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382 results for “Dune”
Dune Biomass on Hog Island, Virginia Coastal Barrier Islands, 1993-2012 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-vcr/70/25. The abstract below was extracted from the Level 0 data package and is included for context:
Soil Moisture at Three Different Dune Elevations on the Hog Island, Northampton County, VA 2023
In summer 2023, soils were collected from swale grasslands (embryonic, Intermediate [swale 1] and Inland [swale 2]) on southern Hog Island. They were kept in plastic bags to quantify soil moisture content, which was determined by weighing cores to obtain water mass before and after drying at 105 deg_C for 72 hours. For details, see: Woods, N.N., Zinnert, J.C. Shrub encroachment of coastal ecosystems depends on dune elevation. Plant Ecol 225, 1047-1057 (2024). https://doi.org/10.1007/s11258-024-01453-2
Dune Biomass on Hog Island, Virginia Coastal Barrier Islands, 1993-2012 (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/323/2, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-vcr/70/25. The abstract below was extracted from the Level 0 data package and is included for context:
LIDAR Derived Dune-Crest Elevation Values and Shrub Prediction Morphometrics for the Virginia Coast Reserve Barrier Islands: 2010 - 2017
This dataset includes LIDAR derived dune-crest elevation values for the VCR for 2010-2017. Dune-crest elevation values were sampled every 100 m from Smith to Cedar islands. The ArcGis Pro file includes the location of dune-crest transects. Additionally, this dataset includes island characteristics related to predicting shrub presence or absence for 2010, 2016, and 2017.
Dataset for 'Meteorological Conditions Influence the Migration of a Marine Dune Field in the Southern North Sea'
<p>This dataset complements the paper 'Meteorological Conditions Influence the Migration of a Marine Dune Field in the Southern North Sea' accepted <span>for publication in Journal of Geophysical Research - Earth Surface</span>.</p> <p> </p> <p>This dataset contains Digital Terrain Maps (DTMs) of specific areas offshore from Dunkirk, on the northern coast of France, opening to the Southern Bight of the North Sea. These areas host marine dunes (sand waves) that have been numerically investigated in this research to identify the parameters influencing their migration. The openTELEMAC system (version v8p4) can be downloaded from <a href="https://opentelemac.org/">https://opentelemac.org/</a>. The model development, calibration and validation is described in Durand (2024).</p> <p> </p> <p>The site-specific data collected by France Energies Marines (2021) are currently proprietary. To protect these data, DTMs of bathymetric changes are provided, calculated as the difference in metres between the final and initial seabed levels. Negative values indicate lowering of the seabed (erosion) and positive values indicate rising (accretion).</p> <p>The initial and final periods are:</p> <ul> <li>S1: 17-Nov-2019</li> <li>S2: 17-Mar-2020</li> <li>S5: 5-Dec-2020</li> </ul> <p>The DTMs are provided for two areas (refer to paper for locations):</p> <ul> <li>Tile #1</li> <li>Tile #3</li> </ul> <p> </p> <p>Included in the dataset are observations, Case I model output (without wind and atmospheric pressure), and Case II model output (with wind and atmospheric pressure).</p>
Figure 9 of paper: It's not only the sea: a history of human intervention in the beach-dune ecosystem of Costa da Caparica (Portugal)
<p>This if the figure 9 of paper with DOI 10.5894/rgci-n432.</p> <p>Dunes of Trafaria and Costa da Caparica. This figure was adapted by Dissanayake M. Ruwan Sampath.</p> <p>The original source can be found at Archive from Instituto para a Conservação da Natureza e Florestas (Portugal).</p> <p>Representation of the works made by the Forestry Services between 1884 and 1910. Reference to an area flooded by the ocean in 1905 [sementeira de 1905 inundada pelo mar] and areas where new sowings had to be done [resementeiras]. Notice the drainage systems [valla] and the fences [sébe] near the coastline to protect the plants.</p>
Figure 11 of paper: It's not only the sea: a history of human intervention in the beach-dune ecosystem of Costa da Caparica (Portugal)
<p>This is the figure 11 of the article with DOI 10.5894/rgci-n432.</p> <p>Forests of Trafaria and Costa da Caparica in the 1930s-1940s. This figure was adapted by Dissanayake M. Ruwan Sampath.</p> <p>Original source can be found at the Archive of Instituto para a Conservação da Natureza e Florestas.</p> <p>In green, the existing forests. In pink, the Forestry Services areas given to other institutions or services for public uses. In yellow, the dunes to be afforested.</p> <p> </p>
Figure 3: Scheme of the procedure adopted for implementing the Sand Dune Acts of 1903/1908, to reclaim the lands affected by sand drifting
<p>Figure 3 of article: Managing Coastal Sand Drift in the Anthropocene: A Case Study of the Manawatū-Whanganui Dune Field, New Zealand, 1800s–2020s</p> <p>DOI zenodo: 10.5281/zenodo.5075980</p>
Data used in 'Local Wind Regime Induced by Giant Linear Dunes: Comparison of ERA5-Land Reanalysis with Surface Measurements.'
