swiss-canopy-crane-ii-research-site

en:
![alt text](https://www.envidat.ch/dataset/fc69e369-eee9-42ab-8486-d2c38cff317d/resource/68fa7065-de32-4343-aa26-71094c5254ae/download/sccii.jpg "Swiss Canopy Crane II")
This research site is located near Hölstein in Canton Basel-Landschaft in a mature temperate forest that harbours more than 400 trees from 14 different species. The 1.6 ha site is equipped with the latest infrastructure, including 60 automated point dendrometers, automated soil respiration chambers, 72 ceramic suction cups at various locations and depths across the site, and a range of automated environmental sensors in the soil, the forest floor and in the canopy. A key piece of infrastructure is the new Swiss Canopy Crane II (SCC II), a 50 m tall crane with a 50 m jib that provides canopy access to 250 trees from 12 different species.

en: forest climate-change ecosystem-functions infrastructure drought damage-of-tree swiss-forest-lab

en: Swiss Canopy Crane II research site

title_for_slug: swiss-canopy-crane-ii-research-site

de:
Resource Title: Swiss Canopy Crane II
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    URL: https://www.envidat.ch/dataset/fc69e369-eee9-42ab-8486-d2c38cff317d/resource/68fa7065-de32-4343-aa26-71094c5254ae/download/sccii.jpg
    Description:

de:
Resource Title: Research site metadata
    Size: 14985 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/fc69e369-eee9-42ab-8486-d2c38cff317d/resource/f2c3b300-e834-438a-84cb-a93b7a273fdd/download/meta-data-sccii-sfl_env.xlsx
    Description:

climate-change

display_name: climate-change

damage-of-tree

display_name: damage-of-tree

drought

display_name: drought

ecosystem-functions

display_name: ecosystem-functions

infrastructure

display_name: infrastructure


$oid: OBSOLETE

en:
These are condensed notes covering selected key points in data analysis and statistics.  They were developed by James Kirchner for the course "Analysis of Environmental Data" at Berkeley in the 1990's and 2000's.  They are not intended to be comprehensive, and thus are not a substitute for a good textbook or a good education!
License: These notes are released by James Kirchner under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.

en: statistics uncertainty error-propagation hypothesis-testing bayesian-inference data-analysis

envidat

approval_status: approved

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

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

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

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

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

state: active

type: organization

wsl

capacity: public

de: EnviDat

en: Data analysis toolkits

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

de:
Resource Title: Toolkit 2: quantifying data distributions using descriptive statistics
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    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/90b055a6-f835-4029-b926-52d5b96c9022/download/toolkit-02-quantifying-distributions.pdf
    Description:

format: PDF

media_type: application/pdf

de:
Resource Title: Toolkit 4: confidence intervals
    Size: 155626 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/a06f6e74-1065-4dd3-af6b-9af0acf44f61/download/toolkit-04-confidence-intervals.pdf
    Description:

de:
Resource Title: Toolkit 12: weighted averages and their uncertainties
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    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/78995d64-cf7c-4564-96f2-72763bc2288b/download/toolkit-12-weighted-averages-and-uncertainties.pdf
    Description: There are two different ways to calculate the uncertainty of a weighted average.  Software packages often don't tell you which one they are reporting, and the resulting errors can be huge.

de:
Resource Title: Toolkit 10: linear regression
    Size: 695233 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/12fa9470-b900-4858-9ba9-ddf0978c2470/download/toolkit-10-linear-regression.pdf
    Description: An overview of the math, logic, and pitfalls of ordinary least-squares linear regression

de:
Resource Title: toolkit 6: averaging in nonlinear functions
    Size: 112578 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/35650811-6329-4025-9240-bad37169e31f/download/toolkit-06-nonlinear-averaging.pdf
    Description: The average of a nonlinear function will not equal the function evaluated at its average inputs (the mean of the function will differ from from the function of the mean).  This toolkit shows how to estimate that averaging bias.

de:
Resource Title: Toolkit 1: graphically displaying data distributions
    Size: 198277 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/40c94808-e96f-4826-812d-844de07dfe42/download/toolkit-01-graphing-distributions.pdf
    Description:

de:
Resource Title: Toolkit 9: Experimentwise error rates and statistical fishing expeditions
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    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/557b276f-7c37-4687-bb6b-665bb6e040eb/download/toolkit_09.pdf
    Description: Statistical fishing expeditions, and how to avoid being misled by them

de:
Resource Title: Toolkit 7: hypothesis testing, significance, and power
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    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/8c6c7021-0811-480f-ab52-00fbe3886591/download/toolkit-07-hypothesis-testing.pdf
    Description: An overview of basic concepts -- and common pitfalls -- in statistical hypothesis testing, statistical significance, and power analysis

de:
Resource Title: Toolkit 8: Hodges-Lehmann estimators
    Size: 87630 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/b796d0b3-535a-481c-8110-4947b8408186/download/toolkit-08-hodges-lehmann-estimators.pdf
    Description: A quick introduction to Hodges-Lehmann estimators (nonparametric measures of effect size)

de:
Resource Title: Toolkit 5: uncertainty analysis and error propagation
    Size: 308876 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/5074901f-3168-4b40-b8f0-20dc5acc6248/download/toolkit-05-error-propagation.pdf
    Description: Gaussian error propagation, monte carlo methods, and first-order, second-moment error propagation

de:
Resource Title: Toolkit 11: serial correlation
    Size: 284194 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/8ed5ef3a-173f-4089-b6f0-f5095c9d1b2d/download/toolkit-11-serial-correlation.pdf
    Description: Tools for detecting and correcting for serial correlation in analyses of time series

de:
Resource Title: Toolkit 3: tools for transforming data
    Size: 223799 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/4652e79e-1ad4-4045-a6c8-1b621ba18b28/resource/bb3d4263-4c88-4886-bc74-ba48f0398144/download/toolkit-03-transforming-distributions.pdf
    Description: The ladder of powers and a few other cool transformations

bayesian-inference

display_name: bayesian-inference

data-analysis

display_name: data-analysis

error-propagation

display_name: error-propagation

hypothesis-testing

display_name: hypothesis-testing

statistics

display_name: statistics

uncertainty

display_name: uncertainty


en: Although much of the endemic biodiversity of Madagascar can be attributed to its isolation as an island in the Indian Ocean, the high rates of speciation throughout its geologic history suggest an influence of local-scale landscape dynamics. The topographic evolution of Madagascar is dominated by the formation of high-relief continental rift escarpment and we argue that the erosion and landward retreat of this topography creates habitat heterogeneity that has served as a speciation pump for the island. The highest plant richness is found along the escarpment and is characterized by steady diversification rates over the last 45 Ma. Modeled landscape evolution by escarpment retreat demonstrates opportunities for allopatric speciation by transient habitat fragmentation through multiple mechanisms, including catchment expansion, isolation of highland remnants and formation of topographic and river barriers The segregation of floral phylogenetic turnover parallel to the escarpment is consistent with these mechanisms and indicates the importance of erosion-driven landscape dynamics on speciation.

en: endemism madagascar landscape-evolution flora geology

de:
Resource Title: Data v1.0
    Size: 777360730 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/5fe6b561-d506-47f4-944b-a5fdc8fc3fe2/resource/b2aad61d-11f6-48ca-94d3-056c95916c7c/download/data.zip
    Description:

format: ZIP

media_type: application/zip

de:
Resource Title: Escarpment evolution drives the diversification of the Madagascar flora
    Size: 2087857142 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/5fe6b561-d506-47f4-944b-a5fdc8fc3fe2/resource/896d9a92-5cde-4259-a50b-3762698a1d61/download/data.zip
    Description: This upload contains the data (species checklist, species range maps, time-calibrated phylogenies, topographic change, etc.) generated, analyzed, and presented in the paper .

endemism

display_name: endemism

flora

display_name: flora

geology

display_name: geology

landscape-evolution

display_name: landscape-evolution

madagascar

display_name: madagascar


swiss-municipal-spatial-planning-survey

en: Survey of spatial planning instruments and the organization of land use planning in Swiss municipalities. In 2014, the survey was sent to all Swiss municipalities in letter and online form. The response rate of 69% (i.e. 1619 of 2352 municipalities at this time) results in a representative sample of Swiss municipalities. The survey contains questions on the implementation of 20 specific planning instruments and the decade they had been implemented at first, as well as details on the local planning regimes.

en: densification policy-analysis settlement-development spatial-planning urban-sprawl

envidat

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

de: EnviDat: das Umwelt-Datenportal

en: Swiss Municipal Spatial Planning Survey

title_for_slug: swiss-municipal-spatial-planning-survey

de:
Resource Title: Glossary
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    URL: https://www.envidat.ch/dataset/a1032456-f016-42f4-a21f-849a26c5ce43/resource/0f76de0f-4c0e-4ed9-b302-6a39cea9cbd5/download/glossaire_fr.pdf
    Description: francais

de:
Resource Title: Municipal Planning Survey
    Size: 1309046 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/a1032456-f016-42f4-a21f-849a26c5ce43/resource/22d4c7c5-ed39-4f2a-a7d6-2957b9e3956b/download/municipal-planning-survey_de.pdf
    Description: german

de:
Resource Title: Municipal Planning Survey
    Size: 240214 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/a1032456-f016-42f4-a21f-849a26c5ce43/resource/ae5941d5-886d-43bb-a62e-90bb35145acc/download/municipal-planning-survey_fr.pdf
    Description: francais

de:
Resource Title: Municipal Planning Survey Data for SPSS
    Size: 2007126 bytes
    Format: No Info
    URL: https://www.envidat.ch/dataset/a1032456-f016-42f4-a21f-849a26c5ce43/resource/f9d0d695-36d9-47f0-a6f3-abc247e401ec/download/municipal-planning-survey_spss
    Description: SPSS

de:
Resource Title: Municipal Planning Survey Data for Stata
    Size: 980633 bytes
    Format: application/x-stata
    URL: https://www.envidat.ch/dataset/a1032456-f016-42f4-a21f-849a26c5ce43/resource/99851c4c-2bf3-48ae-8e4b-e41d7e2ffe35/download/municipal-planning-survey.dta
    Description: STATA 13

de:
Resource Title: Glossary
    Size: 167301 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/a1032456-f016-42f4-a21f-849a26c5ce43/resource/7b8f1dc3-57ec-4f6c-82d5-ee6ffaa5e885/download/glossar_dt.pdf
    Description: german

densification

display_name: densification

policy-analysis

display_name: policy-analysis

settlement-development

display_name: settlement-development

spatial-planning

display_name: spatial-planning

urban-sprawl

display_name: urban-sprawl


en:
![alt text](https://www.envidat.ch/dataset/39a232b5-c50e-490c-9bee-f04c2f697e14/resource/6d38da33-adc3-498e-aa48-7faf60a50a02/download/lwf_irrigation_experiment-pfynwald_2013.jpg "LWF experimental irrigation site Pfynwald")
As the largest contiguous pine forest in Switzerland, the Pfyn forest in Canton Valais (46° 18' N, 7° 36' E, 615 m ASL) offers the best conditions for such measurements.  In light of this, a WSL research team installed a long-term experiment of 20 years duration in the Pfyn forest.  The average temperature here is 9.2°C, the yearly accumulated precipitation is 657 mm (average 1961-1990). The pines in the middle of the forest are about 100 years old and 10.8 m high. The test area has 876 trees covering 1.2 ha divided into 8 plots of 1'000 m2 each. Between the months of April and October, four of these plots are irrigated by a sprinkler system providing an additional 700 mm of water, annually. In the other four plots, the trees grow under natural, hence relatively dry conditions.

