en: castanea-sativa torymus-sinensis biological-control pest-invasion southern-switzerland population-evolution dryocosmus-kuriphilus

en:
Data has been collected from summer 2011 to 2016.

Main reasons for NA values:

-	D. kuriphilus still not arrived in the site.
-	No galls were found during the field sampling.
-	Site has not been sampled in the correspondent year because of time, weather or money constrains.
-    T. sinensis not found in the galls

Variable description:

Year = sampling year.

Coordinate, elevation, slope, aspect = average site characteristics.

ts_arrival = T. sinensis arrival year.

dk_arrival = D. kuriphilus arrival year.

dk_parasitism_by_ts = D. kuriphilus parasitism rate by T. sinensis. Formula: (no. of T. sinensis/ no. of D. kuriphilus chambers) * 100 at gall level.

dk_infestation = Percentage of attacked buds. Formula: no. of attacked buds / no. of total developed buds*100 (as presented in Kotobuki et al. 1985) at shoot level.

ts_per_bud = number of alive T. sinensis individual calculated at bud level.

dk_per_bud = number of alive D. kuriphilus individual calculated at bud level.

dk_pot_per_bud = overall number (dead and alive) D. kuriphilus individual calculated at bud level.

total_gall_nr_per_bud = average gall number per bud.

chamber_per_galls = average D. kuriphilus chamber number per gall.

For details see the submitted publication with the same name

References
Kotobuki, K., Mori, K. and Sato, Y. (1985) ‘2 methods to estimate the tree damage by chestnut gall wasp Dryocosmus-kuriphilus’, Bulletin of the fruit tree research station A (Yatabe), 2(12), pp. 29–36.

en:
This dataset contains the population evolution of a pest and its biocontrol agent in terms of presence proportion at gall level and absolute number of insects.

The study area extends from the Cuneo region (Piedmont, Italy) to southern Switzerland. In order to provide a complete range of data covering the entire process from the pest arrival to complete biological control by its natural enemy T. sinensis, a space-for-time substitution approach has been adopted so as to create a temporal gradient of the epidemic stages over the whole study area.

The southernmost Swiss sites roughly represent the arrival and establishment of the pest without the presence of the natural enemy, the central ones the early epidemic stage and the epidemic peak, whereas the northern ones the end of the epidemic with the beginning of the biocontrol. The Italian ones represent the beginning of the equilibrium between the two population as well as the situation with stable T. sinensis populations on the long term.

These data are used in the paper entitled: Torymus sinensis local and regional early population dynamics in the Insubrian and Piedmont regions

biological-control

display_name: biological-control

castanea-sativa

display_name: castanea-sativa

dryocosmus-kuriphilus

display_name: dryocosmus-kuriphilus

pest-invasion

display_name: pest-invasion

population-evolution

display_name: population-evolution

southern-switzerland

display_name: southern-switzerland

torymus-sinensis

display_name: torymus-sinensis


maintainer: EnviDat Support

en: blowing-snow drifting-snow eddy-covariance-measurements mass-flux turbulence meteorological-data large-eddy-simulations micro-rain-radar antarctica

state: active

type: dataset

en: Measurement data from micro-meteorological station

media_type: zip-compressed folder of ASCII files

en: Automatic in-situ measurements of micro-meteorology and snow transport

format: SERVICE

en: Simulation data for a situation with negligible snow transport

media_type: zip-compressed folder of csv

en: Results from two LES-LSM simulations called NoDrift_1 and NoDrift_2, which reproduce the conditions observed at the S17 site on 12th January 2019, 05:40 to 05:50 UTC.

en: Measurement data from Micro Rain Radar (MRR)

media_type: NetCDF

en: Remote sensing data of blowing snow from a Micro Rain Radar (MRR).

media_type: zip-compressed folder of text files (f90)

en: Source code of the large-eddy simulations coupled with a Lagrangian stochastic model (LES-LSM), available on GitLab.

en: Measurement data from snow particle counter at MRR site

en: Near-surface observations of drifting snow from a Snow Particle Counter (SPC) installed at the location of the Micro Rain Radar.

en: Readme

media_type: TXT

en: Meta data

format: TXT

en: Simulation data for a situation with significant snow transport

en: Data from two LES-LSM simulations called Drift_1 and Drift_2, which reproduce the conditions observed at the S17 site on 11th January 2019, 00:40 to 00:50 UTC.

en:
This dataset contains measurement and simulation data. The measurements characterize the standard meteorology, turbulence, and snow transport at the S17 site near Syowa Station in East Antarctica during an expedition in austral summer 2018/2019. Large-eddy simulations with sublimating particles provide additional insight into the latent and sensible heat exchange between snow and air in two example situations observed at the S17 site.

A part of the measurement data was recorded by an automatic measurement station from 10th January 2019 to 26th January 2019. This measurement station was equipped with standard meteorological sensors, a three-dimensional ultrasonic anemometer, an open-path infrared gas analyzer, a snow particle counter, an infrared radiometer for measurements of the surface temperature, and a sonic ranging sensor measuring changes in snow surface elevation.

At a horizontal distance of approximately 500 m, a Micro Rain Radar (MRR) was installed in a tilted configuration with an elevation angle of 7° for remote sensing of blowing snow between 25th December 2018 and 24th January 2019. In addition, near-surface in-situ measurements of snow transport were performed at the location of the MRR by deploying a snow particle counter from 27th December 2018 to 24th January 2019.

The simulations cover a 18 x 18 x 6 m³ domain and reproduce the steady-state conditions during a 10-min interval with significant snow transport and another 10-min interval with negligible snow transport. We provide the model source code and the post-processed simulation data, i.e., horizontally averaged quantities as a function of height and time.

antarctica

display_name: antarctica

blowing-snow

display_name: blowing-snow

drifting-snow

display_name: drifting-snow

eddy-covariance-measurements

display_name: eddy-covariance-measurements

large-eddy-simulations

display_name: large-eddy-simulations

mass-flux

display_name: mass-flux

meteorological-data

display_name: meteorological-data

micro-rain-radar

display_name: micro-rain-radar

turbulence

display_name: turbulence

education

fr: Education, science

de: Bildung, Wissenschaft

en: Education and science

it: Formazione e scienza

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

it: EnviDat: il portale dei dati ambientali

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

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

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

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

political_level: other

wsl

capacity: public

type: organization

approval_status: approved

key: guid

key: uri

value: Envidat Harvester

en: Meteorology and snow transport at S17 near Syowa, Antarctica, in austral summer 2018/2019


causal-effect-of-lup

title_for_slug: causal-effect-of-lup

en: plan-evaluation land-use-planning annual-effect psm-did land-use-change

en: causal-effect-of-lup

media_type: ZIP

en:
Data.dbf contains the varibles of 1662 villages in Zhangzhou Cities
PSM_DID.R can re-produce the results

format: ZIP

en:
Title: Does zoning contain built-up land expansion? Causal evidence from Zhangzhou City, China.

Research objective: Built-up land zoning is an imporatant policy measure of land use planning (LUP) to contain built-up land expansion in China. We used a difference-indifference model with propensity score matching to estimate the average and annual effect of built-up land zoning on built-up land expansion in Zhangzhou City, China between 2010 and 2020.

Data: Data.dbf contains the varibles of 1662 villages in Zhangzhou Cities in 1995, 2000, 2005, 2010, 2013, 2015, 2018, and 2020.
XZQDM2 is villages' unique administrative ID;
Area is the land area of village i;
Dis2water is the Euclidean distance from village i to the nearest waterbody;
Dis2coastl is the Euclidean distance from village i to the nearest coastline;
Dis2city is the the Euclidean distance from village i to the city center;
Dis2county is the the Euclidean distance from village i to the nearest county center;
Elevation is the the average elevation within village i;
Dis2road is the the Euclidean distance from village i to the nearest road;
Nei_Built_ is the the area of built-up land (Nei Built.upit) in the neighboring villages of village i in year t;
Treated is a binary variable, Treated = 1 to the villages that were partially or entirely located inside the development-permitted zones, and Treated = 0 to the villages that were entirely located outside the development-permitted zones;
Intensity is the percentage of land that was assigned to the development-permitted zones in village i;
Year represent the year in 1995, 2000, 2005, 2010, 2013, 2015, 2018, and 2020;
BuLE is the dependent variable, representing built-up land expansion in village i in year t;
Town is town' unique administrative ID.

Method: First, we employed propensity score matching to overcome the selection bias and satisfy the parallel trend
assumption. Second, we built four Difference-in-Difference models to estimate the average and annual effect.

annual-effect

display_name: annual-effect

land-use-change

display_name: land-use-change

land-use-planning

display_name: land-use-planning

plan-evaluation

display_name: plan-evaluation

psm-did

display_name: psm-did

en: Causal effect of LUP


en: stable-water-isotope 18o snow metamorphism advection

en: Read-me

media_type: text/plain

en: A short description of the data contained in the zipped-file. Recommended for reading before downloading the dataset.

mimetype: text/plain

en: Data files and read-me

media_type: application/zip

en: raw data and micro-CT data available from contacts

mimetype: application/zip

en:
Stable water isotopes (δ18O) obtained from snow and ice samples of polar regions are used to reconstruct past climate variability, but heat and mass transport processes can affect the isotopic composition. Here we present an experimental study on the effect on the snow isotopic composition by airflow through a snow pack in controlled laboratory conditions. The influence of isothermal and controlled temperature gradient conditions on the δ18O content in the snow and interstitial water vapor is elucidated. The observed disequilibrium between snow and vapor isotopes led to exchange of isotopes between snow and vapor under non-equilibrium processes, significantly changing the δ18O content of the snow. The type of metamorphism of the snow had a significant influence on this process.
Ebner, P. P., Steen-Larsen, H. C., Stenni, B., Schneebeli, M., and Steinfeld, A.: Experimental observation of transient δ18O interaction between snow and advective airflow under various temperature gradient conditions, The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-16, accepted, 2017.

advection

display_name: advection

metamorphism

display_name: metamorphism

snow

display_name: snow

stable-water-isotope

display_name: stable-water-isotope

en: Experiments on stable water isotopes,  snow metamorphism, and advection


en: swiss-geo-downloader swiss-geodata geoinformation-system digital-elevation-model qgis-plugin cable-yarding seilaplan digital-planning open-source

en: View video on youtube!

en: Herunterladen des DHM mit SwissGeoDownloader

en:
In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. The plugin ‘Swiss Geo Downloader’, which is available for the open source geoinformation software QGIS, allows freely available Swiss geodata to be downloaded and displayed directly within QGIS. It was developed in 2021 by Patricia Moll in collaboration with the Swiss Federal Institute for Forest, Snow and Landscape Research WSL. In this tutorial we describe how to download the high accuracy elevation model ‘swissALTI3D’ with the help of the ‘Swiss Geo Downloader’ and how to use it for digital planning of a cable line with the plugin ‘Seilaplan’. Please note that the tutorial language is German!

