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7 results for “Spectral Features”
Supraglacial features of debris covered glaciers in the Himalaya from Landsat-8 spectral umixing and Pleiades
<p>This dataset contains the spectral unmixing output files for the debris covered glacier surfaces based on Landsat-8 OLI imagery and Pleiades imagery of 2015. Files are provided for two domains, the Khumbu reference region of Nepal and the greater Himalaya region (76.3 to 92.6° W and 26.3 to 34.2° N), which covers covering most area from Himachal/Jammu and Kashmir border to Bhutan Himalaya. </p> <ul> <li>Landsat surface reflectance : Himalaya_L8_6S_surface_reflectance_scenes_2015 .zip <ul> <li>Contains surface reflectance images of Landsat-8 OLI scenes mostly from 2015 (two images are from 2014 and 2016 due to clouds in 2015) </li> <li>Collection 1 Level 1 (L1TP)</li> <li>Atmospherically and topographically corrected using the ARCSI routine, supplied in .kea format. These can be converted to GeoTifs using the GDAL command.</li> <li>Naming structure: LS8_yyyymmdd_latYYlongXXXX_rRRpPPP_vmsk_topshad_rad_srefdem_stdsref.kea</li> <li>Projection is UTM (zones depending on the image), from the original Landsat L1TP files</li> <li>The file naming convention, which is a standard output from ARCSI routine, include the image date ("yyyy" = year, mm = "month", "dd" = day), latitude ("YY") and longitude ("XXXX") of the image center, path/row ("PPP" = path, "RRR" = row), and the output products generated by ARCSI ("rad" = radiation, "topshad" = topographic shadows, "srefdem" indicates the use of elevation data, "stdsref" = standardized surface reflectance)</li> </ul> </li> <li>Fractional maps for the Khumbu: LS8_20150930_r41p140_frac_files. zip <ul> <li>Raster format (GeoTiffs) </li> <li>Non-normalized fractional water, light and dark debris and vegetation maps for the Khumbu reference image (Sept 30, 2015, path 140 row 40)</li> <li>Output from the linear mixing model routine used to produce binary maps of surfaces with values ranging from 0 to 1 (0% to 100% pixel coverage)</li> </ul> </li> <li>Binary surface maps for the Himalaya: Himalaya_L8_raw_binary_surface_maps.zip <ul> <li>Vector format (ArcGIS shapefiles)</li> <li>Raw, unprocessed binary maps of ponds, vegetation debris, ice and clouds over the debris covered glacier tongues in the Himalaya around the year 2015 (binary files) </li> <li>Derived from tresholding the fractional maps using a variable threshold (see publication)</li> <li>Maps in this pre-release version have not been manually corrected for misclassified areas due to confusion of classes, and the ice and cloud classes are not highly accurate</li> <li>These are not the final coverages of these surfaces over the domain and should not be used as such</li> <li>The supraglacial pond maps will undergo manual corrections and the datasets will be updated on this page</li> </ul> </li> <li>Dataset for analysis, glacier-by-glacier: Himalaya_SDC_LS_for_analysis_gt1km2_with_frac_and_debris_attributes.txt <ul> <li>original data from the SupraGlacial Debris Cover dataset (Sherler et al 2018)</li> <li>updated with the preliminary fractional cover of each surface (in %) on a glacier-by-glacier basis</li> <li>contains only debris covered tongues >1 km2 </li> <li>debris covered attributes were calculated from the ALOS Global Digital Surface Model (AW3D30 DEM) for each debris covered tongue <ul> <li>DC_area_km2 = recalculated debris covered area</li> <li>DCmin = minimum debris cover elevation (meters)</li> <li>DCmax = maximum debris cover elevation (meters)</li> <li>DCrange = altitudinal range (meters)</li> <li>DCmed = median elevation (meters)</li> <li>SLmean = mean slope (degrees)</li> <li>SLrange = slope range (degrees)</li> <li>SLmin = min slope (degrees)</li> <li>SLmax = max slope (degrees)</li> </ul> </li> </ul> </li> </ul>
Data sets for "MELISSA: System description and spectral features of pre‐ and post‐midnight F‐region echoes. Journal of Geophysical Research: Space Physics" by Rodrigues et al.
<p>Observations used in the study "Rodrigues, F. S., Zhan, W., Milla, M. A., Fejer, B. G., de Paula, E. R., Neto, A. C., et al ( 2019). MELISSA: System description and spectral features of pre‐ and post‐midnight <em>F</em>‐region echoes. <em>Journal of Geophysical Research: Space Physics</em>, 124. <a href="https://doi.org/10.1029/2019JA027445">https://doi.org/10.1029/2019JA027445</a>."</p> <p>The uploaded files include the RTI maps measured by the MELISSA radar system between 2014 and 2018 (.tif files). They also include values of SNR versus local time and height and the spectra presented in the manuscript (.mat files).</p> <p>Please, see README.txt for additional details.</p>
Supplementary material S26: A comparison of the spectral features of the loudest jolt against walking vibrations on each substrate.
