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4,107 results for “pointing”
Soil properties as point estimations over the Cypriot pilot area (2021)
<p>In the context of the EU-funded project DIONE (No. 870378), VNIR topsoil reflectance was captured with DIONE’s Soil Scanning System and transformed through Machine Learning modelling to a set of soil properties that are meaningful for the assessment of soil health. The captured reflectance measurements correspond to locations distributed within the pilot areas of Cyprus and are indicated after the analysis of EO multispectral imagery, aiming to create a collection of point locations that well represent the soil characteristics of the area, and provide valuable information about soil condition through the estimations of the following soil properties:</p> <ul> <li>Sand %</li> <li>Clay %</li> <li>Silt %</li> <li>Electrical Conductivity (mS/m)</li> <li>pH</li> <li>Calcium carbonate %</li> <li>Soil Organic Carbon %</li> </ul> <p>The dataset is delivered in a shapefile format (DIONE_CY_point_estimations_2021.shp - EPSG:4326 - WGS 84) containing the following fields:</p> <table> <caption><strong>Description of the information contained in the corresponding "CY points estimations" dataset</strong></caption> <thead> <tr> <th scope="col">Field</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>Sample_ID</td> <td>String</td> <td>Unique ID</td> </tr> <tr> <td>lat</td> <td>Real</td> <td>Latitude </td> </tr> <tr> <td>lon</td> <td>Real </td> <td>Longitude</td> </tr> <tr> <td>Sand</td> <td>Real</td> <td>Sand fraction</td> </tr> <tr> <td>Clay</td> <td>Real</td> <td>Clay fraction</td> </tr> <tr> <td>Silt</td> <td>Real</td> <td>Silt fraction</td> </tr> <tr> <td>EC</td> <td>Real</td> <td>Electrical Conductivity</td> </tr> <tr> <td>pH_H20</td> <td>Real</td> <td>pH</td> </tr> <tr> <td>CaCO3</td> <td>Real</td> <td>Calcium Carbonate </td> </tr> <tr> <td>SOC</td> <td>Real</td> <td>Soil Organic Carbon</td> </tr> </tbody> </table> <p> </p>
LOFAR Observation (MS file) from the Boötes Field and the Toothbrush cluster used in the paper: "Looking beyond pixels with continuous-space EstimAtion of Point sources"
<p>The dataset contains the measurement sets (MS file) of the LOFAR observations from the Boötes field and the Toothbrush cluster. The dataset was used in the experiments of the paper: </p> <blockquote> <p>LEAP: Looking beyond pixels with continuous-spaceEstimAtion of Point sources</p> <p>Pan, H., Simeoni, M., Hurley, P., Blu, T. & Vetterli, M. In: Astronomy & Astrophysics, in press, 2017</p> </blockquote> <p>The data was provided as a collaboration between ASTRON and IBM within the DOME project. The data was acquired for a LOFAR sky survey of the Boötes field:</p> <blockquote> <p>LOFAR 150-MHz observations of the Boötes field: Catalogue and Source Counts</p> <p>Williams, W. L. , Hardcastle, M. J. & 33 others In: Monthly Notices of the Royal Astronomical Society. 460, 3, p. 2385–2412</p> </blockquote> <p>and the Toothbrush cluster (RX J0603.3+4214):</p> <blockquote> <p>Simulating the toothbrush: evidence for a triple merger of galaxy clusters</p> <p>Brüggen, M., van Weeren, R. J., Röttgering, H. J. A. In: Monthly Notices of the Royal Astronomical Society: Letters. 425, 1, p. L76--L80</p> </blockquote> <p>In case of questions concerning the measurement set, please contact the original authors for details.</p> <p> </p> <p>We have also included the three catalogs used in the experiments, which are converted from their original FITS table to Numpy arrays:</p> <ul> <li>skycatalog.npz is the catalog of the Boötes field: https://academic.oup.com/mnras/article-lookup/doi/10.1093/mnras/stw1056</li> <li>TGSSADR1_7sigma_catalog.npz is the TGSS ADR1 source catalog: http://tgssadr.strw.leidenuniv.nl/catalogs/TGSSADR1_7sigma_catalog.fits</li> <li>NVSS_CATALOG.npz is the NRAO/VLA Sky Survey: ftp://nvss.cv.nrao.edu/pub/nvss/CATALOG/</li> </ul>
Hong Kong Annotated Airborne LiDAR Point Clouds
