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2,591 results for “remotization”
Remote detection and recording of atomic-scale spin dynamics
<p>This folder contains all data and all processing files for the paper titled "Remote detection and recording of atomic-scale spin dynamics". View full paper here: https://www.nature.com/articles/s42005-020-0361-z</p>
Data used to create figures in the ACP Letters manuscipt "The value of remote marine aerosol measurements for constraining radiative forcing uncertainty" by Regayre et al. (2020)
<p>This dataset was created from perturbed parameter ensembles (PPEs) using the HadGEM-UKCA atmospheric composition climate model. All data needed to reproduce figures in the Regayre et al. (2020) ACP Letters article "The value of remote marine aerosol measurements for constraining radiative forcing uncertainty" are included. Other output from the PPEs can be obtained by contacting the lead author.</p> <p>The following data are included here:</p> <ul> <li>CCN measurement data degraded to match the model-measurement comparison resolution.</li> <li>Unconstrained and constrained CCN<sub>0.2</sub> output from the PPE used to make Figure 1. These compressed files contain 48 .dat files. Each .dat file contains the PPE mean, variance and 95% creidble interval data. Files are named consecutively, containing data from 90<sup>o</sup>S to 90<sup>o</sup>N at 0<sup>o</sup>E, then continuing Eastward. When combined, these files provide data for each latitude/longitude pair at the N48 spatial resolution.</li> <li>A zip file of an netcdf file containing 26-dimensional data for parameter values, used to create the sample of 1 million model variants from our statistical emulators of model output.</li> <li>A zip file containing a folder of files made of one million ones and zeros that indicate the retention/rejection criteria from applying our constraint methodology for various constraint combination scenarios, for each model variant. A value of 1 indicates the model variant was retained. Data in these files is in the same order as the unconstrained sample file of parameter values.</li> <li>Compressed files containing global, annual mean RF<sub>aci</sub> and ERF<sub>aci</sub> values for the unconstrained set of one million model variants. The compressed netcdf files contain RF (ERF), RF<sub>aci</sub> (ERF<sub>aci</sub>) and RF<sub>ari</sub> (ERF<sub>ari</sub>) values.</li> </ul>
Global vegetation productivity from 1981 to 2018 estimated from remote sensing data
<p>The MUltiscale Satellite remotE Sensing (MUSES) global vegetation productivity dataset includes gross primary productivity (GPP) and net primary productivity (NPP) data from 1981 to 2018. GPP and NPP were estimated with a light use efficiency (LUE) model and MUSES leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR) products.</p> <p>The MUSES product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>The detail information of the MUSES 5-km global GPP and NPP products are as below:</p> <p>Name: MUSES 5-km global GPP and NPP products</p> <p>Period: 1981-2018</p> <p>Spatial resolution: 0.05°</p> <p>Temporal resolution: 8 days</p> <p>Projection: geographic latitude/longitude</p> <p>Data format: Tiff</p> <p>Data type: integer (16bit)</p> <p>Upper left coordinates: -180°E, 90°N</p> <p>Scale factor: 100</p> <p>Unit: gCm<sup>-2</sup>d<sup>-1</sup></p> <p> </p> <p><span>Citation (Please cite these papers when these data are used)</span></p> <p><span>1. Wang, J.M., Sun, R., Zhang, H.L., Xiao, Z.Q., Zhu A.R., Wang, M.J., Yu, T., Xiang, K.L.,</span><span> </span><span>New global MuSyQ GPP/NPP remote sensing products from 1981 to 2018. </span><span>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14, 5596-5612.</span></p> <p><span>2. Wang, M.J.; Sun, R.; </span><span>Zhu, A.R.;</span><span> Xiao, Z. Q. Evaluation and Comparison of Light Use Efficiency</span><span> </span><span>and Gross Primary Productivity Using Three</span><span> </span><span>Different Approaches. <span>Remote Sensing</span>. <span>2020</span>, 12, 1003.</span></p> <p><span>3. Yu, T.; Sun, R.; Xiao, Z.Q. ;Zhang , Q.; Liu, G.; Cui, T.X.; Wang, J.M. Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data. <span>Remote sensing. </span><span>2018, </span>10, 327.</span></p>
A global dataset of SST anomaly evolving processes retrieved from remote sensing products (GDSSTAEP V1.0)
<p> The GDSSTAEP includes three datasets and two relationship files with a time range from January 1982 to December 2009. Three datasets formatted in SHP are a dataset of process object-oriented SSTA, named DSPOSSTA, storing SSTA process objects, a dataset of sequence object-oriented SSTA, named DSSOSSTA, storing SSTA sequence objects, and a dataset of variation object-oriented SSTA, named DSVOSSTA, storing SSTA variation objects, respectively. And two relationship files formatted in CSV store the evolving behaviors among sequence objects of SSTA and variation objects of SSTA, respectively. </p> <p> </p>
