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Figure 4 in Climate Changes of the Temperature of the Surface and Level of the Black Sea by the Data of Remote Sensing at the Coast of the Krasnodar Krai and the Republic of Abkhazia
Figure 4. Spatial variability of the climatic rate of the Black Sea level change (cm/yr) for period from 1993 to 2015.
Green Roofs Footprints for New York City, Assembled from Available Data and Remote Sensing
<p><strong><em>Summary:</em></strong></p> <p>The files contained herein represent green roof footprints in NYC visible in 2016 high-resolution orthoimagery of NYC (described at <a href="https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_AerialImagery.md">https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_AerialImagery.md</a>). Previously documented green roofs were aggregated in 2016 from multiple data sources including from NYC Department of Parks and Recreation and the NYC Department of Environmental Protection, greenroofs.com, and greenhomenyc.org. Footprints of the green roof surfaces were manually digitized based on the 2016 imagery, and a sample of other roof types were digitized to create a set of training data for classification of the imagery. A Mahalanobis distance classifier was employed in Google Earth Engine, and results were manually corrected, removing non-green roofs that were classified and adjusting shape/outlines of the classified green roofs to remove significant errors based on visual inspection with imagery across multiple time points. Ultimately, these initial data represent an estimate of where green roofs existed as of the imagery used, in 2016.</p> <p>These data are associated with an existing GitHub Repository, <a href="https://github.com/tnc-ny-science/NYC_GreenRoofMapping">https://github.com/tnc-ny-science/NYC_GreenRoofMapping</a>, and as needed and appropriate pending future work, versioned updates will be released here.</p> <p><strong><em>Terms of Use:</em></strong></p> <p>The Nature Conservancy and co-authors of this work shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any sale, distribution, loan, or offering for use of these digital data, in whole or in part, is prohibited without the approval of The Nature Conservancy and co-authors. The use of these data to produce other GIS products and services with the intent to sell for a profit is prohibited without the written consent of The Nature Conservancy and co-authors. All parties receiving these data must be informed of these restrictions. Authors of this work shall be acknowledged as data contributors to any reports or other products derived from these data.</p> <p><strong><em>Associated Files:</em></strong></p> <p>As of this release, the specific files included here are:</p> <ul> <li><em>GreenRoofData2016_20180917.geojson</em> is in the human-readable, GeoJSON format, in geographic coordinates (Lat/Long, WGS84; EPSG 4263).</li> <li><em>GreenRoofData2016_20180917.gpkg</em> is in the GeoPackage format, which is an Open Standard readable by most GIS software including Esri products (tested on ArcMap 10.3.1 and multiple versions of QGIS). This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.</li> <li><em>GreenRoofData2016_20180917_Shapefile.zip</em> is a zipped folder containing a Shapefile and associated files. Please note that some field names were truncated due to limitations of Shapefiles, but columns are in the same order as for other files and in the same order as listed below. This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.</li> <li><em>GreenRoofData2016_20180917.csv</em> is a comma-separated values file (CSV) with coordinates for centroids for the green roofs stored in the table itself. This allows for easily opening the data in a tool like spreadsheet software (e.g., Microsoft Excel) or a text editor.</li> </ul> <p><strong><em>Column Information for the datasets:</em></strong></p> <p>Some, but not all fields were joined to the green roof footprint data based on building footprint and tax lot data; those datasets are embedded as hyperlinks below.</p> <ul> <li><em>fid</em> - Unique identifier</li> <li><em>bin</em> - NYC Building ID Number based on overlap between green roof areas and a building footprint dataset for NYC from August, 2017. (Newer building footprint datasets do not have linkages to the tax lot identifier (bbl), thus this older dataset was used). The most current building footprint dataset should be available at: <a href="https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh">https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh</a>. Associated metadata for fields from that dataset are available at <a href="https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md">https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md</a>.