Link to the Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader

Link to Seilaplan website: https://seilaplan.wsl.ch

*********************

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. Das Plugin Swiss Geo Downloader, welches für das Open Source Geoinformationssystem QGIS zur Verfügung steht, ermöglicht frei verfügbare Schweizer Geodaten direkt innerhalb von QGIS herunterzuladen und anzuzeigen. Es wurde 2021 von Patricia Moll in Zusammenarbeit mit der eidgenössischen Forschungsanstalt Wald, Schnee und Landschaft WSL entwickelt. In diesem Tutorial beschreiben wir, wie man mit Hilfe des Swiss Geo Downloaders das hochgenaue Höhenmodell swissALTI3D herunterladen und für die Seillinienplanung mit dem Plugin Seilaplan verwenden kann.

Link zum Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader

Link zur Seilaplan-Webseite: https://seilaplan.wsl.ch

cable-yarding

display_name: cable-yarding

digital-elevation-model

display_name: digital-elevation-model

digital-planning

display_name: digital-planning

geoinformation-system

display_name: geoinformation-system

open-source

display_name: open-source

qgis-plugin

display_name: qgis-plugin

seilaplan

display_name: seilaplan

swiss-geo-downloader

display_name: swiss-geo-downloader

swiss-geodata

display_name: swiss-geodata

en: Seilaplan Tutorial: DTM download with SwissGeoDownloader


en: biodiversity-assessment environmental-dna dna-metabarcoding spatial-ecology biomonitoring

en: Analysis and Figures

media_type: .zip

en: This zipped file includes R scripts and the datasets required to run the analyses. A README describes the columns in each dataset.

en: Field Metadata

media_type: .csv

en: Information concerning the locations and times of sampling, as well as the identity of the samplers and relevant comments.

en: Genomic data

media_type: link

en: Fastq files, primer descriptions and the final output dataset.

en: This dataset contains the results of a five-day field excursion which the extent to which eDNA sampling can capture the diversity of a region with highly heterogeneous habitat patches across a wide elevation gradient through multiple hydrological catchments of the Swiss Alps. Using peristaltic pumps, we filtered 60 L of water at five sites per catchment for a total volume of 1 800 L. Using an eDNA metabarcoding approach focusing on vertebrates and plants, we detected 86 vertebrate taxa spanning 41 families and 263 plant taxa spanning 79 families across ten catchments. This dataset includes two sets of data. The first (Genomic data) includes all the necessary data for the bioinformatic pipeline, whereas the second (Analysis Figures) contains tidied data and scripts for the reproduction of all figures/analyses in the article describing this study.

biodiversity-assessment

display_name: biodiversity-assessment

biomonitoring

display_name: biomonitoring

dna-metabarcoding

display_name: dna-metabarcoding

environmental-dna

display_name: environmental-dna

spatial-ecology

display_name: spatial-ecology

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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: Vertebrate and plant taxa recovered from 10 catchments in Vaud using an eDNA-metabarcoding approach


en: photogrammetry davos snow-hydrology snow-depth-distribution avalanches dischma

en: Snowdepth maps

media_type: geotiff

format: GeoTIFF

en:
The available datasets are snow depth maps with a spatial resolution of 0.5 m derived from images of the survey camera Vexcel Ultracam mounted on a piloted airplane. Image acquisition was carried out during the approximately peak of winter (time when the thickest snowpack is expected) in spring. The snow depth maps are calculated by the subtraction of a summer-DTM from the processed winter- DSM of the corresponding date. The summer-DTM used was derived from a point cloud of an airborne laser scanner from 2020.

Due to the occurrence of inaccuracies of the calculated snow depth values caused by the photogrammetric method, we applied different masks to significantly increase the reliability of the snow depth maps. We masked out settled areas, high-frequented streets and technical constructions, pixels with high vegetation (height > 0.5 m) , outliers and unrealistic snow depth values. In addition, we modified the snow depth values of snow-free pixels to 0.
The information on buildings and infrastructure comes from the exactly classified ALS point cloud and the TLM dataset from Swisstopo (https://www.swisstopo.admin.ch/de/geodata/landscape/tlm3d.html#links). High vegetation is also derived from the classification and the calculated object height from the point cloud.
Outliers and unrealistic snow depth values are defined as negative snow depth values and snow depths exceeding 10 m. The classification of each pixel of the corresponding orthophoto into snow-covered or snow-free is based on the application of a threshold of the NDSI or manually determined ratios of the RGB values.

An extensive accuracy assessment proves the high accuracy of the snow depth maps with a root mean square error of 0.25 m for the year 2017 and 0.15 m for the following snow depth maps.

The work is published in:

avalanches

display_name: avalanches

davos

display_name: davos

dischma

display_name: dischma

photogrammetry

display_name: photogrammetry

snow-depth-distribution

display_name: snow-depth-distribution

snow-hydrology

display_name: snow-hydrology

en: Snow depth mapping by airplane photogrammetry (2017 -  ongoing)


maintainer: Albrecht von Boetticher

en: natural-hazard hangmuren barrier-impact-simulation debris-flow dberisintermixing interfoam murgang shallow-landslide finite-volumes

media_type: application/pdf

en: These are the installation instructions for debrisInterMixing4

format: PDF

mimetype: application/pdf

en: debrisInterMixing for OF-Version 6

en:
This version of debrisInterMixing is based on OpenFOAM Version 4.x and was tested on Ubuntu 18.04.1 LTS.
Please follow the instructions given in ReadMe_debrisInterMixing4.pdf for installation.

en: Installation Instructions for debrisInterMixing-6

en:
Here the updated versions of debrisInterMixing are provided for download. The first OpenFoam-compatible Version 2.3.x are available as supplement to v. Boetticher, A., Turowski, J. M., McArdell,W. B., Rickenmann, D., Hürlimann, M., Scheidl, C., and Kirchner, J. W.: DebrisInterMixing-2.3: A Finite Volume solver for three dimensional debris flow simulations based on two calibration parameters. Part two: model validation with experiments. Geoscientific Model Development, 10, 11: 3963-3978. doi: 10.5194/gmd-10-3963-2017.
DebrisInterMixing is a Volume-of-Fluid based Finite Volume code that accounts for shear-thinning sensitive shares of fine sediment suspension together with pressure-sensitive components of the gravel grains within debris flow mixtures. All model properties can be derived from a material sample except for a grid-sensitive calibration parameter.
For more information, please contact albrecht.vonboetticher@wasserbau.ch.

barrier-impact-simulation

display_name: barrier-impact-simulation

dberisintermixing

display_name: dberisintermixing

debris-flow

display_name: debris-flow

finite-volumes

display_name: finite-volumes

hangmuren

display_name: hangmuren

interfoam

display_name: interfoam

murgang

display_name: murgang

natural-hazard

display_name: natural-hazard

shallow-landslide

display_name: shallow-landslide

en: Three-dimensional debris flow simulation tool debrisInterMixing


en: built-up-classification urban-compactness basic-services nature-conservation topography urban-sprawl planning-outcome forest-gain forest-persistence land-use-and-cover-change spatial-modelling urban-morphology

en: Topographic Index Position (TPI)

media_type: SHP

en: Ecological-Economic Zoning (EEZ)

format: SHP

en: Increase in Normalized Difference Vegetation Index

en: Native forest cover gains from 2005 to 2015

en: isolated

en: New built-up pixels classified as isolated

en: Descricao dos dados em portiuguês

media_type: PDF

en: Portugues description

en: Increase in sanitation service

en: Variables description

en: Table of content. Variables description, source, and other important information

en: Master plan Ihabela (zones)

en: Population density per pixel (change per year)

en: Mean income in Reais (R$) per census sector (change per year)

media_type: .shp

en: Master plan Ubatuba (zones)

en: Human Development Index (HDI) per municipality (change per year)

en: Urban persistence: Built-up 2005 = Built-up 2015

en: infill

en: New built-up pixels classified as infill

en: Axial

en: New built-up pixels classified as axial

en: Native forest cover persistence from 2005 to 2015

en: Permanent housing density increase

en:
Basic education in % per census sector (change per
year)

en: Increase in clean water provision service

en: Increase in waste collection service

en: slope

en: Slope °

en:
The present dataset is part of the published scientific paper entitled “The role of spatial planning in land change: An assessment of urban planning and nature conservation efficiency at the southeastern coast of Brazil” (Pierri Daunt, Inostroza and Hersperger, 2021). In this work, we evaluated the conformance of stated spatial planning goals and the outcomes in terms of urban compactness, basic services and housing provision, and nature conservation for different land-use strategies. We evaluate the 2005 Ecological-Economic Zoning (EEZ) and two municipal master plans from 2006 in a coastal region in São Paulo State, Brazil. We used Partial Least Squares Path Modelling (PLS-PM) to explain the relationship between the plan strategies and land-use change ten years after implementation in terms of urban compactness, basic services and housing increase, and nature conservation.

We acquired the data for the explanatory variables from different sources listed on Table 1. Since the model is spatially explicit, all input data were transformed to a 30 m resolution raster. Regarding the evaluated spatial plans, we acquired the zones limits from the São Paulo State Environmental Planning Division (CPLA-SP), Ilhabela and Ubatuba municipality.

1)	Land use and cover data: Urban persistence, Urban axial, Urban infill, Urban Isolates, Forest cover persistence, Forest cover gain, NDVI increase

We acquired two Landsat Collection 1 Higher-Level Surface Reflectance images distributed by the U.S. Geological Survey (USGS), covering the entire study area (paths 76 and 77, row 220, WRS-2 reference system, https://earthexplorer.usgs.gov/). We classified one image acquired by the Landsat 5 Thematic Mapper (TM) sensor on 2005-05-150, and one image from the Landsat 8 Operational Land Imager (OLI) sensor from 2015-08-15. We collected 100 samples for forest cover, 100 samples for built-up cover and 100 samples for other classes. We then classified these three classes of land cover at each image date using the Support Vector Machine (SVM) supervised algorithm (Hsu et al., 2003), using ENVI 5.0 software.

Land-use and land-cover changes from 2005 to 2015 were quantified using map algebra, by mathematically adding them together in pairs (10*LULC2015 + LULC2005). We reclassified the LULC data into forest gain (conversion of any 2005 LULC to forest cover in 2015); forest persistence (2005 forested pixels that remained forested in 2015); new built-up area (conversion of any 2005 LULC to built-up in 2015); and urban maintenance (2005 built-up pixels that remained built-up in 2015).

To describe the spatial configuration of the urban expansion, we classified the new built-up areas into axial, infill and isolated, following Inostroza et al. (2013) (For details, please refer to Supplementary Material I at the original publication).

The NDVI was obtained from the same source used for the LULC data. With the Google Engine platform, we used an annual average for the best pixels (without clouds) for 2005 and 2015, and we calculated the changes between dates. We used increases of > 0.2 NDVI to represent an improvement in forest quality.