en: forest plot experiment lwf irrigation infrastructure swiss-forest-lab

de: EnviDat

en: Pfynwald long-term experimental irrigation site

envi

de: Umwelt

de:
Resource Title: LWF Irrigation Plot Pfynwald
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    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/39a232b5-c50e-490c-9bee-f04c2f697e14/resource/6d38da33-adc3-498e-aa48-7faf60a50a02/download/lwf_irrigation_experiment-pfynwald_2013.jpg
    Description: Photo: Maria Schmitt, 2013

format: jpeg

de:
Resource Title: Tree measurements 2002-2016 from the long-term irrigation experiment Pfynwald, Switzerland
    Size: 1 bytes
    Format: No Info
    URL: https://doi.org/10.16904/11
    Description:

experiment

display_name: experiment

forest

display_name: forest

infrastructure

display_name: infrastructure

irrigation

display_name: irrigation

lwf

display_name: lwf

plot

display_name: plot

swiss-forest-lab

display_name: swiss-forest-lab


stable-isotope-research-lab-wsl

en:
![alt text](https://www.envidat.ch/dataset/6480bbef-06bf-4da8-8502-96f4def23358/resource/0a9d712c-38ad-4f55-842e-36b21a7e1b97/download/isotopelab_wsl.jpg "Isotope Laboratory WSL")
The lab uses stable isotope ratios of the light elements hydrogen, carbon, nitrogen and oxygen as a universal tool for studying physical, chemical and biological processes in forests and other ecosystems. Due to natural isotope fractionations, environmental changes leave unique fingerprints in organic matter, like tree-rings. It is, therefore, possible to detect the influence of ongoing climate changes on plant physiology. By applying isotopically labelled substrate, matter fluxes through plants and soil can be traced and better understood. The facility has isotope-Ratio mass-spectrometers and dedicated periphery for the analysis of organic matter, gas samples and water samples. With HPLC and GC we apply compound-specific isotope ratio analysis of sugars and organic acids. Additional isotope mass-spectrometers are operated by the Zentrallabor WSL.

en: forest swiss-forest-lab stable-isotopes infrastructure isotopes laboratory

en: Stable Isotope Research Lab WSL

title_for_slug: stable-isotope-research-lab-wsl

isotopes

display_name: isotopes

laboratory

display_name: laboratory

stable-isotopes

display_name: stable-isotopes


massimo

en:
MASSIMO is a distance-independent individual-tree simulator that represents demographic processes (regeneration, growth and mortality) with empirical models that have been parameterized with data from the Swiss NFI. Tree regeneration, growth and mortality are simulated on the regular grid of sample plots of the Swiss NFI, which allows for statistically representative simulations of forest development.
![alt text](https://www.envidat.ch/dataset/8fd996d1-aa7e-41b1-ae6d-1192582c62cc/resource/a12e2cfd-da45-4faf-8291-446c5763ac3c/download/massimo2swissforlab.png)

en: massimo timber-harvesting nfi biomass tree-growth forest model swiss-forest-lab forest-management

en: MASSIMO

title_for_slug: massimo

format: PNG

biomass

display_name: biomass

forest-management

display_name: forest-management

massimo

display_name: massimo

model

display_name: model

nfi

display_name: nfi

timber-harvesting

display_name: timber-harvesting

tree-growth

display_name: tree-growth


en:
This repository consists of the merged data from WaMos2 (2010) and WaMos3 (2020) and also includes both Corona-related surveys that have been conducted within the phase of WaMos3.
WaMos3 is the third assessment of the relationship of the Swiss population to the forest after 1997 and 2010 and was conducted in 2020. As in WaMos2 in 2010, the attitude of the population to the forest as a recreation area, to wood production and to the protective and ecological functions were examined. The topic of climate change was also included. In addition, the views of adolescents between 15 and 18 years of age were taken into account for the first time.
A detailed description of the provided data  can be found in accompanied file "WaMos_Metadatenbeschreibung_221027.pdf" which also contains explanations and examples of the merging process from WaMos2 to WaMos3 as well as sampling procedures. Further, the samples itself can be processed with the help of the provided R-file "EnviDat_WaMos_dataset.R".

en: switzerland corona forest-recreation ppgis questionnaire spatio-temporal-data survey forest-monitoring representative-online-panel

envidat

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

en: EnviDat: the Environmental Data Portal

en: Dataset of the Socio-cultural Forest Monitoring Switzerland for WaMos2 and WaMos3

de:
Resource Title: WaMos data scripts and pre-processing information
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    URL: https://www.envidat.ch/dataset/1fed4aeb-4879-4a86-8106-d4ac084f4819/resource/2d769e5d-6367-44d0-98f2-6ea661d05ff3/download/wamos_sample_processing.zip
    Description:

de:
Resource Title: WaMos data
    Size: 22248789 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/1fed4aeb-4879-4a86-8106-d4ac084f4819/resource/bd3721d4-1966-4834-b09f-50bd393658e0/download/wamos_samples.zip
    Description:

de:
Resource Title: WaMos meta data description
    Size: 544437 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/1fed4aeb-4879-4a86-8106-d4ac084f4819/resource/72a4cba1-bc0f-4045-afe3-fd781903f9de/download/wamos_metadatenbeschreibung_221027.pdf
    Description:

corona

display_name: corona

forest-monitoring

display_name: forest-monitoring

forest-recreation

display_name: forest-recreation

ppgis

display_name: ppgis

questionnaire

display_name: questionnaire

representative-online-panel

display_name: representative-online-panel

spatio-temporal-data

display_name: spatio-temporal-data

survey

display_name: survey

switzerland

display_name: switzerland


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

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

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

envi

en: Environment

de:
Resource Title: Station forcing data
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    Format: application/zip
    URL: https://www.envidat.ch/dataset/04470a99-93e5-499d-b869-6c30dffdc2f5/resource/282b7bc4-a607-4927-a2fd-858610a973c8/download/forcings.zip
    Description: All forcing data need to run JIM at HS and SWE stations. These are data downscaled directly from COSMO NWP data (WY14, WY15) and further enhanced by additional station observations of snow depth (HS) and air temperature (Ta) in WY17. Further information can be found in README file. Note that in WY14 and WY15, shortwave radiation was not split into direct and diffuse (not necessary for JIM) and that the default method in JIM for splitting precipitation phase was used. In WY 17, direct and diffuse were combined as input to JIM and JIM was forced with pre-processed liquid and solid precipitation phases.

de:
Resource Title: READ_ME
    Size: 1134 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/04470a99-93e5-499d-b869-6c30dffdc2f5/resource/30d1a503-fd18-43a5-9af9-e0e0f1855272/download/read_me.txt
    Description: File/data descriptor

format: TXT

media_type: text/plain

de:
Resource Title: DEM
    Size: 568273 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/04470a99-93e5-499d-b869-6c30dffdc2f5/resource/705365e0-bdd9-4dca-966d-e8f9b9b7ede5/download/dem.txt
    Description: 1-km resolution DEM used for downscaling gridded NWP data to stations and for distributing BDE evaluations at HS-stations to SWE-stations

de:
Resource Title: HS and SWE observations
    Size: 1214806 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/04470a99-93e5-499d-b869-6c30dffdc2f5/resource/d1a89df0-4991-48a0-8fbc-35f72afb953e/download/validation.zip
    Description: Automated (HS) and manual (SWE) data used to evaluate model simulations

jim

display_name: jim

jules-investigation-model

display_name: jules-investigation-model

snow

display_name: snow

snow-depth

display_name: snow-depth

snow-simulation

display_name: snow-simulation


en:
Ontologies used for the case study in the publication.
Creative Commons (CC) license: CC BY-NC-SA -https://creativecommons.org/licenses/by-nc-sa/3.0/-

en: conflict-resolution meaning-negotiation ontology-authoring disease-control infectious-disease

envidat

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

de: EnviDat: das Umwelt-Datenportal

conflict-resolution

display_name: conflict-resolution

disease-control

display_name: disease-control

infectious-disease

display_name: infectious-disease

meaning-negotiation

display_name: meaning-negotiation

ontology-authoring

display_name: ontology-authoring


en:
This data-set contains Land Surface Albedo (LSA) data obtained via a UAV sytem with up and downlooking shortwave radiation sensors, as described in the JGR-Atmospheres paper "Effect of forest canopy structure on wintertime Land Surface Albedo: Evaluating CLM5 simulations with in-situ measurements", by Malle et al. (2021, under review). This publication must be cited when using the data.
Data was collected across a large range of forest structures and solar angles in Switzerland (Davos Laret) and in Finland (Sodankylä). For each waypoint location at each site, data includes measured LSA, incoming SWR, reflected SWR and sunlit snow-view fraction alongside zenith angle, azimuth angle and measurement time (local time). Please refer to the abovementioned article for more detailed explanation.

en: uav forest-snow snow-modelling albedo climate

en: Wintertime UAV-based land surface albedo data over forested environments

de:
Resource Title: Davos Laret 2018
    Size: 12033 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/e2f6c051-40b9-469d-9276-c8980b4b53e6/resource/0eb24bd9-413a-4e44-8c89-3fbc72616882/download/davos-laret.zip
    Description: UAV-based LSA data collected in Davos Laret between January and May 2018. The zip file contains one csv file per waypoint location, please refer to the following paper on further information on waypoint locations etc. (to be updated once published): Malle et al. (2021): Effect of forest canopy structure on wintertime Land Surface Albedo: Evaluating CLM5 simulations with in-situ measurements in Journal of Geophysical Research: Atmospheres (under review).