</li> <li><em>bbl</em> - Boro Block and Lot number as a single string. This field is a tax lot identifier for NYC, which can be tied to the Digital Tax Map (<a href="http://gis.nyc.gov/taxmap/map.htm">http://gis.nyc.gov/taxmap/map.htm</a>) and PLUTO/MapPLUTO (<a href="https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page">https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page</a>). Metadata for fields pulled from PLUTO/MapPLUTO can be found in the PLUTO Data Dictionary found on the aforementioned page. All joins to this bbl were based on MapPLUTO version 18v1.</li> <li><em>gr_area</em> - Total area of the footprint of the green roof as per this data layer, in square feet, calculated using the projected coordinate system (EPSG 2263).</li> <li><em>bldg_area</em> - Total area of the footprint of the associated building, in square feet, calculated using the projected coordinate system (EPSG 2263).</li> <li><em>prop_gr</em> - Proportion of the building covered by green roof according to this layer (<em>gr_area</em>/<em>bldg_area</em>).</li> <li><em>cnstrct_yr</em> - Year the building was constructed, pulled from the Building Footprint data.</li> <li><em>doitt_id</em> - An identifier for the building assigned by the NYC Dept. of Information Technology and Telecommunications, pulled from the Building Footprint Data.</li> <li><em>heightroof</em> - Height of the roof of the associated building, pulled from the Building Footprint Data.</li> <li><em>feat_code</em> - Code describing the type of building, pulled from the Building Footprint Data.</li> <li><em>groundelev</em> - Lowest elevation at the building level, pulled from the Building Footprint Data.</li> <li><em>qa</em> - Flag indicating a positive QA/QC check (using multiple types of imagery); all data in this dataset should have 'Good'</li> <li><em>notes</em> - Any notes about the green roof taken during visual inspection of imagery; for example, it was noted if the green roof appeared to be missing in newer imagery, or if there were parts of the roof for which it was unclear whether there was green roof area or potted plants.</li> <li><em>classified</em> - Flag indicating whether the green roof was detected image classification. (1 for yes, 0 for no)</li> <li><em>digitized</em> - Flag indicating whether the green roof was digitized prior to image classification and used as training data. (1 for yes, 0 for no)</li> <li><em>newlyadded</em> - Flag indicating whether the green roof was detected solely by visual inspection after the image classification and added. (1 for yes, 0 for no)</li> <li><em>original_source</em> - Indication of what the original data source was, whether a specific website, agency such as NYC Dept. of Parks and Recreation (DPR), or NYC Dept. of Environmental Protection (DEP). Multiple sources are separated by a slash.</li> <li><em>address</em> - Address based on MapPLUTO, joined to the dataset based on <em>bbl</em>.</li> <li><em>borough</em> - Borough abbreviation pulled from MapPLUTO.</li> <li><em>ownertype</em> - Owner type field pulled from MapPLUTO.</li> <li><em>zonedist1</em> - Zoning District 1 type pulled from MapPLUTO.</li> <li><em>spdist1</em> - Special District 1 pulled from MapPLUTO.</li> <li><em>bbl_fixed</em> - Flag to indicate whether <em>bbl</em> was manually fixed. Since tax lot data may have changed slightly since the release of the building footprint data used in this work, a small percentage of bbl codes had to be manually updated based on overlay between the green roof footprint and the MapPLUTO data, when no join was feasible based on the bbl code from the building footprint data. (1 for yes, 0 for no)</li> </ul> <p>For <em>GreenRoofData2016_20180917.csv</em> there are two additional columns, representing the coordinates of centroids in geographic coordinates (Lat/Long, WGS84; EPSG 4263):</p> <ul> <li><em>xcoord</em> - Longitude in decimal degrees.</li> <li><em>ycoord</em> - Latitude in decimal degrees.</li> </ul> <p><strong><em>Acknowledgements: </em></strong></p> <p>This work was primarily supported through funding from the J.M. Kaplan Fund, awarded to the New York City Program of The Nature Conservancy, with additional support from the New York Community Trust, through New York City Audubon and the Green Roof Researchers Alliance.</p>