<p>A series of spectrograms allowing the comparison of the vibration originating from the loudest jolt on the left column and walking behaviour on the right column, on three separate substrates (petri-dish (a and b), empty honeycomb (c and d), brood-comb (e and f). The magnitude of acceleration is logarithmic (to the base 10), where the highest magnitude is dark red (2.1x10<sup>-3</sup> m/s<sup>2</sup>) and the lowest magnitude dark blue (and forced to 1/200 of the maximum). All six panels are scaled identically for ease of comparison. In each spectrogram, the regular background vibration inherent to the room was calculated and subtracted, but still results in red pixels on the very lowest frequency band. The data observed in each panel is that also used in S24.</p>
From mass spectral features to molecules in molecular networks: MolNotator LDB dataset.
<p>Finding actual molecules in LC-MS/MS experiments can prove challenging due to the considerable amount of redundant ions generated during ionization. In this context, MolNotator was created and validation with this dataset.</p> <p>MolNotator is a Python 3.7 package designed to predict molecules (molecular masses) by combinatorial triangulation in LC-MS/MS experiments after a preprocessing step using MZMine. An MGF and a CSV files output from MZmine are required as input for MolNotator which are placed in the "mzmine_out" folder of the project folder (the uploaded dataset).</p> <p>Instructions for the use of MolNotator are available on GitHub (https://github.com/ZzakB/MolNotator), Pypi (https://pypi.org/project/MolNotator/) and in the associated publication.</p> <p>The dataset consists of 193 LC-MS/MS analyses of lichen pure standards (previously used for the Lichen Database, LDB), 156 of which were detected by manual curation and served to benchmark MolNotator. Results indicated more than 90% of the 156 molecular masses were predicted by MolNotator under 2 ppm error on average.</p> <p>The project folder contains, in addition to the mzmine_out folder, a database and a params folder (see GitHub and Pypi) as well as a styles folder, containing different styles that can be imported on Cytoscape (https://cytoscape.org/) to visualise MolNotator's network output. </p>
Data from: Spectral and temporal acoustic features modulate response irregularities within primary auditory cortex columns
Assemblies of vertically connected neurons in the cerebral cortex form information processing units (columns) that participate in the distribution and segregation of sensory signals. Despite well-accepted models of columnar architecture, functional mechanisms of inter-laminar communication remain poorly understood. Hence, the purpose of the present investigation was to examine the effects of sensory information features on columnar response properties. Using acute recording techniques, extracellular response activity was collected from the right hemisphere of eight mature cats (felis catus). Recordings were conducted with multichannel electrodes that permitted the simultaneous acquisition of neuronal activity within primary auditory cortex columns. Neuronal responses to simple (pure tones), complex (noise burst and frequency modulated sweeps), and ecologically relevant (con-specific vocalizations) acoustic signals were measured. Collectively, the present investigation demonstrates that despite consistencies in neuronal tuning (characteristic frequency), irregularities in discharge activity between neurons of individual A1 columns increase as a function of spectral (signal complexity) and temporal (duration) acoustic variations.
Spectral-temporal features over South Africa (2013-2015) derived from multi-sensor Landsat imagery
<p>The three data sets are spectral-temporal features derived from multi-sensor Landsat imagery over South Africa. They were derived as follows:</p> <p>All Landsat-5, -7 and -8 data from 2013 to 2015 covering South Africa (70 tiles) were acquired and pre-processed following the procedure implemented in Hansen et al. (2013), Potapov et al. (2014), Potapov et al. 2012. Note that four tiles (path/row: 176/77, 175/78, 174/78, 174/79) were discarded because no crop is grown there. Four spectral bands were kept: the red, the near-infrared (NIR), and the two short-wave infrared (SWIR) bands. The blue and green bands were discarded due to their sensitivity to atmospheric effects. We applied a three-step procedure to normalize the radiometry. First, Landsat data were converted to top-of-atmosphere reflectance and then normalized by taking the corresponding MODIS top-of-canopy reflectance data as target. Third, we adjusted cross-track surface anisotropy effects by modeling the Landsat reflectance per spectral band as a function of the viewing angle. The above-mentioned processing steps incrementally improved the appearance of the data, providing more spatial coherence and increasing the generalization and internal consistency of the multi-spectral feature space.</p> <p> </p> <p>Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova S, Tyukavina A, et al. High-resolution global maps of 21st-century forest cover change. science. 2013;342(6160):850{853.</p> <p><br> Potapov P, Dempewolf J, Talero Y, Hansen M, Stehman S, Vargas C, et al. National satellite-based humid tropical forest change assessment in Peru in support of REDD+ implementation. Environmental Research Letters. 2014;9(12):124012.</p> <p><br> Potapov PV, Turubanova S, Tyukavina A, Krylov A, McCarty J, Radeloff V, et al. Eastern Europe's forest cover dynamics from 1985 to 2012 quantified from the full Landsat archive. Remote Sensing of Environment. 2015;159:28 43.</p>
Data from: Spectral and temporal acoustic features modulate response irregularities within primary auditory cortex columns
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