<p>This repository contains the data used in:</p> <blockquote> <p>Gadal, C., Delorme, P., Narteau, C. et al. Local Wind Regime Induced by Giant Linear Dunes: Comparison of ERA5-Land Reanalysis with Surface Measurements. Boundary-Layer Meteorol 185, 309–332 (2022). <a href="https://doi.org/10.1007/s10546-022-00733-6">https://doi.org/10.1007/s10546-022-00733-6</a></p> </blockquote> <p>where wind data measured at 4 different places in and across the Namib Sand Sea are compared to the data from the ERA5/ERA5Land climate reanalyses.</p> <p>The use this data, one should first look at the GitHub repository <a href="https://github.com/Cgadal/GiantDunes">https://github.com/Cgadal/GiantDunes</a> and at the corresponding documentation <a href="https://cgadal.github.io/GiantDunes/">https://cgadal.github.io/GiantDunes/</a>. The description sometimes refers to scripts used in <a href="https://github.com/Cgadal/GiantDunes/tree/master/Processing">https://github.com/Cgadal/GiantDunes/tree/master/Processing</a>.</p> <p>The two folders 'raw_data' and 'processed_data' contain the input raw_data, and the output data after processing used to make the paper figures, respectively. In each of them, '.npy' files contain Python dictionaries with different variables in them. They can be loaded using the Python library <code>numpy</code> as <code>data = np.load('file.npy', allow_pickle=True).item()</code>; and the different keys (variables) can be printed with <code>data.keys()</code> or <code>data[station].keys()</code> if <code>data.keys()</code> return the different stations. Unless specified otherwise below, note that all variables are given in the International System of Units (SI), and wind direction is given anticlockwise, with the 0 being a wind blowing from the West to the East.</p> <ul> <li>raw_data: <ul> <li>DEM: contains the Digital Elevation Models of the two stations from the SRTM30, downloaded from here: https://dwtkns.com/srtm30m/</li> <li>ERA5: hourly data from the ER5 climate reanalysis, on surface (_BLH) and pressure levels (_levels). Downloaded from https://cds.climate.copernicus.eu/</li> <li>ERA5Land: hourly data from the ER5Land climate reanalysis Downloaded from https://cds.climate.copernicus.eu/</li> <li>KML_points: kml points of the measurement station. It can be opened directly in GoogleEarth.</li> <li>measured_wind_data: contains the measured in situ data. The windspeed is measured using Vector Instruments A100-LK cup anemometers, the wind direction using Vector Instruments W200-P wind vane and the time using Campbell Instruments CR10X and CR1000X dataloggers.<br> </li> </ul> </li> <li>processed_data: <ul> <li>'Data_preprocessed.npy': preprocessed_data, output of 1_data_preprocessing_plot.py</li> <li>'Data_DEM.npy': properties of the processed DEM, the output of 2_DEM_analysis_plot.py</li> <li>'Data_calib_roughness.npy': data from the calibration of the hydrodynamic roughnesses, the output of 3_roughness_calibration_plot.py</li> <li>'Data_final.npy': file containing all computed quantities</li> <li>'time_series_hydro_coeffs.npy': file containing the time series of the calculated hydrodynamic coefficients by '5_norun_hydro_coeff_time_series.npy'.</li> </ul> </li> </ul> <p> Depending on the loaded data file, main dictionary keys can be:</p> <ul> <li>'lat': latitude, in degree</li> <li>'lon': longitude, in degree</li> <li>'time': time vector, in datetime objects (https://docs.python.org/3/library/datetime.html)</li> <li>'DEM': elevation data array in [m], with dimensions matching 'lat' and 'lon' vectors</li> <li>'z_mes', 'z_insitu', 'z_ERA5LAND': height of the corresponding velocity</li> <li>'direction': measured wind direction, in [degrees]</li> <li>'velocity': measured wind velocity, in [m/s]</li> <li>'orientaion': dune pattern orientation, [deg]</li> <li>'wavelength': dune pattern wavelength, [km]</li> <li>'z0_insitu': chosen hydrodynamic roughness for the considered station.