Sentinel_NFI_2022
    Size: 794990349 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/3b1cae17-fc7a-4722-95da-92d3be869273/resource/12ed6aff-aeed-45f4-aed3-81e17797a8da/download/landesforstinventar-vegetationshoehenmodell_sentinel_2022_2056.tif
    Description:

en:
Resource Title: VHM_Sentinel_NFI_2020
    Size: 793832708 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/3b1cae17-fc7a-4722-95da-92d3be869273/resource/f0496550-ac9a-4dfd-bfea-68e601e9f3f7/download/landesforstinventar-vegetationshoehenmodell_sentinel_2020_2056.tif
    Description:

en:
Resource Title: VHM_Sentinel_NFI_2023
    Size: 794605160 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/3b1cae17-fc7a-4722-95da-92d3be869273/resource/c57585ec-51da-4aeb-bf90-c5598e14d3c6/download/landesforstinventar-vegetationshoehenmodell_sentinel_2023_2056.tif
    Description:

en:
Resource Title: VHM_Sentinel_NFI_2017
    Size: 791212333 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/3b1cae17-fc7a-4722-95da-92d3be869273/resource/771519e8-6435-463c-a2cb-57031ee25861/download/landesforstinventar-vegetationshoehenmodell_sentinel_2017_2056.tif
    Description:

en:
Resource Title: VHM_Sentinel_NFI_2025
    Size: 789365244 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/3b1cae17-fc7a-4722-95da-92d3be869273/resource/cd9f3543-87a3-4eb5-ab05-a4388a7967c4/download/landesforstinventar-vegetationshoehenmodell_sentinel_2025_2056.tif
    Description:

en:
Resource Title: VHM_Sentinel_NFI_2024
    Size: 799033995 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/3b1cae17-fc7a-4722-95da-92d3be869273/resource/284e4292-df10-4227-85b5-1fc7024def5b/download/landesforstinventar-vegetationshoehenmodell_sentinel_2024_2056.tif
    Description:

en:
Resource Title: VHM_Sentinel_NFI_2019
    Size: 796005325 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/3b1cae17-fc7a-4722-95da-92d3be869273/resource/1320cdac-5544-47d8-acaf-983fd88744a4/download/landesforstinventar-vegetationshoehenmodell_sentinel_2019_2056.tif
    Description:

cnn

display_name: cnn

forest

display_name: forest

nfi

display_name: nfi

remote-sensing

display_name: remote-sensing

switzerland

display_name: switzerland

vegetation-height

display_name: vegetation-height


en:
Landolt et al. (2010) indicator values are widely used in plant related ecological assessments. However, since the publication of the dataset, the national checklist of Switzerland (see Juillerat et al. 2017) has further evolved and the taxon names of the two are not congruent. Here, we present a translation between the two datasets. Translations were made by expert knowledge.
Juillerat, P., et al. 2017. _Flora Helvetica Checklist 2017: der Gefässpflanzen der Schweiz / de la flore vasculaire de la Suisse / della flora vascolare della Svizzera._ Info Flora, Genève.
Landolt, E., et al. 2010. _Flora indicativa: Ökologische Zeigerwerte und biologische Kennzeichen zur Flora der Schweiz und der Alpen._ Haupt,

en: taxonomy translation switzerland indicator-values vascular-plants

envidat

en: EnviDat: the Environmental Data Portal

en: Translation between Checklist 2017 and Flora Indicativa

envi

en: Environment

indicator-values

display_name: indicator-values

taxonomy

display_name: taxonomy

translation

display_name: translation

vascular-plants

display_name: vascular-plants


vegetation-height-model-lidar-nfi

en:
Countrywide **vegetation height models (VHM)** were generated for Switzerland based on
LiDAR data acquired in the framework of the **swissSURFACE3D** campaign (swisstopo).
To derive vegetation height, the classified point cloud got normalized by calculating height above ground for all points not belonging to the class ground (2). Vegetation point (class 3,4,5) got then rasterized to a grid size of 0.5 m using an interpolation distance of one grid cell. Raster cells not belonging to a vegetation class got set to 0 m above ground.
These VHM's are produced in the framework of the Swiss National forest Inventory (NFI).
Each year about 1/6 of switzerland surface is updated using swissSURFACE3D data in leaf-off state.

en: point-cloud vegetation-classification vegetation forest three-dimensional vegetation-height nfi

en: Vegetation Height Model LiDAR NFI

title_for_slug: vegetation-height-model-lidar-nfi

en:
Resource Title: 2025 Vegetation Height Model LiDAR NFI (current)
    Size: 160202280141 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.664%2F2025%2F
    Description: Vegetation Height Model of Switzerland for the year 2025 (cloud optimized geotiff)
and Metadata file with information about acquisition date and point densities of used LiDAR data for tiles of 1 x 1 km (geopackage).

