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98 results for “UAS”
BST/NOAA PSL Level 3 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH
<p>This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). While Level 2 provides each product at their highest retrieved spatial resolution, Level 3 provides all four products on a common grid at each flight location. These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 18th, 2023. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies, Inc. </p> <p> </p> <p>Each Level 3 NetCDF file contains all four UAS parameters at a flight location interpolated to a common rectilinear grid at ~50 cm resolution. Soil moisture retrievals were downscaled to a higher resolution grid using bilinear interpolation while surface temperature, NDVI, and digital elevation were upscaled to a lower resolution grid using conservative interpolation. The data was regridded using the Python package xESMF which is based on code developed for the Earth System Modeling Framework (ESMF) project. </p> <p> </p> <p>The file name convention for the Level 3 NetCDF files is as follows.</p> <p> </p> <p>uas_L3_yyyymmdd_hhmmss_vx.x.nc</p> <p>where</p> <p>L3 = Level 3 data </p> <p>yyyymmdd = year,month,day</p> <p>hhmmss = hour,minute,second</p> <p>x.x = version number </p> <p>Time is the flight start time in UTC.</p> <p>Version number description is provided in the NetCDF global attributes.</p> <p> </p> <p>Note that each flight location using the E2 aerial platform required two flights with different starting flight times for the soil moisture and the other three products. The flight start time is the time of the first flight. The total time for the two flights at each location was ~1 hour. </p> <p><strong>November 2023 update</strong>: Version 2.0 added flight data from 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data, and a mask was applied to the soil moisture retrieval over water surfaces for both 2022 and 2023 data. Version 2.1 adds data file uas_L3_20221018_171650_v2.1.nc that was missing in Version 2.0.</p> <p><strong>December 2023 update</strong>: Version 2.2 updated soil moisture data with a wet bias in v2.1 for flights #2 (17:40:35 UTC) and #3 (19:24:45 UTC) on July 27, 2022.</p>
IDLAB-UA Dataset for Traffic Classification using Spectrum Data
<p>This dataset contains IQ values of physical layer (L1) packets associated with WLAN transmission and the set of labels that associated each of the packets to properties/features at different radio stack layer (from L1 to L7). </p>
Solotvyno hazard&risk maps_ImProDiReT-783232_UA
<p>As a result of the analysis of geological natural environment of Solotvyno, several natural and anthropogenic processes those are potentially dangerous for the population have been identified: karst and suffosion (subsidence, sinkholes, collapses), seasonal and flash floods, flooding, slope erosion, landslides.</p> <p>A set of hazards / risk maps has been elaborated based on expert complex assessment of the natural and anthropogenic hazardous processes manifestations:</p> <p>1. Inventory map of hazardous technogenic-geological and engineering-geological processes manifestations and phenomena for Solotvyno</p> <p>2. Zonation of the hazardous technogenic-geological and engineering-geological processes manifestations;</p> <p>3. Specific land use for Solotvyno;</p> <p>4. Category of land for Solotvyno (according to StateGeoCadastre data);</p> <p>5. Risk Map of Natural and Natural-Antropogenic Hazards for Solotvyno;</p> <p>6. Risk Map of Natural and Natural-Antropogenic Hazards for Solotvyno (with critical infrastructure objects).</p>
BST/NOAA PSL Level 2 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH
<p>This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 18th, 2023. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies LLC. </p> <p> </p> <p>Each zip file contains a set of four Level 2 NetCDF files which provides the highest spatial resolution available for each of four products for a given flight location. With the Level 2 data, each flight location and variable can have different spatial resolutions depending on the sensor type, retrieval algorithm, and flight altitude. The file name convention for the zip files is as follows.</p> <p> </p> <p>uas_L2_yyyymmdd_hhmmss_vX.X.zip </p> <p>where</p> <p>L2 = Level 2 data </p> <p>yyyymmdd = year,month,day</p> <p>hhmmss = hour,minute,second</p> <p>vX.X = version number</p> <p>Time is the flight start time in UTC.</p> <p> </p> <p>The NetCDF file format contained in the zip files has a similar format to the zip files with convention</p> <p> </p> <p>uas_<var>_L2_yyyymmdd_hhmmss.nc </p> <p>where</p> <p><var> = vsm, dem, ndvi, or stmp</p> <p>vsm = volumetric soil moisture</p> <p>dem = digital elevation</p> <p>ndvi = normalized difference vegetation index</p> <p>stmp = surface temperature</p> <p> </p> <p>Note that each flight location using the E2 aerial platform required two flights so starting flight times for the soil moisture NetCDF files are different from the other three products.</p> <p><strong>November 2023 update</strong>: Version 2.0 added flight data from 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data, and a mask was applied to the soil moisture retrieval over water surfaces for both 2022 and 2023 data.</p> <p><strong>December 2023 update</strong>: Version 2.1 updated soil moisture data with a wet bias in v2.0 for flights #2 (17:40:35 UTC) and #3 (19:24:45 UTC) on July 27, 2022.</p>
DUCC - Dataset for UAS Cellular Communications
<p><strong>Motivation</strong><br>The Dataset for Unmanned Aircraft System (UAS) Cellular Communications, short DUCC, was created with the aim of advancing communications for Beyond Visual Line of Sight (BVLOS) operations. With this objective in mind, datasets were generated to analyse the behaviour of cellular communications for UAS operations.</p> <p><strong>Measurement</strong><br>A measurement setup was implemented to execute the measurements. Two Sierra Wireless EM9191 modems possessing both LTE and 5G capabilities were utilized in order to establish a connection to the cellular network and measure the physical parameters of the air-link. Every modem was equipped with four Taoglas antennas, two of type TG 35.8113 and two of type TG 45.8113. To capture the measurements a Raspberry Pi 4B is used. All hardware components were integrated into a box and attached to a DJI Matrice 300 RTK. A connection to the drone controller has been established to obtain location, speed and attitude. To measure end-to-end network parameters, dummy data was exchanged bidirectionally between the Raspberry Pi and a server. Both the server as well as the Raspberry Pi are synchronized with the GPS time in order to measure the one-way packet delay. For this purpose, we utilised Iperf3 and customised it to suit our requirements. To ensure precise positioning of the drone a Real Time Kinematik (RTK) station was placed on the ground during the measurements.</p> <p>The measurements were performed at three distinct rural locations. Waypoint flights were undertaken with the points arranged in a cuboid formation maximizing the coverage of the air volume. Thereby, the campaigns were conducted with varying drone speeds. Moreover, for location A, different flight routes with rotated grids were implemented to reduce bias. Finally, a validation dataset is provided for location A, where the waypoints were calculated according to Quality of Service (QoS) based path-planning.</p> <p><strong>Dataset Structure and Usage</strong><br>The dataset's structure consists of:<br>-- Dataset<br> |-- LocationX<br> |-- RouteX (in case different routes at LocationX were created)<br> |-- LocXRouteX.kml (file containing the waypoints in the kml format)<br> |-- SpeedXMeterPerSecond (folder containing the datasets recorded with a specific drone speed)<br> |-- YYYY-MM-DD hh_mm_ss.s.pkl.gz (Dataset file)<br> |-- RouteY<br> |-- ...<br> |-- ...</p> <p>The dataset files can be loaded using the pandas module in python3. The file "load.py" provides a sample script for loading a dataset as well as the corresponding .kml file which contains the predefined waypoints. In the file "Parameter_Description.csv" each parameter measured is further explained.</p> <p><strong>License</strong><br>All datasets are copyright by us and published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International. This means that you must attribute the work in the manner specified by the authors, you may not use this work for commercial purposes and if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license. This dataset is made available for academic use only. However, we take your privacy seriously! If you find yourself or personal belongings in this dataset and feel unwell about it, please contact us at automotive@oth-aw.de and we will immediately remove the respective data from our server.