<p>The annotated point clouds were generated to train the weakly supervised semantic segmentation algorithm Semantic Query Network (SQN) to classify point clouds <sup>[1]</sup>. The dataset covers 16 tiles of airborne LiDAR data in an area of 7.2 km2 in Shatin, Hong Kong, China. 11 tiles were used for training, while 5 tiles were used for validation. There are multiple types of construction in the dataset including high-rise residential buildings, low-rise village houses, and large public buildings. Green spaces are mainly composed of wood areas in open spaces (e.g., in parks and hills) and planted trees in residential gardens and nearby roads. Point clouds are classified in ground, buildings, and trees.</p> <p>The LiDAR data is owned by the Hong Kong government. Please visit the Spatial Data Portal, Survey Division, CEDD (https://sdportal.cedd.gov.hk/#/en/) for more details.</p> <p> </p>
RGB orthophoto mosaic, DSM, 3d point cloud and LIDAR LAZ of the flash flood damages in Karavelovo and Bogdan vilages, Bulgaria- September 2, 2022
<p>The present dataset contains geospatial resources aimed at investigating and assessing the consequences of a flash flood of debris flow character, relatively significant in extent and magnitude of damage, in the area of two villages in the Municipality of Karlovo, located in central Bulgaria, which happened on September 2, 2022. For this purpose, an integrated approach based on the combination of digital photogrammetry with high spatial resolution and spatial accuracy, based on a fixed wing unmanned aerial system, and laser altimetry (LIDAR), based on a multirotor unmanned platform, was used. The data collection was carried out 2 days after the occurrence of the disaster, resulting in the generation of valuable information resources that allow not only to spatially and quantitatively determine the damage of the disaster, but also to reveal the mechanism of occurrence of the phenomenon: 1) orthophoto mosaic, Digital surface model-DSM and 3D point cloud (from photogrammetry) 2) Classified 3D point cloud- from LIDAR survey.</p>
Harmonized Tree Species Occurrence Points for Europe
<p>This data set is a harmonized collection of existing data from GBIF, the EU-Forest project and the LUCAS survey. It has about 3 million observations and is supplemented by variables (e.g. location accuracy, land cover type, canopy height, etc.) which enable precise filtering for specific user applications.</p> <p>The <em>RDS </em>file is created from an sf-object and suitable for fast reading in the R-programming environment. The <em>CSV.GZ</em> file contains records as a table with Easting and Northing in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035) and can be fed in a GIS after being unzipped.</p> <p><strong>The code producing this data set is <a href="https://gitlab.com/openlandmap/eu-forest-tree-point-data">publicly available on GitLab.</a></strong></p> <p>Data sets were last updated in September 2021.</p> <p>Variables:</p> <ul> <li><strong>id</strong> = unique point identifier</li> <li><strong>easting</strong> = x coordinate</li> <li><strong>northing </strong>= y coordinate</li> <li><strong>country </strong>= ISO country code</li> <li><strong>species </strong>= Latin species name</li> <li><strong>genus </strong>= genus name</li> <li><strong>scientific_name </strong>= long species name</li> <li><strong>gbif_taxon_key </strong>= taxon key from GBIF</li> <li><strong>gbif_genus_key </strong>= genus key from GBIF</li> <li><strong>taxon_rank </strong>= species or genus</li> <li><strong>year </strong>= year of observation</li> <li><strong>accessed_through </strong>= database through which data was accessed (GBIF, LUCAS, EU-Forest)</li> <li><strong>dataset_info </strong>= data set name (individual sub-data-set)</li> <li><strong>citation </strong>= DOI citation of the individual data set</li> <li><strong>license </strong>= distribution license</li> <li><strong>location_accuracy </strong>= spatial accuracy of observation (meters)</li> <li><strong>flag_location_issue </strong>= known location issues present</li> <li><strong>flag_date_issue </strong>= known date issues present</li> <li><strong>eoo </strong>= Extent of occurrence (applying the concept of natural geographical range used for the EU-Forest data set (<a href="https://www.nature.com/articles/sdata2016123">Mauri et al., 2017</a>) to all other data points. 