en:
Resource Title: 2024 Vegetation Height Model LiDAR NFI
    Size: 149786984448 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.664%2F2024%2F
    Description: Vegetation Height Model of Switzerland for the year 2024 (cloud optimized geotiff)
and Metadata file with information about acquisition date and point densities of used LiDAR data for tiles of 1 x 1 km (geopackage).

point-cloud

display_name: point-cloud

three-dimensional

display_name: three-dimensional

vegetation

display_name: vegetation

vegetation-classification

display_name: vegetation-classification


en:
**Schweizweite Karte des sky view factors (Kronenöffnungsgrad im Wald) in 5 m Auflösung (Stand LFI VHM 2024)**
This is a dataset for Swiss forestry, for a description in English please see below.
Il s'agit d'un ensemble de données relatives à la sylviculture suisse. Pour une description en français, voir ci-dessous.
Si tratta di una serie di dati relativi alla silvicoltura svizzera. Per una descrizione in francese, vedere sotto.
Dieser Datensatz enthält eine schweizweite Karte mit einer Auflösung von 5 m, die den sky view factor darstellt. In Wäldern gibt sie den Kronenöffnungsgrad während der Vegetationsperiode wieder, ausserhalb von Wäldern zeigt die Karte lediglich den Terrain-Effekt. Wichtig: Der Kronenöffnungsgrad wurde anhand einer 180° Perspektive ermittelt, was dazu führt, dass die Werte nicht direkt vergleichbar sind mit Schätzungen im Feld, welche meistens auf einem kleineren Sichtradius basieren. Der Datensatz wurde mit dem auf synthetischen hemisphärischen Bildern basierendem Raytracing-Canopy-Radiation-Modell (CanRad) berechnet, unter Verwendung des LiDAR-basierten Vegetationshöhenmodells gemäss LFI (Stand 2024). Die Karte stellt die Bedingungen zum Zeitpunkt der LiDAR Datenerfassung dar. Informationen zu den Jahren der LiDAR-Erfassung finden Sie im Abschnitt „Data and ressources“. Der Datensatz wird jährlich aktualisiert, sobald ein neues LFI-LiDAR-Vegetationshöhenmodell verfügbar ist
**Carte nationale du facteur de vue du ciel (degré d'ouverture de la canopée dans la forêt) avec une résolution de 5 m (état IFN VHM 2024)**
Cet ensemble de données contient une carte couvrant l'ensemble du territoire suisse, avec une résolution de 5 m, qui représente le « sky view factor ». Dans les forêts, elle reflète le degré d'ouverture de la canopée pendant la période de végétation ; en dehors des forêts, la carte indique uniquement l'effet du relief. Important : le degré d'ouverture de la canopée a été déterminé à partir d'une perspective à 180°, ce qui signifie que les valeurs ne sont pas directement comparables aux estimations effectuées sur le terrain, qui se basent généralement sur un rayon de vision plus petit. L'ensemble de données a été calculé à l'aide du modèle CanRad (Raytracing-Canopy-Radiation), basé sur des images hémisphériques synthétiques, en utilisant le modèle altimétrique de végétation basé sur le LiDAR selon l'IFN (état en 2024). La carte représente les conditions au moment de la collecte des données LiDAR. Vous trouverez des informations sur les années de collecte LiDAR dans la section « Données et ressources ». L'ensemble de données est mis à jour chaque année dès qu'un nouveau modèle d'altitude de la végétation LFI-LiDAR est disponible.
**Mappa svizzera dello sky view factor (grado di apertura della chioma nella foresta) con risoluzione di 5 m (stato LFI VHM 2024)**
Questo set di dati contiene una mappa a copertura nazionale con una risoluzione di 5 m che rappresenta lo sky view factor. Nelle aree boschive essa riflette il grado di apertura della chioma durante il periodo vegetativo, mentre al di fuori delle aree boschive la mappa mostra semplicemente l'effetto del terreno. Importante: il grado di apertura della chioma è stato determinato sulla base di una prospettiva a 180°, il che comporta che i valori non siano direttamente comparabili con le stime sul campo, che si basano per lo più su un raggio visivo più ridotto. Il set di dati è stato calcolato con il modello Raytracing-Canopy-Radiation (CanRad), basato su immagini emisferiche sintetiche, utilizzando il modello altimetrico della vegetazione basato su LiDAR secondo l'IFN (aggiornato al 2024). La mappa rappresenta le condizioni al momento dell'acquisizione dei dati LiDAR. Le informazioni relative agli anni di acquisizione LiDAR sono disponibili nella sezione «Data and resources». Il set di dati viene aggiornato annualmente non appena è disponibile un nuovo modello di altezza della vegetazione LFI-LiDAR.
**Switzerland-wide map of the sky view factor (canopy openness in forests) with 5 m resolution (as of NFI VHM 2024)**
This dataset contains a map covering the whole of Switzerland with a resolution of 5 m, showing the sky view factor. In forests, it reflects the degree of canopy openness during the growing season; outside forests, the map simply shows the terrain effect. Important: The degree of canopy openness was determined using a 180° perspective, which means that the values are not directly comparable with field estimates, which are usually based on a smaller field of view. The dataset was calculated using the Raytracing Canopy Radiation model (CanRad), which is based on synthetic hemispheric images, utilising the LiDAR-based vegetation height model in accordance with the LFI (as of 2024). The map depicts conditions at the time of LiDAR data acquisition. Information on the years of LiDAR acquisition can be found in the ‘Data and resources’ section. The dataset is updated annually as soon as a new LFI LiDAR vegetation height model becomes available.

