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5,023 results for “landing”
Edge length of land use classes aggregated from Corine 2018 in 100 - 5000 meter radius areas around Landklif study plots
<p><span>Based on CORINE land cover data 2018, we aggregated the original land use classes into 8 classes: urban, agriculture, grassland, broad-leaved forest, coniferous forest, mixed forest, natural/seminatural vegetation, and water (see clc_legend.txt). Based on this new land use classification, we calculated the edge length of landuse patches (total length of edges between different habitat in meter) for 100 - 5000 meters (100-1000 meters with 100 meter interval, 1000 - 5000 meter with 500 meter interval) buffer area around LandKlif plots</span>.</p> <p>LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif). Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1975
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1975.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1976
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1976.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1977
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1977.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1979
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1979.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1978
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1978.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1974
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1974.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1970
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1970.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p> </p>
ERA5-Land selected indicators daily aggregates for Africa, 1973
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1973.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1971
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1971.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1972
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1972.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
Dataset for Paper Titled "First assessment of cloud-land coupling in LASSO Large-Eddy Simulations"
<div>The attached two files were used for analysis in the paper "First assessment of cloud-land coupling in LASSO Large-Eddy Simulations." The NetCDF file included planetary boundary layer heights derived from lidar and radiosondes. The CSV file detailed the model configurations for selected case days in simulation sets ID1-5.</div> <div> <div> <p> </p> </div> </div>
Evaluating Automated Seismic Event Detection Approaches: An Application to Victoria Land, East Antarctica
<p>This repository contains the waveform data used by Ho et al. (2024), along with all generated fine-tuned models and event catalogs. See the README file for a summary. The corresponding software packages are available on GitHub at <a href="https://github.com/jakewalter/easyQuake.git">https://github.com/jakewalter/easyQuake.git</a>, <a href="https://github.com/seisbench/seisbench">https://github.com/seisbench/seisbench</a>, and <a href="https://github.com/longmho/Transfer_Learning_and_Seisbench">https://github.com/longmho/Transfer_Learning_and_Seisbench</a>. See the README.md file at <a href="https://github.com/longmho/Transfer_Learning_and_Seisbench">https://github.com/longmho/Transfer_Learning_and_Seisbench</a> for additional details.</p>
Monthly time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2023) derived from ERA5-Land data
<p>Overview:<br>ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Processing steps:<br>The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: <br>1. spatially aggregate CHELSA to the resolution of ERA5-Land <br>2. calculate difference of ERA5-Land - aggregated CHELSA <br>3. interpolate differences with a Gaussian filter to 30 arc seconds <br>4. add the interpolated differences to CHELSA</p> <p>Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 12/2023.</p> <p>Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):</p> <p><code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code></p> <p><code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code></p> <p><code>relative humidity = actual water pressure / maximum water pressure</code></p> <p>The resulting relative humidity has been aggregated to monthly averages.</p> <p>Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].</p> <p>File naming scheme (YYYY = year; MM = month):<br><code>ERA5_land_rh2m_avg_monthly_YYYY_MM.tif</code></p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br>north: 82:00:30N<br>south: 18N<br>west: 32:00:30W<br>east: 70E</p> <p>Spatial resolution:<br>30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br>Monthly</p> <p>Pixel values:<br>Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)</p> <p>Software used:<br>GDAL 3.2.2 and GRASS GIS 8.0.0/8.3.2</p> <p>Original ERA5-Land dataset license:<br><a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a></p> <p>CHELSA climatologies (V1.2):<br>Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br>Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC</p> <p>Data is also available in EU LAEA (EPSG: 3035) projection: <a href="https://doi.org/10.5281/zenodo.7427021">https://doi.org/10.5281/zenodo.7427021</a></p>
