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3,105 results for “vegetation”
Leaf water and stem cellulose oxygen isotope ratios simulated with global dynamic vegetation model LPX-Bern
<p>Description of leaf water and stem cellulose oxygen isotope ratios simulated with LPX-Bern</p> <p>Citation of describing paper:</p> <p>Keel SG, Joos F, Spahni R, Saurer M, Weigt RB, Klesse S. 2016. Simulating oxygen isotope ratios in tree ring cellulose using a dynamic global vegetation model, Biogeosciences, 13, 3869–3886, 2016 doi:10.5194/bg-13-3869-2016</p> <p>download: www.biogeosciences.net/13/3869/2016/</p> <p>General Information: Format: NetCDF, gridded</p> <p>Model: Dynamic global vegetation model LPX-Bern Version 1.0 (Land surface Processes and eXchanges, Bern) (Spahni et al., 2013; Stocker et al., 2013)</p> <p>Resolution: 3.75° x 2.5° lat/lon global Time: Monthly from Jan 1960 to Dec 2012</p> <p>Variables:</p> <p>cellu18: monthly stem cellulose δ18O (per mil) lw18: monthly leaf water δ18O (per mil) -2 NPP: monthly net primary production (g C m ) FPC: monthly fractional plant cover</p> <p>Dimensions: i=longitude, j=latitude, l=time, k=plant functional type Codes for plant functional types (k):</p> <ol> <li> <p>1 tropical broad-leaved evergreen</p> </li> <li> <p>2 tropical broad-leaved deciduous (raingreen)</p> </li> <li> <p>3 temperate needle-leaved evergreen</p> </li> <li> <p>4 temperate broad-leaved evergreen</p> </li> <li> <p>5 temperate broad-leaved deciduous (summergreen)</p> </li> <li> <p>6 boreal needle-leaved evergreen</p> </li> <li> <p>7 boreal needle-leaved deciduous (summergreen)</p> </li> <li> <p>8 boreal broad-leaved deciduous (summergreen)</p> </li> <li> <p>9 temperate herbaceous</p> </li> <li> <p>10 tropical herbaceous</p> </li> </ol>
PixelCropRobot dataset: images of vegetables crops in different phenological stages taken in greenhouses
<p><em>Dataset created under the PixelCropRobot project, developed by FCUP, INESC TEC and FEUP.</em></p> <p><strong>Dataset folder:</strong></p> <blockquote> <p>This folder contains the images of each species in two formats (3456 × 4608 pixels and 864 × 1152 pixels), the annotations of the 864 × 1152 px. images, in Pascal VOC (.xml) and YOLO (.txt) formats and also a set of Python scripts useful for managing the dataset.</p> </blockquote> <p>The aim was to capture images of eight crops selected taking into account the length of the crop cycle (annual), the intensity of agricultural practices (mainly weed removal) and the low impact of pests and diseases.</p> <p>The images were captured using a smartphone (Huawei Mate 10 Lite), with 16 megapixels (MP) resolution (3456 × 4608 px.), in Professional mode (no flash, continuous autofocus, automatic ISO and shutter speed). Image collection took place at different hours of the day, with variable lighting conditions.</p> <p>The images are divided as follows (in parenthesis are the classes):</p> <ul> <li>Arugula - 312 (coty, minus9, plus9)</li> <li>Carrot - 533 (coty, smallleaves, carrot)</li> <li>Coriander - 321 (coty, smallleaves, coriander)</li> <li>Lettuce - 1426 (coty, minus9, plus9, ready)</li> <li>Radish - 494 (coty, smallleaves, bigleaves, root)</li> <li>Spinach - 270 (spinach, big)</li> <li>Swiss chard - 454 (coty, chard)</li> <li>Turnip - 313 (coty, smallleaves, turnip)</li> </ul> <p>To standardise the dataset, each image was renamed according to the corresponding EPPO (European and Mediterranean Plant Protection Organization) code and the date of creation of that image. The size of each image was also reduced four times (to 864 × 1152 pixels) to facilitate processing. For example, an image of lettuce captured on June 22 presents the name as follows: LACSA_Jun_22_x_864_1152.jpg.</p>
Tower-based solar-induced fluorescence and vegetation index data for Southern Old Black Spruce forest
<p>Data includes remote sensing products from PhotoSpec (a scanning spectrometer) from September 2019-December 2020 at the Southern Old Black Spruce site in Saskatchewan Canada. We provide half-hourly averaged vegetation indices (NIRv, NDVI, PRI, CCI) and solar-induced fluorescence (SIF) and for a stand-representative mix of black spruce and larch. Additionally, we provide photosynthetically active radiation (PAR), absorbed photosynthetically active radiation (APAR), and the escape fraction of SIF photons (fesc). Version 2 also provides half-hourly averaged SIF and SIFrelative for black spruce (evergreen) and larch (deciduous) separately. </p>
Submerged aquatic vegetation biomass from the Saint Lawrence River (2006-2016) to compare estimation from quadrat-diver technique to rake collection and echosounding
