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5,023 results for “landing”
FIGURE 1 in Increasing diversity of land planarians (Platyhelminthes: Geoplanidae) in the Interior Atlantic Forest with the description of two new species and new records from Argentina
FIGURE 1. Map with the known distribution of Geoplana quagga, Obama ladislavii, and Paraba multicolor throughout Brazil (original extension of the Atlantic Forest in dark grey). In detail, the Argentine portion of the Atlantic Forest (Misiones province) with the new records of these species and localities of the new species here described. (CIAR) Centro de Investigaciones Antonia Ramos, (MO) Moconá Provincial Park, (OS) Osununú Private Reserve, (SA) San Antonio, (SE) Salto Encantado Provincial Park.
Monthly MODIS LST data related to the article: A new fully gap-free time series of land surface temperature from MODIS LST data
<p>Temperature time series with high spatial and temporal resolutions are important for several applications. The new MODIS Land Surface Temperature (LST) collection 6 provides numerous improvements compared to collection 5. However, being remotely sensed data in the thermal range, LST shows gaps in cloud-covered areas. With a novel method [1] we fully reconstructed the daily global MODIS LST products MOD11C1 and MYD11C1 (spatial resolution: 3 arc-min, i.e. approximately 5.6 km at the equator). For this, we combined temporal and spatial interpolation, using emissivity and elevation as covariates for the spatial interpolation. Here we provide a time series of these reconstructed LST data aggregated as monthly average, minimum and maximum LST maps.</p> <p>[1] Metz M., Andreo V., Neteler M. (2017): A new fully gap-free time series of Land Surface Temperature from MODIS LST data. Remote Sensing, 9(12):1333. DOI: http://dx.doi.org/10.3390/rs9121333</p> <p>LICENSE: Open Data Commons Open Database License (ODbL) http://opendatacommons.org/licenses/odbl/</p> <p>Acknowledgments: We are grateful to the NASA Land Processes Distributed Active Archive Center (LP DAAC) for making the MODIS LST data available. The dataset is based on MODIS Collection V006.</p> <p><strong>The data available here for download are the reconstructed global MODIS LST products MOD11C1/MYD11C1 at a spatial resolution of 3 arc-min</strong> (approximately 5.6 km at the equator; see https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table), <strong>aggregated to monthly data</strong>. The data are provided in GeoTIFF format. The Coordinate Reference System (CRS) is identical to the MOD11C1/MYD11C1 product as provided by NASA. In WKT as reported by GDAL:<br> <br> GEOGCS["Unknown datum based upon the Clarke 1866 ellipsoid",<br> DATUM["Not specified (based on Clarke 1866 spheroid)",<br> SPHEROID["Clarke 1866",6378206.4,294.9786982138982,<br> AUTHORITY["EPSG","7008"]]],<br> PRIMEM["Greenwich",0],<br> UNIT["degree",0.0174532925199433]]<br> </p> <p><strong>File name</strong> abbreviations:</p> <ul> <li>avg = average of daily averages</li> <li>min = minimum of daily minima</li> <li>max = maximum of daily maxima</li> </ul> <p>Meaning of <strong>pixel values</strong>:</p> <ul> <li>The <strong>pixel values</strong> are coded in <strong>degree Celsius * 100</strong> (hence, to obtain °C divide the pixel values by 100.0).</li> </ul> <p>Version <strong>changelog</strong>:</p> <ul> <li>V1.1.0: GeoTIFF metadata updated.</li> <li>V1.0.0: original upload</li> </ul>
Results in raster format of Land Cover in the Mendoza and Tunuyán River Basins, Argentina (1986–2018) published in Rojas et al. 2020
<p>Results in raster format of "Land Use Changes" in the Mendoza and Tunuyán River Basins, Argentina (1986-2018) published in Rojas et al. 2020, available at: </p> <p>Resultados en formato raster de "Cambios de Uso del Suelo" en las cuencas de los ríos Mendoza y Tunuyán, Argentina (1986-2018) publicados en Rojas et al. 2020, disponible en: </p> <p>https://doi.org/10.1007/s12061-020-09335-6<br>https://link.springer.com/article/10.1007/s12061-020-09335-6</p> <p>Rojas, F., Rubio, C., Rizzo, M., Bernabeu, M., Akil, N., & Martín, F. (2020). Land Use and Land Cover in Irrigated Drylands: a Long-Term Analysis of Changes in the Mendoza and Tunuyán River Basins, Argentina (1986–2018). Applied Spatial Analysis and Policy, 13(4), 875–899. doi:10.1007/s12061-020-09335-6 </p>
The derived data in manuscript Oblique impact adjacent to Chang'E-5 landing site: Fine-scale analysis and implication on the provenance of returned samples
<p>This website contains the derived data in the <em>manuscript <span>Oblique impact adjacent to Chang’E-5 landing site: </span><span>F</span><span>ine-scale analysis and implication on the provenance of returned samples </span></em>by <span>Wenhui Wu</span><span>, </span><span>Zhaopeng Chen</span><span>, Xin Ren</span><span>, Dawei Liu</span><span>,</span><span> </span><span>Xingguo Zeng, Yuan Chen, Wangli Chen, Wei Yan, Bin Liu,Xiaoxia Zhang, Jianjun Liu</span> for <em><span>Journal of Geophysical Research: Planets</span></em></p>
Field investigation of unsaturated seepage process and its influence on soil behavior for land creation in Loess plateau with fiber-optic technology
Open the record for dataset details and reuse information.