2)	Federal Census data organization: Urban Basic Services and Housing indicator, socioeconomic and population:

The data used to infer the values of basic services provision, socioeconomic and population drivers was derived from the Brazilian National Census data (IBGE, 2000 and 2010). Population density, permanent housing unit density, mean income, basic education, and the percentage of houses receiving waste collection, sanitation and water provision services, called basic services in the context of this study, were calculated per 30 m pixel. The Human Development Index is only available at the municipality level. We attributed the HDI for the vector file with the municipality border, and we rasterized (30 m resolution) this file in QGIS. Annual rates of change were then calculated to allow comparability between LULC periods. To infer the BSH, we used only areas with an increase in permanent housing density and basic services provision (See Supplementary Material I at the original publication).

3)	Topographic drivers

To infer the values of the topographic driver, we used the slope data and the Topographic Index Position (TPI) based on the digital elevation model from SRTM (30 m resolution) produced by ALOS (freely available at eorc.jaxa.jp/ALOS/en/about/about_index.htm), and both variables were considered constant from 2005 to 2015 (See Supplementary Material I at the original publication).

basic-services

display_name: basic-services

en: Spatially explicit data to evaluate spatial planning outcomes in a coastal region in São Paulo State, Brazil


maintainer: Leo Gallus Bont

en: qgis forest openstreetmap accessible-forest seilaplan geoprocessing

en: Preview

media_type: image/png

format: PNG

mimetype: image/png

en: Forest vector data (for sample forest range)

en: Railways vector data (close to the sample forest range)

en: Juypter Notebook for Data Preparation (online)

media_type: ipynb

en: Power lines vector data (close to the area of the sample forest range)

en: Juypter Notebook for Data Preparation

en: DEM (for the area of the sample forest range)

media_type: image/tiff

format: TIFF

mimetype: image/tiff

en: Digitised sample perimeter

en:
This dataset contains open vector data for railways, forests and power lines, as well an open digital elevation model (DEM) for a small area around a sample forest range in Europe (Germany, Upper Bavaria, Kochel Forest Range, some 70 km south of München, at the edge of Bavarian Alps).
The purpose of this dataset is to provide a documented sample dataset in order to demonstrate geospatial preprocessing at FOSS4G2019 based on open data and software. This sample has been produced based on several existing open data sources (detailed below), therefore documenting the sources for obtaining some data needed for computations related to forest accessibility and wood harvesting. For example, they can be used with the open methodology and QGIS plugin [Seilaplan]( https://doi.org/10.16904/envidat.software.1) for optimising the geometric layout cable roads or with additional open software for computing the forest accessibility for wood harvesting.
The vector data (railways, forests and power lines) was extracted from OpenStreetMap (data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org). The railways and forests were downloaded and extracted on 18.05.2019 using the open sources QGIS (https://www.qgis.org) with the QuickOSM plugin, while the power lines were downloaded a couple of days later on 23.05.2019.

Additional notes for vector data:
Please note that OpenStreeMap data extracts such as forests, roads and railways (except power lines) can also be downloaded in a GIS friendly format (Shapefile) from http://download.geofabrik.de/ or using the QGIS built-in download function for OpenStreetMap data. The most efficient way to retrieve specific OSM tags (such as power=line) is to use the QuickOSM plugin for QGIS (using the Overpass API - https://wiki.openstreetmap.org/wiki/Overpass_API) or directly using overpass turbo (https://overpass-turbo.eu/). Finally, the digitised perimeter of the sample forest range is also made available for reproducibility purposes, although any perimeter or area can be digitised freely using the QGIS editing toolbar.

The DEM was originally adapted and modified also with QGIS (https://www.qgis.org) based on the elevation data available from two different sources, by reprojecting and downsampling datasets to 25m then selecting, for each individual raster cell, the elevation value that was closer to the average. These two different elevation sources are:

- Copernicus Land Monitoring Service - EU-DEM v.1.1 (TILE ID E40N20, downloaded from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1; this original DEM was produced by the Copernicus Land Monitoring Service “with funding by the European Union” based on SRTM and ASTER GDEM)
- Digitales Geländemodell 50 m Gitterweite (https://opendata.bayern.de/detailansicht/datensatz/digitales-gelaendemodell-50-m-gitterweite/), produced by the Bayerische Vermessungsverwaltung – www.geodaten.bayern.de –and downloaded from http://www.geodaten.bayern.de/opendata/DGM50/dgm50_epsg4258.tif

This methodology was chosen as a way of performing a basic quality check, by comparing the EU-DEM v.1.1 derived from globally available DEM data (such as SRTM) with more authoritative data for the randomly selected region, since using authoritative data is preferred (if open and available). For other sample regions, where authoritative open data is not available, such comparisons cannot longer be performed.

Additional notes DEM: a very good DEM open data source for Germany is the open data set collected and resampled by Sonny (sonnyy7@gmail.com) and made available on the Austrian Open Data Portal http://data.opendataportal.at/dataset/dtm-germany. In order to simplify end-to-end reproducibility of the paper planned for FOSS4G2019, we use and distribute an adapted (reprojected and resampled to 25 meters) sample of the above mentioned dataset for the selected forest range.

This sample dataset is accompanied by software in Python, as a Jupiter Notebook that generates harmonized output rasters with the same extent from the input data. The extent is given by the polygon vector dataset (Perimeter). These output rasters, such as obstacles, aspect, slope, forest cover, can serve as input data for later computations related to forest accessibility and wood harvesting questions. The obstacles output is obtained by transforming line vector datasets (railway lines, high voltage power lines) to raster. Aspect and slope are both derived from the sample digital elevation model.

accessible-forest

display_name: accessible-forest

forest

display_name: forest

geoprocessing

display_name: geoprocessing

openstreetmap

display_name: openstreetmap

qgis

display_name: qgis

seilaplan

display_name: seilaplan

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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


en: cable-yarding geodata-processing qgis-plugin digital-elevation-model seilaplan digital-planning swiss-geodata geoinformation-system open-source raster-processing

en: View video on youtube!

en: DHM-Kacheln zusammenfügen

en:
In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. In this tutorial video, we show how to merge multiple DTM raster tiles into one file, using the QGIS tool ‘Virtual Raster’. This simplifies the digital planning of a cable line using the QGIS plugin ‘Seilaplan’. Please note that the tutorial language is German!

Link to Seilaplan website: https://seilaplan.wsl.ch

***************************

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. In diesem Tutorialvideo zeigen wir, wie man mit dem QGIS-Plugin Virtuelles Raster mehrere DHM-Kacheln zu einem einzigen Rasterfile zusammenfügen und abspeichern kann. Für die Seillinienplanung mit Seilaplan muss nun nur noch eine Datei, mein neues virtuelles Raster, ausgewählt werden.

Link zur Seilaplan-Website: https://seilaplan.wsl.ch

cable-yarding

display_name: cable-yarding

digital-elevation-model

display_name: digital-elevation-model

digital-planning

display_name: digital-planning

geodata-processing

display_name: geodata-processing

geoinformation-system

display_name: geoinformation-system

open-source

display_name: open-source

qgis-plugin

display_name: qgis-plugin

raster-processing

display_name: raster-processing

swiss-geodata

display_name: swiss-geodata

en: Seilaplan Tutorial: Merge DTM tiles


photogrammetric-drone-data-dorfberg

title_for_slug: photogrammetric-drone-data-dorfberg

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

en: Data Access

media_type: TIF

en:
- 3cm-resolution Orthophoto
- 10cm-resolution DSM
- 10cm-resolution snow depth raster
- original RGB images
- Agisoft Metashape Report

All products are also available as a cloud optimized geotiff.

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

drone-data

display_name: drone-data

dsm

display_name: dsm

orthophoto

display_name: orthophoto

photogrammetry

display_name: photogrammetry

snow

display_name: snow

snow-depth-mapping

display_name: snow-depth-mapping

snow-distribution

display_name: snow-distribution

uav

display_name: uav

en: Photogrammetric Drone Data Dorfberg


en: snow-depth snow-density model-validation-data snow-water-equivalent model-calibration-data

en: swe2hs calibration and validation data

media_type: zip-compressed folder of csv

en:
 Contents of this archive

 README.md

 A readme file containing a description of the other files.

 automatic_stations_validation_data.csv

 Daily data from the automatic weather stations used for validation.

 Data sources and providers are listed in the SWE2HS paper, Table 1.

 Columns:

  * date: timestamp of the format YYYY-MM-DD
  * HS_[m]: snow depth in [m]
  * SWE_[m]: snow water equivalent in [m]
  * site_id: code for the measurement site
  - HS_interpolated: boolean flag for interpolated values
  - SWE_interpolated: : boolean flag for interpolated values

 coordinates_swe2hs_paper_stations.csv

 Coordinates of all measurement sites used in the SWE2HS paper.

 Columns:

  * site_id: code for the measurement site
  * location_name: location name of the measurement site
  * lon_[wgs84]: longitude in WGS84
  * lat_[wgs84]: latitude in WGS84
  * elevation_[m]: atitude of the measurement site in [m]
  * dataset: dataset in which the station is included

 manual_station_calibration_data.csv

 Calibration data from the Swiss manual observer stations. Long station records
 trmmed to 15 random water-years.

 Columns:

  * date: timestamp of the format YYYY-MM-DD
  * HS_[m]: snow depth in [m]
  * SWE_[m]: snow water equivalent in [m]
  * SWE_from_profile_[m]: biweekly measurement from the SWE in the measurement
   pit. Corrected with the bulk density in order to match the snow depth from
   the measurement stake. Please refer to the paper for further details.
  * site_id: code for the measurement site

 manual_stations_validation_data.csv

 Validation data from the Swiss manual observer stations. Remaining water-years
 from the long sttaion record not used for the calibration data.

 Columns:

  * date: timestamp of the format YYYY-MM-DD
  * HS_[m]: snow depth in [m]
  * SWE_[m]: snow water equivalent in [m]
  * SWE_from_profile_[m]: biweekly measurement from the SWE in the measurement
   pit. Corrected with the bulk density in order to match the snow depth from
   the measurement stake. Please refer to the paper for further details.
  * site_id: code for the measurement site

 manual_stations_used_deltasnow_parameters.csv

 Site specific DeltaSNOW model parameters used for the calculation of the daily
 SWE series in 'manual_stations_calibration_data.csv' and
 'manual_stations_validation_data.csv'.

 Parameters were optimized by reducing RMSE to the biweekly snow pit SWE measurements.
 Please refer to the paper for further details.

 A python implementation of the DeltaSNOW model was used for the calculation:
 pydeltasnow (https://pypi.org/project/pydeltasnow/)

 For a detailed description of the DeltaSNOW model parameters please see the
 respective publication:

 Winkler, M., Schellander, H., & Gruber, S. (2021). Snow water equivalents
 exclusively from snow depths and their temporal changes: the Δ snow model.
 Hydrology and Earth System Sciences, 25(3), 1165-1187.

