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3,105 results for “Vegetation =”
An ArcGIS Pro workflow to extract vegetation indices from aerial imagery of small‐plot turfgrass research
<p>Collection of multispectral imagery from an aerial sensor is a means to obtain plot-level vegetation index (VI) values; however, post-capture image processing and analysis remain a challenge for small-plot researchers. An ArcGIS Pro workflow of two task items was developed with established routines and commands to extract plot-level VI values (Normalized Difference VI, Ratio VI, and Chlorophyll Index-Red Edge) from multispectral aerial imagery of small-plot turfgrass experiments. Users can access and download task item(s) from the ArcGIS Online platform for use in ArcGIS Pro. The workflow standardizes the processing of aerial imagery to ensure repeatability between sampling dates and across site locations. A guided workflow saves time with assigned commands, ultimately allowing users to obtain a table with plot descriptions and index values within a .csv file for statistical analysis. The workflow was used to analyze aerial imagery from a small-plot turfgrass research study evaluating herbicide effects on St. Augustinegrass [<em>Stenotaphrum secundatum</em> (Walt.) Kuntze] grow-in. To compare methods, index values were extracted from the same aerial imagery by TurfScout, LLC and were obtained by handheld sensor. Index values from the three methods were correlated with visual percentage cover to determine the sensitivity (i.e., the ability to detect differences) of the different methodologies.</p>
Victorian Water and Climate dataset: long-term streamflow, climate, and vegetation observation records and catchment attributes
<p>This dataset contains streamflow, climate, and vegetation data for 155 minimally impaired catchments in Victoria, Australia. </p> <p>What's included:</p> <p>- Streamflow long-term observation records in daily, monthly, and hydroannual (based on March to February water year) resolution</p> <p>- Catchment climate characteristics in daily, monthly, and hydroannual resolution</p> <p>- Catchment vegetation characteristics in monthly resolution and actual evapotranspiration estimate in monthly and hydroannual resolution</p> <p>- Catchment boundaries</p> <p>- Catchment attributes (topographic, geological, soil, groundwater, vegetation, and human impacts characteristics)</p> <p>- Hydroclimatic characteristics for 3 different periods.</p> <p>Precipitation (p), streamflow (q), and PET (pet) data and their derivatives are in mm (i.e. normalised by catchment area). </p> <p>A paper containing a detailed description of the data including units, data sources, and processing details is submitted, please contact Margarita Saft for a private copy of the draft.</p>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2001 (001–177)
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2001 (001–177). <em>Please <a href="https://zenodo.org/record/7563466#.Y8_P3nZByUk"><strong><em>click</em> here</strong></a> to download the MUSES FVC product <strong>in 2000 (185–361)</strong></em>, and <em><a href="https://zenodo.org/record/7559544#.Y8-QO8lBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2001 (185–361</strong></em><em><strong>)</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2001 (001–177)</li> <li>Spatial Resolution: 250m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 100</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et al. </em>(2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
You eat what you find – local patterns in vegetation structure control diets of African fungus-growing termites
<p>Fungus-growing termites and their symbiotic <em>Termitomyces</em> fungi are critically important carbon and nutrient recyclers in arid and semiarid environments of sub-Saharan Africa. A major proportion of plant litter produced in these ecosystems is decomposed within nest chambers of termite mounds, where temperature and humidity are kept optimal for the fungal symbionts. While fungus-growing termites are generally believed to exploit a wide range of different plant substrates, the actual diets of most species remain elusive. We studied dietary niches of two <em>Macrotermes</em> species across the semiarid savanna landscape in the Tsavo Ecosystem, southern Kenya, based on carbon (C) and nitrogen (N) stable isotopes in <em>Termitomyces</em> fungus combs. We applied Bayesian mixing models to determine the proportion of grass and woody plant matter in the combs, these being the two major food sources available for <em>Macrotermes</em> species in the region. Our results showed that both termite species, and colonies cultivating different <em>Termitomyces</em> fungi, occupied broad and largely overlapping isotopic niches, indicating no dietary specialization. Including laser scanning derived vegetation cover estimates to the dietary mixing model revealed that the proportion of woody plant matter in fungus combs increased with increasing woody plant cover in the nest surroundings. Nitrogen content of fungus combs was positively correlated with woody plant cover around the mounds and negatively correlated with the proportion of grass matter in the comb. Considering the high N demand of large <em>Macrotermes</em> colonies, woody plant matter seems to thus represent a more profitable food source than grass. As grass is also utilized by grazing mammals, and the availability of grass matter typically fluctuates over the year, mixed woodland-grasslands and bushlands seem to represent more favorable habitats for large <em>Macrotermes</em> colonies than open grasslands.</p>