<p>Here we provide 4 datasets that describes 1) the comparison between quadrat and rake collected biomass (QR), 2) the comparison of rake biomass and biovolume, a biomass proxy derived from echosounding (RE), 3) a validation dataset that confronts quadrat measurements to quadrat prediction measured from echosounding using two intercalibration equations (derived from QR and RE datasets), and 4) a whole-system biomass estimation comparing biomass predicted from echosounding and from rake.</p> <p>Original data comes from the Saint Lawrence River, mainly from Lac Saint-Pierre, but for the QR dataset also from Lac Saint-François and Lac Saint-Louis. Data from the QR (2006-2009) and validation dataset (2016) were collected by Christiane Hudon, Environment and Climate Change Canada, while the RE dataset and part of the validation dataset were collected as part of 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). Data were created for an article on a method to estimate SAV biomass, led by Morgan Botrel and supervisor Roxane Maranger, with co-supervisor Christiane Hudon and Pascale Biron.</p> <p>For the second version, the data is more clearly organized in the four categories mentioned above. Additionally, revised prediction equations were applied which modifies results used in the validation and whole-system datasets (dataset 2 and 4). Equations are presented in the associated publication:</p> <p>Botrel, M., C. Hudon, P.M. Biron, R. Maranger. Combining quadrat, rake and echosounding to estimate submerged aquatic vegetation biomass at the ecosystem scale. Limnology & Oceanography: Methods. Accepted (as of 2023/02/08)</p>
Satellite soil and vegetation water content data capturing main terrestrial ecosystem changes
<p>I)SUMMARY</p> <p>This repository contains a harmonized database for the study of terrestrial ecosystem changes published in [Bueso et al., 2021]. It covers the period June 2010 - July 2020 and includes the following variables, which were harmonized to a common spatial scale of 25km and monthly temporal resolution and clustered as detailed in [Bueso et al., 2021]:</p> <p>- SM: soil moisture from SMOS-IC v2<br> - VOD: vegetation optical depth from SMOS-IC v2<br> - NDVI: Normalized Vegetation Difference Index from MODIS, product MOD13Q1 v6<br> - PREC: Rainfall from PERSIANN-CDR v2.2.</p> <p>Additionally, land cover information from MODIS MCD12Q1 collection 6 for years 2011 and 2019 is provided for each cluster.</p> <p>II) CONTACT</p> <p>For questions, please e-mail Diego Bueso at diego.bueso@uv.es</p> <p>III) DATABASE</p> <p>We provide the maps of identified clusters by quantile of SM and VOD and the code to generate Figs 1 and 2 of supplementary material in [Bueso et al., 2023]. We then provide for each identified cluster .mat files containing the variables described above. Further details are in the readme.txt file</p> <p>IV) CITE</p> <p>To properly acknowledge the dataset we kindly encourage users to (1) cite the DOI as an in-text citation and/or in the data acknowledgements in any publication and (2) reference the following publication: </p> <p>D. Bueso, M. Piles, P. Ciais, J-P. Wigneron, Á. Moreno-Martínez, G. Camps-Valls, "Soil and vegetation water content identify the main terrestrial ecosystem changes", National Science Review, 2023, <a href="https://doi.org/10.1093/nsr/nwad026">https://doi.org/10.1093/nsr/nwad026</a> </p>
Data from: Vegetation change in acidic dry grasslands in Moravia (Czech Republic) over three decades: slow decrease in habitat quality after grazing cessation
<p>This dataset contains the original data used in the article:</p> <p>Harásek M., Klinkovská K. & Chytrý M. (2023) Vegetation change in acidic dry grasslands in Moravia (Czech Republic) over three decades: slow decrease in habitat quality after grazing cessation. <em>Applied Vegetation Science</em>, 26, e12726. https://doi.org/10.1111/avsc.12726</p> <p>The data contain plant species composition data from resurveyed vegetation plots in southwestern and central Moravia (Czech Republic). The plots were first surveyed by Milan Chytrý in 1986–1991 (“old plots”) and resurveyed by Martin Harásek, under the supervision of Milan Chytrý, in 2018–2019 (“new plots”).</p> <p>Of the old plots, 86 were sampled between 26 June and 16 September and 8 in May. Their size ranged from 5 to 49 m<sup>2</sup> (mean 33 m<sup>2</sup>). These plots were subjectively selected at different sites to document maximum variation in species composition and environmental conditions of the grasslands and heathlands studied. In each plot, all vascular plant species were recorded, and their covers were estimated using the nine-grade Braun-Blanquet scale (van der Maarel 1979). Plot locations were recorded in the form of text descriptions. Geographic coordinates of approximate location were added for each plot prior to the resurvey by the original surveyor using georeferenced aerial photographs and various information recorded in the field during the first survey, including slope, aspect and elevation. Location uncertainty (mean = 139 m) was indicated as the possible distance of the actual location from the given coordinates.