 Columns:

  * site_id: code for the measurement site
  * rho_max: Maximum density of an individual snow layer produced by the DeltaSNOW model in [kg/m3]
  * rho_null: Fresh snow density for a newly created layer [kg/m3
  * c_ov: Overburden factor due to fresh snow [-]
  * k_ov: Defines the impact of the individual layer density on the compaction due to overburden [-]
  * k: Exponent of the exponential-law compaction [m3/kg]
  * tau: Uncertainty bound [m]
  * eta_null: Effective compactive viscosity of snow for “zero-density” [Pa s]

en:
The data in this repository was used for the calibration and validation of the
SWE2HS model in the following publication:
Aschauer, J., Michel, A., Jonas, T., & Marty, C. (2023). An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0. Geoscientific Model Development Discussions, 1-19. https://doi.org/10.5194/gmd-2022-258
Contains daily snow water equivalent and snow depth timeseries from stations in the European Alps.

model-calibration-data

display_name: model-calibration-data

model-validation-data

display_name: model-validation-data

snow-density

display_name: snow-density

snow-depth

display_name: snow-depth

snow-water-equivalent

display_name: snow-water-equivalent

en: SWE2HS model calibration and validation data


en: runoff alptal meteorological-data subalpine snow first-order-catchment

en: Daily runoff data from three steep torrents in the Alptal (1968-2022)

media_type: XLSX

en: Runoff, water temperature and electrical conductivity have been measured since 1968 at the outlet of three torrential catchments (with an area of approximately 1 km2 each) located in the Alptal valley: Erlenbach, Lümpenenbach and Vogelbach. This data set provides daily mean values; data at 10-min resolution can be provided on request for the period 1984-2022 (contact: alptal@wsl.ch).

format: XLS

en: Manual snow measurements Alptal (1968-2022)

en:
Snow depth, snow density and snow water equivalent have been measured since winter 1968/69 along more than 15 transects distributed across the Alptal valley representing different altitudes, aspects and land use (closed forest and open meadows). Length of the snow courses: 20 to 30 m. The measurement interval has been weekly to monthly.
More details about the locations and methods can be found in Stähli, M.; Gustafsson, D., (2006). Long-term investigations of the snow cover in a subalpine semi-forested catchment. Hydrological Processes, 20, 411-428. doi: 10.1002/hyp.6058.

en: Daily meteorological and snow data (1968-2022) Alptal

en:
Air temperature, dew point temperature, relative humidity, wind speed, precipitation and global radiation have been measured (since 1981) on a meadow (Erlenhöhe) in the Erlenbach catchment. It is located at 1'220 m a.s.l. with an exposure towards west. In order to assess the spatial variability of precipiation, two additional rain gauges have been running in the Vogelbach and Lümpenenbach catchment, respectively.
For the period before 1981, meteorological variables have been extrapolated from the nearby MeteoSwiss station Einsiedeln (ca. 15 km north) to the WSL station Erlenhöhe using a linear correction factor for air temperature, wind speed and precipitation.
Snow depth has been recorded automatically since 2003. Snow water equivalent has been calculated using two numerical models: COUP model (Jansson, 2012) and DeltaSnow model (Winkler et al., 2021)

Jansson, P.-E. (2012). COUP model: model use, calibration, and validation.Transactions of the ASABE, 55(4), 1335–1344.
Winkler, M., et al. (2021): Snow water equivalents exclusively from snow depths and their temporal changes: the Δsnow model, Hydrol. Earth Syst. Sci., 25, 1165-1187, 10.5194/hess-25-1165-861 2021.

en:
This data set includes 54 years of hydrometeorological measurements from small (first-order) catchments in the pre-alpine valley Alptal. Here we provide daily mean values; values in sub-daily resolution can be provided on demand.
Runoff has been measured at the outlet of three small (first-order) catchments of approximately 1 km2 area: Erlenbach (two independent runoff measurements), Vogelbach and Lümpenenbach. The catchments are similar with regard to geology (Flysch) and soil conditions (clay soils), but differ in forest coverage (20 to 60%). A detailed description of the catchments can be found at https://www.wsl.ch/alptal .
Runoff in these small catchments is typically very dynamic and can temporally carry large amounts of sediment and large wood. Thus, the accuracy of the measurements at very large flow is limited.
Meteorological variables have been measured on a meadow (Erlenhöhe) located in the Erlenbach catchment at 1220 m a.s.l. using a standard meteorological station (incl. ventilated air temperature and heated rain gauges). In addition, precipitation has also been recorded at two other locations (in the Vogelbach and Lümpenenbach catchments).
Snow measurements have been conducted weekly to monthly since 1968 at more than 15 locations (30-m transects) representing different altitudes, aspects and land uses (meadow, forest). In addition, snow depth has been recorded continuously since 2003 at Erlenhöhe, and for this location we also include a simulation of snow depth and SWE (using the numerical models COUP and DeltaSnow) that assimilates the manual weekly snow-course measurements. Details on these snow measurements can be found in Stähli, M. and Gustafsson, D. 2006. Hydrol. Proc., 20, 411-428. doi: 10.1002/hyp.6058.

Further information on the methods and sensors can be found at https://www.wsl.ch/alptal .

A first version of this data set (for the period 1968-2017) was uploaded in June 2018 at the occasion of the 50-year anniversary. This original data set was updated in February 2021 (with data from 2018 and 2019), and this data set was used for a longterm trend analysis, submitted for publication in a special issue of Hydrological Processes. A second update of the data set (with data from 2020 to 2022) was uploaded in March 2023.

alptal

display_name: alptal

first-order-catchment

display_name: first-order-catchment

meteorological-data

display_name: meteorological-data

runoff

display_name: runoff

subalpine

display_name: subalpine

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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: Longterm hydrological observatory Alptal (central Switzerland)


en: antarctica gridded-data terrestiral-laserscanning depth-of-snowfall terrestrial-laser-scans

en: Scan from 11 January 2019, 1 mm resolution

media_type: ascii x, y, z

en: Scan from 2 January 2019, 1 mm resolution

en: Scan from 27 December 2018, 10 cm resolution

en: Scan from 11 January 2019, 10 cm resolution, version 2

en: Scan from 21 December 2018, 1 mm resolution

en: Scan from 21 December 2018, 10 cm resolution

en: Deprecated: Scan from 11 January 2019, 10 cm resolution, version 1

en: This resource is deprecated. It contains erroneous data.

en: Scan from 4 January 2019, 10 cm resolution

en: Scan from 4 January 2019, 1 mm resolution

en: Scan from 2 January 2019, 10 cm resolution

en: Scan from 27 December 2018, 1 mm resolution

en:
Surface topography maps (spatial extent: 400 m x 400 m) obtained at approximately 300 m from the top of the Hammarryggen Ice Rise in Dronning Maud Land, East Antarctica, using a Riegl VZ-6000 Terrestrial Laser Scanner (TLS). Scans were obtained on 5 days in the 2018-2019 Austral summer: on December 21 and 27, and January 2, 4 and 11. By using reflectors installed on bamboo poles, scans were registered with respect to the reflectors, such that the difference between two successive scans reveals the spatial patterns of erosion and deposition of snow. On each scan day, we used multiple scan positions to create one combined point cloud. After applying an octree filter on the point cloud, a 3D surface was obtained. For each day, the dataset contains a 1 mm and a 10 cm octree filter resolution file, only including points in a 400 m x 400 m area centered around the scan positions.

Notes:

* All files in the dataset are in the same coordinate system. However, this coordinate system is arbitrary (i.e., not related to any global coordinate system).

* From the installed reflectors, 4 reflectors could be used over the full period. The scan accuracy is generally higher within the area enclosed by the reflectors.

* The scans from January 2 were found to have exhibited small tilt during the scan and are of lesser accuracy.

* By walking along fixed corridors, disturbance of the snow was limited.

antarctica

display_name: antarctica

depth-of-snowfall

display_name: depth-of-snowfall

gridded-data

display_name: gridded-data

terrestiral-laserscanning

display_name: terrestiral-laserscanning

terrestrial-laser-scans

display_name: terrestrial-laser-scans

en: Terrestrial laser scans on Hammarryggen Ice Rise, Dronning Maud Land, East Antarctica


en: 3d-model snow-cast 3d-print rounded-snow snow new-snow weak-layer depth-hoar

en: Snow models in STL-format

media_type: ZIP

en: The zipped file contains all data described in the file 3d_snow_models_metadata. The stl-files should be ready for additive manufacturing.

en: Description of snowtypes, filenames and other metadata

en: This file contains information about the stl-files of the different snow models. Snowtype (Schneeart), International Snow Classification, preview picture, notes about the origin of the snow, filename of the stl-data, and physical size of the model are given.

en: The dataset contains several snow models in the Standard Tesselated Geometry File Format (stl) for 3D visualization, printing and additive manufacturing. Different snow types are available (new snow, rounded snow, depth hoar, buried surface hoar, graupel).

depth-hoar

display_name: depth-hoar

new-snow

display_name: new-snow

rounded-snow

display_name: rounded-snow

snow

display_name: snow

snow-cast

display_name: snow-cast

weak-layer

display_name: weak-layer

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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: acoustic-emissions snow-avalanche snow snow-failure sintering fiber-bundle-model viscous-deformation snow-mechanics

en: fiber-bundle-model-for-snow-failure

media_type: application/zip

en: Data underlying Figures in publication "Modelling Snow Failure Behavior and Concurrent Acoustic Emissions Signatures with a Fiber Bundle Model" submitted to "Geophysical Research Letters"

en:
This dataset contains modeled and experimental results for laboratory snow failure experiments and the concurrent acoustic emissions signatures for different loading rates. For modelling the snow failure we used a fiber bundle model that includes sintering and viscous deformation.

The data underlay the figures in the publication "Modelling Snow Failure Behavior and Concurrent Acoustic Emissions Signatures with a Fiber Bundle Model" submitted for publication to "Geophysical Research Letters".

acoustic-emissions

display_name: acoustic-emissions

fiber-bundle-model

display_name: fiber-bundle-model

sintering

display_name: sintering

snow-avalanche

display_name: snow-avalanche

snow-failure

display_name: snow-failure

snow-mechanics

display_name: snow-mechanics

viscous-deformation

display_name: viscous-deformation

en: Fiber Bundle Model for snow failure and concurrent Acoustic Emissions


en: satellite natural-hazard hazard-mapping avalanche snow

en: SPOT6 Avalanche outlines 24 January 2018

en: Shapefile of the manually mapped avalanche outlines. Please only use this data together with the example key document provided with this dataset.

en:
Outlines of 18'737 avalanches mapped from SPOT6 satellite data over the Swiss Alps on 24 January 2018. The outlines have different attributes described in the data example key (ExampleKey_AvalMapping.pdf)

The generation of the data is described in:

Bühler, Y., E. D. Hafner, B. Zweifel, M. Zesiger, and H. Heisig (2019), Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps, The Cryosphere, 13(12), 3225-3238, doi:10.5194/tc-13-3225-2019.

Abstract.
Accurate and timely information on avalanche occurrence are key for avalanche warning, crisis management and avalanche documentation. Today such information is mainly available at isolated locations provided by observers in the field. The achieved reliability considering accuracy, completeness and reliability of the reported avalanche events is limited. In this study we present the spatial continuous mapping of a large avalanche period in January 2018 covering the majority of the Swiss Alps (12’500 km2).

