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3,105 results for “vegetation”
Sabana pasture permanent plot vegetation sampling
Permanent plot data is expected to show these temporal patterns: (1) rapid increases in percent cover and tree stem density, and (2) rapid turnover from early to late successional plant species. Plant-plant competition should show quick increases in intensity with native grass species and exotic as top competitors. These may lead to exclusion of some trees common after landslide disturbance. Spatial patterns of invading trees should include edge effects due to dispersal limitation with clumping of bird-dispersed species before the first five years after cow exclusion. Because of intact soil and low vegetation in the pasture trees should grow, as measured by biomass(productivity), height, and basal diameter increases, significantly faster compared to colonization of landslides. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Saddle catchment Distributed Hydrology Soil Vegetation Model Simulation (DHSVM) surface variable outputs (SWE, snowmelt, streamflow, soil moisture), 2 meter, 2000-2019.
The Saddle Catchment of the Niwot Ridge LTER is a densely observed, high elevation site that is ideal for hydrological model simulation and calibration. The files produced are the result of a calibration of the Distributed Hydrology Soil Vegetation model (DHSVM) using observationally based states and forcings. Input state files of vegetation, soil properties, shading, and elevation were generated using ground and satellite observations, which, in the case of coarse-resolution or point scale observations, were then interpolated to match the high resolution of the model (2-meter grid cells). Temporally continuous meteorological forcings at the hourly time-step were used to force the model to produce an hourly simulation of the surface and subsurface hydrology within the Saddle catchment. DHSVM was calibrated to effectively reproduce the annual cycle (r^2) and total volume (percent bias) of observed runoff using observations of streamflow at the outflow pour point of the Saddle Catchment from 2001-2019. Calibrated parameters include the lateral conductivity of soil types, exponential decrease of soil conductivity, snow roughness, the snow melting temperature threshold, and the vertical conductivity of the soils. The resulting simulation generated spatially distributed time series of the snow water equivalent, snow melt, precipitation, total evapotranspiration, potential evapotranspiration, and a time-series of the total runoff generated at the outflow pour-point of the Saddle catchment. This data package contains the spatially distributed time series of snow water equivalent, snow melt, and runoff, as well as the model configuration file. Outputs of precipitation, total evapotranspiration, actual evapotranspiration, as well as model inputs are archived separately on the Environmental Data Initiative.
Saddle catchment Distributed Hydrology Soil Vegetation Model Simulation (DHSVM) precipitation and transpiration variable outputs (precipitation, total, potential and actual evapotranspiration), 2 meter, 2000-2019.
The Saddle Catchment of the Niwot Ridge LTER is a densely observed, high elevation site that is ideal for hydrological model simulation and calibration. The files produced are the result of a calibration of the Distributed Hydrology Soil Vegetation model (DHSVM) using observationally based states and forcings. Input state files of vegetation, soil properties, shading, and elevation were generated using ground and satellite observations, which, in the case of coarse-resolution or point scale observations, were then interpolated to match the high resolution of the model (2-meter grid cells). Temporally continuous meteorological forcings at the hourly time-step were used to force the model to produce an hourly simulation of the surface and subsurface hydrology within the Saddle catchment. DHSVM was calibrated to effectively reproduce the annual cycle (r^2) and total volume (percent bias) of observed runoff using observations of streamflow at the outflow pour point of the Saddle Catchment from 2001-2019. Calibrated parameters include the lateral conductivity of soil types, exponential decrease of soil conductivity, snow roughness, the snow melting temperature threshold, and the vertical conductivity of the soils. The resulting simulation generated spatially distributed time series of the snow water equivalent, snow melt, precipitation, total evapotranspiration, potential evapotranspiration, and a time-series of the total runoff generated at the outflow pour-point of the Saddle catchment. This data package contains the spatially distributed time series of precipitation, total evapotranspiration and actual evapotranspiration Outputs of snow water equivalent, snow melt, and runoff, as well as the model configuration file, as well as model inputs are archived separately on the Environmental Data Initiative.
Future hydrologic outputs using the Distributed Hydrology Soil Vegetation Model (DHSVM) for the Saddle Catchment, 2001 - 2100.
