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

en: seilaplan openstreetmap qgis forest accessible-forest geoprocessing

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
    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: 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

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

en:
Resource Title: Juypter Notebook for Data Preparation (online)
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    Format: application/x-ipynb+json
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    Description:

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: 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

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


$oid: OBSOLETE

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

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

envidat

approval_status: approved

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

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

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

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

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

state: active

type: organization

wsl

capacity: public

de: EnviDat

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

en:
Resource Title: Further details on the data repository
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    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.

format: PDF

media_type: application/pdf

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).

format: jpeg

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).

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.

format: CSV

media_type: text/csv

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 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 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

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.

climate-change

display_name: climate-change

long-term

display_name: long-term

meteo

display_name: meteo

meteorology

display_name: meteorology

par

display_name: par

precipitation

display_name: precipitation

relative-humidity

display_name: relative-humidity

swiss-forest-lab

display_name: swiss-forest-lab

temperature

display_name: temperature

wind-speed

display_name: wind-speed


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: Data Broedlin CNP

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

title_for_slug: data-broedlin-cnp

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:

format: XLS

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.

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


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: Automatic Classification of Avalanches

en: avalanche monitoring localization seismic machine-learning

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

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

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

envi

en: Environment

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.

format: ZIP

media_type: application/zip

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

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

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: 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.

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: 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: 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.

hydrological-simulations

display_name: hydrological-simulations

low-flow

display_name: low-flow

prevah

display_name: prevah


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

en: nitrogen soil picea-abies co2 face forest

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: 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 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: 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: 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

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

face

display_name: face

forest

display_name: forest

nitrogen

display_name: nitrogen

picea-abies

display_name: picea-abies


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 Depth Mapping

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

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

title_for_slug: snow-depth-mapping

envi

en: Environment

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 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 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 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

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 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 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 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

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

en: erosion mars model snow sediment-transport

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: 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

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: 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: 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

erosion

display_name: erosion

mars

display_name: mars

model

display_name: model

sediment-transport

display_name: sediment-transport

snow

display_name: snow


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: Base cation dynamics in an Oriental beech forest

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

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

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: Seilaplan

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

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


stem-radius-change-data-pfynwald

en:
**Continuously measured stem radius change data (time series) from high-precision point dendrometers (Natkon) mounted on the stems of Pinus sylvestris trees at 1.5 m in the Pfynwald VPDrought experimental area.**
The data originates from the **TreeNet database**. The published data represents the highest quality data following automatic and manual corrections (LM data level).
**The dataset contains the following files:**
- 10-minute resolution time series (.parquet)
- Daily resolution time series (.csv and .parquet)
- Metadata (.csv)
- Readme (.txt)
Main variables are stem radius changes (SRC), tree water deficit (TWD) and radial stem growth (GRO).

**The site area contains the following site_ids (treatments)**
| 86 | Pfynwald-Control
| 89 | Pfynwald-Irr_Stop
| 90 | Pfynwald-Irrigation
| 145 | Pfynwald-Irrigation_VPD
| 146 | Pfynwald-Roof
| 147 | Pfynwald-Roof_VPD
The measurements cover data from 2011 to 2025. All data is in UTC + 1 hour (Central European Time CET, winter time, no DST).

en: Stem Radius Change Data Pfynwald

en: drought-stress automated-monitoring wood-growth climate-change forest treewaterdeficit drought-legacy treenet pinus-sylvestris drought-experiment time-series bark-growth alps dendrometer ecophysiology data-publication

title_for_slug: stem-radius-change-data-pfynwald

en:
Resource Title: Pfynwald_README.txt
    Size: 12082 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/60f3c95c-fe5d-49f0-89af-f325ef864cf0/resource/8c24a11c-ecda-404c-9dd4-078629e89559/download/pfynwald_readme.txt
    Description:

en:
Resource Title: Pfynwald_Dendro_2010-2025.zip
    Size: 446097419 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/60f3c95c-fe5d-49f0-89af-f325ef864cf0/resource/aec4c998-c6dd-4000-bd4e-c9bc79fe7b22/download/pfynwald_dendro_2010-2025.zip
    Description:

alps

display_name: alps

automated-monitoring

display_name: automated-monitoring

bark-growth

display_name: bark-growth

climate-change

display_name: climate-change

data-publication

display_name: data-publication

dendrometer

display_name: dendrometer

drought-experiment

display_name: drought-experiment

drought-legacy

display_name: drought-legacy

drought-stress

display_name: drought-stress

ecophysiology

display_name: ecophysiology

forest

display_name: forest

pinus-sylvestris

display_name: pinus-sylvestris

time-series

display_name: time-series

treenet

display_name: treenet

treewaterdeficit

display_name: treewaterdeficit

wood-growth

display_name: wood-growth


en:
**Continuously measured stem radius change data (time series) from high-precision point dendrometers (Natkon) mounted on the stems of Pinus sylvestris trees at 1.5 m in the Pfynwald VPDrought experimental area.**
The data originates from the TreeNet database. The published data represents the highest quality data following automatic and manual corrections (LM data level).
The dataset contains the following files:
- 10-minute resolution time series (.parquet)
- Daily resolution time series (.parquet)
- Readme (.txt)
Main variables are stem radius changes (SRC), tree water deficit (TWD) and radial stem growth (GRO).
The measurements cover data from 2011 to 2025. All data is in UTC + 1 hour (Central European Time CET, winter time, no DST).