</p> <p>The resurvey was conducted between 4 June and 16 August. Care was taken to select the most likely location of the original plot based on the coordinates of the approximate location, the original site description, and the occurrence of the species recorded during the first survey. New plots always had the same plot size as in the original sampling. Each old plot was resurveyed using 1–3 new plots depending on the uncertainty of the location of the old plot. A total of 94 old plots were resurveyed at 47 sites with 153 new plots. Of these, 71 old plots at 32 sites were in current protected areas, while 23 old plots at 15 sites were outside protected areas. All new plots were located using GPS with a location uncertainty of approximately 5 m.</p> <p>For each old plot resurveyed with more than one new plot, the most similar new plot (based on Bray-Curtis dissimilarity in species composition) was selected, resulting in a dataset of 94 old and 94 new plots (“best-fit dataset”). To test the robustness of the results, we created another dataset (“validation dataset”) that included the least similar of the corresponding new plots for each old plot. This dataset also included the 94 old and 94 new plots. If the old plot was resurveyed using a single new plot, that new plot was included in both the best-fit and validation datasets.</p> <p>The header data structure follows that of the ReSurveyEurope Database (<a href="http://euroveg.org/eva-database-re-survey-europe">http://euroveg.org/eva-database-re-survey-europe</a>). In addition, fields are added to indicate whether the new plot was used in the best-fit dataset (Best_fit) or the validation dataset (Validation). The information about location within or outside the protected area is given in the field Protection.</p> <p>The data on species composition and environmental variables are provided in two formats:</p> <ul> <li>Turboveg 2 database (see <a href="https://www.synbiosys.alterra.nl/turboveg/">https://www.synbiosys.alterra.nl/turboveg/</a>) – file <strong>TurbovegDbBackup_SW_moravia_acidgrass.zip</strong>. For using this dataset in Turboveg, the database dictionary (TurbovegDdBackup_Default dictionary.zip) and the species list (TurbovegSlBackup_Czechia_slovakia_2015.zip) must be installed.</li> <li>Three TXT files with columns separated by tabs: <ul> <li><strong>SW_moravia_acidgrass_species.txt</strong> contains the percentage covers of plant species in the plots, which are mid-values for cover-abundance categories of the Braun-Blanquet scale. Plant nomenclature was harmonised according to Danihelka et al. (2012).</li> <li><strong>SW_moravia_acidgrass _head.txt</strong> contains information on the number of species in each plot (number_species), the number, proportion and relative cover of threatened species (IUCN categories CR, EN, VU, NT, columns CR_NT_number, CR_NT_perc_number and CR_NT_perc_cover), alien species (alien_number, alien_perc_number, alien_perc_cover), species characteristic of dry grasslands (TH_number, TH_perc_number, TH_perc_cover), sand and rock-outcrop grasslands (TF_number, TF_perc_number, TF_perc_cover), mesotrophic grasslands (TD_number, TD_perc_number, TD_perc_cover) and herbaceous ruderal vegetation (XA_XC_number, XA_XC _perc_number, XA_XC _perc_cover) and unweighted means of Ellenberg-type indicator values for light (light), temperature (temperature), moisture (moisture), soil reaction (reaction) nutrients (nutrients) and salinity (salinity) used to test changes in these variables through time.</li> <li><strong>SW_moravia_life_forms.txt </strong>contains information about the assignment of individual species to the life form, which was used to analyse changes in frequency and cover of the life forms.</li> </ul> </li> </ul> <p>These data are also stored in the Czech National Phytosociological Database (Chytrý & Rafajová 2003; <a href="https://botzool.cz/vegsci/phytosociologicalDb">https://botzool.cz/vegsci/phytosociologicalDb</a>) and the ReSurveyEurope database (Knollová et al. 2023; <a href="http://euroveg.org/eva-database-re-survey-europe">http://euroveg.org/eva-database-re-survey-europe</a>).</p>
Updated fruits and vegetable parameters for swat models
<p>Ensuring accurate crop yield simulations in ecohydrological models such as the Soil and Water Assessment Tool (SWAT) is crucial to improve our understanding of agricultural systems and productivity. This, in turn, can facilitate the development of more sustainable agricultural practices. In this study, we focused on validating the crop growth parameters of the SWAT model for 24 table food fruits and vegetables in Iowa, located in the western Corn Belt region of the United States.</p> <p>To estimate these parameters, we used five primary sources: a) existing parameters in the SWAT crop parameter database, b) alternative parameters in the Environmental Policy Integrated Climate (EPIC) and Agricultural Policy/Environmental eXtender (APEX) crop parameter databases, c) literature, d) PHU fraction for scheduling dates, and e) expert communication among modeling team members. Among the 24 crops tested, 15 initial parameter data sets were already available in the SWAT database, and the remaining crop types were added to this plant.dat file.</p>
