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3,105 results for “Vegetation =”
SGS-LTER GIS layer with detailed information on IBP Vegetation on Central Plains Experimental Range, Nunn, Colorado, USA 2012
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available
Vegetation and Morphology of Hog and Metompkin Islands of the Virginia Coast Reserve 2010
For complete information regarding sampling and analytical methods, and for contextualization of the data and variables, please see C. Wolner's 2011 thesis, available on the VCR LTER website.
Vegetation and Building Heights for Northampton and Accomac Counties, Virginia 2010
This dataset contains heights of vegetation and buildings above ground level as indicated by the 2010 LiDAR from the Virginia Information Technology Agency (VITA) (datasets: VCR12202 [knb-lter-vcr.202] and VCR12195 [knb-lter-vcr.203]). Ground levels were subtracted from the maximum elevations reported in the LiDAR point cloud to create a layer containing vegetation and structure heights above the ground. Some of the structures recorded include radio an cell towers, so there is a wide range of elevations represented, but most are less than 40 meters tall. Data are projected in UTM zone 18N, using the WGS84 datum, and with heights above ground-level in meters.Â
Wave Dynamics and Fluid Stresses in Vegetated and Unvegetated Coastal Lagoons in Virginia, 2010-2011
Wave, turbulence and wind measurements were performed within a Zostra marina seagrass (eelgrass) meadow in South Bay, Virginia, USA a coastal bay within the Virginia Coast Reserve where ongoing seagrass restoration efforts are being performed, and in adjacent areas without seagrass. These datasets were published in: Hansen J.C.R. and Reidenbach M.A., 2013, Seasonal growth and senescence of a Zostera marina seagrass meadow alters wave-dominated flow and sediment suspension within a coastal bay, Estuaries and Coasts, 36, 1099-1114.
Vegetation and Building Heights for Accomack and Northampton Counties, Virginia, April 2015
This dataset gives the aboveground height of vegetation and buildings in Northampton and Accomack Counties, VA in April 2015. LiDAR data were collected April 11-24, 2015. A mosaiced DEM of ground elevations was assembled from DEM tiles from USGS. A LAS dataset of the classified LiDAR point cloud was assembled and a DEM of first-returns (representing the top of vegetation of buildings) created. The ground-elevation DEM was subtracted from the first-return DEM to yeild a difference layer that reflects the height of the vegetation or buildings above the ground level. Areas classified as Ponds, Lakes or Tidal areas were all set to 0 to avoid artifacts caused by waves etc. The resulting DEM was reprojected from State Plane to UTM coordinates and elevation units were changed from US Feet to Meters. The data contains some odd values in the seaside portion of the data. Heights > 10 meters in marsh areas are highly suspect.
Vegetation Density in a 100x50 meter grid at the Eastern Shore Wildlife Refuge
We surveyed vegetation cover within a grid 100x50 m grid of 10x10m grid cells. The attribute table contains a column identifying the transect (1-5), the box within each transect (1-10), and then indicators for sapling density (0-1; low and high density) based on field measurements (SapDenBin), visual interpretation of aerial imagery (SapDenVis) and a logistic regression model (SapDenLog). Data is provided as a ZIP-compressed shapefile.
