en:
The present dataset is part of the report titled Gradinaru S.R., Hersperger A.M., Schmid F. (2021). Deriving Planning Intentions from written planning documents. Report on CONCUR Project- From plans to land change: how strategic spatial planning contributes to the development of urban regions.
The data corresponds to the data collected as part of the DPI Method for deriving all PIs contained in a plan (open coding) as detailed in section 4 of the report. The method involved reading the plans to break down of information in meaningful discrete “incidents” or planning intentions. To identify the planning intentions, the starting points were represented by a) the structuring of the plans in chapters and sub chapters and b) the themes that the plans addressed. Thus, the collected information was not grouped according to pre-defined categories of planning intentions, but rather put together as a list of intentions as revealed by each plan. As a result, we provide, for each case study, a document (named [Urban region name] PI as defined in the plan) which contains:
	Date when the information was filled in.
	Name of the urban region and analysed strategic spatial plan .
	A list of all planning intentions contained in a plan, with each PI being addresses as follows:
	Name of PI as it appears in the plan
	Translated name of the PI (i.e. short name for easy understanding of the meaning)
	Explanation regarding the meaning of the PI
	Why the PI is considered a priority for the urban region
	Spatial information on the PI (text and cartographic representations).
In total, 14 documents are available, one for each case study. Documents contain up to 20 pages of information extracted from the plans together with explanations and notes taken during plan reading.

en: europe development-priorities spatial-information strategic-plans urban-region

envidat

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

en: Planning intentions in strategic plans of European urban regions

development-priorities

display_name: development-priorities

europe

display_name: europe

spatial-information

display_name: spatial-information

strategic-plans

display_name: strategic-plans

urban-region

display_name: urban-region


$oid: OBSOLETE

en:
This python code uses the Finite Element library FEniCS (via docker) to solve the one dimensional partial differential equations for heat and mass transfer in snow. The results are written in vtk format.
The dataset contains the code and the output data to reproduce the key Figure 5 from the related publication:
_Schürholt, K., Kowalski, J., Löwe, H.; Elements of future snowpack modeling - Part 1: A physical
instability arising from the non-linear coupling of transport and phase changes, The Cryosphere, 2022_
The code and potential updates can be accessed directly through git via:
https://gitlabext.wsl.ch/snow-physics/snowmodel_fenics

en: python pattern-formation vapor-transport snow numerical-model

envidat

approval_status: approved

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

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

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

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

state: active

type: organization

wsl

capacity: public

de: EnviDat

en: A numerical solver for heat and mass transport in snow based on FEniCS

type: dataset

key: guid

key: uri

value: Envidat Harvester

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

format: ZIP

numerical-model

display_name: numerical-model

pattern-formation

display_name: pattern-formation

python

display_name: python

snow

display_name: snow

vapor-transport

display_name: vapor-transport


causal-effect-of-mfoz

en:
Title: Closer to causality: How effective is spatial planning in governing built-up land expansion in Fujian Province, China?
Research objective: The Major Function Oriented Zone (MFOZ), the first strategic spatial plan in China, is developed to achieve a coordinated regional development, through spatial regulation and zoning of development. The MFOZ he MFOZ divided land into four major function-oriented zones: The development-optimized zone, the development-prioritised zone, the development-restricted zone, and the development-prohibited zone. We used propensity score marching to evaluate the effect of the MFOZ on built-up land expansion in Fujian Province over three time intervals (2013–2015, 2013–2018 and 2013–2020).
Data: Data.xlsx contains the variables of 954 towns in Fujian Province.
Town_ID is the town unique ID;
County_ID is the county unique ID;
City_ID is the city unique ID;
MFOZ is the the development-prioritised zone and the development-restricted zone (The development-optimized zone and the development-prohibited zone are excluded);
Builtup_13_15 is the built-up land expansion from 2013 to 2015;
Builtup_13_18 is the built-up land expansion from 2013 to 2018;
Builtup_13_20 is the built-up land expansion from 2013 to 2020;
Dis2water is the Euclidean distance from the town to the nearest waterbody;
Slope is the the average slope within the town;
GDP is the average GDP in 2010 within the town;
Pop is the average population in 2010 within the town;
Road is the average population in 2010 within the town;
Dis2city is the Euclidean distance from the town to the nearest prefectural city centre;
Nei_Arable, Nei_Forest, and Nei_Built.up are the area of arable land, forest land, and built-up land neighbouring town i in 2010.
Method: we used the propensity score matching to compare the changes in the amount of built-up land in the towns of the development-prioritised zone with the matched towns of the development-restricted zone. Additionally, we used three evaluation intervals (2013–2015, 2013–2018 and 2013–2020) to evaluate temporal variation in the causal effect of the MFOZ on built-up land expansion.

en: selection-bias propensity-score-matching built-up-land-change fujian-province causality the-major-function-oriented-zone

en: Causal effect of MFOZ

title_for_slug: causal-effect-of-mfoz

built-up-land-change

display_name: built-up-land-change

causality

display_name: causality

fujian-province

display_name: fujian-province

propensity-score-matching

display_name: propensity-score-matching

selection-bias

display_name: selection-bias

the-major-function-oriented-zone

display_name: the-major-function-oriented-zone


greenland-shrubs-and-microclimate

en:
Study Aim
We collected these data to alternatively train and validate high resolution (~ 90 m) Species Distribution Models (SDMs) and Species Abundance Models (SAMs) for _Betula nana_ L. (dwarf birch, Betulaceae) and _Salix glauca_ L. (grey willow, Salicaceae) in Southwest Greenland to assess how well such models can predict local-scale patterns.
 Data Description
Individual (presence-absence, abundance, maximum vegetative height) and community (species composition, maximum canopy height) shrub data for two fjords near Nuuk, Southwest Greenland. Also provided are corresponding downscaled climate data as well as calculated topographic and terrain wetness indicator variables.
 Nuup Kangerlua (Godthåbsfjord)
_Betula nana_ and _Salix glauca_ presence-absence, abundance, community species richness
 Kangerluarsunnguaq (Kobbefjord)
Shrub presence-absence, abundance, maximum vegetative height, community composition, maximum shrub canopy height
 Methods
 Field survey in Nuup Kangerlua
We conducted a stratified systematic plant survey along the length of Nuup Kangerlua (NK) fjord in Soutwesth Greenland (Fig. 1 in Chardon et al. 2022; following Nabe-Nielsen et al., 2017). At five distinct sites, we sampled along elevational gradients to collect data on presences, absences, abundance, and species composition of all woody species using a 0.7 x 0.7 m pin-point frame (Fig. 1e in Chardon et al. 2022). For model training, we converted these pin-point data to percent cover estimates based on the number of pins dropped (n = 25 per plot) and averaged them across the 119 spatio-climatic grids (see next section) corresponding to the plot locations (for details see Appendix S2 in Chardon et al. 2022).
 Field survey in Kangerluarsunnguaq
We conducted a random stratified plant survey in Kangerluarsunnguaq (K) fjord in Southwest Greenland. We used a preliminary Species Abundance Model trained with summed pin counts of _Betula nana_ in NK fjord (see Fig. S1.3 in Chardon et al. 2022) to stratify the ~ 27 x 17 km fjord landscape into low, medium, and high abundances classes. We randomly selected 90 x 90 m spatio-climatic grids to survey in each class for a total of 200 grids, ensuring that they were accessible by foot or boat (for details see Appendix S2 in Chardon et al. 2022). Within each grid, we sampled within three 1 m2 quadrats arranged in a randomly rotated equilateral triangle centered on the mid-point of the cell. We used a gridded sampling quadrat with 1% delineations (Fig. 1h in Chardon et al. 2022) to record woody species presences, absences, and composition, estimated percent cover, and measured maximum shrub species vegetatitve height. At every plot, we also visually scanned the area in a 20 m radius from the plot and recorded the presence of any additional shrub species to estimate grid-level species richness. As in NK fjord, we averaged these data at the grid level (for details see Appendix S2 in Chardon et al. 2022).
 Biotic variables
We calculated biotic microscale variables from the plant survey data collected in NK and K fjords. We calculated shrub species richness, diversity, and competition (i.e. sum of non-B. nana or non-S. glauca pin hits or percent cover). In K fjord, we also calculated canopy height as the community weighted mean (by abundance) of maximum vegetative shrub height.
 Climate variables
We computed high resolution temperature, precipitation, and insolation for local scale data for the study area by statistically downscaling climate time series (1982 - 2013) from the monthly CHELSA data (Karger et al. 2017). We downscaled these data from 30 arc sec (~ 400 m at the latitude of our study) to our target grid size of ~ 90 m with geographic weighted regression and using the MEaSUREs Greenland Ice Mapping Project (GIMP) Digital Elevation Model (DEM) v. 1 (Howat et al., 2014, 2015). We then calculated 30-year averages of the climate parameters: average summer (June – August) maximum temperature, yearly maximum temperature, yearly minimum temperature, temperature continentality (yearly max. - min. temperatures), cumulative Spring (March – May) precipitation, cumulative summer precipitation, and average summer incident solar radiation (henceforth, insolation) (for calculation details see Appendices S2, S3 in Chardon et al. 2022 and Appendix S2 in von Oppen et al. 2021).
 Topography and terrain wetness indicator variables
We calculated several topographic and terrain wetness indices at a local scale. We derived slope, aspect, and the SAGA wetness index (hereafter TWI; Boehner et al., 2002; Boehner and Selige, 2006) from the GIMP DEM. TWI is a measure of how ‘wet’ an area is, based on water drainage from the surrounding landscape. We also calculated the tasseled cap wetness component (hereafter TCW, Crist and Cicone 1984) from satellite images (for details see Appendices S2, S3 in Chardon et al. 2022) as an alternative measure of wetness.
 Computer code
Attached as zip file and available on GitLab (https://gitlab.com/nathaliechardon/gl_microclim)
 Third-party data
Data used to calculate climate, topography, and terrain wetness indicator variables are publicly available (see Appendix S2 in Chardon et al. 2022 for all data references).

