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3,105 results for “Vegetation =”
Top-down photographs of Columbia-0 Arabidopsis thaliana vegetative growth under 16-hour days
<p>Photograph with Raspberry Pi camera v1. Six overlapping fields of view (07 to 12). Two photos per hour from 11:30 UTC to 0300 UTC. (Photographed at 5-minute intervals -- this is a subset of the data.) Manifest file with checksums for the 2712 photos included.</p>
Top-down time-lapse photograph dataset: Columbia-0 Arabidopsis thaliana vegetative growth under 8-hour days #2
<p>Photographed with Raspberry Pi camera v1. Twelve overlapping fields of view (01 to 12). Two photos per hour from ZT 0030 to ZT 0800 (9 AM to 4:30 PM local time). Photographs were taken at 5-minute intervals -- this is a subset of the data. Manifest file with checksums for the 4752 photos (JPEG files) included.</p>
Top-down time-lapse photograph dataset: Columbia-0 Arabidopsis thaliana vegetative growth under 8-hour days #1
<p>Photographed with Raspberry Pi camera v1. Twelve overlapping fields of view (01 to 12). Two photos per hour from ZT 0030 to ZT 0800 (9 AM to 4:30 PM local time). Photographs were taken at 5-minute intervals -- this is a subset of the data. Manifest file with checksums for the 6181 photos (JPEG files) included.</p> <p>First timepoint photo was not captured for fov-04. An extra photo was captured right at dawn (8:30 AM) for both fov-04 and fov-06 and is included. Additional metadata will be deposited in a separate Zenodo record.</p>
Top-down time-lapse photograph dataset: Landsberg erecta Arabidopsis thaliana vegetative growth under 8-hour days
<p>Photographed with Raspberry Pi camera v1. Twelve overlapping fields of view (01 to 12). Two photos per hour from ZT 0030 to ZT 0800 (15:00 to 22:30 UTC). Photographs were taken at 5-minute intervals -- this is a subset of the data. Manifest file with checksums for the 5004 photos (JPEG files) included.</p>
Supplementary Material Chapter 4 - Decomposition of aquatic pioneer vegetation in newly constructed wetlands
<p>Supplementary Material to Chapter 4 "Decomposition of aquatic pioneer vegetation in newly constructed wetlands" of PhD thesis from Ciska Overbeek, "Peat formation on a former landfill - Production and decomposition of aquatic pioneer vegetation". </p> <p>Published by Overbeek et al in 2018 in Ecological Engineering 114: 154-161. https://doi.org/10.1016/j.ecoleng.2017.06.046. </p>
Urban stormwater pond water quality and vegetation cover data
<p>These data include measurements of water physical and chemical parameters and littoral vegetation measurements in urban stormwater ponds located in three different urban land use types in Orlando, Florida, USA.</p>
Model simulated potential natural vegetation state in the western US under preindustrial, historic, and future (RCP8.5) atmospheric conditions using multiple parameterizations of the dynamic vegetation model TRIFFID.