High Resolution 30m Land Surface Parameters for Europe
<p><strong>General Description</strong></p> <p>The <em>High Resolution 30m Land Surface Parameters for Europe</em> dataset is derived from <a href="../records/7676373">Global Ensemble DTM</a>. Data is computed using GRASS GIS and SAGA GIS. Original DTM data is in projection EPSG:4326, and reprojects to Equi7 (EPSG:27704), computes the parameters, and eventually reprojects to EPSG:3035. High resolution layers are computed in tiles. In order to eliminate boundary effects and reprojection resampling, Below is the list of land-surface parameters. Hillshade and Slope in degree are available to download through this resporitory, others are available to access from public S3 server. All files are in COG.</p> <p><strong>slope in degree (slope): </strong>steepness at each cell</p> <ul> <li>S3 path: <a href="https://s3.eu-central-1.wasabisys.com/openlandmap/dtm/dtm.bareearth_ensemble_p10_30m_s_2018_go_epsg4326_v20230221.tif" target="_blank" rel="noopener ugc nofollow">https://s3.eu-central-1.wasabisys.com/arco/slope_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif</a></li> </ul> <p><strong>hillshade:</strong> visualizing of terrain determined by a light source and the slope and aspect of the elevation surface</p> <ul> <li>S3 path: <a href="https://s3.eu-central-1.wasabisys.com/arco/hillshade_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif">https://s3.eu-central-1.wasabisys.com/arco/hillshade_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif</a></li> </ul> <p><strong>minimum curvature (minic): </strong>valleys in negative value and local convex landform in positive value</p> <ul> <li>S3 path: <a href="https://s3.eu-central-1.wasabisys.com/arco/hillshade_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif">https://s3.eu-central-1.wasabisys.com/arco/minic_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif</a></li> </ul> <p><strong>maximum curvature (maxic):</strong> ridges in positive values and local concave landform in negative value</p> <ul> <li>S3 path: <a href="https://s3.eu-central-1.wasabisys.com/arco/hillshade_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif">https://s3.eu-central-1.wasabisys.com/arco/maxic_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif</a></li> </ul> <p><strong>positive openness (pos.openness): </strong>the "dominance" of an elevated location over its surroundings</p> <ul> <li>S3 path: <a href="https://s3.eu-central-1.wasabisys.com/arco/hillshade_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif">https://s3.eu-central-1.wasabisys.com/arco/pos.openness_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif</a></li> </ul> <p><strong>negative openness (neg.openness):</strong> the "enclosure" of a lower location by elevated surroundings</p> <ul> <li>S3 path: <a href="https://s3.eu-central-1.wasabisys.com/arco/hillshade_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif">https://s3.eu-central-1.wasabisys.com/arco/neg</a><a href="https://s3.eu-central-1.wasabisys.com/arco/hillshade_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif">.openness</a><a href="https://s3.eu-central-1.wasabisys.com/arco/hillshade_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif">_edtm_m_30m_s_20000101_20221231_eu_epsg.3035_v20240528.tif</a></li> </ul> <p><strong>Data Details</strong></p> <ul> <li><strong>Time period:</strong> January 2000 – December 2022</li> <li><strong>Type of data:</strong> Land surface parameters of geomorphometry</li> <li><strong>How the data was collected or derived:</strong> Derived from <a href="../records/7676373">Global Ensemble DTM</a> in 30m using GRASS GIS and SAGA GIS running in a local HPC.</li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900000 899000 7401000 5501000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216700, 153400</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <p><strong>Support</strong></p> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">https://github.com/AI4SoilHealth/SoilHealthDataCube/issues</a></p> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> slope = slope in degree</li> <li><strong>variable procedure combination:</strong> edtm = Ensemble digital terrain model</li> <li><strong>Position in the probability distribution / variable type:</strong> m = measurement</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20000101 = 2000-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = Europe</li> <li><strong>EPSG code:</strong> epsg.3035 = EPSG:3035</li> <li><strong>Version code:</strong> v20240528 = 2024-05-28 (creation date)</li> </ol> <p> </p>
