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3,105 results for “vegetation”
Plot Observations of Wetland Vegetation in Sub-Saharan Africa
<p>An R-Image containing plot-observations, Cocktail definitions and syntaxonomy using the packages <a href="https://docs.ropensci.org/taxlist/">taxlist</a> and <a href="https://github.com/kamapu/vegtable">vegtable</a>.</p>
2-round Delphi study on digital technologies in vegetable farming in Switzerland
<p>This dataset contains survey data including the codebook for a 2-round Delphi study we conducted in Switzerland in autumn 2020. Selected experts were asked to describe the future of digital technologies in vegetable farming in Switzerland.</p>
Disturbances in vegetation detected with BFAST in the Purapel fluvial catchment
<p>This dataset contains the results (69 TIFF files) of seasonal disturbances detected in vegetation in the Purapel catchment (southern Chile) for the period from 2002 to 2019. These disturbances were obtained by applying the Breaks for Additive Season and Trend (BFAST, Verbesselt et al., 2010) algorithm to 745 Landsat 5, 7 and 8 imagery. We used Collection 2 Level 2 surface reflectance products and applied the CFMask algorithm (Foga et al., 2017) for cloud masking before utilizing the BFAST algorithm.</p> <p>The BFAST algorithm detects changes in the NDVI time series of each pixel. To determine which event was considered a disturbance, we used the same intensity thresholds as in Cabezas and Fassnacht (2018). We then filtered the results to keep just the disturbances with areas greater than 1 hectare, eliminating noisy data.</p> <p>Except for 2 big wildfires (2015 and 2017) it was assumed that all of the disturbances were clear cuts, since forestry is the main productive activity in the region. This was confirmed by validating the data with 35 manually drawn polygons that were randomly distributed across the catchment. Then, we performed an accuracy assessment, obtaining a confusion matrix with a balanced accuracy of 0,86 and a F1 score of 0,69.</p> <p>Each TIFF file is a binary grid with “zeros” representing no disturbance and “ones” representing a disturbance in the season that the name of the file indicates.</p> <p>A GIF file is also included, which contains the time series of the disturbances for easier graphical purposes.</p> <p>References</p> <p>J. Cabezas and F. E. Fassnacht. Reconstructing the Vegetation Disturbance History of a Biodiversity Hotspot in Central Chile Using Landsat, Bfast and Landtrendr. In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, pages 7636–7639. IEEE, 7 2018. ISBN 978-1-5386-7150-4. doi: 10.1109/IGARSS.2018.8518863.</p> <p>S. Foga, P. L. Scaramuzza, S. Guo, Z. Zhu, R. D. Dilley, T. Beckmann, G. L. Schmidt, J. L. Dwyer, M. Joseph Hughes, and B. Laue. Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194:379–390, 6 2017. ISSN 00344257. doi: 10.1016/j.rse.2017.03.026.</p> <p>J. Verbesselt, R. Hyndman, G. Newnham, and D. Culvenor. Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114(1):106–115, 1 2010. ISSN 00344257. doi: 10.1016/ j.rse.2009.08.014. URL http://linkinghub.elsevier.com/retrieve/pii/S003442570900265X.</p>
Modern pollen data from the East Asian Pollen Database (EAPD): pollen, vegetation and climate relationship
<p>This is a modern pollen dataset of eastern Asia, in which a total of 1756 sample sites is selected from the original database EAPD (East Asian Pollen Database) which consists of 2858 samples. The sample types are mainly surface soil, moss, sediment top (lake, delta, peatland, river basin, reservoir and so on), and dust capture. The pollen data are mostly count numbers, but a few was originally given in percentage (marked with TRUE for proportion or percentage). We have checked pollen taxonomic nomenclature and combined some synonym pollen types from different original sources.