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691 results for “Vegetation Data”
Data from: Vegetation management shapes arthropod and bird communities in an African savanna
<p>Habitat heterogeneity is a key driver of the diversity and distribution of species. African savannas are experiencing changes to their vegetation structure causing shifts towards increased woody plant cover, which results in vegetation structure homogenization. Given the impact that increasing woody plant cover has on patterns of animal use, resource managers across Africa are implementing habitat management practices that are intended to reduce woody plant cover. To understand the ecological implications of various habitat management practices on arthropod and bird communities, we leveraged large-scale tree clearing and subsequent mowing in an African savanna to understand how changes to both the herbaceous layer and woody plant cover (i.e., structural heterogeneity) may shape arthropod and bird communities at the local scale. We focused on four replicated treatments: 1) annual summer mow, 2) annual winter mow, 3) >5 years since last mow (rest), and 4) an adjacent unmanipulated savanna to act as a control. We found that the mowing treatments significantly influenced vegetation structure both with respect to tree density and herbaceous layer. Both arthropod and bird community composition varied across treatments. Grass biomass was the best predictor of arthropod richness and abundance, with arthropods selecting for areas with high biomass. Insectivorous bird richness and abundance was driven by tree density (i.e., perching locations) and not arthropod abundance. Our results suggest that vegetation management practices contribute to habitat heterogeneity at the landscape scale and increase bird species richness through species turnover. However, we caution that if a single vegetation management practice dominates the landscape, it is plausible that it could lead to the simplification of the avian community.</p>
Hurricane Disturbance Vegetation Anomaly (HDVA) [Data set for Turner et al.]
<p>The Hurricane Disturbance Vegetation Anomaly (HDVA) is a rapid assessment approach to understand the severity of ecological damage from a high intensity storm event on an otherwise healthy, mature mangrove forest. Data archived here focuses on Cuba, where Hurricane Irma (Category 5) hit the northern coast in September 2017 and caused wide-spread damage to mangroves and coastal forests. Local scientists were not able to assess the full extent or severity of damages through field work due to limited infrastructure and resources, so they turned to remote sensing analysis. We developed a multitemporal, multiresolution approach to assess the damage 1) extent and 2) relative severity using changes from the typical green-leaf phenology represented by the Enhanced Vegetation Index with MODIS and Sentinel-2 data. All data was processed in Google Earth Engine API to access and utilize large amounts of historical data, as well as compare sensors’ spatial resolution impacts on results. This data set includes the HDVA products and categorization of data by quartile of damage (catastrophic, severe, moderate, mild, no loss). </p>
Dataset used in "Comment on "Soil salinity assessment by using near-infrared channel and Vegetation Soil Salinity Index derived from Landsat 8 OLI data: a case study in the Tra Vinh Province, Mekong Delta, Vietnam" by Kim-Anh Nguyen, Yuei-An Liou, Ha-Phuong Tran, Phi-Phung Hoang and Thanh-Hung Nguyen"
<p>The Excel file provides all the data included in Tab.4 of Nguyen et al. 2020 plus reflectances extracted from the Landsat 8 OLI image acquired on 14 February 2017 and downloaded from the USGS Earth Explorer website. Observations on the number of pixels falling of water, land and mixed water/land surfaces are provided as well as water percentage cover estimated using regular spaced points. </p> <p>The dataset includes vector files (kml format) of the grids corresponding to the selected L8 pixels as well as the regularly spaced points generated within the selected pixels. These files can be imported in QGIS, Google Earth Pro and other free GIS software.</p>
Measured data of global fractional vegetation cover from 2013-2021 and algorithm code for calculating remote sensing products
<p>These data come from "A new computationally efficient algorithm to generate global fractional vegetation cover from Sentinel-2 imagery at 10m resolution", these include:</p> <p>1. Measured data of global fractional vegetation cover from 2013-2021 </p> <p>2. Algorithm code for calculating fractional vegetation cover, these codes are written by JavaScript in GEE (Google Earth Engine).</p>
Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part I: Pre-processing)
<p>This is Part I of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to <em>Limnology and Oceanography Letters.</em> It contains all input and output files to generate the simulation grids.</p>
Vegetation Density Across NYC: Analysis of Land Cover Data (2017) within 200 meter Buffers of Points
<p><strong>Summary:</strong></p><p>This repository contains spatial data files representing the density of vegetation cover within a 200 meter radius of points on a grid across the land area of New York City (NYC), New York, USA based on 2017 six-inch resolution land cover data, as well as SQL code used to carry out the analysis. The 200 meter radius was selected based on a study led by researchers at the NYC Department of Health and Mental Hygiene, which found that for a given point in the city, cooling benefits of vegetation only begin to accrue once the vegetation cover within a 200 meter radius is at least 32% (Johnson et al. 2020). The grid spacing of 100 feet in north/south and east/west directions was intended to provide granular enough detail to offer useful insights at a local scale (e.g., within a neighborhood) while keeping the amount of data needed to be processed for this manageable. </p><p>The contained files were developed by the NY Cities Program of <a href="https://www.nature.org/newyork">The Nature Conservancy</a> and the <a href="https://nyc-eja.org/">NYC Environmental Justice Alliance</a> through the <a href="https://medium.com/gage-nyc/introducing-the-just-nature-nyc-partnership-513612e8c3b4">Just Nature NYC Partnership</a>. Additional context and interpretation of this work is available in a <a href="https://medium.com/gage-nyc/looking-at-cooling-benefits-of-plants-through-nyc-vegetation-data-ccdeb33cbe17">blog post</a>.