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31 results for “vegetation density”
Datasets for testing the robustness of LiDAR vegetation metrics to varying point densities
<p><span>The calculation of vegetation metrics from LiDAR point clouds might be affected by the available point density of a dataset. Testing how the same LiDAR vegetation metrics differ with different point densities can therefore inform about their robustness for upscaling metrics to other areas or other LiDAR point clouds. The datasets made available here were generated to test the robustness of LiDAR vegetation metrics to varying point densities and spatial resolutions (i.e., plots of 1 × 1 m, 2 × 2 m, 5 × 5 m and 10 × 10 m size). A total of 25 LiDAR vegetation metrics representing different aspects of vegetation height, vegetation cover and structural complexity were tested (see metric definition in Kissling et al. 2023, </span><span><a href="https://doi.org/10.1016/j.dib.2022.108798"><span>https://doi.org/10.1016/j.dib.2022.108798</span></a></span><span>). The metric calculation was similar to the metric calculation in the Laserchicken software (Meijer et al. 2020, </span><span><a href="https://doi.org/10.1016/j.softx.2020.100626"><span>https://doi.org/10.1016/j.softx.2020.100626</span></a></span><span>) and the Laserfarm workflow (Kissling et al. 2022, https://doi.org/10.1016/j.ecoinf.2022.101836). The Dutch AHN4 dataset from the years 2020–2022 with a point density of 20–30 points/m<sup>2</sup> was used. Initially, 100 plots (i.e., squared polygons around centre points) were randomly placed across the Netherlands in Dutch Natura 2000 sites that predominantly contain woodland habitats (using shapefiles from the European Environmental Agency). For each centre point, square polygons of the desired resolutions (i.e., 1 × 1 m, 2 × 2 m, 5 × 5 m or 10 × 10 m plot size) were generated. The square polygons were subsequently used to clip the LiDAR point clouds from the Dutch AHN4 point cloud dataset. Since not all locations of the 100 randomly placed plots contained points, the actual sample sizes were slightly smaller than 100, i.e., 94 plots for the 1 × 1 m, 2 × 2 m and 5 × 5 m resolution and 95 plots for the 10 × 10 m resolution. Metrics were calculated with the original point density of the Dutch AHN4 dataset (20–30 points/m2) and with six systematically down-sampled point clouds for the same plots (i.e., keeping 5%, 10%, 20%, 40%, 60% and 80% of the points in the original point clouds). For each clipped point cloud of a plot at a given resolution, the points were first sorted according to their GPS acquisition time (from earliest to latest). Points were then systematically discarded and only 5%, 10%, 20%, 40%, 60% and 80% of the points in the original point clouds were kept. The kept points were used for calculating the 25 LiDAR vegetation metrics. </span></p>
Datasets for testing the robustness of LiDAR vegetation metrics to varying point densities
<p><span>The calculation of vegetation metrics from LiDAR point clouds might be affected by the available point density of a dataset. Testing how the same LiDAR vegetation metrics differ with different point densities can therefore inform about their robustness for upscaling metrics to other areas or other LiDAR point clouds. The datasets made available here were generated to test the robustness of LiDAR vegetation metrics to varying point densities. A total of 25 LiDAR vegetation metrics representing different aspects of vegetation height, vegetation cover and structural complexity were tested (see metric definition in Kissling et al. 2023, <a href="https://doi.org/10.1016/j.dib.2022.108798">https://doi.org/10.1016/j.dib.2022.108798</a>). The metric calculation was similar to the metric calculation in the Laserchicken software (Meijer et al. 2020, <span><a href="https://doi.org/10.1016/j.softx.2020.100626">https://doi.org/10.1016/j.softx.2020.100626</a>) and the Laserfarm workflow (Kissling et al. 2022, https://doi.org/10.1016/j.ecoinf.2022.101836). The Dutch AHN4 dataset from the years 2020–2022 with a point density of 20–30 points/m<sup>2</sup> was used. A number of plots (i.e., squared polygons around centre points) were randomly placed across the Netherlands within Dutch Natura 2000 sites (using shapefiles from the European Environmental Agency). Different Dutch Natura 2000 sites were distinguished based on their dominant habitat type (dunes, grassland, marsh, shrubland, and woodland). About 100 plots were randomly placed in each habitat type. The AHN4 point cloud of each plot was clipped and then randomly downsampled to 1, 2, 5, 10, 15, 20 points per square meter, respectively. This was done for six different spatial resolutions (1, 2, 5, 10, 20 and 30 meter). The clipped points were then used to calculate the 25 LiDAR vegetation metrics for the original point density and for the six down-sampled point densities.</span></span></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>
Bulk density, Soil:Effect of Burning Patterns on Vegetation in the Fish Lake Burn Compartments