</p> <p><strong>Achnowledgement</strong><br>The authors gratefully acknowledge the following European Union H2020 -- ECSEL Joint Undertaking project for financial support including funding by the German Federal Ministry for Education and Research (BMBF): ADACORSA (Grant Agreement No. 876019, funding code 16MEE0039).</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>
High precision photogrammetry data of Lascar Volcano acquired by UAS survey in 2017 and 2020
<p>Here we present a high precision photogrammetry dataset of Lascar summit crater, which acquired by unmanned aircraft system (UAS) in Nov 2017 and Feb 2020 respectively, and reconstructed by Structure-from-Motion (SfM) method. In which, optical orthomosaic and DEM (Digital Elevation Model) were processed in Agisoft Metashape (version 1.7.3), preprocessing of thermal data was conducted in Thermoviewer (v3.0.7) and thermal mosaic was generated by Pix4Dmapper (v4.5.6). All data was projected to global coordinates (WGS 1984 UTM Zone 19 South). Thermal orthomosaic was georeferenced to 2020 orthomosaic in ArcMap (version 10.8). Employed UAS and SfM-derived product are given as follows:</p> <p>(1) 2017 orthomosaic (7.7 cm/pix) and DEM (15.6 cm/pix): DJI Mavic Pro Platinum </p> <p>(2) 2020 orthomosaic (7.0 cm/pix) and DEM (13.7 cm/pix): DJI Phantom 4 RTK</p> <p>(3) 2020 additional orthomosaic (5.3 cm/pix): DJI Mavic 2</p> <p>(4) 2020 thermal orthomosaic (spatial resolution: 45.0 cm/pix, radiometric resolution: 0.04 degree/pix): FLIR Tau 2 640 attached to DJI Phantom 4 RTK</p>
HELiX UAS data for SPLASH 2022
<p>This dataset includes data files collected during flights of the HELiX uncrewed aircraft system (UAS) for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH). Flights were conducted at the Avery Picnic and Kettle Ponds SPLASH sites during spring 2022 and capture the evolution of the snow surface during spring snow melt.</p>
UAS survey on a small earthen dam in Romania
<p>The present dataset provides the UAS imagery and the GPCs of a survey carried out within the HARMONIOUS COST Action on the earthen dam of Pischia in Romania. The subset could be used to test SfM methods and the results can be compared with those provided on the manuscript by Manfreda et al. (2019). It important to clarify that this is just a subset of the series of surveys described in the mentioned manuscript and is made available for training exercises. </p> <p>Reference</p> <p>Manfreda, S., P. Dvorak, J. Mullerova, S. Herban, P. Vuono, J.J. Arranz Justel, M. Perks, <strong>Assessing the Accuracy of Digital Surface Models Derived from Optical Imagery Acquired with Unmanned Aerial Systems</strong>, <strong>Drones</strong>, 3(1), 15; (doi: 10.3390/drones3010015), 2019.</p>
◂Fig. 1 Morphology of thecate and coccoid cells, with labelled thecal plates. a–c, i, m Light microscopy, d–h, k–l scanning electron microscopy. a Ventral view of strain GeoM*788; b dorsal view of strain GeoM*793; c apical view of strain GeoK*044; d ventral view of strain GeoK*037; e dorsal view of strain GeoM*788; f apical view of strain GeoK*024, with the dehiscence of epithecal opening indicated by a blue line; g antapical view of strain GeoK*044; h leftlateral view of strain GeoM*866; i motile cell of strain GeoK*037; k–m coccoid cells showing variability in shape and size of strains k GeoM*866, l GeoM*793 and m GeoK*024. Abbreviations: n′: apical plate, n′′: precingular plate, n′′′: postcingular plate, n′′′′: antapical plate, na: anterior intercalary plate, nC: cingular plate, Sa: anterior sulcal plate, Sd: right sulcal plate, Sp: posterior sulcal plate. Ss: left sulcal plate. Scale bar: 10 µm. UA: 15 kV in Morphological and molecular variability of Peridinium volzii Lemmerm. (Peridiniaceae, Dinophyceae) and its relevance for infraspecific taxonomy