en: lidar canrad light-availability forestry canopy-openness

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: Switzerland-wide maps of sky view factor (canopy openness in forests) at 5 m resolution (as of NFI VHM 2024)

en:
Resource Title: Lidar_Befliegungsjahr_VHM_2024.png
    Size: 536750 bytes
    Format: image/png
    URL: https://www.envidat.ch/dataset/fa3b2b7f-c0f0-4fd0-b04e-78b0b60ebb4b/resource/d55237b2-558a-49c5-a26d-7c558f5a54c8/download/lidar_befliegungsjahr_vhm2024.png
    Description:

en:
Resource Title: Sky_view_factor_CH_LI_2024.tif
    Size: 1642824991 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/fa3b2b7f-c0f0-4fd0-b04e-78b0b60ebb4b/resource/ca5d0cc0-1838-4e3d-90da-4c5e0b1992ff/download/map_chli.tif
    Description:

canopy-openness

display_name: canopy-openness

canrad

display_name: canrad

forestry

display_name: forestry

lidar

display_name: lidar

light-availability

display_name: light-availability


en:
**Article: "Species Species composition shapes canopy microclimate and foliar traits without modifying photosynthetic thermal acclimation in a beech-oak ecotone"**
Authors: **Janisse Deluigi**, Charlotte Grossiord, Alvaro Poretti, Lorenz Walthert, Christoph Bachofen
The study was conducted along a south-exposed elevational gradient ranging from 770 to 860 m a.s.l. in a central alpine valley (46°10`14``N, 7°10`01``E, Saillon, Switzerland). Along the elevational gradient, we selected three forest stands characterized by high coverage of mature European beech (Fagus sylvatica L.) and downy oak (Quercus pubescens Willd.) trees, with are naturally distributed in three species combinations: one pure beech stand (i.e., at least 80% of the basal area) at 860 m a.s.l. , one mixed beech-oak stand (i.e., similar relative abundances of the two species – at least 40% of the basal area each – and minimal presence of other tree species) at 785 m a.s.l., and one pure oak stand (i.e., at least 80% of the basal area) at 770 m a.s.l. An additional forest clearing of around 0.04 ha next to the pure oak stand was selected as reference for conditions outside the forest  (i.e., clearing or gap). At each forest stand, we selected five healthy, mature, overstory trees per tree species, resulting in a total of 20 individuals (5 individuals x 2 species x 2 mixture combinations, i.e., mixed vs. pure forest stand).
**Dataset 1: Climatic conditions at the study sites**
In 2023 and 2024 we monitored climatic conditions at each forest stand and in the clearing using climatic stations with air temperature, relative humidity and radiation sensors installed at 1.8 m height in a central position relative to the selected trees for each site. The meteo station in the clearing site had additionally a rain gauge and wind sensor.
**Dataset 2: Tree and canopy sizes**
Once in 2023 we measured tree height and diameter at breast height of all selected trees using a vertex hypsometer and a caliper. In August 2023 and 2024 we estimated canopy size measuring the height at which canopy starts (CS), canopy depth (= tree height - CS) and maximum canopy radius as the average of two orthogonal measurements (south and east direction) using a vertex hypsometer. We also estimated canopy total leaf area using species- and site-specific allometric equations from collected branches upscaled to the tree level.
**Dataset 3: Microclimatic conditions measured in tree canopies**
We monitored canopy microclimatic conditions in 2023 and 2024 attaching one air temperature and air humidity sensor (HOBO) on one branch at the top and one branch at the bottom of the south-exposed side of the canopy for each selected tree in June 2023.
**Dataset 4: Physiological traits**
During three field campaigns (June, August, September) per year (2023, 2024) we measured temperature response curves of leaf net photosynthesis and dark respiration. As in-situ measurements were not possible, tree-climbers collected one representative branch at the top and the bottom of the canopy from each selected tree at each site. Estimates of the thermal optimum of photosynthesis (Topt), photosynthetic rate at Topt (Aopt), respiration rate at a standard temperature of 25°C (R25) and thermal sensitivity of respiration (Q10) were extracted from the temperature response curves of photosynthesis and respiration, respectively.
Leaf water potentials at predawn and midday, as well as leaf chlorophyll content, were also measured on the same day for each selected tree at each site.
**Dataset 5: Physiological traits - specific leaf area and leaf size**
In August 2023 and 2024 we collected a random subset of 10 representative fully developed leaves of different sizes from a representative branch harvested at the top and bottom of the canopy of each selected tree to estimate the specific leaf area (SLA) and leaf size (average of the 10 leaves).
**Dataset 6: Soil water potentials**
Hourly soil water potentials and soil temperature were recorded during the two years of measurements (2023, 2024) at each forest site at different soil depths: 20 cm, 80 cm, 140 cm and 200 cm.