The Saddle Catchment of the Niwot Ridge LTER is subject to warming in a future climate and thus changes in precipitation phase, precipitation redistribution, and timing and distribution of surface water inputs (the summation of rainfall and snowmelt) as well as changes in atmospheric demand (potential evapotranspiration, PET) and the amount of evapotranspiration (ET). The input warming data were developed to first force a future climate across the Saddle Catchment and evaluate resultant hydrologic outputs using the Distributed Hydrology Soil Vegetation Model (DHSVM). Future forcing data were generated by calculating and implementing delta values between daily average historical data and those generated from end-of-current-century Weather Research Forecasting model data. The variables perturbed in the warming DHSVM simulation were: precipitation, air temperature, relative humidity and longwave radiation. Target outputs included: daily spatially distributed precipitation (historical and future), daily spatially distributed surface water inputs (historical and future), total spatially distributed PET (historical and future), and total spatially distributed ET (historical and future). The precipitation and surface water inputs products are orthorectified (UTM projection) raster products, and the forcing data and PET and ET are CSV files. The forcing data represent catchment averages, which are distributed within DHSVM, and all other files are at the 2 m resolution.
PIE LTER locations of vegetation transects in Rowley salt marsh sites used for measuring long term changes.
A description of transects used for long term studies of marsh vegetation in Rowley, MA. Four sites ((12 transects) were originally set up to study the impact of salt marsh haying. Two of these sites (labeled McH and EPH) were regularly hayed until 2002. The other two (PUH and CC) were reference sites. Two additional sites labeled RM (8 transects) were originally set up to track invasion by Phragmites australis.
PIE LTER study of marsh vegetation percent cover at Greenwood Creek, Ipswich, MA effluent enrichment and Clubhead Creek, Rowley, MA reference sites
Vegetation percent cover data along transects through a nutrient enriched marsh receiving wastewater effluent and a reference (unenriched) marsh. Nutrient enrichment comes from the Ipswich Wastewater Treatment Facility on Greenwood Creek in Ipswich. The marsh around Clubhead Creek, Rowley, MA was used as a reference.
PIE LTER, geographic information for the transects that were set up to study the impacts on the salt marsh vegetation of nutrient enrichment from the Ipswich Wastewater Treatment Facility on Greenwood Creek in Ipswich, MA.
A description of the transects that were set up to study the impacts on the salt marsh vegetation of nutrient enrichment from the Ipswich Wastewater Treatment Facility on Greenwood Creek in Ipswich, MA, USA. The marsh around Clubhead Creek, Rolwey, MA, USA was used as a reference.
PIE LTER marsh vegetation species composition and elevation along nine transects in 2000 and 2001
We selected 9 transects from a set of 40 marsh surveys conducted in 2000 and 2001 to use as a baseline for monitoring changes in species composition and marsh elevation across a broad spatial scale in the Plum Island Estuary. In the early surveys, latitude, longitude and elevation, as well as salt and brackish marsh vegetation composition were documented using cover classes on individual plots. Results from those surveys have been compiled here.
PIE LTER marsh vegetation species composition and elevation along nine transects in 2021
Salt and brackish marsh vegetation distribution was documented using cover classes (modified Braun-Blanquet) on individual plots along 9 transects in the Plum Island Estuary. The plots were also surveyed for elevation rel mNAVD88 using RTK GPS . The plots and transects had been surveyed for vegetation and elevation in 2001. The re-survey is designed to monitor changes in species composition and marsh elevation across a broad spatial scale over the previous 20 years. The survey will also serve as a baseline for future marsh monitoring work with UAVs.
Gunnison's Prairie Dog Restoration Experiment (GPDREx): Vegetation Cover Data from the Sevilleta National Wildlife Refuge, New Mexico (2011-2016)
Prairie dogs (Cynomys spp.) are burrowing rodents considered to be ecosystem engineers and keystone species of the central grasslands of North America. Yet, prairie dog populations have declined by an estimated 98% throughout their historic range. This dramatic decline has resulted in the widespread loss of their important ecological role throughout this grassland system. The 92,060 ha Sevilleta NWR in central New Mexico includes more than 54,000 ha of native grassland. Gunnison's prairie dogs (C. gunnisoni) were reported to occupy ~15,000 ha of what is now the SNWR during the 1960's, prior to their systematic eradication. In 2010, we collaborated with local agencies and conservation organizations to restore the functional role of prairie dogs to the grassland system. Gunnison's prairie dogs were reintroduced to a site that was occupied by prairie dogs 40 years ago. This work is part of a larger, long-term study where we are studying the ecological effects of prairie dogs as they re-colonize the grassland ecosystem.