de:
Resource Title: Sodankylä (FIN) pine 2019
    Size: 8316 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/e2f6c051-40b9-469d-9276-c8980b4b53e6/resource/e4009b6d-1e1b-46ae-aa19-8a34be3f9cab/download/sodankylae_pine.zip
    Description: UAV-based LSA data collected in Sodankylae (FIN) between April and May 2019. The zip file contains one csv file per waypoint location, please refer to the following paper on further information on waypoint locations etc. (to be updated once published): Malle et al. (2021): Effect of forest canopy structure on wintertime Land Surface Albedo: Evaluating CLM5 simulations with in-situ measurements in Journal of Geophysical Research: Atmospheres (under review).

albedo

display_name: albedo

climate

display_name: climate

forest-snow

display_name: forest-snow

snow-modelling

display_name: snow-modelling

uav

display_name: uav


large-wood-event-database

en: In the context of the WoodFlow project (https://woodflow.wsl.ch), an extensive database was developed which documents recruited and transported quantities of large wood (woody debris) together with the associated catchment and flood-specific parameters. Transported large wood volumes were related to catchment area, forest cover, stream length, peak discharge, runoff volume, sediment load, and precipitation. The dataset covers flood events mostly from Switzerland, but also from other alpine catchments in Germany, Italy France and Japan.

en: dead-wood empirical-data geomorphology mountain-stream natural-hazards large-wood wood-transport event-analysis flood

en: Large wood event database

title_for_slug: large-wood-event-database

format: XLS

dead-wood

display_name: dead-wood

empirical-data

display_name: empirical-data

event-analysis

display_name: event-analysis

flood

display_name: flood

geomorphology

display_name: geomorphology

large-wood

display_name: large-wood

mountain-stream

display_name: mountain-stream

natural-hazards

display_name: natural-hazards

wood-transport

display_name: wood-transport


lwf-lageren-long-term-research-site

en:
![alt text](https://www.envidat.ch/dataset/b763c4e1-2de3-4e8f-9bb7-2ca533624060/resource/b6e747c6-7a85-43e0-958b-3522f370bbad/download/lwf_laegeren.jpg "LWF Lägeren")
This research site is located on the southern slope of the Lägern, which forms the eastern most part of the Jura mountains, within a managed mixed deciduous forest. The forest is highly diverse, dominated by beech, but also including ash, maple, spruce and fir trees. Eddy covariance flux measurements were started in April 2004. The site was part of the international CarboEurope IP network and currently part of the following national networks:
* National Air Pollution Monitoring Network ([NABEL](https://www.empa.ch/web/s503/nabel))
* [TreeNet](https://treenet.info/switzerland/laegeren): The biological drought and growth indicator network
* Long-term Forest Ecosystem Research ([LWF](https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/laegeren.html))
* [Swiss FluxNet](https://www.swissfluxnet.ethz.ch/index.php/sites/ch-lae-laegeren/site-info-ch-lae/)
The site measurements are jointly run by the Swiss Federal Laboratories for Materials Science and Technology ([EMPA](https://www.empa.ch)), the groups [Grassland Sciences](https://www.gl.ethz.ch) and [Land-Climate Dynamics](https://iac.ethz.ch/group/land-climate-dynamics.html) from the Swiss Federal Institute of Technology Zurich, the unit [Soil Science & Biogeochemistry](https://www.geo.uzh.ch/en/units/2b.html) from the University of Zurich, and the Swiss Federal Research Institute ([WSL](https://www.wsl.ch)).
LWF Plot Lägeren
- Community: Wettingen / canton AG
- Date of installation: 1.05.2012
- Size of the plot: 1.34 ha
- Altitude: 643 - 718 m
- Mean slope: 37 %
- Geology (in German): Kettenjura; Jura: Malm, Molassehangschutt
- Soil types (WSL) : calcareous brown soil, chromic luvisol, mixed rendzina
- Woodland association after Ellenberg and Klötzli's classification (1972): Galio odoratio-Fagetum typicum bis - Pulmonario-Fagetum typicum
- Main tree species: fagus sylvatica
- Management system: high forest
- Silvicultural system: forest reserve
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 72.18 cm
- Number of trees BHD -= 12 cm (2011): 503
- Maximum tree age: picea abies: 120-170 years, fagus sylvatica: ca. 150 years

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

en: LWF Lägeren long-term research site

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

forest

display_name: forest

infrastructure

display_name: infrastructure

lwf

display_name: lwf

meteorological-data

display_name: meteorological-data

meteorological-measurements

display_name: meteorological-measurements

plot

display_name: plot

swiss-fluxnet

display_name: swiss-fluxnet

swiss-forest-lab

display_name: swiss-forest-lab


en:
Wind-Topo is a statistical downscaling model for near surface wind fields especially suited for highly complex terrain.
It is based on deep learning and was trained (calibrated) using the hourly wind speed and direction from 261 automatic measurement stations (IMIS and SwissMetNet) located in Switzerland. The periods 1st October 2015 to 1st October 2016 and 1st October 2017 to 1st October 2018 were used for training. The model was validated using 60 other stations on the period 1st October 2016 to 1st October 2017. Wind-Topo was trained using COSMO-1 data and a 53-meter Digital Elevation Model as input.
This dataset provides all the necessary code to understand, use and incorporate Wind-Topo in a new downscaling scheme. Specifically, the dataset contains the architecture of Wind-Topo and its optimized parameters, as well as a python script to downscale uniform wind fields with a prescribed vertical profile for any given 53-meter DEM.
Accompanies the publication "Wind-Topo: Downscaling near-surface wind fields to high-resolution topography in highly complex terrain with deep learning" Dujardin and Lehning, Quarterly Journal of the Royal Meteorological Society, 2022. https://doi.org/10.1002/qj.4265
Please cite this publication if you use Wind-Topo or derive new models from it.
The code can be used under the GNU Affero General Public License (AGPL).

en: convolutional-neural-networks high-resolution surface-winds machine-learning meteorology complex-terrain topography downscaling

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

complex-terrain

display_name: complex-terrain

convolutional-neural-networks

display_name: convolutional-neural-networks

downscaling

display_name: downscaling

high-resolution

display_name: high-resolution

machine-learning

display_name: machine-learning

meteorology

display_name: meteorology

surface-winds

display_name: surface-winds

topography

display_name: topography


en:
Model code, technical documentation and auxiliary files for the dynamic ecohydrological model FORHYCS (FORests and HYdrology under Climate change in Switzerland). FORHYCS combines two pre-existing models, the hydrological model PREVAH and the forest landscape model TreeMig.
License: GPL v3

en: modeling hydrology climate-change-impact forest forest-dynamics

en: FORHYCS v. 1.0.0 model code

de:
Resource Title: Documentation
    Size: 293179 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/41bdb335-fa7d-4682-b51a-1c28867f9fed/resource/438b76c4-eebe-4722-9f66-dd012eac183f/download/forhycs_v110_manual.pdf
    Description: Technical documentation for the FORHYCS model. Includes an overview of the model code; changes from the parent models PREVAH and TreeMig; a description of the file format for grid inputs/outputs and a description of the file structure on a HPC cluster.

de:
Resource Title: FORHYCS v 1.0.0 model code (28-10-2019)
    Size: 485913 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/41bdb335-fa7d-4682-b51a-1c28867f9fed/resource/057ee6c3-428d-47f5-a9ed-1b5b27a1e177/download/src.zip
    Description: Model code for FORHYCS as described in the Speich et al. discussion paper in GMDD. Includes code for the parent models PREVAH and TreeMig, as well as the new interface routines and output modules.

de:
Resource Title: Scripts and sample datasets v. 1.0.0
    Size: 2596848 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/41bdb335-fa7d-4682-b51a-1c28867f9fed/resource/0416f804-6b86-4d1b-afd2-423b6467d52f/download/scripts_and_aux.zip
    Description: Scripts and sample files for the documentation of FORHYCS (version 1.0.0)

de:
Resource Title: Link to FORHYCS in Github
    Size: 0
    Format: No Info
    URL: https://github.com/mspeich/FORHYCS/
    Description:

climate-change-impact

display_name: climate-change-impact

forest-dynamics

display_name: forest-dynamics

hydrology

display_name: hydrology

modeling

display_name: modeling


en:
In the associated study [1], two time-lapse temperature gradient metamorphism series of three-dimensional micro-computed tomography images of snow (obtained by [2]) have been used to model the decrease of specific surface area (SSA) over time based on the pore-scale physics. We conducted finite element simulations of one-way coupled heat and mass diffusion in order to estimation the spatial pattern of water vapor deposition and sublimation, which controls the evolution of the SSA over time. We notably studied the influence of the condensation coefficient, a key but poorly constrained physical parameter.
This dataset provides the parameters used for the mesh generation and the finite element simulations. It also includes the ice fraction, specific surface area per unit volume and surface average of mean curvature and vapor field obtained as outputs from the mesh process and the simulations.
[1]: Braun, A., Fourteau, K., and Löwe, H.: A rigorous approach to the specific surface area evolution in snow during temperature gradient metamorphism, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-1947, 2023.
[2]: Pinzer, B. R., Schneebeli, M., and Kaempfer, T. U.: Vapor flux and recrystallization during dry snow metamorphism under a steady temperature gradient as observed by time-lapse micro-tomography, The Cryosphere, 6, 1141–1155, https://doi.org/10.5194/tc-6-1141-2012, 2012.