</li> <li>'U_insitu', 'Orientation_insitu': hourly averaged measured wind velocities and direction</li> <li>'U_era', 'Orientation_era': hourly 10m wind data from the ERA5Land data set</li> <li>'Boundary layer height', 'blh': boundary layer height from the hourly ERA5 dataset</li> <li>'Pressure levels', 'levels': Pressure levels from the pressure levels ERA5 dataset</li> <li>'Temperature', 't': Temperature from the pressure levels ERA5 dataset</li> <li>'Specific humidity', 'q': Specific humidity from the pressure levels ERA5 dataset</li> <li>'Geopotential', 'z': Geopotential from the pressure levels ERA5 dataset</li> <li>'Virtual_potential_temperature': Virtual potential temperature calculated from the pressure levels ERA5 dataset</li> <li>'Potential_temperature': Potential temperature calculated from the pressure levels ERA5 dataset</li> <li>'Density': Density calculated from the pressure levels ERA5 dataset</li> <li>'height': Vertical coordinates calculated from the pressure levels ERA5 dataset</li> <li>'theta_ground': Averaged virtual potential temperature within the ABL.</li> <li>'delta_theta': Virtual potential temperature at the ABL.</li> <li>'gradient_free_atm': Virtual potential temperature gradient in the FA.</li> <li>'Froude': time series of the Froude number U/((delta_theta/theta_ground)*g*BLH)</li> <li>'kH': time series of the number 'kH'</li> <li>'kLB': time series of the internal Froude number kU/N</li> </ul> <p>Other keys are not relevant and are stored for verification purposes. For more details, please contact Cyril Gadal (see authors), and look at the following GitHub repository: <a href="https://github.com/Cgadal/GiantDunes">https://github.com/Cgadal/GiantDunes</a>, where all the codes are present.<br> </p>
Data from: A semi-automated approach to classify and map ecological zones across the dune-beach interface
<p>This is the raw data behind the publication: </p> <p><strong>A semi-automated approach to classify and map ecological zones across the dune-beach interface</strong></p> <p><strong>Abstract: </strong>Habitat classification and mapping underpins most conservation and management tools, because habitats are often used as a surrogate for all biodiversity. Some habitat boundaries are easy to delineate; however, sandy shores are ecotones or ecoclines given their dynamic interface between the marine and the terrestrial realms. Although methods for mapping habitats along shorelines have been broadly applied, we aim to test a semi-automated approach to mapping across-shore “sub-environments” in this transition zone at a finer scale. Using an empirical dataset of photographs covering a small area (three across-shore transects from each of two different areas) with a high resolution, we tested seven machine learning algorithms to determine which one had the best classification accuracy, and to identify which environmental variables are the main determinants of classifications. The randomForest, stochastic gradient boosting, and C5.0 algorithms most accurately classified the photographs as the correct sub-environment. Based on the randomForest algorithm, the variables entropy, drift cover rate, local slope, segmented vegetation cover and number of points with sand or marine litter had the highest influence on the classification. There was no sensitivity to spatial variation alongshore. This approach can be used to map sub-environments at larger scales using drone technology to capture georeferenced digital photographs systematically. Consequently, coastal habitats can be mapped at a finer scale without causing disturbance to this especially sensitive ecotone.</p>