We tested different satellite sensors available for rapid mapping during a first avalanche period. Based on these experiences, we tasked SPOT6/7 data for data acquisition to cover the second, much larger avalanche period. We manually mapped the outlines of 18’737 individual avalanche events, applying image enhancement techniques to analyze regions in cast shadow as well as brightly illuminated ones. The resulting dataset of mapped avalanche outlines, having a unique completeness and reliability, is evaluated to produce maps of avalanche occurrence and avalanche size. We validated the mapping of the avalanche outlines using photographs acquired from helicopters just after the avalanche period.

This study demonstrates the applicability of optical, very high spatial resolution satellite data to map an exceptional avalanche period with very high completeness, accuracy and reliability over a large region. The generated avalanche data is of great value to validate avalanche bulletins, complete existing avalanche databases and for research applications by enabling meaningful statistics on important avalanche parameters.

Koordinate System: CH1903+ LV95 LN02

avalanche

display_name: avalanche

hazard-mapping

display_name: hazard-mapping

natural-hazard

display_name: natural-hazard

satellite

display_name: satellite


maintainer: Andrin Caviezel

en: rockfall-runout natural-hazards rockfall induced-rockfall stonenodedata dem rockfall-experiments stonenode deposition-points sensor-stream

en: StoneNode v1.0 Data for equipped experimental Runs.

en: Corresponding StoneNode v1.0 data streams belonging to the deposition points.

en: Induced Rockfall Data, Tschamut, GR

en:
 Induced Rockfall Experimental Data, Tschamut, GR, Switzerland

The archive contains site specific geographical data as well as the deposition points of manually induced rockfall by releasing differently shaped boulders with 30–80 kg of mass. Additionally availabe are the StoneNode v1.0 sensor streams for the equipped rocks.

The _OverviewFigures_ file serves as quick representation of the included data. It also contains the overview table for the rock masses in order to set the dimensions accordingly in RAMMS::ROCKFALL.

The archive contains the following folders:
<\ul>
* DEM: ArcGIS compatible DEM Tschamut_DEM_2m (grid resolution of 2 m) as well as the .asc grid used in RAMMS::ROCKFALL simulations
* _DepositionPoints_: containing two folders _RF Tschamut Frozen_ and _RF Tschamut WET_ containing each a shapefile with the complete deposition data of each series as well as deposition shapfiles for each rock shape.
* _DigitizedRocks_: Digitized point clouds of the used rocks as used for RAMMS::ROCKFALL simulations (.pts files) (dimensions have to be checked).
* _Orthofoto_: Orthophoto of the experimental site obtained via UAV with 5 cm resolution.

en:
 Dataset of an experimental campaign of induced rockfall in Tschamut, Grisons, Switzerland.

The data archive contains site specific geographical data such as DEM and orthophoto as well as the deposition points of manually induced rockfall by releasing differently shaped boulders with 30–80 kg of mass. Additionally available are all the StoneNode data streams for rocks equipped with a sensor. The data set consists of

* Deposition points from two series (wet (27/10/2016) and frozen (08/12/2016) ground)
* Digital Elevation Model (grid resolution 2 m) obtained via UAV
* Orthophoto (5 cm resolution) obtained via UAV
* Digitized rock point clouds (.pts input files for RAMMS::ROCKFALL)
* StoneNode v1.0 raw data stream for equipped rocks.

Further information is found in

* A. Caviezel et al., _Design and Evaluation of a Low-Power Sensor Device for Induced Rockfall Experiments_, IEEE Transactions on Instrumentation and Measurement, 2018, 67, 767-779, http://ieeexplore.ieee.org/document/8122020/
* P. Niklaus et al., _StoneNode: A low-power sensor device for induced rockfall experiments_, 2017 IEEE Sensors Applications Symposium (SAS), 2017, 1-6, http://ieeexplore.ieee.org/document/7894081/

dem

display_name: dem

deposition-points

display_name: deposition-points

induced-rockfall

display_name: induced-rockfall

natural-hazards

display_name: natural-hazards

rockfall

display_name: rockfall

rockfall-experiments

display_name: rockfall-experiments

rockfall-runout

display_name: rockfall-runout

sensor-stream

display_name: sensor-stream

stonenode

display_name: stonenode

stonenodedata

display_name: stonenodedata

en: Induced Rockfall Dataset (Small Rock Experimental Campaign), Tschamut, Grisons, Switzerland


geography

fr: Géographie

de: Geographie

en: Geography

it: Geografia

territory

fr: Territoire et environnement

de: Raum und Umwelt

en: Territory and environment

it: Territorio e ambiente

de: amtliche-vermessungav basiskarte referenzsystem topographie kartenwerk

label: geocat.ch permalink

spatial: Kanton Luzern

protocol: https

de: Datenshop

de: Produktansicht Geodatenshop

de: Übersichtsplan 1:10'000, schwarz-weiss: geometrischer Plan im Massstab 1:10'000, der wenig generalisiert ist. Der Übersichtsplan schliesst die Lücke zwischen dem Landeskartenwerk und dem Grundbuchplan.

fr: Géoinformation du canton de Lucerne

de: Geoinformation Kanton Luzern

en: Geoinformation Canton of Lucerne

it: Geoinformazione del Cantone di Lucerna

fr: La géoinformation du canton de Lucerne est responsable de la collecte, du traitement et de la gestion des données à référence spatiale. Elle dirige, coordonne et surveille les travaux de la géoinformation et de la mensuration officielle.

en: Geoinformation of the Canton of Lucerne is responsible for the collection, processing and management of spatial data. It manages, coordinates and monitors the work of geoinformation and of the cadastral surveying.

de: Die Geoinformation des Kantons Luzern ist für die Erhebung, Verarbeitung und Verwaltung von raumbezogenen Daten zuständig. Sie leitet, koordiniert und überwacht die Arbeiten der Geoinformation und der amtlichen Vermessung.

it: La Geoinformation del Cantone di Lucerna è responsabile della raccolta, dell'elaborazione e della gestione dei dati territoriali. Gestisce, coordina e monitora il lavoro della geoinformazione e della misurazione ufficiale.

political_level: canton

value: LU-Kt - Geocat Harvester

de: Übersichtsplan 1:10'000 (UP10)


maintainer: Dirk Nikolaus Karger

en: climate bioclim precipitation temperature climatology free-climate-data climate-layers climate-change

en: CHELSA V2.1 (current)

en: CHELSA Version 2.1 based on ERA5 forcings and CMIP6

en: CHELSA V1.2 (deprecated)

en: CHELSA Version 1.2 based on ERA-Interim forcings and CMIP5

en: CHELSA Technical Documentation

en: Technical documentation document including the variable names, units, scaling, offset, and background.

en:
High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth’s land surface areas) data of downscaled temperature and precipitation to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction.

CHELSA data published in EnviDat includes the deprecated version 1.2 (originally published under 10.5061/dryad.kd1d4). Please use the current 2.1 version.

Paper Citation:
 > _Karger DN. et al. Climatologies at high resolution for the earth’s land surface areas, Scientific Data, 4, 170122 (2017) [doi: 10.1038/sdata.2017.122](https://doi.org/10.1038/sdata.2017.122)._

bioclim

display_name: bioclim

climate

display_name: climate

climate-change

display_name: climate-change

climate-layers

display_name: climate-layers

climatology

display_name: climatology

free-climate-data

display_name: free-climate-data

precipitation

display_name: precipitation

temperature

display_name: temperature

education

en: Education and science

envidat

en: EnviDat: the Environmental Data Portal

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.


lidar-davos-wolfgang

maintainer: Patric Seifert

title_for_slug: lidar-davos-wolfgang

en: polly lidar tropos clouds raclets

en: Raman Lidar Davos

en: A portable Raman lidar system (Polly) from Leibnitz Institute for Tropospheric Research (Tropos) was deployed at Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Please use this [link](http://polly.tropos.de/?p=lidarzeit&Ort=39), to be directly forwarded to the Davos location and select the date of interest from the calendar (bold numbers). The data can be requested directly at the Polly team.

clouds

display_name: clouds

lidar

display_name: lidar

polly

display_name: polly

raclets

display_name: raclets

tropos

display_name: tropos

en: LIDAR Davos Wolfgang


geography

en: Geography

territory

en: Territory and environment

de: boden bodennutzung landwirtschaft militar umweltbeobachtung umweltschutz ver--und-entsorgung verkehr

de: Der Prüfperimeter für Bodenverschiebungen (PBV) zeigt Gebiete, deren Böden mit hoher Wahrscheinlichkeit schadstoffbelastet sind. Als schadstoffbelastet gelten Ober- und Unterböden, bei denen Richtwerte der Verordnung über Belastungen des Bodens (VBBo) überschritten werden.

en: Geoinformation Canton of Lucerne

de: Prüfperimeter Bodenverschiebung


vegetation-height-model-nfi

maintainer: Christian Ginzler

title_for_slug: vegetation-height-model-nfi

en: vegetation-height-model photogrammety forest 3d point-cloud

en: 2012 Vegetation Height Model NFI

en:
Data 'Vegetation Height Model NFI 2012' available on request (christian.ginzler@wsl.ch).

A national vegetation height model was calculated for Switzerland using digital aerial images. We used the stereo aerial images acquired by the Federal Office of Topography swisstopo using the ADS80 sensor to first calculate a digital surface model (DSM) with a very high spatial resolution (1 × 1 m). The DSM was then normalized to obtain the actual vegetation heights using the digital terrain model (DTM) SwissAlti3D based on airborne laser data. Buildings are masked out using the NDVI information of the aerial images to produce a vegetation height model, and to produce a vegetation height model (VHM). Such a model will be calculated in the framework of the Swiss National Forest Inventory (NFI) with consistent methods and a very high level of detail. For covering the whole of Switzerland, we used summer aerial images from 2007 to 2012.

en: 2015 Vegetation Height Model NFI

en:
Data 'Vegetation Height Model NFI 2015' available on request (christian.ginzler@wsl.ch).

A national vegetation height model was calculated for Switzerland using digital aerial images. We used the stereo aerial images acquired by the Federal Office of Topography swisstopo using the ADS80 sensor to first calculate a digital surface model (DSM) with a very high spatial resolution (1 × 1 m). The DSM was then normalized to obtain the actual vegetation heights using the digital terrain model (DTM) SwissAlti3D based on airborne laser data. Buildings are masked out using the NDVI information of the aerial images to produce a vegetation height model, and to produce a vegetation height model (VHM). Such a model will be calculated in the framework of the Swiss National Forest Inventory (NFI) with consistent methods and a very high level of detail. For covering the whole of Switzerland, we used summer aerial images from 2010 to 2015.

en: Countrywide Stereo-Image Matching for Updating Digital Surface Models in the Framework of the Swiss National Forest Inventory

en: Thumbnail

media_type: image/jpeg

mimetype: image/jpeg

en: 2016 Vegetation Height Model NFI

en:
Data 'Vegetation Height Model NFI 2016' available on request (christian.ginzler@wsl.ch).