</p> <p>This dataset includes only the samples collected in the areas under natural vegetation or land cover with low human disturbance, that the sites located in the agriculture areas or strong human intervention have been excluded. This screening procedure makes the pollen data readily available for biome and climate reconstructions. The contributors' original research concerning pollen-climate relationship from EAPD sources have been published in Zheng, et al. (2014 and 2008), which have revealed that pollen taxa in the dataset have significant relationship with climate variables. This dataset is potentially useful for multiscale paleovegetation and paleoclimate reconstruction studies in Asia. </p> <p>EAPD is developed and maintained by the Laboratory of Quaternary Science and Palynology in the School of Earth Sciences and Engineering, Sun Yat-sen University, Zhuhai, China.</p>
Submerged aquatic vegetation and nitrogen retention data from 2012 to 2017 in lake Saint-Pierre, Saint Lawrence River
<p>Here we provide seven datasets that describes plant biomass (2012 to 2016), environmental variables and nitrogen retention time series (2012 to 2016) in a submerged aquatic vegetation (SAV) meadow at the confluence of two agricultural tributaries (Saint-François and Yamaska) with the St. Lawrence River in southern Lake Saint-Pierre.</p> <p>Version 2 adds the dataset 6 and 7.</p> <p>The seven datasets are:</p> <p>1) Growing season (June 21 to September 22) daily environmental variables (water level, water temperature, light, tributaries input, and SAV biomass indicator)</p> <p>2) Mean SAV biomass measured using rake or quadrat samples in the meadow</p> <p>3) Modelled daily nitrate tributary inputs to the SAV bed</p> <p>4) Daily nitrate output to the SAV bed estimated from a sensor</p> <p>5) Daily nitrate budget</p> <p>6) Hourly nitrate output to the SAV bed and signal decomposition from ensemble empirical mode decomposition (EEMD)</p> <p>7) Hourly dissolved oxygen and gas exchange velocities at the SAV bed outflow for 2016</p> <p>Original data comes from Lake Saint-Pierre, either from publicly available government agencies data, from a project led by the Groupe de recherche interuniversitaire en limnologie (GRIL, 2012-2015) and by Morgan Botrel Ph.D. candidate (2016-2017, Université de Montréal) or from Christiane Hudon (ECCC). Data were created for an article on climate-driven variation in nitrogen retention, led by Morgan Botrel and supervisor Roxane Maranger, with Christiane Hudon, James B. Heffernan and Pascale M. Biron (https://doi.org/10.1029/2022WR032678).</p>
Data from: Cross-scale regulation of seasonal microclimate by vegetation and snow in the Arctic tundra
<p>The zip file contains data and code from the analyses for von Oppen et al. (2022) <em>Global Change Biology</em> (<a href="https://doi.org/10.1111/gcb.16426">https://doi.org/10.1111/gcb.16426</a>). Access through the provided R project file (e.g. with RStudio) is recommended for seamless running of the code. Please see the paper (link below) for methodological details, results and discussion, and the ReadMe included in the archive for further detail and usage policy.</p>
Model codes and simulation data for "Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS's Earth system model (ModelE-BiomeE v.1.0)"
<p>ModelE-BiomeE v1.0 model codes and data This folder contains the simulation data and model codes that were used in the paper ‘Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS’s Earth system model (ModelE-BiomeE v.1.0)’ (https://doi.org/10.5194/gmd-2022-72). We included the data simulated by ModelE-BiomeE v.1.0 with settings of full demography (folder FullDemography) and single cohort (folder SingleCohort), and initial settings of land grids and vegetation data (folder GlobalVegetation). The codes include the full ModelE 2.1, module BiomeE files in ModelE, and the standalone BiomeE. In the folder FullDemography, we have 4 netcdf files for global output and 25 files for single grids output. The files ‘FullDM_2588_JAN.nc’ and ‘FullDM_2588_JUL.nc’ are the original model output of January and July in the year 2588. The file ‘FullDM_2588_Annual.nc’ is the yearly summary of model simulations. The file ‘FullDM_Selected.nc’ is an annual summary of 588 years of model simulation only