Data from: Davison et al. (2023) Vegetation structure from LiDAR explains the local richness of birds across Denmark
<p>Environmental and biodiversity data associated with the article: Davison et al. (2023) <strong>Vegetation structure from LiDAR explains the local richness of birds across Denmark</strong>, <em>Journal of Animal Ecology</em>.</p> <p>Bird richness and abundance at points across Denmark, with matched land cover and LiDAR structural data. Bird observations are a subset of the Common Bird Monitoring programme (DOF – Birdlife Denmark) and pooled from summer counts of 2014, 15, and 16. Bird functional group assignments and environmental data are from open access data sets (see below).</p> <table> <tbody> <tr> <td>Data source</td> <td>Reference</td> </tr> <tr> <td>Danish Common Bird Monitoring programme</td> <td>Eskildsen, D. P., Vikstrøm, T., & Jørgensen, M. F. (2021). Overvågning af de almindelige fuglearter i Danmark 1975-2020. Dansk Ornitologisk Forening.</td> </tr> <tr> <td>EcoDes-DK15 LiDAR data set of Denmark</td> <td>Assmann, J. J., Moeslund, J. E., Treier, U. A., & Normand, S. (2022). EcoDes-DK15: high-resolution ecological descriptors of vegetation and terrain derived from Denmark’s national airborne laser scanning data set. Earth System Science Data, 14(2), 823–844. https://doi.org/10.5194/essd-14-823-2022</td> </tr> <tr> <td>Pan-European land cover map of the year 2015 </td> <td>Pflugmacher, D., Rabe, A., Peters, M., & Hostert, P. (2019). Mapping pan-European land cover using Landsat spectral-temporal metrics and the European LUCAS survey. Remote Sensing of Environment, 221, 583–595. https://doi.org/10.1016/j.rse.2018.12.001</td> </tr> <tr> <td>AVONET bird traits data</td> <td>Tobias, J. A., Sheard, C., Pigot, A. L., Devenish, A. J. M., Yang, J., Neate-Clegg, M. H. C., Alioravainen, N., Weeks, T. L., Barber, R. A., Walkden, P. A., MacGregor, H. E. A., Jones, S. E. I., Vincent, C., Phillips, A. G., Marples, N. M., Montaño-Centellas, F., Leandro-Silva, V., Claramunt, S., Darski, B., … Schleunning, M. (2022). AVONET: morphological, ecological and geographical data for all birds. Ecology Letters, 25(3), 581–597. https://doi.org/10.1111/ele.13898</td> </tr> <tr> <td>Birds of the Palearctic - original source of trait data </td> <td>Cramp, S. (2006). The birds of the western Palearctic interactive. Oxford University Press and BirdGuides.</td> </tr> <tr> <td>Life-history characteristics of European birds - trait database</td> <td>Storchová, L., & Hořák, D. (2018). Life-history characteristics of European birds. Global Ecology and Biogeography, 27(4), 400–406. https://doi.org/10.1111/geb.12709</td> </tr> </tbody> </table>
Copernicus High Resolution Vegetation Phenology and Productivity for Doñana Natural Space
<p>GeoTIFF rasters with the following phenometrics obtained from Sentinel 2 Data:.</p> <p> </p> <p>* Start of the season Day of the Year (SOSD)</p> <p>* Maximun of the Season Day of the Year (MAXD)</p> <p>* End of the Season Day of the Year (EOSD)</p> <p>* Start of the season Value (SOSV)</p> <p>* Maximun of the Season Value (MAXV)</p> <p>* End of the Season Value (EOSV)</p> <p> </p> <p>These rasters have been downloaded, mosaicked and croped with Doñana Natural Space DEIMS.ID through Pyvpp python package. </p>
Harmonized Vegetation Continuous Fields (VCF)
<p><strong>Motivation</strong></p> <p><a href="https://www.nature.com/articles/s41586-018-0411-9">Song’s Vegetation Continuous fields (VCF) product</a>, based on AVHRR satellite data, is the longest time-series of its type, but lacks updates past 2016 due to the extensive degradation of the sensor. We used machine learning to extend this time-series using data from the <a href="https://land.copernicus.eu/global/products/lc">Copernicus Land Cover dataset</a>, which provides per-pixel proportions of different land cover classes between 2015 and 2019. In addition, we included <a href="https://modis.gsfc.nasa.gov/data/dataprod/mod44.php">MODIS VCF data</a>.</p> <p><strong>Content</strong></p> <p>This repository contains the infrastructure used to model Song-like VCF data past 2016. This infrastructure contains a yaml file that configures the modelling framework (e.g. variables, directories, hyper-parameter tuning), and that interacts with a standardized folder structure.</p> <p><strong>Modelling approach</strong></p> <p>Song's VCF dataset includes data on generic categories, namely “tree cover”, “non-tree vegetation”, and “non vegetated”. Given the Copernicus dataset has a higher thematic detail, we first aggregated these data into comparable classes. We created a “Non-tree vegetation” layer (i.e. total per-pixel proportion of crops, grasses, shrubs, and mosses), and a “Non Vegetated” layer (i.e. total per-pixel proportion of bare land, permanent water, urban, and snow). Independent data on “Tree cover” was already present.