Data for the article "Organic farming increases functional diversity and ecosystem service provision of spontaneous vegetation in Mediterranean vineyards"
<p>Research data for the article "<strong>Organic farming increases functional diversity and ecosystem service provision of spontaneous vegetation in Mediterranean vineyards</strong>" made by Roser Rotchés-Ribalta, Joan Marull and Joan Pino published in Ecological Indicators.</p> <p>It includes:</p> <p>- Species and functional diversities of spontaneous plant species in the different plots surveyed per field</p> <p>- The cover of each species per plot in each field</p> <p>- The functional traits used for each species</p> <p>- The physico-chemical soil data of each plot per field </p>
FIGURE 2 in Kalanchoe ×sampsonii [K. ×hankeyi × K. sexangularis] (Crassulaceae subfam. Kalanchooideae), a horticulturally successful nothospecies from South Africa with enhanced vegetative and reproductive characters
FIGURE 2. Kalanchoe ×sampsonii. A. At flowering maturity plants of this shrubby nothospecies reach a height of about 0.5 m. B. Through heterosis, inflorescences are more branched and have a wider spread than in either of the parents. C. Peduncles are more intensely reddish-infused than in K. ×hankeyi, but less than in K. sexangularis. D. Flowers (corolla tube and corolla lobes) are more intensely yellow than those of either of the parents. E. Flowers, here in lateral view, of K. ×sampsonii (centre) are often intermediate between those of K. longiflora (left, one of the parents of K. ×hankeyi) and K. sexangularis (right), but the corolla tube is more intensely yellow. F. Corolla lobes of K. ×sampsonii (centre) are more intensely yellow than those of K. longiflora (left, one of the parents of K. ×hankeyi) and K. sexangularis (right, the other parent of K. ×hankeyi). G. A White-bellied sunbird, Cinnyris talatala, feeding on the copiously nectariferous flowers of K. ×sampsonii. H. Jason D.S. Sampson (1979–), after whom K. ×sampsonii is named, next to a very large specimen of K. ×estrelae on the Hatfield campus of the University of Pretoria; photograph taken on 10 June 2022. All photographs taken by Gideon F. Smith.
FIGURE 1 in Kalanchoe ×sampsonii [K. ×hankeyi × K. sexangularis] (Crassulaceae subfam. Kalanchooideae), a horticulturally successful nothospecies from South Africa with enhanced vegetative and reproductive characters
FIGURE 1. Kalanchoe ×hankeyi (A–B) and K. sexangularis (C–D). A. Leaves of K. ×hankeyi are light green to strongly red-infused, especially when grown in full sun. The marginal leaf crenations of K. ×hankeyi are situated more towards the upper parts of the leaf margins, as in one of its parents, K. longiflora, rather than virtually along the whole margin, as is found in the other parent, K. sexangularis. B. Flowers of K. ×hankeyi have yellow corolla lobes and a greenish yellow tube. C. Leaves of K. sexangularis turn a bright crimson red when exposed to high levels of solar irradiation. D. As in the case of K. ×hankeyi, flowers of K. sexangularis have a greenish yellow corolla tube and yellow corolla lobes. All photographs taken by Gideon F. Smith.
Tidal channel formation in a rapidly invaded micro-tidal marsh: the role of dynamic vegetation pattern and concurrent channel deepening
<p>The channel formation during the spartina alterniflora invasion influenced by vegetation pattern dynamic and concurrent channel deepening.</p>
FIGURE 2. Eugenia aeterna. A. Vegetative branch. B in A new species of Eugenia sect. Eugenia (Myrtaceae) from Atlantic Forest in São Paulo, Brazil
FIGURE 2. Eugenia aeterna. A. Vegetative branch. B. Detail of the branch with distinctive persistent cataphylls. C. Branch with inflorescences in late stage. D. Branch with maturing fruits. E. Branch with mature fruit (Photo E by Miguel Nema Neto).
Vegetation table
<p>Vegetation plot data for the following article : https://doi.org/10.5194/egusphere-2022-1164</p> <p>Please contact the authors and quote the article if using this dataset.</p>
Data from: General patterns of beavers' selective foraging: How to evaluate the effects of a re-emerging driver of vegetation change along Central European small watercourses
<p>This Excel table contains information about the woody plant supply and its utilization by the Eurasian beaver at 11 study sites along small watercourses located in Hungary. The following data are provided about each woody plant unit (trunk or branch): taxon group, genus, diameter class, and type of utilization. Two taxon groups were differentiated: softwood species (<em>Salix</em> and <em>Populus</em> spp.) and other species. The units were categorized into six diameter classes (d1-d6). Thin branches with a diameter of 0.8–4.9 cm were classified into the first one (d1), and five more diameter classes (d2–d6) were defined using Jenks natural breaks method: d2 – 5–9 cm, d3 – 10–18 cm, d4 – 19–32 cm, d5 – 33–54 cm, d6 – 55–146 cm. Three types of utilization were defined: <em>cutting</em>, <em>carving</em>, and <em>debarking</em>. We refer to trunks and branches not utilized by beavers as intact units. We also distinguished between fresh and old signs of utilization. In addition, a fresh dataset was defined, which consists of intact units, units with fresh signs of utilization, and formerly beaver-harmed but living trees.</p> <p> </p>
Evaluating impacts of non-native submerged aquatic vegetation on native nekton