en: accessible-shrub-forest climate arctic validation field-data sdm

en: Greenland shrubs and microclimate

title_for_slug: greenland-shrubs-and-microclimate

en:
Resource Title: Nuup Kangerlua
    Size: 27166 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/040d25dd-71c0-40e6-99a8-849427250f5d/resource/49bbe7fa-5694-4aff-9754-1d66bdc0978e/download/nuupkangerlua.csv
    Description: ## Collected July-August 2011-2013 (PI: Jacob Nabe-Nielsen)

These data collected July-August 2011-2013 (PI: Jacob Nabe-Nielsen). Please contact J. Nabe-Nielsen (jnn_at_ecos.au.dk) to request access to data on all surveyed shrub species. Available here are only data used in Chardon et al. 2022.

## COLUMN ABBREVIATIONS

### Metadata for 90 m spatial grid

year: sampling year

alt: average altitude of grid as recorded during field surveys [m]

long: WGS84 longitude

lat: WGS84 latitude

x_stere: longitude in Polar Stereographic (EPSG 4326)

y_stere: latitude in Polar Stereographic (EPSG 4326)

### Species presence [1] or absence [0] at grid level

pa_bena: _Betula nana_

pa_sagl: _Salix glauca_

### Shrub species cover (calculated from pin hits in 0.7 x 0.7 m frame, based on total of 25 hits) [%]

cov_bena: _Betula nana_ cover

cov_sagl: _Salix glauca_ cover

### Downscaled climate (time series 1984 – 2013)

tempjja: average maximum June-August temperature [ºC]

tempmax: yearly maximum temperature [ºC]

tempmin: yearly minimum temperature [ºC]

tempcont: yearly maximum – minimum temperature [ºC]

precipmam: cumulative March-May precipitation [mm]

precipjja: cumulative June-August precipitation [mm]

insoljja: average June-August insolation [MJ cm-2 yr-1]

### Topography and terrain wetness (calculated from)

twi: SAGA wetness index (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015)

slope: slope angle (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

asp: aspect (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

tcws: Tasseled Cap Wetness component (Sentinal images)

### Community characteristics

rich: shrub species richness averaged across pin-point frames per grid

shan: Shannon diversity (‘vegan’ package; Oksanen et al. 2019)

comp_bena: shrub competition on Betula nana (i.e. summed cover of non-_B. nana_ species) [% cover]

comp_sagl: shrub competition on Salix glauca (i.e. summed cover of non-_S. glauca_ species) [% cover]

format: CSV

media_type: text/csv

en:
Resource Title: Kangerluarsunnguaq
    Size: 93883 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/040d25dd-71c0-40e6-99a8-849427250f5d/resource/dc1bf94c-e651-4193-9a42-de3c9f3b11e9/download/kangerluarsunnguaq.csv
    Description: ## Collected August 2020 (PI: Nathalie Isabelle Chardon)

These data collected August 2020 (PI: Nathalie Isabelle Chardon). Please contact N. I. Chardon (nathalie.chardon_at_gmail.com) for individual quadrat (i.e. 1 m x 1 m) data, which are averaged at the 90 m spatial grid here.

## COLUMN ABBREVIATIONS

### Metadata for 90 m spatial grid

id: sampling zone and unique 90 m grid identifier

long: WGS84 longitude

lat: WGS84 latitude

x_stere: longitude in Polar Stereographic (EPSG 4326)

y_stere: latitude in Polar Stereographic (EPSG 4326)

date: sampling day.month.year

### Species presence [1] or absence [0] at grid level

alcr: _Alnus crispa_

bena: _Betula nana_

dila: _Diapensia lapponica_

emni: _Empetrum nigrum_

hahy: _Harimanella hypoides_

lopr: _Loiseleuria procumbens_

oxpa: _Oxycoccus palustre_

phca: _Phyllodoce caerulea_

rhod: _Rhododendron_ sp.

saar: _Salix arctica_

sagl: _Salix glauca_

sahe: _Salix herbacea_

vaul: _Vaccinium uliginosum_

drin: _Dryas integrifolia_

thpr: _Thymus praecox_

### Habitat characteristics [% cover]

shrub: all shrub species

gram: graminoids

forb: forbs

bryo: bryophytes

lich: lichens

litt: litter

bare: bare ground

stone: stones (> 2 cm)

org: organic crust

### Shrub species height (maximum vegetative height) [mm] and cover (estimated in 1x1 m quadrat) [%]

alcr_c: _Alnus crispa_ cover

alcr_h: _Alnus crispa_ height

bena_c: _Betula nana_ cover

bena_h: _Betula nana_ height

dila_c: _Diapensia lapponica_ cover

dila_h: _Diapensia lapponica_ height

emni_c: _Empetrum nigrum_ cover

emni_h: _Empetrum nigrum_ height

hahy_c: _Harimanella hypoides_ cover

hahy_h: _Harimanella hypoides_ height

lopr_c: _Loiseleuria procumbens_ cover

lopr_h: _Loiseleuria procumbens_ height

oxpa_c: _Oxycoccus palustre_ cover

oxpa_h: _Oxycoccus palustre_ height

phca_c: _Phyllodoce caerulea_ cover

phca_height: _Phyllodoce caerulea_ height

rhod_c: _Rhododendron_ sp. cover

rhod_h: _Rhododendron_ sp. height

saar_c: _Salix arctica_ cover

saar_h: _Salix arctica_ height

sagl_c: _Salix glauca_ cover

sagl_h: _Salix glauca_ height

sahe_c: _Salix herbacea_ cover

sahe_h: _Salix herbacea_ height

vaul_c: _Vaccinium uliginosum_ cover

vaul_h: _Vaccinium uliginosum_ height

drin_c: _Dryas integrifolia_ cover

drin_h: _Dryas integrifolia_ height

### Community characteristics

rich_pa: shrub species richness at grid level

rich: shrub species richness averaged across 3 1x1 m sampling quadrats per grid

shan: Shannon diversity (‘vegan’ package; Oksanen et al. 2019)

comp_bena: shrub competition on _Betula nana_ (i.e. summed cover of non-_B. nana_ species) [% cover]

comp_sagl: shrub competition on _Salix glauca_ (i.e. summed cover of non-_S. glauca_ species) [% cover]

ht_cwm: maximum shrub canopy height (i.e. community weighted mean by abundance of maximum shrub height) [mm]

### Topography and terrain wetness (calculated from)

tcws: Tasseled Cap Wetness component (Sentinal images)

slope: slope angle (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

twi: SAGA wetness index (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015)

asp: aspect (MEaSUREs Greenland Ice Mapping Project Digital Elevation Model v. 1; Howat et al., 2014, 2015) [º]

### Downscaled climate (time series 1984 – 2013)

insoljja: average June-August insolation [MJ cm-2 yr-1]

precipmam: cumulative March-May precipitation [mm]

tempcont: yearly maximum – minimum temperature [ºC]

tempmin: yearly minimum temperature [ºC]

precipjja: cumulative June-August precipitation [mm]

tempjja: average maximum June-August temperature [ºC]

tempmax: yearly maximum temperature [ºC]

en:
Resource Title: GitLab Repository
    Size: 235019467 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/040d25dd-71c0-40e6-99a8-849427250f5d/resource/9a2b483b-baf7-40ff-98a2-faf92ef7b7a2/download/gl_microclim.zip
    Description: Zip drive of GitLab repository contents (https://gitlab.com/nathaliechardon/gl_microclim).

media_type: application/zip

accessible-shrub-forest

display_name: accessible-shrub-forest

arctic

display_name: arctic

climate

display_name: climate

field-data

display_name: field-data

sdm

display_name: sdm

validation

display_name: validation


causal-effect-of-lup

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.