Potential distribution of land cover classes (Potential Natural Vegetation) at 250 m spatial resolution
<p>Potential distribution of land cover classes (Potential Natural Vegetation) at 250 m spatial resolution based on a compilation of data sets (Biome6000k, Geo-Wiki, LandPKS, mangroves soil database, and from various literature sources; total of about 65,000 training points). We used a comparable thematic legend used to produce the Dynamic Land Cover 100m: Version 2. Copernicus Global Land Operations product (Buchhorn et al. 2019), which is based on the UN FAO Land Cover Classification System (LCCS), so that users can compare actual (https://lcviewer.vito.be/) vs potential (this data set) land cover. Two classes not available in the LCCS were added: "subtropical/tropical mangrove vegetation" and "sub-polar or polar barren-lichen-moss, grassland". The map was created using relief and climate variables representing conditions the climate for the last 20+ years and predicted at 250 m globally using an Ensemble Machine Learning approach as implemented in the mlr package for R. Processing steps are described in detail <a href="https://github.com/Envirometrix/PNVmaps"><strong>here</strong></a>. Maps with "_sd_" contain estimated model errors per class. Antarctica is not included.</p> <p>Produced for the needs of the <a href="https://naturemap.earth/"><strong>NatureMap</strong></a> which is project run by the <strong>International Institute for Applied Systems Analysis</strong> (IIASA), the <strong>International Institute for Sustainability</strong> (IIS), the <strong>UN Environment Programme World Conservation Monitoring Centre</strong> (UNEP-WCMC), and the <strong>UN Sustainable Development Solutions Network</strong> (SDSN). NatureMap is funded by Norway’s International Climate Initiative (NICFI).</p> <p>Maps will also be made available via: <a href="https://OpenLandMap.org">OpenLandMap.org</a>. These are initial predictions for testing purposes only. A publication explaining all processing steps is pending.</p> <p>If you discover a bug, artifact or inconsistency in the predictions, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://github.com/Envirometrix/PNVmaps/issues">https://github.com/Envirometrix/PNVmaps/issues</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>pnv = theme: potential natural vegetation,</li> <li>potential.landcover = variable: potential land cover type (e.g. "open forest, evergreen needleleaf"),</li> <li>probav.lc100 = classification model: ProbaV-based land cover mapping legend (LCCS),</li> <li>c = factor,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..0cm = vertical reference: land surface,</li> <li>2000..2017 = time reference: period 2000-2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>
Supplementary Files to "Towards a global understanding of vegetation-climate dynamics at multiple time scales"
<p><strong>TimeSeriesPaper_main_processing.html</strong></p> <p>Html version of Jupyter notebook exemplifying main data analysis steps in "Towards a global understanding of vegetation-climate dynamics at multiple time scales" (doi:10.5194/bg-17-945-2020). Please see the publication for further details. Notebook written in Julia v0.6</p> <p><strong>Fig2_Co_Oscillation_Regimes.nc</strong></p> <p>Data underlying Fig. 2 in "Towards a global understanding of vegetation-climate dynamics at multiple time scales" (doi:10.5194/bg-17-945-2020).</p> <p>Classification of land surface by dominant scale of variability in Normalized Difference Vegetation Index (NDVI), air temperature, and precipitation. Dominant scale of variability was determined after Fourier decomposition, separately for each grid cell and summarized as unique combinations between variables (S - short-term, A - seasonal, L - longer-term, T - trend, listed in order NDVI, air temperature, precipitation). The semi-annual cycle is included in the seasonal band. Please see the publication for further details.</p>