Territorial land use data relative to Pesa Basin ( Tuscany) and its recent changes ( 2016- 2007)
<p>Data and elaborations are provided by IBIMET CNR , Accademia Georgofili and <a href="http://www.cbmv.it/">Consorzio di Bonifica 3 Medio Valdarno. </a></p>
Dakar very-high resolution land cover map
<p>This land cover map of Dakar (Senegal) was created from a Pléiades very-high resolution imagery with a spatial resolution of 0.5 meter. The methodology followed a open-source semi-automated framework [1] that rely on <a href="https://grass.osgeo.org/">GRASS GIS</a> using a local unsupervised optimization approach for the segmentation part [2-3].</p> <p>Description of the files:</p> <ul> <li>"Landcover.zip" : The direct output from the supervised classification using the Random Forest classifier.</li> <li>"Landcover_Postclassif_Level8_Splitbuildings.zip" : Post-processed version of the previous map ("Landcover"), with reduced misclassifications from the original classification (rule-based used to reclassify the errors, with a focus on built-up classes).</li> <li>"Landcover_Postclassif_Level8_modalfilter3.zip" : Smoothed version of the previous product (modal filter with window 3x3 applied on the "Landcover_Postclassif_Level8_Splitbuildings"). </li> <li>"Landcover_Postclassif_Level9_Shadowsback.zip" : Corresponds to the "level8_Splitbuildings" with shadows coming from the original classification.</li> <li>"Dakar_legend_colors.txt" : Text file providing the correspondance between the value of the pixels and the legend labels and a proposition of color to be used.</li> </ul> <p> </p> <p>References:</p> <p>[1] Grippa, Taïs, Moritz Lennert, Benjamin Beaumont, Sabine Vanhuysse, Nathalie Stephenne, and Eléonore Wolff. 2017. “An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification.” <em>Remote Sensing</em> 9 (4): 358. <a href="https://doi.org/10.3390/rs9040358">https://doi.org/10.3390/rs9040358</a>.</p> <p>[2] Grippa, Tais, Stefanos Georganos, Sabine G. Vanhuysse, Moritz Lennert, and Eléonore Wolff. 2017. “A Local Segmentation Parameter Optimization Approach for Mapping Heterogeneous Urban Environments Using VHR Imagery.” In <em>Proceedings Volume 10431, Remote Sensing Technologies and Applications in Urban Environments II.</em>, edited by Wieke Heldens, Nektarios Chrysoulakis, Thilo Erbertseder, and Ying Zhang, 20. SPIE. <a href="https://doi.org/10.1117/12.2278422">https://doi.org/10.1117/12.2278422</a>.</p> <p>[3] Georganos, Stefanos, Taïs Grippa, Moritz Lennert, Sabine Vanhuysse, and Eleonore Wolff. 2017. “SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas.” In <em>Proceedings of the 2017 Conference on Big Data from Space (BiDS’17)</em>.</p> <p> </p> <p>Founding: </p> <p>This dataset was produced in the frame of two research project : MAUPP (<a href="http://maupp.ulb.ac.be">http://maupp.ulb.ac.be</a>) and REACT (<a href="http://react.ulb.be">http://react.ulb.be</a>), funded by the Belgian Federal Science Policy Office (<a href="http://eo.belspo.be/About/Stereo3.aspx">BELSPO</a>).</p>
Ouagadougou very-high resolution land cover map
<p>This land cover map of Ouagadougou (Burkina Faso) was created from a WorldView3 very-high resolution imagery with a spatial resolution of 0.5 meter. The methodology followed a open-source semi-automated framework [1] that rely on <a href="https://grass.osgeo.org/">GRASS GIS</a> using a local unsupervised optimization approach for the segmentation part [2-3].</p> <p>Description of the files:</p> <ul> <li>"Landcover.zip" : The direct output from the supervised classification using the Random Forest classifier.</li> <li>"Landcover_Postclassif_Level5_Splitbuildings.zip" : Post-processed version of the previous map ("Landcover"), with reduced misclassifications from the original classification (rule-based used to reclassify the errors, with a focus on built-up classes).</li> <li>"Landcover_Postclassif_Level5_modalfilter3.zip" : Smoothed version of the previous product (modal filter with window 3x3 applied on the "Landcover_Postclassif_Level5_Splitbuildings"). </li> <li>"Landcover_Postclassif_Level6_Shadowsback.zip" : Corresponds to the "level5_Splitbuildings" with shadows coming from the original classification.</li> <li>"Ouaga_legend_colors.txt" : Text file providing the correspondance between the value of the pixels and the legend labels and a proposition of color to be used.</li> </ul> <p> </p> <p>References:</p> <p>[1] Grippa, Taïs, Moritz Lennert, Benjamin Beaumont, Sabine Vanhuysse, Nathalie Stephenne, and Eléonore Wolff. 2017. “An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification.” <em>Remote Sensing</em> 9 (4): 358. <a href="https://doi.org/10.3390/rs9040358">https://doi.org/10.3390/rs9040358</a>.