with selected variables. The csv files are for single grids output at the time steps of daily and yearly. The last digit 1~8 represents the sites of 'BNC','MNT','HF','OKR','KZ','SV','WGK','TPJ', respectively (Table 1). Table 1 Site ID and file number ['BNC', 'MNT', 'HF', 'OKR', 'KZ', 'SV', 'WGK', 'TPJ'] ['8991', '8992', '8993', '8994', '8995', '8996', '8997', '8998'] ['8971', '8972', '8973', '8974', '8975', '8976', '8977', '8978'] ['8961', '8962', '8963', '8974', '8965', '8966', '8977', '8968'] Please refer to Table 2 in the paper for the detail of these 8 sites. ‘DailyLAIGPP.csv’ is a summary of all ‘DailyEcosystem’ files with LAI and GPP data. We included the Python scripts that can be used to generate the figures in out paper (Plotting-BiomeE-MsTMIP.py, Plotting-Scatter-Comparison.py, PlottingBiomeEMaps.py, and PlottingGridOutput.py). For the convenience of readers (in reproducing our figures), we included the summary of reanalysis of the data from observations and MsTMIP in folder ‘Sum-Obs-Simu’. Please refer to the original sources listed in our paper for the detail of these data.</p>
The Standardized Vegetation Optical Depth Index SVODI
<p><strong>Related paper with detailed description: </strong><a href="https://doi.org/10.5194/bg-19-5107-2022">https://doi.org/10.5194/bg-19-5107-2022</a></p> <p><strong>Short summary:</strong> The Standardized Vegetation Optical Depth index (SVODI) can be used to monitor the vegetation condition, such as whether the vegetation is unusually dry or wet. SVODI has global coverage and spans the past three decades and is derived from multiple space-borne passive microwave sensors of that period. SVODI is based on a new probabilistic merging method that allows the merging of normally distributed data, even if the data is not gap-free.</p> <p><strong>Files: </strong></p> <ul> <li>"SVODI_v01.zip" <ul> <li>Contains the bulk SVODI data, globally, from 1987-07-10 to 2019-12-31. Files are daily global netcdf images, with a 0.25 degree spatial resolution. Is unzipped roughly the same size.</li> </ul> </li> <li>"svodi_v01_0_2006-06-26.nc" <ul> <li>An arbitrary file from SVODI_V01.zip. For your convenience in case you want to see an example first without downloading the whole thing.</li> </ul> </li> <li>"ESA-CCI-SOILMOISTURE-LAND_AND_RAINFOREST_MASK-fv04.2.nc" <ul> <li>Grid of SVODI, Source: https://github.com/TUW-GEO/smecv-grid</li> </ul> </li> </ul> <p><strong>Data fields:</strong></p> <ul> <li>"svodi" <ul> <li>The standardized vegetation optical depth index</li> <li>unitless</li> <li>range: -inf, inf</li> </ul> </li> <li>"flag" <ul> <li>Bit-flag indicating which <sensor>-<band> combination contributed to each observation <ul> <li>1: AMSRE-C</li> <li>2: AMSR2-C</li> <li>3: WindSat-C</li> <li>4: AMSRE-X</li> <li>5: AMSR2-X</li> <li>6: WindSat-X</li> <li>7: TMI-X</li> <li>8: AMSRE-Ku</li> <li>9: AMSR2-Ku</li> <li>10: WindSat-Ku</li> <li>11: TMI-Ku</li> <li>12: SSMI-Ku</li> </ul> </li> </ul> </li> </ul>
Oregon Wolfe Barley (Hordeum vulgare) Informative & Spectacular Subset (ISS) vegetative stage growth data
<p>Oregon Wolfe Barley Informative & Spectacular Subset (ISS) was raised at the Ag Alumni Seed Phenotyping Facility (AAPF) at Purdue University (West Lafayette, Indiana, USA) for 42 days. There were 18 genotypes, with two replicates for each genotype (total plants: 36). AAPF is a controlled environment high-throughput phenotyping facility with automated imaging and irrigation systems. A virtual tour of AAPF can be found at <a href="https://ag.purdue.edu/aapf/virtual-tour.html">https://ag.purdue.edu/aapf/virtual-tour.html</a>.</p> <p>Seeds were sown in a 6 L pot with 2.8 L of Profile Porous Ceramic Greens Grade and Berger BM6 each with 10g of Osmocote. Five hundred ml of Turface was laid on top of each pot to avoid effect of algae for RGB data derivation. The growth temperature in the chamber was 72/68 degrees Fahrenheit day/night. Relative humidity was set at 60%. Lighting was 16 h day/8 h night.</p> <p>Plants were imaged with RGB camera from one top and 12 side views three times a week, ranging between 10 days from planting (equivalent to sowing, Dfp) to 42 Dfp. Ground reference data of plant height and tiller count were measured twice a week. </p> <p> </p> <p>RGB imaging data were stored in “OWB_RGB.xlsx”. Datasheet “Information” describes the variables in datasheets for top view, side average view and every side view.</p> <p> </p> <p>Ground reference data for plant height and tiller count were stored in “OWB_ground_reference.xlsx”. Datasheet “Information” describes the variables in datasheet “Data”.</p>