<p>These data accompany the publication of the same name. We performed a quantitative meta-analysis to quantify impacts of non-native submerged aquatic vegetation on native crabs, fishes, and shrimps in coastal, estuarine, and marine systems. We found that nekton abundance, species richness, and biomass were the most assessed metrics of nekton performance. We extracted data from 35 studies and evaluated 11 response metrics related to inter-specific facilitation, restricting our analysis to studies that compared at least one of these metrics in nekton from co-occurring native and non-native SAV habitats in marine, coastal, or estuarine systems.</p>
Seasonally Varying Vegetation Impacts on Surface Fluxes (VegFlux)
<p>VegFlux introduced observationally based, seasonal variations in vegetation and bare-soil fractions into the Met Office Unified Model (UM) and quantify impacts on dust emission and moist processes, allowing improvement of climate projections for Africa.</p> <p>In the zipped folder (VegetFlux_data.tar.gz) uploaded here, the data is grouped into five folders containing each one of the following parameters: temperature (tewnt15m), precipitation (tewnpre), latent heat (tewnlhf), sensible heat (tewnshf) and vegetation fraction (vegfrac). In each folder, we have two types of simulation, one whose file names begin with tewnfa and the other whose file names begin with tewnha. The one begining with tewnfa is the simulation using fix vegetation fraction in the unified model (UM) and the tewnha using varying vegetation fraction. The timestep is 12 hours meaning two measures per day (00h and 12h) during five months (may to september).</p>
Detection of Ore-forming Elements Migrated from Rock and Soil to Vegetation Leaves using Hyperspectral Data
<p>In this paper we present the detection of ore-forming elements migrated from soil and rock to vegetation leaves using hyperspectral data. The rock, soil (C, B, A layer) and vegetation (root, stem, leaf) samples with the measurements of spectra and element concentrations in the vertical section of the abandoned pits were collected. The wavelet approach is applied to analyze the correlations between spectral features and element concentrations. The results show that the significant correlations are found at a lower order mother wavelets such as Haar. Moreover, the significant correlations are negative between Cu and Mo elements and the wavelet energy vector of the soil and rock to vegetation leaves spectrum. The results also show that ore-forming elements migrated from rock and soil to vegetation leaves can be detected using hyperspectral data in the study area.</p>
The Vegetation Dynamics in Response to Cooling Events from the Medieval Warm Period to the Little Ice Age in Southeast China
<p>Full palynological dataset from three-peats (Wangdongyang peat bog: WDY, 27°40′48″N、119°38′15″E, elevation: 1303 m; Xiyaohu peat bog: XYH15, 28°44′N, 115°40′E, elevation: 735m and XYH14 is close to XYH15; Yuhuashan peat bog: YHS2, 27°50′29″N, 115°38′54.98″E, elevation: 882 m) in the southeast of China.</p> <p>The dataset contains raw counts for pollen and ferns.</p>
Impact of updating vegetation information on land surface model performance
<p>ECLand model outputs generated and analyzed during the study "<span>Impact of updating vegetation information on land surface model performance</span>"</p>
MUSES Fractional Vegetation Coverage (FVC) Monthly Global 0.05º Geographic Grid Since 1981
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 0.05º spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on Geographic grid and spans from 1981 to 2018 (continuously updated). It was generated from the MUSES LAI product at 0.05º resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: 180º W – 180º E, 90º S – 90º N;</li> <li>Temporal Coverage: 1981 – 2018;</li> <li>Spatial Resolution: 0.05º (approximately 5 km);</li> <li>Temporal Resolution: 1 month;</li> <li>Projection: Geographic;</li> <li>Data Format: HDF;</li> <li>Scale: 0.004;</li> <li>Valid Range: 0 – 250.</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Fractional Vegetation Coverage (FVC) Monthly Global 1km SIN Grid in 2015
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 1 km spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2015. <em>Please <a href="https://zenodo.org/record/7928103#.ZF3z6XZBypo"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2014</strong></em>, <em>and <a href="https://zenodo.org/record/7927598#.ZF3QURFBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2016</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2015</li> <li>Spatial Resolution: 1 km</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
Dataset of Flow Velocity Prediction in Vegetated Alluvial Channels Comparing Empirical and State-of-the-art Hybrid Machine Learning Models
<p>We compiled 447 datasets from different sources and lab- and field-based measurements. These datasets included Einstein and Banks (1950), Fenzl (1962), Kouwen et al. (1969), Ree and Crow (1977), Murota (1984), Tsujimoto and Kitamura (1990), Tsujimoto (1991), Tsujimoto (1993), Shimizu (1994), Dunn et al. (1996), Ikeda and Kanazawa (1996), Meijer (1998), Jarvela (2002), Rowinski and Kubrak (2002), Stone and Shen (2002), Poggi et al. (2004), Carollo et al. (2005), and Murphy et al. (2007).</p>
FIGURE 7. Microlicia pumila. A. Fruiting branches. B in Microlicia delicata and Microlicia pumila (Melastomataceae), two new species from unprotected mountains with campo rupestr vegetation in eastern Brazil
FIGURE 7. Microlicia pumila. A. Fruiting branches. B. Branchlet terminating in a floral bud. C. Type locality, Serra da Formosa, Monte Azul, Minas Gerais, Brazil. Photos by R. Pacifico. Voucher: R. Pacifico et al. & V.E. Bressan 725.
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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.