en: Pfynwald VPDrought  experiment stem radius changes (TreeNet)

en: vpd pinus-sylvestris point-dendrometer irrigation stem-growth pfynwald drought vpdrought twd

en:
Resource Title: pfynwald_vpdrought_dendrometer_2024.zip
    Size: 61781663 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/571cf7ad-edda-4e1f-9e9d-45014c39a9e2/resource/425594e2-7210-49a8-82b7-1bdfc4cbaf33/download/pfynwald_vpdrought_dendrometer_2024.zip
    Description:

en:
Resource Title: pfynwald_vpdrought_dendrometer_2025.zip
    Size: 62493309 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/571cf7ad-edda-4e1f-9e9d-45014c39a9e2/resource/cea43ffe-6437-4b0e-8d58-398026cbde41/download/pfynwald_vpdrought_dendrometer_2025.zip
    Description:

drought

display_name: drought

irrigation

display_name: irrigation

pfynwald

display_name: pfynwald

point-dendrometer

display_name: point-dendrometer

stem-growth

display_name: stem-growth

twd

display_name: twd

vpd

display_name: vpd

vpdrought

display_name: vpdrought


en: This dataset includes the diversity of soil prokaryotes, fungi and micro-eukaryotes in unmanaged forest reserves with managed forests across nine locations within Switzerland. Forests have been exploited for resources for centuries, which has caused disturbances, altered community structure, and reduced diversity at different scales. Strict forest reserves, where no forest management is practiced, have been implemented to promote biodiversity, by allowing old trees with microhabitats and high deadwood amounts. However, the effectiveness of forest reserves on soil microbial diversity and functional traits are little known at a national scale in Swiss forests. The associated metadata includes local environmental conditions, such as soil pH, organic C, N, deadwood volume, MAT and MAP, and forest type (coniferous vs deciduous).

en: Management practices shape soil fungal diversity and functional traits in Swiss forests

en: forest-management prokaryotes microeukaryotes fungi soil-biodiversity

en:
Resource Title: Microeukaryotes
    Size: 2512555868 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.780%2FForestMan_SoilBD_Microeukaryotes%20%2818S%29%2F
    Description:

en:
Resource Title: Environmental data
    Size: 13926 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/04e9459c-7024-46c3-9c33-a50a33da55b2/resource/c27f01d0-217e-4b50-88c0-70421ec0334f/download/forest-management-soilbd-environmental-data-envidat.csv
    Description: Environmental Data such as soil abiotic properties

en:
Resource Title: Prokaryotes
    Size: 2738041651 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.780%2FForestMan_SoilBD_Prokaryotes%20%2816S%29%2F
    Description:

en:
Resource Title: Fungi
    Size: 2931315180 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.780%2FForestMan_SoilBD_Fungi%20%28ITS%29%2F
    Description:

forest-management

display_name: forest-management

fungi

display_name: fungi

microeukaryotes

display_name: microeukaryotes

prokaryotes

display_name: prokaryotes

soil-biodiversity

display_name: soil-biodiversity


en: This dataset comprises qualitative data for the development of the five Swiss Shared Socioeconomic Pathways (SSP-CH) up to the year 2100. First, it comprises projections of 22 socioeconomic factors, each represented by four to five factor-specific trajectories that form the basis of the qualitative scenario storylines. Second, it includes an input table providing concise qualitative characterizations of 63 socioeconomic factors across the five SSP-CH scenarios.

en: Shared Socioeconomic Pathways for Switzerland (SSP-CH): qualitative data for scenario development

en: scenario-development ssp-storylines socioeconomic-scenarios qualitative-scenarios national-ssps

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

envi

en: Environment

en:
Resource Title: SSP-CH_Faktorprojektionen_d.pdf
    Size: 3030385 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/3e5310e5-4038-4782-b3c9-b64100709e32/resource/11d2b1ab-8a77-407e-9732-db22a853e03f/download/faktorprojektionen_beschreibung_d.pdf
    Description: SSP-CH factor projections of 20 socioeconomic factors (german). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Programme. The following individuals participated in the compilation of this data: Gubler, L., Tschumi, P., Pütz, M., Seidl, I.

en:
Resource Title: Input-Tabelle_final_d.xlsx
    Size: 198502 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/3e5310e5-4038-4782-b3c9-b64100709e32/resource/3458c684-6a22-437e-99cf-20c9f494d521/download/input-tabelle_final_d.xlsx
    Description: SSP-CH Input table: qualitative description of 63 socioeconomic factors in the 5 SSP-CH (german). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Programme. The following individuals participated in the compilation of this data: Gubler, L., Tschumi, P., Pütz, M., Seidl, I.

en:
Resource Title: Input-table_final_e.xlsx
    Size: 45599 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/3e5310e5-4038-4782-b3c9-b64100709e32/resource/9eef7ee5-8a6d-47c2-9952-447cd39018e4/download/input-table_final_e.xlsx
    Description: SSP-CH Input table: qualitative description of 63 socioeconomic factors in the 5 SSP-CH (english). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Programme. The following individuals participated in the compilation of this data: Gubler, L., Tschumi, P., Pütz, M., Seidl, I.

national-ssps

display_name: national-ssps

qualitative-scenarios

display_name: qualitative-scenarios

scenario-development

display_name: scenario-development

socioeconomic-scenarios

display_name: socioeconomic-scenarios

ssp-storylines

display_name: ssp-storylines


en:
This dataset contains the results of greenhouse gas emission modeling for the five Swiss Shared Socioeconomic Pathways (SSP-CH) up to the year 2100. Six sector-specific reduced-complexity models were developed specifically for this analysis, enabling the modelling of the SSP-CH storylines. Accordingly, the dataset comprises the outputs of all six models, generated both in the absence of and under the implementation of additional climate policy packages, referred to as Shared Policy Assumptions (SPAs; see related publications).
- Transport model (energy consumption by energy source and transport type/fleet mix)
- Housing model (heating demand and GHG emissions per building category)
- Electricity model (electricity consumption, electricity generation by energy source, installed capacity, district heating)
- Industry model (energy consumption by energy source, subdivided into raw materials/building materials, consumer goods, and processed goods)
- Agriculture model (feed digestion in livestock, manure storage, and nitrogen fertilization)
- Export/Import model (product quantities in import and export, broken down into consumer goods, raw materials/building materials, and processed goods)
- Synthesis model

en: Results of greenhouse gas modelling of the Swiss Shared Socioeconomic Pathways (SSP-CH)

en: national-ssp greenhousegas-modelling emissions-scenarios socioeconomic-scenarios qualitative-scenarios

en:
Resource Title: SSP-CH_GHG_transport_model.xlsx
    Size: 9070182 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c02b9e4b-32dc-46bb-aa43-9d648665b089/resource/be82872b-5fd7-4498-a4b9-2141dd5eb00f/download/ssp-ch_ghg_transport_model.xlsx
    Description: SSP-CH GHG modelling results of Transport model (energy consumption by energy source and transport type/fleet mix). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Program. The following individuals participated in the compilation of this data: Oberpriller, Q., Schmid, N., Kemmler, A., Gubler, L., Bogler, S., Giger, D., Piegsa, A., Brutsche, A., Kreidelmeyer, S., Vu M. Ph., Black, B., Kirchner, A., Füssler, J.