Snow accumulation patterns in a high mountain Andean catchment from optical tri-stereoscopic remote sensing
<p><strong>1) DBSM_Data_RioYeso'</strong> = Automatic weather station (AWS) data from Yeso Embalse and Termas del Plomo meteorological stations (available from Chilean Water Directorate, 'Dirección General de Aguas' or 'DGA' http://www.arcgis.com/apps/OnePane/basicviewer/index.html?appid=d508beb3a88f43d28c17a8ec9fac5ef0), used to force a distributed blowing snow model of Essery et al. (1999) to derive spatial snow depth of the Rio del Yeso catchment, Chile. The format is as follows:</p> <p><em>{'Year','Month','Day','Hour','Incoming shortwave radiation (Wm2)','Incoming longwave radiation (Wm2)','SnowfallRate(mm/hr)','RainfallRate(mm/hr)','Air temperature (celsius)','Relative humidity (%)','Wind speed (m s-1)','Compass wind direction','Air pressure (hPa)'};</em></p> <p><strong>2) 'snowHeightPleiadesREG' </strong>= A snow depth map (horizontal resolution 4m) derived from triplets of high resoution stereo optical satellite images (Pléiades) following the methodology of Marti et al. (2016). The snow depth map is derived for a high mountain catchment (Rio del Yeso) of the central Chilean Andes (see Burger et al., 2018).</p> <p><strong>3) 'L2_LiDAR_4m'</strong> = A LiDAR (Light detection and Ranging) spatial snow depth map at a horizontal resolution of 4 m. The data were captured by a Reigl VZ-6000 LiDAR scanner and generated from the difference of two constructed digital elevation models (DEMs) between the dates 13th September, 2017 (with snow) and 12th December, 2017 (without snow). </p> <p><strong>4) 'L2_Pleiades_SDLidar_NEW' </strong>= The Pléiades snow depth map as described in <strong>2)</strong>, extracted by the areas of LiDAR scan described in <strong>3)</strong>. </p> <p><strong>5) 'SnowDepthResults'</strong> = A folder containing a corrected and gap-filled Pléiades snow depth map (<strong>'SD_PleiadesCORR'</strong>) and for comparison: <strong>'SD_TOPO'</strong>, a statistical estimation of snow depth using topographic parameters and the regression equation of Grünewald et al. (2013) and; The physically based estimates of snow depth using the DBSM model as in <strong>1)</strong> without snow transport for the 4th September, 2017 (<strong>'SD_EXTP_Sep04'</strong>) and 13th September, 2017 ('<strong>SD_EXTP_Sep13'</strong>) and with snow transport for those dates (<strong>'SD_Wind_Sep04','SD_Wind_Sep13'</strong>).</p> <p><strong>6) 'rdyDEM'</strong> = An independent ASTER GDEM (https://asterweb.jpl.nasa.gov/gdem.asp) cut to the area of the study catchment (horizontal resolution = 30 m). </p> <p><strong>7) '</strong><strong>PlanetScope_20170907_TPK' </strong>= An stitched optical PlanetScope image of the catchment (horizontal resolution of 3.25 m) derived from access under the research and teaching iniative (planet.com). </p> <p><strong>Cited work:</strong></p> <p><strong>Burger, F. et al.</strong> (2018) ‘Interannual variability in glacier contribution to runoff from a high ‐ elevation Andean catchment : understanding the role of debris cover in glacier hydrology’, Hydrological Processes, pp. 1–16. doi: 10.1002/hyp.13354.</p> <p><strong>Essery, R</strong>., Li, L. and Pomeroy, J. (1999) ‘A distributed model of blowing snow over complex terrain’, Hydrological Processes, 13(14–15), pp. 2423–2438. doi: 10.1002/(SICI)1099-1085(199910)13:14/15<2423::AID-HYP853>3.0.CO;2-U.</p> <p><strong>Grünewald, T. et al.</strong> (2013) ‘Statistical modelling of the snow depth distribution in open alpine terrain’, Hydrology and Earth System Sciences, 17(8), pp. 3005–3021. doi: 10.5194/hess-17-3005-2013.</p> <p><strong>Marti, R. et al</strong>. (2016) ‘Mapping snow depth in open alpine terrain from stereo satellite imagery’, The Cryosphere, pp. 1361–1380. doi: 10.5194/tc-10-1361-2016.</p>
Dataset for "A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets"
<p>This dataset is associated with a research article entitled "A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets".</p>
Dataset - Impact of 3D radiative transfer on airborne NO2 imaging remote sensing over cities with buildings
<p>This dataset was created by Marc Schwaerzel (marc.schwaerzel@empa.ch) and is intended to get along with the Schwaerzel et al. (2021) AMT publication (amt-2020-146) . The data and the data structure is described in the<em> <strong>readme.md </strong></em>text file.</p> <p>The dataset contains:</p> <p>- libRadtran output (radiances and AMFs)</p> <p>- Synthetic SCDs</p>
