snow-depth-mapping

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

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

envidat

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

en: Snow Depth Mapping

title_for_slug: snow-depth-mapping

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

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

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

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

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

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

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

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

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

aerial-imagery

display_name: aerial-imagery

avalanche

display_name: avalanche

dsm

display_name: dsm

image-matching

display_name: image-matching

snow-depth

display_name: snow-depth

snow-hydrology

display_name: snow-hydrology

snow-water-equivalent

display_name: snow-water-equivalent


$oid: OBSOLETE

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

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

envidat

approval_status: approved

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

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

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

de: EnviDat: das Umwelt-Datenportal

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: Base cation dynamics in an Oriental beech forest

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

format: XLS

biogeochemical-cycling

display_name: biogeochemical-cycling

calcium

display_name: calcium

fagus-orientalis-lipsky

display_name: fagus-orientalis-lipsky

magnesium

display_name: magnesium

potassium

display_name: potassium


seilaplan

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

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

en: Seilaplan

title_for_slug: seilaplan

format: ZIP

cable-planning

display_name: cable-planning

cable-yarding

display_name: cable-yarding

catenary-load-path

display_name: catenary-load-path

python

display_name: python

qgis

display_name: qgis

software

display_name: software

steep-terrain-harvesting

display_name: steep-terrain-harvesting


vegetation-height-model-sentinel-nfi

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

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

envidat

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

en: Vegetation Height Model Sentinel NFI

title_for_slug: vegetation-height-model-sentinel-nfi

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

format: TIFF

media_type: image/tiff

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

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

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

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

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

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

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

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

cnn

display_name: cnn

forest

display_name: forest

nfi

display_name: nfi

remote-sensing

display_name: remote-sensing

switzerland

display_name: switzerland

vegetation-height

display_name: vegetation-height


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

en: taxonomy translation switzerland indicator-values vascular-plants

envidat

en: EnviDat: the Environmental Data Portal

en: Translation between Checklist 2017 and Flora Indicativa

envi

en: Environment

format: CSV

indicator-values

display_name: indicator-values

taxonomy

display_name: taxonomy

translation

display_name: translation

vascular-plants

display_name: vascular-plants


vegetation-height-model-lidar-nfi

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

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

en: Vegetation Height Model LiDAR NFI

title_for_slug: vegetation-height-model-lidar-nfi

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

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

point-cloud

display_name: point-cloud

three-dimensional

display_name: three-dimensional

vegetation

display_name: vegetation

vegetation-classification

display_name: vegetation-classification


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

en: lidar canrad light-availability forestry canopy-openness

envidat

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

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

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

format: PNG

media_type: image/png

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

canopy-openness

display_name: canopy-openness

canrad

display_name: canrad

forestry

display_name: forestry

lidar

display_name: lidar

light-availability

display_name: light-availability


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

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

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

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

media_type: text/csv

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

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

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

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

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

acclimation

display_name: acclimation

climate-change

display_name: climate-change

forest-canopy

display_name: forest-canopy

photosynthesis

display_name: photosynthesis

respiration

display_name: respiration

species-interaction

display_name: species-interaction


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

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

envidat

en: EnviDat: the Environmental Data Portal

envi

en: Environment

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

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

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

experiment

display_name: experiment

slab-avalanche

display_name: slab-avalanche

snow-mechanics

display_name: snow-mechanics

strength

display_name: strength

weak-layer

display_name: weak-layer


en:
Monitoring fish diversity through eDNA metabarcoding along the Kinabatangan river
In 2019, water samples were collected in 25 locations along the Kinabatangan in Malaysia. Samples were taken either from a boat or by filtering from the side of the river. At each station, two filtration replicates were performed using a peristaltic pump to conduct environmental DNA (eDNA) sampling. Each filtration targeted a maximum duration of 30 min, during which approximately 30 liters of water were filtered through each filtration capsule. After filtration, the water inside the capsules was removed, and the capsules were filled with 50 ml of conservation buffer for preservation at room temperature. We followed strict contamination control protocols throughout both the fieldwork and laboratory processes, adhering to the guidelines of Valentini et al. (2016). To prevent contamination, each sample was processed using disposable gloves and single-use filtration equipment. We use primers for eukaryote and fish. The MiSeq Reagent Kit v3 (2x75 bp) (Illumina, San Diego, CA, USA) was used for paired-end sequencing at a theoretical sequencing depth of 200,000 reads per sample.
Data content:
	* rawdata/: contains the raw reads for each individual sample. One archive contains the paired-end reads specified by the _R1 or _R2 suffix as well as individually tagged PCR replicates (if available) together with an archive containing all extraction and PCR blank samples of the library. Reads have been demultiplexed using cutadapt but not trimmed, individual demultiplexing tags and primers remain present in the sequences.
	* taxadata/: contains the table with all detected taxonomy for each sample after bioinformatic processing (see Polanco et al. 2020 for details; https://doi.org/10.1002/edn3.140) and associated field metadata.
	* metadata/: contains two metadata files, one related to the data collected in the field for each filter, and the second related to the sequencing process in the lab (including the tag sequence, library name, and marker information for each sample)