Model outputs: Vegetation biomass change in China in the 20th century: An assessment based on a combination of multi-model simulations and field observations
<p>This dataset contains six model outputs of vegetation biomass on plant functional type level and total vegetation biomass on grid cell with and without bias-correction in different sensitivity experiments, as described in Readme.txt.</p>
Database of 242 vegetation surveys from Ticino Canton (Switzerland) grasslands
<p>This is a database of 242 vegetation surveys carried out in semi-natural grasslands of Ticino Canton (Switzerland), managed as conventionally managed grasslands (CMG) or biodiversity promotion areas (BPA). Many topographic, climatic, ecological, and vegetation variables are reported.</p>
Data for: Thresholds of freshwater biodiversity in response to riparian vegetation loss in the Neotropical region
<p>These files contain all the data and R scripts used for analyses of the manuscript "Thresholds of freshwater biodiversity in response to riparian vegetation loss in the Neotropical region", published in Journal of Applied Ecology.</p> <p>See Metadata (README).xls file for a detailed description of these files.</p> <p>Dala-Corte RB, Melo AS, Siqueira T, et al. Thresholds of freshwater biodiversity in response to<br> riparian vegetation loss in the Neotropical region. J Appl Ecol. 2020;00:1–12. https://doi.org/10.1111/1365-2664.13657</p>
Dataset accompanying Riesch et al. 2020. Grazing by wild red deer maintains characteristic vegetation of semi-natural open habitats: Evidence from a 3-year exclusion experiment. Applied Vegetation Science
<p>This repository contains vegetation community data used by Riesch et al. 2020 in an article accepted in Applied Vegetation Science.</p> <p>Metadata are provided in the first excel worksheet. For further details please see the original article.</p>
Vegetation data along alpine pasture-shrub gradients
<p>Columns of dataset:</p> <ul> <li>Species_richness: Number of species per plot</li> <li>Species_evenness: Pielou's eveness of species cover values</li> <li>Heterogeneity: Beta-diversity, average Jaccard distance to the other three plots within cover zone</li> <li>Nutrient_index: Cover-weighted average nutrient indicator value</li> <li>Light_index: Cover-weighted average light indicator value</li> <li>Water_index: Cover-weighted average humidity indicator value</li> <li>Gradients_ID: gradient identifier</li> <li>Dominant_shrub: Dominant shrub species (3 categories)</li> <li>Zonal_cover: Value of zonal cover in percent</li> <li>Plot_cover: Value of plot cover in percent</li> <li>Geology_4level: Substrate of parent material (4 categories)</li> <li>Geology_2level: Substrate of parent material (2 categories)</li> <li>Aspect_3level: Aspect (3 categories)</li> <li>Elevation: Elevation in m above sea level</li> <li>Steepness_perc: Terrain slope in percent</li> <li>Precipitation: Annual precipitation sum in mm</li> <li>Temperature: Mean annual temperature in °C</li> <li>Soil_phosphorus: Soil phosphorus extracted by AA-EDTA in mg/kg</li> <li>Soil_pH: pH of soil-water suspension</li> </ul>
Rangeland Analysis Platform - vegetation cover 1988
<p>Rangeland Analysis Platform vegetation cover 1988</p> <p>These data represent rangeland cover estimates determined by Jones et al. (2018)<br> and as accessible on the Rangeland Analysis Platform (https://rangelands.app).<br> Values are percent aerial cover of the following rangeland functional groups:</p> <p>Band 1 - annual forbs and grasses<br> Band 2 - bare ground<br> Band 3 - litter<br> Band 4 - perennial forbs and grasses<br> Band 5 - shrubs<br> Band 6 - trees<br> No Data value = 255</p> <p>Although these data were produced across a broad region, they are primarily<br> intended for rangeland ecosystems. Cover estimates may not be suitable in other<br> ecosystems, e.g., forests, agricultural lands.</p> <p>Data are in WGS84 Geographic Coordinate System (EPSG:4326); spatial resolution<br> is approximately 30m.</p> <p>Please attribute these data to:<br> Jones, M. O., B. W. Allred, D. E. Naugle, J. D. Maestas, P. Donnelly, L. J.<br> Metz, J. Karl, R. Smith, B. Bestelmeyer, C. Boyd, J. D. Kerby, and J. D. McIver.<br> 2018. Innovation in rangeland monitoring: annual, 30 m, plant functional type<br> percent cover maps for U.S. rangelands, 1984-2017. Ecosphere 9:e02430.<br> http://dx.doi.org/10.1002/ecs2.2430</p>
