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3,105 results for “Vegetation =”
New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities
<p>The dataset is the vegetation type map for India as per <a href="https://www.sciencedirect.com/science/article/pii/S0303243415000574?via%3Dihub">Roy et 2015 "<span>New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities". </span></a></p> <p><span>The dataset consistes of two files- (1) a raster GIS file at 60m spatial resolution (EPSG 32643 WGS 84/ UTM Zone 34) in which each pixel value means a vegetation class as defined and mapped in Roy et al., 2015 and (2) a csv file which contains information matching the pixel value with the vegetation type. <br><br>For all additional information, please refer to the pper reviewed publication. </span></p>
Data from: Trends in plant cover derived from vegetation-plot data using ordinal zero-augmented beta regression
<p><strong>Questions.</strong> Plant cover values in vegetation-plot data are bounded between 0 and 1, and cover is typically recorded in discrete classes with non-equal intervals. Consequently, cover data are skewed and heteroskedastic, which hampers the application of conventional regression methods. Recently developed ordinal beta regression models consider these statistical difficulties. Our primary question is if we can detect species trends in vegetation-plot time series data with this modelling approach. A second question is whether trends in cover have additional value compared to trends in occurrence, which are easier to assess for practitioners.</p> <p><strong>Location</strong>. The Netherlands, Western Europe.</p> <p><strong>Methods. </strong>We used vegetation-plot data collected from 10.000 fixed plots which were surveyed once every four years during 1999-2022. We used the ordinal zero-augmented beta regression (OZAB) model, a hierarchical model consisting of a logistic regression for presence and an ordinal beta regression for cover. We adapted the OZAB model for longitudinal data and produced estimates of cover and occurrence for each four-year period. Thereafter we assessed trends in cover and in occurrence across all periods.</p> <p><strong>Results.</strong> We found evidence of a trend in cover in 318 out of the 721 species (44%) with sufficient data. Most species showed similar directional trends in occurrence and percent cover. No trend in occurrence was detected for 64 species that had evidence of a trend in cover. Declining species had stronger relative changes in cover than in occurrence.</p> <p><strong>Conclusions. </strong>Our model enables researchers to detect trends in cover using longitudinal vegetation-plot data. Cover trends often corroborated trends in occurrence, but we also regularly found trends in cover even in the absence of evidence for trends in occurrence. Our approach thus contributes to a more complete picture of (changes in) vegetation composition based on large monitoring datasets.</p>
Relationship between Channel Island Melospiza melodia (Song Sparrow) bill size and vegetation, seed extraction time, bite force, and climate (2014-2016)
<p>Inferring the environmental selection pressures responsible for phenotypic variation is a challenge in adaptation studies as traits often have multiple functions and are shaped by complex selection regimes. We provide indirect evidence that morphology of the multifunctional avian bill is primarily shaped by climate and thermoregulatory ability in <em>Melospiza melodia</em> (Song Sparrows) on the California Channel Islands. Our research builds on a study in Song Sparrow museum specimens that demonstrated a positive correlation between bill surface area and maximum temperature, suggesting a greater demand for dry heat dissipation in hotter, xeric environments. We sampled contemporary sparrow populations across three climatically distinct islands to test the hypotheses that bill morphology is influenced by habitat differences with functional consequences for foraging efficiency and is related to maximum temperature and, consequently, important for thermoregulation. Measurements of >500 live individuals indicated a significant, positive relationship between maximum temperature and bill surface area when correcting for body size. In contrast, maximum bite force, seed extraction time, and vegetation on breeding territories (a proxy for food resources) were not significantly associated with bill dimensions. While we cannot exclude the influence of foraging ability and diet on bill morphology, our results are consistent with the hypothesis that variation in Song Sparrows' need for thermoregulatory capacity across the northern Channel Islands selects for divergence in bill surface area.</p>