◂Fig. 1 Morphology of thecate and coccoid cells, with labelled thecal plates. a–c, i, m Light microscopy, d–h, k–l scanning electron microscopy. a Ventral view of strain GeoM*788; b dorsal view of strain GeoM*793; c apical view of strain GeoK*044; d ventral view of strain GeoK*037; e dorsal view of strain GeoM*788; f apical view of strain GeoK*024, with the dehiscence of epithecal opening indicated by a blue line; g antapical view of strain GeoK*044; h leftlateral view of strain GeoM*866; i motile cell of strain GeoK*037; k–m coccoid cells showing variability in shape and size of strains k GeoM*866, l GeoM*793 and m GeoK*024. Abbreviations: n′: apical plate, n′′: precingular plate, n′′′: postcingular plate, n′′′′: antapical plate, na: anterior intercalary plate, nC: cingular plate, Sa: anterior sulcal plate, Sd: right sulcal plate, Sp: posterior sulcal plate. Ss: left sulcal plate. Scale bar: 10 µm. UA: 15 kV
UAS-SfM data from Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA
<p>Data for:</p> <p>Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA<br>Sean Reilly 1, Matthew L. Clark 2, Lika Loechler 2, Jack Spillane 2, Melina Kozanitas 3, Paris Krause 4, David Ackerly 3, Lisa Patrick Bentley 4, and Imma Oliveras Menor 1,5</p> <p>1 Environmental Change Institute, University of Oxford, Oxford OX1 3QY, UK<br>2 Center for Interdisciplinary Geospatial Analysis, Department of Geography, Environment, and Planning, Sonoma State University, Rohnert Park, CA 94928, USA<br>3 Departments of Integrative Biology and Environmental Science, Policy, and Management, University of California, Berkeley, CA 94720, USA<br>4 Department of Biology, Sonoma State University, Rohnert Park, CA 94928, USA<br>5 AMAP (Botanique et Modélisation de l’Architecture des Plantes et des Végétations), CIRAD, CNRS, INRA, IRD, Université de Montpellier, Montpellier, France</p> <p>Study abstract:</p> <p>There is a pressing need for well-informed management to reduce wildfire hazard and restore fire’s beneficial ecological role in the Mediterranean- and temperate-climate forests of California, USA. These efforts rely upon the accessibility of high spatial and temporal resolution data on biomass and canopy fuel parameters such as canopy base height (CBH), mean canopy height, canopy bulk density (CBD), canopy cover, and leaf area index (LAI). Remote sensing using unoccupied aerial system Structure-from-Motion (UAS-SfM) presents a promising technology for this application due to its accessibility, relatively low cost, and possibility for high temporal cadence. However, to date, this method has not been studied in the complex mosaic of forest types found across California. In this study we examined the capacity of structural and multispectral information obtained from UAS-SfM, in conjunction with machine learning methods, to model aboveground biomass and forest canopy fuel structural parameters using an area-based approach across multiple sites representing a diversity of forest types in California.</p> <p>Based on correlations with field measurements, fuel parameters separated into vertical (biomass, CBH, and mean height) and horizontal (LAI, CBD, canopy cover) groups. UAS-SfM random forest models performed well for modelling the vertical structure canopy fuels parameters (R2 0.69 – 0.75). These models exhibited strong performance in comparison to ALS, as well as when transferred to a novel site. Vertical structure predictors were prominent in these models, and did not improve with the addition of spectral predictors. UAS-SfM random forest models of horizontal structure parameters mainly used raster-based spectral indices (primarily NDVI) and had relatively low performance (R2 0.49 – 0.59). In addition, these models underperformed ALS and had poor performance when applied to a novel site. When applied to a region with widespread UAS-SfM coverage, models from both groups successfully produced contiguous maps that could be used for modelling fire behavior or in management decision making and monitoring.