en: climate-change photosynthesis acclimation respiration forest-canopy species-interaction

en: Dataset Article "Species composition shapes canopy microclimate and foliar traits without modifying photosynthetic thermal acclimation in a beech-oak ecotone", Deluigi et al.

en:
Resource Title: 5_SLA and leaf size measurements Saillon 2023-2024.csv
    Size: 4444 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/6d43eea8-791c-440c-9f96-d02098025f64/resource/b825d938-273b-43a5-83a8-9d5c89c895e5/download/5_sla-and-leaf-size-measurements-saillon-2023-2024.csv
    Description: #Dataset 5: Physiological traits - specific leaf area and leaf size
Year = year of measurement
Site	= forest site (pure beech, mixture, pure oak)
Species	= tree species (B = beech, O = downy oak)
Tree = tree ID within each site (5 trees per site and tree species)
Canopy_level = canopy level (top, bottom)
SLA	= specific leaf area (cm2 g-1)
leaf_size = leaf area (cm2)

en:
Resource Title: 3_Microclimatic measurements day and night Saillon 2023-2024.csv
    Size: 179804 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/6d43eea8-791c-440c-9f96-d02098025f64/resource/69ce25f7-f502-4461-8336-0e0400182d03/download/3_microclimatic-measurements-day-and-night-saillon-2023-2024.csv
    Description: #Dataset 3: Microclimatic conditions measured in tree canopies
Site	= forest site (pure beech, mixture, pure oak)
Species	= tree species (B = beech, O = downy oak)
Tree = tree ID within each site (5 trees per site and tree species)
Time = time of the day (HH:MM:SS)
Tair_top	= air temperature at the top of the canopy (°C)
Tair_low	= air temperature at the bottom of the canopy (°C)
VPD_top = vapor pressure deficit at the top of the canopy (kPa)
VPD_low = vapor pressure deficit at the bottom of the canopy (kPa)
Tair_gap	= air temperature in the clearing (i.e., outside the forest) (°C)
VPD_gap = vapor pressure deficit in the clearing (kPa)
Tair_T_L	= difference in air temperature between the top and bottom of the canopy (°C)
VPD_T_L = difference in vapor pressure deficit between the top and bottom of the canopy (kPa)
Tair_T_gap	 =  difference in air temperature between the top of the canopy and the clearing (°C)
VPD_T_gap = difference in vapor pressure deficit between the top of the canopy and the clearing (kPa)
Tair_L_gap	= difference in air temperature between the bottom of the canopy and the clearing (°C)
VPD_L_gap = difference in vapor pressure deficit between the bottom of the canopy and the clearing (kPa)

en:
Resource Title: 2_Tree size and canopy Saillon 2023-2024.csv
    Size: 4465 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/6d43eea8-791c-440c-9f96-d02098025f64/resource/b1b06195-b354-404b-b2e2-c44217446c54/download/2_tree-size-and-canopy-saillon-2023-2024.csv
    Description: #Dataset 2: Tree and canopy sizes
Year = year of measurement
Site = forest site (pure beech, mixture, pure oak)
Species = tree species (B = beech, O = downy oak)
Tree = tree ID within each site (5 trees per site and tree species)
height = tree height (m)
DBH = diameter at breast height (cm)
sapwood_depth = depth of sapwood from tree cores (cm)
sapwood_area = area of sapwood in tree trunks (cm2)
Al_As = ratio between branch total leaf area (Al, cm2) and branch section area (As, cm2) from sampled branches
WT_LA = canopy total leaf area estimated for each tree using allometric equations (m2)
radius_max = average maximum radius of the canopy (m) from two orthogonal measurements
radius_east = radius of the canopy (m) measured direction east
radius_south = radius of the canopy (m) measured direction south
crown_start = height at which the canopy starts (m)
canopy_depth = difference between tree height and height at which the canopy starts (m)
Asymetric = TRUE/FALSE to indicate whether the canopy was asymetric
Direction = direction to which the canopy was asymetric

en:
Resource Title: 6_Soil moisture and temperature Saillon 2023-2024.csv
    Size: 16861102 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/6d43eea8-791c-440c-9f96-d02098025f64/resource/305fbfa7-56fa-4f62-88d2-f5b87cb51452/download/6_soil-moisture-and-temperature-saillon-2023-2024.csv
    Description: #Dataset 6: Soil water potentials
Datetime = date and time (CEST)
Site	= forest site (pure beech, mixture, pure oak)
Depth	= soil depth (cm)
WP_corr	 = soil water potential corrected by temperature (MPa)
Temp = soil temperature (°C)
Date = date of measurement (mm/dd/yyyy)
Year = year of measurement