Ground-Based Remote Sensing Observations at Marquette, Michigan
<p>This dataset contains observations used in "Multi-year analysis of rain-snow levels at Marquette, Michigan," Shates, Pettersen, L'Ecuyer, and Kulie, submitted to Journal of Geophysical Research - Atmospheres, in revision.</p> <p> </p> <p>The dataset includes daily files of Micro Rain Radar2 (MRR) and Precipitation Imaging Package (PIP) observations. The MRR and PIP are both hosted at the National Weather Service office in Marquette, MI (Pettersen, Kulie, et al., 2020; Kulie et al., 2021). The MRR is a 24 GHz vertically profiling radar and observations have been post-processed using Maahn and Kollias (2012). Key variables include radar reflectivity, Doppler velocity and spectral width. The PIP is a custom video disdrometer that records shadows of hydrometeors to obtain key microphysical variables that include particle size distributions and vertical velocity distributions (Pettersen, Bliven, et al., 2020). Additional processing provides precipitation rates in liquid water equivalent and the effective density (Pettersen et al., 2021). </p> <p>The files are separated into daily MRR files, daily PIP Particle Size Distribution (PSD) files, daily PIP Vertical Velocity Distribution (VVD) files, and daily PIP precipitation rate (rain and non-rain) and effective density (edensity) files. The PSD and VVD files contain one-minute resolution particle counts and fall speeds, respectively, for particle diameter bins.</p>
SNAPPING PSI surface motion measurements over selected sites presented in MDPI Remote Sensing paper "SNAPPING Services on the Geohazards Exploitation Platform for Copernicus Sentinel-1 Surface Motion Mapping"
<p>SNAPPING PSI surface motion measurements over selected sites as presented in the paper with the title "SNAPPING Services on the Geohazards Exploitation Platform for Copernicus Sentinel-1 Surface Motion Mapping" by Michael Foumelis, Jose Manuel Delgado Blasco, Fabrice Brito, Fabrizio Pacini, Elena Papageorgiou, Panteha Pishehvar and Philippe Bally on Remote Sensing Open Access Journal.</p> <p>Whenever using this dataset, please cite its original paper (<a href="https://doi.org/10.3390/rs14236075">https://doi.org/10.3390/rs14236075</a>) and include the reference to this dataset (<a href="https://doi.org/10.5281/zenodo.7369653">https://doi.org/10.5281/zenodo.7369653</a>).</p> <p>This dataset includes average Line-of-Sight velocities for the following sites and dates:</p> <table> <tbody> <tr> <td><strong>Site name</strong></td> <td><strong>Country</strong></td> <td><strong>Period</strong></td> <td><strong>Relative orbit</strong></td> <td><strong>Orbit direction</strong></td> </tr> <tr> <td>Cap-Haïtien</td> <td>Haiti</td> <td>Jan-2017 / Dec-2019</td> <td>106</td> <td>ascending</td> </tr> <tr> <td>Gran Renaissance Ethiopian Dam</td> <td>Ethiopia</td> <td>Jan-2019 / Jun-2021</td> <td>50</td> <td>descending</td> </tr> <tr> <td>La Palma Volcano</td> <td>Spain</td> <td>Jun-2019 / Dec-2021</td> <td>169</td> <td>descending</td> </tr> <tr> <td>Santorini Volcano</td> <td>Greece</td> <td>Apr-2015 / May-2021</td> <td>29</td> <td>ascending</td> </tr> <tr> <td>San Francisco</td> <td>USA</td> <td>Jan-2016 / Dec-2020</td> <td>115</td> <td>descending</td> </tr> <tr> <td>Thessaloniki International Airport (SKG)</td> <td>Greece</td> <td>Apr-2015 / Dec-2020</td> <td>102</td> <td>ascending</td> </tr> </tbody> </table>
Boreal forest tower-based remote sensing data (solar-induced fluorescence and reflectance-based vegetation indices)
<p>Data includes remote sensing products from PhotoSpec (a scanning spectrometer) from August 2019-December 2021 at the Southern Old Black Spruce site in Saskatchewan Canada and the National Ecological Observatory Network (NEON) Delta Junction. We provide half-hourly averaged vegetation indices (NIRv, NDVI, PRI, CCI) and Solar-Induced Fluorescence (SIF) and for stand-representative targets. Additionally, we provide half-hourly Photosynthetically Active Radiation (PAR), a fraction of direct vs. diffuse radiation (Df), Air Temperature (Tair) and Gross Primary Productivity (GPP). </p>
Dataset: Remotely sensed soil moisture can capture dynamics relevant to plant water uptake
