The UAS Facility Maps are designed to identify permissible altitudes (above ground level) at which UAS, operating under the Small UAS Rule (14 CFR 107), can be authorized to fly within the surface areas of controlled airspace. These altitude parameters, provided by the respective air traffic control facilities, are criteria used to evaluate airspace authorization requests (14 CFR 107.41), submitted via FAA.GOV/UAS. Airspace authorization requests for altitudes in excess of the predetermined map parameters will require a lengthy coordination process. This dataset will be continually updated and expanded to include UAS Facility Maps for all controlled airspace by Fall 2017. This map is not updated in real time. Neither the map nor the information provided herein is guaranteed to be current or accurate. Reliance on this map constitutes neither FAA authorization to operate nor evidence of compliance with applicable aviation regulations in or during enforcement proceedings before the National Transportation Safety Board or any other forum. Disclaimer of Liability. The United States government will not be liable to you in respect of any claim, demand, or action—irrespective of the nature or cause of the claim, demand, or action—alleging any loss, injury, or damages, direct or indirect, that may result from the use or possession of any of the information in this draft map or any loss of profit, revenue, contracts, or savings or any other direct, indirect, incidental, special, or consequential damages arising out of any use of or reliance upon any of the information in this draft map, whether in an action in contract or tort or based on a warranty, even if the FAA has been advised of the possibility of such damages. The FAA’s total aggregate liability with respect to its obligations under this agreement or otherwise with respect to the use of this draft map or any information herein will not exceed $0. Some States, Territories, and Countries do not allow certain liability exclusions or damages limitations; to the extent of such disallowance and only to that extent, the paragraph above may not apply to you. In the event that you reside in a State, Territory, or Country that does not allow certain liability exclusions or damages limitations, you assume all risks attendant to the use of any of the information in this draft map in consideration for the provision of such information. Export Control. You agree not to export from anywhere any of the information in this draft map except in compliance with, and with all licenses and approvals required under, applicable export laws, rules, and regulations. Indemnity. You agree to indemnify, defend, and hold free and harmless the United States government from and against any liability, loss, injury (including injuries resulting in death), demand, action, cost, expense, or claim of any kind or character, including but not limited to attorney’s fees, arising out of or in connection with any use or possession by you of this draft map or the information herein. Governing Law. The above terms and conditions will be governed by the laws of each and every state within the United States, without giving effect to that state’s conflict-of-laws provisions. You agree to submit to the jurisdiction of the state or territory in which the relevant use of any of the information in this draft map occurred for any and all disputes, claims, and actions arising from or in connection with this draft map or the information herein.

Geospatial data about Federal Aviation Administration UAS Facility Map Data. Export to CAD, GIS, PDF, CSV and access via API.

The FAA LAANC Grid Zero Ceiling layer was developed for users who may need to quickly assess whether an object falls within a zero max ceiling grid from the FAA's UAS Facility Map Data.

The UAS Facility Maps are designed to identify permissible altitudes (above ground level) at which UAS, operating under the Small UAS Rule (14 CFR 107), can be authorized to fly within the surface areas of controlled airspace. These altitude parameters, provided by the respective air traffic control facilities, are criteria used to evaluate airspace authorization requests (14 CFR 107.41), submitted via FAA.GOV/UAS. Airspace authorization requests for altitudes in excess of the predetermined map parameters will require a lengthy coordination process. This dataset will be continually updated and expanded to include UAS Facility Maps for all controlled airspace by Fall 2017. This map is not updated in real time. Neither the map nor the information provided herein is guaranteed to be current or accurate. Reliance on this map constitutes neither FAA authorization to operate nor evidence of compliance with applicable aviation regulations in or during enforcement proceedings before the National Transportation Safety Board or any other forum. Disclaimer of Liability. The United States government will not be liable to you in respect of any claim, demand, or action-irrespective of the nature or cause of the claim, demand, or action-alleging any loss, injury, or damages, direct or indirect, that may result from the use or possession of any of the information in this draft map or any loss of profit, revenue, contracts, or savings or any other direct, indirect, incidental, special, or consequential damages arising out of any use of or reliance upon any of the information in this draft map, whether in an action in contract or tort or based on a warranty, even if the FAA has been advised of the possibility of such damages. The FAA's total aggregate liability with respect to its obligations under this agreement or otherwise with respect to the use of this draft map or any information herein will not exceed $0. Some States, Territories, and Countries do not allow certain liability exclusions or damages limitations; to the extent of such disallowance and only to that extent, the paragraph above may not apply to you. In the event that you reside in a State, Territory, or Country that does not allow certain liability exclusions or damages limitations, you assume all risks attendant to the use of any of the information in this draft map in consideration for the provision of such information. Export Control. You agree not to export from anywhere any of the information in this draft map except in compliance with, and with all licenses and approvals required under, applicable export laws, rules, and regulations. Indemnity. You agree to indemnify, defend, and hold free and harmless the United States government from and against any liability, loss, injury (including injuries resulting in death), demand, action, cost, expense, or claim of any kind or character, including but not limited to attorney's fees, arising out of or in connection with any use or possession by you of this draft map or the information herein. Governing Law. The above terms and conditions will be governed by the laws of each and every state within the United States, without giving effect to that state's conflict-of-laws provisions. You agree to submit to the jurisdiction of the state or territory in which the relevant use of any of the information in this draft map occurred for any and all disputes, claims, and actions arising from or in connection with this draft map or the information herein.