<p>DATA DESCRIPTION<br> Author contact information:<br> Linnia R. Hawkins<br> Oregon State University<br> lhawkins@oregonstate.edu; linnia.hawkins@gmail.com<br> Data supporting 2019 Journal of Advances in Modeling Earth Systems publication</p> <p>Simulations of the equilibrium vegetation distribution in the western US performed with the climate model HadAM3p-HadRM3p-MOSES2-TRIFFID</p> <p>step1: Identify Influential Parameters<br> All files labeled step1.<br> EXPERIMENT DESCRIPTION: Data used in step 1: identify influential parameters <br> sensitivity experiment adjusting one parameter at a time 38 individual parameters were adjusted to 7 values, equally spaced<br> over a defined plausible range. For reference nine simulations with the default model parameterization are included, initiated with unique initial potential temperature perturbations. </p> <p>The data contains the vegetation state variables at the end of four-year simulations (January 2004 to December 2007) during which two equilibrium time steps with the dynamic vegetation model TRIFFID (Cox et al., 2001). The results are averaged over three simulations initiated with unique atmospheric potential temperature perturbations.</p> <p>FILE DESCRIPTION:<br> NETCDF: Each netcdf file contains the fractional coverage (field1391), leaf area index (field1392), and the canopy height (field1393) for 5 plant functional types (PFTs: broadleaf, needleleaf, c3 grass, c4 grass, shrub) simulated for November 29, 2007. </p> <p> File labeling scheme: <br> step1_parameter_settingindex_3ICave.nc</p> <p> parameter: the name of the only model parameter adjusted<br> setting index: the index of the parameter setting (1-7)<br> index of 1 references the lowest plausible parameter setting<br> index of 7 references the highest plausible parameter setting<br> index of 4 references the parameter setting half way between the lowest and highest plausible parameter settings. <br> 3ICave: references that the results have been averaged over 3 initial conditions.</p> <p> Variables:<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> </p> <p>step2: ParameterSensitivity<br> all files labeled step2<br> EXPERIMENT DESCRIPTION:<br> Data used in step 2: parameter sensitivity <br> Perturbed Parameter Experiment (PPE) simultaneously adjusting 18 parameters.<br> Latin hypercube sampling was employed to generate 250 unique parameterizations references with a SETID (ranging from 1-359)<br> The data provided contains the simulated vegetation state after 4 year simulations (December1903-November1907) with two TRIFFID equilibrium rounds. <br> Data is averaged over 5 initial atmospheric conditions.</p> <p>FILE DESCRIPTION:<br> NETCDF: files contain either the fractional coverage (field1391) or the above ground biomass (field1512) for 5 plant functional types (PFTs) simulated for November 1907.</p> <p> File labeling scheme: <br> step2_variable_parametersetindex_5ICave.nc</p> <p> variable: the name of the variable contained in the file<br> setting index: the index of the parameter setting corresponding to the parameter set text files<br> 5ICave: references that the results have been averaged over 5 initial atmospheric conditions.</p> <p> Variables:<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]</p> <p>TXT: files contain a list of the model parameterizations (for each PFT and variable) and the corresponding to the parameter set index. <br> Parameters are labeled in row 1<br> Parameter set indices are shown in column 1</p> <p>RESTART: restart_region_TPPE_c374_1903-12-01.nc<br> The restart file contains the model state variables after spinup. This file was used to initiate all model simulations in step2.</p> <p>step3: ParameterSetSelection<br> All files labeled step3<br> EXPERIMENT DESCRIPTION:<br> Data used in step 3: parameter set selection<br> PPE simultaneously adjusting 10 parameters. <br> Latin hypercube sampling was employed to generate 140 unique model parameterizations, referenced with a SETID (ranging from 3-276).<br> The data provided contains the simulated vegetation state after 4 year simulations (December1903-November1907) with two TRIFFID equilibrium rounds. <br> Data is averaged over 5 initial atmospheric conditions.</p> <p><br> FILE DESCRIPTION:<br> NETCDF: files contain either the biomass (field1512) fractional coverage (field1391) canopy height (field1393) for 5 plant functional types (PFTs) simulated for November 1907 or the net primary productivity (NPP; item3262_monthly_mean) for December 1903 through November 1907. </p> <p> File labeling scheme: <br> step3_variable_parametersetindex_5ICave.nc</p> <p> variable: the name of the variable(s) contained in the file<br> setting index: the index of the parameter setting corresponding to the parameter set text files<br> 5ICave: references that the results have been averaged over 5 initial atmospheric conditions.</p> <p> Variables:<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> item3262_monthly_mean – Net primary productivity – units: (kgC/m2/sec) – all PFTs</p> <p>TXT: files contain a list of the model parameterizations (for each PFT) and the corresponding to the parameter set index. <br> Parameters are labeled in row 1<br> Parameter set indices are shown in column 1</p> <p><br> production_runs<br> files labeled PI, historical, and future<br> EXPERIMENT DESCRIPTION:<br> Data simulated in the production runs. Model spinup was performed under preindustrial conditions with 10 unique model parameterizations (pset0-pset9). The resulting vegetation distribution for each parameterization after spinup are included and labeled PIrestarts. These restarts were used to initiate (or restart) the simulations under historic and future (RCP8.5) climate conditions. Files labeled historic contains the simulated vegetation state after 5 year simulations (2004-09-01 to 2009-08-30) with one TRIFFID equilibrium round occurring at the end. Files labeled future contain the simulated vegetation state after 5 year simulations (2054-09-01 to 2059-08-30) with one TRIFFID equilibrium round occurring at the end. </p> <p>FILE DESCRIPTION:<br> NETCDF: files contain the fractional coverage (field1391), leaf area index (field1392), canopy height (field1393), and biomass(field1512) for 5 plant functional types (in the order broadleaf, needleleaf, C3 grass, C4 grass, shrub).