</p> <p>These findings indicate that UAS-SfM, without the need for multispectral sensors, is well suited for mapping area-based vertical-structure canopy parameters across diverse landscapes supporting a wide range of forest types. In contrast, the identification of spectral mean variables for modelling horizontal structure canopy fuels suggests the potential of multi- or hyperspectral sensors or high-resolution satellite imagery for meeting management information needs. </p> <p>Published in Remote Sensing of Environment</p> <p><br>Contents:</p> <p>This repository contains multispectral UAS-SfM data from four sites around California, USA:<br>jcksn: Jackson Demonstration State Forest<br>ltr: LaTour Demonstration State Forest<br>ppwd: Pepperwood Preserve<br>sdlmtn: Saddle Mountain Open Space Preserve</p> <p>Data were collected during a series of campaigns:<br>c1: Pepperwood, 2019-09-01 to 2019-10-15<br>c3: Jackson, 2020-06-15 to 2020-07-02<br>c4: LaTour, 2020-07-07 to 2020-07-17<br>c6: Saddle Mountain, 2020-08-04 to 2020-08-09<br>c9: Jackson, 2021-07-08 to 2021-07-12</p> <p>Data are included in three formats:<br>raw: Raw outputs from Pix4D (spectral and las)<br>reg_grnd, reg_cnpy: Las files with merged multispectral data and classified ground, registered to ALS using either ground points (grnd) or, in cases with insufficient ground points for registration, to the canopy (cnpy)<br>hnrm: Height normalized las files, normalization performed using ALS terrain model</p> <p>File naming structure:<br>site_campaign_flightzone_uas_processedstate</p> <p>See accompanying paper for methods on data collection and processing</p> <p>Data are grouped into zipped folder by product type</p> <p>Funding:</p> <p>Funding for this research was supported by CAL FIRE Forest Health and Forest Legacy (8GG18806) and California State University, Agricultural Research Institute (20-01-106) awards to L.P.B and M.L.C. S.R. was funded by the Rhodes Trust and through the University of Oxford Environmental Change Institute Small Grant Scheme. Pepperwood ground data collection was supported by funding from the Gordon and Betty Moore Foundation and National Science Foundation grants 1754475 and 1835086.</p> <p>Citation:</p> <div> <div>Reilly, S., Clark, M.L., Loechler, L., Spillane, J., Kozanitas, M., Krause, P., Ackerly, D., Bentley, L.P., Menor, I.O., 2024. Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA. Remote Sensing of Environment 312, 114310. <a href="https://doi.org/10.1016/j.rse.2024.114310">https://doi.org/10.1016/j.rse.2024.114310</a></div> </div> <p> </p> <p> </p>
Text-fig. 1. Schematic drawing, demonstrating the basic endoskeletal elements of the craniate head. Sclerotomic derivatives red and pharyngoqualar derivatives yellow. b: basal pharyngoqualar segment; c: ceratal pharyngoqualar segment; ct: cartilago teniformis (blue); e: epal pharyngoqualar segment; gr: gill rays; h: hypal pharyngoqualar segment; la: lower arcual element; lt: laterotectal element; mt: mediotectal element; n: notochord (green); s: summital pharyngoqualar segment; t: telical pharyngoqualar segment; ua: upper arcual element. in Cartilago Teniformis And Its Derivatives: Additional Information On The Basic Composition And Evolution Of The Skull
Text-fig. 1. Schematic drawing, demonstrating the basic endoskeletal elements of the craniate head. Sclerotomic derivatives red and pharyngoqualar derivatives yellow. b: basal pharyngoqualar segment; c: ceratal pharyngoqualar segment; ct: cartilago teniformis (blue); e: epal pharyngoqualar segment; gr: gill rays; h: hypal pharyngoqualar segment; la: lower arcual element; lt: laterotectal element; mt: mediotectal element; n: notochord (green); s: summital pharyngoqualar segment; t: telical pharyngoqualar segment; ua: upper arcual element.
Comparison of tetracycline and temperature sensitive GAL80 transgenes with UAS-lacZ
<p>ß-galactosidase specific activity was measured during development and aging (Experimental workflow) as described in <a href="https://star-protocols.cell.com/protocols/2150">STAR Protocols 3, 101843, 2022</a>. Two different muscle-specific GAL4 drivers, Mef2-Gal4 (BDSC:27390) and DJ694 (BDSC: 8176), were each crossed with a second chromosome insertion of UAS-lacZ (Bg2) (BDSC: 1776), Bg2 recombined with third chromosome insertions of TetOFF-GAL80 (3.3+1077, <a href="https://peerj.com/articles/4167/">PeerJ 5:e4167</a>), and Bg2 recombined with third chromosome insertions of GAL80ts (BDSC:7017). Flies were raised at 20˚C and 29℃. Two independent sets of parents were used for each cross (biological replicates 1 and 2). After 2-3 days, parents were flipped into a new bottles (technical replicates 1 and 2 reported as replicate 3 and 4 in the dataset). CPRG and Bradford assays were performed on whole animal for the third instar larvae (L3), early pupae (EP) and late pupae (LP) stages, and on dissected thoraces for adult stages. Five extracts (1 individual in each) were measured per experimental replicate. Raw microplate readings are available upon request.</p>