en: fem time-series snow microstructure metamorphism x-ray-tomography

de: EnviDat

en: Simulation parameters and outputs for a rigorous approach to the specific surface area evolution in snow during temperature gradient metamorphism

envi

de: Umwelt

en: Environment

format: ODS

fem

display_name: fem

metamorphism

display_name: metamorphism

microstructure

display_name: microstructure

snow

display_name: snow

time-series

display_name: time-series

x-ray-tomography

display_name: x-ray-tomography


en:
The available lichen data consists of detection/nondetection data (1/0) of 373 tree-inhabiting (*corticolous*) lichen species from 416 plots surveyed 1-2 times. The lichen data were originally collected for the purpose of the Red List of epiphytic lichen species in Switzerland ([Scheidegger *et al.* 2002](https://www.bafu.admin.ch/bafu/de/home/themen/biodiversitaet/publikationen-studien/publikationen/rote-liste-gefaehrdete-arten-baum-erdbewohnende-flechten.html)), but updated to recent nomenclature for the purpose of this study.
This repository contains all the supporting data and R code for the paper:
von Hirschheydt, G., Kéry, M., Ekman, S., Stofer, S., Dietrich, M., Keller, C., Scheidegger, C. (2024) **Occupancy model reveals limited detectability of lichens in a standardised large-scale monitoring**. *Journal of Vegetation Science*.
Results and figures presented in the manuscript should be reproducible (with small differences in the latter digits due to stochasticity of the MCMC sampler) with the provided data and code.
The downloadable `.zip` folder has the following structure:
* `0_data/`
* `1_code/`
* `2_output/`
* `lichen_detectability.Rproj`
* `README.txt`
* `workflow.html`
- `workflow.Rmd`

The main folder and the three subordinate folders each have their own `README*.txt` file. These describe each available file in detail and should be consulted prior to using the data or running any code. The file `lichen_detectability.Rproj` stores the information about the R project. The user can open the project by clicking/double-clicking on this file which will automatically define the repository as working directory for the R session. If the user does not use RStudio/Posit, they may have to set the working directory manually to the stored location in the R files. The files `workflow.*` guide the user through the analysis (`1_code/*.R`) in the correct order so that they can:
- bundle the cleaned data into a data list readable for JAGS
- fit the multi-species occupancy model to the data and store the output
- assess the goodness-of-fit of the model to the data
- conduct a prior sensitivity analysis with 2 additional sets of priors
- extract the summary statistics reported in the manuscript and supplementary materials
- generate the figures shown in the manuscript and supplementary materials

en: bayesian-inference biodiversity cryptogams detectability occupancy-model observer-error detection-probability monitoring sessile-species swisslichens inter-observer-difference lichens

en: Repeated detection-nondetection data of corticolous lichens from a standardised monitoring across Switzerland

bayesian-inference

display_name: bayesian-inference

biodiversity

display_name: biodiversity

cryptogams

display_name: cryptogams

detectability

display_name: detectability

detection-probability

display_name: detection-probability

inter-observer-difference

display_name: inter-observer-difference

lichens

display_name: lichens

monitoring

display_name: monitoring

observer-error

display_name: observer-error

occupancy-model

display_name: occupancy-model

sessile-species

display_name: sessile-species

swisslichens

display_name: swisslichens


en: This dataset collection contains all datasets collected during the ring wind tunnel experiments in January and May 2023 at the SLF cold laboratory facilities in Davos. A full description of the experiment set-up can be found in Wahl et al. (2024). The collection contains data from 19 experiments. The dataset collection entails measurements of the stable water isotopic composition of snow samples and the water vapour inside the wind tunnel, measurements of the meteorological variables inside the wind tunnel and snow sample properties as measured with microCT measurements (sphere size distribution and specific surface area (SSA)).

en: snow-saltation drifting-snow metamorphism cold-lab wind-tunnel experiments water-isotopes blowing-snow

en: Ring wind tunnel experiments - airborne snow metamorphism and stable water isotopes

de:
Resource Title: Meteorological variables measured inside the wind tunnel during the January 2023 experiments
    Size: 41430 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/f2ebc8e9-e757-47a8-8713-8892cb1cb25f/resource/a9edeb00-0547-47a3-821d-0a70b86af7ab/download/windtunnel2023_janmetvariables_1min.txt
    Description: The dataset contains the 1-minute averages of the calibrated meteorological variables: temperature (T, °C), relative humidity (RH, %), relative humidity referenced against saturation over ice (RHice, %), specific humidity (q, g/kg) and wind speed (WS, m/s) as measured in the observation section of the wind tunnel at different heights. The measurement heights correspond to: "Top"=42.5cm, "Mid"=30cm, "Bot"=15cm, "PT100_Ta"=3cm. "WS" was measured at 36.5cm height. The timestamp is in local time (UTC+1) and indicates the start of the averaging period.

de:
Resource Title: Meteorological variables measured inside the wind tunnel during the May 2023 experiments
    Size: 290145 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/f2ebc8e9-e757-47a8-8713-8892cb1cb25f/resource/1fcb9bb3-769d-40ee-948f-1b6899c6d488/download/windtunnel2023_metvariables_1min.txt
    Description: The dataset contains the 1-minute averages of the calibrated meteorological variables: temperature (T, °C), relative humidity (RH, %), relative humidity referenced against saturation over ice (RHice, %), specific humidity (q, g/kg) and wind speed (WS, m/s) as measured in the observation section of the wind tunnel at different heights and the room temperature of the laboratory measured outside the wind tunnel (Lab_Ta, °C). The measurement heights correspond to: "Top"=42.5cm, "Mid"=30cm, "Bot"=15cm, "PT100_Ta"=3cm. "WS" was measured at 36.5cm height. The time was synchronised to the concurrent CRDS measurements (which are UTC) and adjusted for the time lag resulting from pumping vapour from inside the wind tunnel to the CRDS. The timestamps indicate the beginning of the averaging period.

de:
Resource Title: Wind tunnel water vapour isotopic composition
    Size: 46068957 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/f2ebc8e9-e757-47a8-8713-8892cb1cb25f/resource/e1ffe37c-e289-4eb5-a65f-af474e56fae7/download/windtunnel2023_vapor_isotopes.txt
    Description: The water vapour and its calibrated isotopic composition sampled inside the wind tunnel at 46cm height during the experiments. The vapour was pumped through a heated copper tube at a rate of 0.2 l/min to the CRDS instrument located in a temperate room outside the cold laboratory. The data has 1 second resolution with the timestamp in UTC (CRDS reference time). The measured variables are the calibrated d18O (‰), dD (‰) and d-excess (‰) signals and the uncalibrated ("H2Oraw", ppmv) and calibrated ("H2Ocali",ppmv) humidity readings of the CRDS and converted to specific humidity ("spechum", g/kg). The isotope data was corrected for its humidity-isotope dependency and referenced against the VSMOW-SLAP scale. Calibration runs were performed daily and details can be found in Wahl et al. (2024) including the Supplementary Materials.

de:
Resource Title: Snow samples - microCT measurements
    Size: 3800 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/f2ebc8e9-e757-47a8-8713-8892cb1cb25f/resource/93606a69-9f2e-4f8d-bca5-3f28001ee056/download/windtunnel2023_ct_measurements.txt
    Description: The data of the snow samples that were analysed in a Scanco® CT-40 scanner using a sample holder of 15 mm diameter which allowed for a high-resolution analysis with voxel size of 8 μm. The timestamp (local time, Davos, Switzerland) of snow sampling is given as well as the type of snow sample (see Snow samples - stable water isotopic composition dataset), the corresponding wind tunnel experiment number ("ExpNo", Wahl et al. (2024), Table 1) and the time since the snow introduction to the wind tunnel. The measured SSA is given in units 1/mm. The column "hist_filename" lists the name of the corresponding .txt file which contains the sphere size statistics of the snow samples. The sphere size statistics files are compressed in a .zip file.

de:
Resource Title: Snow samples - stable water isotopic composition
    Size: 20365 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/f2ebc8e9-e757-47a8-8713-8892cb1cb25f/resource/68873e4c-7b30-4f82-b97c-446c80546216/download/windtunnel2023_snow_isotopes.txt
    Description: The dataset contains the stable water isotopic composition (d18O, dD. d-excess) of the snow samples taken during the ring wind tunnel experiments in January and May 2023 at the SLF cold laboratory facilities in Davos. The snow samples were collected in air-tight WhirlPaks and kept frozen at -20°C until they were melted in the closed sampling bags, filtered and 1.8ml were transferred into a 2ml glass vial and shipped to the Paul Scherrer Institut in Villigen, Switzerland where they were analysed following established laboratory procedures. The combined uncertainty on the individual isotopic composition is <0.5‰ for dD and <0.1‰ for d18O which yields an uncertainty of 0.9‰ on d-excess.
Timestamps are given for local Davos time (LT) and synchronised to the CRDS instrument which is in UTC. The "ExpNo" column refers to the experiment numbers given in Wahl et al. (2024), Table 1. The "type" of the snow samples is: "ini" = initial snow before introduction to the wind tunnel, "airborne" = snow sampled out of the air, "acc_in" = inner part of accumulated snow, "acc_out" = outer surface layer of accumulated snow.

de:
Resource Title: Snow samples - sphere size statistics files
    Size: 62459 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/f2ebc8e9-e757-47a8-8713-8892cb1cb25f/resource/d91180d7-3057-462d-b893-20a01974b0a1/download/snowsamples_spheresizedistributions.zip
    Description: The individual measurement reports of the microCT scan for each snow sample as combined zip file. The file names and the sample number indicate which snow sample it is, see "Snow samples - microCT measurements". Each file contains the statistics and the count of fitted sphere size diameters and the respective share (in-%) in the frequency distribution. Immediately after snow sampling, a small portion of snow was transferred to the cylindrical μCT sample holder with a diameter of 15 mm and analysed at -20°C using a Scanco® CT-40 scanner. The analysis followed Hildebrand and Rüegsegger (1997).

blowing-snow

display_name: blowing-snow

cold-lab

display_name: cold-lab

drifting-snow

display_name: drifting-snow

experiments

display_name: experiments

metamorphism

display_name: metamorphism

snow-saltation

display_name: snow-saltation

water-isotopes

display_name: water-isotopes

wind-tunnel

display_name: wind-tunnel


en:
This dataset includes the necessary files to reproduce the results presented in Melo et al. (2024):
- code version used in the simulations (also available at the institutional [GitLab repository](https://gitlabext.wsl.ch/atmospheric-models/les-lsm) - tag 'r2024')
- input files to run the simulations
- post-processing scripts (MATLAB and FORTRAN)
- selected raw output files
- post-processed results (MATLAB data files).
 	**Note:** Direct model outputs are not provided because of their size, but they can be shared upon request. They can be reproduced by running the code. The post-processed results allow you to reproduce all figures in Melo et al. (2024).