en:
Resource Title: SSP-CH_GHG_electricity_model.xlsx
    Size: 1195377 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c02b9e4b-32dc-46bb-aa43-9d648665b089/resource/b543e472-b1c0-488c-8a71-ff09be0d140d/download/ssp-ch_ghg_electricity_model.xlsx
    Description: SSP-CH GHG modelling results of Electricity model (electricity consumption, electricity generation by energy source, installed capacity, district heating). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Program. The following individuals participated in the compilation of this data: Oberpriller, Q., Schmid, N., Kemmler, A., Gubler, L., Bogler, S., Giger, D., Piegsa, A., Brutsche, A., Kreidelmeyer, S., Vu M. Ph., Black, B., Kirchner, A., Füssler, J.

en:
Resource Title: SSP-CH_GHG_Synthesis_model.xlsx
    Size: 386540 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c02b9e4b-32dc-46bb-aa43-9d648665b089/resource/a1e54990-11c5-43cf-b922-d839925b4078/download/ssp-ch_ghg_synthesis_model.xlsx
    Description: SSP-CH GHG modelling results of Synthesis model. This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Program. The following individuals participated in the compilation of this data: Oberpriller, Q., Schmid, N., Kemmler, A., Gubler, L., Bogler, S., Giger, D., Piegsa, A., Brutsche, A., Kreidelmeyer, S., Vu M. Ph., Black, B., Kirchner, A., Füssler, J.

en:
Resource Title: SSP-CH_GHG_agriculture_model.xlsx
    Size: 624865 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c02b9e4b-32dc-46bb-aa43-9d648665b089/resource/b8d73680-8159-46ac-98f6-9d97131f5c2e/download/ssp-ch_ghg_agriculture_model.xlsx
    Description: SSP-CH GHG modelling results of Agriculture model (feed digestion in livestock, manure storage, and nitrogen fertilization). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Program. The following individuals participated in the compilation of this data: Oberpriller, Q., Schmid, N., Kemmler, A., Gubler, L., Bogler, S., Giger, D., Piegsa, A., Brutsche, A., Kreidelmeyer, S., Vu M. Ph., Black, B., Kirchner, A., Füssler, J.

en:
Resource Title: SSP-CH_GHG_industry_model.xlsx
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    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c02b9e4b-32dc-46bb-aa43-9d648665b089/resource/3e81b975-33bf-4327-bf18-3ae32f36b21d/download/ssp-ch_ghg_industry_model.xlsx
    Description: SSP-CH GHG modelling results of Industry model (energy consumption by energy source, subdivided into raw materials/building materials, consumer goods, and processed goods). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Program. The following individuals participated in the compilation of this data: Oberpriller, Q., Schmid, N., Kemmler, A., Gubler, L., Bogler, S., Giger, D., Piegsa, A., Brutsche, A., Kreidelmeyer, S., Vu M. Ph., Black, B., Kirchner, A., Füssler, J.

en:
Resource Title: SSP-CH_GHG_housing_model.xlsx
    Size: 3051356 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c02b9e4b-32dc-46bb-aa43-9d648665b089/resource/913c1ae0-418b-4f31-8b51-9ef1e364b3fd/download/ssp-ch_ghg_housing_model.xlsx
    Description: SSP-CH GHG modelling results of Housing model (heating demand and GHG emissions per building category). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Program. The following individuals participated in the compilation of this data: Oberpriller, Q., Schmid, N., Kemmler, A., Gubler, L., Bogler, S., Giger, D., Piegsa, A., Brutsche, A., Kreidelmeyer, S., Vu M. Ph., Black, B., Kirchner, A., Füssler, J.

en:
Resource Title: SSP-CH_GHG_transport_model.xlsx
    Size: 9070182 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c02b9e4b-32dc-46bb-aa43-9d648665b089/resource/c147c40f-3431-4847-a954-5a80b6a38cf1/download/ssp-ch_ghg_transport_model.xlsx
    Description: This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Program. The following individuals participated in the compilation of this data: Oberpriller, Q., Schmid, N., Kemmler, A., Gubler, L., Bogler, S., Giger, D., Piegsa, A., Brutsche, A., Kreidelmeyer, S., Vu M. Ph., Black, B., Kirchner, A., Füssler, J.

en:
Resource Title: SSP-CH_GHG_imp_exp_model.xlsx
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    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/c02b9e4b-32dc-46bb-aa43-9d648665b089/resource/c80f618c-fd87-4c43-920e-77c8081ccd5a/download/ssp-ch_ghg_imp_exp_model.xlsx
    Description: SSP-CH GHG modelling resluts of Export/Import model (product quantities in import and export, broken down into consumer goods, raw materials/building materials, and processed goods). This data was compiled as part of the Project "Socioeconomic Scenarios for Switzerland" within the NCCS-Impacts Program. The following individuals participated in the compilation of this data: Oberpriller, Q., Schmid, N., Kemmler, A., Gubler, L., Bogler, S., Giger, D., Piegsa, A., Brutsche, A., Kreidelmeyer, S., Vu M. Ph., Black, B., Kirchner, A., Füssler, J.

