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3,105 results for “Vegetation =”
FIGURE 19 in " Cerrado " vegetation, with descriptions of three new species
FIGURE 19. Tarsonemus longisetae sp. nov. (female). Dorsal surface.
FIGURE 14 in " Cerrado " vegetation, with descriptions of three new species
FIGURE 14. Metatarsonemus megasolenidii sp. nov. (male). Ventral surface.
FIGURE 13 in " Cerrado " vegetation, with descriptions of three new species
FIGURE 13. Metatarsonemus megasolenidii sp. nov. (male). Dorsal surface.
FIGURE 7 in " Cerrado " vegetation, with descriptions of three new species
FIGURE 7. Metatarsonemus megasolenidii sp. nov. (female). Dorsal surface.
FIGURE 8 in " Cerrado " vegetation, with descriptions of three new species
FIGURE 8. Metatarsonemus megasolenidii sp. nov. (female). Ventral surface.
FIGURE 2 in " Cerrado " vegetation, with descriptions of three new species
FIGURE 2. Daidalotarsonemus folisetae sp. nov. (female). Ventral surface.
FIGURE 26 in " Cerrado " vegetation, with descriptions of three new species
FIGURE 26. Tarsonemus longisetae sp. nov. (male). Ventral surface.
FIGURE 1 in " Cerrado " vegetation, with descriptions of three new species
FIGURE 1. Daidalotarsonemus folisetae sp. nov. (female). Dorsal surface.
Dataset and R code (Effect of Vegetation Structure on Secondary Wind Dispersal Distance of Diaspores)
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Flume data on flow resistance of flexible foliated woody vegetation
<p>This dataset contains flume data on flow resistance of flexible foliated woody vegetation. The data have been previously reported and analyzed by Västilä et al. (2013), Västilä & Järvelä (2014), and Box et al. (2021), respectively.</p> <p>The data were used in developing and validating the modeling of flexible woody vegetation in D-FLOW FM module of the Delft3D Flexible Mesh (Delft3D FM) Suite, as reported in Västilä et al. (submitted). Two leaf-aread-index based flow resistance formulas (Järvelä 2004, Eq. 16, JAR; and Västilä & Järvelä 2014, Eq. 17, VAS) were implemented into D-FLOW FM by Västilä et al. (submitted). </p> <p>This contribution consists of three datasets, in all of which the plants were approximately homogeneously distributed across the entire flume bed: 1) artificial nature-like shrubs with 2 cm tall understory grasses under low relative submergence (h/hd=0.9-3.1) conditions (Box et al., 2021), 2) Black Poplars at three plant densities under just submerged conditions (Västilä et al., 2013), and 3) four natural deciduous riparian species at different leaf-area-to-stem-area ratios (AL/AS) under just submerged conditions (Västilä & Järvelä, 2014). </p> <p>The measured data are compiled in the file "Vastila_etal_2024_Flume data on flow resistance of flexible foliated woody vegetation.xlsx". Dataset 1 is located in sheet "Box_etal_2021_data.xlsx", while Datasets 2 and 3 are combined in sheet "Västilä&Järvelä_2013+2014_data". The corresponding metadata descriptions are included in separate sheets. The sheet "VAS+JAR_parameters" includes tables reporting the recommended parameter values of the JAR (Table 1) and VAS (Table 2) formulas as well as the parameter values of both formulas as derived at low and high levels of AL/AS (Table 3) to analyze the uncertainty of the formulas related to the parameter dependency on AL/AS (see Västilä et al. submitted). The datasets include a specific model ID code for each case in column entitled "Model ID".</p> <p>References<br>- Box, W., Järvelä, J. & Västilä, K. 2021. Flow resistance of floodplain vegetation mixtures for modelling river flows. Journal of Hydrology 601: 126593. https://doi.org/10.1016/j.jhydrol.2021.126593</p> <p>- Järvelä, J. 2004. Determination of flow resistance caused by non-submerged woody vegetation. International Journal of River Basin Management 2(1): 61–70. https://doi.org/10.1080/15715124.2004.9635222</p> <p>- Västilä, K., Järvelä, J., & Aberle, J. 2013. Characteristic reference areas for estimating flow resistance of natural foliated vegetation. Journal of Hydrology 492: 49-60. DOI: http://dx.doi.org/10.1016/j.jhydrol.2013.04.015</p> <p>- Västilä, K. & Järvelä, J. 2014. Modeling flow resistance of woody vegetation using physically-based parameters for foliage and stem. Water Resources Research 50(1): 229-245. DOI: 10.1002/2013WR013819</p> <p>- Västilä, K., Berends, K.D., Järvelä, J., Penning, E.W.E. (submitted) Development of flow resistance modeling using state-of-the-art formulas for flexible woody vegetation in Delft3D FM. </p>