<p><strong>Dataset Description</strong><br> Stable isotope water uptake profiles were consulted across 45 datasets to determine the primary zone of root water uptake ("Uptake Range Top" to "Uptake Range Bottom"), whether the uptake increases in proportion nearer to the surface ("Decay of Water Uptake With Depth"), and whether uptake temporarily switches to shallow soils ("Temporary Uptake of Upper Layers"). More details on the data collection are shared in our Water Resources Research publication (in revision).</p> <p>Correlation length scales, or the effective depth of representation of L-band satellite soil moisture, are estimates in Short Gianotti et al. 2019 using SMAP surface soil moisture and GPM precipitation retrievals.</p> <p><strong>Citations</strong><br> Those that use the stable isotope table are asked to cite our Water Resources Research publication (in revision) as well as the 45 references contributing to the table.<br> Those that use the correlation length scale dataset are asked to cite:<br> Short Gianotti, D.J., Salvucci, G.D., Akbar, R., McColl, K.A., Cuenca, R., Entekhabi, D., 2019. Landscape water storage and subsurface correlation from satellite surface soil moisture and precipitation observations. Water Resour. Res. 9111–9132. https://doi.org/10.1029/2019wr025332</p>
Remote sensing data for crop yield in CONUS
<p><strong>I) SUMMARY</strong></p> <p>This database contains harmonized time series for the study of crop yields using remote sensing data and meteorological data. We collected information on soybean, corn, and wheat yields (t/ha) over the CONUS (continuous US) from <a href="http://quickstats.nass.usda.gov/USDA-NASS">USDA-NASS</a> for years 2015–2018 at a county level, and collocated time series for the following variables:</p> <ul> <li>Enhanced Vegetation Index (EVI) from <a href="https://lpdaac.usgs.gov">MODIS</a> satellite (MOD13C1 v6 product)</li> <li>Soil Moisture (SM) from SMAP satellite through <a href="https://zenodo.org/record/5619583#.Y2OkiXbMKUl">MT-DCA algorithm</a></li> <li>Vegetation Optical Depth (VOD) from SMAP satellite through <a href="https://zenodo.org/record/5619583#.Y2OkiXbMKUl">MT-DCA algorithm</a></li> <li>Maximum temperature (TMAX) from <a href="https://daac.ornl.gov">Daymet</a> v3</li> <li>Precipitation (PRCP) from <a href="https://daac.ornl.gov">Daymet</a> v3</li> </ul> <p><strong>II) CONTACT</strong></p> <p>For questions, please email Laura Martínez-Ferrer at <a href="mailto:laura.martinez-ferrer@uv.es">laura.martinez-ferrer@uv.es</a></p> <p><strong>III) DATABASE</strong></p> <p>For each crop type, we provided CSV files containing the time series of the variables and yield described above. Furthermore, additional information for spatial and temporal identification such as a county identifier and a year are included. Lastly, country-shapefiles (.shp) are added for geospatial representation. Further details in readme.txt file.</p> <p><strong>IV) CITE</strong></p> <p>We kindly encourage to cite the following works if this database is used</p> <p>L. Martínez-Ferrer, M. Piles, G. Camps-Valls, Crop Yield Estimation and Interpretability With Gaussian Processes, IEEE Geoscience and Remote Sensing Letters, 2020, vol. 18, no 12, p. 2043-2047, DOI: <a href="https://doi.org/10.1109/LGRS.2020.3016140">10.1109/LGRS.2020.3016140</a> </p> <p>A. Mateo-Sanchis, J. E. Adsuara, M. Piles, J. Muñoz-Marí, A. Pérez-Suay and G. Camps-Valls, "Interpretable Long-Short Term Memory Networks for Crop Yield Estimation," in IEEE Geoscience and Remote Sensing Letters, DOI: <a href="https://ieeexplore.ieee.org/document/10041987">10.1109/LGRS.2023.3244064</a></p>
An Agnostic Benchmark for Optical Remote Sensing Image Super-Resolution
<p>In remote sensing, image super-resolution (ISR) is a technique used to create high-resolution (HR) images from low-resolution (R) satellite images, giving a more detailed view of the Earth’s surface. However, with the constant development and introduction of new ISR algorithms, it can be challenging to stay updated on the latest advancements and evaluate their performance objectively. To address this issue, we introduce SRcheck, a Python package that provides an easy-to-use interface for comparing and benchmarking various ISR methods. SRcheck includes a range of datasets that consist of high-resolution and low-resolution image pairs, as well as a set of quantitative metrics for evaluating the performance of SISR algorithms.</p>
Utilizing traditional and remote sensing techniques to assess Colorado potato beetle host preference in the Columbia Basin -- 2021 Data