1 = point inside species range; 0 = point outside; NA = EOO polygon not available for this species)</li> <li><strong>dbh </strong>= Diameter Breast Height (only recorded for observations from the EU-Forest data set (<a href="https://www.nature.com/articles/sdata2016123">Mauri et al., 2017</a>))</li> <li><strong>lc1 </strong>= <a href="https://ec.europa.eu/eurostat/web/lucas/data/primary-data/2018">LUCAS</a> land cover type 1 (only recorded for observations from LUCAS data)</li> <li><strong>lc2 </strong>= <a href="https://ec.europa.eu/eurostat/web/lucas/data/primary-data/2018">LUCAS </a>land cover type 2 (only recorded for observations from LUCAS data)</li> <li><strong>landmask_country </strong>= land mask overlay 30 meters (NA = not on land)</li> <li><strong>corine </strong>= <a href="https://land.copernicus.eu/pan-european/corine-land-cover/clc2018">CORINE 2018</a> land cover type (extracted from the 100 meter raster data set)</li> <li><strong>nightlights</strong> = <a href="https://eogdata.mines.edu/download_dnb_composites.html">light pollution</a> observed by VIIRS (proxy for remoteness / distance to human structures)</li> <li><strong>canopy_height </strong>= <a href="https://zenodo.org/record/4057883#.X3sx4-2xW9I">canopy height</a> derived from GEDI waveform LiDAR point data</li> <li><strong>natura_2000 </strong>= Natura 2000 site code (if a point falls inside a protected area (<a href="https://www.eea.europa.eu/data-and-maps/data/natura-11">GIS-layer</a>) this variable contains the site identification code; all sites can be explored on an <a href="https://natura2000.eea.europa.eu/">interactive map</a>)</li> <li><strong>freq_location </strong>= number of points with identical location (in some cases one location has multiple observation, differing in species and/or year. This may lead to difficulties in certain modeling tasks)</li> <li><strong>geometry </strong>= point geometry in ETRS89 / LAEA Europe</li> </ul> <p>See <strong><a href="https://docs.google.com/spreadsheets/d/1WM0BIaVEKxTsCISEaF76RJ8F1iWiZlDfuyQCBiF2Sxw/edit?usp=sharing">this detailed documentation</a></strong> for more insights into each variable and individual GBIF data set citations.</p> <p>If you would like to know more about the creation of this data set, see</p> <ol> <li>the R-Markdown documenting the process (<a href="https://gitlab.com/openlandmap/eu-forest-tree-point-data">GitLab repository</a>)</li> <li>the talk at OpenGeoHub Summer School 2020 (<a href="https://www.youtube.com/watch?v=5HhmLGcqXLs&list=PLXUoTpMa_9s0Ea--KTV1OEvgxg-AMEOGv&index=40">Youtube</a>)</li> </ol> <p>Some advice: This data set is a puzzle with pieces from many different sources. Take some time to explore before including it in your work. Use summary statistics to see which variables have NAs and how many. Choose your filtering criteria wisely. For example, some points with the highest location accuracy have no record for the year of observations. You would exclude these, if "year > 1990" was your criteria.</p> <p> </p> <p>This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (<a href="https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095">https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095</a>).</p>
H2020 Platone German Demonstrator - Baseline Active Power Exchange at Grid Connection Point (Medium Voltage/Low Voltage)
<p>The given data are computed values for the active power exchange at the medium (MV)/low voltage grid connecting feeder (active power). The data are provided as 15-minutes mean values in kilowatt. The computed indicate the power exchange that would have been measured, in case no use case would have been applied in the field (control of batteries).</p> <p><strong>Data Description:</strong></p> <ul> <li>p_tei_c_mean = arithmetic mean of p_tei computed in 1-minute intervals devided by number of samples available for computing within 15 minutes (p_tei_count)</li> <li>p_tei_c_min = the minimum value (1-minute mean) computed within the period of p_tei_mean (15-minutes)</li> <li>p_tei_c_max = the maximum value (1-minute mean) computed within the period of p_tei_mean period (15-minutes)</li> </ul> <p><strong>Field Test Setup</strong></p> <p>The field test setup of the demonstrator consists of a MV/LV substation, 89 households, 450kW of installed PV generation capacity, a large scale battery with 300 kW and 850 kWh capacity. </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 864300</p>
Data for: Attractive solution of binary Bose mixtures: Liquid-vapor coexistence and critical point