This dataset consists of UAS flight images from three sites along an elevation and precipitation gradient within Reynolds Creek Experimental Watershed collected between June 4 and July 9, 2019. The lowest elevation site ('wbs1', 1,425 m) was vegetated by shrub steppe dominated Wyoming big sage (Artemisia tridentata ssp. wyomingensis). Vegetation at the middle elevation site ('los1', 1,680 m) was shrub steppe dominated by low sage (Artemisia arbuscula). Shrub steppe at the highest elevation site ('mbs1', 2,110 m) was dominated by mountain big sage (Artemisia tridentata ssp. vaseyana) and Utah snowberry (Symphoricarpos oreophilus utahensis). A MicaSense RedEdge 3 sensor mounted on a DJI Matrice 600 Pro UAS platform was used to collect multispectral imagery of each site. The drone was flown by a Federal Aviation Administration (FAA) Part 107 certified remote pilot between June 5 and July 9 2019. All flights were completed within two hours of solar noon. The RedEdge is a broadband multispectral sensor: blue (475nm), green (560nm), red (668nm), red edge (717nm), and near-infrared (840nm). The RedEdge sensor was radiometrically calibrated using a reflectance panel before and after each flight. A DJI Phantom 4 with the stock FC330 Red Green Blue (sRGB) camera was flown over each site to collect imagery at a finer spatial resolution to assist with training and test data for vegetation type classification.Resources in this dataset:Resource Title: UAS Imagery and Location Data - SCINet.File Name: Web Page, url: https://app.globus.org/file-manager?origin_id=904c2108-90cf-11e8-9672-0a6d4e044368&origin_path=/LTS/ADCdatastorage/NAL/published/node424632/Folder containing imagery (.zip) and location (.csv) data. The .zip files contain unprocessed visual (RGB) imagery in .jpg format acquired with a 12-MP DJI (Sony) FC330 camera and unprocessed multispectral, 5-band imagery in .tif format acquired with a MicaSense RedEdge-M sensor. Camera settings and EXIF information are embedded in the imagery files. The .csv files contain ground control point (GCP) labels and coordinate information recorded with an RTK instrument for GCP target (black/white cross) locations at the relevant study areas.SCINet users: The files can be accessed/retrieved with valid SCINet account at this location: /LTS/ADCdatastorage/NAL/published/node424632/ See the SCINet File Transfer guide for more information on moving large files: https://scinet.usda.gov/guides/data/datatransferGlobus users: The files can also be accessed through Globus by following this data link. The user will need to log in to Globus in order to retrieve this data. User accounts are free of charge with several options for signing on. Instructions for creating an account are on the login page.

MotivationThe 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.

MeasurementA 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.

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.

Dataset Structure and UsageThe dataset's structure consists of:-- Dataset |-- LocationX |-- RouteX (in case different routes at LocationX were created) |-- LocXRouteX.kml (file containing the waypoints in the kml format) |-- SpeedXMeterPerSecond (folder containing the datasets recorded with a specific drone speed) |-- YYYY-MM-DD hh_mm_ss.s.pkl.gz (Dataset file) |-- RouteY |-- ... |-- ...