Assessment of current and future invasive plants in protected dune habitats of the Atlantic coastal region for the LIFE DUNIAS project (LIFE20 NAT/BE/001442)
<p>This .csv file contains the raw data from the risk screening supplementing the LIFE DUNIAS horizon scan for (invasive) alien species in protected habitats of Atlantic coastal dune ecosystems (<a href="https://doi.org/10.21436/inbor.86703335">Adriaens et al. 2022</a>). We gladly refer to the annexes and methods section in this report for more explanation about the fields and their contained values.</p> <p>The file contains the following fields:</p> <p><em>TaxonName</em>: original taxonomic name of the considered alien species</p> <p><em>WorkName</em>: taxonomic name of the considered alien species after lumping of subspecies, closely related species of a complex, functionally similar species of the same genus (see chapter 3.1)</p> <p><em>hab_xxxx</em> (1110, 1130, 1140, 1210, 1230, 1310, 1320, 1330, 2110, 2120, 2130, 2140, 21A0, 2150, 2190, 2160, 2170, 2180): susceptibility of habitat for the alien species (4-digit code refering to the Annex I habitat under the Habitats Directive) </p> <p><em>occ_XX</em> (BE, FR, IE, NL, ES, UK, DK, DE, PT, ALL): occupancy of the alien species in different countries of the Atlantic European region (as the number of 10km<sup>2</sup> squares per country). Country codes: BE = Belgium, FR = France, IE = Ireland, NL = Netherlands, ES = Spain, UK = United Kingdom, DK = Denmark, DE = Germany, PT = Portugal, ALL = total for all countries.</p> <p><em>scor_XXX_xxxx</em>: score of the assessment per criterium (INT = introduction, EST = establishment, SPR = spread, IMP = ecological impact, ALL = overall score) and per habitat group (salt = salties, sand = sandies, shru = shrubbies) conf_<em>XXX_xxxx</em>: confidence on the scores of the assessment per criterium (INT = introduction, EST = establishment, SPR = spread, IMP = ecological impact, ALL = overall score) and per habitat group (salt = salties, sand = sandies, shru = shrubbies)</p> <p><em>scor_ALL_MAX</em>: maximum ecological impact score of the alien taxon across all habitats</p>
Spatial data sets of the paper Heinrich Stadial 1 continental sand dunes and Middle to Late Holocene paleosol sequences in SE Iberia: implications for human occupation and site formation processes
<p>Spatial data sets of the paper Heinrich Stadial 1 continental sand dunes and Middle to Late Holocene paleosol sequences in SE Iberia: implications for human occupation and site formation processes. This data set is composed by 3 shapefiles:</p> <ol> <li>Dune_field: Feature class polygon shapefile geometry representing the individual dunes identified in the Villena dune field.</li> <li>Sampled dunes: Shapefile of point geometry representing the location of the stratigraphic sequences of CC1, CC2 and CC3 sampled for texture, soil chemistry, OSL and radiocarbon dating. </li> <li>Sediment sourcing samples: Shapefile of point geometry representing the location of the reference samples of El Moron, El Arenal de la Virgen and Sierra del Castellar. </li> </ol> <p>The spatial reference system is EPSG 25830.</p>
Figure 1 in How extensive is the effect of modern farming on bird communities in a sand dune desert?
Figure 1. Dendrogram of F- and C-transects, using group-average clustering from Bray-Curtis similarities on log-transformed bird abundances. Similarity coefficient in percent.