en: large-eddy-simulation drifting-snow snow-saltation splash saltation-parameterizations aerodynamic-entrainment

en: Understanding snow saltation parameterizations - Reproducibility

aerodynamic-entrainment

display_name: aerodynamic-entrainment

large-eddy-simulation

display_name: large-eddy-simulation

saltation-parameterizations

display_name: saltation-parameterizations

splash

display_name: splash


en:
Dominant tree species map of Switzerland
We created a tree species map of Switzerland for the dominant tree species in the forested areas. The spatial resolution of the map is 10 m and the coordinate system is ETRS89-extended / LAEA Europe (EPSG 3035). The map comprises Sentinel-2 index time series from the year 2020, a digital elevation model and species reference data from the Swiss National Forest Inventory. The map is available as raster (.tif) or vector dataset (.gpkg).
**Access will be granted upon request.**
In total, the following 15 species were mapped:
*Abies alba*, *Acer pseudoplatanus*, *Alnus glutinosa*, *Alnus incana*, *Betula pendula*, *Castanea sativa*, *Fagus sylvatica*, *Fraxinus excelsior*, *Picea abies*, *Pinus cembra*, *Pinus mugo arborea*, *Pinus sylvestris*, *Quercus petraea*, *Quercus robur*, *Sorbus aucuparia*.
-br/--br/-
 Approach
-br/--br/-
 Data
- Swiss National Forest Inventory Data (stand species with - 60 % dominance in upper canopy; on at least more than 9 plots dominant)
- Sentinel-2 time series (2020, Indices: CCI, CIRE, NDMI, EVI, NDVI)
- Digital elevation model (DEM) (swissalti3d, 5 m)
- Biogeographical regions (Federal Office for the Environment FOEN)
- Forest mask 2017 (Approach: Waser et al., 2015)
-br/--br/-
 Modeling approach
We identified the most meaningful variables that led to separation of the respective groups by using random forest models with a forward feature selection (Meyer et al., 2018; Ververidis & Kotropoulos, 2005). In this approach, the final random forest model is solely built from the selected meaningful variables. By identifying meaningful variables, we can determine which variables might influence the grouping. Further, to avoid overfitting and overly optimistic results, we applied 10-fold spatial cross-validation and put all pixels from a plot in the same spatial fold.
The modeling was realized using the CAST package in R (Meyer et al., 2022), based on the well-known caret package (Kuhn, 2022). We used the ranger package in R (Wright & Ziegler, 2017) to implement the random forest models, due to its short computation time.
-br/--br/-
 Training data for modeling
- 295 Sentinel-2, DEM & Biogeographical variables
- 10525 tree species pixels
-br/--br/-
 Selected variables for final model
1. EVI of 2020.05.16
2. NDMI of 2020.03.12
3. CIRE of 2020.04.16
4. NDMI of 2020.07.05
5. CCI of 2020.05.11
6. dem
7. CCI of 2020.08.14
8. NDMI of 2020.08.24
9. CCI of 2020.12.22
10. NDMI of 2020.04.21
11. NDMI of 2020.11.17
12. NDMI of 2020.08.09
13. CIRE of 2020.03.22
14. CIRE of 2020.08.09
14. CCI of 2020.11.02
15. CIRE of 2020.06.10
-br/--br/-
 Overall Accuracy of final model
- 0.759
-br/--br/-
 Nationwide prediction
- Predicted throughout forest mask 2017 (Approach: Waser et al., 2015)
- Not applied on incomplete Sentinel-2 time series (own category in final map: incomplete_ts)
- Applied the Area of Applicability (Meyer 2022) to sort out pixels outside of the feature space; basically where the model had not the same values for pixels as in the available training data
-br/--br/-
-br/--br/-
 *Be aware that the map is only validated with the training data itself, an independent validation with other data sources remains missing*
-br/--br/-
-br/--br/-
 References
- Kuhn, M. (2022). Classification and Regression Training.  6.0-93.
- Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., & Nauss, T. (2018). *Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation*. Environmental Modelling and Software, 101, 1-9. https://doi.org/10.1016/j.envsoft.2017.12.001
- Meyer, H., Milà, C., & Ludwig, M. (2022). *CAST: 'caret' Applications for Spatial-Temporal Models*.  0.7.0.
- Ververidis, D., & Kotropoulos, C. (2005). *Sequential forward feature selection with low computational cost*. 2005 13th European Signal Processing Conference.
- Waser, L., Fischer, C.,Wang, Z., & Ginzler, C. (2015). *Wall-to-Wall Forest Mapping Based on Digital Surface Models from Image-Based Point Clouds and a NFI Forest Definition*. Forests, 6, 12, 4510–4528.
- Wright, M. N., & Ziegler, A. (2017). *ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R*. Journal of Statistical Software, 77(1), 1-17. https://doi.org/doi:10.18637/jss.v077.i01

en: map forest remote-sensing tree-species switzerland

envidat

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

en: EnviDat: the Environmental Data Portal

en: Tree species map of Switzerland

forest

display_name: forest

map

display_name: map

remote-sensing

display_name: remote-sensing

switzerland

display_name: switzerland

tree-species

display_name: tree-species


en:
On this repository you can find the monitoring data of the 3 PV power-plants analysed in the article _"Confirmation of the power gain for solar photovoltaic systems in alpine areas"_ (doi: 10.3389/fenrg.2024.1372680) . Each folder contains the following two elements:
1.  **README.txt**  : describes the content of the folder. Especially it describes the plant's monitoring system's architecture and relates it to the various files included in the data sub-folder.  It also provides a detailed description of all processing steps leading to the data sets as available in the data sub-folder and used in the pre-cited article.
3. **data/** : sub-folder containing the file(s) used in the referenced article.

en: winter-electricity photovoltaic bifaciality snow alps

en: Monitoring data sets of alpine photovoltaic power-plants

de:
Resource Title: A1.zip
    Size: 5999205 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/40024d58-c912-499e-8f4f-8a9b9189955a/resource/1a7597fd-203a-427f-851a-009673c29374/download/a1.zip
    Description: Title: Monitoring data set at A1 (test-site of Totalp), years 2018-2022
Provider: ZHAW - EKZ

Edition: Y.Frischholz, Laboratory of Cryospheric Sciences, EPFL
Last edition: 17.01.2024

File name structure:
The test site contains 6 segments named from A to F. The six files provided in the data/ subfolder follow the same naming convention (e.g. for segment A, the file name is: “A1_A.csv”). Refer to the publication for further details on the setups of the segments.

File format:
All files are provided in the CSV format.

Column headings:
	•	timestamp (datetime64): time stamp in format “YYYY-MM-DD HH:mm:ss”, time-zone: UTC
	•	poa_front (double): front plane-of-array irradiation (W/m**2)
	•	poa_back (double): rear plane-of-array irradiation (W/m**2)
	•	mod_temp (double): module’s temperature (°C)
	•	P_AC (double): output AC power (W)

Preprocessing:
	Outliers:
	⁃	Quantiles Q1 and Q99 are computed for each variable
	⁃	Values below Q1 and above Q99 are considered outliers
	⁃	Outliers are replaced by NaNs.

	Missing data:
	⁃	Data holes of less than 3 hours are interpolated linearly for all variables
	⁃	Data holes of more than 3 hours are replaced using the monthly nan-mean of the considered hour if more than 50% of the qualifying data points are available. For instance, if the data point 2022-12-12 12:12:00, is missing for a given variable and is part of a data hole larger than 3 hours, then it is replaced by the mean of all data points with hour = 12 of the month of December 2022, if 50% of all the qualifying data points are not NaN. In case more than 50% of the qualifying points are missing, the data point remains as missing (NaN).

	Wiring losses :
	⁃	As described in [1] , the test-site setup caused abnormally large wiring losses,  mainly due to the distance to optimisers and to the low installed power per DC-wire.
	⁃	The wiring losses comprised between 10% and 15%, are averaged hourly and added to the measured DC power.

	Misc.:
	⁃	P_AC is computed for each segment based on the measured DC power and the average inverter’s efficiency (96.1%).
	⁃	As described in [1],  the peak power of the installed module, used to compute the final yield is slightly different from the values announced by the manufacturer.

Mean uptime:
	Raw:
	⁃	95.2%
	Processed:
	⁃	100%

References:
[1] 	D. Anderegg, S. Strebel, and J. Rohrer, “Alpine Photovoltaik Versuchsanlage Davos Totalp : Erkenntnisse aus 5 Jahren Betrieb,” de, Sep. 	2023, Accepted: 2023-09-29T09:13:16Z Publisher: ZHAW Zürcher Hochschule für Angewandte Wissenschaften.DOI: 10.21256/	zhaw-2524. [Online]. Available: https://digitalcollection.zhaw.ch/handle/11475/28797 (visited on 12/15/2023).

de:
Resource Title: A2.zip
    Size: 27177550 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/40024d58-c912-499e-8f4f-8a9b9189955a/resource/e68bb1d0-915a-49a5-a108-c607356b1889/download/a2.zip
    Description: Title: Monitoring data set at A2 (utility-scale power-plant Muttsee), months 10.2022-09.2023
Provider: AXPO - IWB Industrielle Werke Basel
Last edition: 17.01.2024
Editor: Y.Frischholz, Laboratory of Cryospheric Sciences, EPFL

File name structure:
The power-plant is made of 23 string boxes (GAK) and inverters (WR) named after their given number. One file per device is provided in the data/ subfolder, following the same naming convention  (e.g. for the string box number 1, the file name is: “A2_GAK01.csv” and for the corresponding inverter, the file name is “A2_WR01.csv”). Refer to the publication for further details on the setup of the power plant.

File format:
All files are provided in the CSV format.

Column headings:
	String boxes and inverters (GAK & WR):
	⁃	device_name (string): name of the device (same as file name)
	⁃	timestamp (datetime64): time stamp in format “YYYY-MM-DD HH:mm:ss”, time-zone: UTC

	String boxes (GAK):
	⁃	I_# (double): input current from the substring number # (W)
	⁃	I_SUM (double): output current of string box (A)
	⁃	U_DC (double): output voltage of string box (V)
	⁃	P_DC_# (double):  input DC power from the substring number # (W)
	⁃	P_DC (double): output DC power of string box (W)
	⁃	$S$_T#_$T$ (double): temperature from the sensor connected to the string box (empty if not existing), placed on the “front” or “back” side (S)  of table T. Two types of temperature measurements (#) are available: 1=standard cell temperature (°C) , 2=module temperature on the rear side (°C) .
	⁃	$S$_SRAD_T (double): poa irradiation (W/m**2) from the sensor connected to the string box (empty if not existing), placed on the “front” or “back” side (S)  of table T.