</p> <p>We then constructed a Random Forest Regression (RFReg) model to predict Song-like VCF layers between 2016 and 2019. The predictions were informed by variables on topography, climate, and fires (which limit the density of vegetation), and by variables on differences between the Copernicus VCF and MODIS-based VCF data. Because MODIS data is available past 2016, its inclusion informs our models on how MODIS data, and their differences compared to Copernicus data, relate to the values reported in Song's data.</p> <p><strong>Sampling scheme</strong></p> <p>For each VCF category, we collected samples on a country-by-country basis. Within each country, we estimated the difference in percent cover between the Song's and Copernicus VCF data, and sampled across a gradient of differences, from -100% (no cover in AVHRR and full cover in Copernicus) to +100% (full cover in AVHRR and no cover in Copernicus). We iterated through this range in intervals of 10% and sampled across a gradient of “tree cover”, “non-tree vegetation”, and “non vegetated”, in intervals of 10% from 0% to 100%. We collected at least one sample per 50 km<sup>2</sup> in 2016, the last year where all VCF-related variables (Song's, Copernicus, MODIS) are available simultaneously. The amount of samples attributed to each range of differences is proportional to the area covered by this range within the country of reference. The sampling approach was repeated for each VCF class, and the outputs were later combined into a single set of samples that exclude duplicates, resulting in 238,052 samples.</p> <p><strong>Validation</strong></p> <p>The model outputs were validated using leave-one-out cross-validation. For each VCF class, the validation framework iterates through each country where samples were collected, excluding it for validation and using the remaining samples to train a RFReg models.This resulted in R<sup>2</sup> values of 0.91, 0.87 and 0.91 for “tree cover”, “non-tree vegetation”, and “non vegetated”. respectively. The RMSE values were of 2.31%, 3.05%, and 2.25%.</p> <p>The model was applied to data from 2015, which was not used to neither predict nor validate our models. A comparison between the 2015 Song data against our predictions, which consist of 8,764,232 pixels, yielded R<sup>2</sup> values of 0.94, 0.91, and 0.97. The RMSE were 6.65%, 8.92%, and 5.96%. Additionally, we compared changes between 2015 and 2016, resulting in RMSE values of 2.83%, 3.69%, and 2.57%.</p> <p><strong>Post-processing</strong></p> <p>When observing annual VCF time-series based on Song's data, we noted that our predictions were the most plausible for “tree cover” and “non-tree vegetation”. In turn, our “non vegetated” are seemingly underestimated (see "temporal_trend_check.png"), reporting large year-to-year decreases om cover (-3.05% between 2016 and 2017, compared to -0.14% for "tree cover" and -0.26% for “non-tree vegetation”). To address this issue, we recommend deriving data on “non-vegetated” cover by computing the difference between 100% and the sum of "tree cover” and “non-tree vegetation”.</p> <div class="notranslate"> </div>
Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022 (V1.2)
<p><strong>Brief Introduction:</strong></p> <p>The PKU GIMMS Normalized Difference Vegetation Index product (PKU GIMMS NDVI, version 1.2) provides spatiotemporally consistent global NDVI data in half-month and 1/12° from 1982 to 2022. It is created to address the major uncertainties presented in current global long-term NDVI products, i.e., the effects of NOAA satellite orbital drift and AVHRR sensor degradation.</p> <p> </p> <p>The PKU GIMMS NDVI was generated based on biome-specific BPNN models that employed GIMMS NDVI3g product and 3.6 million high-quality global Landsat NDVI samples. It was then consolidated with the MODIS NDVI (MOD13C1) to extend the temporal coverage to 2022 via a pixel-wise Random Forests fusion method.</p> <p> </p> <p>The PKU GIMMS NDVI exhibits overall high accuracy evaluated by Landsat NDVI samples. Besides, it efficiently eliminated the effects of satellite orbital drift and sensor degradation and presents a good temporal consistency with MODIS NDVI in terms of pixel value and global vegetation trend. It could potentially provide a more solid data basis for global change studies.</p> <p> </p> <p>Here we provide two versions of PKU GIMMS NDVI for download, one solely based on AVHRR data (1982−2015) and the other consolidated with the MODIS NDVI (1982−2022). <strong>We strongly recommend an adequate use of the quality control (QC) layer in the product. </strong>Please refer to the Readme file for more details. <strong>We also recommend removing sparse vegetation by a threshold (e.g., 0.1) in trend analysis (Zhou et al., 2001; Liu et al., 2016)</strong></p> <p> </p> <p><strong>Major updates:</strong></p> <p>Version 1.0 (December 15, 2022):</p> <p>· The original version of the product.