<p>Path-integral Monte-Carlo results for a balanced Bose mixture with attractive interspecies interaction. In the files named "press_*" we provide the pressure data, presented in figures 1, 2, and S1, and then used to extract the coexistence regions. In the files named "coexistence_region_*" we provide the data extracted using the Maxwell construction (as well as the condensate fraction for a12=-1.2a) and presented in figures 3, 4, 5 and S2.</p>
Last Interglacial sea-level data points from Northwest Europe
<p>This dataset contains known preserved Last Interglacial (Eemian, Ipswichian, MIS 5e, MIS 5) sea level proxies for NW Europe: from along the shores of the English Channel and North Sea, and from their offshore areas. This V2 version includes 146 sea-level indicator data points from in and around the North Sea (35 entries in Netherlands, 10 Belgium, 23 in Germany, 17 in Denmark, 9 in Britain) and the English Channel (24 entries for the British and 25 for the French side, 3 on the Channel Isles), believed to be a representative and fairly complete inventory and assessment coming from some 80 published sites. The database also includes a modest subselection of data points from older interglacials (six sites), for comparative use. The dataset has been constructed in and has been exported from the World Atlas of Last Interglacial Shorelines (WALIS) database (<a href="https://warmcoasts.eu/world-atlas.html">https://warmcoasts.eu/world-atlas.html</a>).</p> <p>The sea level proxies in majority are obtained from localities with well developed lithostratigraphic (MIS-6 aged ‘Saalian’ glaciogenic landforms and deposits as the substrate), morphostratigraphic (position in terrace flights), Amino-Acid Racemization, and biostratigraphic constraints ('Lusitanian' molluscan, foraminifera, pollen successions, other). The majority of European continental sites have chronostratigraphic age-control (regional encoding scheme included in dataset) notably by correlation to regional Pollen Association Zones with varve-count based durations (reckoned to span at least 11,000 years of temperate interglacial conditions; matching a good part of MIS 5e), and/or Foraminiferal Association Zones (echoing marine environmental and paleogeographic changes), and/or from AAR zones (majority of British sites, selected sites of mainland NW Europe; albeit for many sites older, lesser quality applications of the technique). In all regions, some of the marine deposits and bracketing terrestrial ones have also been independently dated using luminescence (IRSL, OSL, TL), U-series and ESR techniques.</p>
Optimized structures of the stationary points on the potential energy surface of the OH(2Π) + C2H4 reaction
<p>This Zip file contains the cartesian coordinates of optimized stationary points of the OH(<sup>2</sup>Π) + C<sub>2</sub>H<sub>4</sub> potential energy surface published in our article “OH(<sup>2</sup>Π) + C<sub>2</sub>H<sub>4</sub> Reaction: A Combined Crossed Molecular Beam and Theoretical Study” (P<em>hys. Chem. A</em> 2023, 127, 21, 4609–4623), that can be found in <a href="https://doi.org/10.1021/acs.jpca.2c08662">https://doi.org/10.1021/acs.jpca.2c08662</a>.</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.</p>
Optimized structures of the stationary points on the potential energy surface of the O(3P, 1D) + HCCCN(X1Σ+) reaction
<p>This Zip file contains the cartesian coordinates of optimized stationary points of the O(<sup>3</sup>P, <sup>1</sup>D) + HCCCN(X<sup>1</sup>Σ<sup>+</sup>) potential energy surface published in our article “Reactions O(<sup>3</sup>P, <sup>1</sup>D) + HCCCN(X<sup>1</sup>Σ<sup>+</sup>) (Cyanoacetylene): Crossed-Beam and Theoretical Studies and Implications for the Chemistry of Extraterrestrial Environments” (<em>J. Phys. Chem. A</em> 2023, 127, 3, 685–703), that can be found in <a href="https://doi.org/10.1021/acs.jpca.2c07708">https://doi.org/10.1021/acs.jpca.2c07708</a>.</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.</p>
Optimized structures of the stationary points on the potential energy surface of the dissociation of the CH3OH˙+ cation