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.

LicenseAll 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.

AchnowledgementThe 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).

This snow depth map was generated 14 January 2015, close to peak of winter accumulation, applying Unmanned Aerial System digital surface models with a spatial resolution of 10 cm. The covered area is 285'000 m2 at the top of Brämabüel, 2490 m a.s.l. covering all expositions. Coordinate system: CH1903LV03. A detailed description is given here: Bühler, Y., Adams, M. S., Bösch, R., and Stoffel, A.: Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations, The Cryosphere, 10, 1075-1088, 10.5194/tc-10-1075-2016, 2016. Abstract: Detailed information on the spatial and temporal distribution, and variability of snow depth (HS) is a crucial input for numerous applications in hydrology, climatology, ecology and avalanche research. Nowadays, snow depth distribution is usually estimated by combining point measurements from weather stations or observers in the field with spatial interpolation algorithms. However, even a dense measurement network is not able to capture the large spatial variability of snow depth in alpine terrain. Remote sensing methods, such as laser scanning or digital photogrammetry, have recently been successfully applied to map snow depth variability at local and regional scales. However, such data acquisition is costly, if manned airplanes are involved. The effectiveness of ground-based measurements on the other hand, is often hindered by occlusions, due to the complex terrain or acute viewing angles. In this paper, we investigate the application of unmanned aerial systems (UAS), in combination with structure-from-motion photogrammetry, to map snow depth distribution. Such systems have the advantage that they are comparatively cost-effective and can be applied very flexibly to cover also otherwise inaccessible terrain. In this study we map snow depth at two different locations: a) a sheltered location at the bottom of the Flüela valley (1900 m a.s.l.) and b) an exposed location (2500 m a.s.l.) on a peak in the ski resort Jakobshorn, both in the vicinity of Davos, Switzerland. At the first test site, we monitor the ablation on three different dates. We validate the photogrammetric snow depth maps using simultaneously acquired manual snow depth measurements. The resulting snow depth values have a root mean square error (RMSE) better than 0.07 to 0.15 m on meadows and rocks and a RMSE better than 0.30 m on sections covered by bushes or tall grass. This new measurement technology opens the door for efficient, flexible, repeatable and cost effective snow depth monitoring for various applications, investigating the worlds cryosphere.

Remote sensing maps of plant functional type (PFT) fractional cover (FCover), dominant PFT, and FCover uncertainty derived from NASA's Airborne Visible / Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG). The AVIRIS-NG imaging spectroscopy data (380-2510 nm) was collected as a part of the collaboration between NASA's Arctic-Boreal Vulnerability Experiment (ABoVE; Miller et al., 2019) and DOE's Next Generation Ecosystem Experiment in the Arctic (NGEE-Arctic). This package includes maps of the NGEE-Arctic Council watershed on the Seward Peninsula, Alaska, created using AVIRIS-NG imagery collected on July 9th, 2019. The map data and metadata are provided as GeoTIFF (.tif), ENVI image (.dat), and text (*.txt, *hdr) formats. Additional map quicklooks are provided as *.pdf files and GIS *.kml files. These datasets are provided in support of Yang et al., (2023), "Integrating Very-High-Resolution UAS Data and Airborne Imaging Spectroscopy to Map the Fractional Composition of Arctic Plant Functional Types in Western Alaska".The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