This study examines the effects of long-term prescribed burning treatments on vegetation structure and composition, productivity, and nutrient cycling in upland oak savanna and woodland vegetation. The basis for the study is an ongoing, experimental prescribed burning program begun in 1964 at Cedar Creek, and a similar program operating since 1962 on the adjacent Helen Allison Savanna property (owned by The Nature Conservancy). These prescribed burning programs are designed to subject upland oak communities (and some old fields) to different burn frequencies and patterns of burning, with the ultimate objectives of 1) restoring and maintaining the historically important savanna and open woodland vegetation, and 2) providing information about the effects of different burning patterns on vegetation structure and composition. This study addresses the latter of these two purposes and expands on it by also investigating possible influences of fire on resource availability (nutrients, water, and light) and net primary productivity. This study represents a continuation and expansion of experiments 015 and 094.
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.
Data for: The interplay of environmental cues and wood density in the vegetative and reproductive phenology of seasonally dry tropical forest trees
<p>The great phenological diversification characteristic of seasonally dry tropical forests (SDTF) suggests that these patterns result from a complex interplay between exogenous (e.g., climatic) and endogenous (e.g., morphological, physiological, anatomical) factors. Based on the well-established relationships of wood density with water-storing capacity and cavitation vulnerability in woody plants, we hypothesized differential vegetative and reproductive phenological responses to environmental cues for hardwood and softwood species. To test this hypothesis, we compared phenological patterns of pairs of conspecific populations of 10 species differing in wood density, occurring in two localities with slightly different climatic regimes, and evaluated the influence of three environmental variables (rainfall, photoperiod, temperature) on them. Our results, based on the assessment of the overlap of the phenological curves of conspecific populations occurring in different sites and on linear modeling, showed different effects of the environmental factors on phenophase attributes, depending on wood density of the study species, thus supporting our hypothesis. Leaf out in softwood species took place in the dry season, they shed the foliage at the first signs of drought, and once leafless, they flowered and fruited shortly after. By contrast, hardwood species bore leaves and flowers in the rainy season, shed their leaves several months after the rain ceased, and produced fruits during the dry season. We conclude that the role of environmental variables in cueing growth and reproduction cycles in SDTF tree species is interrelated with their wood density, a key endogenous factor crucially linked to plant hydraulics in these water-limited ecosystems.</p>
Early nest initiation and vegetation density enhance nest survival in Wild Turkeys
<p>The theory of adaptive habitat selection suggests resource selection by animals should reflect underlying quality, such that individual selection confers an adaptive advantage via increased fitness. Using resource selection functions and nest survival models, we demonstrated that visual obstruction at the nest site was adaptively significant but timing of nest initiation had the greatest effect on nest survival for eastern wild turkeys (Meleagris gallopavo silvestris). Predation risk is a selective pressure, and if individuals can perceive predation risk, they may respond by altering selection of nest site characteristics based on prior experience. We evaluated patterns in nest site selection of 387 wild turkeys and consequences of selection on reproductive success across the southeastern United States from 2014-2019. We monitored 549 nest sites and found that nest initiation date had the strongest effect on daily nest survival rates, wherein adult females at our earliest nest initiation date were ~ 4 times more likely to successfully nest than females at our latest nest initiation dates. Selection of nest sites with greater visual obstruction also increased daily nest survival rates as females were 1.17 (1.100 – 1.234; 95% CI) and 1.37 (1.258 – 1.486; 95% CI) times more likely to select sites for every 10 cm increase in visual obstruction and maximum vegetation height, respectively. Collectively, our results indicate that nest initiation date is likely the critical parameter driving wild turkey nest success, whereas vegetative conditions play a lesser role influencing nest success. Females nesting earlier may be in better body condition and show increased nest attentiveness, which may mediate nest success more than vegetation conditions around nest sites. Our work indicates that increasing the reproductive success of wild turkeys may hinge on females being able to nest as early as possible within the reproductive season.</p>
Vegetation Density and Greenness Change Index (GCI), Southeast Michigan, 1990-2000-2010
<p>The vegetation density composites in 1990, 2000, and 2010 and greenness change index (GCI) between 2000 and 2010 for the Detroit-area counties of Oakland, Macomb, and Wayne.</p>
Evaluation of Predictive Capabilities of Regression Models and Artificial Neural Networks for Density and Viscosity Measurements of Different Biodiesel-Diesel-Vegetable Oil Ternary Blends