emissions-scenarios

display_name: emissions-scenarios

greenhousegas-modelling

display_name: greenhousegas-modelling

national-ssp

display_name: national-ssp


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

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

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

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

en:
Resource Title: Description input data
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    Format: application/pdf
    URL: https://www.envidat.ch/dataset/516d259a-3b10-419c-a033-980bedf6ade1/resource/3db8ca57-2838-4b26-82a9-9770b75b7096/download/readme_descriptioninputdata.pdf
    Description: Details where the input data come from and how they have been processed

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

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

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

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

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

bioclimatic-variables

display_name: bioclimatic-variables

forest

display_name: forest

forest-dynamics

display_name: forest-dynamics

forest-model

display_name: forest-model

gui

display_name: gui

land-use

display_name: land-use

r-package

display_name: r-package

spatial-modelling

display_name: spatial-modelling

species-interaction

display_name: species-interaction

species-migration

display_name: species-migration

treemig

display_name: treemig


en:
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: Code of TreeMig Package version 1

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

envidat

en: EnviDat: the Environmental Data Portal

envi

en: Environment

dynamic-modelling

display_name: dynamic-modelling

landscape

display_name: landscape


en:
We used 1463 observations of early leaf discoloration presence or absence in Switzerland's European beech (*Fagus sylvatica L.*) forests between 2018 and 2023 and interpolated Sentinel-2 vegetation index time series from Koch et al. (2024) to train a support vector classifier of early leaf discoloration. We applied this classifier to the European beech dominated forest area of Switzerland over the same time period to estimate a probability of early leaf discoloration between 0 and 1 (high probability of early leaf discoloration). The maps intentionally exclude legacy effects of drought like crown dieback or mortality which occurred in prior years.
Early leaf discoloration maps are available at the end of every summer month (June, July, August, and September) 2017 to 2023. Additional maps are available every 5-days during the summer months of 2018. Probability of early leaf discoloration at each date is provided as a GeoTIFF file with a scaled value range of 0 to 10,000 such that a probability of 0.9 is represented by a value of 9000 (a high probability of early leaf discoloration). An optimized probability threshold of 0.4 (4000) is recommended for classification into discolored and not discolored pixels. More details on the methods and results can be found in the publication of Bloom et al. (2026).
***These maps represent an interpreted remote sensing product that approximates ground observations of early leaf discoloration across Switzerland's European beech dominated forests.***
Given ongoing efforts to operationalize an early discoloration pipeline, all data is currently available upon reasonable request. An example of the 2018 5-day time series in the Hardwald (Canton Basel-Land) is available for immediate download.

en: Early leaf discoloration maps for Switzerland's European beech forests

en: early-defoliation early-warning drought drought-2018 switzerland forest european-beech sentinel-2

drought

display_name: drought

early-defoliation

display_name: early-defoliation

early-warning

display_name: early-warning

european-beech

display_name: european-beech

switzerland

display_name: switzerland


en:
The TreeMig framework allows for an easy application of the forest landscape model TreeMig for simulating forest dynamics in space under changing environmental and land use conditions.
Here, case study examples in Switzerland are given that simulate the dynamics and spatial spread of competing tree species in a region in Switzerland, the invasion of a hypothetical invasive tree species, and the control of this species via a management model coupled to TreeMig.  The datasets consist of the installation- and GUI starting-script,  example environmental input data for Switzerland, the simulation environment, and further R-scripts for running the simulations and plotting the simulation results directly from R and for coupling TreeMig to the forest management model.

en: Case study applications demonstrating the use and potential of the TreeMig framework v1

en: example-applications forest forest-management treemig forest-dynamics spatial-modelling bioclimatic-variables climate-change land-use species-migration r-package

en:
Resource Title: readme_descriptioninputdata.pdf
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    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/5abbe67b-ab73-413b-bff8-5cccf5347b1c/download/readme_descriptioninputdata.pdf
    Description:

en:
Resource Title: ExampleApplicationsTreeMig.zip
    Size: 1676301558 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/609e85c8-98d0-4043-be91-48bd32c162ea/download/exampleapplicationstreemig.zip
    Description: 1) Simulation environment TMEnvironmentExampleApplications for the simulation of the case study applications. It contains a) simulation settings (folder C),  b) species parameters (folder P), c) folders S, R and P for the state files and results and plots to be produced, and the binaries of the TreeMig model.
2) Input data for Switzerland in TMInputData (Climate, soil, land cover, DEM)
3) Several R scripts (folder Scripts) to run and plot the simulations directly from R without having to use the graphical user interface.
For learning how to use TreeMig we recommend the starter kit https://www.doi.org/10.16904/envidat.533.

en:
Resource Title: InstructionsForRunningTheSimulations.pdf
    Size: 127302 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/32c74611-c61e-48be-998b-6722bbb697c8/download/instructionsforrunningthesimulations.pdf
    Description: A condensed instruction for running the presented simulations.

en:
Resource Title: ExampleApplicationsTreeMig.zip
    Size: 1686005000 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/0f59997f-3e90-4b25-9e5b-37b641d9769b/download/exampleapplicationstreemig.zip
    Description: 1) Simulation environment TMEnvironmentExampleApplications for the simulation of the case study applications. It contains a) simulation settings (folder C),  b) species parameters (folder P), c) folders S, R and P for the state files and results and plots to be produced, and the binaries of the TreeMig model.
2) Input data for Switzerland in TMInputData (Climate, soil, land cover, DEM)
3) Several R scripts (folder Scripts) to run and plot the simulations directly from R without having to use the graphical user interface.
For learning how to use TreeMig we recommend the starter kit https://www.doi.org/10.16904/envidat.533.