Global Physically-Constrained Deep Learning Water Cycle Model with Vegetation: Model Simulations
<p>Welcome to our repository, which features simulations from the Hybrid Hydrological Model with Vegetation (H2MV). This collection includes 11 NetCDF files, representing temporal model simulations on a monthly scale and the static output of maximum soil moisture capacity (also known as plant rooting water storage) derived from a 10-fold cross-validation (CV) setup:</p> <ul> <li><strong>Temporal Simulations</strong>: The files named "fold1.nc" through "fold10.nc" contain the temporal model simulations, aggregated to a monthly scale, from 10 fold cross-validation (CV) setup.</li> <li><strong>Static Output</strong>: The "sm_max.nc" file presents the H2MV's estimation of the maximum soil moisture capacity</li> </ul> <h3>Contents of the Temporal Simulation Files</h3> <p>Each of the "fold" files ("fold1.nc" to "fold10.nc") contains the following variables:</p> <ul> <li><strong>Snow Dynamics</strong> <ul> <li><span><code>snow_acc</code></span>: Snow accumulation (mm/day)</li> <li><span><code>snow_melt</code></span>: Snow melt (mm/day)</li> <li><span><code>swe</code></span>: Snow water equivalent (mm)</li> </ul> </li> <li><strong>Evapotranspiration and its components</strong> <ul> <li><span><code>Ei</code></span>: Interception evaporation (mm/day)</li> <li><span><code>Es</code></span>: Soil evaporation (mm/day)</li> <li><span><code>T</code></span>: Transpiration (mm/day)</li> <li><span><code>ET</code></span>: Evapotranspiration (mm/day)</li> </ul> </li> <li><strong>Recharge</strong> <ul> <li><span><code>r_soil</code></span>: Soil recharge (mm/day)</li> <li><span><code>r_gw</code></span>: Groundwater recharge (mm/day)</li> </ul> </li> <li><strong>Runoff</strong> <ul> <li><span><code>runoff_surface</code></span>: Surface runoff (mm/day)</li> <li><span><code>baseflow</code></span>: Baseflow (mm/day)</li> <li><span><code>runoff_total</code></span>: Total runoff (mm/day)</li> </ul> </li> <li><strong>Water Storages </strong> <ul> <li><span><code>GW</code></span>: Groundwater (mm)</li> <li><span><code>SM</code></span>: Soil moisture (mm)</li> <li><span><code>tws</code></span>: Terrestrial water storage (mm)</li> <li><span><code>tws_anomaly</code></span>: Anomalies of terrestrial water storage (mm)</li> </ul> </li> <li><strong>Vegetation</strong> <ul> <li><span><code>fapar</code></span>: Fraction of absorbed photosynthetically active radiation (-)</li> </ul> </li> </ul> <h3>Contents of the Static Output File</h3> <p>The "sm_max.nc" file contains 10 variables corresponding to the 10 folds of CV, with each variable (e.g., "fold1") referring to the respective fold.</p> <h3>Additional Information</h3> <p>It's important to note that the original model simulations were conducted with a daily temporal resolution, but the data shared here have been aggregated to a monthly scale. We are open to sharing the original daily simulations and additional variables not included in this repository upon request. Please feel free to reach out to us for more information or data requests.</p>
Fig. 6 in The Impact Of Forest Harvesting Machines On Understorey Vegetation In Hylocomiosa Forest Type In Zemgale, Latvia
Fig. 6. The proportion of evergreen and summer green plant species in the herbaceous' layer.
Fig. 4 in The Impact Of Forest Harvesting Machines On Understorey Vegetation In Hylocomiosa Forest Type In Zemgale, Latvia
Fig. 4. The projective coverage of herbaceous' and moss' layers (mean values are shown ± SE).
Fig. 3 in The Impact Of Forest Harvesting Machines On Understorey Vegetation In Hylocomiosa Forest Type In Zemgale, Latvia
Fig. 3. The number of species of herbaceous and moss layers (mean values are shown ± SE).
Fig. 1 in Short-Term Impact Of Nitrogen Fertilizer And Wood Ash On Forest Ground Vegetation
Fig. 1. Sample plot design in vegetation field surveyed.
Fig. 1 in Macrophyte Vegetation Assessment In Streams Of The Venta River Basin District
Fig. 1. Location of sampling sites in the Venta River Basin district, Latvia.
Fig. 3 in Macrophyte Vegetation Assessment In Streams Of The Venta River Basin District
Fig. 3. Correlation between species richness and ammonium nitrogen (N-NH4+).
FIGURE 1 in The Integrated Plant Record (IPR) analysis: Methodological advances and new insights into the evolution of European Palaeogene/Neogene vegetation
FIGURE 1. Zelkova zelkovifolia is a representative of the broad-leaved deciduous component.
Figure 3 in Preliminary assessment of weed population in vegetable and fruit farms of Taif, Saudi Arabia
Figure 3. The density of various weed families in vegetable (CROP A) and fruit (CROP B) farms.