</p> <p> </p> <p>Version 1.1 (June 17, 2023):</p> <p>· A pixel-wise Random Forests consolidation method is used to replace the linear one.</p> <p>· The data files have been re-organized on a decade basis.</p> <p> </p> <p>Version 1.2 (August 17, 2023):</p> <p>· The BPNN model without explanatory variables of NOAA satellite number and years since launch is used to generate NDVI values of EBF during the periods of 1982−1984 and all October to April, when the Landsat NDVI samples were relatively scarce.</p> <p> </p> <p><strong>Dataset Characteristics:</strong></p> <p>Spatial Coverage: 180ºW~180ºE, 63ºS~90ºN</p> <p>Projection: Geographic</p> <p>Spatial Resolution: 1/12 degree</p> <p>Temporal Resolution: Half month</p> <p>Temporal Coverage: January 1982 to December 2022</p> <p>Image Dimension: Rows-2160; Columns-4320</p> <p>Units: unitless</p> <p>Fill Value: 65535</p> <p>Data Type: uint16</p> <p>Valid Range: 0-1000</p> <p>Scale Factor: 0.001</p> <p>File Format: TIFF(.tif)</p> <p>File Size: ~8Mb each file</p> <p> </p> <p><strong>References:</strong></p> <p>Li, M., Cao, S., Zhu, Z., Wang, Z., Myneni, R. B., and Piao, S.: Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022, Earth Syst. Sci. Data, 15, 4181–4203, <a href="https://doi.org/10.5194/essd-15-4181-2023">https://doi.org/10.5194/essd-15-4181-2023</a>, 2023.</p> <p>Liu, Q., Fu, Y. H., Zhu, Z., Liu, Y., Liu, Z., Huang, M., Janssens, I. A., and Piao, S.: Delayed autumn phenology in the Northern Hemisphere is related to change in both climate and spring phenology, Global Change Biology, 22, 3702–3711, <a href="https://doi.org/10.1111/gcb.13311">https://doi.org/10.1111/gcb.13311</a>, 2016.</p> <p>Zhou, L., Tucker, C. J., Kaufmann, R. K., Slayback, D., Shabanov, N. V., and Myneni, R. B.: Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999, J. Geophys. Res., 106, 20069–20083, <a href="https://doi.org/10.1029/2000JD000115">https://doi.org/10.1029/2000JD000115</a>, 2001.</p> <p> </p>
BgMA-ESy: Expert system for automatic classification of vegetation plots of subalpine tall-herb vegetation (class Mulgedio-Aconitetea) from Bulgaria
<p>*****</p> <p>BgMA-ESy is an expert system that classifies vegetation plots of the class <em>Mulgedio-Aconitetea</em> (<a href="https://doi.org/10.1111/avsc.12257">Mucina et al. 2016</a>) occurring in Bulgaria. The expert system can be run using the JUICE program (<a href="https://doi.org/10.1111/j.1654-1103.2002.tb02069.x">Tichý 2002</a>; <a href="https://www.sci.muni.cz/botany/juice/">https://www.sci.muni.cz/botany/juice/</a>).</p> <p>The aggregation of vascular plants included within the BgMA-ESy is adopted from EUNIS-ESy (<a href="https://doi.org/10.1111/avsc.12519">Chytrý et al. 2020</a>; <a href="https://doi.org/10.5281/zenodo.4812736">https://doi.org/10.5281/zenodo.4812736</a>), and in a few cases, it is adjusted.</p> <p>*****</p> <p><strong>Specifications</strong></p> <p>The analyzed data (vegetation plots) cannot: </p> <ul> <li>include scrub vegetation (cover of tall shrub species > 8%; e.g., <em>Pinus mugo</em>, <em>Salix </em>spp.).</li> <li>contain tree species with cover > 1% (e.g., <em>Fagus sylvatica</em>, <em>Picea abies</em>).</li> <li>contain <em>Pteridium aquilinum </em>as a dominant species.</li> </ul> <p>The expert system was trained on vegetation plots with 5–100 m<sup>2</sup> area that occur above 1000 m a. s. l.</p> <p>* Exceptions from EUNIS-ESy aggregation:</p> <p>Heracleum sphondylium agg. does not include H. sphondylium subsp. verticillatum.</p> <p> </p> <p>*****</p> <p>When using this work, please cite:</p> <p>Szokala D., Kočí M. & Vassilev K. (2024): Subalpine tall-herb vegetation in Bulgaria: diversity and ecology. – Plant Biosystems 158: 490–510. <a href="https://doi.org/10.1080/11263504.2024.2327865">https://doi.org/10.1080/11263504.2024.2327865</a>.</p> <p>*****</p>
Methane and carbon dioxide fluxes from vegetated and open water zones of lakes in the Peace-Athabasca Delta, Alberta, Canada, 2019
Shallow areas of lakes, known as littoral zones, emit disproportionately more methane than open water but are sometimes ignored in upscaled estimates of lake greenhouse gas emissions. Littoral zone coverage may be estimated through synthetic aperture radar (SAR) mapping of emergent aquatic vegetation, which only grows in water less than ~1.5 m deep. In an accompanying publication, we combine airborne SAR mapping with field measurements of littoral and open-water methane flux to assess the importance of littoral zones to landscape-scale methane emissions. This dataset contains the field measurements of chamber methane flux from vegetated littoral zones and open water used for the accompanying publication. Measurements come from 24 distinct sampling events of 15 lakes in the Peace-Athabasca Delta, Alberta, Canada in July through August, 2019. The dataset also includes within-lake locations, carbon dioxide measurements, simple characterizations of vegetation type, and associated limnological and meteorological measurements, when available: water and air temperature, water depth, wind speed and direction, and relative humidity.