<p>In this section, it was given that Annex Figures and Annex Tables related to the article "Evaluation of Predictive Capabilities of Regression Models and Artificial Neural Networks for Density and Viscosity Measurements of Different Biodiesel-Diesel-Vegetable Oil Ternary Blends" published in "Environmental and Climate Technologies" journal. </p>
Effects of long-term fixed fire regimes on African savanna vegetation biomass, vertical structure and tree stem density
<ol> <li><span>Fire plays an integral role in shaping the vegetation structure of savanna ecosystems. However, effects of fire regime characteristics, such as frequency and season of burn, on savanna vegetation structure, biomass and tree abundance across landscape types are largely unknown. </span></li> <li><span>We used high-resolution airborne Light Detection and Ranging (LiDAR) to investigate the long-term effects of fire manipulation on savanna vegetation in Kruger National Park, South Africa. We analysed the effects of fire exclusion and experimental burns every 1, 2, 3, 4 and 6 years and during different seasons on aboveground biomass (AGB), tree stem densities and vegetation vertical height profiles across a rainfall gradient and on contrasting geologies. </span></li> <li><span>Across savanna types, and especially in drier savannas, fire season was more influential for constraining AGB than was fire frequency. Plots experiencing fires during the late- and mid-dry season had 44.50% and 43.60%, respectively, lower AGB relative to unburnt plots than wet-season fires. However, in mesic savannas, fire frequency interacted with fire season to influence AGB: plots subjected to high frequency, dry season fires had 55.35% lower AGB than unburnt plots, whereas plots burnt in the wet season at lower frequencies had lower AGB (24.40% lower than unburnt plots) than plots subjected to high frequency, wet-season fires (13.74% lower AGB than unburnt plots). </span></li> <li><span>Fire regimes had variable effects on tree densities, and effects varied with savanna type. Woody vertical vegetation profiles showed the largest differences in response to dry season fires, with the greatest divergence in vegetation height classes < 5m. </span></li> <li><span><em>Synthesis and applications</em>. Understanding the influence of fire regimes on vegetation structure has important implications for the management of savanna heterogeneity, and for predicting trajectories of change in savanna vegetation as fire regimes vary with climate change. We show that the magnitude of the effect of fire on woody vegetation structure varies with savanna context. Our results suggest that heterogeneous vegetation structure can be achieved by applying fires in the dry season in mesic savannas, whereas in dry savannas, variation in fire regimes is less consequential for constraining biomass accumulation and altering vegetation structure. </span></li> </ol>
Soundscapes and airborne laser scanning identify vegetation density and its interaction with elevation as main driver of bird diversity and community composition
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Data from: Effects of vegetation density on the diversity of lizards in an area of the Brazilian Cerrado
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Data from: Mowing effects on woody stem density and woody and herbaceous vegetation heights along Mississippi highway right-of-ways
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Data for: The interplay of environmental cues and wood density in the vegetative and reproductive phenology of seasonally dry tropical forest trees
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Early nest initiation and vegetation density enhance nest survival in Wild Turkeys
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Effects of long-term fixed fire regimes on African savanna vegetation biomass, vertical structure and tree stem density
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Plant density: Effect of Burning Patterns on Vegetation in the Fish Lake Burn Compartments
This study examines the effects of long-term prescribed burning treatments on vegetation structure and composition, productivity, and nutrient cycling in upland oak savanna and woodland vegetation. The basis for the study is an ongoing, experimental prescribed burning program begun in 1964 at Cedar Creek, and a similar program operating since 1962 on the adjacent Helen Allison Savanna property (owned by The Nature Conservancy). These prescribed burning programs are designed to subject upland oak communities (and some old fields) to different burn frequencies and patterns of burning, with the ultimate objectives of 1) restoring and maintaining the historically important savanna and open woodland vegetation, and 2) providing information about the effects of different burning patterns on vegetation structure and composition. This study addresses the latter of these two purposes and expands on it by also investigating possible influences of fire on resource availability (nutrients, water, and light) and net primary productivity. This study represents a continuation and expansion of experiments 015 and 094.