Impact crater identification results for Chang'e-4 satellite image area and landing camera image area
<p>Here are the results of impact crater identification from Chang'e-4 satellite image data and Chang'e-4 landing camera image data.</p> <p>There are 99 impact craters identified in the landing camera, with diameters ranging from 1.55m to 50.87m.There are 947 impact craters identified in the satellite images, with diameters ranging from 48.37m to 2,222.34m in diameter.</p> <p>All impact craters are categorised into five degradation classes (A, AB, B, BC, C).</p> <p>Landing camera image source: <a href="https://doi.org/10.5281/zenodo.3600427" target="_blank" rel="noopener noreferrer">https://doi.org/10.5281/zenodo.3600427</a><br>Satellite image source: <span>https://darts.isas.jaxa.jp/planet/pdap/selene/index.html.en</span></p> <p> </p>
ERA5-Land selected indicators daily aggregates for Africa, 2005
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 2005.</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>
FIGURE 2 in New distributional record of Sri Lankan wet land species Hylarana gracilis (Gravenhorst, 1829) (Anura: Ranidae) from the Eastern Ghats, India
FIGURE 2. Maximum likelihood tree for Hylarana species from India and Sri Lanka based on DNA sequences of the mitochondrial 16S rRNA gene (539 bps data).
FIGURE 3 in New distributional record of Sri Lankan wet land species Hylarana gracilis (Gravenhorst, 1829) (Anura: Ranidae) from the Eastern Ghats, India
FIGURE 3. Map showing the known distribution of Hylarana gracilis from Sri Lanka and Eastern Ghats, India (Red circles, genetically confirmed records from Sri Lanka (Biju et al. 2014); Blue circles, unconfirmed records from India (Deuti et al. 2014; Ganesh et al. 2021) and Green star, genetically confirmed record from the Eastern Ghats, India, reported herein.
FIGURE 1 in New distributional record of Sri Lankan wet land species Hylarana gracilis (Gravenhorst, 1829) (Anura: Ranidae) from the Eastern Ghats, India
FIGURE 1. Hylarana gracilis (ZSI/FBRC/VER/345A). A, lateral view; B, dorsal view; C, Head lateral view; D, ventral view; E, ventral view of foot; F, ventral view of hand.
Threats of land use to the global diversity of vascular plants
<p>Inputs, outputs, and scripts used in the model that supports the findings of the article "Threats of Land Use to the Global Diversity of Vascular Plants". We first calculated the extinction threat at ecoregion level using the countryside species-area model. Next, we allocated the regional extinction threats to land use. Finally, we calculated the global extinction threat per ecoregion and land use.</p> <p>In this repository, you will find the inputs necessary to run the model, the R scripts to run the model, and the outputs of the model.</p>
Model dataset for the journal publication titled "Improved snow albedo evolution in Noah-MP land surface model coupled with a physical snowpack radiative transfer scheme"
<div> <p>This is the Noah-MP model simulation dataset for the journal publication titled "Improved snow albedo evolution in Noah-MP land surface model coupled with a physical snowpack radiative transfer scheme"</p> <p> </p> </div>
Data related to: Global land-water competition and synergy between solar energy and agriculture
Open the record for dataset details and reuse information.