</p> <p><br> File labeling scheme:<br> pset* where * refers to the model parameterization 0-9<br> files were simulated with the model parameterization *, initiated with a unique initial condition (perturbation to the potential temperature field). </p> <p> Variables (PIrestart)<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf] <br> field1391_1 – fractional coverage of PFT – units: fraction – [needleleaf]<br> field1391_2 – fractional coverage of PFT – units: fraction – [c3grass]<br> field1391_3 – fractional coverage of PFT – units: fraction – [c4grass]<br> field1391_4 – fractional coverage of PFT – units: fraction – [shrub]<br> field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf]<br> field1392_1 – leaf area index of PFT – units: m2/m2 – [needleleaf]<br> field1392_2 – leaf area index of PFT – units: m2/m2 – [c3grass]<br> field1392_3 – leaf area index of PFT – units: m2/m2 – [c4grass]<br> field1392_4 – leaf area index of PFT – units: m2/m2 – [shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf]<br> field1393_1 – canopy height of PFT – units: meters – [needleleaf]<br> field1393_2 – canopy height of PFT – units: meters – [c3grass]<br> field1393_3 – canopy height of PFT – units: meters – [c4grass]<br> field1393_4 – canopy height of PFT – units: meters – [shrub]<br> </p> <p> Variables (historic/future)<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> <br> </p>
JEt interacting with VEgetation in a Rotating Basin
<p>Contaminants, nutrients and sediment particles flow into our inland and coastal water bodies often forming turbulent jets.<br> Some examples are outflows from rivers, sources and discharges entering the ocean through natural obstructions.<br> Mixing processes are rather well understood for unobstructed jets, and they are mainly controlled by the initial jet characteristics,<br> the boundary conditions and the hydrodynamic features of the ambient current.<br> Jets obstructed by vegetation have been investigated only recently and have shown to play an important role.<br> For example, while in the absence of vegetation the entrainment in the jet is a dominant process,<br> the vegetation canopy has been found to induce detrainment.<br> In the case of a uniform flow, previous research has demontrated that vegetation canopy strongly perturbs advection<br> and dispersion depending on its density and geometry.<br> In realistic contexts, the effect of rotation on the turbulence induced by an obstructed pattern is felt indirectly<br> through the modification of the mean flow,<br> and so the subsequent transport and spreading of turbulent kinetic energy and scalars (tracers).<br> Furtherly, rotation may induce the development of Ekman boundary layers,<br> which effectively increases bottom friction and could alter the turbulence characteristics within the jet.<br> Laboratory studies investigating the effect of the earth's rotation of a jet propagating through vegetation do not exist to date<br> and are the goal of the present experiments.<br> Variation parameters are the vegetation disposition and the different background rotation speeds,<br> for a given output flow rate of the jet.<br> Velocity fields are captured at different horizontal 2D cross sections using a PIV measurement technique,<br> ADV point measurements and dye visualizations and will enable to obtain detailed information about the mean and turbulent flow characteristics.</p>
Drought impacts on Australian vegetation during the Millennium Drought measured with multi-source spaceborne remote sensing
<p>During the period from 1997 to 2009, Australia experienced a severe and persistent drought known as the Millennium Drought (MD). Major water shortages were reported across the continent as were various field accounts of tree mortality and dieback, but large-area assessment has been lacking. Given uncertain projections of future drought conditions in South-East Australia, analysis of the MD presents a valuable opportunity to assess possible impacts of these future trends. In this study, we analyzed the <strong>magnitude and sensitivity of vegetation responses to the MD</strong> with satellite-derived information including the fraction of photosynthetically absorbed radiation (FPAR), photosynthetic vegetation cover (PVC), canopy density derived from vegetation optical depth (VOD) and aboveground biomass carbon (ABC). <strong>Bioclimatic diferences in drought impacts and sensitivity</strong> were examined as well. Here are generated datasets and related codes for processing. Drought impcats and sensitivities for FPAR and PVC are too large to be uploaded but users could calculate their own results with the codes provided here. See Metadata.txt for details.</p>
Ecosystem_resilience_study_vegetation_images
<p>Processed and cropped satellite images of vegetation patterns in the Sahel.</p>
Figure 2 in Community analysis of soil-inhabiting nematodes in natural vegetations of Singalila National Park, West Bengal (India)
Figure 2. Taxonomic diversity (abundance and genera) of nematode orders in Singalila National Park.