This dataset represents active recreational flyer fixed sites (commonly referred to as flying fields) that are established by an agreement with the FAA. The fixed sites depicted here are located in controlled airspace two or more miles from an airport. At these sites, recreational UAS operations are authorized up to the unmanned aircraft system (UAS) facility map (UASFM) altitudes. If you fly at the fixed sites depicted in this dataset within controlled airspace, you must adhere to the operating limitations of the agreement, which is available from the fixed site sponsor.The FAA currently is upgrading LAANC (Low Altitude Authorization and Notification Capability) to enable recreational flyers to obtain automated authorization to controlled airspace. The FAA is committed to quickly implementing LAANC for recreational flyers. The FAA also is exploring upgrades to DroneZone to enable access for recreational flyers. Until LAANC is available for recreational operations, the FAA is granting temporary airspace authorizations to operate at certain fixed sites (commonly referred to as flying fields) that are established by an agreement with the FAA. For fixed sites that are located in controlled airspace two or more miles from an airport, operations are authorized up to the unmanned aircraft system (UAS) facility map (UASFM) altitudes. The FAA is reviewing fixed sites located within two miles of an airport and will make individualized determinations of what airspace authorization is appropriate. Aeromodelling organizations that sponsor fixed sites, regardless of their location within controlled airspace, can obtain additional information about requesting airspace authorization by email at UAShelp@faa.gov. During this interim period, you may fly in controlled airspace only at authorized fixed sites. The list of authorized fixed sites is available on the FAA’s website at www.faa.gov/uas and will be depicted on the maps on the FAA’s UAS Data Delivery System, which is available at https://udds-faa.opendata.arcgis.com. Agreements establishing fixed sites may contain additional operating limitations. If you fly at a fixed site in controlled airspace, you must adhere to the operating limitations of the agreement, which is available from the fixed site sponsor.As a reminder, existing FAA rules provide that you may not operate in any designated restricted or prohibited airspace. This includes airspace restricted for national security reasons or to safeguard emergency operations, including law enforcement activities. The easiest way to determine whether any restrictions or special requirements are in effect as well as the authorized altitudes where you want to fly is to use the maps on the FAA’s UAS Data Delivery System, which is available at https://udds-faa.opendata.arcgis.com, and to check for the latest FAA Notices to Airmen (NOTAMs). This information may also be available from third-party applications.The FAA will provide notice when LAANC is available for use by recreational flyers.Alternatively, during this interim period, the FAA directs recreational flyers to existing basic safety guidelines, which are based on industry best practices, on its website (faa.gov/uas): • Fly only for recreational purposes • Keep your unmanned aircraft within your visual line-of-sight or within the visual line of sight of a visual observer who is co-located and in direct communication with you • Do not fly above 400 feet in uncontrolled (Class G) airspace • Do not fly in controlled airspace without an FAA authorization • Follow all FAA airspace restrictions, including special security instructions and temporary flight restrictions • Never fly near other aircraft • Always give way to all other aircraft • Never fly over groups of people, public events, or stadiums full of people • Never fly near emergency response activities • Never fly under the influence of drugs or alcoholYou also should be able to explain to an FAA inspector or law enforcement official which safety guidelines you are following if you are flying under the exception for limited recreational unmanned aircraft operations.Please do not contact FAA Air Traffic facilities for airspace authorization because these facilities will no longer accept requests to operate recreational unmanned aircraft in controlled airspace.Please continue to check faa.gov/uas on a regular basis for the most current directions and guidance.

These datasets were acquired in the 2010/2011 and 2012/2013 Summer seasons from a multi-rotor unmanned aircraft system (UAS), also known as OktoKopter. This research focuses on the development of novel remote sensing techniques to map the spatial variability of moss bed health. At each site we flew the OktoKopter with three sensors: 1) an RGB digital SLR camera resulting in ultra-high resolution aerial photography, 2) a 6-band multispectral sensor, and 3) a thermal imaging sensor.

A structure-from-motion (SfM) workflow was applied to derive orthomosaics from the hundreds of overlapping images acquired by each sensor. For the 2011 datasets, a very detailed description of the data collection process is provided in Turner et al. (2014) and Lucieer et al. (2013).

For ASPA135, Robinson Ridge, and Red Shed the following datasets were generated: - "name of site"_vis_1cm.bsq: RGB orthomosaic from overlapping DSLR photography - "name of site"_mca_3cm.bsq: 6-band multispectral image mosaic - "name of site"_tir_10cm.bsq: thermal image mosaic

These datasets are provided in the Exelis IDL/ENVI band sequential (.bsq) format, which is compatible with ArcGIS.