Dataset: Six years of surface remote sensing of stratiform warm clouds in marine and continental air over Mace Head, Ireland
<p>A total of 118 stratiform water clouds observed by ground-based remote sensing instruments at the Mace Head Atmospheric Research Station at the West coast of Ireland from 2009 to 2015 were analyzed in terms of microphysical and optical characteristics as well as the impact of aerosols on these properties. The microphysical and optical cloud properties in the files were obtained using the algorithm SYRSOC (SYnergistic Remote Sensing Of Clouds).</p>
Data for: Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) 2: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for O2 and O3
<p>We present all of the data across our SNR and abundance study for the molecules O2 and O3 for an exoEarth twin. The wavelength range is from 0.515-1 micron, with 25 evenly spaced 20% bandpasses in this range. The SNR ranges from 3-20, and the abundance values range in log space in steps of 0.5 and/or 0.25 (all presented in VMR in the associated table). We present the lower and upper wavelength per bandpass, the input O2 and O3 values (abundance case), the retrieved O2 and O3 values (presented as the log10(VMR)), the lower and upper limits of the 68% credible region (presented as the log10(VMR)), and the log-Bayes factor for O2 and O3. For more information about how these were calculated, please see Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) 2: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for O2 and O3, accepted and currently available on arXiv. </p> <p>To open this csv as a Pandas dataframe, use the following command:</p> <p>your_dataframe_name = pd.read_csv(f'zenodo_table.csv', dtype={'Input O2': str, {'Input O3': str}})</p>
GLAB-VOD: Global L-band AI-Based Vegetation Optical Depth Dataset Based on Machine Learning and Remote Sensing
<p>GLAB VOD is a Global L-band Ai-Based vegetation optical depth dataset with 18-day temporal and 25 km spatial resolution, covering 2002 to 2020. The dataset is created using a neural network with SMOS-SMAP-INRAE-BORDEAUX (SMOSMAP-IB) VOD product as a target (over 2015-2020) and brightness temperatures (TB) from the SMOS, AMSR-E, and AMSR-2 spaceborne missions alongside with a novel soil moisture dataset (CASM) as inputs. The GLAB-VOD dataset was created using a recently developed methodology previously used to create a long-term consistent soil moisture dataset CASM, adapted to the VOD retrievals. First, the TB and VOD signals were divided into fixed seasonal cycle and residuals, where the residual part of the signal contains sub-seasonal periodic signals, trends, extremes, and noise. Then, a multi-staged neural network training scheme was used to achieve internally consistent predictions by merging data from different sources without introducing biases or compromising data distribution. A side-product of this project is GLAB TB - a global long-term brightness temperature dataset that matches SMOS TB quality and spawns back to 2002. GLAB TB has daily temporal resolution and 25 km spatial resolution. </p>
e-DIPLOMA - Dataset: European remote e-learning ecosystem survey data
<p>This is the dataset "European remote e-learning ecosystem survey data" of the e-DIPLOMA project.</p> <p>In 2022 the evaluation of the European tertiary training ecosystem capacity for using disruptive technologies in practice based e-learning was explored. It was done in the eDiploma project WP2. The research problem was: What are the main gaps in tertiary education in the institutional capacity to perform practice based e-learning with disruptive technologies? Three survey instruments were developed for three target groups in institutions: technology specialists, educators and students. The survey was composed of four blocks of capacity elements: </p> <ul> <li> <p>infrastructural capacities, </p> </li> <li> <p>normative and regulatory capacities (institutional level), </p> </li> <li> <p>teaching cultures (community level), </p> </li> <li> <p>competences, attitudes and values (personal level). </p> </li> </ul> <p>The data were collected with the anonymous web based survey approach in countries: Spain, Estonia, Hungary, Bulgaria, Italy, Cyprus. </p> <p>In each HEI or VET institution the respondents were:</p> <ul> <li> <p>Technical and didactical support staff: educational technologist, IT or technical support specialists, lecturers responsible for technology training, Digital policy administrative