Field-Collected Spectral Reflectance of Dominant Vegetation at the Sevilleta National Wildlife Refuge
This dataset includes field-collected spectral reflectance of dominant vegetation species in grassland and shrubland at the Sevilleta National Wildlife Refuge collected monthly May – September 2019. A spectroradiometer was used to collect the percent spectral reflectance of electromagnetic radiation (range 400-2500nm) of a sample of dominant vegetation species ("spectra"), yielding a spectral curve for each species. At least ten individuals per species were sampled. These data form a spectral library which was used to calibrate a multiple-endmember spectral mixture analysis (MESMA) of satellite imagery of the Sevilleta NWR, as part of an ongoing collaboration between the LTER and the Center for the Advancement of Spatial Informatics Research and Education (ASPIRE). Ultimately, we aim to produce fractional images of green vegetation, non-photosynthetic vegetation, bare soil, and shade to form a synoptic thirty-year record of vegetation dynamics at the Refuge. The spectral library can be referenced by future researchers using remote sensing methods to examine vegetation dynamics at the Sevilleta NWR.
Vegetation surveys in the riparian (bosque) corridor of the Middle Rio Grande valley, NM
This dataset contains vegetation cover information from 34 long-term Bosque Ecosystem Monitoring Program (BEMP) sites from 2000 – 2021. Data were collected along ten 30-m transects at each site at the centimeter scale each year in August-early October as funding and site access allowed. At the fullest extent, sites spanned 520 km of the riparian forest along the Rio Grande. The purpose of this dataset is to track plant species at sites along the Rio Grande in New Mexico. From this dataset, changes in plant species abundance, richness, and species diversity can be tracked and analyzed with ecosystem drivers such as flooding, fire, species removal/fuel reduction projects, and climate change. Species are coded using USDA Plant Database codes, allowing species information to be added to each species, including origin (native or nonnative), duration (e.g., annual, biennial, perennial), and plant type (e.g., grass, forb, vine, shrub, tree). This dataset has allowed the tracking of the ascendance of nonnatives in some sites, the recovery of natives in other sites, success or lack of success following restoration projects, and records of new species occurring in various counties and the state of New Mexico.
SEV LTER: Tracking Vegetation Phenology Using PhenoCam Imagery at the Sevilleta National Wildlife Refuge, New Mexico, 2014-2024
As of 03/03/2024, the Sevilleta Long-Term Ecological Research Program is equipped with a total of 65 digital RGB cameras, or PhenoCams, across the Sevilleta National Wildlife Refuge. These cameras are installed on eddy covariance flux towers and at a number of precipitation manipulation experiments to track vegetation phenology and productivity across dryland ecotones. PhenoCams have been paired with eddy covariance flux tower data at the site since 2014, while some Mean-Variance Experiment PhenoCams were installed as recently as June 2023. For information on PhenoCam data processing and formatting, see Richardson et al., 2018, Scientific Data (https://doi.org/10.1038/sdata.2018.28), Seyednasrollah et al., 2019, Scientific Data (https://doi.org/10.1038/s41597-019-0229-9), and the PhenoCam Network web page (https://phenocam.nau.edu/webcam/). The PhenoCam Network uses imagery from digital cameras to track vegetation phenology and seasonal changes in vegetation activity in diverse ecosystems across North America and around the world. Imagery is uploaded to the PhenoCam server hosted at Northern Arizona University, where it is made publicly available in near-real time, every 30 minutes from sunrise to sunset, 365 days a year. The data are processed using simple image analysis tools to yield a measure of canopy greenness, from which phenological metrics are extracted, characterizing the start and end of the growing season. These transition dates have been shown to align well with on-the-ground observations at various research sites. Long-term PhenoCam data can be used to track the impact of climate variability and change on the rhythm of the seasons.
