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opencc-by-4.0Aug 2024View details →

US Emission Facilities Land Cover Area Derived At Parcel Scale

<p>This dataset includes US industrial facilities from the Environmental Protection Agency's (EPA) 2017 National Emissions Inventory (NEI), combined with location data from the EPA's Facility Registry Service and land cover classes from the United States Geological Survey's (USGS) National Land Cover Data (NLCD). These land cover classes are measured in square meters at the parcel scale. The matching of facility parcels was done using a tiered approach to enhance spatial accuracy. The parcel data was provided by Homeland Infrastructure Foundation-Level Data (HIFLD) US Parcel Data. Because this parcel data is proprietary, the parcel geometries and fields were removed from the final dataset. However, centroid latitude and longitude coordinates were derived to allow spatial joins with publicly available parcel data.</p> <p>This dataset is organized by unique EPA NEI facilities data fields. Unique facility observations are identified by the field <code>cleaned_name</code> which represents the concatenated address and/or place name for the facility (dependent on data availability). Each row represents a facility matched to parcel scale land cover information and are associated with the unique identifier <code>MatchID</code>. NLCD land cover fields are described as the total area in meters squared of each facility parcel. Please see data README file for more information on individual data fields. </p> <h3>Known Limitations</h3> <ul> <li>Parcels are matched to the facility and in some cases multiple facilities are matched to the same parcel. Data users may want to omit these multiple match parcels and there is a data flag called <code>multi_match</code> that enables this.</li> <li>Approximately 15% of the dataset includes facilities where the latitude/longitude coordinates are over 200 meters from the matched parcel. Spot checking these instances revealed that, in many cases, the facility latitude and longitude locations did not accurately match the street address, city, or postal zip code associated with the facility. These instances are flagged as a potential source of inaccuracy and can be removed at the users discretion.</li> </ul> <p> </p>

opencc-by-4.0Sep 2024View details →

Global FLEXPART-ERA5 simulations using 30 million atmospheric parcels since 1980

<h2><strong>Abstract</strong></h2> <p>This database compiles the outputs of the global experiment performed with the Lagrangian particle dispersion model FLEXPART since 1980. The experiment was conducted using the ERA5 reanalysis data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) and homogeneously dividing the atmosphere into 30 million particles. The database can be used to investigate global moisture and heat transport and to establish sink-source relationships.</p> <h2><strong>Input data</strong></h2> <p>The data employed for FLEXPART running was the ERA5 reanalysis dataset from the ECMWF (Hersbach et al., 2020). To feed the model, the input data was downloaded and pre-processed by using the software Flex_extract v7.1 (Tipka et al., 2020).</p> <p>The original available ERA5 resolution is 0.1-degree and 1-hour. For this experiment, ERA5 input data was retrieved for the global area (90ᵒS to 90ᵒN and 180ᵒW to 180ᵒE) at a 0.5-degree horizontal resolution for 137 level from the surface to 1 hPa and a 3-hour temporal resolution (00, 03, 06, 09, 12, 15,18 and 21 UTC).</p> <p>The data is stored in individual GRIB files for each time step, following the name criteria "EAYYMMDDHH". The size of each file is approximately 530 MB. The variables included in each file are: temperature, specific humidity, u- and v-wind components, Eta-coordinate vertical velocity, divergence, specific cloud liquid water content, specific cloud ice water content, and the logarithm of surface pressure on model levels; and 2m temperature an dew-point temperature, 10m u and v wind component, geopotential, land-sea mask, mean sea level pressure, snow depth, the standard deviation of orography, surface pressure, total cloud cover, convective precipitation, large-scale precipitation, surface sensitive heat flux, eastward and northward turbulent surface stress and surface net solar radiation at the surface level.</p> <h2><strong>Software and running</strong></h2> <p>The software used for the simulations is the Lagrangrian particle dispersion model FLEXPART on version 10.4 (Pisso et al., 2019). The software is configured for a global experiment, and the simulations were obtained from 1980 to the present with a temporal resolution of 3-h. For the experiment, 30 million particles were homogeneously distributed on the global area, and their trajectories were followed according to the model configuration specified in the COMMAND and RELEASES files. The complete period is distributed in individual annual experiments, with each annual experiment obtained continuously running the model from October of the previous year to December of that year.</p> <h2><strong>Outputs characteristics</strong></h2> <p>The outputs were stored in individual GRIB files for each time step, with the file name following the naming convention "partposit_YYYYMMDDHH". Each file has a size of 1,76 GB, and the total size of the annual experiment is 6 TB. Each file contains information about each particle of the experiment: the particle identification number (particle ID), the particle's position (latitude, longitude, and altitude), topographic height, potential vorticity, specific humidity, air density, atmospheric boundary layer height, and temperature. The file corresponding to the 1st January 2023 at 00UTC is provided in this repository as an example. Due to the size of each file, the complete dataset is accessible by personal contact (see <em>Data Access</em> section). </p> <h2><strong>Post-process and applications</strong></h2> <p>The dataset presented here allows for the analysis of moisture and heat transport in the atmosphere for any region of the world up to 3-h temporal resolution and different horizontal resolutions. The transport may be established between sources and sinks, both in a forward or backward tracking in time. Currently, two open-source post-processing options developed within the EPhyslab-UVigo group are available for the analysis of these data: TROVA (Fernadez-Alvarez et al., 2022) and LATTIN (Perez-Alarcón et al., 2024) with different moisture tracking calculation options, and the latter including tools for heat transport analysis. Both options allow different methodologies (those most widely used) for the moisture transport analysis. The studies can be configured for any region of the planet, specifying it by a NetCDF 2-D mask, and the moisture transport can be set for different time periods (from 1 to 15 days, being from 8 to 10 days the periods most commonly applied according to the mean residence time of water vapor in the atmosphere). For further discussion on the residence time of water vapor in the atmosphere and its application for Lagrangian studies see Gimeno et al. (2021) and Nieto and Gimeno (2019).</p> <h2><strong>Example of application</strong></h2> <p>J. C. Fernández-Álvarez,  M. Vázquez, A. Pérez-Alarcón, R. Nieto, L. Gimeno (2023) Comparison of moisture sources and sinks estimated with different versions of FLEXPART and FLEXPART-WRF models forced with ECMWF reanalysis data, Journal of Hydrometeorology, doi: 10.1175/JHM-D-22-0018.1.</p> <p>A. Pérez-Alarcón, R. Sorí, M. Stojanovic, M. Vázquez, R.M. Trigo, R. Nieto, L. Gimeno (2024) Assessing the Increasing Frequency of Heat Waves in Cuba and Contributing Mechanisms, Earth Systems and Environment, DOI: 10.1007/s41748-024-00443-8</p> <h2><strong>Validation</strong></h2> <p>The moisture transport analysis provided by this dataset was validated by Fernández-Alvarez et al. (2023) through an in-depth comparison with different versions of the model, horizontal resolutions and input data, including the ERA-Interim reanalysis from the ECMWF, which has been widely used for this purpose over the past decades.</p> <h2><strong>Data Access</strong></h2> <p>Data access is available by contacting the EPhysLab group via:  rnieto[at]uvigo.gal  or  l.gimeno[at]uvigo.gal</p>