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

en: Causal effect of LUP

title_for_slug: causal-effect-of-lup

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: The present dataset is part of the published scientific paper Zhao C, Weng Q, Hersperger A M. Characterizing the 3-D urban morphology transformation to understand urban-form dynamics: a case study of Austin, Texas, USA. Landscape and urban planning, 2020, 203:103881. The overall objective of this paper is to understand urban form dynamics in the Austin metropolitan area for the periods 2006–2011 and 2011–2016. The study also aims to understand to what extent the changes in the built environment (in terms of ‘efficient growth’ versus ‘inefficient growth’) from the 1990s to 2016 in the Austin metropolitan area corresponded with ‘compact and efficient growth’ planning policy documents. The UMT distribution can be found in the paper. The area of transitioning UMT was provided in Table 2 and Table 3 can be found in the Appendix of the paper. A protocol was developed to perform the content analysis of the strategic plans and gather the data. The detailed list of protocol items can be found in Appendix B of the paper. This study demonstrates the advantage of applying Lidar data to characterize 3-D urban morphology type (UMT) transition and understand its dynamics, which helps develop a comprehensive understanding of the urbanization process and provides a tool for planning intentions and policies evaluation on urban development over time. The UMT maps can be found in Appendix A of the paper. The Lidar point datasets and the 30 × 30 m National Land Cover Database (NLCD) are the two main data sources of UMT mapping. Lidar datasets were gathered from different projects that had been conducted and collected by state agencies and other organizations between 2007 and 2017. Table A1 in the appendix in the paper shows the accuracies and acquisition parameters of the Lidar projects from 2007 to 2017. Land use/cover dynamics in Austin metropolitan area dataset provides Land use/cover patterns in the years 1992, 2001, 2004, 2006, 2008, 2011, 2013, 2016 with a spatial resolution of 30 meters. Since NLCD 1992 used a different classification system for the urban land classes, we first reclassified the NLCD 1992 using a customized Arcpy package.

en: austin urban-morphology landscape land-dynamics land-use

envidat

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

en: Land use/cover dynamics in Austin metropolitan area since 1992

austin

display_name: austin

land-dynamics

display_name: land-dynamics

land-use

display_name: land-use

landscape

display_name: landscape

urban-morphology

display_name: urban-morphology


en: The data results from a questionnaire survey conducted at 8 schools in the cantons Zurich, Aargau and St. Gallen. Respondents aged 13-22 years. The aim of the survey was to gain insight into teenagers' relationship to the forest, reasons for visiting or not visiting the forest and activities in the forest.

en: recreational-activities childhood forest adolescents teenagers

envidat

en: EnviDat: the Environmental Data Portal

en: Factors influencing teenagers' forest visit frequency

envi

en: Environment

en:
Resource Title: Dataset survey at schools
    Size: 844175 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/04ecf3b8-76aa-4b45-af4e-e06da88a8fcd/resource/9b9bee0b-a041-4870-b845-1115c5deed6d/download/datensatz-umfrage-schulen.xlsx
    Description: The dataset of the questionnaire survey at schools in excel format.

format: XLS

en:
Resource Title: Dataset comments
    Size: 32680 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/04ecf3b8-76aa-4b45-af4e-e06da88a8fcd/resource/29308f48-7be2-4afb-9b4d-829c5618e50a/download/datensatz-kommentar.xlsx
    Description: The codebook to the dataset

en:
Resource Title: Questionnaire school survey
    Size: 141278 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/04ecf3b8-76aa-4b45-af4e-e06da88a8fcd/resource/c7f5fdd3-361e-44bf-ba4c-fa97f6164bf0/download/fragebogenv2.pdf
    Description: The questionnaire used in the survey at 8 schools aimed at teenagers in German.

format: PDF

media_type: application/pdf

en:
Resource Title: Survey at schools 2017
    Size: 638125 bytes
    Format: application/x-spss-sav
    URL: https://www.envidat.ch/dataset/04ecf3b8-76aa-4b45-af4e-e06da88a8fcd/resource/fe6746a6-96c4-484e-99c4-8f8556d76d86/download/datensatz-umfrage-schulen.sav
    Description: The dataset of the questionnaire survey at schools in SPSS-format.

adolescents

display_name: adolescents

childhood

display_name: childhood

forest

display_name: forest

recreational-activities

display_name: recreational-activities

teenagers

display_name: teenagers


en: The data includes (1) amphibian occurrence data (2017-2019) for ten species across the cantons of Aargau and Zürich gathered from the Coordination Center for the Protection of Amphibians and Reptiles of Switzerland (http://www.karch.ch), (2) amphibian whole-life cycle environmental predictors (i.e. topographic, hydrologic, edaphic, vegetation, land-use derived, movement-ecology related), and (3) local urban "green" and "grey" landcover data which can be used to identify opportunities for Blue-Green Infrastructure (through green or grey transitions) in support of regional landscape connectivity.

en: urban-spatial-planning circuit-theory integrated-modelling ecological-corridors human-dominated-landscapes biodiversity

en: Amphibian and urban-rural landscape data Swiss Lowlands

en:
Resource Title: Binarized "green" and "grey" landcover data (Cantons of Aargau and Zürich)
    Size: 531399 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/8197fa13-272f-4ab0-b8fa-061269b079b6/resource/5752ef42-b6cc-4365-bb61-288dfa3d5d95/download/urban_green_grey.zip
    Description: "Green" landcover refers to lawns and vegetated urban surfaces such as, for example private gardens, public parks, cemeteries, green stripes along roadsides, etc. While "grey" landcover refers to impervious grounds such as roads, troittoirs, other impervious. Binarized maps, 30x30m resolution.

en:
Resource Title: Amphibian breeding sites of national importance - cantons of Aargau and Zürich
    Size: 39808424 bytes
    Format: application/pgp-keys
    URL: https://www.envidat.ch/dataset/8197fa13-272f-4ab0-b8fa-061269b079b6/resource/fd5b1fe3-bdf9-4095-816a-edaf22178df2/download/breeding_sites_raster_30m.asc
    Description: Amphibian breeding sites of national importance (focal nodes) in the cantons of Aargau and Zürich, 30x30 m resolution, binarised ascii format.

format: pgp-keys

en:
Resource Title: Amphibian monitoring data
    Size: 310101 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/8197fa13-272f-4ab0-b8fa-061269b079b6/resource/ce42d731-4b85-4da0-b356-35deb8f80607/download/amphibian_presence_2017_2019.csv
    Description: Processed amphibian occurrence data (2017-2019) in the cantons of Aargau and Zürich. The dataset includes information for the following species:  _Alytes obstetricans_,_Bombina variegata_, _Bufo bufo_, _Hyla arborea_, _Ichtyosaura alpestris_, _Lissotriton helveticus_, _Lissotriton vulgaris_, _Rana temporaria_, _Salamandra salamandra_, and _Triturus cristatus_.

en:
Resource Title: Amphibian species specific landscape resistance maps.
    Size: 51864736 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/8197fa13-272f-4ab0-b8fa-061269b079b6/resource/acaac64b-3ebe-4e97-ae24-a35736dfa1d5/download/resistance.zip
    Description: For each amphibian species we used the weighted average Ensemble model suitability map and converted it to a resistance map using an exponential decay function following (Duflot et al., 2018) using the species-specific mpa threshold previously computed (Preau et al., 2020). The function assigns a resistance value of 1,000 when habitat suitability equals zero, and a value of one when habitat suitability is greater than or equal to the binarization threshold. The exponential shape strengthens the barrier effect of less favorable areas.