en:
Resource Title: 4_Physiological measurements Saillon 2023-2024.csv
    Size: 46961 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/6d43eea8-791c-440c-9f96-d02098025f64/resource/e9f0e85f-b12b-4b29-a4e3-44600d37c34a/download/4_physiological-measurements-saillon-2023-2024.csv
    Description: #Dataset 4: Physiological traits
Year = year of measurement
Campaign = campaign of measurement (June, August, September)
Site	= forest site (pure beech, mixture, pure oak)
Species	= tree species (B = beech, O = downy oak)
Tree = tree ID within each site (5 trees per site and tree species)
Canopy_level = canopy level (top, bottom)
WP_predawn = leaf water potential at predawn (MPa)
WP_midday = leaf water potential at midday (MPa)
CHL = chlorophyll content (CCI)
Date = date of measurement (mm/dd/yyyy)
Aopt = photosynthetic rate at thermal optimum (µmol m-2 s-1)
sd_err_Aopt = standard error of Aopt estimates from temperature response curves of photosynthesis
b	= curvature coefficient of the temperature response curve of photosynthesis
sd_err_b = standard error of b estimates  from temperature response curves of photosynthesis
Topt = thermal optimum of photosynthesis (°C)
sd_err_Topt = standard error of Topt estimates from temperature response curves of photosynthesis
R25	= respiration rate at a standard temperature of 25°C (µmol m-2 s-1)
sd_err_R25 = standard error of R25 estimates from temperature response curves of dark respiration
Q10	= thermal sensitivity of respiration (i.e., increase in respiration rates for a 10°C increase in measurement temperature)
sd_err_Q10 = standard error of Q10 estimates from temperature response curves of dark respiration
Collection_time = time of the day at which the branch was collected (morning, midday)

en:
Resource Title: 1_Climatic conditions at the study sites Saillon 2023-2024.csv
    Size: 22240297 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/6d43eea8-791c-440c-9f96-d02098025f64/resource/85ccee2d-7e73-40b2-ad63-c6ce1b030e26/download/1_climatic-conditions-at-the-study-sites-saillon-2023-2024.csv
    Description: #Dataset 1: Climatic conditions at the study sites
Datetime = date and time (CEST)
PAR	= photosynthetic photon flux density (µmol m-2 s-1)
RH = air relative humidity (%)
Temp = air temperature (°C)
site = forest site (pure beech, mixture, pure oak) and clearing (gap)
date = date (mm/dd/yyyy)
Precip	= rain precipitation (mm)
Wind = wind speed (m s-2)
Gusts = wind gusts (m s-2)
Direction = direction of the wind (°)
month = month of the year (1:12)
VPD = vapor pressure deficit (kPa)
Time = time of the day (HH:MM:SS)
Year = year of measurement

acclimation

display_name: acclimation

climate-change

display_name: climate-change

forest-canopy

display_name: forest-canopy

photosynthesis

display_name: photosynthesis

respiration

display_name: respiration

species-interaction

display_name: species-interaction


en: This repository hosts the experimental data accompanying our publication "Testing the strength of buried surface hoar weak layers under combined compression and shear loading" submitted to Geophysical Research Letters.

en: snow-mechanics strength experiment slab-avalanche weak-layer

envidat

en: EnviDat: the Environmental Data Portal

envi

en: Environment

en:
Resource Title: experiments_dataset_v1.csv
    Size: 9277 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/cfac66e0-5864-4ee4-a2fc-470b1168927c/resource/d5fff209-9307-44cf-aa73-c43bdcb873d5/download/experiments_dataset_v1.csv
    Description: Dataset containing the experimental results. The column "CT ID" allows to link this data to the CT parameter dataset.

en:
Resource Title: Example_Video.mp4
    Size: 7402947 bytes
    Format: video/mp4
    URL: https://www.envidat.ch/dataset/cfac66e0-5864-4ee4-a2fc-470b1168927c/resource/e0ec0699-86b1-462c-ae52-aab3ab644f26/download/20250601_sh25-1_ps2s4_30deg2.mp4
    Description: Example video of an biaxial experiment performed at a reaction-force angle of approximately 30 degree (sample SH25-1_PS2S4).

en:
Resource Title: CT_dataset_v1.csv
    Size: 9605 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/cfac66e0-5864-4ee4-a2fc-470b1168927c/resource/66fe8b1c-f13d-473a-8e9e-731ddb6ae09b/download/ct_dataset_v1.csv
    Description: Dataset containing the microstructural parameters obtained from the CT data.