Data from: Geographic Object-Based Image Analysis Framework for Mapping Vegetation Physiognomic Types at Fine Scales in Neotropical Savannas
<p>Spatially-explicit information featuring a wide range of detailed vegetation structural types are necessary to support conservation and ecological analyses. A systematic approach that accurately maps such detailed categories at regional scales is currently lacking for neotropical savannas. We developed a systematic Geographic Object-Based Image Analysis (GEOBIA) framework that accounts for spectral and spatial properties to map Cerrado vegetation structural types at 5-m resolution. This framework counts with a two-step mapping approach: (1) image segmentation and a Random Forest land cover classification based on spectral information (Level 1 classification), followed by (2) a GEOBIA knowledge-based classification that follows contextual and topological spatial rules developed in a systematic manner for mapping Cerrado ecological classes (Level 2 classification).</p><p>The framework was tested for two large study sites covering most major Cerrado physiognomic types: a control site in central Brazil (Taquara watershed site, located in Brasilia, Federal District, and embraces the IBGE Ecological Reserve) and another larger site that is located in an agricultural landscape in the western portion of Bahia State (encompasses parts of the municipalities of: Sao Desiderio, Luis Eduardo Magalhaes, Barreiras, and Riachao das Neves), which brings additional mapping challenges related to intra-class spectral similarity. </p><p>Results demonstrate that our GEOBIA approach is effective for mapping 13 land cover classes with 87.6% overall accuracy, of which all 11 major vegetation classes were identified. For additional details, please check the associated publication with these datasets (Ribeiro et al. 2020).</p><p>The datasets developed in this study are available in shapefile format with attributed metadata following ISO 19115 standards: </p><ul><li><strong>wba_lcmap_geobiaL2_2011lucmask:</strong> final land cover map (Level 2 -- physiognomic types) for a section of the western bahia region in the Cerrado. This product uses a land use mask of 2011 derived from ancillary data</li><li><strong>wba_lcmap_geobiaL2_2013lucmask:</strong> final land cover map (Level 2 -- physiognomic types) for a section of the western bahia region in the Cerrado. This product accounts for the land use mask of 2011 as well as an updated land use mask of 2013 derived from ancillary data</li><li><strong>ibge_taquara_luc_L1_L2_2013_original:</strong> land cover maps (Levels 1 and 2) developed for the Taquara/IBGE site</li><li><strong>ibge_taquara_detailed_luc_2013:</strong> final land cover map (Level 2 -- physiognomic types) developed for the Taquara/IBGE site with additional detailed land use categories</li></ul><p> </p><p><strong>Data</strong></p><p>Mapped land cover classes: 1) cerrado woodland, 2) savanna, 3) open savanna, 4) shrubby grassland, 5) grassland (only present in the Taquara site), 6) cerrado scrub (only present in the western bahia site), 7) marsh, 8) shrub swamp, 9) palm swamp, 10) riparian forest, 11) semi-deciduous forest, 12) seasonally dry tropical forest (only present in the western bahia site), 13) invasive forbes (only present in the Taquara site), 14) non-natural/barren, 15) water, 16) shade, 17) clouds</p><p>The land-use mask incorporated into our maps feature the following classes: main roads, farming/crops, silviculture, and urban areas. </p><p> </p><p><strong>Coordinate Reference System</strong></p><p>Datasets were projected to <strong>South America Albers Equal Area</strong> <strong>Conic</strong>, with all areal calculations based on this projection.</p><p> </p><p><strong>Data Usage</strong> </p><p>The datasets are publicly available and should be cited appropriately (main publication: Ribeiro et al. 2020).</p><p> </p><p><strong>Additional information</strong></p><p>For any other data requests or questions, please contact Fernanda Ribeiro (fernanda.ffr@gmail.com).</p>
THE IMPACT OF THE EXPORT POTENTIAL OF THE FRUIT AND VEGETABLE INDUSTRY ON THE ECONOMIC GROWTH OF THE REPUBLIC OF UZBEKISTAN.