en: malaysia teleo edna fish metabarcoding

envidat

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

en: Environmental DNA Freshwater Malaysia Kinabatangan 2019

edna

display_name: edna

fish

display_name: fish

malaysia

display_name: malaysia

metabarcoding

display_name: metabarcoding

teleo

display_name: teleo


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

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

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

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

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

format: TXT

media_type: text/plain

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

format: gzip

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

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

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

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

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

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

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

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

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

ecosystem-functioning

display_name: ecosystem-functioning

grassland

display_name: grassland

herbivory

display_name: herbivory

invasive-species

display_name: invasive-species

soil-biodiversity

display_name: soil-biodiversity


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

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

envidat

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

en: EnviDat: the Environmental Data Portal

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

envi

en: Environment

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

format: PDF

media_type: application/pdf

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

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

air-temperature

display_name: air-temperature

century

display_name: century

climate

display_name: climate

climate-layers

display_name: climate-layers

free-climate-data

display_name: free-climate-data

maximum-temperature

display_name: maximum-temperature

minimum-temperature

display_name: minimum-temperature

precipitation

display_name: precipitation

timeseries

display_name: timeseries


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

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

en: Code of TreeMig Package version 1

dynamic-modelling

display_name: dynamic-modelling

forest-model

display_name: forest-model

gui

display_name: gui

landscape

display_name: landscape

r-package

display_name: r-package


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

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

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

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

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

check-dam

display_name: check-dam

fungal-decay

display_name: fungal-decay

long-term-behaviour

display_name: long-term-behaviour

timber-structure

display_name: timber-structure

torrent-control

display_name: torrent-control


soil-erosion-mapping-on-steep-slopes

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

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

en: Soil erosion mapping on steep slopes

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

erosion

display_name: erosion

erosion-scar

display_name: erosion-scar

gully

display_name: gully

monitoring

display_name: monitoring

mountain-stream

display_name: mountain-stream

steep-slope

display_name: steep-slope


en:
Plant ecophysiological and associated environmental data for two tree species (*Fagus sylvatica* and *Quercus pubescens*) grown for five years in a factorial open-top climate chamber experiment (Model Ecosystem Facility, Birmensdorf, Switzerland) to test the long-term effects of heat (+5 °C) and soil drought (irrigation reduced by 50%). n = 4 trees per species and treatment, n = 48 trees total.
 Included data sheets:
- Environmental_Data.csv: Chamber-level environmental recorded every 10 minutes
- Tleaf.csv: Leaf temperatures recorded every 10 minutes
- Gas_exchange.csv: Leaf-level gas exchange data taken diurnally on four days during the growing season
- Leaf_traits.csv: Leaf functional traits
- TSMs_HSMs.csv: Photosynthetic thermal tolerance and leaf water potential data, summarized also in terms of leaf thermal safety margins and hydraulic safety margins, collected 3-4 times during the growing season
- Scorch.csv: Leaf-level leaf scorching data, including leaf thermal safety margins and scorching occurrence and presence, summarized by date

en: thermoregulation hot-drought gas-exchange leaf-scorching hydraulic-safety-margins quercus-pubescens functional-traits fagus-sylvatica thermal-safety-margins

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: Hydraulic stress limits thermal acclimation in trees under chronic drought

en:
Resource Title: Tleaf.csv
    Size: 28028436 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/8780a01e-98aa-46bb-9938-ba8e0568fa20/resource/1860aa5f-68f2-4889-b0c9-bd907e48111f/download/tleaf.csv
    Description: Tree-level leaf temperature data recorded every 10 minutes.

Variable List:
	- Timestamp (MM/DD/YYYY HH:MM)
	- TreeID: Unique identifier for individual trees
	- Chamber: open-top-chamber ID
	- Treatment: chamber abiotic treatment (C = control, H = heated, RM = reduced soil moisture, H_RM = heated and reduced soil moisture)
	- Heating: Yes or no
	- RM: Reduced soil moisture, yes or no
 	- Species: Tree species
	- Tair: Air temperature (degrees C)
	- Tleaf: Leaf temperature (degrees C)
	- Tdiff: Leaf-to-air temperature differential (degrees C)

en:
Resource Title: README.txt
    Size: 8090 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/8780a01e-98aa-46bb-9938-ba8e0568fa20/resource/1f818ee5-bfba-4996-b8fb-e6dc1c96fbb4/download/readme.txt
    Description: README file with more information about the dataset.

en:
Resource Title: README.txt
    Size: 8110 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/8780a01e-98aa-46bb-9938-ba8e0568fa20/resource/1fe7f58f-356a-4ed4-ae5f-a2b16755e9de/download/readme.txt
    Description: README file with more information about the dataset.

en:
Resource Title: Gas_exchange.csv
    Size: 4413 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/8780a01e-98aa-46bb-9938-ba8e0568fa20/resource/cc7e702f-6d53-4192-944c-7671eaecf5bc/download/gas_exchange.csv
    Description: Mean values taken for each tree across the growing season. Measurements were taken diurnally (~1 measurement/hr) with a Licor LI6800 matching ambient chamber conditions on 4 days during the growing season (1 day in June, July, August, September).

- Number of variables: 11
- Number of cases/rows: 48
- Variable List:
	- TreeID: Unique identifier for individual trees
	- Chamber: open-top-chamber ID
	- Species: Tree species
	- Treatment: chamber abiotic treatment (C = control, H = heated, RM = reduced soil moisture, H_RM = heated and reduced soil moisture)
	- Heating: Yes or no
	- RM: Reduced soil moisture, yes or no
	- gsw.mean: Mean stomatal conductance to water vapor (mol m^-2 s^-1)
	- gsw.mean: Mean stomatal conductance rate (mol m^-2 s^-2)
	- Tdiff.mean: Mean difference between leaf and air temperature on the day of Licor measurements
	- lngsw.mean: Mean of the natural log of stomatal conductance
	- lngsw.mean~Tdiff.mean_slope: The slope of the linear least-squares relationship between the natural log of lngsw.mean and Tdiff.mean

en:
Resource Title: TSMs_HSMs.csv
    Size: 15217 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/8780a01e-98aa-46bb-9938-ba8e0568fa20/resource/6c897b79-4cc6-46f5-87f0-83275921ede9/download/tsms_hsms.csv
    Description: Tree-level photosynthetic thermal tolerance, leaf water potentials, thermal safety margins, and hydraulic safety margins measured 3-4 times per tree during the growing season.