en:
Resource Title: installAndRunTreeMigWrapperAndGUI_std.R
    Size: 553 bytes
    Format: text/x-r
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/916c8a50-1d57-4e28-97c8-f50b8a9f7bf7/download/installandruntreemigwrapperandgui_std.r
    Description: R-script to install the TreeMig framework and start the graphical user interface

en:
Resource Title: InstructionsForRunningTheSimulations.pdf
    Size: 68995 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4e735cce-be7e-4279-9f96-893d8d789924/resource/830a45db-7985-42f6-8864-4961fe5ef5a2/download/instructionsforrunningthesimulations.pdf
    Description: A condensed instruction for running the presented simulations.

bioclimatic-variables

display_name: bioclimatic-variables

climate-change

display_name: climate-change

example-applications

display_name: example-applications

forest

display_name: forest

forest-management

display_name: forest-management


en:
Regional disturbance predisposition maps for windthrow, bark beetle and snow breakage. The maps are created using spatially explicit data of classified forest structure and site factor parameters. The classified input parameters were weighted according to their effect on windthrow, bark beetle and snow breakage predisposition using an expert-based model. The predisposition raster maps (10 x 10 m) represent the sum of expert-weighted effects on the predisposition and are provided as raw values (sum of the effects on predisposition) and as classified layers.
Classified predisposition maps are encoded as follows:
1: low predisposition (-50% quantile based on raw predisposition values)
2: increased predisposition (50–75% quantile)
3: high predisposition (-75–95% quantile)
4: extreme predisposition (-95% quantile)
Also included are the derived forest structure maps for development stage, dominant stand height, number of stems and canopy cover used for the predisposition mapping, as well as the R scripts and example data to re-create these parameters. Forest structure parameters are based on a data set of individual trees detected using a high-resolution vegetation height model. Further included are ArcPy scripts and example data to re-create the predisposition maps. Running the ArcPy scripts requires a valid ArcGis license including the extensions "SpatialAnalyst", "ImageAnalyst" and "3D".
The example data provided here is assembled from different sources; please check the related publication Bührle et al. (in review) for data sources and further information about the processing, the expert-based model, the forest structure derivation and the predisposition mapping.
Also consider reviewing the related datasets and publications Bast et al. (2025) for more information about canopy layering derivation and individual tree detection (ITD).

en: Regional bark beetle windthrow and snow breakage disturbance predisposition maps

en: predisposition-mapping als stand-height tree-detection canopy-cover itd stem-number forest-structure predisposition windthrow snow-breakage development-stage bark-beetle

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:
Resource Title: 02_python_scripts_predisposition_mapping.zip
    Size: 438323 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/171b832f-9fd1-40c0-b2d6-f8f137b572e8/resource/e165fc12-ca86-423f-807a-d7cf1c8535a5/download/02_python_scripts_predisposition_mapping.zip
    Description: ArcPy-Python scripts and demo data to create the bark beetle, windthrow and snow breakage predisposition maps.

en:
Resource Title: 04_forest_structure_maps.zip
    Size: 34068234 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/171b832f-9fd1-40c0-b2d6-f8f137b572e8/resource/cd162993-bc30-46c1-816b-0722c3d83a50/download/04_forest_structure_map.zip
    Description: Maps of development stage, number of stems, dominant stand height and canopy coverage based on single trees detected from a vegetation height model.

en:
Resource Title: 01_predispostion_maps.zip
    Size: 19881001 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/171b832f-9fd1-40c0-b2d6-f8f137b572e8/resource/59df8818-2e56-4794-9152-3652c20fe6fc/download/01_predispostion_maps.zip
    Description: Classified and raw value predisposition maps for bark beetle, windthrow and snow breakage

en:
Resource Title: 03_rcode_forest_structure.zip
    Size: 103693681 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/171b832f-9fd1-40c0-b2d6-f8f137b572e8/resource/42199442-7c55-48ed-a3a7-04aaf9967da0/download/03_rcode_forest_structure.zip
    Description: R-codes and demo data to derive the forest structure parameters: development stage, number of stems, dominant stand height, and canopy coverage based on single trees detected from a vegetation height model. These parameters are necessary input parameters for predisposition mapping.

als

display_name: als

bark-beetle

display_name: bark-beetle

canopy-cover

display_name: canopy-cover

development-stage

display_name: development-stage

forest-structure

display_name: forest-structure

itd

display_name: itd

predisposition

display_name: predisposition

predisposition-mapping

display_name: predisposition-mapping

snow-breakage

display_name: snow-breakage

stand-height

display_name: stand-height

stem-number

display_name: stem-number

tree-detection

display_name: tree-detection

windthrow

display_name: windthrow


en:
Phenology, growth, NSC and Amax data used for the following paper:
Early-season warming advances autumn senescence in temperate tree saplings in association with early growth dynamics  published in Plant Cell & Environment 2026.

en: Data for the paper: Early-season warming advances autumn senescence in temperate tree saplings in association with early growth dynamics

en: growth warming-experiment leaf-senescence tree-phenology nsc

envidat

en: EnviDat: the Environmental Data Portal

envi

en: Environment

en:
Resource Title: ReadMe_Data information.docx
    Size: 17668 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/90ccc75b-bc5e-4968-92a2-38d3452341a8/resource/28146b13-81fd-4625-bc7a-6f55ee39796d/download/readme_data-information.docx
    Description:

en:
Resource Title: Data.zip
    Size: 42598 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/90ccc75b-bc5e-4968-92a2-38d3452341a8/resource/6a336540-7ba5-471e-b452-db20bc540b6a/download/data.zip
    Description: Data used in the paper published in Plant Cell & Environment Vitasse et al. 2026

growth

display_name: growth

leaf-senescence

display_name: leaf-senescence

nsc

display_name: nsc

tree-phenology

display_name: tree-phenology

warming-experiment

display_name: warming-experiment


en: This dataset contains R scripts and pre-processed files to reconstruct the analyses made in the technical note submitted to Ecology Letters as a response to Li & He (2026, https://doi.org/10.1111/ele.70326). In short, I show that the claims made by LH26 (variation in tree growth has increased due to global warming, and that the power exponent between growth variance and mean is b=1.4) are questionable. I show that the reported variance increase in ring widths is an artefact caused by division-based detrending. The reported exponent of 1.4 reflects systematic underestimation, not biological reality of a steep variance-mean scaling. The ecological interpretations built on these foundations cannot be considered reliable.