		Inverters (WR):
	⁃	I_DC (double): input DC current(A)
	⁃	U_DC(double): input DC voltage(V)
	⁃	P_DC (double): input DC power (W)
	⁃	P_AC (double): output AC power (W)

Preprocessing:
	1. Outliers:
	⁃	Quantiles Q1 and Q99 are computed for each variable
	⁃	Values below Q1 and above Q99 are considered outliers
	⁃	Outliers are replaced by NaNs.

	2. Missing data:
	⁃	Data holes of less or equal to 3 hours are interpolated linearly for all variables
	⁃	Data holes of more than 3 hours are replaced using the monthly nan-mean of the considered hour if more than 50% of the qualifying data points are available. For instance, if the data point 2022-12-12 12:12:00, is missing for a given variable and is part of a data hole larger than 3 hours, then it is replaced by the mean of all data points with hour = 12 of the month of December 2022, if 50% of all the qualifying data points are not NaN. In case more than 50% of the qualifying points are missing, the data point remains as missing (NaN).

	3. Wiring losses:
	⁃	To be consistent with A1, wiring losses are corrected at A2. The wiring loss between the string boxes and the inverters is removed by multiplying the value of input P_DC at the inverter level (P_DC,inv) by the mean ratio P_DC,inv / P_DC,arr.
	⁃	The computed mean wiring loss is 2.3\%. It is computed from 2023-02-01 to 2023-09-30, after the long data hole in the data sets of the string boxes.

Mean uptime:
	Raw:
	⁃	WR: 98.2%
	⁃	GAK*: 38.5%
	Processed:
	⁃	WR: 99.6%
	⁃	GAK*: 56.4%

	*The uptime at the string box level (GAK) is low because PMUs at this level shut down during steady and low signal leading to unavailable data at night. In addition, the 23 days downtime of 	January 2023 could not be interpolated nor replaced.

alps

display_name: alps

bifaciality

display_name: bifaciality

photovoltaic

display_name: photovoltaic

snow

display_name: snow

winter-electricity

display_name: winter-electricity


en:
Environmental DNA complements scientific trawling in surveys of marine fish biodiversity (Dataset 2019)
In October 2019 we chose 15 sites from the 2019 EVHOE survey for eDNA sampling. The French international EVHOE bottom trawl survey is carried out annually during autumn in the BoB to monitor demersal fish resources. At each site, we sampled seawater using Niskin bottles deployed with a circular rosette. There were nine bottles on the rosette, each of them able to hold ∼5 l of water. At each site, we first cleaned the circular rosette and bottles with freshwater, then lowered the rosette (with bottles open) to 5 m above the sea bottom, and finally closed the bottles remotely from the boat. The 45 l of sampled water was transferred to four disposable and sterilized plastic bags of 11.25 l each to perform the filtration on-board in a laboratory dedicated to the processing of eDNA samples. To speed up the filtration process, we used two identical filtration devices, each composed of an Athena® peristaltic pump (Proactive Environmental Products LLC, Bradenton, Florida, USA; nominal flow of 1.0 l min–1 ), a VigiDNA 0.20 μm filtration capsule (SPYGEN, le Bourget du Lac, France), and disposable sterile tubing. Each filtration device filtered the water contained in two plastic bags (22.5 l), which represent two replicates per sampling site. We followed a rigorous protocol to avoid contamination during fieldwork, using disposable gloves and single-use filtration equipment and plastic bags to process each water sample. At the end of each filtration, we emptied the water inside the capsule that we replaced by 80 ml of CL1 conservation buffer and stored the samples at room temperature following the specifications of the manufacturer (SPYGEN, Le Bourget du Lac, France). We processed the eDNA capsules at SPYGEN, following the protocol proposed by Polanco-Fernández et al., (2020). We performed library preparation and sequencing at Fasteris (Geneva, Switzerland). Specifically, we prepared four libraries using the MetaFast protocol (a ligation-based method) and sequenced them separately. We carried out paired-end sequencing using a MiSeq sequencer (2 × 125 bp, Illumina, San Diego, CA, USA) on two MiSeq Flow Cell Kits (v3; Illumina), following the manufacturer’s instructions. We analysed the sequence reads using the OBITools package (http://metabarcoding.org/obitools; Boyer et al., 2016), following the protocol described by Valentini et al. (2016).
Data content:
	* rawdata/: contains the raw reads for each individual sample. One archive contains the paired-end reads specified by the _R1 or _R2 suffix as well as individually tagged PCR replicates (if available) together with an archive containing all extraction and PCR blank samples of the library. Reads have been demultiplexed using cutadapt but not trimmed, individual demultiplexing tags and primers remain present in the sequences.
	* taxadata/: contains the table with all detected taxonomy for each sample after bioinformatic processing (see Polanco et al. 2020 for details; https://doi.org/10.1002/edn3.140) and associated field metadata.
	* metadata/: contains two metadata files, one related to the data collected in the field for each filter, and the second related to the sequencing process in the lab (including the tag sequence, library name, and marker information for each sample)

en: bayofbiscay fish-composition chondrichthyes teleo atlantic-region actinopterygii edna metabarcoding

envidat

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

de: EnviDat: das Umwelt-Datenportal

en: Environmental DNA Marine France Evhoe 2019

envi

en: Environment

actinopterygii

display_name: actinopterygii

atlantic-region

display_name: atlantic-region

bayofbiscay

display_name: bayofbiscay

chondrichthyes

display_name: chondrichthyes

edna

display_name: edna

fish-composition

display_name: fish-composition

metabarcoding

display_name: metabarcoding

teleo

display_name: teleo


en: This dataset contains the raw ring-width and climate data (1952-2020), as well as the R code to run the model and the model output used in the research article "No future growth enhancement expected at the northern edge for European beech due to continued water limitation".

en: tree-ring beech network-analysis fagus-sylvatica dendroecology dendrochronology

en: European Beech tree-ring dataset 1952-2020

de:
Resource Title: Model output
    Size: 679105614 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/6b052bdb-9e18-4f00-92b6-e102347245ec/resource/c0117599-93c9-4ea0-8f12-753c711f003c/download/ebtrnplus_model_newcwbmean2
    Description: Output of the Mixed-effects model

format: OCTET

de:
Resource Title: beech_rawtrw.txt
    Size: 332605512 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/6b052bdb-9e18-4f00-92b6-e102347245ec/resource/f3bc670c-3474-4987-966e-c9a2e94bebd3/download/beech_rawtrw.txt
    Description: This dataset contains the raw ring-width and climate data (1952-2020) used in the research article "No future growth enhancement expected at the northern edge for European beech due to continued water limitation".

RW= ring width
Year= Calendar year
CN = core number
ID3= plot identifier
longitude = longitude (°)
latitude = latitude (°)
elevation = elevation (m)
age= estimated age in calendar year
radius= estimated radius in calendar year (mm)
MAT= mean annual temperature (°C)
MAP= mean annual precipitation (mm)
MCWB= mean climatic water balance (P-PET; mm)
amplitude= simple continentality measure (July - January mean maximum temperatures)
MATspline1 and 2, MCWBspline1 and 2, radiusspline4 and 5= natural spline transformed values of MAT, MCWB and radius #ns(x,2)
CWD_pMay_Jun= Climatic Water Balance of previous year May to June #!!!#please note CWD here is NOT Climatic Water Deficit, this was a labeling error in the creation of the dataset headers
CWD_May_Jun= Climatic Water Balance of current year May to June
Tmax_pMay_Jun= mean monthly maximum temperature of previous year May to June
Tmax_May_Jun= mean monthly maximum temperature of current year May to June
weight = site replication-based weighting input for model
SOM= membership of regional cluster 1-8 based on Supervised Organized Mapping

de:
Resource Title: final_cwb_model.R
    Size: 13781 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/6b052bdb-9e18-4f00-92b6-e102347245ec/resource/5d54048a-25a6-4861-89a3-6586b970177d/download/final_cwb_model.r
    Description: R code to run the model behind the results of the research article "No future growth enhancement expected at the northern edge for European beech due to continued water limitation".
Requires beech_rawtrw.txt.

beech

display_name: beech

dendrochronology

display_name: dendrochronology

dendroecology

display_name: dendroecology

fagus-sylvatica

display_name: fagus-sylvatica

network-analysis

display_name: network-analysis

tree-ring

display_name: tree-ring


en:
Transport infrastructure such as **highways** are important corridors for the spread of invasive species. In this project, an automated detection and mapping of **two invasive alien plant species**  was developed and applied along highways in the **Swiss Central Plateau**, based on **artificial intelligence (AI, Deep Learning)**. The two target species are **tree of heaven *(Ailanthus altissima)*** and **narrow-leaved ragwort *(Senecio inaequidens)***.
The data is part of the project report and shows the mapping results in **interactive maps**. Technically speaking, these are **self-contained HTML files** that can be displayed with standard web browsers. To reduce the file size, the downloads are provided in zip format.
**Report**: [https://www.mobilityplatform.ch/de/research-data-shop/product/1690](https://www.mobilityplatform.ch/de/research-data-shop/product/1690)
**Project**: [https://www.wsl.ch/en/projects/automated-detection-of-invasive-alien-plants-along-highways/](https://www.wsl.ch/en/projects/automated-detection-of-invasive-alien-plants-along-highways/)

en: biological-invasions artificial-intelligence invasive-species plants automatic-detection computer-vision

en: Automated detection of invasive alien plants along highways

de:
Resource Title: Senecio_frame_min3_Pts_strips_v2024.html.zip
    Size: 21519085 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/1bf97970-cc9e-4101-aaf3-b47a3906e7a1/resource/0b6a46c9-53e5-4420-a77a-883b2e4ac351/download/senecio_frame_min3_pts_strips_v2024.html.zip
    Description: Detected occurrences of Senecio inaequidens along highways on the Swiss Plateau. To reduce false-positives, only images in which the species was recognized in at least three of 16 image tiles are considered as occurrences (Fig. 40 in the report). Image coordinates of the middle and side strips can be displayed separately in the interactive map.

de:
Resource Title: Senecio_maxF1_Pts_strips_v2024.html.zip
    Size: 24847774 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/1bf97970-cc9e-4101-aaf3-b47a3906e7a1/resource/ffd01a5d-4711-4bc7-b1fd-8afe0db89e50/download/senecio_maxf1_pts_strips_v2024.html.zip
    Description: Detected occurrences of Senecio inaequidens along highways on the Swiss Plateau. The number of image tiles in which the species was recognized is shown (Fig. 25 in the report). Image coordinates of the middle and side strips can be displayed separately in the interactive map.