Soil bulk density:Effect of Burning Patterns on Vegetation in the Fish Lake Burn Compartments
This study examines the effects of long-term prescribed burning treatments on vegetation structure and composition, productivity, and nutrient cycling in upland oak savanna and woodland vegetation. The basis for the study is an ongoing, experimental prescribed burning program begun in 1964 at Cedar Creek, and a similar program operating since 1962 on the adjacent Helen Allison Savanna property (owned by The Nature Conservancy). These prescribed burning programs are designed to subject upland oak communities (and some old fields) to different burn frequencies and patterns of burning, with the ultimate objectives of 1) restoring and maintaining the historically important savanna and open woodland vegetation, and 2) providing information about the effects of different burning patterns on vegetation structure and composition. This study addresses the latter of these two purposes and expands on it by also investigating possible influences of fire on resource availability (nutrients, water, and light) and net primary productivity. This study represents a continuation and expansion of experiments 015 and 094.
SGS-LTER Effects of grazing on ecosystem structure and function (GZTX): Vegetation density on the Central Plains Experimental Range, Nunn, Colorado, USA 1992-2008, ARS Study Number 32
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. When the CPER was established in 1939, researchers constructed a .5-1 ha grazing exclosure in each of the pastures. These areas have remained protected from grazing for the past 70 years. The remaining areas have been grazed for the past 20+ years. This collection of pastures and exclosures provided an extraordinary opportunity to reinitiate grazing and protection, and evaluate the balance between degradation and aggradation. We proposed to rearrange fences and expose areas to grazing that have been protected for 50 years, and protect areas from grazing that had been grazed for 50 years. The combinations of grazing conditions were: 1. Long-term protection 2. Long-term grazing (moderate) 3. 50 years of protection followed by grazing 4. 50 years of grazing followed by protection Net primary production, nitrogen dynamics, cattle utilization, and community dynamics of vegetation were measured. Additional information and referenced materials can be found: http://hdl.handle.net/10217/85596.
SGS-LTER Bouteloua gracilis Removal Experiment Vegetation Density Data on the Central Plains Experimental Range, Nunn, Colorado, USA 1997-2008
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. Six sites approximately 6 km apart were selected at the Central Plains Experimental Range in 1997. Within each site, there was a pair of adjacent ungrazed and moderately summer grazed (40-60% removal of annual aboveground production by cattle) locations. Grazed locations had been grazed from 1939 to present and ungrazed locations had been protected from 1991 to present by the establishment of exclosures. Within grazed and ungrazed locations, all tillers and root crowns of B. gracilis were removed from two treatment plots (3 m x 3 m) with all other vegetation undisturbed. Two control plots were established adjacent to the treatment plots. Plant density was measured annually by species in a fixed 1m x 1m quadrat in the center of treatment and control plots. For clonal species, an individual plant was defined as a group of tillers connected by a crown Coffin & Lauenroth 1988, Fair et al. 1999). Seedlings were counted as separate individuals. In the same quadrat, basal cover by species, bare soil, and litter were estimated annually using a point frame. A total of 40 points were read from four locations halfway between the center point and corners of the 1m x 1m quadrat. Density was measured from 1998 to 2005 and cover from 1997 to 2006. All measurements were taken in late
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