Rangeland Analysis Platform - vegetation cover 1990
<p>Rangeland Analysis Platform vegetation cover 1990</p> <p>These data represent rangeland cover estimates determined by Jones et al. (2018)<br> and as accessible on the Rangeland Analysis Platform (https://rangelands.app).<br> Values are percent aerial cover of the following rangeland functional groups:</p> <p>Band 1 - annual forbs and grasses<br> Band 2 - bare ground<br> Band 3 - litter<br> Band 4 - perennial forbs and grasses<br> Band 5 - shrubs<br> Band 6 - trees<br> No Data value = 255</p> <p>Although these data were produced across a broad region, they are primarily<br> intended for rangeland ecosystems. Cover estimates may not be suitable in other<br> ecosystems, e.g., forests, agricultural lands.</p> <p>Data are in WGS84 Geographic Coordinate System (EPSG:4326); spatial resolution<br> is approximately 30m.</p> <p>Please attribute these data to:<br> Jones, M. O., B. W. Allred, D. E. Naugle, J. D. Maestas, P. Donnelly, L. J.<br> Metz, J. Karl, R. Smith, B. Bestelmeyer, C. Boyd, J. D. Kerby, and J. D. McIver.<br> 2018. Innovation in rangeland monitoring: annual, 30 m, plant functional type<br> percent cover maps for U.S. rangelands, 1984-2017. Ecosphere 9:e02430.<br> http://dx.doi.org/10.1002/ecs2.2430</p>
Rangeland Analysis Platform - vegetation cover 1986
<p>Rangeland Analysis Platform vegetation cover 1986</p> <p>These data represent rangeland cover estimates determined by Jones et al. (2018)<br> and as accessible on the Rangeland Analysis Platform (https://rangelands.app).<br> Values are percent aerial cover of the following rangeland functional groups:</p> <p>Band 1 - annual forbs and grasses<br> Band 2 - bare ground<br> Band 3 - litter<br> Band 4 - perennial forbs and grasses<br> Band 5 - shrubs<br> Band 6 - trees<br> No Data value = 255</p> <p>Although these data were produced across a broad region, they are primarily<br> intended for rangeland ecosystems. Cover estimates may not be suitable in other<br> ecosystems, e.g., forests, agricultural lands.</p> <p>Data are in WGS84 Geographic Coordinate System (EPSG:4326); spatial resolution<br> is approximately 30m.</p> <p>Please attribute these data to:<br> Jones, M. O., B. W. Allred, D. E. Naugle, J. D. Maestas, P. Donnelly, L. J.<br> Metz, J. Karl, R. Smith, B. Bestelmeyer, C. Boyd, J. D. Kerby, and J. D. McIver.<br> 2018. Innovation in rangeland monitoring: annual, 30 m, plant functional type<br> percent cover maps for U.S. rangelands, 1984-2017. Ecosphere 9:e02430.<br> http://dx.doi.org/10.1002/ecs2.2430</p>
Rangeland Analysis Platform - vegetation cover 1991
<p>Rangeland Analysis Platform vegetation cover 1991</p> <p>These data represent rangeland cover estimates determined by Jones et al. (2018)<br> and as accessible on the Rangeland Analysis Platform (https://rangelands.app).<br> Values are percent aerial cover of the following rangeland functional groups:</p> <p>Band 1 - annual forbs and grasses<br> Band 2 - bare ground<br> Band 3 - litter<br> Band 4 - perennial forbs and grasses<br> Band 5 - shrubs<br> Band 6 - trees<br> No Data value = 255</p> <p>Although these data were produced across a broad region, they are primarily<br> intended for rangeland ecosystems. Cover estimates may not be suitable in other<br> ecosystems, e.g., forests, agricultural lands.</p> <p>Data are in WGS84 Geographic Coordinate System (EPSG:4326); spatial resolution<br> is approximately 30m.</p> <p>Please attribute these data to:<br> Jones, M. O., B. W. Allred, D. E. Naugle, J. D. Maestas, P. Donnelly, L. J.<br> Metz, J. Karl, R. Smith, B. Bestelmeyer, C. Boyd, J. D. Kerby, and J. D. McIver.<br> 2018. Innovation in rangeland monitoring: annual, 30 m, plant functional type<br> percent cover maps for U.S. rangelands, 1984-2017. Ecosphere 9:e02430.<br> http://dx.doi.org/10.1002/ecs2.2430</p>