de:
Resource Title: Ailanthus_maxF1_Pts_strips_v2024.html.zip
    Size: 18633094 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/1bf97970-cc9e-4101-aaf3-b47a3906e7a1/resource/8a96b1cf-2d57-45f4-bc79-280dd5603f99/download/ailanthus_maxf1_pts_strips_v2024.html.zip
    Description: Detected occurrences of Ailanthus altissima along highways on the Swiss Plateau. The number of image tiles in which the species was recognized is shown (Fig. 23 in the report). Image coordinates of the middle and side strips can be displayed separately in the interactive map.

de:
Resource Title: Ailanthus_frame_min3_Pts_strips_v2024.html.zip
    Size: 18185776 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/1bf97970-cc9e-4101-aaf3-b47a3906e7a1/resource/26102858-9e76-41ac-b4b9-7e1a9f03dd02/download/ailanthus_frame_min3_pts_strips_v2024.html.zip
    Description: Detected occurrences of Ailanthus altissima along highways on the Swiss Plateau. To reduce false-positives, only images in which the species was recognized in at least three of 16 image tiles are considered as occurrences (Fig. 38 in the report). Image coordinates of the middle and side strips can be displayed separately in the interactive map.

artificial-intelligence

display_name: artificial-intelligence

automatic-detection

display_name: automatic-detection

biological-invasions

display_name: biological-invasions

computer-vision

display_name: computer-vision

invasive-species

display_name: invasive-species

plants

display_name: plants


en:
This dataset includes geomorphic predictors and plant biodiversity patterns in the Hengduan Mountains, published in the Geological Society of London special volume: Asian Geodynamics, Climate and Biodiversity (https://doi.org/10.1144/SP549-2024-17).
The geomorphic predictors include catchment-restricted relief and chi maps, while the climate predictors comprise site water balance, growing degree days, and climate change velocity from the Last Glacial Maximum (LGM) to the present.
The plant biodiversity patterns are represented by a plant richness map for 13,321 species in the Hengduan Mountains. Species-level plant range maps are available in the dataset, previously published under DOI: 10.16904/envidat.424.

en: tectonic-faults endemism plant-biodiversity relief climate-history

de: EnviDat

en: Tectonic and climatic controls on plant biodiversity in the Hengduan Mountains China

envi

de: Umwelt

climate-history

display_name: climate-history

endemism

display_name: endemism

plant-biodiversity

display_name: plant-biodiversity

relief

display_name: relief

tectonic-faults

display_name: tectonic-faults


lwf-visp-long-term-research-site

en:
![alt text](https://www.envidat.ch/dataset/1a43f9fa-e36c-46b9-a409-367fce3ce48b/resource/757c846b-c266-4fd4-b6ad-5f5f327ffcb4/download/lwf_visp.jpg "LWF Visp")
LWF Plot Visp
- Community: Visp / canton VS
- Date of installation: 13 March 1996
- Size of the plot: 2 ha
- Altitude: 657-733 m
- Mean slope: 80%
- Geology (in German): Penninikum, Jura, Bündnerschiefer; Kalkphyllite, Hängeschutt
- Provisional soil type (WSL): Calcaric Cambisol
- Woodland association after EK72: =~= 38: Arabidi turritae-Quercetum pubescentis
- Main tree species: Pinus sylvestris
- Management system: high forest
- Silvicultural system: unmanaged
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 53.9 cm
- Number of trees BHD -= 12 cm (2011): 650
- Maximum tree age: Pinus sylvestris uneven-aged 40-80 yr
More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/visp.html

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

envidat

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

en: EnviDat: the Environmental Data Portal

en: LWF Visp long-term research site

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

de:
Resource Title: Visp Meteorological Data LWF
    Size: 1 bytes
    Format: No Info
    URL: https://doi.org/10.16904/envidat.108
    Description:

de:
Resource Title:
    Size: 134960 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/1a43f9fa-e36c-46b9-a409-367fce3ce48b/resource/757c846b-c266-4fd4-b6ad-5f5f327ffcb4/download/lwf_visp.jpg
    Description: LWF plot Visp. Photo: Peter Waldner, 2017

forest

display_name: forest

infrastructure

display_name: infrastructure

lwf

display_name: lwf

meteorological-data

display_name: meteorological-data

meteorological-measurements

display_name: meteorological-measurements

plot

display_name: plot

swiss-forest-lab

display_name: swiss-forest-lab


lwf-schanis-long-term-research-site

en:
![alt text](https://www.envidat.ch/dataset/b21e7c90-7d1f-4940-82ab-d29c0dcf5fcf/resource/5b41f2a1-847f-4a0f-aa48-cd530ace3827/download/lwf_schaenis.jpg "LWF Schänis")
LWF Plot Schänis
- Community: Schänis / canton SG
- Date of installation: 17 September 1997
- Size of the plot: 2 ha
- Altitude: 693-773 m
- Mean slope: 60%
- Geology (in German): Tertiär. Subalpine Molasse, Oligocaen, Chattien, Kalknagelfluh
- Soil types (WSL) : n.d.
- Woodland association after EK72: 13: Cardamino-Fagetum tilietosum
- Main tree species: Fagus sylvatica
- Management system: high forest
- Silvicultural system: group selection
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 55.8 cm
- Number of trees BHD -= 12 cm (2011): 611
- Maximum tree age: Abies alba130-150 yr - Fraxinus excelsior 130-150 yr - Fagus sylvatica 130-150 yr
More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/schaenis.html

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

en: LWF Schänis long-term research site

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

de:
Resource Title:
    Size: 225843 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/b21e7c90-7d1f-4940-82ab-d29c0dcf5fcf/resource/5b41f2a1-847f-4a0f-aa48-cd530ace3827/download/lwf_schaenis.jpg
    Description: LWF plot Schänis. Photo: Anne Thimonier Rickenmann, 2019

de:
Resource Title: Schänis Meteorological Data LWF
    Size: 1 bytes
    Format: No Info
    URL: https://doi.org/10.16904/envidat.107
    Description:


en:
![alt text](https://www.envidat.ch/dataset/ebb37e73-1280-4a06-81d8-e18bc5d9c1cf/resource/64291bfd-37d4-4f88-b505-81fc85a109c2/download/lwf_othmarsingen.jpg "LWF Othmarsingen")
LWF Plot Othmarsingen
- Community: Othmarsingen / canton AG
- Date of installation: 9 September 1994
- Size of the plot: 1 ha
- Altitude (m): 467-500
- Mean slope: 27%
- Soil types (WSL): Stagnic Luvisols, Haplic Luvisols
- Woodland association after EK72: 7: Galio odorati-Fagetum typicum
- Main tree species: Fagus sylvatica
- Management system: former coppices w. standards
- Silvicultural system: group selection
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 62.8 cm
- Number of trees BHD -= 12 cm (2011): 167
- Maximum tree age: Fagus sylvatica 120-140 yr - Tilia sp. 120-140 yr
More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/othmarsingen.html

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

en: LWF Othmarsingen long-term research site

envi

en: Environment

de:
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    Description: LWF plot Othmarsingen. Photo: Christian Hug, 2017


lwf-novaggio-long-term-research-site

en:
![alt text](https://www.envidat.ch/dataset/e87358e9-4beb-487e-af68-66633e9cfc96/resource/91bd053a-61c4-4b20-b0d3-a572c983e647/download/lwf_novaggio.jpg "LWF Novaggio")
LWF Plot Novaggio
- Community: Novaggio / canton TI
- Date of installation: 8.3.95
- Size of the plot: 1.5 ha
- Altitude (m): 902-997
- Mean slope: 68%
- Geology (in German): Untergrund: Südalpin, präpermisches Grundgebirge; Orthogneis, schiefriger Biotitplagioklasgneis - Oberfläche: Quartär; karbonatfreie würmeiszeitliche Moräne
- Provisional soil type (WSL): Kryptopodzole
- Woodland association after EK72: 42: Phyteumo betonicifoliae-Quercetum castanosum
- Main tree species: Quercus cerris
- Management system: former coppice
- Silvicultural system: unmanaged
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 27.0 cm
- Number of trees BHD -= 12 cm (2011): 1130
- Maximum tree age: Castanea sativa 90 yr- Betula pendula 70 yr - Quercus cerris 70 yr
More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/novaggio.html

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

envidat

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

en: LWF Novaggio long-term research site

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

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    Description: LWF plot Novaggio. Photo: Peter Waldner, 2010

de:
Resource Title: Novaggio Meteorological Data LWF
    Size: 1 bytes
    Format: No Info
    URL: https://doi.org/10.16904/envidat.105
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lwf-neunkirch-long-term-research-site

en:
![alt text](https://www.envidat.ch/dataset/86603cf3-979c-4b28-a63e-9852bfb969bd/resource/69a58300-d372-4487-8596-5403254d539d/download/lwf_neunkirch.jpg "LWF Neunkirch")
LWF Plot Neunkirch
- Community: Neunkirch / canton SH
- Date of installation: 14 July 1995
- Size of the plot: 2 ha
- Altitude (m): 554-609
- Mean slope: 58%
- Geology (in German): Tafeljura, oberer Malmkalk; Malmhängeschutt
- Soil types (WSL) : Rendzic Leptosols
- Woodland association after EK72: 13: Cardamino-Fagetum tilietosum
- Main tree species: Fagus sylvatica
- Management system: former coppices w. standards
- Silvicultural system: reserve
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 56.5 cm
- Number of trees BHD -= 12 cm (2011): 442
- Maximum tree age: Fagus sylvatica 160 yr - Acer pseudoplatanus 160 yr - Tilia sp. 110 yr
More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/neunkirch.html

envidat

de: EnviDat: das Umwelt-Datenportal

en: LWF Neunkirch long-term research site

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

de:
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    URL: https://doi.org/10.16904/envidat.104
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    Size: 217751 bytes
    Format: image/jpeg
    URL: https://www.envidat.ch/dataset/86603cf3-979c-4b28-a63e-9852bfb969bd/resource/69a58300-d372-4487-8596-5403254d539d/download/lwf_neunkirch.jpg
    Description: LWF plot Neunkirch. Photo: Anne Thimonier Rickenmann, 2011