Paired Vegetation and Soil Burn Severity Metrics and Associated Climate, Weather, Topographical, and Land Cover Attributes
<p>This dataset pairs differenced Normalized Burn Ratio (dNBR) and soil burn severity (SBS) for 254 large (>400 ha in size) fires across the western US. Dataset also includes climate, weather, topography, physical and chemical soil characteristics, and land cover attributes of each burned pixel at the time of fire. This effort provided a table of 16.3 million burned pixels and their associated characteristics including dNBR, SBS, and 94 biological and physical covariates. After removing correlated features, the final data includes 18 fire covariates namely: dNBR, elevation, slope, aspect, land cover type, wind speed, energy release component, vapor pressure deficit, annual precipitation, and annual average daily max temperature, as well as the clay, sand and silt content of the soil and volumetric fraction of coarse fragments and soil organic carbon content. We also included spatial coherence metrices for dNBR, including DVAR, SHADE and SAVG. This data is provided as CSV files in Xtrain, Xvalidation, Xtest, as well as Ytrain, Yvalidation, and Ytest; in which X files (model input) provide all features except for SBS and Y files (model output) include SBS.</p><p>We also provided this data for an additional 16 large fires across the western US ("Extra Test" folder, including Dataset – X file – and Label – Y file).</p><p>Finally, the trained XGBoost model to translate dNBR to SBS using the associated features is also provided in this folder.</p>
Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part IV: Post-processing)
<p>This is Part IV of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to <em>Limnology and Oceanography Letters.</em> It contains all input and output files for the post-processing of all model results.</p> <p>To be able to run the scripts as is, the folder structure should be as follows:</p> <p>Runs (includes all model run folders from Part II and Part III)<br>Post/Basic/Channels<br>Post/Basic/Cross_sections<br>Post/Basic/Integrals<br>Post/Basic/Median_neighborhood_analysis (includes all unzipped MNA_TIGER_XX.zip folders)<br>Post/Basic/Skeleton_clean<br>Post/Basic/Skeleton_final<br>Post/Basic/Skeleton_raw<br>Post/Basic/Unchanneled_path_length<br>Post/Basic/Watersheds<br>Post/Basic/Scenarios.txt<br>Post/Basic/TIGER_2km_5m.slf<br>Post/Paper_1/Erosion-deposition<br>Post/Paper_1/Fluxes<br>Post/Paper_1/Profiles<br>Post/Paper_1/Std</p>
Structural, ecological and biogeographical attributes of European vegetation alliances
<p>This is a database of structural, ecological and biogeographical attributes of 1115 European phytosociological alliances. The original version was published by Preislerová et al. (2024). This article also contained definitions and descriptions of individual attributes.</p> <p>Version 2 of this dataset has been updated to match Version 3 of EuroVegChecklist (Mucina et al. 2016) published on https://floraveg.eu/download/. This version contains syntaxonomic changes in the vegetation of coastal dunes (classes <em>Ammophiletea arundinaceae</em>, <em>Helichryso-Crucianelletea maritimae</em> and <em>Honckenyo peploidis-Leymetea arenarii</em>), Mediterranean pine forests (order <em>Pinetalia halepensis</em>) and bogs (class <em>Oxycocco-Sphagnetea</em>) approved by the European Vegetation Classification Committee in January 2024 following the proposals published by Marcenò et al. (2018, 2024), Bonari et al. (2021) and Jiroušek et al. (2022), respectively.