<p>This Zip file contains the optimized stationary points structures of the potential energy surface (PES) for the dissociation of the CH3OH˙+ cation.</p> <p>The PES has been published in our paper “Fragmentation of interstellar methanol by collisions with He˙<sup>+</sup>: an experimental and computational study” (<em><strong>Phys. Chem. Chem. Phys.</strong></em>, 2022, <strong>24</strong>, 22437-22452), that can be found in https://doi.org/10.1039/D2CP02458F .</p> <p>All calculations have been performed with Gaussian 09, Revision D.01 and the structures were optimized at ωB97X-D/aug-cc-pVTZ level of theory.</p>
CoCO2 global emission point source database
<p>This dataset contains a global emission catalogue of CO2 and co-emitted species (NOx, SO2, CO, CH4) from thermal power plants for the year 2018. The dataset contains annual emission information for individual thermal power plants at their exact geographical location. Each facility is linked to a specific temporal (i.e., monthly, day-of-the-week and hourly) and vertical distribution profile to derive spatial- and temporal-resolved emissions for modelling efforts. The dataset was produced as part of the CoCO2 project, which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 958927.</p>
Structural Diversity from the NEON Discrete-Return LiDAR Point Cloud in 2013-2022
Structural diversity, characterizing the volumetric capacity and physical arrangement of biotic components in an ecosystem, controls critical ecosystem functions like light interception, hydrology, and microclimate. This product generates structural diversity metrics for the NEON sites, sourced from the Discrete-Return LiDAR Point Cloud from the NEON Aerial Observation Platform (DP1.30003.001; collected in March 2023). Using R programming, we computed the metrics detailing height, heterogeneity, and density at 30 m, aligned to the Landsat grids, for 243 site years in 57 NEON sites from 2013 to 2022.
CVPIA Predation Contact Point Study - 2020: Impacts of Artificial Light At Night in the Upper Sacramento River
The Central Valley Project Improvement Act (CVPIA) has led to the implementation of a Decision Support Model (DSM) to assist in the prioritization of CVPIA restoration actions. The fall-run Chinook salmon DSM depends on a coarse-resolution salmon life-cycle model to predict the population benefits of different restoration actions and scenarios. One critical element of the life-cycle model is how to incorporate predation mortality during the juvenile rearing and outmigration portion of the salmon life-cycle in the Sacramento-San Joaquin Delta. Of particular importance to potential restoration activities, is the predation mortality that occurs in proximity to, and as a result of contact points between predator and prey fishes. Sacramento River winter-run Chinook salmon (Oncorhynchus tshawytscha) are a genetically distinct Evolutionary Significant Unit (ESU) with a unique life history and are listed as endangered at both state and federal levels. Predation of juvenile winter-run by piscivorous fishes is considered to be an important stressor that may reduce the population size of this ESU. Notably, the presence of artificial illumination at night (ALAN) has been shown to aggregate and slow out-migrating salmonids and increase predation by piscivores, and may be an important contact point affecting winter-run Chinook salmon, especially given the vast majority of winter-run Chinook salmon are born and rear within the city limits of Redding, CA. Perhaps the most significant source of ALAN in this region of the Sacramento River is the iconic Sundial Bridge. This bridge is illuminated at night and regional biologists have long been concerned on its potential impacts on winter-run Chinook salmon, as mediated through predation by Rainbow Trout. We therefore performed a field-based experiment to better inform this management concern. To assess the impacts of Sundial Bridge ALAN on fishes, our first objective was to determine whether variable ALAN intensities altered the relati
Understory percent cover, plant traits, canopy LAI, PAR, temperature, and soil moisture data at multiple time points for sites in the burn chronosequence and Indian Point forest at the University of Michigan Biological Station, Pellston, MI (2022-2023)