en:
Resource Title: Amphibian whole life cycle predictors - 10m resolution
    Size: 734915785 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/8197fa13-272f-4ab0-b8fa-061269b079b6/resource/26ceef50-633c-469b-970d-cd6e453e9500/download/amphibian_predictors_sdms.zip
    Description: Amphibian whole life cycle predictors include 6 variable types:
(1)	Topographic – slope [°]: derived from the Swiss Topographical Landscape model (swissTLM 3D 2015) at 2 m resolution (elevation uncertainty ± 0.5m) and, subsequently, aggregated to a median value;
(2)	Hydrologic: two predictors including nearest distance to water [m] (all water-related objects and rasterized lotic, lentic and reedbeds from the regional cadastral maps – AV: Daten der Amtliche Vermessung) and runoff coefficient [C] (between 0 and 1 relating amount of runoff to amount of precipitation, mapped AV: Daten der Amtliche Vermessung data against literature values for different land covers);
(3)	Edaphic – soil moisture variability (not available): can gathered from the spatial modelling of Ecological Indicator Values in Switzerland (Descombes et al., 2020) and resampled from 100m to 10m;
(4)	Vegetation: we calculated the median and standard deviation of the Normalized Difference Vegetation Index (NDVI) from all available cloud free Sentinel-2 pixels available between April and October for the years 2016 to 2019 (Drusch et al., 2012) and the median vegetation height [m] (not available) can gathered from Ginzler and Hobi (2015);
(5)	Land-use derived: includes three predictors, grassland density [grassland/cell] (not available) - can be gathered from Pazur et al. (2021), total grasslands area evaluated using a spatial weight matrix and a 250m neighborhood radius around each cell; urbanization [buildings/cell] – average building density for a 250m neighborhood around each cell based on the AV: Daten der Amtliche Vermessung ; and traffic intensity [vehicles/day] – average daily vehicle count (Nationales Personenverkehrsmodells);
(6)	Movement-ecology related: includes three predictors of proximity – nearest forest distance [m]; nearest rock-gravel and sandy area (RGS) distance [m] and nearest road distance [m] (considering any road type) – all calculated based on the AV: Daten der Amtliche Vermessung maps.

biodiversity

display_name: biodiversity

circuit-theory

display_name: circuit-theory

ecological-corridors

display_name: ecological-corridors

human-dominated-landscapes

display_name: human-dominated-landscapes

integrated-modelling

display_name: integrated-modelling

urban-spatial-planning

display_name: urban-spatial-planning


en: L'objectif de ce livre blanc est de fournir aux décideurs, aux administrations et aux parties prenantes les résultats de recherche les plus récents afin de promouvoir l'utilisation optimale de la bioénergie issue des engrais de ferme dans la transition énergétique suisse. A cette fin, les résultats du centre de compétence suisse pour la recherche en bioénergie - SCCER BIOSWEET - sont résumés et présentés dans un contexte plus large. Si rien d'autre n'est mentionné, les résultats se réfèrent à la Suisse et, dans le cas de la matière première, aux potentiels nationaux de biomasse.

en: review manure bioenergy biogas switzerland

envidat

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

en: Production de biogaz à partir d’engrais de ferme en Suisse

bioenergy

display_name: bioenergy

biogas

display_name: biogas

manure

display_name: manure

review

display_name: review

switzerland

display_name: switzerland


biogas-aus-hofdunger-in-der-schweiz

en: Ziel dieses Whitepapers ist es, Entscheidungsträgern, Verwaltungen und Stakeholdern die aktuellsten Forschungsergebnisse zur Verfügung zu stellen, um die optimale Nutzung von Bioenergie aus Hofdünger in der Schweizer Energiewende zu fördern. Zu diesem Zweck werden die Ergebnisse des Schweizer Kompetenzzentrums für Bioenergieforschung - SCCER BIOSWEET - zusammengefasst und in einem breiteren Kontext dargestellt. Wenn nichts anderes erwähnt wird, beziehen sich die Ergebnisse auf die Schweiz und im Falle der Ressourcen auf die heimischen Biomassepotenziale.

envidat

en: EnviDat: the Environmental Data Portal

en: Biogas aus Hofdünger in der Schweiz

title_for_slug: biogas-aus-hofdunger-in-der-schweiz

envi

en: Environment


en: Aim of this white paper is to provide decision-makers, administrations and stakeholders with the most current research findings in order to promote the optimal use of bioenergy from manure in the Swiss energy transition. For this purpose, the results of the Swiss competence center for bioenergy research - SCCER BIOSWEET - are summarized and presented in a broader context. If nothing else is mentioned, the results refer to Switzerland and in case of the feedstock to the domestic biomass potentials.

en: review manure bioenergy switzerland biogas

en: Biogas from animal manure in Switzerland


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).

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

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

en:
Resource Title: slope
    Size: 10630454 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/3ad576ef-181c-485a-b794-53381eed55d5/download/slope.tif
    Description: Slope °

format: TIFF

media_type: image/tiff

en:
Resource Title: income_0010
    Size: 2453471 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/d4800254-34c8-4699-ad14-78a32d90a8e8/download/income_0010.tif
    Description: Mean income in Reais (R$) per census sector (change per year)

en:
Resource Title: isolated
    Size: 1365075 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/08978ae8-8980-446a-af9b-28b6e18b8dcb/download/isolated.tif
    Description: New built-up pixels classified as isolated

en:
Resource Title: water_inc_0010
    Size: 1464652 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/5f52216a-8f44-472b-b70d-6bb003968d1d/download/water_inc_0010.tif
    Description: Increase in clean water provision service

en:
Resource Title: master_plan_Ubatuba
    Size: 458342 bytes
    Format: application/x-7z-compressed
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/3c1e0ad8-fb01-447a-b325-a9574bdaeb06/download/master_plan_ubatuba.7z
    Description: Master plan Ubatuba (zones)

en:
Resource Title: Axial
    Size: 1360715 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/7dc6ab63-6a3b-4744-8b8e-b899427e95a5/download/axial.tif
    Description: New built-up pixels classified as axial

en:
Resource Title: alfa_0010
    Size: 2405447 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/b0604e68-15bf-4c85-a4ca-9bfe519fd03a/download/alfa_0010.tif
    Description: Basic education in % per census sector (change per
year)

en:
Resource Title: urban_persistence
    Size: 1412319 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/09b0444f-8dbd-4a8a-b5ac-26ba6d463401/download/urban_persistence.tif
    Description: Urban persistence: Built-up 2005 = Built-up 2015

en:
Resource Title: house_inc_0010
    Size: 1559472 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/309672b6-4fa2-411c-999a-5b119c72fbef/download/house_inc_0010.tif
    Description: Permanent housing density increase

en:
Resource Title: EEZ_2005
    Size: 755680 bytes
    Format: application/x-7z-compressed
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/df399923-2f14-45df-bc12-707385ea2fe7/download/eez_2005.7z
    Description: Ecological-Economic Zoning (EEZ)

en:
Resource Title: Variables description
    Size: 89561 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/8a4afdc9-9af4-4922-8856-6c0e67a1bcca/download/table.pdf
    Description: Table of content. Variables description, source, and other important information

en:
Resource Title: TPI300
    Size: 11941205 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/3a51f451-aa57-4d4e-b4a8-a07786d7a18b/download/tpi300.tif
    Description: Topographic Index Position (TPI)

en:
Resource Title: master_plan_Ilhabela
    Size: 92087 bytes
    Format: application/x-7z-compressed
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/b3f65b7e-8f44-4cb1-8f3b-21e45da6ecd4/download/master_plan_ilhabela.7z
    Description: Master plan Ihabela (zones)

en:
Resource Title: infill
    Size: 1337272 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/5b598ade-cb86-4f8b-aadc-c18e12814d15/download/infill.tif
    Description: New built-up pixels classified as infill

en:
Resource Title: ndvi_inc
    Size: 1552732 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/3f14dfcc-668a-4321-9719-f0b58737deda/download/ndvi_inc.tif
    Description: Increase in Normalized Difference Vegetation Index

en:
Resource Title: forest_gain_0515
    Size: 1359475 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/cc2499a2-e179-473d-ba04-ba7502d0e1ca/download/forest_gain_0515.tif
    Description: Native forest cover gains from 2005 to 2015

en:
Resource Title: sanit_inc_0010
    Size: 1424839 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/a9a2d0d1-2de5-4a14-9b82-eb22e9dd2a8f/download/sanit_inc_0010.tif
    Description: Increase in sanitation service

en:
Resource Title: Descricao dos dados em portiuguês
    Size: 93276 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/3142ea4a-a5cf-41e9-b04e-3eea956e32af/download/tabela.pdf
    Description: Portugues description