</p> <p>[2] Grippa, Tais, Stefanos Georganos, Sabine G. Vanhuysse, Moritz Lennert, and Eléonore Wolff. 2017. “A Local Segmentation Parameter Optimization Approach for Mapping Heterogeneous Urban Environments Using VHR Imagery.” In <em>Proceedings Volume 10431, Remote Sensing Technologies and Applications in Urban Environments II.</em>, edited by Wieke Heldens, Nektarios Chrysoulakis, Thilo Erbertseder, and Ying Zhang, 20. SPIE. <a href="https://doi.org/10.1117/12.2278422">https://doi.org/10.1117/12.2278422</a>.</p> <p>[3] Georganos, Stefanos, Taïs Grippa, Moritz Lennert, Sabine Vanhuysse, and Eleonore Wolff. 2017. “SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas.” In <em>Proceedings of the 2017 Conference on Big Data from Space (BiDS’17)</em>.</p> <p> </p> <p>Founding: </p> <p>This dataset was produced in the frame of two research project : MAUPP (<a href="http://maupp.ulb.ac.be">http://maupp.ulb.ac.be</a>) and REACT (<a href="http://react.ulb.be">http://react.ulb.be</a>), funded by the Belgian Federal Science Policy Office (<a href="http://eo.belspo.be/About/Stereo3.aspx">BELSPO</a>).</p>
Atmospheric_river_land_hydrology_western_US_HUC8_datasets
<p>Dataset for the manuscript entitled "Impact of Atmospheric Rivers on Surface Hydrological Processes in Western U.S. Watersheds".</p> <p> </p> <p>It includes daily meteorological and surface hydrological data from western U.S. WRF simulation. Data is aggregated to 8-digit Hydrological Unit (HUC8) watersheds.</p> <p> </p> <p>To use this dataset, please cite the following two publications:</p> <p> </p> <p>Chen, X., Leung, L. R., Gao, Y., Liu, Y., Wigmosta, M., & Richmond, M. (2018). Predictability of extreme precipitation in western U.S. watersheds based on atmospheric river occurrence, intensity, and duration. <em>Geophysical Research Letters</em>, 45, 11,693–11,701. <a href="https://doi.org/10.1029/2018GL079831">https://doi.org/10.1029/2018GL079831</a></p> <p> </p> <p>Chen, X., Leung, L. R., Wigmosta, M., & Richmond, M. (2019). Impact of Atmospheric Rivers on Surface Hydrological Processes in Western U.S. Watersheds. <em>Journal of Geophysical Research: Atmospheres</em>, <a href="http://doi.org/10.1029/2019JD03468">https://doi.org/10.1029/2019JD03468</a></p>
INEGI Uso del Suelo y Vegetacion Land Cover Classifications for Mexico (1985, 1993, 2002, 2007, 2011), Harmonized with NLCD 2011 Legend
<p>We have taken the Uso del Suelo y Vegetacion land cover classification products for Mexico (courtesy of Mexico's Instituto Nacional de Estadistica y Geografia, or INEGI) for years 1985, 1993, 2002, 2007, and 2011 (INEGI, 2015); and harmonized their classes with the classes of the Multi-Resolution Land Characteristics Consortium (MRLC) National Land Cover Database (NLCD) (Homer et al., 2015). Details of processing, along with the processing scripts, are archived in GitHub in the <a href="https://github.com/tbohn/NLCD_INEGI/tree/v1.5">NLCD_INEGI</a> project (Bohn, 2019).</p> <p>This project contains the following g-zipped tar files:</p> <ul> <li>SERIE_I.tgz - land cover from 1985</li> <li>SERIE_II.tgz - land cover from 1993</li> <li>SERIE_III.tgz - land cover from 2002</li> <li>SERIE_IV.tgz - land cover from 2007</li> <li>SERIE_V.tgz - land cover from 2011</li> </ul> <p>On LINUX, the contents of these files can be extracted via "tar":</p> <p>tar -xvzf SERIE_I.tgz >& log.tar.txt</p> <p>On Windows, applications such as "7-zip" can extract the contents.</p> <p>Each of these .tgz files contain a folder with the same name but without the ".tgz". Within each of these folders are the following sub-folders:</p> <ul> <li>For SERIE_I to SERIE_IV: <ul> <li>metatiles/ - original land cover shapefiles, with Mexico divided into "metatiles" along UTM zones, as documented in <a href="https://github.com/tbohn/NLCD_INEGI/tree/v1.0/docs/Processing_of_INEGI_USOSV_dataset.docx">Processing_of_INEGI_USOSV_dataset.docx</a></li> <li>geo/ - shapefiles from "metatiles", reprojected into geographic</li> <li>entire/ - shapefiles from "geo" merged into a single file for the entire country</li> </ul> </li> <li>For SERIE_V: <ul> <li>entire/ - original land cover shapefile in Lambert Conical projection, covering all of Mexico</li> <li>geo/ - shapefile from "entire" reprojected into geographic</li> </ul> </li> <li>SERIE_I to SERIE_V: <ul> <li>cve_union/ - shapefiles covering all of Mexico, in geographic projection, with land cover reclassified to NLCD 2011 legend</li> <li>rasters/ - files from "cve_union", rasterized at 0.000350884 degree resolution</li> <li>ascii/ - raster files from "rasters", exported to ascii ESRI grid file format</li> </ul> </li> </ul> <p>Output files (in the "ascii" folders) are ESRI ascii raster grid files, in geographic projection, with cellsize = 0.000350884 degrees.</p>
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