A national vegetation height model was calculated for Switzerland using digital aerial images. We used the stereo aerial images acquired by the Federal Office of Topography swisstopo using the ADS80 sensor to first calculate a digital surface model (DSM) with a very high spatial resolution (1 × 1 m). The DSM was then normalized to obtain the actual vegetation heights using the digital terrain model (DTM) SwissAlti3D based on airborne laser data. Buildings are masked out using the NDVI information of the aerial images to produce a vegetation height model, and to produce a vegetation height model (VHM). Such a model will be calculated in the framework of the Swiss National Forest Inventory (NFI) with consistent methods and a very high level of detail. For covering the whole of Switzerland, we used summer aerial images from 2011 to 2016.

en: 2019 Vegetation Height Model NFI (current)

en:
Data 'Vegetation Height Model NFI 2019' available on request (christian.ginzler@wsl.ch).

A national vegetation height model was calculated for Switzerland using digital aerial images. We used the stereo aerial images acquired by the Federal Office of Topography swisstopo using the ADS80 sensor to first calculate a digital surface model (DSM) with a very high spatial resolution (1 × 1 m). The DSM was then normalized to obtain the actual vegetation heights using the digital terrain model (DTM) SwissAlti3D based on airborne laser data. Buildings are masked out using the NDVI information of the aerial images to produce a vegetation height model, and to produce a vegetation height model (VHM). Such a model will be calculated in the framework of the Swiss National Forest Inventory (NFI) with consistent methods and a very high level of detail. For covering the whole of Switzerland, we used summer aerial images from 2013 to 2019.

en:
A national vegetation height model was calculated for Switzerland using digital aerial images.
We used the stereo aerial images acquired by the Federal Office of Topography swisstopo using the ADS80 sensor to first calculate a digital surface model (DSM) with a very high spatial resolution
(1 × 1 m and 0.5 x 0.5 m). The DSM was then normalized to obtain the actual
vegetation heights using a digital terrain model (DTM)
based on laser data with the buildings masked out, and to
produce a vegetation height model (VHM). Such a model
will be calculated in the framework of the Swiss National Forest Inventory (NFI) with consistent methods and a very
high level of detail. For covering the whole of Switzerland,
we use summer aerial images from six years.

Latest version is from 2019.

forest

display_name: forest

photogrammety

display_name: photogrammety

point-cloud

display_name: point-cloud

vegetation-height-model

display_name: vegetation-height-model

en: Vegetation Height Model NFI


de: amtliche-vermessungav basiskarte gelandemodell referenzsystem topographie kartenwerk

de: Übersichtsplan 1:10'000, Graustufen, mit schattiertem Relief: geometrischer Plan im Massstab 1:10'000, der wenig generalisiert ist. Der Übersichtsplan schliesst die Lücke zwischen dem Landeskartenwerk und dem Grundbuchplan.

en: Geoinformation of the Canton of Lucerne is responsible for the collection, processing and management of spatial data. It manages, coordinates and monitors the work of geoinformation and of the cadastral surveying.

de: Übersichtsplan 1:10'000 (UP10), mit schattiertem Relief


geography

en: Geography

de: amtliche-vermessungav basiskarte referenzsystem topographie kartenwerk siedlung

de: Ortsplan 1:5'000, farbig, mit schattiertem Relief: geometrischer Plan im Massstab 1:5'000, der wenig generalisiert ist. Der Ortsplan schliesst die Lücke zwischen dem Grundbuchplan und dem Übersichtsplan 1:10'000.

de: Ortsplan 1:5'000, farbig


bienenstandorte

agriculture

fr: Agriculture, sylviculture

de: Land- und Forstwirtschaft

en: Agriculture, forestry

it: Agricoltura, selvicoltura

de: Geographie

title_for_slug: bienenstandorte

de: fauna landwirtschaft lebensraum wirtschaftliche-aktivitaten

de: Standorte der Bienenstöcke im Kanton Luzern

name: Dienststelle Landwirtschaft und Wald (lawa)

de: Bienenstandorte


territory

de: Raum und Umwelt

en: Territory and environment

de: amtliche-vermessungav basiskarte referenzsystem topographie kartenwerk

de: Datenshop

de: Produktansicht Geodatenshop

de: Übersichtsplan 1:10'000, farbig, mit schattiertem Relief: geometrischer Plan im Massstab 1:10'000, der wenig generalisiert ist. Der Übersichtsplan schliesst die Lücke zwischen dem Landeskartenwerk und dem Grundbuchplan.

de: Geoinformation Kanton Luzern

en: Geoinformation Canton of Lucerne

de: Übersichtsplan 1:10'000 (UP10), farbig


stand-der-amtl-vermessung

title_for_slug: stand-der-amtl-vermessung

spatial: Kanton Bern

fr: Lien vers le téléchargement de données (Zip)

de: Link zum Daten-Download (Zip)

fr: Sur la base du fichier cantonal Limites communale 1:5'000, les données relatives aux périmètres approuvé et en les périmètres en cours de la mensuration officielle du canton de Berne, depuis lintroduction du CC en 1910, ont été saisies dans un même fichier AMO. Le fichier AMO montre létat actuel de la mensuration officielle dans le canton de Berne et permet den exploiter les données.

de: Im Datensatz AMO sind auf der Grundlage des Kantonsdatensatzes Gemeindegrenzen 1:5'000 für den Kanton Bern die anerkannten Bearbeitungsperimeter und die sich in Bearbeitung befindenden Bearbeitungsperimeter der amtlichen Vermessung erfasst, welche nach Einführung des ZGB im Jahr 1910 bearbeitet wurden resp. bearbeitet werden. Der Datensatz AMO zeigt den heutigen Stand der amtlichen Vermessung im Kanton Bern.

fr: Office de l'information géographique du canton de Berne

de: Amt für Geoinformation des Kantons Bern

fr: L'Office de la géoinformation est le centre de compétence pour l'information spatiale dans le canton de Berne. Les géodonnées cantonales peuvent être utilisées gratuitement, sous réserve des conditions d'utilisation. Le Géoportail du canton de Berne est le point d'entrée central pour les cartes interactives, les géodonnées et les géoservices de l'administration cantonale.

de: Das Amt für Geoinformation ist das Kompetenzzentrum für räumliche Informationen im Kanton Bern. Die kantonalen Geodaten sind unter Einhaltung der Nutzungsbedingungen kostenlos nutzbar. Das Geoportal des Kantons Bern ist der zentrale Einstiegspunkt für interaktive Karten, Geodaten und Geodienste der kantonalen Verwaltung.

value: Bern-Kt - Geocat Harvester

fr: Etat actuel de la mensuration officielle

de: Stand der amtl. Vermessung


agriculture

de: Land- und Forstwirtschaft

fr: zone-agricole agriculture

de: landwirtschaftszone landwirtschaft

en: agricultural-zone agriculture

it: zona-agricola agricoltura

spatial: Jura (JU)

fr: Accès aux données / fichiers de téléchargement

protocol: OGC:service WMS

fr: Le cadastre viticole décrit les parcelles plantées en vignes, celles en cours de reconstitution et celles bénéficiant d'une autorisation pour une nouvelle plantation. Les nouvelles plantations de vigne nécessitent une autorisation du canton. Les reconstitutions doivent être annoncées au canton. Cette donnée correspond exactement au modèle minimum fédéral Cadastre viticole ID151.

canton-du-jura

fr: Canton du Jura

de: Kanton Jura

en: Canton of Jura

it: Canton Giura

fr: Le canton du Jura publie sur Open Data Swiss différents types de données de l'administration cantonale qui répondent au principe de l'Open Government Data.

en: The canton of Jura publishes on Open Data Swiss various types of data of the cantonal administration that meets the principle of Open Government Data.

de: Der Kanton Jura veröffentlicht auf Open Data Swiss verschiedene Arten von Daten der kantonalen Verwaltung, die dem Prinzip von Open Government Data entsprechen.

it: Il Cantone del Giura pubblica su Open Data Swiss vari tipi di dati dell'amministrazione cantonale che soddisfano il principio degli Open Government Data.

value: JU-Kt - Geocat Harvester

fr: Exploitation agricole_cadastre viticole


fr: batiment police station-de-reference-de-surveillance surveillance-dinstallations video inventaire-environnement monument-historique bien-culturel

de: gebaude polizei grunduberwachung anlagenuberwachung video inventar baudenkmal kulturgut

fr: Service d'image

de: Darstellungsdienst

format: WMS

fr: Le service représente les données de base du canton de Berne selon la géocatégorie O conforme à eCH-0166. Il comprend le recensement architectural et la vidéosurveillance dans le secteur public.

de: Der Dienst stellt Geobasisdatensätze des Kantons Bern der Geokategorie O gemäss eCH-0166 dar. Er enthält die Datensätze Bauinventar des Kantons Bern und Videoüberwachung im öffentlichen Raum.

fr: Edifices et infrastructures du canton de Berne

de: Gebäude und Anlagen des Kantons Bern


maintainer: Ana Beatriz Pierri Daunt

en: socio-ecological-system multifunctionality ecosystem-services planning landscape-planning landscape-ecology landscape-perception sense-of-place landscape-services pdfsearch resilience decision-making green-infrastructure literature-review

en: 4. Use of any additional concept.

media_type: CSV

format: CSV

en: 5. Planning steps.

en: 2. Frequency of additional concepts.

en: 1. Frequency of early concepts.

en: 3. Use of any early concept.

en: 6. Protocol

en:
Table of content: 1. Frequency of early concepts; 2. Frequency of additional concepts; 3. Use of any early concept; 4. Use of any additional concept, 5. Planning steps; 6. Protocol.

The present dataset is part of the published scientific paper entitled “Landscape ecological concepts in planning: review of recent developments” (Hersperger et al., 2021). The goal of this research was to review recent publications to assess the use of landscape ecological concepts in planning. Specifically, we address the following research questions: Q1. Landscape ecological concepts: What are they? How frequently are they mentioned in current research? Q2. How are landscape ecological concepts integrated in landscape planning?

We analysed all empirical and overview papers that have been published in four key academic journals in the field of landscape ecology and landscape planning in the years 2015–2019 (n = 1918). Four key journals in the field of landscape ecology were selected to conduct the analysis, respectively Landscape Ecology (LE), Landscape Online (LO), Current Landscape Ecology Reports (CLER), and Landscape and Urban Planning (LUP). The title, abstract and keywords of all papers were read in order to identify landscape ecological concepts. Then, all 1918 papers went through a keyword search to identify the use of early and additional concepts. We used the “pdfsearch” package in R programming language and searched for singular and plural forms and different variations of the concepts (see Supplementary material 1, Table A). As a result, we provided four outputs:

  1. Frequency of early concepts. This data provides the total number of times each article used each early concept (Q1). This data was used to produce the Figure 2a at the original publication.

  2. Frequency of additional concepts. This data provides the total number of times each article used each additional concept (Q1). This data was used to produce the Figure 2b at the original publication.

  3. Use of any early concept. This data provides the total number of times each article used any early concept (Q1).
This data was used to produce the Figure 3a at the original publication.