Variable List:
	- TreeID: Unique identifier for individual trees
 	- Species: Tree species
	- Campaign: Month of the growing season in which data was collected
	- Heating: Yes or no
	- RM: Reduced soil moisture, yes or no
	- Treatment: chamber abiotic treatment (C = control, H = heated, RM = reduced soil moisture, H_RM = heated and reduced soil moisture)
	- Waterpotential: midday leaf waterpotential (MPa)
	- WPtlp: Waterpotential at leaf turgor-loss-point (MPa) *Measured by Mas, E., Cochard, H., Deluigi, J., Didion-Gency, M., Martin-StPaul, N., Morcillo, L., Valladares, F., Vilagrosa, A., & Grossiord, C. (2024). Interactions between beech and oak seedlings can modify the effects of hotter droughts and the onset of hydraulic failure. New Phytologist, 241(3), 1021-1034. https://doi.org/https://doi.org/10.1111/nph.19358
	- HSM: Hydraulic safety margin (difference between WPtlp and Waterpotential, in MPa)
	- Tcrit: Critical thermal threshold of Photosystem II (degrees C)
	- T50: High leaf temperature at which Photosystem II function declines by 50% (degrees C)
	- Tleaf.max: Maximum leaf temperature during the week preceding thermal tolerance measurement
	- TSM.min: Minimum leaf thermal safety margin during the week preceeding thermal tolerance measurement (difference between Tcrit and Tleaf.max, in degrees C)

en:
Resource Title: Leaf_traits.csv
    Size: 7772 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/8780a01e-98aa-46bb-9938-ba8e0568fa20/resource/eaf9d8ce-80f5-489f-af97-e80760927aee/download/leaf_traits.csv
    Description: Tree-level leaf functional trait data.

Variable List:
	- TreeID: Unique identifier for individual trees
	- Chamber: open-top-chamber ID
	- Treatment: chamber abiotic treatment (C = control, H = heated, RM = reduced soil moisture, H_RM = heated and reduced soil moisture)
	- Heating: Yes or no
	- RM: Reduced soil moisture, yes or no
 	- Species: Tree species
	- Leaf_width (cm)
	- Leaf_area (cm^2)
	- Stomatal_density (count per cm^2)
	- Pore_length: Stomatal pore length (micrometers)
	- Guard_cell_width: Stomatal guard cell width (micrometers)
	- Leaf_thickness (mm)
	- Epidermal_thickness (mm)
	- Parenchyma_thickness (mm)
	- Parenchyma:Epidermis: Ratio between parenchyma and epidermal thickness
	- Reflectance: Reflectance of photosynthetically active radiation (400-700), fraction
	- Transmittance: Transmittance of photosynthetically active radiation (400-700), fraction
	- Absorptance: Absorptance of photosynthetically active radiation (400-700), fraction
	- Absorbance: Optical absorbance of photosynthetically active radiation (400-700 nm), unitless
	- g1: Medlyn-style stomatal slope calculated with Licor LI6800 data

en:
Resource Title: Scorch.csv
    Size: 129956 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/8780a01e-98aa-46bb-9938-ba8e0568fa20/resource/e153f13e-330e-4c0a-8cff-59e625dc7e7c/download/scorch.csv
    Description: Tree-level leaf scorching occurrence and severity data summarized by date with daily leaf thermal safety margins.

Variable List:
	- TreeID: Unique identifier for individual trees
	- Date (MM/DD/YYYY)
	- Heating: Yes or no
	- RM: Reduced soil moisture, yes or no
	- Treatment: chamber abiotic treatment (C = control, H = heated, RM = reduced soil moisture, H_RM = heated and reduced soil moisture)
	- Species: Tree species
	- Tleaf: Leaf temperature (degrees C) *measured by an infrared sensor integrated into the Cavicam
	- Tcrit: Critical thermal threshold of Photosystem II (degrees C) *daily entries inherit the Tcrit measured onthe next measurement date and applied to the interval preceding it (or are forward-filled when Tcrit could not be estimated in August or September)
	- TSM: Leaf thermal safety margin (difference between Tcrit and Tleaf, in degrees C)
	- Scorch_binary: Binary indicating whether scorching occurred on that date (0 = no, 1 = yes)
	- Percent_affected: Percent of the camera window affected by scorching during a scorching event

en:
Resource Title: Environmental_Data.csv
    Size: 19996344 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/8780a01e-98aa-46bb-9938-ba8e0568fa20/resource/8113d780-83c8-43bd-a823-f726ad3f21ab/download/environmental_data.csv
    Description: Chamber-level abiotic data recorded every 10 minutes.

Variable List:
	- Time (MM/DD/YYYY HH:MM)
	- Chamber: open-top-chamber ID
	- Treatment: chamber abiotic treatment (C = control, H = heated, RM = reduced soil moisture, H_RM = heated and reduced soil moisture)
	- Heating: Yes or no
	- RM: Reduced soil moisture, yes or no
	- Tair: Air temperature (degrees C)
	- Tair_sd: Standard deviation of air temperature
	- RH: Relative humidity
	- RH_sd: Standard deviation of relative humidity
	- VPD: Vapor pressure deficit (kPa)
	- Tsoil: Soil temperature (degrees C)
	- Soil_m: Volumetric soil water content (m^3/m^3)
	- PPFD: Photosynthetic photon flux density (micromoles m^-2 s^-1) *collected from a weather station next to the open-top-chambers (50% intensity inside chambers)
	- windspeed: (m/s) *collected from a weather station next to the open-top-chambers

en:
Resource Title: Gas_exchange.csv
    Size: 4424 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/8780a01e-98aa-46bb-9938-ba8e0568fa20/resource/df082744-69a0-49b6-81b7-c3f02d24c9f0/download/gas_exchange.csv
    Description: Leaf-level gas exchange collected diurnally for each tree on 4 days of the growing season and summarized to growing-season mean.