Remote sensing based species distribution modelling based on GLCM and vegetation fractions for the city of Leipzig
<p>Modelling dataset and fractional vegetation cover dataset used in the study "Earth observation based indication for avian species distribution models using the spectral trait concept and machine learning in an urban setting" Wellmann et al. 2020.</p> <p> </p> <p>Reference:</p> <p></p> <p>Wellmann, T., Lausch, A., Scheuer, S., & Haase, D. (2020). Earth observation based indication for avian species distribution models using the spectral trait concept and machine learning in an urban setting. <em>Ecological Indicators</em>, <em>111</em>(April 2020), 106029. https://doi.org/10.1016/j.ecolind.2019.106029</p> <p></p>
Simulations from the LPJmL3.5 dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the LPJmL3.5 dynamic global vegetation model are provided for two global simulations. One forced by CRU-NCEP v5 climate data for the period 1901-2014 and one forced by bias-corrected IPSL-CM5A-LR RCP 8.5 climate data for the period 1901-2099. Only potential natural vegetation was simulated, i.e. no human land-use. Both simulations are at 0.5 x 0.5 degree spatial resolution. Carbon turnover fluxes for live vegetation for each individual turnover-causing process in the model were outputted individually. For a full description of the simulations, please refer to the accompanying paper.</p>
Simulations from the ORCHIDEE dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the ORCHIDEE dynamic global vegetation model are provided for two global simulations. One forced by CRU-NCEP v5 climate data for the period 1901-2014 and one forced by bias-corrected IPSL-CM5A-LR RCP 8.5 climate data for the period 1901-2099. Only potential natural vegetation was simulated, i.e. no human land-use. Both simulations are at 0.5 x 0.5 degree spatial resolution. Carbon turnover fluxes for live vegetation for mortality, leaf phenological turnover and fine root phenological turnover were outputted individually. For a full description of the simulations, please refer to the accompanying paper.</p>
Simulations from the JULES dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the JULES dynamic global vegetation model are provided for two global simulations. One forced by CRU-NCEP v5 climate data for the period 1901-2014 and one forced by bias-corrected IPSL-CM5A-LR RCP 8.5 climate data for the period 1901-2099. Only potential natural vegetation was simulated, i.e. no human land-use. Both simulations are at 1.875 x 1.25 degree spatial resolution. Carbon turnover fluxes for live vegetation for each individual turnover-causing process in the model were outputted individually. For a full description of the simulations, please refer to the accompanying paper.</p>
Simulations from the LPJ-GUESS dynamic global vegetation model v3.0 for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the LPJ-GUESS dynamic global vegetation model v3.0 are provided for two global simulations. One forced by CRU-NCEP v5 climate data for the period 1901-2014 and one forced by bias-corrected IPSL-CM5A-LR RCP 8.5 climate data for the period 1901-2099. Only potential natural vegetation was simulated, i.e. no human land-use. Both simulations are at 0.5 x 0.5 degree spatial resolution. Carbon turnover fluxes for live vegetation for mortality, leaf phenological turnover and fine root phenological turnover were outputted individually. For a full description of the simulations, please refer to the accompanying paper.</p>
CO2 fertilization, transpiration deficit and vegetation period drive the response of mixed broadleaved forests to a changing climate in Wallonia: Dataset
<p>This repository is linked to the paper "CO2 fertilization, transpiration deficit and vegetation period drive the response of mixed broadleaved forests to a changing climate in Wallonia" submitted to Annals of Forest Science and written by Louis DE WERGIFOSSE (corresponding author), Frédéric ANDRE, Hugues GOOSSE, Steven CALUWAERTS, Lesley DE CRUZ, Rozemien DE TROCH, Bert VAN SCHAEYBROECK and Mathieu JONARD.</p> <p>The files stored in the repository are the input files that should be used in the model HETEROFOR to retrieve the results displayed in the study and the corresponding results themselves. The source code of the model HETEROFOR can be freely accessed and downloaded (https://doi.org/10.5281/zenodo.3591348). Additional information on the model can be found in the following description papers: Jonard et al., 2020 (https://doi.org/10.5194/gmd-13-905-2020) and de Wergifosse et al., 2020 (https://doi.org/10.5194/gmd-13-1459-2020).