  4. Use of any additional concept. This data provides the total number of times each article used any additional concept (Q1). This data was used to produce the Figure 3b at the original publication.

To address the second question (Q2), the title, abstract and keywords of the papers included in our sample (n=1918 articles) were screened to identify papers that might show how landscape ecological concepts are integrated into planning. We selected 52 empirical papers (see Supplementary material – 4 Integration of landscape ecological concepts into planning), and we provided two outputs:

  5. Planning steps. This data provides the number of times landscape ecological concepts were addressed in each
planning steps in 52 empirical papers analysed in detail (Q2). This data was used to produce the Figure 4 at the
original publication.

  6. Protocol for assessing the integration of landscape ecological concepts into planning. To systematically collect the
data, we used this protocol which addressed the following questions: (a) which type of planning is addressed by the
paper? (b) to which planning level does the paper refer to? (c) which concepts are integrated in any of the planning
steps described above?

decision-making

display_name: decision-making

ecosystem-services

display_name: ecosystem-services

green-infrastructure

display_name: green-infrastructure

landscape-ecology

display_name: landscape-ecology

landscape-perception

display_name: landscape-perception

landscape-planning

display_name: landscape-planning

landscape-services

display_name: landscape-services

literature-review

display_name: literature-review

multifunctionality

display_name: multifunctionality

pdfsearch

display_name: pdfsearch

planning

display_name: planning

resilience

display_name: resilience

sense-of-place

display_name: sense-of-place

socio-ecological-system

display_name: socio-ecological-system

education

fr: Education, science

de: Bildung, Wissenschaft

en: Education and science

envidat

fr: EnviDat: le portail de données environnementales

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

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

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

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

en: The usage of landscape ecological concepts in the planning literature


bauinventar

construction

fr: Construction, logement

de: Bau- und Wohnungswesen

en: Construction and housing

it: Costruzione e abitazione

geography

fr: Géographie

de: Geographie

en: Geography

title_for_slug: bauinventar

fr: batiment inventaire-environnement monument-historique bien-culturel

de: gebaude inventar baudenkmal kulturgut

spatial: Kanton Bern (erweitert)

fr: Lien vers le téléchargement de données (Zip)

fr: Le recensement architectural du canton de Berne décrit et évalue les monuments historiques (objets, ensembles bâtis ou structurés). Il constitue une base solide pour le travail pratique du Service des monuments historiques et pour la recherche scientifique. En publiant le recensement architectural, le Service des monuments historiques fournit aux communes et aux propriétaires, mais aussi à la population, une vue d’ensemble du patrimoine histo-rique du canton. Les données du géoportail sont un extrait actuel et juridiquement non contraignant sur le plan du recensement architectural.. La version légalement valide des recensements architecturaux sont disponibles auprès des communes concernées, dans les préfectures, au Ser-vice des monuments historiques et à l’Office des affaires communales et de l’organisation du territoire. Depuis 2010, toutes les communes du canton ont leur propre recensement. Le recensement architectural des quartiers de la ville de Berne a, quant à lui, été élaboré par le Service des monuments historiques de la ville de Berne. Il est également disponible en ligne (en allemand) : [Bauinventar Stadt Bern](https://bauinventar.bern.ch/) Le Service cantonal des monuments historiques révise régulièrement le recensement architectural. Toute modification doit d’abord être mise à l’enquête publique, puis mise en vigueur par une décision de l’Office de la culture du canton de Berne, avant d’être reportée dans le géoportail. Vous trouverez de plus amples informations sur le Service des monuments historiques du canton de Berne ici : [Service des monuments historiques](https://www.kultur.bkd.be.ch/fr/start/themen/denkmalpflege.html) et [Recensement architectural en ligne](https://www.kultur.bkd.be.ch/fr/start/themen/denkmalpflege/baudenkmaeler-im-kanton-bern/bauinventar/bauinventar-online.html)

de: Das kantonale Bauinventar erfasst, beschreibt und bewertet Baudenkmäler (Einzelobjekte sowie Bau- und Strukturgruppen). Es bildet eine fundierte Grundlage für die praktische Arbeit der Denkmalpflege und für die wissenschaftliche Forschung. Mit dem Bauinventar stellt die Denkmalpflege nicht nur Gemeinden und Grundeigentümerinnen und -eigentümern, sondern auch der breiten Öffentlichkeit eine qualifizierte Gesamtschau des historischen Baubestandes im Kanton zur Verfügung. Die Daten im Geoportal präsentieren einen aktuellen, aber rechtlich nicht verbindlichen Datenauszug aus dem Bauinventar. Die rechtlich verbindliche Ausgabe kann auf den Gemeindeverwaltungen und den Regierungsstatthalterämtern sowie bei der Denkmalpflege und dem Amt für Gemeinden und Raumordnung eingesehen werden. Seit 2010 liegen die Bauinventare für alle Gemeinden des Kantons vor. Die Quartierinventare der Stadt Bern werden durch die städtische Denkmalpflege bearbeitet. Sie sind online zugänglich: [Bauinventar Stadt Bern](bauinventar.bern.ch) Das Bauinventar wird durch die kantonale Denkmalpflege regelmässig revidiert. Änderungen müssen zuerst zur öffentlichen Einsichtnahme aufgelegt und per Verfügung durch das kantonale Amt für Kultur in Kraft gesetzt werden, bevor sie im Geoportal abgebildet werden. Weitere Informationen zur Denkmalpflege des Kantons Bern finden Sie hier: [Denkmalpflege](https://www.kultur.bkd.be.ch/de/start/themen/denkmalpflege.html) und [Bauinventar Online](https://www.kultur.bkd.be.ch/de/start/themen/denkmalpflege/baudenkmaeler-im-kanton-bern/bauinventar/bauinventar-online.html)

fr: Le recensement architectural

de: Bauinventar


amtliche-vermessung-vereinfacht

territory

fr: Territoire et environnement

de: Raum und Umwelt

en: Territory and environment

title_for_slug: amtliche-vermessung-vereinfacht

fr: cadastre planification

de: kataster planung

de: Link zum Daten-Download (Zip)

fr: Le géoproduit mensuration officielle simplifiée (MOPUBE) contient les données relatives à la structure de propriété et à l'utilisation du sol dans le canton de Berne. Ces données sont les plus précises disponibles actuellement. Elles se prêtent bien à un fond de plan pour la représentation d'informations thématiques. Le géoproduit mensuration officielle simplifiée est mis à jour chaque semaine ; il n'a pas de portée légale. Il constitue un sous-ensemble des données complètes de la mensuration officielle (MO). Il contient les données géométriques et les attributs des objets décrivant la propriété et l'utilisation du sol. Ces données sont à la base de la plupart des applications SIG. Elles ont été élaguées des informations plus détaillées n'intéressant principalement que les spécialistes de la mensuration cadastrale. Ce jeu de données comprend des extraits des couches d'information suivantes: points fixes, couverture du sol, objets divers, nomenclature, biens-fonds, divisions administratives et techniques de la mensuration officielle. La structure du geoproduit correspond à celle du modèle de données de la Confédération. Il a été toutefois complété avec des exigences cantonales.

de: Das Geoprodukt Amtliche Vermessung vereinfacht (MOPUBE) enthält die genauesten, verfügbaren Daten über Grund und Boden des Kantons Bern. Es eignet sich optimal  insbesondere die Bodenbedeckung und Liegenschaften  als Kartenhintergrund für die Darstellung thematischer Informationen. Das Geoprodukt Amtliche Vermessung vereinfacht wird wöchentlich aktualisiert; es besitzt keine Rechtswirkung. Es enthält eine Teilmenge der Daten der Amtlichen Vermessung (AV), namentlich diejenigen geometrischen und attributiven Daten zu Grund und Boden, die von den meisten GIS-Anwendern benötigt werden. Weitergehende Informationen, die vorwiegend Vermessungsspezialisten interessieren, werden weggelassen. Der Geodatensatz umfasst Auszüge aus den Informationsebenen Fixpunkte, Bodenbedeckung, Einzelobjekte, Nomenklatur, Liegenschaften, administrative und technische Einteilungen der Amtlichen Vermessung. Das Geoprodukt entspricht von der Struktur her dem Datenmodell «MOpublic» des Bundes. Es ist jedoch mit einigen kantonalen Anforderungen ergänzt worden.

fr: Office de l'information géographique du canton de Berne

de: Amt für Geoinformation des Kantons Bern

fr: L'Office de la géoinformation est le centre de compétence pour l'information spatiale dans le canton de Berne. Les géodonnées cantonales peuvent être utilisées gratuitement, sous réserve des conditions d'utilisation. Le Géoportail du canton de Berne est le point d'entrée central pour les cartes interactives, les géodonnées et les géoservices de l'administration cantonale.

de: Das Amt für Geoinformation ist das Kompetenzzentrum für räumliche Informationen im Kanton Bern. Die kantonalen Geodaten sind unter Einhaltung der Nutzungsbedingungen kostenlos nutzbar. Das Geoportal des Kantons Bern ist der zentrale Einstiegspunkt für interaktive Karten, Geodaten und Geodienste der kantonalen Verwaltung.

fr: Mensuration officielle simplifiée

de: Amtliche Vermessung vereinfacht


kommunale-nutzungsplanung

title_for_slug: kommunale-nutzungsplanung

fr: protection-de-la-nature corps-executif risque-naturel planification-de-lespace-physique amenagement-du-paysage commune planification-regionale eaux-geographie cadastre amenagement-du-territoire bois polluant-sonore plan planification

de: naturschutz behorde naturgefahr raumplanung landschaftsplanung gemeinde regionalplanung gewasser kataster flachennutzungsplanung wald larmbelastung plan planung

fr: Thème du cadastre RDPPF L' ordonnance classe les jeux de géodonnées de base communales qui relèvent du domaine des plans d'affectation dans le cadastre des restrictions de droit public à la propriété foncière (cadastre RDPPF). Les thèmes du cadastre RDPPF suivantes se trouvent dans le géoproduit NUPLA: - Plans d'affectation communaux (OGéo ID 73C), - Degré de sensibilité au bruit (OGéo ID 145), - Distances par rapport à la forêt communales (OGéo ID 159C). Les espaces réservés aux eaux déterminés par les communes se trouvent aussi dans le géoproduit (couches 'UEGEWRF: Espaces réservés aux eaux (surfaces)' et 'UEGEWRL: Espaces réservés aux eaux (lignes)'). **Le jeu de données est en construction et n'est pas encore disponible pour l'ensemble du canton de Berne. **

de: ÖREB-Katasterthema Die kommunalen Geobasisdatensätze im Bereich Nutzungsplanung sind gemäss der Verordnung über den Kataster der öffentlich-rechtlichen Eigentumsbeschränkungen (ÖREBKV) Bestandteile des ÖREB-Katasters. Folgende ÖREB-Katasterthemen sind in diesem Geoprodukt NUPLA enthalten: - Kommunale Nutzungsplanung (GeoIV ID 73C), - Lärmempfindlichkeitsstufen (GeoIV ID 145), - Kommunale Wald-Baulinien (GeoIV ID 159C). Enthalten sind auch die von den Gemeinden festgelegten Gewässerräume (Ebenen 'UEGEWRF: Gewässerraumflächen' und 'UEGEWRL: Gewässerraumlinien'). **Der Datensatz befindet sich im Aufbau und ist noch nicht flächendeckend für den ganzen Kanton Bern vorhanden. **

fr: Plans d'affectation communaux

de: Kommunale Nutzungsplanung


gebaudeadressen

title_for_slug: gebaudeadressen

fr: batiment liste-de-diffusion

de: gebaude adressenliste

fr: Le fichier GEBADR contient les adresses postales des bâtiments, adresses spatialisées et enregistrées dans la base de données de la mensuration officielle. Il contient aussi les bâtiments souterrains. En règle générale, une adresse de bâtiment se compose des éléments suivants : un nom de rue, ou de place, ou un lieu dénommé; un numéro de maison; le numéro postal d'acheminement (NPA) et la localité. Les bâtiments annexes (garages, remises, etc.) ne possèdent pas tous un numéro de maison ; ils sont désignés par la mention « nn » (qui veut dire non numéroté). L'objectif à moyen terme est d'attribuer aussi à ces bâtiments annexes un numéro de maison univoque. Cette désignation se fera en collaboration avec les communes bernoises.