en: No increased tree growth variability due to global warming

en: climate-change global-warming tree-rings estimation-methods variability

en:
Resource Title: R scripts.zip
    Size: 24412 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/b1e75fa6-f232-45af-a524-d2088ff2757b/resource/15fb0bd5-ad52-4cf3-ad52-3573063232d2/download/r-scripts.zip
    Description: R scripts to reconstruct analyses made in technical note.

mean_variance_ITRDB_SM1: SM1, calculation of running variances, calculation of outliers, diagnostic plots
power_estimate_clean_simulation: Simulation for SM5
fair_power_comparison_persample: power estimation per 50-year period per sample, SM3
fair_power_comparison: population power estimation per 50-year period, SM3
powertest3.1: Simulation to estimate power given the shape of the ITRDB raw ring-width data, SM4
different power exponent estimations: SM4

en:
Resource Title: preprocessed data.zip
    Size: 3199750636 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/b1e75fa6-f232-45af-a524-d2088ff2757b/resource/3ada643d-214e-4573-a4d6-3c49736f5e3f/download/preprocessed-data.zip
    Description: pre-processed data produced in the accompanying R scripts.

en:
Resource Title: Diagnostic plots.zip
    Size: 279812506 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/b1e75fa6-f232-45af-a524-d2088ff2757b/resource/e51b22cb-ff0f-40cb-b302-bfc24e2a3a48/download/diagnostic-plots.zip
    Description: Diagnostic plots presented in SM1.

Outlier_plots_negexp contains the outlier screening where the arithmetic mean at the population levels creates much larger values than the robust mean.

varmax_diagnostic_pages contains the outlier screening for running 5-year variance >5.

climate-change

display_name: climate-change

estimation-methods

display_name: estimation-methods

global-warming

display_name: global-warming

tree-rings

display_name: tree-rings

variability

display_name: variability


en:
Richness, site occurrence and abundance data of bees, beetles, birds, hoverflies, net-wingeds, true bugs, snails, spiders, milipides, wasps collected in the city of Zurich using different sampling techniques, and the environmental variables for each sampling site.
Data are provided on request to contact person against bilateral agreement.

en: Ecological properties of urban ecosystems. Biodiversity dataset of Zurich

en: insects species-richness-models sdms diversity urbanization species-abundance-distributions spiders snails birds urban-ecology arthropods

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:
Resource Title: Dataset_1_SAD_SOD.txt
    Size: 73482 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/0f81429a-204e-4720-929e-5b251e9ab1c4/resource/a7a96a85-dc46-4b5f-bcf4-4d91485e3d03/download/dataset_1_sad_sod.txt
    Description: abundance and occurrence data of the 1446 species studied, used to investigate species-abundance distributions and species-occurrence distributions

en:
Resource Title: Dataset_5_Species_richness_all.txt
    Size: 2453668 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/0f81429a-204e-4720-929e-5b251e9ab1c4/resource/f3abc8d4-c92e-4a57-aea4-39ba8c93158d/download/dataset_5_species_richness_all.txt
    Description: contains the raw data to perform the species richness models of all species

en:
Resource Title: Dataset_3_Species_richness_hyper.txt
    Size: 1373635 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/0f81429a-204e-4720-929e-5b251e9ab1c4/resource/0ef3b508-b8e7-4404-a7b9-edc51c7f2375/download/dataset_3_species_richness_hyper.txt
    Description: contains the raw data to perform the species richness models of the hypercommon species

en:
Resource Title: Dataset_2_SDM_data.txt
    Size: 65682801 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/0f81429a-204e-4720-929e-5b251e9ab1c4/resource/0e2e7fb0-88ba-4aa4-92ef-079191b4a668/download/dataset_2_sdm_data.txt
    Description: contains the raw data to perform species distribution models, only for common species

en:
Resource Title: Dataset_4_Species_richness_rare.txt
    Size: 2275410 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/0f81429a-204e-4720-929e-5b251e9ab1c4/resource/3bbbc78a-92c3-4832-b51b-61d65346460e/download/dataset_4_species_richness_rare.txt
    Description: contains the raw data to perform the species richness models of the rare species

arthropods

display_name: arthropods

birds

display_name: birds

diversity

display_name: diversity

insects

display_name: insects

sdms

display_name: sdms

snails

display_name: snails

species-abundance-distributions

display_name: species-abundance-distributions

species-richness-models

display_name: species-richness-models

spiders

display_name: spiders

urban-ecology

display_name: urban-ecology

urbanization

display_name: urbanization


en: This repository contains plant community data collected at 180 sites across five types of urban green spaces (allotments, gardens, green areas in residential estates, parks, and ruderal sites) in three Swiss cities (Lugano, Geneva, Zurich). There are 12 sites per UGS type and city (i.e., 60 sites per city). Plant communities were sampled in 100 m2 plots, and coverage was recorded using the extended Braun-Blanquet scale. The data is part of the SNF Synergia project PAPPUS.

en: Data on the plant composition in 5 urban green space types and 3 Swiss Cities (project PAPPUS)

en: urban-greenspaces urban-plants human-preferences ubran-biodiversity origin-status

envidat

en: EnviDat: the Environmental Data Portal

envi

en: Environment

en:
Resource Title: Non-native plant community
    Size: 183641 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/3bb645df-e8d4-4887-93c4-d91a24afdea5/resource/b0270e86-5580-4b99-8f94-a903178db451/download/community_matrix_nonnative_f.csv
    Description: The dataset contains the non-native plant communities  sampled in the 180 study sites. Rows are sites and columns are plant species. Cells contain the numerical values of the extended Braun-Blanquet coverage classes (e.g., https://doi.org/10.1658/1100-9233(2007)18[413:HRITMO]2.0.CO;2). Native species are considered as those whose natural distribution ranges are outside continental Europe, following (https://doi.org/10.1016/j.dib.2021.107243). They include invasive species and exotics.