Variable List:
	- TreeID: Unique identifier for individual trees
	- Chamber: open-top-chamber ID
	- Species: Tree species
	- Treatment: chamber abiotic treatment (C = control, H = heated, RM = reduced soil moisture, H_RM = heated and reduced soil moisture)
	- Heating: Yes or no
	- RM: Reduced soil moisture, yes or no
	- gsw.mean: Mean stomatal conductance to water vapor (mol m^-2 s^-1)
	- E.mean: Mean transpiration rate (mol m^-2 s^-2)
	- Tdiff.mean: Mean difference between leaf and air temperature on the day of Licor measurements
	- lnE.mean: Mean of the natural log of the transpiration rate
	- lnE.mean~Tdiff.mean_slope: The slope of the linear least-squares relationship between the natural log of lnE.mean and Tdiff.mean

fagus-sylvatica

display_name: fagus-sylvatica

functional-traits

display_name: functional-traits

gas-exchange

display_name: gas-exchange

hot-drought

display_name: hot-drought

hydraulic-safety-margins

display_name: hydraulic-safety-margins

leaf-scorching

display_name: leaf-scorching

quercus-pubescens

display_name: quercus-pubescens

thermal-safety-margins

display_name: thermal-safety-margins

thermoregulation

display_name: thermoregulation


en:
This dataset includes measurements of lethal temperatures (LT) of buds and leaves collected in winter and spring 2025, along with phenological observations recorded during 2024–2025 in a forest plantation in Switzerland. The data were collected to assess frost tolerance and phenological timing in native and non-native tree species under current climatic conditions.
The species investigated are Abies bornmuelleriana, Abies alba, Cedrus libani, Fagus orientalis, Fagus sylvatica, Tilia tomentosa, Tilia platyphyllos, and Quercus petraea.
Publications related to original data set: https://doi.org/10.1016/j.foreco.2026.123788

en: tilia-tomentosa abies-bornmuelleriana bud-break abies-alba spring-phenophases tilia-platyphyllos frost-hardiness climate-warming frost-risk phenology quercus-petraea fagus-orientalis fagus-sylvatica cedrus-libani

en: Frost hardiness and phenological events of native and non-native species in Switzerland

envi

en: Environment

en:
Resource Title: Mutrux(CH)_Frost_hardiness.csv
    Size: 29338 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/193dcaf9-38b1-49b7-9895-2aeaacbf4893/resource/db481da7-1b4f-411b-b9fd-3fb3bea25d11/download/mutruxch_frost_hardiness.csv
    Description: The dataset contains measurements of frost lethal temperatures for buds and leaves of native and non-native species in Switzerland. It also includes the geographic coordinates of the study site and information on the origin of the seed material used for the non-native species.

en:
Resource Title: Mutrux(CH)_Phenology2024-2025.csv
    Size: 17961 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/193dcaf9-38b1-49b7-9895-2aeaacbf4893/resource/4cbd7f00-42a5-4b91-95ba-25c204fac81d/download/mutruxch_phenology2024-2025.csv
    Description: The dataset contains measurements of phenological development assessed using a six-stage categorical scale, ranging from stage 0 (dormant buds with no visible development) to stage 5 (fully unfolded leaves and needles).

abies-alba

display_name: abies-alba

abies-bornmuelleriana

display_name: abies-bornmuelleriana

bud-break

display_name: bud-break

cedrus-libani

display_name: cedrus-libani

climate-warming

display_name: climate-warming

fagus-orientalis

display_name: fagus-orientalis

frost-hardiness

display_name: frost-hardiness

frost-risk

display_name: frost-risk

phenology

display_name: phenology

quercus-petraea

display_name: quercus-petraea

spring-phenophases

display_name: spring-phenophases

tilia-platyphyllos

display_name: tilia-platyphyllos

tilia-tomentosa

display_name: tilia-tomentosa


en: Photosynthetic thermal tolerance estimates for 36 tropical species following the static water bath method and measuring maximum quantum yield (Fv/Fm). Thermal tolerance assays were performed once each in near-darkness (~4 μmol photons m^-2 s^-1) and in sub-saturating light (280 μmol photos m^-2 s^-1).

en: chlorophyll-fluorescence tcrit tropical-plants irradiance thermal-tolerance photosystem-ii photoinhibition

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: Light exposure reshapes photosynthetic thermal tolerance curves, with idiosyncratic responses across 36 species.