FIGURE 4 in Note sur des crustacés décapodes du Rupélien de Gaas (Landes, France) avec description d'un nouveau crabe (Brachyura: Leucosiidae)
FIGURE 4: A-E: AquitQinotlos gQQsensis gen.nov.,sp.nov.,holotype MHNBx 2021. 29.1,du Rupélien de Gaas Espibos.A: vue dorsale; B: vue frontale; C: vue ventrale; D: vue latérale droite; E: vue latérale gauche.Abréviations,es: endostome; ic: canal inhalant.(Barre d'échelle 5 mm).(Photos par À.Ossó). A-E: AquitQinotlos gQQsensis gen. nov., sp. nov., holotype MHNBx 2021.29.1, from the Rupelian of Gaas Espibos.A: dorsal view;B: frontal view; C: ventral view;D: right lateral view;E:lef lateral view.Abbreviations,es:endostome;ic: inhalant channel.(Scale bar = 5 mm).(Pictures by À.Ossó).
FIGURE 1 in Note sur des crustacés décapodes du Rupélien de Gaas (Landes, France) avec description d'un nouveau crabe (Brachyura: Leucosiidae)
FIGURE 1. Carte de la situation géographique des trois gisements de Gaas (Landes) et photographies des sites d'Espibos et de Lagouarde (A-D). (Photos par A.Cluzaud) Geographical location map of the three Gaas sites (Landes) and photographs of the Espibos and Lagouarde outcrops (A-D).(Pictures by A.Cluzaud).
ERA5-Land monthly: Total precipitation, monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
<p>ERA5-Land total precipitation monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)</p> <p>Source data:<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>Total precipitation:<br>Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step.</p> <p>Processing steps:<br>The original hourly ERA5-Land 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 proportion of ERA5-Land / aggregated CHELSA <br>3. interpolate proportion with a Gaussian filter to 30 arc seconds <br>4. multiply the interpolated proportions with CHELSA <br>Using proportions ensures that areas without precipitation remain areas without precipitation. Only if there was actual precipitation in a given area, precipitation was redistributed according to the spatial detail of CHELSA.</p> <p>The spatially enhanced daily ERA5-Land data has been aggregated to monthly resolution, by calculating the sum of the precipitation per pixel over each month.</p> <p>File naming:<br><code>ERA5_land_monthly_prectot_sum_30sec_YYYY_MM_01T00_00_00_int.tif</code> <br>e.g.:<code>ERA5_land_monthly_prectot_sum_30sec_2023_12_01T00_00_00_int.tif</code></p> <p>The date within the filename is year and month of aggregated timestamp.</p> <p>Pixel values:<br>mm * 10<br>Scaled to Integer, example: value 218 = 21.8 mm</p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br>north: 28:18N<br>south: 14:42N<br>west: 17:05W<br>east: 4:49W</p> <p>Temporal extent:<br>January 2019 - December 2023</p> <p>Spatial resolution:<br>30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br>monthly</p> <p>Lineage:<br>Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.</p> <p>Software used:<br>GRASS GIS 8.3.2</p> <p>Format: GeoTIFF</p> <p>Original ERA5-Land dataset license:<br><a href="https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf</a></p> <p>CHELSA climatologies (V1.2): 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>Representation type: Grid</p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Contact: <br>mundialis GmbH & Co. KG, info@mundialis.de</p> <p> </p>
Land Subsidence in Pekalongan City Central Java from Remote Sensing Perspective
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
Risk Level Analysis of Land Contaminated with B3 Waste from Traditional Oil Mining Activities in Wonocolo Village, Kedewan District, Bojonegoro Regency, East Java Province
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
Remote Sensing DInSAR Method for Land Subsidence Detection in Semarang, Central Java, Indonesia
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
Human-driven land surface transformations are major influences in global drought propagation
<p>This upload contains two data sets </p> <p>1) oldata.zip : This dataset contains time series for soil moisture, groundwater, precipitation and evapotranspiration within Open loop simulations </p> <p>2) dadata.zip : This dataset contains time series for soil moisture, groundwater, precipitation and evapotranspiration within Data assimilation simulations </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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