en:
Resource Title: waste_inc_0010
    Size: 1528622 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/e50c4311-9521-4ec1-a177-cc9952a5cff3/download/waste_inc_0010.tif
    Description: Increase in waste collection service

en:
Resource Title: hdi_0010
    Size: 1672974 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/4e9794cf-e518-4b03-abe9-8c797b3a0562/download/hdi_0010.tif
    Description: Human Development Index (HDI) per municipality (change per year)

en:
Resource Title: forest_persist_0515
    Size: 1675441 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/a7d6f906-ee5d-4d64-a713-754764577b8a/download/forest_persist_0515.tif
    Description: Native forest cover persistence from 2005 to 2015

en:
Resource Title: pop_0010.
    Size: 2604307 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/cdbd343b-fe3b-4404-a9ed-6a2516ec4610/resource/21d0b38e-7066-4ac1-9c43-4c6fb732f337/download/pop_0010.tif
    Description: Population density per pixel (change per year)

basic-services

display_name: basic-services

built-up-classification

display_name: built-up-classification

forest-gain

display_name: forest-gain

forest-persistence

display_name: forest-persistence

land-use-and-cover-change

display_name: land-use-and-cover-change

nature-conservation

display_name: nature-conservation

planning-outcome

display_name: planning-outcome


en: This database contains GNSS derived snow water equivalent (SWE), liquid water content (LWC), and snow height (HS)  and reference data collected during the two winter 2018-2020 at 4 sites Weissfluhjoch (2540 m asl, 46°49’47’’ N, 9°48’34’’E), Laret (1515 m asl, . 46°50’2’’N, 9°52’17’’E), Klosters (1200 m asl, 46°51’49’’N, 9°53’17’’E), and Küblis (815 m asl, 46°54’48’’N, 9°46’54’’E).

en: gps snow swe gnss lwc liquid-water-content snow-water-equivalent

envidat

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

en: EnviDat: the Environmental Data Portal

en: Snow water equivalent measurements with low-cost GNSS receivers along a steep elevation gradient in the East-ern Swiss Alps

envi

en: Environment

en:
Resource Title: Meteo_LAR
    Size: 2994700 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/e582db3a-a0ba-4527-9c2f-ca961562c317/download/lar_meteo.csv
    Description: Snow and meteo data from automatic sensors for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

en:
Resource Title: Obs_WFJ
    Size: 23691 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/4b796a10-3ab7-42c9-ab1f-3d6218692326/download/wfj_obs.csv
    Description: Daily observer data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en:
Resource Title: Obs_Kueblis
    Size: 10744 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/ba8b38d2-fcc3-4345-b763-4b618e49836f/download/kub_obs.csv
    Description: Daily observer data from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020.

en:
Resource Title: day_Laret
    Size: 27183 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/3af4ec59-d2ff-425e-ba49-996cd5a85247/download/lar_day.csv
    Description: Daily data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en:
Resource Title: GNSS_WFJ
    Size: 54679 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/f791ca79-0c9a-4055-9989-d5176e222935/download/wfj_gnss.csv
    Description: GNSS derived SWE, HS, and LWC for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en:
Resource Title: Prof_Laret
    Size: 3424 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/d18ef060-e61f-4196-82ad-859e3faa1c9b/download/lar_prof.csv
    Description: Profile data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

en:
Resource Title: GNSS_klosters
    Size: 19682 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/bc65a02c-f311-49c7-9007-a8df51a87610/download/klo_gnss.csv
    Description: GNSS derived SWE, LWC and HS from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-20

en:
Resource Title: day_WFJ
    Size: 71919 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/01d5ba5c-4734-43b0-b1cd-68e97e0fd711/download/wfj_day.csv
    Description: Daily data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en:
Resource Title: Meteo_Kueblis
    Size: 674638 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/398c7a10-d1bf-4fd3-b7a9-8175843077d7/download/kub_meteo.csv
    Description: Snow and meteo data from automatic sensors from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020.

en:
Resource Title: Profiles_WFJ
    Size: 2365 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/8a7318b9-571d-4da9-9686-2330bf0f373a/download/wfj_prof.csv
    Description: Snow profiles data for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en:
Resource Title: Webcam images
    Size: 236611230 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/292ddd39-edd8-4df4-8dc7-f79137c6494d/download/pictures_webcam.zip
    Description: Webcam images showing some typical differences in the snow conditions at the four sites (Küblis, Klosters, Laret and Weissfluhjoch) for two time periods (14-18.3.2019 and 27.1-11.2.2020). Both time periods included a precipitation event with partial rain-on–snow occurring at the lower locations.

en:
Resource Title: day_Klosters
    Size: 28691 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/40726e6f-f175-4033-8df7-526e42cdc266/download/klo_day.csv
    Description: Daily data from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en:
Resource Title: GNSS_kueblis
    Size: 17196 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/973a7cde-cf6e-484e-8da1-a2da1de4fc49/download/kub_gnss.csv
    Description: GNSS derived SWE, LWC and HS from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-20

en:
Resource Title: GNSS_laret
    Size: 15587 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/0a95d946-3fd0-4d83-bfb9-4fdcb210a0d3/download/lar_gnss.csv
    Description: GNSS derived data for Laret at 1510 m a.s.l. (46°50’2’’N, 9°52’17’’E) for the two winter 2018-2020.

en:
Resource Title: day_Kueblis
    Size: 28335 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/df76c91e-b71e-4441-a905-31b32b8782fb/download/kub_day.csv
    Description: Daily data from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020. These data were processed to daily values from the other data files.

en:
Resource Title: Manual profiles
    Size: 4065671 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/f7ac1de9-3b24-47fa-9f67-cd6560674a18/download/profiles.zip
    Description: Data of the traditional manual profiles for the locations Weissfluhjoch, Klosters and Küblis. For the location of Küblis there are no traditional manual profile data.  We provided the data as both PDF and .CAAML format (See https://niviz.org/  or http://caaml.org/Schemas/V5.0/Profiles/SnowProfileIACS/CAAMLv5_SnowProfileIACS.pdf). Moreover, we added LWC and density profiles when available (all locations).

en:
Resource Title: Obs_Klosters
    Size: 13499 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/9bb74e8a-d19b-49ca-aecf-feed84c3e324/download/klo_obs.csv
    Description: Daily observer data from automatic sensors from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020.

en:
Resource Title: Meteo_WFJ
    Size: 2779069 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/be4ca553-12f8-4a2a-94bc-1672dfbac5b9/download/wfj_meteo.csv
    Description: Snow and meteo data from automatic sensors for the site Weissfluhjoch at 2540 m a.s.l (46°49’47’’ N, 9°48’34’’E) for the two winters 2018-2020.

en:
Resource Title: Profiles_Kueblis
    Size: 1151 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/e6d6f714-5f1a-4cc9-9ab1-3cf5417073a7/download/kub_prof.csv
    Description: Snow profiles data from Küblis at 820 m a.s.l. (46°54’48’’N, 9°46’54’’E) for the two winters 2018-2020.

en:
Resource Title: Profiles_Klosters
    Size: 2530 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/dc102dd3-9d9d-4757-9263-9306c6474b5c/download/klo_prof.csv
    Description: Snow profiles data from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020.

en:
Resource Title: Meteo_Klosters
    Size: 697178 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/9e303b80-9c88-407c-b926-e8f2f7eb63da/resource/0ba3714e-3caa-47d3-8b8c-c235fc57760e/download/klo_meteo.csv
    Description: Snow and meteo data from automatic sensors from Klosters (1210 m a.s.l. ,46°51’49’’N, 9°53’17’’E) for the two winters 2018-2020.

gnss

display_name: gnss

gps

display_name: gps

liquid-water-content

display_name: liquid-water-content

lwc

display_name: lwc

snow

display_name: snow

snow-water-equivalent

display_name: snow-water-equivalent


en:
Avalanche problem types were derived from snow cover simulations with the models Crocus and SNOWPACK at the Weissfluhjoch study plot, Davos, CH. The data include annual frequencies of avalanche problem types for the seasons 1999-2017 and daily presence of avalanche problem types for the period 01.01.2016 - 30.04.2016.
Avalanche activity was derived from two seismic sensor arrays deployed no further than 15 km from Weissfluhjoch, Davos, CH. The data cover the period 01.01.2016 - 30.04.2016.