</p> <p>The data are provided in two files with identical contents, one in the XLSX format, and the other in the TXT format with columns separated by tabs.</p> <p><strong>References</strong></p> <div> <div> <div> <ul> <li>Bonari G., Fernández‐González F., Çoban S., Monteiro‐Henriques T., Bergmeier E., Didukh Ya. P. … Chytrý, M. (2021). Classification of the Mediterranean lowland to submontane pine forest vegetation. <em>Applied Vegetation Science</em>, 24, e12544. <a href="https://doi.org/10.1111/avsc.12544">https://doi.org/10.1111/avsc.12544</a></li> <li>Jiroušek, M., Peterka, T., Chytrý, M., Jiménez-Alfaro, B., Kuznetsov, O.L., Pérez-Haase, A. … Hájek, M. (2022). Classification of European bog vegetation of the Oxycocco-Sphagnetea class. <em>Applied Vegetation Science</em>, 25, e12646. <a href="https://doi.org/10.1111/avsc.12646">https://doi.org/10.1111/avsc.12646</a></li> <li>Marcenò, C., Guarino, R., Loidi, J., Herrera, M., Isermann, M., Knollová, I. … Chytrý, M. (2018). Classification of European and Mediterranean coastal dune vegetation. <em>Applied Vegetation Science</em>, 21, 533–559. <a href="https://doi.org/10.1111/avsc.12379">https://doi.org/10.1111/avsc.12379</a></li> <li>Marcenò, C., Danihelka, J., Dziuba, T., Willner, W. & Chytrý, M. (2024). Nomenclatural revision of the syntaxa of European coastal dune vegetation. <em>Vegetation Classification and Survey</em>, 5, 27–37. <a href="https://doi.org/10.3897/VCS.108560">https://doi.org/10.3897/VCS.108560</a></li> <li>Mucina, L., Bültmann, H., Dierßen, K., Theurillat, J.-P., Raus, T., Čarni, A. … Tichý, L. (2016). Vegetation of Europe: Hierarchical floristic classification system of vascular plant, bryophyte, lichen, and algal communities. <em>Applied Vegetation Science</em>, 19(Suppl. 1.), 3–264. <a href="https://doi.org/10.1111/avsc.12257">https://doi.org/10.1111/avsc.12257</a></li> <li>Preislerová Z., Marcenò C., Loidi J., Bonari G., Borovyk D., Gavilán R.G., Golub V., Terzi M., Theurillat J.-P., Argagnon O., Bioret F., Biurrun I., Campos J.A., Capelo J., Čarni A., Çoban S., Csiky J., Ćuk M., Ćušterevska R., Dengler J., Didukh Ya., Dítě D., Fanelli G., Fernández-González F., Guarino R., Hájek O., Iakushenko D., Iemelianova S., Jansen F., Jašková A., Jiroušek M., Kalníková V., Kavgacı A., Kuzemko A., Landucci F., Lososová Z., Milanović Đ., Molina J.A., Monteiro-Henriques T., Mucina L., Novák P., Nowak A., Pätsch R., Perrin G., Peterka T., Rašomavičius V., Reczyńska K., Rūsiņa S., Sánchez Mata D., Santos Guerra A., Šibík J., Škvorc Ž., Stešević D., Stupar V., Świerkosz K., Tzonev R., Vassilev K., Vynokurov D., Willner W. & Chytrý M. (2024) Structural, ecological and biogeographical attributes of European vegetation alliances. <em>Applied Vegetation Science</em>, 27, e12766. <a href="https://doi.org/10.1111/avsc.12766">https://doi.org/10.1111/avsc.12766</a></li> </ul> </div> </div> </div>
Global bare soil, photosynthetic and non-photosynthetic vegetation fraction annual at 500 m resolution
<p>Annual mean and std for (1) bare soil fraction, and (2) photosynthetic and (3) non-photosynthetic vegetation annual at 500 m resolution for 2001–2023. The dataset was obtained from: <a href="https://thredds.nci.org.au/thredds/catalog/tc43/modis-fc/v310/tiles/monthly/cover/catalog.html">https://thredds.nci.org.au/thredds/catalog/tc43/modis-fc/v310/tiles/monthly/cover/catalog.html</a> (monthly values; 320GB in total). Mean and std was derived using terra package in R using functions "mean" and "std" from 12 monthly values; missing values were ignored during derivation.</p> <p>Note: the Global Vegetation Fractional Cover Product (GVFCP) v3.1 (<a href="https://doi.org/10.1016/j.agee.2021.107719">Hill and Guerschman, 2022</a>) is derived from spectral unmixing of all seven optical bands from the 500 m MODIS (Moderate Resolution Imaging Spectroradiometer) Nadir BRDF (Bidirectional Reflectance Distribution Function)-adjusted Reflectance Product (NBAR, MCD43A4 Collection 6). A similar dataset has been produced by <a href="https://doi.org/10.5194/essd-16-1333-2024">Sun et al., (2024)</a>, covering period 2001–2022. Below is the sample code explaining how were the mean, max and std derived.