<p>This is a remote sensing dataset collected in 2021 that contains orthomosaic images, shape files, analysis scripts, and derived numerical data from each plot. Data was collected using the protocol described here:</p> <p><a href="https://www.protocols.io/view/usda-ars-potato-genetics-lab-drone-data-collection-bp2l6148dvqe/v1">https://www.protocols.io/view/usda-ars-potato-genetics-lab-drone-data-collection-bp2l6148dvqe/v1</a></p> <p>Provided are "field map" files that denote the location and contents of each plot, a folder from each date that contains the 10 band orthomosiac, surface model image, a cropped and rotated image, shape files indicating the location of each plot, and derived data. The analysis can be replicated by following along with workflow listed in file named: rondon_cpb_2021.R. Derived data from this experiment can be found it the file named: "Rondon_CPB_data_2021_UAS_all.csv"<br> <br> If you have any questions or comments regarding this dataset please contact Dr. Max Feldman via email: max.feldman@usda.gov</p> <p> </p>
Quantifying flood exposure for Pakistan's 2022 floods from remotely sensed data
<p>Workflow for a rapid assessment of flood depth from remotely sensed data for Pakistan's 2022 floods. This workflow is designed to inform strategic and trans-sectoral reconstruction and adaptation to flood hazards. </p>
Remotely Sensed Paddy Rice Map of South Korea (2017-2021)
<p>This dataset includes paddy rice maps in South Korea from 2017 to 2021 with 10 m resolution, which was produced by analyzing time-series Sentinel-1 images with recurrent U-Net deep learning architecture. The paddy rice maps are a product of deep learning model predictions and DO NOT represent ground truth information.</p> <p>The detailed algorithm for producing the dataset can be found in the following paper: <a href="https://doi.org/10.1080/15481603.2023.2206539">https://doi.org/10.1080/15481603.2023.2206539</a></p> <p>The used modeling architecture is indicated by "RU-net 3" in the paper, and the consisting products in the dataset is as follows:</p> <ul> <li>PR_in_[Year] : Probabiltiy of paddy rice cultivation area inside the paddy boundary (levee)</li> <li>PR_bd_[Year] : Probabiltiy of levee</li> <li>PR_in and PR_bd files have a scale factor vaule of 1,000,000.</li> <li>PR_bi_[Year] : Binary map of paddy rice, Due to the pixel size much larger than the levee width, the pixels labeled as levees inevitably include a large portion of the cultivation area. Therefore, binary map represent the sum of PR_in and PR_bd exceeding 0.5 threshold.</li> </ul> <p>Please cite the paper when using this dataset.</p> <p>This work was supported by the International Research and Development Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT [2021K1A3A1A78097879], and partially supported by the European Commission under contract H2020-CALLISTO [101004152]</p>
Primary data for: "Remotely sensed localised primary production anomalies predict the burden and community structure of infection in long-term rodent datasets"
<p>Datasets</p>
Measured data of global fractional vegetation cover from 2013-2021 and algorithm code for calculating remote sensing products
<p>These data come from "A new computationally efficient algorithm to generate global fractional vegetation cover from Sentinel-2 imagery at 10m resolution", these include:</p> <p>1. Measured data of global fractional vegetation cover from 2013-2021 </p> <p>2. Algorithm code for calculating fractional vegetation cover, these codes are written by JavaScript in GEE (Google Earth Engine).</p>
Data for: Extensive coral mortality and critical habitat loss following dredging and their association with remotely-sensed sediment plumes
<p>This work describes impacts to coral reefs surrounding the 2013-2015 dredging of the Port of Miami based on data collected before, during, and after dredging by Dial Cordy and Associates (DCA) on behalf of Great Lakes Dredge and Dock Company, the dredging contractors for the U.S. Army Corps of Engineers (USACE) and for the Port of Miami (Miami-Dade County). A front page for this repository can be accessed at <a href="http://jrcunning.github.io/pom-dredge">jrcunning.github.io/pom-dredge</a> containing rendered R Markdown detailing all analyses conducted as part of this work.</p>
Data for: A generalized area-based framework to quantify river mobility from remotely sensed imagery
Open the record for dataset details and reuse information.
Data and code for: Remote sensing of riverbank migration using particle image velocimetry
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Remotely sensed crown nutrient concentrations modulate forest reproduction across the contiguous United States
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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.