Text-fig. 1 Map of the area surrounding Bohutín and the Litavka River. PA – locality. www.mapy.cz in Reconstruction Of Vegetation Development On The Floodplain Of The Litavka River In The Holocene (Central Bohemia, Brdy Mts.)
Text-fig. 1 Map of the area surrounding Bohutín and the Litavka River. PA – locality. www.mapy.cz
Figure 2 in Bird communities of different woody vegetation types from the Niraj Valley, Romania
Figure 2. PCoA of the bird communities from the woody vegetation based on the Bray–Curtis index.
Figure 1 in Bird communities of different woody vegetation types from the Niraj Valley, Romania
Figure 1. PCoA of the bird communities from the woody vegetation based on the Jaccard index.
Data from: Birds influence vegetation coverage and structure on sandy biogeomorphic islands in the Dutch Wadden Sea.
<p><span>Small uninhabited islands form important roosting and breeding habitats for many coastal birds. Here, we assess the role of external nutrient input by coastal birds on the vegetation structure and coverage on sandy biogeomorphic islands in the Dutch Wadden Sea, where island-forming processes depend on vegetation-sedimentation feedbacks. We Used a combination of bird observations and plant stable isotope (<em>δ</em><sup>15</sup>N) analyses, to demonstrate that (i) breeding birds transport large quantities of nutrients via their faecal outputs to these islands annually and that (ii) this external nitrogen source influences vegetation development on these sandy, nutrient-limited, islands. We further discuss how this avian nutrient pump could impact island development and habitat suitability for coastal birds and discuss future directions for research. For the conservation of both threatened coastal birds and sandy back-barrier islands and the design of appropriate management strategies, we argue that three-way interactions between birds, vegetation and sandy island morphodynamics need to be further elucidated. </span></p> <p> </p> <p><span>Methods</span></p> <p><span>Data stored in this repository are bird counts from all three islands [Breeding_total.csv] and the relation between total faecal excretion rates (estimated using bird counts) and average vegetation coverage (estimated using satellite imagery) [Vegetation_bird.csv]. Additionally, we gathered data in the field including vegetation charactersitics [field.data.xls], isotopic data of the vegetation [new_isotopes.csv] and soil charactersitics [Soil_prop.csv]. Detailed description of these methods and the results can found in the accompanying publication (Reijers <em>et al. STOTEN</em>).</span></p>
Figure 1 in The determination some biological parameters of Phenacoccus madeirensis Green (Hemiptera: Pseudococcidae) on vegetable plants
Figure 1. Survival ratio and life table parameters of Phenacoccus madeirensis on Tomato (Hazera).
Figure 2 in The determination some biological parameters of Phenacoccus madeirensis Green (Hemiptera: Pseudococcidae) on vegetable plants
Figure 2. Survival ratio and life table parameters of Phenacoccus madeirensis on Tomato (Torry).
Figure 4 in The determination some biological parameters of Phenacoccus madeirensis Green (Hemiptera: Pseudococcidae) on vegetable plants
Figure 4. Survival ratio and life table parameters of Phenacoccus madeirensis on Eggplant (Anamur).
The US National Vegetation Classification (USNVC)
The USNVC Hierarchy provides detailed descriptions of vegetation types in the U.S. <p></p>http://usnvc.org/explore-classification/<p></p>
Canadian National Vegetation Classification (CNVC)
The CNVC is an ecological classification of natural and semi-natural Canadian vegetation. The classification is a hierarchical taxonomy, describing vegetation conditions at different levels of generalization from global to local. The purpose of the CNVC is to act as a "dictionary" of vegetation units with standardized names, specific definitions, and factsheet descriptions. <p></p>http://www.cnvc-cnvc.ca/index.cfm<p></p>
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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.