Rangeland Analysis Platform - vegetation cover 1994
<p>Rangeland Analysis Platform vegetation cover 1994</p> <p>These data represent rangeland cover estimates determined by Jones et al. (2018)<br> and as accessible on the Rangeland Analysis Platform (https://rangelands.app).<br> Values are percent aerial cover of the following rangeland functional groups:</p> <p>Band 1 - annual forbs and grasses<br> Band 2 - bare ground<br> Band 3 - litter<br> Band 4 - perennial forbs and grasses<br> Band 5 - shrubs<br> Band 6 - trees<br> No Data value = 255</p> <p>Although these data were produced across a broad region, they are primarily<br> intended for rangeland ecosystems. Cover estimates may not be suitable in other<br> ecosystems, e.g., forests, agricultural lands.</p> <p>Data are in WGS84 Geographic Coordinate System (EPSG:4326); spatial resolution<br> is approximately 30m.</p> <p>Please attribute these data to:<br> Jones, M. O., B. W. Allred, D. E. Naugle, J. D. Maestas, P. Donnelly, L. J.<br> Metz, J. Karl, R. Smith, B. Bestelmeyer, C. Boyd, J. D. Kerby, and J. D. McIver.<br> 2018. Innovation in rangeland monitoring: annual, 30 m, plant functional type<br> percent cover maps for U.S. rangelands, 1984-2017. Ecosphere 9:e02430.<br> http://dx.doi.org/10.1002/ecs2.2430</p>
Rangeland Analysis Platform - vegetation cover 1995
<p>Rangeland Analysis Platform vegetation cover 1995</p> <p>These data represent rangeland cover estimates determined by Jones et al. (2018)<br> and as accessible on the Rangeland Analysis Platform (https://rangelands.app).<br> Values are percent aerial cover of the following rangeland functional groups:</p> <p>Band 1 - annual forbs and grasses<br> Band 2 - bare ground<br> Band 3 - litter<br> Band 4 - perennial forbs and grasses<br> Band 5 - shrubs<br> Band 6 - trees<br> No Data value = 255</p> <p>Although these data were produced across a broad region, they are primarily<br> intended for rangeland ecosystems. Cover estimates may not be suitable in other<br> ecosystems, e.g., forests, agricultural lands.</p> <p>Data are in WGS84 Geographic Coordinate System (EPSG:4326); spatial resolution<br> is approximately 30m.</p> <p>Please attribute these data to:<br> Jones, M. O., B. W. Allred, D. E. Naugle, J. D. Maestas, P. Donnelly, L. J.<br> Metz, J. Karl, R. Smith, B. Bestelmeyer, C. Boyd, J. D. Kerby, and J. D. McIver.<br> 2018. Innovation in rangeland monitoring: annual, 30 m, plant functional type<br> percent cover maps for U.S. rangelands, 1984-2017. Ecosphere 9:e02430.<br> http://dx.doi.org/10.1002/ecs2.2430</p>
Rangeland Analysis Platform - vegetation cover 2000
<p>Rangeland Analysis Platform vegetation cover 2000</p> <p>These data represent rangeland cover estimates determined by Jones et al. (2018)<br> and as accessible on the Rangeland Analysis Platform (https://rangelands.app).<br> Values are percent aerial cover of the following rangeland functional groups:</p> <p>Band 1 - annual forbs and grasses<br> Band 2 - bare ground<br> Band 3 - litter<br> Band 4 - perennial forbs and grasses<br> Band 5 - shrubs<br> Band 6 - trees<br> No Data value = 255</p> <p>Although these data were produced across a broad region, they are primarily<br> intended for rangeland ecosystems. Cover estimates may not be suitable in other<br> ecosystems, e.g., forests, agricultural lands.</p> <p>Data are in WGS84 Geographic Coordinate System (EPSG:4326); spatial resolution<br> is approximately 30m.</p> <p>Please attribute these data to:<br> Jones, M. O., B. W. Allred, D. E. Naugle, J. D. Maestas, P. Donnelly, L. J.<br> Metz, J. Karl, R. Smith, B. Bestelmeyer, C. Boyd, J. D. Kerby, and J. D. McIver.<br> 2018. Innovation in rangeland monitoring: annual, 30 m, plant functional type<br> percent cover maps for U.S. rangelands, 1984-2017. Ecosphere 9:e02430.<br> http://dx.doi.org/10.1002/ecs2.2430</p>
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