Surveys of coastal foredune topography and vegetation abundance, U.S. North Carolina Outer Banks, 2016-2018
These datasets include paired measurements of beach and foredune topographic profiles and dune vegetation surveys along the North Carolina Outer Banks, collected from October 2016 to June 2018. In October 2016, we conducted topographic and vegetation surveys at 55 transect locations along the southern Outer Banks (Shackleford Banks, South Core Banks, and North Core Banks) that were approximately 2 km apart. We then surveyed an additional 34 transect locations along the northern Outer Banks (Ocracoke Island, Hatteras Island, Pea Island, Bodie Island, and False Cape) that ranged from approximately 2-20 km apart in June 2017. In October 2017 and June 2018, we repeated these surveys at the same transect locations. At each cross-shore transect, we measured elevation and position using an RTK GPS and plant species abundance and dune grass density (within 0.25 m2 quadrats) every 5 m from the foredune toe to the foredune heel. We measured the areal percent cover of all plant species found within each quadrat and counted the live and dead shoot density of four native Atlantic Coast dune building grasses (Uniola paniculata, Ammophila breviligulata, Panicum amarum, and Spartina patens).
Projected climate and canopy change lead to thermophilization and homogenization of forest floor vegetation in a hotspot of plant species richness, Berchtesgaden National Park, Bavaria, Germany
Mountain forests are plant diversity hotspots, but changing climate and increasing forest disturbances will likely lead to far-reaching plant community change. Projecting future change, however, is challenging for forest understory plants, which respond to forest structure and composition as well as climate. Here, we jointly assessed effects of both climate and forest change, including wind and bark beetle disturbances, using the process-based simulation model iLand in a protected landscape in the northern Alps (Berchtesgaden National Park, Germany), asking: (1) How do understory plant communities respond to 21st-century change in a topographically complex mountain landscape, representing a hotspot of plant species richness? (2) How important are climatic changes (i.e., direct climate effects) versus forest structure and composition changes (i.e., indirect climate effects and recovery from past land use) in driving understory responses at landscape scales? Stacked individual species distribution models fit with climate, forest, and soil predictors (248 species currently present in the landscape, derived from 150 field plots stratified by elevation and forest development, overall AUC = 0.86) were driven with projected climate (RCP4.5 and RCP8.5) and modeled forest variables to predict plant community change. Nearly all species persisted in the landscape in 2050, but on average 8% of the species pool was lost by the end of the century. By 2100, landscape mean species richness and understory cover declined (-13% and -8%, respectively), warm-adapted species increasingly dominated plant communities (i.e., thermophilization, +12%), and plot-level turnover was high (62%). Subalpine forests experienced the greatest richness declines (-16%), most thermophilization (+17%), and highest turnover (67%), resulting in plant community homogenization across elevation zones. Climate rather than forest change was the dominant driver of understory responses. The magnitude of unabated 2
Data from "Grassland woody plant management rapidly changes woody vegetation persistence and abiotic habitat conditions but not herbaceous community composition"
These files contain microhabitat, soil, vegetation structure, and woody plant species data used in the paper "Grassland woody plant management rapidly changes woody vegetation persistence and abiotic habitat conditions but not herbaceous community composition". The project was conducted at seven publicly accessible remnant (i.e., unplowed or old-growth) tallgrass prairie within 100 miles of Madison, Wisconsin, United States starting in the 2020 growing season and commencing following the 2022 growing season. The goal was to assess the initial effects of different management interventions on woody vegetation persistence, abiotic habitat conditions, and herbaceous community composition, including physical and chemical management interventions and their combination.