opencc-by-4.0Dec 2023View details →

Agroforestry parcel with broadleaved trees in Domaine de Restinclères - France

Open the record for dataset details and reuse information.

opencc-by-4.0Oct 2021View details →

Dataset with Agricultural Parcels Markup on Satellite Images

<p>The dataset was created for the development and testing of the algorithm proposed in the paper "Segmentation of agricultural parcel in satellite images based on historical vegetation index data". However, this markup can be used to test other algorithms and compare their quality.</p> <p>This work employs data from the remote sensing programs Sentinel-2A, Sentinel-2B.</p> <p>The dataset contains the agricultural parcel markup for four regions within Russia and Ukraine, where agriculture is well developed. The areas were chosen so that each of them had other types of terrain in addition to fields: urban area, water surface, swamps, and forests.</p> <p> </p>

opencc-by-4.0Dec 2022View details →

Backtracked trajectories of passive particles in the Virgin Islands Basin (2007-2017) using Ocean Parcels

<p>Backtracked trajectories of 100 passive particles released in the Virign Islands Basin daily from 2007-2017. Backtracking is done for 100 days. Surface velocity fields are from GLORYS reanalysis. <br> <br> Corresponding author: Giovanni Seijo-Ellis (giovanni.seijo@colorado.edu)</p>

openbsd-3-clauseFeb 2023View details →

Myeloarchitectonic cortical parcellation data for contemporary neuroimaging – The Vogt-Vogt legacy in the 21st century

<p><strong>Full Changelog</strong>: <a href="https://github.com/NOEL-MNI/noelMyelin/compare/v1.0...v2.0">https://github.com/NOEL-MNI/noelMyelin/compare/v1.0...v2.0</a></p>

dryad36/100

Parcel level temporal variance of remotely sensed spectral reflectance predicts plant diversity

<p>Over the last two decades, considerable research has built on remote sensing of spectral diversity to assess plant diversity. The spectral variation hypothesis (SVH) proposes that spatial variation in reflectance data of an area is positively associated with plant diversity. While the SVH has exhibited validity in dense forests, it performs poorly in highly fragmented and temporally dynamic agricultural landscapes covered mainly by grasslands. Such underperformance can be attributed to the mosaic-like spatial structure of human-dominated landscapes with fields in varying phenological and management stages. Therefore, we argued for re-evaluating SVH's flawed window-based spatial analysis and underutilized temporal component. In particular, In particular, we captured the spatial and temporal variation in reflectance and assessed the relationships between spatial and temporal components of spectral diversity and plant diversity at the parcel level as a unit that relates to management patterns. Our investigation spanned three grasslands on two continents covering a wide spectrum of agricultural usage intensities. To calculate different components of spectral diversity, we used multi-temporal spaceborne Sentinel-2 data. We showed that plant diversity was negatively associated with the temporal component of spectral diversity across all sites. In contrast, the spatial component of spectral diversity was related to plant diversity in sites with larger parcels. Our findings highlighted that in agricultural landscapes, the temporal component of spectral diversity drives the spectral diversityplant diversity associations. Consequently, our results offer a novel perspective for remote sensing of plant diversity globally.</p>

opencc-zeroJun 2024View details →

Soil scans for parcels in Netherlands (20180329-101704)

<p>Parcel soil scan with electric conductivity measurement from Netherlands</p>

Soil scans for parcels in Netherlands (20180312-122558)

<p>Parcel soil scan with electric conductivity measurement from Netherlands</p>

Soil scans for parcels in Netherlands (20180418-175137)

<p>Parcel soil scan with electric conductivity measurement from Netherlands</p>

Soil scans for parcels in Netherlands (20180328-151818)

<p>Parcel soil scan with electric conductivity measurement from Netherlands</p>