specialist</p> </li> <li> <p>Lecturers or researchers who have experiences with some forms of group-learning or practice based learning</p> </li> <li> <p>Students from the institution who have experiences with some forms of group-learning or practice based learning / to be spread among each institution, so that different areas students respond, these should not be one group from one class only)</p> </li> </ul> <p>The answers were collected totally from the following number of the technology specialists-experts (N=96), the educators (N=351), and the students (N=516). The generalizability of the data is limited due to the sampling structure: it was not attempted to reach regional coverage because countries in our sample differ greatly in size. In Estonia responses were collected from 9 institutions (3 vocational schools and 6 HEIs). In Bulgaria responses were from 3 institutions (all HEIs). In Cyprus responses were from 3 institutions (all HEIs). In Hungary responses were from 6 institutions (1 vocational school and 5 HEIs). In Spain responses were from 116 institutions (28 high schools, 41 vocational schools, 47 HEIs). In Italy responses were from 9 institutions (4 HEIs and 5 social enterprises). </p>
Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment - Measurements and Locations
<p>Many wetlands in East Africa are farmed and wetland reservoirs are used for irrigation, livestock, and fishing. Water quality and agriculture have a mutual influence on each other. Turbidity is a principal indicator of water quality and can be used for, otherwise, unmonitored water sources. Low-cost turbidity sensors improve in situ coverage and enable community engagement. The availability of high spatial resolution satellite images from the Sentinel-2 multispectral instrument and of bio-optical models, such as the Case 2 Regional CoastColor (C2RCC) processor, has fostered turbidity modeling. However, these models need local adjustment, and the quality of low-cost sensor measurements is debated. We tested the combination of both technologies to monitor turbidity in small wetland reservoirs in Kenya. We sampled ten reservoirs with low-cost sensors and a turbidimeter during five Sentinel-2 overpasses. Low-cost sensor calibration resulted in an R² of 0.71. The models using the C2RCC C2X-COMPLEX (C2XC) neural nets with turbidimeter measurements (R² = 0.83) and with low-cost measurements (R² = 0.62) performed better than the turbidimeter-based C2X model. The C2XC models showed similar patterns for a one-year time series, particularly around the turbidity limit set by Kenyan authorities. This shows that both the data from the commercial turbidimeter and the low-cost sensor setup, despite sensor uncertainties, could be used to validate the applicability of C2RCC in the study area, select the better-performing neural nets, and adapt the model to the study site. We conclude that combined monitoring with low-cost sensors and remote sensing can support wetland and water management while strengthening community-centered approaches.</p> <p>The provided dataset includes a point shapefile with the studied reservoirs in central Kenya and a data table with the sampling date (Sentinel-2 overpass plus/minus one day), low-cost sensor setup number, reservoir ID, sampling location within the reservoir, the voltage measurements of the three respective low-cost sensor heads for sensor setups A and B, the averaged voltage, and the turbidimeter measured turbidity value in nephelometric turbidity units (NTU).</p> <p>The study is available in (please cite):</p> <div> <div>Steinbach, S., Rienow, A., Chege, M.W., Dedring, N., Kipkemboi, W., Thiong’o, B.K., Zwart, S.J., Nelson, A., 2024. Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment. <em>IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing</em> <em>17</em>, 8490–8508. <a href="https://doi.org/10.1109/JSTARS.2024.3381756">https://doi.org/10.1109/JSTARS.2024.3381756</a></div> </div> <p>This research was supported in part by the German Federal Ministry of Education and Research (BMBF) through the Project “Participatory Approach to Environmental Conservation of the Muringato Catchment Area for Sustainable Management and Enhanced Ecosystem Health” (CITGI4Muringato) under Grant Agreement No. 01DG20022.</p>
Remotely-sensed Arctic Discharge Reanalysis (RADR)
<p>This repository contains the dataset RADR generated from the work <em>Recent changes to Arctic river discharge (2021), Nature Communications, DOI: 10.1038/s41467-021-27228-1</em></p> <p>The authors caution users that discrepancy in flow magnitude exists for a few small rivers between 1984-2013 and 2014-2019 due to the different climate forcings used for these two periods.</p>