</p> <p>The repository contains three directories. The first (HETEROFOR_input_files) comprises the additional files to those in the model repository presented in the previous paragraph needed to run the model for the purpose of this study. The second directory (Simulation_outputs_raw) contains the data directly provided by the model without any processing. The third directory (Simulation_outputs_raw) includes the model outputs after processing.</p> <p>The directory "HETEROFOR_input_files" is constituted of two directories called "Climate_files" and "Stand_files". "Climate_files" is subdivided in three sub-directories. Sub-directory "Original_downscaled_CORDEX_timeseries" contains the climate projections of the four sites and scenarios described in the study. These downscaled timeseries have been produced by the Royal Meteorological Institute of Belgium under the program CORDEX.be, which is part of EURO-CORDEX. A bias correction has been further applied to these climate timeseries that are stored in the "Bias_corrected_timeseries" sub-directory. The files of these two sub-directories should be used in HETEROFOR as "Meteorological data" input files. The "CO2_concentrations" sub-directory includes the yearly averaged projected concentrations for the three RCP scenarios described in the paper. In HETEROFOR, they should be put as input in the "Atmospheric CO2 concentration" part after selecting the option "Variable over time". The second directory called "Stand files" contain the six inventory files described in the study for which a thinning has been applied. They should be used in HETEROFOR as "Inventory data" input files.</p> <p>The directory "Simulation_outputs_raw" is divided similarly to the study into two simulation experiments. The "First simulation experiment" directory is further subdivided into constant and time-dependent CO2 concentrations like in the study and contains one file for the regular modality and one for the thinning modality. All the files are constructed the same way with, for each tree and site (or stand, soil and climate), annual values of Net Primary Production (NPP) in kg of carbon, transpiration and potential transpiration in L under the different climate scenarios. In addition, the "Phenology" directory contains, for each day and under all climate scenarios, the green proportion (proportion of green leaves comprised between 0 and 1) for the two tree species considered in the study (Common oak and European beech).</p> <p>Finally, the directory "Simulation_outputs_processed" is constructed similarly to "Simulation_outputs_raw" but all the data are integrated in one file at the yearly time step. However, the units change with the NPP expressed in gC/m2 and transpiration and potential transpiration in mm (or L/m2) while the vegetation period is averaged according to the percentage of species occurrence<br> in the different stands.</p> <p><br> For more information concerning this repository or the study, please do not hesitate to contact Louis DE WERGIFOSSE (louis.dewergifosse@uclouvain.be) or Mathieu JONARD (mathieu.jonard@uclouvain.be).</p>
Global vegetation productivity from 1981 to 2018 estimated from remote sensing data
<p>The MUltiscale Satellite remotE Sensing (MUSES) global vegetation productivity dataset includes gross primary productivity (GPP) and net primary productivity (NPP) data from 1981 to 2018. GPP and NPP were estimated with a light use efficiency (LUE) model and MUSES leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR) products.</p> <p>The MUSES product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>The detail information of the MUSES 5-km global GPP and NPP products are as below:</p> <p>Name: MUSES 5-km global GPP and NPP products</p> <p>Period: 1981-2018</p> <p>Spatial resolution: 0.05°</p> <p>Temporal resolution: 8 days</p> <p>Projection: geographic latitude/longitude</p> <p>Data format: Tiff</p> <p>Data type: integer (16bit)</p> <p>Upper left coordinates: -180°E, 90°N</p> <p>Scale factor: 100</p> <p>Unit: gCm<sup>-2</sup>d<sup>-1</sup></p> <p> </p> <p><span>Citation (Please cite these papers when these data are used)</span></p> <p><span>1. Wang, J.M., Sun, R., Zhang, H.L., Xiao, Z.Q., Zhu A.R., Wang, M.J., Yu, T., Xiang, K.L.,</span><span> </span><span>New global MuSyQ GPP/NPP remote sensing products from 1981 to 2018. </span><span>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14, 5596-5612.</span></p> <p><span>2. Wang, M.J.; Sun, R.; </span><span>Zhu, A.R.;</span><span> Xiao, Z. Q. Evaluation and Comparison of Light Use Efficiency</span><span> </span><span>and Gross Primary Productivity Using Three</span><span> </span><span>Different Approaches. <span>Remote Sensing</span>. <span>2020</span>, 12, 1003.</span></p> <p><span>3. Yu, T.; Sun, R.; Xiao, Z.Q. ;Zhang , Q.; Liu, G.; Cui, T.X.; Wang, J.M. Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data. <span>Remote sensing. </span><span>2018, </span>10, 327.</span></p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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