chlorophyll-fluorescence

display_name: chlorophyll-fluorescence

irradiance

display_name: irradiance

photoinhibition

display_name: photoinhibition

photosystem-ii

display_name: photosystem-ii

tcrit

display_name: tcrit

thermal-tolerance

display_name: thermal-tolerance

tropical-plants

display_name: tropical-plants


en:
Hourly Alpine photovoltaic (PV) potential profiles for Switzerland, covering the year 2016 and spatially aggregated to two levels: Swiss alpine municipalities and the nodes of the Swiss electricity transmission grid. Six scenarios combine land suitability mask, terrain restrictions (elevation, slope, aspect), road accessibility, hazard zones, and either an elevation range or a pixel-level winter specific yield filter, providing per-pixel installed capacity and aggregated hourly generation profiles for ~713 alpine communes and ~126 transmission grid nodes.
 What is in this dataset
- **Hourly generation profiles** (`energy_yield`, in Wh) for each aggregation unit
- **Installed capacity** (`installed_capacity_mwp`, in MWp) and **pixel count** (`pixel_count`) per unit
- **Seasonal sums** (`annual_gwh`, `winter_gwh`, `summer_gwh`, in GWh) per unit
- **Polygon geometries** (`geometry`, WKT in EPSG:2056) for each aggregation unit, embedded directly in the NetCDF
- **Global attributes** recording scenario name, panel tilt, GCR method, POA mode, and PV system parameters
 Six scenarios
Scenarios vary the site-selection approach (elevation range vs. pixel-level winter specific yield threshold) and the maximum slope:
- **1. `reusser_nowy_elev1900_3300_slope40`** — slope ≤ 40°, elevation 1900–3300 m, aspect 90–270° on steep slopes, no winter-yield filter
- **2. `reusser_nowy_elev1500_4400_slope40`** — slope ≤ 40°, elevation 1500–4400 m, aspect 90–270° on steep slopes, no winter-yield filter
- **3. `reusser_nowy_elev1900_3300_slope30`** — slope ≤ 30°, elevation 1900–3300 m, aspect 90–270° on steep slopes, no winter-yield filter
- **4. `reusser_nowy_elev1500_4400_slope30`** — slope ≤ 30°, elevation 1500–4400 m, aspect 90–270° on steep slopes, no winter-yield filter
- **5. `reusser_wy500_elev100_4400_slope40`** — slope ≤ 40°, elevation 100–4400 m, all aspects, winter specific yield ≥ 500 kWh/kWp
- **6. `reusser_wy500_elev100_4400_slope30`** — slope ≤ 30°, elevation 100–4400 m, all aspects, winter specific yield ≥ 500 kWh/kWp
The four `nowy_*` scenarios use a conventional elevation range together with an aspect restriction (north-facing pixels excluded on slopes - 10 deg). The two `wy500_*` scenarios apply a pixel-level winter specific yield threshold instead.
 Methodology
A pre-computed binary exclusion raster from the [Swiss SolarWind Explorer](https://swiss-solarwind-explorer.ethz.ch/) (Reusser et al., 2025) serves as the base land suitability mask, integrating land-cover, protected-area, and biodiversity exclusions developed through extensive consultation with governmental, expert, and biodiversity stakeholders. On top of this base mask, the pipeline clips to alpine communes, removes natural hazards (level -= 2), applies a 5 km effective road distance filter (with vertical penalty), and applies scenario-specific terrain restrictions.
For each qualifying 25 m pixel, the ground-cover ratio is computed using the Bao et al. (2022) parametrization with a 60 deg panel tilt, and the hourly yield is derived from the [CH-POA300](https://www.doi.org/10.16904/envidat.632) plane-of-array irradiance dataset (300 m resolution, 5 azimuth bins). A simplified PV system model is applied (module efficiency 20%, performance ratio 0.8, bifacial factor 1.2, inter-row shading loss 10%). For scenarios with the winter yield filter, only pixels whose simulated October--March specific yield exceeds 500 kWh/kWp are retained.
 Two aggregation levels
- **commune** — Swiss alpine municipality; 713 units; source swissBOUNDARIES3D; CRS EPSG:2056
- **node** — Transmission grid Voronoi node; 126 units; source [CH-RPV300 (grid_voronoi_v2)](https://www.doi.org/10.16904/envidat.733); CRS EPSG:2056
 File format
NetCDF-4 files, one per scenario and aggregation level (12 files total). Each file is self-contained: the hourly profiles, installed capacity, seasonal sums, polygon geometries, and scenario parameters are all embedded in the NetCDF. See `data_description_v*.md` included in the dataset for the full data model, coordinate definitions, and usage examples.

en: alps snow alpine-pv renewable-energy photovoltaic

en: CH-ALPV300: Hourly Alpine-PV  Potential Profiles in Switzerland

en:
Resource Title: specs_v1.pdf
    Size: 10108273 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/fb06af0f-1ac6-4d2b-ae8e-1d6950cd0413/resource/216f0204-0ac5-4510-8087-e6dfc7669078/download/specs_v1.pdf
    Description: Specifications, version 1

en:
Resource Title: profiles_2016_v1.zip
    Size: 112449290 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/fb06af0f-1ac6-4d2b-ae8e-1d6950cd0413/resource/ecbc2326-e274-4734-9150-c394c0213a50/download/profiles_2016_v1.zip
    Description: Profiles 2016, version 1

media_type: application/zip

en:
Resource Title: data_description_v1.md
    Size: 12104 bytes
    Format: text/markdown
    URL: https://www.envidat.ch/dataset/fb06af0f-1ac6-4d2b-ae8e-1d6950cd0413/resource/4dc40d28-c071-4463-8030-1a1661b02869/download/data_description_v1.md
    Description:

format: markdown

alpine-pv

display_name: alpine-pv

alps

display_name: alps

photovoltaic

display_name: photovoltaic

renewable-energy

display_name: renewable-energy

snow

display_name: snow


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

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

en: Snow depth mapping by lidar station Braemabuel

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

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

avalanche

display_name: avalanche

lidar

display_name: lidar

low-cost

display_name: low-cost

monitoring

display_name: monitoring

wind-drifted-snow

display_name: wind-drifted-snow


en:
Hourly rooftop photovoltaic (RPV) power generation profiles for Switzerland, covering 12 weather years (2008--2019) and spatially aggregated to the nodes of the Swiss electricity transmission grid. The dataset resolves 48 orientation and tilt combinations, five installation size classes, and three flat roof design scenarios per grid node, providing approximately 8,550 distinct candidate RPV generators nationwide.
 What is in this dataset
- **Hourly generation profiles** (`power`, in Wh) for each grid node, roof orientation, and tilt bin
- **Installed capacity** (`Wp`, in Wp) broken down by size class (1--10, 10--30, 30--100, 100--300, 300--1000 kWp)
- **Normalized capacity factor profiles** (`specificPower`, in Wh/Wp) enabling scaling to arbitrary deployment levels
- **Voronoi grid shapefiles** defining the spatial aggregation zones around each transmission node.
 Flat roof scenarios
Flat roofs (~23% of Swiss roof area) allow different module configurations. Three scenarios are included:
- **Winter** — South-facing, 35° tilt, GCR 0.45 — optimizes winter yield (Oct–Mar)
- **Annual** — South-facing, 15° tilt, GCR 0.70 — optimizes annual yield
- **Flat-EW** — East-West alternating, 15° tilt, GCR 1.04 — maximizes total installed capacity
Tilted roofs (tilt - 0) are identical in all three scenarios.
 Methodology
Generation profiles are derived from satellite-based hourly surface solar radiation data (HelioMont), downscaled to 300 m resolution using the NASADEM digital elevation model with terrain-scale shading and ground reflection ([CH-POA300](https://www.doi.org/10.16904/envidat.632)). The national roof inventory Sonnendach (v1.5) provides orientation, tilt, and area for over 10 million roof surfaces. A simplified PV system model (module efficiency 20%, performance ratio 0.8, roof cover factor 0.7) converts irradiance to AC power output. Only roofs with an installed capacity -= 1 kWp are included.
 Two grid versions
- **v1** —  135 nodes, CRS EPSG:21781
- **v2** —  126 nodes, CRS EPSG:21781
 File format
NetCDF-4 files, one per year and scenario. See `DATA_DESCRIPTION.md` included in the dataset for the full data model, coordinate definitions, and usage examples.

en: renewable-energy energy-transition photovoltaic rooftop-pv solar-energy

en: CH-RPV300: Hourly Rooftop-PV Potential Profiles in Switzerland

envi

en: Environment

en:
Resource Title: profiles_grid_v2.zip
    Size: 4531190497 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/73f6cac8-6a0e-469b-8eed-a9e7fcd75bb8/resource/d6a43092-bdce-4e14-a182-8c37e53b5f65/download/profiles_grid_v2.zip
    Description: This archive contains the RPV profiles aggregated using the second version (v2) of the transmission grid, also included.

en:
Resource Title: profiles_grid_v1.zip
    Size: 5894842614 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/73f6cac8-6a0e-469b-8eed-a9e7fcd75bb8/resource/4e34b9ac-b8d5-408f-b9e0-70a559e44a2b/download/profiles_grid_v1.zip
    Description: This archive contains the RPV profiles aggregated using the first version (v1) of the transmission grid, also included.

en:
Resource Title: DATA_DESCRIPTION.md
    Size: 7762 bytes
    Format: text/markdown
    URL: https://www.envidat.ch/dataset/73f6cac8-6a0e-469b-8eed-a9e7fcd75bb8/resource/78340283-abd2-48e0-82aa-57707c46dc93/download/data_description.md
    Description:

energy-transition

display_name: energy-transition

rooftop-pv

display_name: rooftop-pv

solar-energy

display_name: solar-energy


en:
This repository contains all observational data sets used for the paper:
Sophia Etzold, Arun K. Bose, Arthur Gessler, Frank Krumm, Katrin Meusburger, Marcus Schaub, Anne Thimonier, Peter Waldner, Roman Zweifel (2026): Decreasing water availability reduces productivity in Swiss forests along an altitudinal gradient, Functional Ecology, https://doi.org/xxxxx.
**Dataset 1**: This dataset contains inventory measurements of individual trees, i.e. species, tree status, DBH (diameter at breast height), height, and BAI (Basal area increment).
**Dataset 2**: This dataset contains plotwise aggregated BAI and NPP data per inventory period, as well as stand and soil properties of the site (N trees, QMD, SDI, meanDBH, foliar N and foliar P, N deposition, LAI, main species) and meteorological parameters (deviation of T, Psum and SWP during vegetation period).
**Dataset 3**: This dataset contains site-specific properties (altitude, age in 2000, main species, Tmean long-term, Psum long-term, soil pH, soil C/N).
**Dataset 4**: This dataset contains annually resolved relative NPP data, based on inventory data (Dataset 2) and annual circumference measurements of individual trees, as well as relative annual Tmax and SWP.

en: dendrometer forest-productivity tree-growth drought-stress carbon-sequestration

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  Article “Decreasing water availability reduces productivity in Swiss forests along an altitudinal gradient” Etzold et al.

en:
Resource Title: 4_Annual_NPP_plot.csv
    Size: 98038 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/1388bc90-9e07-4a1e-a3a9-144d319e492a/resource/24458cd4-2a47-45fb-b201-acfb65b186f1/download/4_annual_npp_plot.csv
    Description: #4: Annual NPP per plot
Site: Site name
Subplot: Subplot name
Year
NPPyr_rel: Annual Net Primary Productivity of the stand in relation to long-term mean in [%]
SWP_rel: Mean soil water potential during vegetation period in relation to long-term mean in [%]
Tmax_rel: Maximum temperature during vegetation period in relation to long-term mean in [%]