Community ecology has sought to understand the mechanisms by which plant communities are assembled through time and space. One prominent way to address how communities are assembled is by quantifying functional traits. While there is a tremendous body of literature on functional traits, debate persists about how to account for variation in measured traits. For example, intraspecific trait variation (ITV) can be equal to or greater than interspecific trait variation and ITV has also been found to vary greatly across years. Therefore, there is a need to account for variability in functional trait measures among and within species and through time to improve our understanding of community assembly. Chronosequences are a powerful tool to address temporal changes in community dynamics, however, the inclusion of understory plants in forest chronosequence studies is still relatively uncommon. Previous chronosequence studies have been primarily performed in grasslands or in a limited subset of forest types, so further work is needed in understory plant traits across other ecosystems and climates to improve trait-based understanding of understory plant communities through time. Additionally, because plant traits change as ecosystems age, community interactions are likely to change with ecosystem age. Interactions of particular interest are herbivory, arthropod predation, and the influence of plant traits on arthropod diversity.
SGS-LTER Ecosystem Stress Area - long-term point-frame (percent basal cover) dataset following nutrient enrichment stress on the Central Plains Experimental Range in Nunn, Colorado, USA 1982-2011, ARS Study Number 3 (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-sgs/521/7. The abstract below was extracted from the Level 0 data package and is included for context: 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. Water, nitrogen, and water-plus-nitrogen at levels beyond the range normally experience by shortgrass steppe communities were applied from 1971 through 1975, plant densities were sampled through 1977, and then sampling resumed in 1982, with sampling frequencies changing from annually to every other year. The initial sampling from 1970 to 1974 showed that the water and water plus nitrogen treatments had the strongest effect on plant community structure, both treatments increased biomass, and exotic weed species were noted on the water plus nitrogen treatment. Later sampling from 1982 to 1991 showed a ten-fold increase in exotic weed species on the water plus nitrogen plots as compared to the controls (Milchunas and Lauenroth 1995), a community change that has persisted on this site due to a chronic elevation of soil nitrogen caused by a plant tissue/soil organic matter feedback mec
PIE LTER surveys of salt marsh breeding birds using point counts in Rowley and Newbury, Massachusetts (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/336/2, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-pie/175/8. The abstract below was extracted from the Level 0 data package and is included for context: This data set contains the results of point count surveys of breeding birds in the salt marshes of the Plum Island Estuary. Surveys have been carried out each year in June beginning in 2004 and are ongoing. Currently six circles of 100 m radius are being surveyed. Three of the circles are in plots that had been regularly hayed and three in unhayed plots. Currently only one of the plots is regularly hayed. 85 species recorded as of 2020. Most frequently encountered are Agelaius phoeniceus, Ammospiza caudacuta, Tringa semipalmata. The Plum Island Ecosystems (PIE) LTER is developing a predictive understanding of the response of a linked watershed-marsh-estuarine system in northeastern Massachusetts to rapid environmental change. Over the last 30 years, surface sea water temperatures in the adjacent Gulf of Maine have risen at 3 times the global average, rates of sea-level rise have accelerated, and precipitation has increased. Coupled with these changes in climate and sea level are substantial changes within the rapidly urbanizing watersheds that influence water, sediment, and nutrient delivery to the marsh and estuary. In PIE IV our focus is on: Dynamics of coastal ecosystems in a region of rapid climate change, sea-level rise, and human impacts. NSF OCE LTER-Plum Island Ecosystems: Dynamics of coastal ecosystems in a region of rapid climate change, sea-level rise, and human impacts.