Sites IDs are composed by City_UGStype + Values orthogonal gradient + replicate number:

    City: 3 levels, GE = Geneva, ZH= Zurich, LG = Lugano
    UGStype: 5 levels, Al = Allotments, Ga = private gardens, Es = green areas in residential estates, Pa = parks, Ru = ruderal sites.
    Orthogonal gradient: Landscape urbanization class + Local structural diversity class. 4 combinations: HH, HL, LH, LL. H =high, L = Low
    Replicate, numeric. 1-3

en:
Resource Title: Native plant community
    Size: 262862 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/3bb645df-e8d4-4887-93c4-d91a24afdea5/resource/ca98f7fd-5e48-4ffc-b5cd-b8a9b6fd4b15/download/community_matrix_native_f.csv
    Description: The dataset contains the native plant communities  sampled in the 180 study sites. Rows are sites and columns are plant species. Cells contain the numerical values of the extended Braun-Blanquet coverage classes (e.g., https://doi.org/10.1658/1100-9233(2007)18[413:HRITMO]2.0.CO;2). Native species are considered as thosw whose natural distribution ranges are within continental Europe, following (https://doi.org/10.1016/j.dib.2021.107243)

Sites IDs are composed by City_UGStype + Values orthogonal gradient + replicate number:

    City: 3 levels, GE = Geneva, ZH= Zurich, LG = Lugano
    UGStype: 5 levels, Al = Allotments, Ga = private gardens, Es = green areas in residential estates, Pa = parks, Ru = ruderal sites.
    Orthogonal gradient: Landscape urbanization class + Local structural diversity class. 4 combinations: HH, HL, LH, LL. H =high, L = Low
    Replicate, numeric. 1-3

en:
Resource Title: Full plant community
    Size: 449744 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/3bb645df-e8d4-4887-93c4-d91a24afdea5/resource/70681117-86df-43b9-abf9-870d027eb8af/download/community_matrix.csv
    Description: The dataset contains the full plant communities (including both native and non-native species) sampled in the 180 study sites. Rows are sites and columns are plant species. Cells contain the numerical values of the extended Braun-Blanquet coverage classes (e.g., https://doi.org/10.1658/1100-9233(2007)18[413:HRITMO]2.0.CO;2).

Sites IDs are composed by City_UGStype + Values orthogonal gradient + replicate number:
- City: 3 levels, GE = Geneva, ZH= Zurich, LG = Lugano
- UGStype: 5 levels, Al = Allotments, Ga = private gardens, Es = green areas in residential estates, Pa = parks, Ru = ruderal sites.
- Orthogonal gradient: Landscape urbanization class + Local structural diversity class.  4 combinations: HH, HL, LH, LL.  H =high, L = Low
- Replicate, numeric. 1-3

human-preferences

display_name: human-preferences

origin-status

display_name: origin-status

ubran-biodiversity

display_name: ubran-biodiversity

urban-greenspaces

display_name: urban-greenspaces

urban-plants

display_name: urban-plants


en:
Jikradia olitoria Say is a Nearctic leafhopper that was recently identified in several European countries. Given its role as the vector of phytoplasmas associated with Grapevine Yellows diseases (GYD) in its native range, its presence in European vineyard agroecosystems could represent an important phytosanitary risk.
Systematic monitoring conducted over two consecutive years (i.e., 2023 and 2024) using chromotropic traps in seven sites allowed to confirm its widespread presence in the southern Alps, both inside vineyards as well as in the nearby forest edge compartment. Moreover, a subset of the collected specimens was analyzed to test potential infection by phytoplasmas associated with Flavescence dorée.

en: First detailed record of Jikradia olitoria (Hemiptera: Cicadellidae) in the Swiss southern Alps and evaluation of its current potential as a vector of 16SrV-phytoplasmas

en: flavescence-doree grapevine-yellows epidemiology neobiota nearctic-leafhopper

en:
Resource Title: Table 3 - Insect vectors of 'Candidatus Phytoplasma vitis' associated to grapevine’s Flavescence dorée in Europe
    Size: 21135 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/83726448-91da-423c-8eb4-28a195c7f40a/resource/7b134d20-6f86-41d1-bca1-3610fdf02b07/download/table-3.docx
    Description: Insect vectors of 'Candidatus Phytoplasma vitis' associated to grapevine’s Flavescence dorée in Europe. Insect species categorized according to their vector competence, i.e., main vector: primary epidemic grapevine-to-grapevine vector; alternative vector: FD phytoplasma acquisition and transmission confirmed in laboratory and supported by data collected in the field and usually involving other plant species; putative vector: only FDp and/or FDp-like genotypes acquisition confirmed, no indication of ability to transmit FDp to receipient plant. Associated plant species involved in alternative FDp cycle do not represent the complete and known range of host plant species for the listed insect species. Only major and epidemic map-type genotypes are listed. For further details on map-type gene and grouping (i.e., FD1, FD2, and FD3) and vectotype related gene VmpA, refer to Malembic-Maher et al. (2020).

en:
Resource Title: Supplementary Figure 2
    Size: 13233029 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/83726448-91da-423c-8eb4-28a195c7f40a/resource/41f7fb05-1fac-4ef1-adc6-7362059d972d/download/figure_s2.tiff
    Description: Jikradia olitoria captures on yellow sticky traps placed in the landscape around the vineyards in the years 2023 and 2024.

en:
Resource Title: Table2 - Jikradia olitoria hatching events
    Size: 15278 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/83726448-91da-423c-8eb4-28a195c7f40a/resource/7bf9a0ee-2111-4144-8f11-f4f35784b74c/download/table2.docx
    Description: Jikradia olitoria hatching events divided by year and wood type. For the hatching experiment, wood was collected from the Giornico site due to the large J. olitoria adult population observed in 2023.