en: snowpack snow-instability weissfluhjoch avalanche-problem-type avalanche-activity snow avalanche-danger avalanche snow-cover-modeling crocus

envidat

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

en: EnviDat: the Environmental Data Portal

en: Simulated avalanche problem types and seismic avalanche activity around Weissfluhjoch

envi

en: Environment

en:
Resource Title: 2015-2016 Seismic avalanche acitivity catalogue
    Size: 322 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/67f54960-fb04-4315-980b-029ea5b8b6c6/resource/aee18298-2eea-4876-ba1c-15cd7e69a031/download/2015_16_ava_seismic.csv
    Description: Avalanches were indentified in seismic data recorded by two seismic arrays, one deployed at Wannengrat (2,500 m) and another one deployed in the Dischma valley (2,000 m).
The data cover the period from 01.01.2016 - 30.04.2016 and are presented in Figure 7 in Reuter et al. (2021).

en:
Resource Title: 1999-2017 Avalanche problem types from Crocus
    Size: 507 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/67f54960-fb04-4315-980b-029ea5b8b6c6/resource/3b8f8b29-2be6-473e-8e33-0b598670531d/download/wfj_1999-2017_crocus_avalanche_problem_types.csv
    Description: Avalanche problem types were derived from snow cover simulations with the model Crocus at the Weissfluhjoch study plot, Davos, CH.
The data cover the winter seasons between 1999-2017 and include the simulated annual frequencies of new snow, wind slab, persistent weak layer, aging-persistent weak layer and wet snow problem types.
The data are presented in Figure 8 in Reuter et al. (2021).

en:
Resource Title: 1999-2017 Avalanche problem types from SNOWPACK
    Size: 493 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/67f54960-fb04-4315-980b-029ea5b8b6c6/resource/fd62a04d-b072-46d9-b860-0415656b3e38/download/wfj_1999-2017_snowpack_avalanche_problem_types.csv
    Description: Avalanche problem types were derived from snow cover simulations with the model SNOWPACK at the Weissfluhjoch study plot, Davos, CH.
The data cover the winter seasons between 1999-2017 and include the simulated annual frequencies of new snow, wind slab, persistent weak layer, aging-persistent weak layer and wet snow problem types.
The data are shown in Figure 8 in Reuter et al. (2021).

en:
Resource Title: 2015-2016 Avalanche problem types from Crocus
    Size: 2749 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/67f54960-fb04-4315-980b-029ea5b8b6c6/resource/07bdeaa3-c5c4-4c04-bca2-66a8d84310ab/download/2015-2016_crocus_ava_prob_types.csv
    Description: Avalanche problem types were derived from a snow cover simulation with the model Crocus for the winter season 2015-2016 at the Weissfluhjoch study plot, Davos, CH.
The data cover the period 01.01.2016-30.04.2016. The binary indicator (0,1) tells whether an avalanche problem type was identified in the snow cover simulation (1) or not (0).
The data are presented in Figure 7 in Reuter et al. (2021).

en:
Resource Title: 2015-2016 Avalanche problem types from SNOWPACK
    Size: 2749 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/67f54960-fb04-4315-980b-029ea5b8b6c6/resource/b76405da-ff97-4720-8a1e-323d6c4b6da9/download/2015-2016_snowpack_ava_prob_types.csv
    Description: Avalanche problem types were derived from a snow cover simulation with the model SNOWAPCK for the winter season 2015-2016 at the Weissfluhjoch study plot, Davos, CH.
The data cover the period 01.01.2016-30.04.2016. The binary indicator (0,1) tells whether an avalanche problem type was identified in the snow cover simulation (1) or not (0).
The data are presented in Figure 7 in Reuter et al. (2021).

avalanche

display_name: avalanche

avalanche-activity

display_name: avalanche-activity

avalanche-danger

display_name: avalanche-danger

avalanche-problem-type

display_name: avalanche-problem-type

crocus

display_name: crocus

snow-cover-modeling

display_name: snow-cover-modeling

snow-instability

display_name: snow-instability

snowpack

display_name: snowpack

weissfluhjoch

display_name: weissfluhjoch


en:
This dataset includes the parallel application and the main results supporting the research article "Modeling snow saltation: the effect of grain size and interparticle cohesion" published at the Journal of Geophysical Research: Atmospheres.
The code is a flow solver based on the Large Eddy Simulation (LES) technique coupled with a Lagrangian Stochastic Model (LSM). The interaction of snow particles with the bed is modeled with statistical and physically-based models for aerodynamic entrainment, rebound and splash, following the works of Groot Zwaaftink et al. (2014), Comola and Lehning (2017) and Sharma et al. (2018). This algorithm was also used by Sigmund et al. (2021) to model snow sublimation.

en: large-eddy-simulations saltation drifting-snow mixed-grain-transport snow-cohesion

en:
Resource Title: Data in Figures
    Size: 312666 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/c4543b3d-d898-4364-8194-bb56f2fe41f9/resource/9824aead-cf4d-4499-ac43-d392daca5417/download/figures_data.zip
    Description: Simulation results presented in Figures 1 to 11 of the research article "Modeling snow saltattion: the effect of grain size and interparticle cohesion" in csv format.

en:
Resource Title: LES-LSM code
    Size: 405936 bytes
    Format: application/x-tar
    URL: https://www.envidat.ch/dataset/c4543b3d-d898-4364-8194-bb56f2fe41f9/resource/aa04cdac-a8cc-446d-84ef-b9f69b767d83/download/les-lsm.tar.xz
    Description: Source code used to perform the analysis presented at the research article "Modeling snow saltation: the effect of grain size and interparticle cohesion". The source code is also available at the WSL/SLF GitLab Repository (https://gitlabext.wsl.ch/atmospheric-models/les-lsm).

drifting-snow

display_name: drifting-snow

large-eddy-simulations

display_name: large-eddy-simulations

mixed-grain-transport

display_name: mixed-grain-transport

saltation

display_name: saltation

snow-cohesion

display_name: snow-cohesion


en:
One individual per species, vitality class (low and high) and height class (eight classes: 0–10, 11–20, 21–35, 36–60, 61–90, 91–130, 131–200 and 201–500 cm) was randomly selected and harvested in each of the six plots. This resulted in a sample of 82, 80 and 89 living individuals of A. platanoides, A. pseudoplatanus and F. sylvatica, respectively.
 Additionally stems of dead Acer spp. and F. sylvatica trees that had died within the last three years (2015–2018) were randomly harvested, matching the height classes of the harvested living trees wherever possible. In total, 179 dead young trees (60 A. platanoides, 72 A. pseudoplatanus and 47 F. sylvatica) were collected.
 Variables:
* species_code: a_pla - Acer platanoides, a_pse - Acer pseudoplatanus, f_syl - Fagus sylvatica
* species: as above
* dummy: 0 - living individual, 1 - dead individual
* LAR_cm2_g: leaf area ratio or ratio of leaf area to total plant biomass, [cm2/g]
* tree_age: in years
* avg_ring_micron: average width of the last 5 rings in tree life excluding the last ring
* dry_mass_g: aboveground and belowground biomass
* DLI: direct light index (measured only under living individuals)
* BLI: diffuse light index (measured only under living individuals)
* GLI: global light index

en: shade-tolerance fagus mortality-rate acer dominance lar regeneration

en: Mortality of regeneration: Acer spp. and Fagus sylvatica

en:
Resource Title: Mortality of regeneration
    Size: 60209 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/8d9df0cd-f20c-423f-a4ab-71fc213871de/resource/37fb69d9-c553-4931-89ba-31857ac15c04/download/imputed.csv
    Description: Acer platanoides, Acer pseudoplatanus and Fagus sylvatica living and dead juveniles (0-500 cm in height) grown in the deep shade of a prime beech-dominated forest.

en:
Resource Title: Survival_AFT_R_script
    Size: 18015 bytes
    Format: text/x-r
    URL: https://www.envidat.ch/dataset/8d9df0cd-f20c-423f-a4ab-71fc213871de/resource/6ff5b7a2-6662-41ab-8c2d-68f688cde189/download/survival_script.r
    Description: The R-script of survival analysis for accelerated failure time model

acer

display_name: acer

dominance

display_name: dominance

fagus

display_name: fagus

lar

display_name: lar

mortality-rate

display_name: mortality-rate

regeneration

display_name: regeneration

shade-tolerance

display_name: shade-tolerance


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?