en:
Resource Title: 2_Plotdata_Inventory_NPP_BAI.csv
    Size: 65526 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/1388bc90-9e07-4a1e-a3a9-144d319e492a/resource/05b47422-7f82-45ce-a545-5c6cb14449e6/download/2_plotdata_inventory_npp_bai.csv
    Description: #2: Plotdata_NPP_BAI
Site: Site name
Subplot: Subplot name
INVYEAR: Assigned Year to Inventory (mean over all plots)
INVNR: Inventory number
Csum.yr.gm2: Carbon uptake by growth in [gC/m2/yr]
BAI.yr.m2ha: Basal area increment in [m2/ha/yr]
Ntrees.ha: Number of trees per ha in [N/ha]
QMD: Quadratic mean diameter of all trees in [cm]
SDI: Reineke's Stand density index [-]
meanDBH_plot: mean DBH of all trees per plot in [cm]
LAI: Leaf area index of plot in [m2/m2]
Ndep: total Nitrogen deposition in [kg N/ha/yr]
foliarN_rel: relative foliar N concentration in [%]
foliarP_rel: relative foliar P concentration in [%]
meanT.veg: mean temperature during vegetation period in [°C]
Psum.veg: mean precipitation sum during vegetation period in [mm]
meanSWP.veg: mean soil water potential during vegetation period in [kPa]
dTemp.veg: annual deviation of mean temperature from long-term mean in [%]
dPsum.veg: annual deviation of precipitation sum from long-term mean in [%]
dSWP.veg: annual deviation of mean soil water potential from long-term mean in [%]

en:
Resource Title: 3_Site_properties.csv
    Size: 1011 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/1388bc90-9e07-4a1e-a3a9-144d319e492a/resource/62395d0d-330e-4d35-a022-ca8a47a501fd/download/3_site_properties.csv
    Description: #3: Site properties
Site: Site name
Altitude: in [m a.s.l.]
Age_in_2000: Estimated stand age in the year 2000 in [years]
Main_species: Dominant species of the stand
meanT_long: long-term (1995-2020) mean temperature during vegetation period in [°C]
Psum_long: long-term (1995-2020) mean precipitation sum during vegetation period in [mm]
soil_CN: C/N ratio of the soil
soil_pH: PH of the soil

en:
Resource Title: 1_Treedata_Inventory_BAI.csv
    Size: 3816817 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/1388bc90-9e07-4a1e-a3a9-144d319e492a/resource/47d47817-4612-44bc-9f36-c1f2195d015b/download/1_treedata_inventory_bai.csv
    Description: #1: Treedata_BAI
Site: Site name
Subplot: Subplot name
Treenr: Tree number
SPECIES: Species name
Start.Year: First Year of Inventory
End.Year: Last Year of Inventory
DBH.tree: Diameter at breast height (1.3m) in [cm]
DBH.class: Category of DBH: 'sub': <12cm, '1':small, '2':medium, '3': large
Height: tree height in [m]
Treestatus: Status of tree: '1': alive, '0': dead
BAIyr: Annual basal area increment of tree in [cm2/yr]

carbon-sequestration

display_name: carbon-sequestration

dendrometer

display_name: dendrometer

drought-stress

display_name: drought-stress

forest-productivity

display_name: forest-productivity

tree-growth

display_name: tree-growth


distributed-acoustic-sensing-brienz

en:
This dataset contains the Distributed Acoustic Sensing (DAS), radar detection data used for training and result analysis in the GRL paper titled `Automatic Monitoring of Rock-Slope Failures Using Distributed Acoustic Sensing and Semi-Supervised Learning`.
The DAS dataset (both waveform and cross-spectral density matrices), extracted features, labeled dataset, two trained models (feature extraction model and xgboost classification model), scripts to reproduce the whole training and classification processes, and a notebook to replicate the result analysis part are provided under the MIT license. To provide a reasonable data size, we chunked the raw data to a few hundred channels which we used in our project.
Abstract:
Effective use of the wealth of information provided by Distributed Acoustic Sensing (DAS) for mass movement monitoring remains a challenge. We propose a semi-supervised neural network tailored to screen DAS data related to a series of rock collapses leading to a major failure of approximately 1.2 million cubic meters on 15 June 2023 in Brienz, Eastern Switzerland. Besides DAS, the dataset from 16 May to 30 June 2023 includes Doppler radar data for partially ground-truth labeling. The proposed algorithm is capable of distinguishing between rock-slope failures and background noise, including road and train traffic, with a detection precision of over 95%. It identifies hundreds of precursory failures and shows sustained detection hours before and during the major collapse. Event size and signal-to-noise ratio (SNR) are the key performance dependencies. As a critical part of our algorithm operates unsupervised, we suggest that it is suitable for general monitoring of natural hazards.

en: landslide early-warning machine-learning seismic rockfall natural-hazard

en: Distributed Acoustic Sensing Brienz

title_for_slug: distributed-acoustic-sensing-brienz

early-warning

display_name: early-warning

landslide

display_name: landslide

machine-learning

display_name: machine-learning

natural-hazard

display_name: natural-hazard

rockfall

display_name: rockfall

seismic

display_name: seismic


en:
To validate the critical crack length as implemented in the snow cover model SNOWPACK, PST experiments were conducted for three winter seasons (2015-2017) at two field site above Davos, Switzerland. This dataset contains manually observed snow profiles and stability tests. Furthermore, corresponding SNOWPACK simulations are included.
These data were analyzed and results were published in Richter et al. (2019).
Please refer to the Readme file for further details on the data.
These data are the basis of the following publication:
Richter, B., Schweizer, J., Rotach, M. W., and van Herwijnen, A.: Validating modeled critical crack length for crack propagation in the snow cover model SNOWPACK, The Cryosphere, 13, 3353–3366, https://doi.org/10.5194/tc-13-3353-2019, 2019.

en: snowpack snow-stability-evaluation avalanche-forecast avalanche snow

en: Validating and improving the critical crack length in SNOWPACK

en:
Resource Title: Manually observed snow profiles
    Size: 27333 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/c460111d-cbd0-479d-bd34-48ba27abe5e4/resource/74bf515b-cd6b-4e97-be6d-4356f0af6d3d/download/manual_snow_profiles.zip
    Description: Manually observed snow profiles as csv-files containing depth [cm], thickness [cm], density [kg m-3], hand hardness, first grain type, second grain type, first grain size [mm], and second grain size [mm] of each layer.