Orthophotos, DSMs and interpretation files of the remote sensing assessment of archaeological damage and destruction at Nineveh, Iraq, during the ISIS occupation
<p>Archaeological heritage has long been threatened by damage or destruction during armed conflicts. Recently, however, deliberate destruction has increasingly become a major part of daily threats in some areas. In that context these datasets describe the results of a programme of remote sensing of damage at Nineveh, within a wider research initiative involving six years of monitoring in northern Iraq. Analysis of satellite imagery, low-and level airphotography observation were combined in a comprehensive assessment of the damage. These datasets present an updated topographic map of Nineveh and its city walls, with a summary of the damage encountered.</p>
Stacked remote sensing indices covering OAL-Austria
<p>Stacked indices derived from Sentinel-2A/B imagery (processing level 2A) covering the period from 2017/04/24 to 2022/01/16</p> <p># Normalized Difference Vegetation Index(Rouse etal. 1974) NDVI = (NIR ‒ R)/(NIR + R)<br> # Visible Difference Vegetation Index (Wang et al. 2015) VDVI = ((2*G) - R - B)/((2 * G) + R + B)<br> # Enhanced vegetation index (Schwieder et al. 2022) EVI=G*(nir-red)/(nir+C1*red-C2*blue+X)<br> # Excess green index ExGI=2*g-(r+b)<br> # Green chromatic coordinate GCC=g/(r+g+b)<br> # Normalized difference moisture index (Lastovicka et al. 2020) NDMI = (NIR − SWIR) / (NIR + SWIR)</p>
Remote Sensing VQA - Low Resolution (RSVQA LR)
<p>Remote sensing images contain a wealth of information which can be useful for a wide range of tasks including land cover classification, object counting or detection. However, most of the available methodologies are task-specific, thus inhibiting generic and easy access to the information contained in remote sensing data. As a consequence, accurate remote sensing product generation still requires expert knowledge. With RSVQA, we propose a system to extract information from remote sensing data that is accessible to every user: we use questions formulated in natural language and use them to interact with the images. With the system, images can be queried to obtain high level information specific to the image content or relational dependencies between objects visible in the images. Using an automatic method, we built two datasets (using low and high resolution data) of image/question/answer triplets. The information required to build the questions and answers is queried from OpenStreetMap (OSM). The datasets can be used to train (when using supervised methods) and evaluate models to solve the RSVQA task.</p> <p>This page concerns the low resolution dataset.</p>
Remote Sensing VQA - High Resolution (RSVQA HR)
<p>Remote sensing images contain a wealth of information which can be useful for a wide range of tasks including land cover classification, object counting or detection. However, most of the available methodologies are task-specific, thus inhibiting generic and easy access to the information contained in remote sensing data. As a consequence, accurate remote sensing product generation still requires expert knowledge. With RSVQA, we propose a system to extract information from remote sensing data that is accessible to every user: we use questions formulated in natural language and use them to interact with the images. With the system, images can be queried to obtain high level information specific to the image content or relational dependencies between objects visible in the images. Using an automatic method, we built two datasets (using low and high resolution data) of image/question/answer triplets. The information required to build the questions and answers is queried from OpenStreetMap (OSM). The datasets can be used to train (when using supervised methods) and evaluate models to solve the RSVQA task.</p> <p>This page is about the high resolution dataset.</p>
Data for Li et al., Coupling remote sensing and particle tracking to estimate trajectories in large water bodies, International Journal of Applied Earth Observation and Geoinformation, 2022
<p>This data set contains four parts:</p> <p>1) compressed folder with input parameters and results for the hydrodynamic model</p> <p>2) compressed folder with input parameters and results for the particle tracking</p> <p>3) compressed folder with satellite data </p> <p>4) code used in the article for hydrodynamic model, particle tracking and image processing</p> <p>Each folder contains a readme file,</p>
Data for the publication "Retrieving ice-nucleating particle concentration and ice multiplication factors using active remote sensing validated by in situ observations"