</p> <pre><code>## Download from: https://thredds.nci.org.au/thredds/catalog/tc43/modis-fc/v310/tiles/monthly/cover/catalog.html ## wget -e robots=off -nH --cut-dirs 4 -nc -r -l5 -A '*.nc' -R 'catalog*' -I /thredds/fileServer/,/thredds/catalog/ 'https://thredds.nci.org.au/thredds/catalog/tc43/modis-fc/v310/tiles/monthly/cover/catalog.html' ## 6857 tiles library(terra) modis.tiles = list.files("/mnt/lacus/raw/modis-fc/v310/tiles/monthly/cover/", pattern = glob2rx("*.nc")) mod.lst = unique(sapply(modis.tiles, function(i){strsplit(i, "\\.")[[1]][4]})) str(mod.lst) ## 272 ## aggregate per year per tile nc_tile <- function(i, year, dir.x="/mnt/lacus/raw/modis-fc/v310/tiles/monthly/cover/", mc.cores=parallel::detectCores()){ require(terra) if(year == 2023 | year == 2024){ in.filename = paste0(dir.x, "FC_Monthly_Medoid.v310.MCD43A4.", i, ".", year, ".061.nc") } else { in.filename = paste0(dir.x, "FC_Monthly_Medoid.v310.MCD43A4.", i, ".", year, ".006.nc") } bs.filenames = paste0("./modis-fc/bs_", c("mean", "max", "std"), "/FC_Monthly_Medoid.v310.MCD43A4.", i, ".", year, ".006.tif") dg = terra::rast(in.filename) if(any(!file.exists(bs.filenames))){ bs = dg["bare_soil"] ## 12 months dg.m = app(bs, fun=mean, na.rm=TRUE, filename=bs.filenames[1], wopt=list(gdal=c("COMPRESS=DEFLATE"), datatype='INT1S'), overwrite=TRUE, cores = mc.cores) dg.x = app(bs, fun=max, na.rm=TRUE, filename=bs.filenames[2], wopt=list(gdal=c("COMPRESS=DEFLATE"), datatype='INT1S'), overwrite=TRUE, cores = mc.cores) dg.s = app(bs, fun=sd, na.rm=TRUE, filename=bs.filenames[3], wopt=list(gdal=c("COMPRESS=DEFLATE"), datatype='INT1S'), overwrite=TRUE, cores = mc.cores) } } ## run in parallel for(year in 2001:2024){ x = parallel::mclapply(sample(mod.lst), function(i){try( nc_tile(i, year=year, mc.cores = 2) )}, mc.cores = 40) tmpFiles(remove=TRUE) }</code></pre>
Vegetation of post-mining areas, Upper Silesia, Poland (Floristic composition of the plots_November_2022)
<p><span><span>The data set containing a list of plant species in the research plots along with their percentage coverage.</span></span> <span><span>The selection of plots took into account the occurrence of the dominant species (cover > 40% of the study plot area).</span></span> <span><span>The dominant species represent functional groups: monocots, forbs and legumes.</span></span></p>
Datasets for testing the robustness of LiDAR vegetation metrics to varying point densities
<p><span>The calculation of vegetation metrics from LiDAR point clouds might be affected by the available point density of a dataset. Testing how the same LiDAR vegetation metrics differ with different point densities can therefore inform about their robustness for upscaling metrics to other areas or other LiDAR point clouds. The datasets made available here were generated to test the robustness of LiDAR vegetation metrics to varying point densities. A total of 25 LiDAR vegetation metrics representing different aspects of vegetation height, vegetation cover and structural complexity were tested (see metric definition in Kissling et al. 2023, <a href="https://doi.org/10.1016/j.dib.2022.108798">https://doi.org/10.1016/j.dib.2022.108798</a>). The metric calculation was similar to the metric calculation in the Laserchicken software (Meijer et al. 2020, <span><a href="https://doi.org/10.1016/j.softx.2020.100626">https://doi.org/10.1016/j.softx.2020.100626</a>) and the Laserfarm workflow (Kissling et al. 2022, https://doi.org/10.1016/j.ecoinf.2022.101836). The Dutch AHN4 dataset from the years 2020–2022 with a point density of 20–30 points/m<sup>2</sup> was used. A number of plots (i.e., squared polygons around centre points) were randomly placed across the Netherlands within Dutch Natura 2000 sites (using shapefiles from the European Environmental Agency). Different Dutch Natura 2000 sites were distinguished based on their dominant habitat type (dunes, grassland, marsh, shrubland, and woodland). About 100 plots were randomly placed in each habitat type. The AHN4 point cloud of each plot was clipped and then randomly downsampled to 1, 2, 5, 10, 15, 20 points per square meter, respectively. This was done for six different spatial resolutions (1, 2, 5, 10, 20 and 30 meter). The clipped points were then used to calculate the 25 LiDAR vegetation metrics for the original point density and for the six down-sampled point densities.</span></span></p>