en:
![alt text](https://www.envidat.ch/dataset/4a0ff376-83d2-4ae4-bf2e-c361eb050778/resource/51e98f2d-5524-4ea6-babb-9df0b9aba8f1/download/lwf_nationalpark.jpg "LWF Nationalpark")
LWF Plot Nationalpark
- Community: Zernez / canton GR
- Date of installation: 10 October 1995
- Size of the plot: 2 ha
- Altitude: 1890-1907 m
- Mean slope: 11%
- Geology (in German): Nacheiszeitlicher Schwemmfächer; kalkhaltige Moräne, Dolomite, Kalke, Tonschiefer, Rauhwacken
- Provisional soil types (WSL): Rendzic Leptosol
- Woodland association after EK72: 67: Erico-Pinetum montanae
- Main tree species: Pinus mugo
- Management system: high forest
- Silvicultural system: forest reserve
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 23.7 cm
- Number of trees BHD -= 12 cm (2011): 2450
- Maximum tree age: Pinus mugo 210 yr
More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/nationalpark.html

en: LWF Nationalpark long-term research site

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    Description: LWF plot Nationalpark. Photo: Oliver Schramm, 2011

de:
Resource Title: Nationalpark Meteorological Data LWF
    Size: 1 bytes
    Format: No Info
    URL: https://doi.org/10.16904/envidat.103
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lwf-lens-long-term-research-site

en:
![alt text](https://www.envidat.ch/dataset/87d3ea5a-8d50-446b-9c33-cbe56057f7d3/resource/bf459948-da5a-4b05-9372-83ca5454aeca/download/lwf_lens.jpg "LWF Lens")
LWF Plot Lens
- Community: Lens / canton VS
- Date of installation: 15 March 1996
- Size of the plot: 2 ha
- Altitude: 1033-1093 m
- Mean slope: 75%
- Geology (in German): Untergrund: Penninikum, Ferret-Zone, Trias; sandiger Kalkstein - Oberfläche: Hängeschutt
- Provisional soil type (WSL): Calcaric Cambisol
- Woodland association after EK72: +- 64: Cytiso-Pinetum silvestris
- Main tree species: Pinus sylvestris
- Management system: high forest
- Silvicultural system: unmanaged
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 31.8 cm
- Number of trees BHD -= 12 cm (2011): 2304
- Maximum tree age:150-170 yr
More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/lens.html

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: LWF Lens long-term research site

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

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    Description: LWF plot Lens. Photo: Freddy Potzinger, 2012

de:
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    URL: https://doi.org/10.16904/envidat.102
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lwf-lausanne-long-term-research-site

en:
![alt text](https://www.envidat.ch/dataset/9f35f697-a226-4719-aefd-b8c96fe5ae7c/resource/d6a48c12-62e6-4c04-9f63-a84d5964bc55/download/lwf_lausanne.jpg "LWF Lausanne")
LWF Plot Lausanne
- Community: Lausanne / canton VD
- Date of installation: 5 September 1994
- Size of the plot: 2 ha
- Altitude: 800-814 m
- Mean slope: 7%
- Geology (in German): Untergrund: Tertiär, Miozän, Burdigalien, obere Meeresmolasse; Sandstein - Oberfläche: Quartär, Würm; würmeiszeitliche Moräne
- Soil types (WSL) : Distric Cambisols
- Woodland association after EK72: 8: Milio-Fagetum
- Main tree species: Fagus sylvatica
- Management system: high forest
- Silvicultural system: group selection
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 59.9 cm
- Number of trees BHD -= 12 cm (2011): 650
- Maximum tree age: Abies alba 160-170 yr - Picea abies 160-170 yr - Fagus sylvatica 160-170 yr
More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/lausanne.html

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

envidat

en: EnviDat: the Environmental Data Portal

de: EnviDat

en: LWF Lausanne long-term research site

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

envi

de: Umwelt

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    Description: LWF plot Lausanne. Photo: Anne Thimonier Rickenmann, 2019

de:
Resource Title: Lausanne Meteorological Data LWF
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    URL: https://doi.org/10.16904/envidat.101
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lwf-jussy-long-term-research-site

en:
![alt text](https://www.envidat.ch/dataset/465d852d-af24-415f-9db2-48fe31d6dc20/resource/a39a0e35-efdf-4bef-bab1-4b0532442b34/download/lwf_jussy.jpg "LWF Jussy")
LWF Plot Jussy
- Community: Jussy / canton GE
- Date of installation: 31 May 1995
- Size of the plot: 1.99 ha
- Altitude: 496-506 m
- Mean slope: 3%
- Geology (in German): Quartär; tonreiche würmeiszeitliche Grundmoräne
- Soil types (WSL) : Stagnic Luvisols
- Woodland association after EK72: 35: Galio silvatici-Carpinetum
- Main tree species: Quercus species
- Management system: former coppices w. standards
- Silvicultural system: unmanaged / group selection
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 36.6 cm
- Number of trees BHD -= 12 cm (2011): 1278
- Maximum tree age: Carpinus betulus 60 yr - Populus tremula 60 yr - Quercus petrea 90 yr
More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/jussy.html

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

en: LWF Jussy long-term research site

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    Description: LWF plot Jussy. Photo: Anne Thimonier Rickenmann, 2019

de:
Resource Title: Jussy Meteorological Data LWF
    Size: 1 bytes
    Format: No Info
    URL: https://doi.org/10.16904/envidat.100
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lwf-isone-long-term-research-site

en:
![alt text](https://www.envidat.ch/dataset/f4b44f60-4eed-471f-a09e-749a0f5f0683/resource/cb8e84ea-ad8b-476e-be32-e02a927d2449/download/lwf_isone.jpg "LWF Isone")
LWF Plot Isone
- Community: Isone / canton TI
- Date of installation: 5 September 1995
- Size of the plot: 2 ha
- Altitude (m): 1181-1259
- Mean slope: 58%
- Geology (in German): Untergrund: Südalpin, präpermisches Grundgebirge, Ceneri Zone; schiefriger Biotitplagioklasgneis - Oberfläche: Quartär; Moräne, Hängeschutt-.
- Provisional soil type (WSL) : Humic Cambisol
- Woodland association after EK72: 4: Luzulo niveae-Fagetum dryopteridetosum
- Main tree species: Fagus sylvatica
- Management system: former coppice
- Silvicultural system: unmanaged
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 37.4 cm
- Number of trees BHD -= 12 cm (2011): 1254
- Maximum tree age: Fagus sylvatica uneven-aged - 70-85-100 yr
More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/isone.html

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

en: LWF Isone long-term research site

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

envi

en: Environment

de:
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    URL: https://doi.org/10.16904/envidat.99
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    URL: https://www.envidat.ch/dataset/f4b44f60-4eed-471f-a09e-749a0f5f0683/resource/cb8e84ea-ad8b-476e-be32-e02a927d2449/download/lwf_isone.jpg
    Description: LWF plot Isone. Photo: Anne Thimonier Rickenmann, 2011


lwf-chironico-long-term-research-site

en:
![alt text](https://www.envidat.ch/dataset/67e14643-c7f2-4190-ae6e-c8c94f1c5f01/resource/1d23d53f-8572-47eb-b466-88dd8ad244c9/download/lwf_chironico.jpg "LWF Chironico")
LWF Plot Chironico
- Community: Chironico / canton TI
- Date of installation: 29 August 1995
- Size of the plot: 2 ha
- Altitude: 1342-1387 m
- Mean slope: 35%
- Geology (in German): Untergrund: Penninikum; Paragneisse u. Glimmerschiefer - Oberfläche: Quartär; karbonatfreie Moräne, Hängeschutt
- Provisional soil type (WSL) : Distric Cambisol
- Woodland association after EK72: 47: Calamagrostio villosae-Abietetum
- Main tree species: Picea abies
- Management system: high forest
- Silvicultural system: group selection
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 54.1 cm
- Number of trees BHD -= 12 cm (2011): 750
- Maximum tree age: Picea abies: 160-180 yr - Abies alba: 140-160 yr
More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/chironico.html

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

envidat

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

en: LWF Chironico long-term research site

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

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    Description: LWF plot Chironico. Photo: Anne Thimonier Rickenmann, 2019

de:
Resource Title: Chironico Meteorological Data LWF
    Size: 1 bytes
    Format: No Info
    URL: https://doi.org/10.16904/envidat.98
    Description:


lwf-celerina-long-term-research-site

en:
![alt text](https://www.envidat.ch/dataset/0c244fe7-886a-4a1c-add5-5f706e995a29/resource/28e97caa-65fc-4396-8bca-860721eddfa2/download/lwf_celerina.jpg "LWF Celerina")
LWF Plot Celerina
- Community: Celerina / canton GR
- Date of installation: 3 July 1996
- Size of the plot: 2 ha
- Altitude (m): 1846-1896
- Mean slope: 34%
- Geology (in German): Untergrund: ostalpin; prätriadische Tiefengesteine - Oberfläche: Quartär; karbonatfreie Moräne
- Soil types (WSL): n.d.
- Woodland association after EK72: 59: Larici-Pinetum cembrae
- Main tree species: Pinus cembra
- Management system: high forest
- Silvicultural system: group selection
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 48.6 cm
- Number of trees BHD -= 12 cm (2011): 469
- Maximum tree age: Pinus cembra uneven-aged - 210-250 years
More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/celerina.html

envidat

de: EnviDat: das Umwelt-Datenportal

en: LWF Celerina long-term research site

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    Description: LWF plot Celerina. Photo: Oliver Schramm, 2011

de:
Resource Title: Celerina Meteorological Data LWF
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    URL: https://doi.org/10.16904/envidat.97
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lwf-lantsch-long-term-research-site

en:
![alt text](https://www.envidat.ch/dataset/9e5d6c82-26ac-4818-9458-1ac7c8574bb0/resource/c26169dc-db58-4e69-bc74-9513dbf7bccc/download/lwf_lantsch.jpg "LWF Lantsch")
LWF Plot Lantsch
- Community: Lantsch / canton GR
- Date of installation: 15 September 1997
- Size of the plot: n.d.
- Altitude: 1458-1490 m
- Mean slope: 16%
- Geology (in German): Ostalpin. Gehängeschutt aus mesozoischen Schiefern, Dolomiten und Kalken
- Soil types (WSL) : n.d.
- Woodland association after EK72: 65: Erico-Pinetum silvestris
- Main tree species: Picea abies
- Management system: high forest
- Top-height diameter (quadratic average diameter of the 100 thickest trees per ha): 40.1 cm
- Number of trees BHD -= 12 cm (2011): 709
- Maximum tree age: n.d.
More Information: https://www.wsl.ch/en/forest/forest-development-and-monitoring/long-term-forest-ecosystem-research-lwf/sites/lantsch.html

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

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: LWF Lantsch long-term research site

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