experiment

display_name: experiment

slab-avalanche

display_name: slab-avalanche

snow-mechanics

display_name: snow-mechanics

strength

display_name: strength

weak-layer

display_name: weak-layer


en:
Monitoring fish diversity through eDNA metabarcoding along the Kinabatangan river
In 2019, water samples were collected in 25 locations along the Kinabatangan in Malaysia. Samples were taken either from a boat or by filtering from the side of the river. At each station, two filtration replicates were performed using a peristaltic pump to conduct environmental DNA (eDNA) sampling. Each filtration targeted a maximum duration of 30 min, during which approximately 30 liters of water were filtered through each filtration capsule. After filtration, the water inside the capsules was removed, and the capsules were filled with 50 ml of conservation buffer for preservation at room temperature. We followed strict contamination control protocols throughout both the fieldwork and laboratory processes, adhering to the guidelines of Valentini et al. (2016). To prevent contamination, each sample was processed using disposable gloves and single-use filtration equipment. We use primers for eukaryote and fish. The MiSeq Reagent Kit v3 (2x75 bp) (Illumina, San Diego, CA, USA) was used for paired-end sequencing at a theoretical sequencing depth of 200,000 reads per sample.
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: malaysia teleo edna fish metabarcoding

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: Environmental DNA Freshwater Malaysia Kinabatangan 2019

edna

display_name: edna

fish

display_name: fish

malaysia

display_name: malaysia

metabarcoding

display_name: metabarcoding

teleo

display_name: teleo


en:
Dataset supporting the paper "Herbivory mediates the response of below-ground food-webs to invasive grasses", published in the Journal of Animal Ecology.
The dataset refers to samples collected across 6 farms in the Bega Valley region of Australia. The farms are located in the grassy woodland ecoregion and are extensively managed with sheep and cattle grazing. Livestock shares the landscape with native mammalian and invertebrate herbivores. On each farm, two nested fenced exclosure systems were established. These included an open area, a fence excluding livestock but allowing access to native mammals and invertebrates, a fence excluding all mammals but allowing access to invertebrates and a shielded cage excluding all invertebrate herbivores as well. In each farm, one of these systems was located in a zone  dominated by the native Kangaroo grass, and another in a zone co-dominated by the Invasive African lovegrass. The sample and treatment numbering is provided in the file Sample_metadata.
Soil samples were taken and DNA was extracted and amplified for 16s and ITS markers. The resulting metabarcoding derived ASV tables are presented (16s_ASV_table and ITS_ASV_table), as are the inferred functional profiles for bacteria at the enzyme and pathway level (16s_picrust_enzymes and 16s_picrust_pathways), as derived by the PICRUSt2 pipeline, and the fungal guilds reconstructed with FunGuildR (FunguildR_output).
Chemical, physical and enzymatic assays were also performed on the soil samples (Chemical_data).
Nematodes were extracted from soil cores with a Baermann funnel, counted (Nematodes_counts) and had their DNA extracted and amplified (Nematodes_asv_table). Soil invertebrates were sampled with below-ground pitfall traps and identified to family level (Invertebrates) and earthworms were manually extracted from 20*20*20 cm monoliths ((Earthworm_counts).
Two 20 by 100 cm strips had their vegetation removed and their living and litter biomass quantified by dry weight (Biomass_summary). Sensors measuring moisture and temperature 10 cm beneath the soil surface were installed in each plot and their output is presented in Microclimate_summary.

en: ecosystem-functioning invasive-species soil-biodiversity grassland herbivory

en:
Resource Title: Microclimate_summary.csv.xlsx
    Size: 3575094 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/4bda5372-4c89-4db2-91c2-da8b2f55aacc/resource/54ae1e37-c905-4e7a-84ef-bdd19489fedf/download/microclimate_summary.csv.xlsx
    Description: Summary of sensor-collected soil moisture and temperature data

en:
Resource Title: FunGuildR_output.csv
    Size: 3471 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/4bda5372-4c89-4db2-91c2-da8b2f55aacc/resource/d54a6c74-d9a7-4c1d-bff2-fe2aa355cfdc/download/funguildr_output.csv
    Description: FunGuildR derived ecological profiles of fungal communities

en:
Resource Title: ITS_ASV_table.tsv.txt
    Size: 4203574 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4bda5372-4c89-4db2-91c2-da8b2f55aacc/resource/2569787f-d2d4-427e-a024-3a45d6bc9856/download/its_asv_table.tsv.txt
    Description: ASV table of fungal communities. Each column is a sampling site

en:
Resource Title: 16s_picrust_enzymes.tsv.gz
    Size: 416613 bytes
    Format: application/gzip
    URL: https://www.envidat.ch/dataset/4bda5372-4c89-4db2-91c2-da8b2f55aacc/resource/b3783c93-6e3d-4e1b-a417-cbb1a489699c/download/16s_picrust_enzymes.tsv.gz
    Description: PICRUSt2 derived functional profiles of bacterial communities

format: gzip

en:
Resource Title: 16s_picrust_pathways.tsv.gz
    Size: 175067 bytes
    Format: application/gzip
    URL: https://www.envidat.ch/dataset/4bda5372-4c89-4db2-91c2-da8b2f55aacc/resource/a724bdd1-1af4-4719-a29d-00908d1ec218/download/16s_picrust_pathways.tsv.gz
    Description: PICRUSt2 derived inferred pathways for bacterial communities