<p>This repository contains the data for the paper:</p> <p>Wieder, J., Ihn, N., Mignani, C., Haarig, M., Bühl, J., Seifert, P., Engelmann, R., Ramelli, F., Kanji, Z. A., Lohmann, U., and Henneberger, J.: Retrieving ice nucleating particle concentration and ice multiplication factors using active remote sensing validated by in situ observations, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2022-67, in review, 2022.</p> <p>More information can be found in the README files.</p> <p>Note that the scripts to reproduce the figures of the publication are available on request.</p>
Remote sensing of river discharge (RSQ) estimates derived from multi-temporal Landsat width observations and BAM/geoBAM discharge inversion algorithms
<p><strong>This repository provides three data files in CSV format:</strong><br> 1. Gauge name, lat/lon information<br> 2. Gauge name, date, and multi-temporal river width extracted from Landsat<br> 3. Gauge name, date, and BAM/geoBAM estimates of river discharge with monthly Q priors</p> <p>Note: the multi-temporal river width data were extracted from Landsat imageries using RivWidthCloud, where the river centerline/orthogonal line definition and the cross-section sampling strategies were made prior to, and different from Feng et al. (2022). So the width values may be different from Feng et al. (2022) at some locations due to these differences. The discharge estimates were derived from BAM/geoBAM algorithms with width-only observations. More details of the technical workflow and the inner workings of BAM/geoBAM were provided in the literature below and papers therein.</p> <p> </p> <p><strong>Reference:</strong></p> <p>Lin, P., D. Feng, C.J. Gleason, M. Pan, C.B. Brinkerhoff, X. Yang, H.E. Beck, R. Frasson (2023). Inversion of river discharge from remotely sensed river widths: a critical assessment at three-thousand global river gauges. <em>RSE</em>.</p> <p> </p> <p>Updated: 2022/6/17, 2023/1/19</p> <p> </p>
Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data
<p><strong>Overview:</strong> The Global Inundation Extent from Multi-Satellites (GIEMS; Prigent et al. 2007, Papa et al. 2010) downscaled at 15 arc-second (GIEMS-D15; Fluet-Chouinard et al. 2015) was produced through the downscaling of the GIEMS database (natively at 0.25°). The downscaling procedure predicts the location of surface water cover with an inundation ranking surface generated by bagged decision trees. The decision trees were trained on binary presence/absence of wetland in the GLC2000 global land cover map (Bartholomé & Belward 2005) and used 13 topographic and hydrographic predictors derived from the SRTM-derived HydroSHEDS database (Lehner, Verdin & Jarvis 2008). The downscaling technique to three temporal aggregation of the GIEMS dataset representing three states of land surface inundation extents: mean annual minimum (MA<sub>Min</sub>; total area, 6.5 × 106 km<sup>2</sup>), mean annual maximum (MA<sub>Max</sub>; 12.1 × 106 km<sup>2</sup>), and long-term maximum (LT<sub>Max</sub>; 17.3 × 106 km<sup>2</sup>). The area of MAMin and MAMax from GIEMS were supplemented with the minimum area value from lakes, river and reservoirs from GLWD (Lehner & Döll 2004; classes 1,2,3). LTMax was corrected as the mean area from 3-year rolling maximum from GIEMS and the total wetland area from GLWD (classes 1-12). The accuracy of GIEMS-D15 reflects distribution errors introduced by the downscaling process as well as errors from the original satellite estimates. Yet, a comparison against independent regional wetland maps showed adequate agreement over large floodplains and wetlands. GIEMS-D15 offers a higher resolution delineation of inundated areas than originally offered by GIEMS, allowing for the assessment of global freshwater resources and the study of large floodplain and wetland ecosystems.</p> <p><strong>Projection:</strong> WGS84 (EPSG:4326)</p> <p><strong>Geographic extent:</strong></p> <ul> <li>Longitude: -180° to 180°</li> <li>Latitude: -56° to 84°</li> </ul> <p><strong>Spatial resolution: </strong>15 arc-second (500m at equator)</p> <p><strong>Legend</strong> (for discrete pixel values):</p> <ul> <li>0 = Upland</li> <li>1 = Mean Annual Minimum (MA<sub>Min</sub>)</li> <li>2 = Mean Annual Maximum (MA<sub>Max</sub>)</li> <li>3 = Long Term Maximum (LT<sub>Max</sub>)</li> </ul>
Coswara: A respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection
<p>Coswara is a dataset containing diverse set of respiratory sounds and rich meta-data from COVID-19 positive and Non-COVID subjects.</p>
Data set: UAS-based optical- and thermal infrared remote sensing of the fumarole field of La Fossa cone, Vulcano Island (Italy), reveals the degassing and hydrothermal alteration structure