Vegetation greenesss data for the Aït Benhaddou Catchment, Morocco. Includes: 1984-2019 NDVI time series, breakpoint analysis results, and resillience indicator results, among others.
<p>This dataset is comprised of two main parts, both originating from different but related works. </p> <p>The NDVI timeseries and breakpoint analysis were originally developed by Vermeer (2021) for the MSc thesis: Vermeer, A. L. (2021). <em>Ecological stability in the face of climatic disturbances: a case study of a dryland ecosystem in the Moroccan High Atlas Mountains</em>. These data include a harmonized timeseries of Normalized Difference Vegetation Index from different Landsat missions at 30x30 meter resolution for the Aït Benhaddou catchment in Morocco. It also includes the output of a breakpoint analysis that was conducted using this dataset, which showcases different statistical breakpoints in NDVI after a severe drought that occured between 1998 and 2002. Shapefiles, a DEM and masks of irrigiated areas for the catchment are also included. For more information about these data, consult Vermeer (2021).</p> <p>The secondary part of this dataset was produced by Grootoonk (2024) for the MSc thesis: Grootoonk, W. (2024). <em>Relations between temporal resilience indicators and trend breakpoints in a dryland high-mountain catchment, </em>drawing upon the original dataset from Vermeer (2021). These data include Kendall's tau values for the resillience indicators variance and lag-one autocorrelation, computed using a rolling window for each pixel. Results for differerent window sizes (WS) for both indicators are included. </p> <p>Beyond these main results, a number of additional data sources are provided. These are Kendall's tau for precipitation variance in the area, produced using CHIRPS data (https://www.chc.ucsb.edu/data/chirps) and a NSI soil salinity map produced from Landsat imagery. See Grootoonk (2024) for more information. </p>
Data from a study on the role of the American toad (Anaxyrus americanus) in vegetable pest management in Pennsylvania
<p>Datasets associated with a manuscript on the role of the American toad (<em>Anaryxus americanus</em>) in pest management in vegetable production in Pennsylvania, USA. The datasets derive from three distinct studies that shed light on the ecological role of the toad: (1) gut contents of field-collected individuals from a small diversified vegetable farm, (2) outcomes from a laboratory experiment on toad foraging behavior toward a range of prey, and (3) outcomes from a field mesocosm experiment in which different densities of toads were enclosed with kale plants. </p> <p>Please see the file <code>toad_column_key.xlsx</code> for the definitions of fields in all data files. </p>
Effect size data for the meta-analysis article ""Effects of vegetation management intensity on biodiversity and ecosystem services in vineyards: a meta-analysis"
<p>This Exel file includes the effect size dataset used for the statistical analysis for the paper "Effect of vegetation management intensity on biodiversity and ecosystem services in vineyards: a meta-analysis", which will be published in the Journal of Applied Ecology in 2018.</p> <p>This meta-analysis was conducted in the course of the project VineDivers (<a href="http://www.vinedivers.eu/">www.vinedivers.eu</a>) funded through the 2013-2014 BiodivERsA/FACCE-JPI joint call for research proposals, with the national funders: Austrian Science Fund (FWF), Spanish Ministry for Economy and Competitiveness (MINECO), French National Research Agency (ANR), Romanian Executive Agency for Higher Education, Research, Development and Innovation Funding (UEFISCDI) and Federal Ministry of Education and Research (BMBF/Germany). P. Batáry was supported by the German Research Foundation (DFG BA4438/2-1) and by the Economic Development and Innovation Operational Programme of Hungary (GINOP–2.3.2–15–2016–00019).</p> <p> </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>
Soil moisture maps of Ukraine based on SMAP satellite data, vegetation season 2018
<p>A set of soil moisture maps of Ukraine based on SMAP satellite data</p> <p>Product: SMAP Enhanced L3 Radiometer Global Daily 9 km EASE-Grid Soil Moisture V001</p> <p>Vegetation season 2018</p> <p> </p>
DATA_VEGETATED_DRAINAGE_CHANNEL
<p>The dataset contains all the files related to the experimental measurement of flow parameters in a vegetated drainage channel. Dataset include vegetational parameters, Manning's coefficients estimation, Turbulence analysis, velocity distribution analysis. </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>
[FABSPACE2.0]-Vegetation_Regione_Toscana
<p>Toscana Region (Italy) Vector Data describing land cover and anthropic surfaces. Scale 1: 250000</p>
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OpenNeuro
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