Variation in the Composition of Understory Vegetation in a Tropical Rain Forest as a Function of Soil and Topographic Position. 1986 - 1990
Understory plants are a major contribution to the high plant species diversity of Neotropical rain forests. Shrubs, understory trees, saplings of overstory trees, and herbs occupy a habitat of generally low light levels and high humidity in which there seem to be few obvious mechanisms to support habitat partitioning. Moreover several plant families are characterized by a high number of co-occurring understory species. In 1987-1989 we sampled understory vegetation in 18 sites at the La Selva Biological Station of the Organization for Tropical Studies in Heredia Province, Costa Rica. At each site we used 20 nested quadrats to investigate the effects of soil type on replicated sites of mapped alluvial and residual volcanic soils (5 map units) and topographic positions (ridges, midslopes and flats) on composition, density and diversity of small (1m tall to 5cm dbh, 25 m2 quadrat) and large(5-10cm dbh, 100 m2 quadrat) understory plants. We also measured fine litter dry mass, extractable P, total organic matter, percent slope and percent incident light radiation in each quadrat.
Site environmental, climate, water levels and temperatures, vegetation cover, and GIS change detection for assessing permafrost change in fens on the Tanana Flats, central Alaska
This data package provides data used to assess the roles of climate extremes, ecological succession, and hydrology in repeated permafrost aggradation and degradation in fens on the Tanana Flats, central Alaska. The package provides data on site environmental information, Fairbanks climate, vegetation cover, water levels and temperatures, as well as GIS files for fen change detection. The Site data include information on observers, locations, geomorphology, hydrology, soils, vegetation, and disturbance. The table has numerous fields that uses coding for class characteristics and these codes are described in the metadata as well as compiled in the ELS_Arctic_Boreal_Site_Soil_Veg_Code_Sheet_2020.docx. Alaska Climate records for Fairbanks (UAF Experiment Station) from 1904 to 2019 were acquired from the National Oceanic and Atmospheric Administration (https://www.ncdc.noaa.gov/cdo-web/). Additional data were obtained for the Nenana station (about 70 km southwest of Fairbanks), to fill in small data gaps (particularly precipitation/snow depth ruler measurements) in the Fairbanks record. We attributed the data with fields for summer (May-September) and winter periods (November-March) and hydrologic year (October-September) and calculated mean air temperature, precipitation, and snow depth by seasonal period (average of daily values) and year. The broad summer and winter periods were of interest because warmer and wetter summers increase soil heat input and warmer and snowier winters reduce soil heat loss. Fen hydrology data include information on fen water level/pressure and temperatures collected every two hours at seven sites within fens from 2011 to 2014. Vegetation composition and cover of fens, scrub, and forests was sampled to assess effects of thermokarst on vegetation change. Plant cover was determined by point-sampling at 100 points (including repetitive “hits” for all layers) distributed along 5 equally spaced rows (4-m long, 20 points per row) across the 10-m l
UCSB SONGS Mitigation Monitoring: Wetland Process Study - Irrigation, Decompaction, Amendment, Planting and Seeding Experiment Vegetation Cover
These data describe estimates of the percent cover of marsh plants in experimental plots designed to evaluate the effectiveness of various soil treatments on increasing vegetation cover at the San Dieguito Wetlands (Del Mar, California). Plots established between 1.61 – 2.1 m MLLW were manipulated to test the effects of irrigation, decompaction, soil amendments, and planting versus seeding, whereas plots between 1.6 – 1.7 m MLLW tested the effects of planting versus seeding alone. Data collection was conducted from 2020 to 2022. During each survey, species of marsh plants were identified and recorded under 98 uniformly spaced points within 4.5 m2 quadrats in each plot.
Stratified Vegetation Survey Data from an Experimental Mangrove Site in Port Aransas, Texas: 2019
We visually surveyed the vegetation at the front and back of ten large experimental plots located in a large stand of mangroves near Port Aransas, Texas on November 28th, 2019. The ten experimental plots had been thinned using a 3 x 3 m grid in 2012 to create a gradient in plot-level mangrove cover from 0 to 100 percent. We estimated percent cover in six “mangrove” and six “cleared” cells at the front and back of each plot.
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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.