en:
Resource Title: Table1 - Jikradia olitoria total captures
    Size: 21647 bytes
    Format: application/vnd.openxmlformats-officedocument.wordprocessingml.document
    URL: https://www.envidat.ch/dataset/83726448-91da-423c-8eb4-28a195c7f40a/resource/05806ade-fd08-4bfa-b2bb-a0991dec3377/download/table1.docx
    Description: Jikradia olitoria total captures divided by plot and trap type, mean captures per trap and ratio between the two sampling years. YST/plot = yellow sticky traps installed in each plot, L = landscape YST, V = vineyard YST, N YST = overall number of processed YST throughout the experiment.

en:
Resource Title: Supplementary_table3 - Hatching events
    Size: 15852 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/83726448-91da-423c-8eb4-28a195c7f40a/resource/588d922d-1acd-4eab-9693-c76d4cf7d9a6/download/supplementary_table3.xlsx
    Description: Hatching events in 2024 and 2025.

en:
Resource Title: Supplementary_table2 - Trap captures 2024
    Size: 165550 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/83726448-91da-423c-8eb4-28a195c7f40a/resource/bba02f83-22d2-48d3-8d60-39e03be1cfb6/download/supplementary_table2.xlsx
    Description: Trap captures in 2024.

en:
Resource Title: Supplementary Figure 1
    Size: 13233029 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/83726448-91da-423c-8eb4-28a195c7f40a/resource/0b456ac4-2091-4836-9b36-065b5fa39f55/download/figure_s1.tiff
    Description: Jikradia olitoria captures on yellow sticky traps placed inside the vineyard canopy in the years 2023 and 2024.

en:
Resource Title: Supplementary_table1 - Trap captures 2023
    Size: 144783 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/83726448-91da-423c-8eb4-28a195c7f40a/resource/8c89de75-a018-44c1-b913-346b1dbc5356/download/supplementary_table1.xlsx
    Description: Trap captures in 2023.

epidemiology

display_name: epidemiology

flavescence-doree

display_name: flavescence-doree

grapevine-yellows

display_name: grapevine-yellows

nearctic-leafhopper

display_name: nearctic-leafhopper

neobiota

display_name: neobiota


en:
This dataset supports the analysis presented in "From point clouds to forest management: Quantifying the sensitivity of a decision support framework to initialization data using close-range remote sensing" (https://doi.org/10.1016/j.ecoinf.2026.103896). The repository covers two parts of the analysis: (1) data preparation from LiDAR-based forest inventories, and (2) sensitivity analysis of decision support system (DSS) outputs. The intermediate forest simulation (ForClim v4.1) and multi-criteria decision analysis (MCDA) were carried out using an external pipeline described in Blattert et al. (2024), using the R package rforclim (v1.01.001) by Hiltner & Marano (2025). The corresponding model outputs are included here as inputs to the sensitivity analysis, making all analyses in this repository fully reproducible without requiring access to the simulation pipeline. The ForClim model (v4.1) (Marano et al., 2025) is openly available from the ETH Zurich Institute of Terrestrial Ecosystems (https://ites-fe.ethz.ch/openaccess/products/forclim). Climate data were sourced from the publicly available Swiss CH2018 dataset (CH2018 Project Team, 2018). Soil data were sourced from Baltensweiler et al. (2024). For a detailed description of the repository structure, scripts, and data files, see the README file included in this dataset.
 References
Blattert C, Mutterer S, Thrippleton T, Diaci J, Fidej G, Bont LG, Schweier J (2024). Managing European Alpine forests with close-to-nature forestry to improve climate change mitigation and multifunctionality. Ecological Indicators, 165, 112154. https://doi.org/10.1016/j.ecolind.2024.112154
Baltensweiler A, Walthert L, Hanewinkel M, Zimmermann S, Nussbaum M (2024). Soil property maps for the Swiss forest. EnviDat. https://doi.org/10.16904/envidat.484
CH2018 Project Team (2018). CH2018 - Climate Scenarios for Switzerland. National Centre for Climate Services, Zurich. https://www.nccs.admin.ch/nccs/en/home/climate-change-and-impacts/swiss-climate-change-scenarios/ch2018---climate-scenarios-for-switzerland.html
Marano, G., Hiltner, U., Meusburger, K., Hands, T., & Bugmann, H. (2025). Predicting drought-induced tree mortality in Swiss beech forests hinges upon predisposing and inciting factors (p. 2025.12.15.694299). bioRxiv. https://doi.org/10.64898/2025.12.15.694299
Hiltner U, Marano G (2025). rforclim: Collection of useful functions to parameterize and run the forest model ForClim. R package version 1.01.001. Available on request from the ETH Zurich Institute of Terrestrial Ecosystems.

en: ecosystem-services uncertainty forest-model decision-support lidar forest-inventory sensitivity-analysis

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:
Resource Title: README.html
    Size: 23631 bytes
    Format: text/html
    URL: https://www.envidat.ch/dataset/6b3838ed-8433-492d-8424-2a19ae134b0a/resource/b331ae91-9d28-4262-9edd-9efeb9f2fc12/download/readme.html
    Description:

format: HTML

media_type: text/html

en:
Resource Title: dss_sensitivity_analysis_envidat.zip
    Size: 20615843021 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.784%2F
    Description:

en:
Resource Title: README.md
    Size: 4149 bytes
    Format: text/markdown
    URL: https://www.envidat.ch/dataset/6b3838ed-8433-492d-8424-2a19ae134b0a/resource/eaa7ed36-52f8-47fc-827c-a54a71292a4a/download/readme.md
    Description:

format: markdown

decision-support

display_name: decision-support

ecosystem-services

display_name: ecosystem-services

forest-inventory

display_name: forest-inventory

forest-model

display_name: forest-model

lidar

display_name: lidar

sensitivity-analysis

display_name: sensitivity-analysis

uncertainty

display_name: uncertainty