Fig. 3. A–C in Description of a new species of Paracrobeles Heyns, 1968 (Nematoda, Rhabditida, Cephalobidae) from Kelso Dunes, Mojave National Preserve, California, USA
Fig. 3. A–C. Paracrobeles kelsodunensis sp. nov. LM micrographs. A. Male anterior end, ventral side to the right. B–C. Female anterior end, ventral side to the right. D–H. Paracrobeles mojavicus Taylor, Baldwin & Mundo-Ocampo, 2004. D–F. Female anterior end, ventral side to the right. G–H. Male anterior end, ventral side to the right. I. Paracrobeles cf. kelsodunensis sp. nov., male anterior end, ventral side to the right. Scale bar: A–I = 10 µm.
Fig. 1 in Description of a new species of Paracrobeles Heyns, 1968 (Nematoda, Rhabditida, Cephalobidae) from Kelso Dunes, Mojave National Preserve, California, USA
Fig. 1. Paracrobeles kelsodunensis sp. nov. A. Pharyngeal region. B. Female gonad. C. Female tail. D. Male tail. Scale bar = 20 µm.
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Great Sand Dunes GeoTIFF
<p>An elevation model of Great Sand Dunes, Colorado, USA</p> <p>Landform features: active dune field, sand sheet, sabkha</p> <p>Resolution: 3.3 meter, 5,300 x 5,300 height samples</p> <p>File format: GeoTIFF</p> <p>This is one model of a set of elevation models: <a href="https://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Dataset from Pardini, E. A., Parsons, L. S., Ştefan, V., & Knight, T. M. (2018). GLMM BACI environmental impact analysis shows coastal dune restoration reduces seed predation on an endangered plant. Restoration Ecology, 26(6), 1190-1194.
<p>Data and its metadata used in the analysis from the publication: Pardini, E. A., Parsons, L. S., Ştefan, V., & Knight, T. M. (2018). GLMM BACI environmental impact analysis shows coastal dune restoration reduces seed predation on an endangered plant. Restoration Ecology, 26(6), 1190-1194. <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/rec.12678">https://onlinelibrary.wiley.com/doi/full/10.1111/rec.12678</a> </p>
Figure 12 of paper: It's not only the sea: a history of human intervention in the beach-dune ecosystem of Costa da Caparica (Portugal)
<p>Figure 12 of research paper with DOI: 10.5894/rgci-n432</p>
Figure 1: Study area: A Digital Elevation Model of the North Island and the Manawatū-Whanganui dune field
<p>Figure 1 of article: Managing coastal sand drift in the Anthropocene: A case study of the Manawatū-Whanganui Dune Field, New Zealand, 1800s-2020s; authors: Sampath, Ruwan; Beattie, James; Freitas, Joana Gaspar; journal Environment & History [forthcoming]</p> <p>an article accepted in June 29, 2021</p> <p>https://zenodo.org/badge/DOI/10.5281/zenodo.5075980.svg</p>
The Provincelands of Cape Cod National Seashore, Barnstable County, Massachusetts, USA. The reddish vegetation in the center of the photo is a cranberry (Vaccinium macrocarpon) bog, a wetland used for breeding by the Fowler's toad. The surrounding landscape is ideal for the Fowler's toad and supports one of the largest populations of this species in the United States. The landscape contains a patchwork of sand, pitch pine (Pinus rigida), scrub oak (Quercus ilicifolia), and dune grass (Ammophila breviligulata). Photo by Rebecca Flaherty. in Fowler's Toad (Anaxyrus fowleri) occupancy in the southern mid-Atlantic, USA
The Provincelands of Cape Cod National Seashore, Barnstable County, Massachusetts, USA. The reddish vegetation in the center of the photo is a cranberry (Vaccinium macrocarpon) bog, a wetland used for breeding by the Fowler's toad. The surrounding landscape is ideal for the Fowler's toad and supports one of the largest populations of this species in the United States. The landscape contains a patchwork of sand, pitch pine (Pinus rigida), scrub oak (Quercus ilicifolia), and dune grass (Ammophila breviligulata). Photo by Rebecca Flaherty.
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