<p>This is the data set supporting the paper "Anatomy of a fumarole field; drone remote sensing and petrological approaches reveal the degassing and alteration structure at La Fossa cone, Vulcano Island, Italy" (DOI: <a href="https://doi.org/10.5194/egusphere-2023-1692" target="_blank" rel="noopener noreferrer">10.5194/egusphere-2023-1692</a>).</p> <p> </p> <p><strong>Short description of the study:</strong> Hydrothermal alteration is common on actively degassing volcanoes and can lead to significant changes in the physical and chemical properties of the volcanic rocks, such as changes in permeability or rock strength. Despite the potentially far-reaching consequences of hydrothermal alteration for volcano stability, less is known about the detailed structures and dynamics of degassing and alteration systems. In this study, we use UAS-derived high-resolution data to analyze the fumarole field at La Fossa cone, Vulcano Island (Italy), aiming to better understand the structures and dynamics of volcanic degassing and alteration systems. By combining Principal Component Analysis, image analysis, and classification applied to high-resolution optical data and analysis of thermal infrared data, we resolve the detailed structure of the surficial degassing and alteration system based on optical and thermal anomalies. We identified characteristic anomaly patterns that indicate local degassing and alteration variability, and larger units of diffuse activity that, next to high-temperature fumaroles, contribute significantly to the total activity. We compared the observed anomaly patterns with the mineralogical and geochemical composition of representative rock samples, and with the surface degassing activity, and are able to provide the anatomy of the La Fossa fumarole field at great resolution. We show local alteration gradients, the presence of larger diffuse active complexes, and evidence for dynamic processes associated with the hydrothermal alteration. For more details, please read on: "<em>Müller, D., Walter, T. R., Troll, V. R., Stammeier, J., Karlsson, A., De Paolo, E., ... & De Jarnatt, B. (2023). Anatomy of a fumarole field; drone remote sensing and petrological approaches reveal the degassing and alteration structure at La Fossa cone, Vulcano Island, Italy. EGUsphere, 2023, 1-45. </em> https://doi.org/10.5194/egusphere-2023-1692".</p> <p> </p> <p> </p> <p><strong>Data set:</strong> We provide a UAS-based high-resolution dataset covering the whole La Fossa cone, including aerial Orthomosaic, Digital Elevation Model, and a Temperature Map derived from an airborne optical- and thermal infrared sensor (acquired in 2018 and 2019). </p> <p>The dataset is organized in 1) photogrammetric data, and 2) relevant processing results and related data. <strong>Filenames</strong> are written in bold letters and are a composite of the file type and the date (YYYYMMDD). </p> <p> </p> <p> </p> <p><strong>1) Photogrammetric data: </strong></p> <ul> <li><strong>Orthomosaic_20191114.tif</strong> is the in Agisoft Metashape processed orthomosaic of a 150 m (above fumarole field) optical overflight (DJI Phantom 4 Pro camera). </li> <li><strong>DigitalElevationModel_20191114.tif</strong> is the in Agisoft Metashape processed Digital Elevation Model (DEM) from the above-mentioned 150 m overflight. </li> <li><strong>Hillshade_20191114.tif</strong> is the 2.5-D representation of the DigitalElevationModel_20191114. Note, for viewing use a stretched (black to white) color scale.</li> <li><strong>TemperatureMap_20181115.tif</strong> is showing the apparent surface temperature for the La Fossa cone, acquired by a Flir Tau 2 thermal infrared camera at ~150 m (above fumarole field) flight altitude in the early morning hours (before sunrise) of 15 November 2018. Note that apparent temperatures shown may underestimate real in situ fumarole temperatures due to pixel-to-vent size ratios and atmospheric- or gas-plume distortion effects. Note further that the data has some processing artifacts, due to blind pixels of our IR camera system. For more detailed information or an updated data set please contact dmueller@gfz-potsdam.de.</li> <li><strong>T_20to40C.tif</strong> shows the diffuse thermally active surface at the fumarole field of the La Fossa cone (units a-g, see Fig. 4 in "Anatomy of a fumarole field...", https://doi.org/10.5194/egusphere-2023-1692). This raster shows the extracted pixels from TemperatureMap_20181115 in the range of 22 - 40 °C.</li> <li><strong>T_higher40C.tif</strong> outlines the high-temperature fumarole locations of the La Fossa fumarole field (HTF, see Fig. 4 in "Anatomy of a fumarole field...", https://doi.org/10.5194/egusphere-2023-1692), based on the extracted pixels with temperatures > 40 °C from TemperatureMap_20181115.