Comparison of tetracycline and temperature sensitive GAL80 transgenes with UAS-grim
<p>In order to assess the ability of GAL80 to repress the expression of a UAS transgene, lethality tests were performed in presence of a UAS-grim reporter (<a href="http://doi.org/10.1002/gene.10128"><em>Genesis</em> 34:34, 2002</a>, <a href="https://peerj.com/articles/4167/"><em>PeerJ</em> 5:e4167, 2017</a>). The Grim reporter gene encodes a strong pro-apoptotic factor, thus the lethality across development (embryonic, larval, pupal) can be scored to assess the repression ability of GAL80. The UAS-grim lethality test facilitates the determination of the developmental period during which GAL4 activity is not inhibited.</p> <p>Two different muscle-specific GAL4 drivers, Mef2-Gal4 (BDSC:27390) and DJ694 (BDSC: 8176), were each crossed with a second chromosome insertion of UAS-grim (<a href="https://doi.org/10.1038/sj.cdd.4400423">Cell Death Differ 5:930,1998</a>), UAS-grim recombined with third chromosome insertions of GAL80TET (3.3+1077, <a href="https://peerj.com/articles/4167/">PeerJ 5:e4167</a>), and UAS-grim recombined with third chromosome insertions of GAL80ts (BDSC:7017). Negative control (no lethality) was generated by crossing the UAS-grim strain with a strain without any GAL4 transgene (w<sup>1118</sup>,<a href="https://www.cell.com/cell/pdf/0092-8674(84)90240-X.pdf?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2F009286748490240X%3Fshowall%3Dtrue">Cell 36:469, 1984</a> ). Crosses were maintained, and eggs were collected at 20˚C (Experimental workflow). Parents were obtained from multiple independent cultures to set-up independent crosses (biological replicates, Set indicates parents from the same culture). Crosses were kept 24 to 48h in culture tubes to allow mating before transferring them to egg collectors. Multiple egg collections were done for each parental set (technical replicates, Date indicates dates of collection). Parents were allowed to lay eggs for 12h to 16h. Up to 25 eggs were aligned on a slice of food and up to 4 slices were used for a given collection from a set. Slices of food are then transferred to culture vials and incubated at the indicated temperature. The scoring of the number of first-instar larvae (L1) was done by scoring the number of empty eggs 25-30h (29˚C) or 43-48h (20˚C) after egg alignment. The number of pupae and adults was scored 5-6 days (29˚C) or 8-10 days (20˚C) after the scoring of the previous stage.</p>
TEAMx-PC22 (TEAMx pre-campaign 2022) – Vertical profiles and Multi-Point In-Situ Measurements at Nafingalm collected with the SWUF-3D UAS fleet
<p>This dataset contains aggregated measurements from a fleet of multicopter UAS. The data was measured during the period 21 June 2022 through 27 June 2022 at Nafingalm, Austria with the SWUF-3D fleet. The data was collected in association to the TEAMx-PC22 field campaign. A maximum of three UAS were operated simultaneously. Processed level-2 data is provided. For level-2 data, time synchronization between individual UAS was done through interpolation, if multiple UAS are operated simultaneously.</p> <p>In this dataset vertical profiles (swuf3dvpro) between 10m and 120m above ground level and time series of UAS hovering for approx. 10 minutes at fixed positions (swuf3dhover) are provided with a temporal resolution of 1 Hz.</p> <p>The data are provided in NetCDF format with metadata and variable descriptions in the style of the SAMD Product standard: Jahnke-Bornemann, Annika. (2022, August 18). The SAMD Product Standard (Standardized Atmospheric Measurement Data) (Version 2.2). http://doi.org/10.25592/uhhfdm.10416</p>
Hydrated DPPC, MD simulation trajectory and related files for UA charmm36 model by Lee et al 2014