de: Der Datensatz GEBADR enthält die postalischen Gebäudeadressen aller ober- und unterirdischen Gebäude, welche in der amtlichen Vermessung erfasst sind. Eine Gebäudeadresse setzt sich im Normalfall aus folgenden Elementen zusammen: Strassenname (oder Platz, benanntes Gebiet), Hausnummer, Postleitzahl und Ortschaft. Nebengebäude (z.B. Garagen, Ökonomiegebäude) verfügen teilweise nicht über eine eigene Hausnummer und sind deshalb mit der Bezeichnung 'nn' (= nicht nummeriert) bezeichnet. Es wird angestrebt, in Zusammenarbeit mit den bernischen Gemeinden künftig auch diese bisher nicht nummerierten Gebäude mit einer eindeutigen Hausnummer zu kennzeichnen.

fr: Adresses des bâtiments

de: Gebäudeadressen


fr: maillage carte registre-foncier laser droit-de-propriete systeme-de-coordonnees cadastre fond-de-plan

de: gitternetz karte grundbuch laser eigentumsordnung koordinatensystem kataster kartenhintergrund

fr: Service d'image

de: Darstellungsdienst

fr: Le service représente les données de base du canton de Berne selon la géocatégorie A conforme à eCH-0166. Il comprend les numéros de parcelles, le plan d’ensemble 1:5'000 (UP5), le réseau kilométrique et la vue d'ensemble des survols LIDAR.

de: Der Dienst stellt Grundlagedaten des Kantons Bern entsprechend der Geokategorie A gemäss eCH-0166 dar. Er enthält das Kilometernetz, die Parzellennummern, die Übersicht der LIDAR-Befliegungen sowie den Übersichtsplan 1:5'000 (UP5).

fr: Cartes de référence, couverture du sol et images aériennes du canton de Berne

de: Basiskarten, Bodenbedeckung und Bilddaten des Kantons Bern


gruda-av-export

geography

fr: Géographie

de: Geographie

territory

de: Raum und Umwelt

title_for_slug: gruda-av-export

fr: batiment registre-foncier droit-de-propriete cadastre nomenclature

de: gebaude grundbuch eigentumsordnung kataster nomenklatur

fr: Lien vers le téléchargement de données (Zip)

fr: Export des données sur les biens-fonds, les bâtiments, désignation de la situation et la couverture du sol de la banque de données des biens-fonds GRUDA-MO. L'export correspond au modèle de données du téléchargement de données issues de GRUDA-MO La correspondance des attributs du modèle de données au géoproduit est décrite dans le fichier [GRUDAEXP_Attribute.pdf](<https://geofiles.be.ch/geoportal/pub/lpi/GRUDAEXP_Attribute.pdf>).

de: Daten-Export zu Grundstücken, Gebäuden, Lagebezeichnung und Bodenbedeckung aus der Grundstückdatenbank GRUDA-AV. Der Export entspricht dem Datenmodell des Datenablads aus GRUDA-AV Die Zuordnung der Attribute des Datenmodells zum Geoprodukt ist in der Datei [GRUDAEXP_Attribute.pdf](<https://geofiles.be.ch/geoportal/pub/lpi/GRUDAEXP_Attribute.pdf>) beschrieben.

fr: GRUDA MO Export

de: GRUDA AV Export


wald-flora-und-fauna-des-kantons-bern

title_for_slug: wald-flora-und-fauna-des-kantons-bern

fr: desserte gibier loisirs-et-detente schema-regional foret-protectrice gestion-des-forets biodiversite conseil fonction-forestiere production-de-bois vegetation plan-forestier planification-regionale comportement-animal routes-forestieres bois trafic-motorise temps-libre gestion-forestiere planification

de: erschliessung wild erholung regionalplan schutzwald waldbewirtschaftung biodiversitat gutachten waldfunktion holzproduktion vegetation waldplan regionalplanung tierverhalten waldstrassen wald motorfahrzeugverkehr freizeit forstwirtschaft planung

fr: Le service représente les données de base du canton de Berne selon la géocatégorie G conforme à eCH-0166. Il comprend la carte de base de la desserte des forêts, l'expertise des dégâts du gibier, les grands prédateurs, l'information sur la fôret, le modèle de hauteur de végétation forêt (LIDAR), le plan des routes forestières, la planification forestière régionale - carte des fonctions forestières ainsi que les zones de gestion du gibier.

de: Der Dienst stellt Geobasisdatensätze des Kantons Bern der Geokategorie G gemäss eCH-0166 dar. Er enthält die Datensätze Grossraubtiere, Grundlagenkarte Walderschliessung, Regionale Waldplanung - Waldfunktionenkarte, Vegetationshöhenmodell Wald und Wildschadengutachten, Waldinformation, Waldstrassenplan sowie Wildräume.

fr: Office de l'information géographique du canton de Berne

de: Amt für Geoinformation des Kantons Bern

fr: L'Office de la géoinformation est le centre de compétence pour l'information spatiale dans le canton de Berne. Les géodonnées cantonales peuvent être utilisées gratuitement, sous réserve des conditions d'utilisation. Le Géoportail du canton de Berne est le point d'entrée central pour les cartes interactives, les géodonnées et les géoservices de l'administration cantonale.

de: Das Amt für Geoinformation ist das Kompetenzzentrum für räumliche Informationen im Kanton Bern. Die kantonalen Geodaten sind unter Einhaltung der Nutzungsbedingungen kostenlos nutzbar. Das Geoportal des Kantons Bern ist der zentrale Einstiegspunkt für interaktive Karten, Geodaten und Geodienste der kantonalen Verwaltung.

fr: Forêt, flore et faune du canton de Berne

de: Wald, Flora und Fauna des Kantons Bern


ubersicht-leitungskataster

territory

fr: Territoire et environnement

title_for_slug: ubersicht-leitungskataster

de: Link zum Daten-Download (Zip)

fr: La vue d'ensemble du cadastre des conduites donne des informations concernant la disponibilité digitale des fluides eaux usées, gaz, eau, chauffage à distance, électricité, communication et des autres fluides, ainsi que l'adresse de contact du service de gestion des données pour obtenir un extrait du cadastre de conduites.

de: Die Übersicht zum Leitungskataster gibt Informationen zum Stand der digitalen Verfügbarkeit der Lage der Medien Abwasser, Gas, Wasser, Fernwärme, Elektrizität, Kommunikation und den weiteren Medien sowie die Kontaktadresse der Datenverwaltungsstelle zum Bezug des Leitungskatasters.

fr: Aperçu du cadastre des conduites

de: Übersicht Leitungskataster


de: naturschutz altlast flughafen naturdenkmal verkehrsinfrastruktur naturgefahr luftverkehr schadstoffbelastung raumplanung flora naturschutzgebiet archaologie landschaftsplanung regionalplanung reservat geologie gewasser zivilluftverkehr offentlicher-verkehr luftsicherheit kataster flachennutzungsplanung grenze wasserbau raumordnung-und--planung wald inventar pflanzenschutz schutzgebiet grundwasserschutz larmbelastung abfall botanik gewasserschutz waldschutzgebiet planung

fr: Le service offre des informations sur les thèmes du Cadastre des restrictions de droit public à la propriété (cadastre RDPPF). Le cadastre RDPPF dresse l’inventaire des principales restrictions applicables aux parcelles conformément aux dispositions légales ainsi qu’aux actes édictés par les autorités (p. ex. zones à bâtir).

de: Der Dienst stellt die Themen des Katasters der öffentlich-rechtlichen Eigentumsbeschränkungen (ÖREB-Kataster) dar. Der ÖREB-Kataster führt die wichtigsten Beschränkungen auf, die aufgrund gesetzlicher Bestimmungen und behördlicher Erlasse auf ein Grundstück wirken (z.B. Bauzonen).

fr: Cadastre RDPPF du canton de Berne

de: ÖREB-Kataster des Kantons Bern


parzellennummern

title_for_slug: parzellennummern

de: grundbuch eigentumsordnung kataster

fr: Le produit Numéros de parcelles contient les numéros géoréférencés de parcelles (bien-fonds ainsi que les droits distincts et permanents) du canton de Berne. Par le fait que les numéros de parcelles soient connues par leurs coordonnées (y et x), DIPANU permet de rechercher des parcelles dans le canton de Berne.

de: Das Produkt Parzellennummern beinhaltet die georeferenzierten Nummern von Grundstücken (Liegenschaften und Selbstständige, dauernde Rechte) im Kanton Bern. Dadurch, dass die Grundstücknummern mit ihren Koordinaten (y und x) abgelegt sind, dient DIPANU als Suchinstrument von Grundstücken innerhalb des Kantons Bern.

fr: Numéros de parcelles

de: Parzellennummern


fr: identification-du-lieu-sig batiment liste-de-diffusion point distribution-spatiale altitude nomenclature geographie

de: ortsbestimmung gebaude adressenliste punkt raumliche-verteilung hohe nomenklatur geographie

fr: Service d'image

de: Darstellungsdienst

fr: Le service représente les données de base du canton de Berne selon la géocatégorie B conforme à eCH-0166. Il comprend les adresses des bâtiments, le canton dans votre région, les localités (pour la recherche de localité), les points fixes planimétriques et altimétriques 1 / 2 ainsi que la répartition des plans de nomenclature.

de: Der Dienst stellt Grundlagedaten des Kantons Bern entsprechend der Geokategorie B gemäss eCH-0166 dar. Er enthält die Datensätze Der Kanton in Ihrer Region, Gebäudeadressen, Lage- und Höhenfixpunkte der Kategorien 1 und 2, Ortschaften (für Suche von Ortschaften) und Planeinteilung Nomenklatur.

fr: Localisation et systèmes de référence du canton de Berne

de: Ortsangaben und Referenzsysteme des Kantons Bern