Growth of Heracleum sosnowskyi Manden. plant in indoor conditions after end of vegetation period
<p>Growth of<em> Heracleum sosnowskyi</em> Manden. plant in indoor conditions after end of vegetation period. The period of observation of plant growth from October 2017 to February 2018. The series of images.</p>
The Global Long-term Microwave Vegetation Optical Depth Climate Archive VODCA
<p><strong>Related paper containing detailed description</strong>: <strong><a href="https://essd.copernicus.org/articles/12/177/2020/essd-12-177-2020.html">Moesinger et al. (2020)</a></strong></p> <p>Vegetation optical depth (VOD) describes the attenuation of radiation by plants. VOD a function of frequency as well as vegetation water content, and by extension biomass. VOD has many possible applications in studies of the biosphere, such as biomass monitoring, drought monitoring, phenology analyzes or fire risk management.</p> <p>We merged VOD observations from various spaceborne sensors (SSM/I, TMI, AMSR-E, AMSR2, WindSat) to create global long-term vod time series. Prior to aggregation the data has been rescaled to AMSR-E, removing systematic differences between them.</p> <p>There is a product for C-band (~6.9 GHz, 2002 - 2018), X-band (10.7 GHz, 1997 - 2018) and Ku-band (~19 GHz, 1987 - 2017). The data is global sampled on a regular 0.25 degrees grid. Each product is available as daily global netcdf4 files.</p> <p> </p> <p>Currently there is an issue with opening the file using ESA SNAP. As an alternative <a href="https://www.giss.nasa.gov/tools/panoply/">Panoply</a> can be used to quickly visualize the data. </p> <p>An update of VODCA, addressing this issue and potentially including an extension of the dataset, is foreseen to be published on Zenodo early 2020.</p> <p> </p> <p><strong>Please contact us if you have any questions, problems or suggestions for improvement!</strong></p> <p> </p> <p><strong>Files:</strong></p> <ul> <li>"VODCA_C-band_2002-2018_v01.0.0.zip" (unzipped size: ~140 GB): <ul> <li>VODCA C-band files, sorted into yearly folders</li> </ul> </li> <li>"VODCA_X-band_1997-2018_v01.0.0.zip" (unzipped size: ~180 GB): <ul> <li>VODCA X-band files, sorted into yearly folders</li> </ul> </li> <li>"VODCA_Ku-band_1987-2017_v01.0.0.zip" (unzipped size: ~270 GB) : <ul> <li>VODCA Ku-band files, sorted into yearly folders</li> </ul> </li> <li>"vodca_v01-0_K-band_2007-06-01.nc" <ul> <li>sample file of the Ku-band product</li> </ul> </li> <li>"ESA-CCI-SOILMOISTURE-LAND_AND_RAINFOREST_MASK-fv04.2.nc" <ul> <li>Contains a global land mask, VODCA only has data for land locations. Source: https://github.com/TUW-GEO/smecv-grid</li> </ul> </li> </ul> <p><strong>Variables of data in VODCA files:</strong></p> <ul> <li>"VOD": Unitless, Vegetation Optical Depth of the respective band</li> <li>"sensor_flag": Bit-flag indicating which sensors contributed to each observation. <ul> <li>Values: <ul> <li>1 = AMSR-E</li> <li>2 = AMSR2</li> <li>3 = SSM/I F8</li> <li>4 = SSM/I F11</li> <li>5 = SSM/I F13</li> <li>6 = TMI</li> <li>7 = WindSat</li> </ul> </li> </ul> </li> <li>"processing_flag": Bit-flag indicating irregularities during processing affecting the quality of the observations <ul> <li>Values: <ul> <li>0 = Everything is fine</li> <li>10 = AMSR-2 7.3 GHz band is used instead of 6.9 GHz</li> <li>11 = Sensor is scaled to matched TMI instead of AMSR-E</li> <li>12 = Sensor scaled without temporally overlapping observations</li> </ul> </li> </ul> </li> <li>"time"/"lon"/"lat": Dimensions of the data.</li> </ul> <p> </p>
Gran Paradiso - Nivolet: binary map vegetated/not vegetated (2016)