May to July 2018 ground control points GPS coordinates of tidal marsh and tidal forest plant species to be used as ground control points in habitat mapping.
We collected field data from sites distributed in habitats along the salinity axis of the Altamaha River estuary and the Duplin River to be used as ground control points (GCP) and ground reference data for habitat mapping. GCPs for tidal marsh (salt, brackish, tidal fresh) and tidal fresh forest vegetation species were acquired. A real time kinematic (RTK) GPS survey of GPS coordinates and ground elevations for tidal marsh vegetation was carried out in May of June of 2018. A handheld GPS was used to collect GPS coordinates for tidal forest plant species in July of 2018. A total of 101 GCPs were collected in tidal habitats, with 26 in salt, 28 in brackish and 29 in tidal fresh marsh, and another 18 in tidal fresh forest. These observations will be used to create habitat maps from aerial photographs of the Altamaha River estuary, GA taken following Hurricane Irma to better understand how the storm surge affected tidal vegetation and to examine any shifts in vegetation type.
Sparse point clouds derived from UAV overflights of the fifteen NPP study sites at Jornada Basin LTER in 2019
This data package contains sparse point clouds for each of the 15 NPP study sites at the Jornada Basin LTER in southern New Mexico, USA. The point clouds were derived from raw images collected during uncrewed aerial vehicle (UAV) overflight missions conducted in late summer and early autumn of 2019 (1 overflight day per site). For each site one or two missions were flown during an afternoon using a DJI Phantom 4 UAV, and between 450 and 1300 12.4 megapixel RGB images were captured. A subset of images captured at each site were loaded into Agisoft Metashape software to derive points using a structure from motion method. The raw images are not provided but can be made available via project PIs. This data package includes point clouds for all 15 sites as geotiff raster files. Other derived products from these UAV missions, including orthomosaic photos, digital elevation models, and digital terrain models, are available in other EDI data packages (knb-lter-jrn.210543001, knb-lter-jrn.210543002, and knb-lter-jrn.210543003, respectively). This study is complete.
Elevation at grid points on the Luquillo Forest Dynamics Plot (LFDP)
This file contains data that describe the physical and environmental attributes of the LFDP. The attributes include elevation, topography type, and percentage slope. All data are given for the 20 m by 20 m quadrat scale. Information on soils are taken form an interpolation of the soil map produced by the Natural Resources Conservation Service, US Department of Agriculture (Soil Survey 1995). Other information from the elevation of each of the corner posts defining the quadrats. The National Science Foundation requires that data from projects it funds are posted on the web two years after any data set has been organized and "cleaned". The data from each census of the LFDP will be updated at intervals as each survey of the LFDP shows errors in the previous data collection. After posting on the web, researchers who are not part of the project are then welcome to use the data. Given the enormous amount of time, effort and resources required to manage the LFDP, obtain these data, and ensure data accuracy, LFDP Principal Investigators request that researchers intending to use this data comply with the requests below. Through complying with these requests we can ensure that the data are interpreted correctly, analyses are not repeated unnecessarily, beneficial collaboration between users is promoted and the Principle Investigators investment in this project is protected. Submit to the LFDP PIs a short (1 page) description of how you intend to use the data; · Invite LFDP PIs to be co-authors on any publication that uses the data in a substantial way (some PIs may decline and other LFDP scientists may need to be included); If the LFDP PIs are not co-authors, send the PIs a draft of any paper using LFDP data, so that the PIs may comment upon it; In the methods section of any publication using LFDP data, describe that data as coming from the "Luquillo Forest Dynamics Plot, part of the Luquillo Experimental Forest Long-Term Ecological Research Program"; Acknowledge in any pu
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Allen Brain Atlas
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OpenNeuro
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