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

envidat

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

en: EnviDat: the Environmental Data Portal

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

en:
Resource Title: 5. Planning steps.
    Size: 3898 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/61f83301-e7aa-4374-a4bd-7fbf35fb12ff/resource/f456f712-f67c-40d0-9f6a-7e18f2746ce4/download/5.-planning_steps.csv
    Description: 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.

en:
Resource Title: 6. Protocol
    Size: 91483 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/61f83301-e7aa-4374-a4bd-7fbf35fb12ff/resource/8675307f-37dc-4062-b990-3202634a5dbc/download/6_protocol.xlsx
    Description: 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?

en:
Resource Title: 3. Use of any early concept.
    Size: 157296 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/61f83301-e7aa-4374-a4bd-7fbf35fb12ff/resource/21b7a6f8-9f37-4ddb-b8fa-b17f25428996/download/3.-use_of_any_early_concept.csv
    Description: 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.

en:
Resource Title: 1. Frequency of early concepts.
    Size: 1002570 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/61f83301-e7aa-4374-a4bd-7fbf35fb12ff/resource/3b4f848b-5dc5-4f24-96b3-09778c0a8655/download/1.-frequency_of_early_concepts.csv
    Description:  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.

en:
Resource Title: 4. Use of any additional concept.
    Size: 146949 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/61f83301-e7aa-4374-a4bd-7fbf35fb12ff/resource/070af529-0b2d-4ead-b0d8-86aaf15afd04/download/4.-use_of_any_additional_concept.csv
    Description: 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.

en:
Resource Title: 2. Frequency of additional concepts.
    Size: 1187545 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/61f83301-e7aa-4374-a4bd-7fbf35fb12ff/resource/7ae0ec68-0675-4d2b-a5d2-51d08e153f99/download/2-frequency_of_additional_concept.csv
    Description: 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.

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


en:
This dataset contains all data, on which the following publication below is based.
Paper Citation:
_Resch, M.C., Schütz, M., Ochoa-Hueso, R., Buchmann, N., Frey, B., Graf, U., van der Putten, W.H., Zimmermann, S., Risch, A.C. (in review). Long-term recovery of above- and belowground interactions in restored grassland after topsoil removal and seed addition. Journal of Applied Ecology_
Please cite this paper together with the citation for the datafile.
Study area and experimental design
The study was conducted in and around two nature reserves, Eigental and Altläufe der Glatt, which were located approximately 5 km apart (47°27´ to 47°29´ N, 8°37´ to 8°32´ E, 417 to 572 m a.s.l., Canton of Zurich, Switzerland; Figure S1 and S2, Table S1). Mean annual temperature and precipitation are 9.8 ± 0.6 °C and 990 ± 168 mm (Kloten climate station 1988-2018; MeteoSchweiz, 2019). TFor this study, we used a space-for-time approach based on eight restoration sites that were between 3 and 32 years old. We measured recovery and restoration success by comparing the restored grasslands with intensively managed and semi-natural grasslands. Using a space-for-time approach requires high similarities in historical properties of the site, such as soil conditions and management regimes, to assure that temporal processes are appropriately represented by spatial patterns (Walker et al., 2010). This was the case in our study. The restored sites had similar soil conditions (i.e., soil type, structure, water availability) as the targeted semi-natural grasslands, while they shared the same agricultural legacy with intensively managed grasslands, i.e., biomass harvest and fertilization (manure and/or slurry) three to five times a year as well as tillage. We randomly established three 5 m x 5 m (25-m2) plots for plant identification and three 2 m x 2 m (4-m2) subplots for soil biotic and abiotic data collection at least 2 m away from the 25-m2 plots in each restoration site. Sites of similar age were grouped into four age classes: Y.4 (3 & 4 years after restoration), Y.18 (17 & 19 years), Y.24 (23 & 25 years), and Y.30 (27 & 32 years). Six intensively managed (Initial) and six semi-natural grassland (Target) sites complemented the experimental set-up, for a total of 36 plots. All plots were sampled under similar conditions, i.e., day of the year, air temperature, soil moisture, and time since last rain event, in June/July 2017 (intensively managed and semi-natural plots) and 2018 (restored plots).
Collection of plants and selected soil biota data
Plant species cover (in %) was visually estimated in each 25-m2 plot in mid-June (Braun-Blanquet, 1964; nomenclature: Lauber & Wagner, 1996). We calculated Shannon diversity and assessed plant community structure. We included soil microbial (fungi, procaryotes) and nematodes in our study as they represent the majority of soil biotic diversity and abundance (Bardgett & van der Putten, 2014), cover various trophic levels of the soil food web (Bongers & Ferris, 1999), and play key roles in soil functioning and ecosystem processes (Bardgett & van der Putten, 2014). In particular, soil nematodes were found to be well suited belowground indicators to evaluate recovery/development after restoration (e.g. Frouz, et al. 2008; Kardol et al., 2009; Resch et al., 2019). We randomly collected ten soil cores (2.2 cm diameter x 12 cm depths; sampler from Giddings Machine Company, Windsor, USA) in the 4-m2 subplots to assess soil nematode and microbial (fungal, prokaryotic) diversities and community structures. For soil nematodes, eight of the soil cores were combined and gently homogenized, placed in coolers and stored at 4 °C and transported to the laboratory (Netherlands Institute of Ecology, NIOO, Wageningen, Netherlands) within three days after collection. Free-living nematodes were extracted from 200 g of fresh soil using Oostenbrink elutriators (Oostenbrink, 1960). After extraction, each sample was divided into three subsamples, two for molecular identification and one to determine nematode abundance (see Resch et al., 2019). For the molecular work, two subsamples were stored in 70% ethanol (final volume 10 mL each) and transported to the laboratory at the Swiss Federal Research Institute WSL (Birmensdorf, Switzerland). Each subsample was reduced to roughly 200 μL by centrifugation and removal of the supernatant. The remaining ethanol was vaporized (65 °C for 3 h). Thereafter, 180 μL ATL buffer solution (Qiagen, Hilden, Germany) was immediately added and samples were stored at 4 °C until further processing. From these samples, nematode metagenomic DNA was extracted using the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany) according to the manufacturer`s protocol, except for the incubation step which was run at 56 °C for 4 h. PCR amplification of the V6-V8 region of the eukaryotic small-subunit (18S) was performed with 7.5 μL of genomic DNA template (ca. 1 ng/μL) in 25 μL reactions containing 5 μL PCR reaction buffer, 2.5 mM MgCL2, 0.2 mM dNTPs, 0.8 μM of each primer (NemF: Sapkota & Nicolaisen, 2015; 18Sr2b: Porazinska et al., 2009), 0.5 μL BSA, and 0.25 μL GoTaq G2 Hot Start Polymerase (Promega Corporation, Madison, USA). Amplification was using an initial DNA denaturation step of 95 °C for 2 min, followed by 35 cycles at 94 °C for 40 sec, 58 °C for 40 sec, 72 °C for 1 min, and a final elongation step at 72 °C for 10 min. Filtering, dereplication, sample inference, chimera identification, and merging of paired-end reads was implemented using the DADA2 pipeline (v.1.12; Callahan et al., 2016) to finally assign amplicon sequence variants (ASVs) as taxonomic units.
We combined and homogenized the remaining two soil cores to assess soil microbes, placed them in coolers (4 °C) and transported them to the laboratory at WSL. Metagenomic DNA was extracted from 8 g sieved soil (2 mm) using the DNAeasy PowerMax Soil Kit (Qiagen, Hilden, Germany) according to the manufacturer´s protocol. PCR amplification of the V3-V4 region of the small-subunit (16S) of prokaryotes (i.e., bacteria and archaea) and the ribosomal internal transcribed spacer region (ITS2) of fungi was performed with 1 ng of template DNA using PCR primers and conditions as previously described (Frey et al., 2016). PCRs were run in triplicates, pooled and sent to the Genome Quebec Innovation Centre (Montreal, QC, Canada) for barcoding using the Fluidigm Access Array technology (Fluidigm) and paired-end sequencing on the Illumina MiSeq v3 platform (Illumina Inc., San Diego, USA). Quality filtering, clustering into operational taxonomic units (OTUs, 97% similarity cutoffs) and taxonomic assignment were performed as previously described (Resch et al., 2021).Taxonomic classification of nematode, prokaryotic and fungal sequences was conducted querying against the most recent versions of PR2 (v.4.11.1; Guillou et al., 2013), SILVA (v.132; Quast et al., 2013), and UNITE (v.8; Nilsson et al., 2019) reference sequence databases. Taxonomic assignment cutoffs were set to confidence rankings ≥ 0.8 (below ranked as unclassified). Prokaryotic OTUs assigned to mitochondria or chloroplasts as well as OTUs or ASVs assigned to other than Fungi or Nematoda were manually removed prior to data analysis. The three datasets were filtered to discard singletons and doubletons. Taxonomic abundance matrices were rarefied to the lowest number of sequences per community to achieve parity of the total number of reads between samples (Prokaryotes: 10,929 reads; Fungi: 18,337 reads; Nematodes: 6,662 reads). We calculated Shannon diversity and assessed community structures for soil nematodes, prokaryotes and fungi based on their relative abundances of ASV or OTU at the taxon level.
Collection of soil physical and chemical properties
We randomly collected one undisturbed soil core (5 cm diameter, 12 cm depth) per 4-m2 subplot using a steel cylinder that fit into the soil corer. The cylinders were capped to avoid disturbance during transport and used to measure field capacity, rock content and fine earth density as previously described (Resch et al., 2021). We randomly collected another three soil cores (5 cm diameter, 12 cm depths) in each 4-m2 subplot to determine soil chemical properties. The cores were pooled, dried at 60 °C for 48 h and passed through a 2 mm sieve. We measured soil pH (CaCl2) on dried samples, total nitrogen (N) and organic carbon (C) concentration on dried and fine-ground samples (≤ 0.5 mm; for details see Resch et al., 2021). We calculated total N and organic C pools after correcting its concentration for soil depth, rock content and fine earth density.