<p>MD simulation files</p> <p>72 hydrated DPPC + 2189 water TIP3P </p> <p>NPgT</p> <p>P=1atm, gamma=0, T=323K (liquid crystalline phase)</p> <p>20 ns equilibration (not here)</p> <p>50 ns trajectory (dcd file)</p> <p>Model : Lee S, Tran A, Allsopp M, Lim JB, Hénin J, Klauda JB. CHARMM36 United Atom Chain Model for Lipids and Surfactants. <em>J Phys Chem B</em>. 2014;118(2):547-556. doi:10.1021/jp410344g.</p> <p>----<br /> * bilayer-72DPPC-c36-AU.psf : NAMD2.10 structure file Obtained with psfgen utility, using</p> <p>1) topology from Lee et al. 2014</p> <p>2) positions from J. Klauda.</p> <p>http://terpconnect.umd.edu/~jbklauda/research/download.html</p> <p>----</p> <p>* dppc_c36_AU.equil.2.dcd : trajectory file of 2635 frames every 20 ps.</p> <p>---<br /> * measure_SCD_heads.tcl : file used to measure order parameters for the head hydrogens using vmd-1.9</p> <p>---</p> <p>*namd_input.tar files usefull to launch the simulations using NAMD(2.10).</p>
Hydrated DPPC, MD simulation trajectory and related files for UA charmm36 model by Lee et al 2014
<p>MD simulation files</p> <p>72 hydrated DPPC + 2189 water TIP3P </p> <p>NPgT</p> <p>P=1atm, gamma=0, T=323K (liquid crystalline phase)</p> <p>20 ns equilibration (not here)</p> <p>50 ns trajectory (dcd file)</p> <p>Model : Lee S, Tran A, Allsopp M, Lim JB, Hénin J, Klauda JB. CHARMM36 United Atom Chain Model for Lipids and Surfactants. <em>J Phys Chem B</em>. 2014;118(2):547-556. doi:10.1021/jp410344g.</p> <p>----<br /> * bilayer-72DPPC-c36-AU.psf : NAMD2.10 structure file Obtained with psfgen utility, using</p> <p>1) topology from Lee et al. 2014</p> <p>2) positions from J. Klauda.</p> <p>http://terpconnect.umd.edu/~jbklauda/research/download.html</p> <p>----</p> <p>* dppc_c36_AU.equil.2.dcd : trajectory file of 2635 frames every 20 ps.</p> <p>---<br /> * measure_SCD_heads.tcl : file used to measure order parameters for the head hydrogens using vmd-1.9</p> <p>---</p> <p>*namd_input.tar files usefull to launch the simulations using NAMD(2.10).<br /> </p>
High resolution wind speed measurements with multicopters of the SWUF-3D UAS fleet - calibration and verification in a wind tunnel with active grid
<p>This dataset contains aggregated measurements from multicopter UAS. The data were measured during the period from October 5, 2022 to October 12, 2022 in the ForWind wind tunnel at the University of Oldenburg with UAS of the SWUF-3D fleet against Constant Temperature Anemometer (CTA). </p><p>Recorded data are provided for measurement flights in different generated wind profiles, i.e. staircase profiles, gusts, velocity steps and statistical turbulence. The measurement data consist of the accelerations measured by the UAS in its longitudinal and lateral axes, as well as the wind speeds measured by the CTA. The latter data were sampled down to the sampling rate of the UAS wind measurement. For the measurements in statistical turbulence, additional files are provided which contain the wind speeds measured by the CTA in its original sampling rate. Each file contains the data for a single measurement flight, as well as information in the header about the ambient conditions in the wind tunnel. The file names contain the following metadata:</p><p>for "gust" files:</p><ul><li>V0 : inertial velocity [m/s]</li><li>V_g : gust velocity amplitude [m/s]</li></ul><p>for "staircase" files:</p><ul><li>uas : the ID of the UAS used [-]</li><li>heading : yaw angle of UAS in relation to longitudinal axes of wind tunnel</li></ul><p>for "turbulence" files:</p><ul><li>V0 : fan wind speed [m/s]</li><li>I : turbulence intensity [%]</li><li>f_cta : sampling frequency of reference sensor [Hz] (for files with original sampling rate)</li></ul><p>for "velocity step" files:</p><ul><li>V0 : lower wind speed</li><li>V_du : wind speed aimed for of upward and downward velocity step</li></ul><p>All filenames end with the test date in YYYY-MM-DD format.</p>
Dataset_study_metadata_UA_Academic_events
<p>This is a dataset with research results among organizers of academic events and scientists in Ukraine for submission.</p>
Judaculla Rock - July 2017 UAS Imagery
This model was done by Dr. Jannie Loubser and Joel Logan, GISP on a field visit on 7/22/2017 for Straum Unlimited, LLC. This model was done after Stratum Unlimited completed a lengthy project of removing grafitti from the rock. This model was done entirely from photos from a DJI Phantom 4 Pro. This site can be visited by the public. It is on a one acre site that Jackson County, NC owns. Source: Objaverse 1.0 / Sketchfab
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.