<p>A vegetated/not vegetated binary map for the Nivolet area in Gran Paradiso (Italy) PA, detected by a thresholding on NDVI spectral index extracted from a Sentinel-2A image dated 13 August 2016, at 10 meters spatial resolution, projected in WGS84/UTM32N. <br> The map has binary values where value 1 indicates pixels of vegetation whereas value 0 indicates No vegetation pixels.<br> </p>
Simulated submerged aquatic vegetation spectral signatures under different water quality conditions using Hydrolight
<p>Reflectance spectra were simulated using the Hydrolight radiative transfer model (Sequoia Scientific, Bellevue, WA) for four different submerged macrophyte species under a range of water quality conditions at two different depths. We used the four-component case-2 model with spectral reflectance of four submerged species, Egeria densa, Ceratophyllum demersum, Cabomba caroliniana, and Stukenia pectinata. These four reflectance spectra were calculated from the median of 10 measurements of the canopies of the representative species placed in clear tap-water made with a handheld ASD FieldSpec Pro spectrometer. Total suspended solids concentration was varied from 1 to 40 g·m<sup>−3</sup>, chlorophyll-a concentration was varied from 0.5 to 50 mg·m<sup>−3</sup>, and colored dissolved organic matter (CDOM) was varied from 0.25 to 3.5 m<sup>−1</sup>. A total of 4,742 spectra were simulated for all four species and a mud substrate at two different depths, 1 m and 5 m, and for optically deep water.</p>
Urban Vegetation Data - Canopy Height Model (Brussels Capital Region, 2021)
<p>This GIS dataset was created for the following scientific publication, as part of the EU-funded <a href="https://coolschools.eu/">Cool Schools</a> research project (under Grant Agreement No. 101003758) : Gallez, E., Canters, F., Gadeyne, S., & Baró, F. (2024). <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000846?via%3Dihub">A multi-indicator distributive justice approach to assess school-related green infrastructure benefits in Brussels - ScienceDirect</a>. Ecosystem Services, 70, 101677. https://doi.org/10.1016/j.ecoser.2024.101677. </p> <p><em>Very-High Resolution Canopy Height Model (resolution : 25cm), distinguishing between 4 vegetation types (trees, high shrubs, low shrubs and grass) in the Brussels Capital Region.</em></p> <p><em>Coordinate system : Lambert_Belge_72.</em></p> <p><em>The CHM was built on </em><em>:</em></p> <ul> <li><em>VHR aerial orthophotos (visible RGB and NIR) (“UrbIS-Ortho N-S, 2021”) for the Brussels Capital Region, of 5x5cm resolution Source: Paradigm. (2021). UrbIS-Ortho N-S. Paradigm.Brussels. <a href="https://datastore.brussels/web/data/dataset/fec72767-d6b6-41b9-a767-616df2779aae#access">https://datastore.brussels/web/urbisdownload</a>. and;</em></li> <li><em>digital terrain models (DSM and DTM) of 50x50cm, captured on 22/09/2021. Paradigm.Brussels. </em><em>Source: Paradigm. (2021). DSM / DTM. Paradigm.Brussels. <a href="https://datastore.brussels/web/data/dataset/1d7bd49d-fe83-4388-af85-6f5dc8ec7909#access">https://datastore.brussels/web/urbisdownload.</a></em></li> </ul> <p><em>Both the orthophotos and digital terrain models were resampled to a 25x25cm resolution, using a bilinear interpolation method. </em></p> <p><em>The Canopy Height Model was then created by selecting NDVI values of 0.2 and higher, - a commonly used threshold value to distinguish vegetated land from built land (Hashim et al., 2019) -, </em><em>and vegetation height thresholds of < 0.5m (for grass), 0.5 - 2m (for low shrubs), 2 - 5m (for high shrubs), and > 5m (for trees) (Derkzen et al., 2015; Sankey et al., 2018). </em><em>Green roofs were excluded.The CHM raster was then converted to polygon features. </em></p> <p><em>Classification :</em></p> <ul> <li><em>From 0 to 0.5 m (nDSM value) : gridcode 1 = </em><em>grass</em></li> <li><em>From 0.5 to 2 m (nDSM value): gridcode 2 = </em><em>low shrubs</em></li> <li><em>From 2 to 5 m (nDSM value): gridcode 3 =</em><em> high shrubs</em></li> <li><em>From 5 to 113.96 m (nDSM value): gridcode 4 = </em><em>trees</em></li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.