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FIGURE 4 in Taxonomic Reconciliation of Smilacaceae in the Indian Subcontinent: Synonymy, Typification, Updated Inventory, and a Vegetative Key for Identification
FIGURE 4. Lectotype of Smilax luzonensis Presl PR290785 © National Museum in Prague.
Slower changes in vegetation phenology than precipitation seasonality in the dry tropics
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Invasive vegetation encroachment modulates dual threats faced by island-endemic scaly crickets
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Soil seepage under different vegetation
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Table 2 in Checklist of bees (Hymenoptera: Apoidea) from small diversified vegetable farms in south-western Montana
<p>Table 2. List of published keys used for species identification. Genus-level identifications were done using Michener et al. (1994).</p><table><tbody><tr><td>Family</td><td>References</td></tr><tr><td>Andrenidae</td><td>Bouseman and LaBerge 1979 Donovan 1977 LaBerge 1967 LaBerge 1969 LaBerge 1973 LaBerge 1977 LaBerge 1980 LaBerge 1985 LaBerge 1986 LaBerge 1989 LaBerge and Bouseman 1970 LaBerge and Ribble 1975 Ribble 1967 Ribble 1974 Thorp 1969 Timberlake 1956 Timberlake 1960 Timberlake 1964 Timberlake 1967</td></tr><tr><td>Apidae</td><td>Brumley 1965 Daly 1973 Hurd and Linsley 1951 Koch et al. 2012 LaBerge 1956b LaBerge 1956a LaBerge 1961 LaBerge 1963 Rightmyer 2008 Sipes 2001 Thorp et al. 1983 Williams et al. 2014</td></tr><tr><td>Colletidae</td><td>Snelling 1966b Snelling 1966a Snelling 1970 Stephen 1954</td></tr><tr><td>Halictidae</td><td>Coelho 2004 Dumesh and Sheffield 2012 Gibbs 2010 McGinley 1986 McGinley 2003 Roberts 1972 Roberts 1973</td></tr><tr><td>Megachilidae</td><td>Baker 1975 De Silva and Packer 2012 Gonzalez and Griswold 2013 Grigarick and Stange 1968 Hurd and Michener 1955 Michener 1938 Michener 1939 Michener 1947 Rightmyer et al. 2010 Sandhouse 1939 Sheffield et al. 2011</td></tr></tbody></table>
Drone Flight Videos for Vegetation Monitoring, Fire Sensor Detection, and Power Line Inspection
<p>This video set showcases drone flights conducted in three different scenarios: vegetation monitoring in natural areas, recognition and localization of a fire detection sensor, and visual inspection of a power line. The videos serve as practical examples of how drones can be used in environmental monitoring and critical infrastructure maintenance applications. The visualization of the results and analysis of the collected data is performed through the GridWatch platform, enabling efficient and detailed monitoring. </p>
Effect of soil stoichiometry mediated by vegetation on carbon emissions in a large wetland under hydrological stress
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Supplementary digital data for Exposure of Insects to Current Use Pesticide Residues in Soil and Vegetation along Spatial and Temporal Distribution in Agricultural Sites
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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.