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ShareScore release 0.9.0
Dataset results
26 results for “Depth Map”
Snow depth mapping with an Unmanned Aerial Vehicles in heterogeneous mountain site (Izas Catchment, Pyrenees)
<p>Recent developments in unmanned aircraft vehicles (UAV) and Structure for Motion (SfM photogrammetry or simply SfM) algorithms have proved their worth for determining snow depth distribution. This dataset presents UAV and Terrestrial Laser Scanner (TLS) snow depth obsrevations obtained in complex alpine terrain in the Pyrenees. UAV observations have been acqired with a fixed-wing UAV working in RTK mode with an RGB camera. During the 2018-19 season, seven field campaigns (13 UAV flights) were undertaken covering 0.48 km2 . Several UAV observations were obtained under different light conditions and flight block configurations (altitude and image overlaps) in the same day with the aim if evaluating UAV observations when compared ti a well-established close range remote sensing technique (TLS).</p>
Maps of northern peatland extent, depth, carbon storage and nitrogen storage
Open the record for dataset details and reuse information.
Maps of Vegetation, NDVI, Snow and Thaw Depths: North Slope, Alaska and NWT, Canada
This dataset includes vegetation cover maps, Normalized Difference Vegetation Index (NDVI) maps, snow depth and thaw depth data that were obtained as part of a biocomplexity project on the North Slope of Alaska, USA, and the Northwest Territories (NWT), Canada. In Alaska, seven sites are located along the Dalton Highway and in the Prudhoe Bay Oilfield area, forming a transect across the climate gradient of the North Slope. From South to North, the sites are Happy Valley, Sagwon (an acidic and nonacidic site), Franklin Bluffs, Deadhorse, West Dock and Howe Island. Four sites are in the NWT, forming a latitudinal gradient from South to North; the sites include Inuvik, Green Cabin, Mould Bay, and Isachsen.
Supplementary material to "SnowPappus v1.0, a blowing-snow model for large-scale applications of Crocus snow scheme" : Pleiades snow depth maps analysis
<p>This Folder contains :</p><p> - necessary codes to reprduce Fig. 5, 12 and 13 of the third version of the submitted manuscript SnowPappus v1.0, a blowing-snow model for large-scale applications of Crocus snow scheme"</p><p> - necessary data to run these codes, including extracts of simulation outputs</p><p> - A readme.txt which explains how to run everything</p><p> </p>
Depth to top of root or water soil restrictive layer (resdept) soil maps of the Upper Colorado River Basin
<p>The data here were originally posted to facilitate timely and transparent peer review. The final public data release with formal metadata is now available from at the following location:</p> <p>Nauman, T.W., and Duniway, M.C., 2020, Predictive soil property maps with prediction uncertainty at 30 meter resolution for the Colorado River Basin above Lake Mead: U.S. Geological Survey data release,<a href="http://https://doi.org/10.5066/P9SK0DO2"> https://doi.org/10.5066/P9SK0DO2</a>.</p> <p>Associated publication:</p> <p>Nauman, T. W., and Duniway, M. C., 2020, A hybrid approach for predictive soil property mapping using conventional soil survey data: Soil Science Society of America Journal, v. 84, no. 4, p. 1170-1194. <a href="https://doi.org/10.1002/saj2.20080">https://doi.org/10.1002/saj2.20080</a>.</p> <p>This version includes updated training data that accounts for making sure that if multiple restrictions are in a soil, the first is chosen. It also incorporates the updates in version 2 that included very deep soils with no restriction not included in version 1.</p> <p>Repository includes maps describing the depth (cm) to the top of any water or root soil restrictive layer (resdept) as defined by United States soil survey program.</p> <p>These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.</p> <p>The creation and interpretation of this data is documented in the following article.</p> <p>Nauman, T. W., and Duniway, M. C., 2020, A hybrid approach for predictive soil property mapping using conventional soil survey data: Soil Science Society of America Journal, v. 84, no. 4, p. 1170-1194.</p> <p>File Name Details:</p> <p>ACCURACY!! Please see manuscript and Github repository (https://github.com/usgs/Predictive-Soil-Mapping/tree/master/SoilSurvReconstrProperties) for full details on accuracy. We do provide 10-fold cross validation (CV) accuracy plots in this repository for the training sample (file ending _CV_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000).</p> <p>Elements are separated by underscore (_) in the following sequence:</p> <p>property_r_depth_cm_geometry_model_additional_elements.extension</p> <p>Example: resdept_r_cm_2D_QRF.tif</p> <p>Indicates depth to top of restriction (resdept; in cm) using a 2D model (separate model for each depth) employing a quantile regression forest. This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions. </p> <p>The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below).</p> <p>_95PI_h: Indicates the layer is the upper 95% prediction interval value.</p> <p>_95PI_l: Indicates the layer is the lower 95% prediction interval value.</p> <p>_95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (2019) for more details on RPI.</p> <p>References</p> <p> Nauman, T. W., and Duniway, M. C., 2019, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma, Vol 347, pp 170-184.</p>
DeepLoop enables robust mapping of DNA loops from low-depth single cell or allele-resolved Hi-C data at high resolution
GEO Series GSE167200. Mus musculus; Homo sapiens. 15 samples. Type: Other; Third-party reanalysis.
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