</li> </ul> <p>Shapefiles for temperatures > 40 °C representing the high-temperature fumarole locations (HTF) and for temperatures of 20 - 40 °C representing diffuse active units, are attached at the end of the upload list and named <strong>T_higher40C_polygon</strong> and <strong>T_20_40C_polygon</strong> and consist of multiple files per shapefile with the file extensions .CPG, .dbf, .prj, .sbn, .sbx, .shp, .shp.xml, .shx. </p> <p>The coordinate system of the data sets is WGS84 EPSG:4326. For nadir projection use WGS 84 / UTM zone 33N - EPSG:32633. Note that the data might have horizontal and vertical offsets in the typical range of SfM-derived products with single-band GPS accuracy.</p> <p> </p> <p> </p> <p><strong>2) Relevant processing steps and related data:</strong></p> <ul> <li>Step 1) Principal Component Analysis applied to Orthomosaic_20191114 results in the following 3 Principal Components (decorrelated variance representations of the initial RGB bands): <ul> <li><strong>1_PCA_PC1.tif </strong>1st principal component </li> <li><strong>1_PCA_PC2.tif</strong> 2nd principal component</li> <li><strong>1_PCA_PC3.tif</strong> 3rd principal component - highlights well the effects of concentrated and diffuse degassing, resulting in different alteration effects from a simple shift from reddish oxidized surface to gray, up to strong silicic alteration effects. This can be used to extract the data of interest, the hydrothermally altered surface, and to create a new alteration sub-dataset. </li> </ul> </li> <li>Step 2) Extraction of hydrothermally altered surface / alteration sub-dataset <ul> <li><strong>2_alteration_subdata_RGB.tif</strong> The alteration sub-data set was extracted from the original Orthomosaic_20191114 based on a mask obtained from Principal Component 3 (1_PCA_PC3) for values > 85. The resulting raster data set is an extract of the original RGB data.</li> </ul> </li> <li>Step 3) PCA applied to 2_alteration_subdata_RGB will adjust to the reduced spectral range of the alteration sub-data set, provide a more sensitive variance representation, and highlight variability within the hydrothermally altered surface. <ul> <li><strong>3_PCA_PC1.tif</strong> 1st principal component of 2_alteration_subdata_RGB</li> <li><strong>3_PCA_PC2.tif</strong> 2nd principal component of 2_alteration_subdata_RGB</li> <li><strong>3_PCA_PC3.tif</strong> 3rd principal component of 2_alteration_subdata_RGB</li> </ul> </li> <li>Step 4) Unsupervised classification <ul> <li><strong>4_classification.tif</strong> is the unsupervised classification result of 3_PCA (all Principal Components), classified into 32 classes to achieve a high class resolution. When combining different classes, they form larger spatial units / surface types with similar spectral characteristics. This way, we divide the alteration surface into 3 surface types (see Fig. 4B in "Anatomy of a fumarole field..." DOI: 10.5194/egusphere-2023-1692) representing different alteration gradients and important structural units. To achieve the same results, combine classes 1 -19 (surface type 3), 20 - 25 (surface type 2), 26 - 30 (surface type 1), and 31 - 32 for sulfur/fumarole plume. See Image <strong>optical_structure.jpg</strong> for comparison. </li> </ul> </li> </ul> <p>Note that Principal Components and Classification of Principal Components highlight data variability along the axes of highest data variance. Results have to be evaluated carefully and may be valid only locally. They are efficient for identifying variability in degassing and alteration areas, but at the same time may also highlight certain fractions of vegetation or settlements for instance. We evaluated the structure defined by our classification results by analyzing the thermal structure (<strong>thermal_structure.jpg</strong>) of the fumarole field and additional geochemical- and mineralogical investigations (XRD and XRF) of rock samples and by measuring the diffuse degassing from surface (see "Anatomy of a fumarole field..." DOI: 10.5194/egusphere-2023-1692) to prove that the observed degassing/alteration units are true.</p> <p>To highlight alteration effects throughout the entire La Fossa cone, including the southern inner and outer crater rim, the alteration zones of La Forgia, or alteration on the outer flanks of La Fossa e.g. the 1988 Landslide, we provide the raster <strong>La_Fossa_alteration.tif </strong>and image <strong>La_Fossa_alteration.jpg (</strong>Note that the color scale for strong alteration (classes 31 - 32) was changed from white to purple for highlighting purpose).</p> <p> </p> <p>In case of further questions about the dataset, please contact dmueller@gfz-potsdam.de.</p> <p> </p> <p> </p> <p> </p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.