en:
Resource Title: Extracted data
    Size: 197562 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/c460111d-cbd0-479d-bd34-48ba27abe5e4/resource/42774021-73e4-49b2-a714-b192a0e3cc14/download/extracted_data.zip
    Description: Extracted data contain validation and results published in Richter et al. (2019).

en:
Resource Title: README
    Size: 18771 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/c460111d-cbd0-479d-bd34-48ba27abe5e4/resource/b6cfb0c3-2bb3-4fa1-859d-5861983aa785/download/readme.txt
    Description: README file explaining data structure and content

en:
Resource Title: SNOWPACK simulations
    Size: 21903741 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/c460111d-cbd0-479d-bd34-48ba27abe5e4/resource/6b0dc93f-34d1-48e2-bf69-3f9254e34692/download/snowpack.zip
    Description: This dataset contains all SNOWPACK simulations, including input files, setup and output files.

en:
Resource Title: Stability tests
    Size: 6975 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/c460111d-cbd0-479d-bd34-48ba27abe5e4/resource/98cbbb6e-bfad-4b71-b920-e3def95143d3/download/stability_tests.zip
    Description: Stability tests including csv-files with CT, ECT and PST experiments for each year and each field site.

avalanche

display_name: avalanche

avalanche-forecast

display_name: avalanche-forecast

snow

display_name: snow

snow-stability-evaluation

display_name: snow-stability-evaluation

snowpack

display_name: snowpack


en:
Continuous stem radius measurements to investigate carbon balance and water relations of trees
 Purpose:
Assessment of growth and water related stem changes

en: icp-forests tree-growth stem-diameter swiss-forest-lab long-term-forest-ecosystem-research

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: Automated point dendrometer measurements at LWF sites

envi

en: Environment

format: HTML

icp-forests

display_name: icp-forests

long-term-forest-ecosystem-research

display_name: long-term-forest-ecosystem-research

stem-diameter

display_name: stem-diameter

swiss-forest-lab

display_name: swiss-forest-lab


en:
These data include all avalanches that were mapped in the region of Davos, Switzerland during the winters 1998-1999 to 2018-2019 (21 years), in total 13,918 avalanches, and the corresponding forecast danger level valid on the day of avalanche occurrence, 3533 days and danger ratings in total.
This avalanche activity data set was analysed and results published by Schweizer et al. (2020). They found that the
number of avalanches per day strongly increased with increasing danger level, but avalanche size was poorly related  to avalanche danger level.
The data are provided in two files: the first includes the avalanche data (13,918 records); the second includes the avalanche activity per day (3533 records).
Please refer to the Read-me file for further details on the data.
These data are the basis of the following publication:
Schweizer, J., Mitterer, C., Techel, F., Stoffel, A. and Reuter, B., 2020. On the relation between avalanche occurrence and avalanche danger level. The Cryosphere, 14, 737-750, https://doi.org/10.5194/tc-14-737-2020.

en: avalanche-forcasting snow-avalanche avalanche-size danger-level avalanche-activity

en: Snow avalanche data Davos, Switzerland, 1999-2019

en:
Resource Title: Avalanche activity data
    Size: 929518 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ce11efbe-4dac-4ff5-9a3d-f01e2c573292/resource/39e6dd9e-9b52-439e-a4d1-5e17a8e18eca/download/data_set_2_danger_avalanches.csv
    Description: This data file includes data on the avalanche activity per day aggregated from the avalanche data.

en:
Resource Title: Readme
    Size: 159745 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/ce11efbe-4dac-4ff5-9a3d-f01e2c573292/resource/f8d6835f-3ef6-4fe6-94a2-6b7f7b5827ce/download/read_me.pdf
    Description: The Readme file contains the description of the two data files.

en:
Resource Title: Snow avalanche data
    Size: 936455 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/ce11efbe-4dac-4ff5-9a3d-f01e2c573292/resource/4c2b7c38-a874-45fc-9833-fdf83823067b/download/data_set_1_avalanche_observations_wi9899_to_wi1819_davos.csv
    Description: This data file includes data on the avalanches mapped in the region of Davos.

avalanche-activity

display_name: avalanche-activity

avalanche-forcasting

display_name: avalanche-forcasting

avalanche-size

display_name: avalanche-size

danger-level

display_name: danger-level

snow-avalanche

display_name: snow-avalanche


en:
Two stem core samples of 10 trees outside the plot of each main species in the plot were taken at breast height (1.3 m above ground) with a SUUNTO corer. Tree ring width and density were determined with the instruments CATRAS and TSAP, the Densitometer DENDRO 2003 and a stereo-microscope. The selected trees included at least 10 pre-dominant or dominant trees.
 Purpose:
N and C stable isotope signals
 Paper Citation:
 * Tomlinson, G., Siegwolf, R.T.W., Buchmann, N., Schleppi, P., Waldner, P., Weber, P., 2014. The mobility of nitrogen across tree-rings of Norway spruce (Picea abies L.) and the effect of extraction method on tree-ring d15N and d13C values. Rapid Commun. Mass Spectrom. 28, 1258-1264.

en: long-term-forest-ecosystem-research dominant-trees dendrochronology tree-ring-width swiss-forest-lab stable-isotopes icp-forests

en: Dendrochronological analyses of tree core samples (IsoN) adjacent to LWF sites

dendrochronology

display_name: dendrochronology

dominant-trees

display_name: dominant-trees

stable-isotopes

display_name: stable-isotopes

tree-ring-width

display_name: tree-ring-width