en:
Resource Title: Nematodes_asv_table.csv
    Size: 1319550 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/4bda5372-4c89-4db2-91c2-da8b2f55aacc/resource/5cd79f48-2967-4687-8a34-cf18c3e3f9b4/download/nematodes_asv_table.csv
    Description: ASV table of nematode communities

en:
Resource Title: Nematodes_counts.csv
    Size: 2438 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/4bda5372-4c89-4db2-91c2-da8b2f55aacc/resource/3b85e403-72c0-4f1c-bde3-c0464a59b660/download/nematodes_counts.csv
    Description: Summary counts and moisture adjustments for Baermann extracted nematode samples

en:
Resource Title: Biomass_summary.csv
    Size: 4005 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/4bda5372-4c89-4db2-91c2-da8b2f55aacc/resource/a2d0a31a-cfb3-421c-9970-36f0f2ba9b5e/download/biomass_summary.csv
    Description: Summary of collected biomass data. Each row is the average of 2 20 by 100 cm strips.

en:
Resource Title: Sample_metadata.csv
    Size: 1295 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/4bda5372-4c89-4db2-91c2-da8b2f55aacc/resource/ef16a55a-9e7e-447e-9fb8-c07818c42b82/download/sample_metadata.csv
    Description: Metadata and treatment information for each of the sampling sites

en:
Resource Title: Chemical_data.csv
    Size: 8011 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/4bda5372-4c89-4db2-91c2-da8b2f55aacc/resource/9303e1cc-6bd5-4274-8e3c-64857ee50dff/download/chemical_data.csv
    Description: Summary of chemical and physical parameters. Each row is a sampling site

en:
Resource Title: Invertebrates.csv
    Size: 6266 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/4bda5372-4c89-4db2-91c2-da8b2f55aacc/resource/46a505cc-ced8-41da-92f6-c2db25b46550/download/invertebrates.csv
    Description: Summary table of pitfall-trap collected invertebrates.

en:
Resource Title: Easrthworm_counts.csv
    Size: 1165 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/4bda5372-4c89-4db2-91c2-da8b2f55aacc/resource/695104b9-95dd-4ee3-bd87-b99f62c82816/download/easrthworm_counts.csv
    Description: Earthworms counts and biomass for each sampling site

en:
Resource Title: 16s_ASV_table.txt
    Size: 10331118 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/4bda5372-4c89-4db2-91c2-da8b2f55aacc/resource/200c7de4-d400-47f6-9b28-a4710766338e/download/16s_asv_table.txt
    Description: ASV table of 16s (bacteria) communities. Columns are sampling sites, rows ASVs

ecosystem-functioning

display_name: ecosystem-functioning

grassland

display_name: grassland

herbivory

display_name: herbivory

invasive-species

display_name: invasive-species

soil-biodiversity

display_name: soil-biodiversity


en:
CHELSAcruts is a delta change monthly climate dataset for the years 1901-2016 for mean monthly maximum temperatures, mean monthly minimum temperatures, and monthly precipitation sum.  Here we use the delta change method by B-spline interpolation of anomalies (deltas) of the CRU TS 4.01 dataset. Anomalies were interpolated between all CRU TS grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2 (Karger et al. 2017, Scientific Data). This method has the assumption that climate only varies on the scale of the coarser (CRU TS) dataset, and the spatial pattern (from CHELSA) is consistent over time. This is certainly a rather crude assumption, and for time periods for which more accurate data is available CHELSAcruts should be avoided if possible (e.g. use CHELSA V1.2 for 1979-2015). Different to CHELSA V1.2, CHELSAcruts does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages.
CHELSAcruts is licensed under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.

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

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

envi

en: Environment

en:
Resource Title: CHELSA_tech_specification_V1.2.pdf
    Size: 1146678 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/9c9c699a-e57f-49e9-b489-c6abdfa9b53b/resource/1d294699-c738-4a00-8500-2d64d3226819/download/chelsa_tech_specification_v1.2.pdf
    Description:

en:
Resource Title: Data Access
    Size: 3298534883328 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fchelsav1%2F&prefix=chelsa_cruts%2F
    Description: CHELSAcruts is a delta change monthly climate dataset for the years 1901-2016 for mean monthly maximum temperatures, mean monthly minimum temperatures, and monthly precipitation sum.

en:
Resource Title: CHELSA Technical Documentation
    Size: 0
    Format: application/pdf
    URL: http://chelsa-climate.org/wp-admin/download-page/CHELSA_tech_specification.pdf
    Description: As the CHELSA project is continuously expanded, certain new parameters, time scales etc. are not included anymore in the original publication (Karger et al. 2017). We are therefore provide this document as a guideline of the current climatic parameters available and will expand it as new parameters or time periods will become available. Please refer to the version history for changes made in the document.