</p><p> </p><p><i>References:</i></p><p>Johnson, S., Z. Ross, I. Kheirbek, and K. Ito. 2020. Characterization of intra-urban spatial variation in observed summer ambient temperature from the New York City Community Air Survey. <i>Urban Climate</i> 31:100583. <a href="https://doi.org/10.1016/j.uclim.2020.100583">https://doi.org/10.1016/j.uclim.2020.100583</a></p><p> </p><p><strong>Files in this Repository:</strong></p><p>Spatial Data (all data are in the New York State Plane Coordinate System - Long Island Zone, North American Datum 1983, <a href="https://epsg.io/2263">EPSG 2263</a>):</p><p>Points with unique identifiers (<i>fid</i>) and data on proportion tree canopy cover (<i>prop_canopy</i>), proportion grass/shrub cover (<i>prop_grassshrub</i>), and proportion total vegetation cover (<i>prop_veg</i>) within a 200 meter radius (same data made available in two commonly used formats, Esri File GeoDatabase and GeoPackage):</p><p><i>nyc_propveg2017_200mbuffer_100ftgrid_nowater.gdb.zip</i></p><p><i>nyc_propveg2017_200mbuffer_100ftgrid_nowater.gpkg</i> </p><p>Raster Data with the proportion total vegetation within a 200 meter radius of the center of each cell (pixel centers align with the spatial point data)</p><p><i>nyc_propveg2017_200mbuffer_100ftgrid_nowater.tif</i></p><p>Computer Code:</p><p>Code for generating the point data in PostgreSQL/PostGIS, assuming the data sources listed below are already in a PostGIS database.</p><p><i>nyc_point_buffer_vegetation_overlay.sql</i></p><p> </p><p><strong>Data Sources and Methods:</strong></p><p>We used two openly available datasets from the City of New York for this analysis:</p><p>Borough Boundaries (Clipped to Shoreline) for NYC, from the NYC Department of City Planning, available at <a href="https://www.nyc.gov/site/planning/data-maps/open-data/districts-download-metadata.page">https://www.nyc.gov/site/planning/data-maps/open-data/districts-download-metadata.page</a> </p><p>Six-inch resolution land cover data for New York City as of 2017, available at <a href="https://data.cityofnewyork.us/Environment/Land-Cover-Raster-Data-2017-6in-Resolution/he6d-2qns">https://data.cityofnewyork.us/Environment/Land-Cover-Raster-Data-2017-6in-Resolution/he6d-2qns</a> </p><p>All data were used in the New York State Plane Coordinate System, Long Island Zone (<a href="https://epsg.io/2263">EPSG 2263</a>). Land cover data were used in a polygonized form for these analyses.</p><p>The general steps for developing the data available in this repository were as follows:</p><p>Create a grid of points across the city, based on the full extent of the Borough Boundaries dataset, with points 100 feet from one another in east/west and north/south directions</p><p>Delete any points that do not overlap the areas in the Borough Boundaries dataset.</p><p>Create circles centered at each point, with a radius of 200 meters (656.168 feet) in line with the aforementioned paper (Johnson et al. 2020).</p><p>Overlay the circles with the land cover data, and calculate the proportion of the land cover that was grass/shrub and tree canopy land cover types. Note, because the land cover data consistently ended at the boundaries of NYC, for points within 200 meters of Nassau and Westchester Counties, the area with land cover data was smaller than the area of the circles.</p><p>Relate the results from the overlay analysis back to the associated points.</p><p>Create a raster data layer from the point data, with 100 foot by 100 foot resolution, where the center of each pixel is at the location of the respective points. Areas between the Borough Boundary polygons (open water of NY Harbor) are coded as "no data."</p><p>All steps except for the creation of the raster dataset were conducted in PostgreSQL/PostGIS, as documented in <i>nyc_point_buffer_vegetation_overlay.sql</i>. The conversion of the results to a raster dataset was done in QGIS (version 3.28), ultimately using the <a href="https://gdal.org/programs/gdal_rasterize.html">gdal_rasterize</a> function.</p>
Data and model code for: Habitat use patterns suggest that climate-driven vegetation changes will negatively impact mammal communities in the Amazon (ACV)
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Data from: Vegetation dynamics 1946–2018 in an old-growth conifer forest
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Data from: Flying high: Sampling savanna vegetation with UAV-lidar
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Camera trap data suggest uneven predation risk across vegetation types in a mixed farmland landscape
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Data from: Reindeer carcasses modulate vegetation composition and greenness in High-Arctic tundra
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Data from: Broad-scale meta-analysis of drivers mediating adverse impacts of flow regulation on riparian vegetation
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Data and scripts for: Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high-throughput phenotyping
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Data from: Long-term impacts of changed grazing regimes on the vegetation of heterogeneous upland grasslands
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Data from: Influence of Myrmecophytic Acacia drepanolobium on the composition and growth of surrounding herbaceous vegetation
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Data from: The role of migration in the change of Northern Hemisphere vegetation for the past 50,000 years
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Data from: Grazing by non-native ungulates negatively impacts vegetation important to a native species of concern
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Data from: Leaf-associated macroinvertebrate assemblage and leaf litter breakdown in headwater streams depend on local riparian vegetation
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Data from: Eco–enzymatic stoichiometry unveils resource limitations in soil microorganisms during subtropical vegetation restoration
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Regional plot x species data for alpine vegetation
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