en: nematodes soil-biodiversity plants bacteria soil restoration fungi soil-biology biodiversity topsoil-removal soil-chemistry grassland

en: Long-term recovery of above-and belowground interactions in restored grasslands

envi

en: Environment

en:
Resource Title: Vegetation data
    Size: 58554 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/9c4d0d64-3ddd-419d-a002-5a725b778274/resource/9940b2c1-35a6-46e8-9604-4fb866f1ddd4/download/vegetation_sample.data_coverage.xlsx
    Description: Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)
column I-LW	Plant abundance per species (in total 327 plant species) expressed as cover percentage estimation according to van der Maarel (vdM 1979; 7 classes: 1-7); nomenclature  according to Lauber & Wagner (1996)

en:
Resource Title: Soil properties
    Size: 23266 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/9c4d0d64-3ddd-419d-a002-5a725b778274/resource/143c6e0e-1aa5-4510-b333-9c00e99c59fd/download/soil.properties_pcoas_h.values.xlsx
    Description: Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)
N.pool	Soil nitrogen pool (June 2017); corrected for bulk density and 12 cm sampling depth [kg ha-1 12cm-1]
Corg.pool	Soil organic carbon pool (June 2017); corrected for bulk density and 12 cm sampling depth [kg ha-1 12cm-1]
pH	soil pH [CaCl2]
BD	Bulk density [g cm-3]
FC	Field capacity [%]
Veg.1	principle coordinate axis 1 (vegetation)
Veg.1	principle coordinate axis 2  (vegetation)
Nem.1	principle coordinate axis 1 (nematoda)
Nem.2	principle coordinate axis 2  (nematoda)
Fun.1	principle coordinate axis 1 (fungi)
Fun.2	principle coordinate axis 2  (fungi)
Prok.1	principle coordinate axis 1 (prokaryota)
Prok.2	principle coordinate axis 2  (prokaryota)
Veg.H	Shannon diversity (vegetation)
Nem.H	Shannon diversity (nematoda)
Fun.H	Shannon diversity (fungi)
Prok.H	Shannon diversity (prokaryota)

en:
Resource Title: Nematode data
    Size: 256130 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/9c4d0d64-3ddd-419d-a002-5a725b778274/resource/3f820b86-ee6b-4466-bc40-4346eac4cbcd/download/nematoda_sample.data_taxonomy_abundance.xlsx
    Description: Sample.data
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)

Taxonomy
ASV.ID	unit number.letter code per amplicon sequence variant (in total 904)
Taxonomic rank	Phylum, Class, Order, Family, Genus, Species

Abundance
columns	Plot.ID (see Sample.data)
rows	ASV.ID (see Taxonomy)

en:
Resource Title: Fungi data
    Size: 1175564 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/9c4d0d64-3ddd-419d-a002-5a725b778274/resource/64d9bac3-d7ce-4ae9-8922-f0ee496292a9/download/fungi_sample.data_taxonomy_abundance.xlsx
    Description: Sample.data
Plot.ID	consecutive numbers for plots (in total 36 plots)
Treatment	One restoration treatment and two reference treatments. Topsoil+Propagules= Topsoil removal (upper 10-30 cm) combined with introduction of target plant species; Initial=intensively managed grasslands; Target= species-rich semi-wet to semi-dry grasslands
treat.yr	Treatment abbreviation. For restored grasslands: Treatment abbreviation + time since restoration; Initial + Target = NA
yr.trt	year when topsoil was removed; Initial + Target = NA
Age	time since restoration (sampling year 2018 - yr.trt); Initial + Target = NA
Age.class	restored sites of similar age grouped into four age classes. Y.4 = Year 3+4 (recently restored); Y.18 = Year 17+19 (mid-term); Y.24 = Year 23+25 (long-term); Y.30 = Year 27+32 (long-term); Initial + Target = NA
Cluster	clustered spatial arrangment of sites in the field (see Supporting Information Fig. S1)
Site	clustered spatial arrangment (replicates) of plots per site (see Supporting Information Fig. S1)

Taxonomy
OTU.ID	consecutive numbers for operational taxonomic units (OTUe 1-7794)
Taxonomic rank	Domain, Phylum, Class, Order, Family, Genus, Species

Abundance
columns	Plot.ID (see Sample.data)
rows	OTU.ID (see Taxonomy)

en:
Resource Title: Networks
    Size: 21006 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/9c4d0d64-3ddd-419d-a002-5a725b778274/resource/3f35aa3b-5f64-4d55-907f-ac6c1f2d490c/download/network.correlations.xlsx
    Description: Treatment	Treatment groups; Y.4-18 = restored grasslands; Initial = intensively managed grasslands; Target = species-rich semi-wet to semi-dry grasslands
Correlation	non-parametric Spearman rank correlations
Vertex.1+2	Comparison between network components; N.pool = total soil nitrogen pool [kg ha-1 12 cm-1]; Corg.pool = soil organic carbon pool [kg ha-1 12 cm-1], pH = soil pH [CaCl2]; BD = bulk density [g cm-3]; FC = field capacity [%]
	PCoA 1 + 2 biotic communities [Veg = vegetation; Nem = nematoda; Fun = fungi; Prok = prokaryota]
Type	Interactions type; A.A = abiotic.abiotic; A.B = abiotic.biotic; B.B = biotic.biotic

en:
Resource Title: Ecosystem coupling_interactions
    Size: 18890 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/9c4d0d64-3ddd-419d-a002-5a725b778274/resource/046d129c-3bc2-4214-8d9c-ffbf8659b245/download/ecosystem.coupling_ecological.interactions.xlsx
    Description: Treatment	Treatment groups; Y.4-18 = restored grasslands; Initial = intensively managed grasslands; Target = species-rich semi-wet to semi-dry grasslands
Type	Interactions type; A.A = abiotic.abiotic; A.B = abiotic.biotic; B.B = biotic.biotic
Correlation	non-parametric Spearman rank correlations between biotic and abiotic parameters (ecological interactions)

en:
Resource Title: Distance to target results
    Size: 13678 bytes
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
    URL: https://www.envidat.ch/dataset/9c4d0d64-3ddd-419d-a002-5a725b778274/resource/ed2658c5-c2c2-4b13-b29a-187f199f9f47/download/distance.to.target.xlsx
    Description: Community	biotic community (vegetation, nematoda, fungi, prokaryota)
Treatment	Treatment groups; Y.4-30= restored grasslands (time since restoration); Initial=intensively managed grasslands (I)
Comparison	within- or between-community Bray-Curtis dissimilarity distance comparison
Label	within-community distances of initial/restored grasslands: treatment abbreviation; between-communities distances of initial/restored-to-target grasslands: treatment abbreviation plus Target (T; species-rich grasslands)
mean	Mean distance to Target  (6 plots per treatment group)
sd	Standard deviation of distances to Target (6 plots per treatment group)

bacteria

display_name: bacteria

biodiversity

display_name: biodiversity

fungi

display_name: fungi

grassland

display_name: grassland

nematodes

display_name: nematodes

plants

display_name: plants

restoration

display_name: restoration

soil

display_name: soil

soil-biodiversity

display_name: soil-biodiversity

soil-biology

display_name: soil-biology

soil-chemistry

display_name: soil-chemistry

topsoil-removal

display_name: topsoil-removal