air-temperature

display_name: air-temperature

century

display_name: century

climate

display_name: climate

climate-layers

display_name: climate-layers

free-climate-data

display_name: free-climate-data

maximum-temperature

display_name: maximum-temperature

minimum-temperature

display_name: minimum-temperature

precipitation

display_name: precipitation

timeseries

display_name: timeseries


en:
Spatio-temporal vegetation models are essential for simulating dynamics and shifts
in species distributions over large areas, particularly under global change. One such
model is the dynamic forest landscape model TreeMig. We have developed a frame-
work that makes the model more user-friendly, improves its functionality, and expands
its possibilities. The framework consists of the improved TreeMig model, the R pack-
age ‘TreeMig-R’ for pre-processing input data, model execution and visualization and a
graphical user interface (GUI) for ease of use. The code of the TreeMig Package and the GUI is stored here.

en: gui dynamic-modelling landscape r-package forest-model

en: Code of TreeMig Package version 1

dynamic-modelling

display_name: dynamic-modelling

forest-model

display_name: forest-model

gui

display_name: gui

landscape

display_name: landscape

r-package

display_name: r-package


en:
Research question / purpose
Timber is widely used for protection measures in torrent control. However, life span of woody constructions such as timber check dams is limited mainly due to fungal decay. In practice a controversial discussion has been going on if either Norway spruce (Picea abies) or silver fir (Abies alba) is more enduring and if bark removal increases resistance against fungal decay.
Description
This dataset contains monitoring information on a series of 15 timber check dams built in 1996 in Hergiswil, Switzerland. The constructions were alternatively realised with Norway spruce and silver fir, half of them each with remaining and removed bark, respectively. The data includes the identification of decayed zones with a simple practical approach (i.e. measurement of the penetration depth of a slotted screwdriver No. 3) from a total of 12 repeated surveys from 2001 to 2025.
The following information is contained (by column and column title):
•	Identification of decayed zone (column title “Decay_Id”)
•	Year of survey (column title “Year”)
•	Identification of check dam (column title “Check dam”)
•	Degree of decay (column title “Degree of decay”): 2 = reduced wood strength (penetration depth 2-10 mm); 3 = low wood strength (penetration depth - 10 mm)
•	Sectors 1 to 7 of check dams (column title “Sector”)
•	Element of check dam (column title “Element”): LH (longitudinal log, German: “Längsholz”), Z (transverse log, German: “Zange”), FH (infill log, German: “Füllholz”)
Plan of check dams and additional information on monitoring surveys: survey information (PDF)

en: fungal-decay check-dam timber-structure long-term-behaviour torrent-control

en: Long-term behaviour of timber constructions in torrent control

en:
Resource Title: decay_envidat.csv
    Size: 13209 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/25bbe989-95e3-4705-9807-248a0ab14884/resource/7db6f23d-ff20-4991-a626-616e2be7aa65/download/decay_envidat.csv
    Description:

en:
Resource Title: Survey Information.pdf
    Size: 357932 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/25bbe989-95e3-4705-9807-248a0ab14884/resource/37048955-41d9-4145-96cd-90c3edc4c83b/download/survey-information.pdf
    Description:

check-dam

display_name: check-dam

fungal-decay

display_name: fungal-decay

long-term-behaviour

display_name: long-term-behaviour

timber-structure

display_name: timber-structure

torrent-control

display_name: torrent-control


soil-erosion-mapping-on-steep-slopes

en:
The dataset contains Digital Terrain Models (DTMs) and orthophotos obtained from successive surveys used to quantify soil erosion on non-vegetated slopes in a torrent catchment. The research area is steep (mainly 30-60°), has a complex topography and the spatial extent is about 0.6 ha. Furthermore, it is partly forested, and the soils consist mainly of glacial till. Digital terrain models were compiled in spring and autumn of the years 2021 to 2025 using UAV based LiDAR (Riegel miniVUX-3) and Photogrammetry (DJI Phantom 4 RTK).
To derive DTMs from the LiDAR (miniVUX-3) and photogrammetric (Phantom 4RTK) point clouds ground classification was processed with lasground_new (LASTools) and further refined by applying a filtering, based on vegetation index (2*green channel – red channel – blue channel) to remove vegetation points falsely classified as ground. Documented accuracies of the DTMs heights compared to dGNSS measurements were mostly around 2-5 cm. All data is supplied in the reference system LV95 (epsg: 2056) / LN02 (epsg: 5728).
Dates of surveys (miniVUX-3UAV, DJI P4 RTK):
 * 15.06.2021
 * 10.11.2021
 * 20.04.2022
 * 17.11.2023
 * 18.04.2023
 * 24.11.2023
 * 19.03.2024
 * 14.11.2024
 * 25.03.2025
 * 12.11.2025

en: mountain-stream monitoring gully erosion-scar steep-slope erosion

en: Soil erosion mapping on steep slopes

title_for_slug: soil-erosion-mapping-on-steep-slopes

erosion

display_name: erosion

erosion-scar

display_name: erosion-scar

gully

display_name: gully

monitoring

display_name: monitoring

mountain-stream

display_name: mountain-stream

steep-slope

display_name: steep-slope