For the Robinson Ridge site several additional data products were generated as described in Turner et al. (2014) and Lucieer et al. (2013): - Robbos50m_DEM2cm.bsq and LnFlowAccMC.bsq: digital elevation model (DEM) and flow accumulation modelled with a Monte Carlo simulation based on a 2 cm resolution DEM derived from SfM (Lucieer et al.,2013). ArcMap project: Lucieer2013_Robbos_DEMFlowAccumulation.mxd - Robbos_MCA_new_3cm_refl.bsq: raw multispectral imagery converted from DN-values to reflectance value based on calibration panels and spectral field observations. - Robbos_MCA_new_3cm_MTVI2.bsq: MTVI2 vegetation index derived from multispectral reflectance imagery - moss_health_map.img: MTVI2 values converted to % moss health based on a regression of quadrat observations - robbos_tir_3cm_calibrated_bl.bsq: thermal imagery resampled to 3 cm to match the multispectral imagery and calibrated to absolute temperature based on field temperature observations - moss_temp_map.img: temperature for moss areas only, where moss was identified through thresholding the MTVI2 image - ArcMap projects and figures corresponding to figure numbers in Turner et al. (2014)

The ASPA135 10 Feb 2013 datasets were collected with an RGB DSLR camera. The datasets in this folder are an RGB orthomosaic and DEM for the main melt puddle in ASPA135 when there was less snow cover than Feb 2011. The processing workflow was identical to the Robbos workflow described in Lucieer et al. (2013).

The Videos folder contains two videos: - 3D model of Robbos Lucieer et al. (2013) - UAS flight with hyperspectral SkyJib multi-rotor 5 Feb 2013 at Robinson Ridge (hyperspectral data still to be processed and published)

1.Assessing habitat quality is a primary goal of ecologists. However, evaluating habitat features that relate strongly to habitat quality at fine-scale resolutions across broad-scale extents is challenging. Unmanned aerial systems (UAS) provide an avenue for bridging the gap between relatively high spatial resolution, low spatial extent field-based habitat quality measurements and lower spatial resolution, higher spatial extent satellite-based remote sensing. Our goal in this study was to evaluate the potential for UAS structure from motion (SfM) to estimate several dimensions of habitat quality that provide potential security from predators and forage for pygmy rabbits (Brachylagus idahoensis) in a sagebrush-steppe environment. 2.At the plant and patch scales, we compared UAS-derived estimates of vegetation height, volume (estimate of food availability), and canopy cover to estimates from ground-based terrestrial laser scanning (TLS), and field-based measurements. Then, we mapped habitat features across two sagebrush landscapes in Idaho, USA, using point clouds derived from UAS SfM. 3.At the individual plant scale, the UAS-derived estimates matched those from TLS for height (r2 = 0.85), volume (r2 = 0.94), and canopy cover (r2 = 0.68). However, there was less agreement with field-based measurements of height (r2 = 0.67), volume (r2 = 0.31), and canopy cover (r2 = 0.29). At the patch scale, UAS-derived estimates provided a better fit to field-based measurements (r2 = 0.51-0.78) than at the plant scale. Landscape-scale maps created from UAS were able to distinguish structural heterogeneity between key patch types. 4.Our work demonstrates that UAS was able to accurately estimate habitat heterogeneity for a key terrestrial vertebrate at multiple spatial scales. Given that many of the vegetation metrics we focus on are important for a wide variety of species, our work illustrates a general remote sensing approach for mapping and monitoring fine-resolution habitat quality across broad landscapes for use in studies of animal ecology, conservation, and land management. Usage notes: Landscape-scale maps of structural quality derived from UAS SfM at the Camas study site, Idaho, USA Unmanned aerial system (UAS) structural quality maps derived from structure from motion (SfM) photogrammetry at the Camas study site in Idaho, USA. The dense point cloud was produced in Agisoft PhotoScan, and then height filtered with the BCAL LiDAR Tools to create a canopy height model (5-cm pixel resolution). Separate maps of maximum vegetation height, volume, and canopy cover were then produced in ArcGIS at 1-m pixel resolution. Camas_landscape_maps.zip Landscape-scale maps of structural quality derived from UAS SfM at the Cedar Gulch study site, Idaho, USA Unmanned aerial system (UAS) structural quality maps derived from structure from motion (SfM) photogrammetry at the Cedar Gulch study site in Idaho, USA. The dense point cloud was produced in Pix4D, and then height filtered with the BCAL LiDAR Tools to create a canopy height model (5-cm pixel resolution). Separate maps of maximum vegetation height, volume, and canopy cover were then produced in ArcGIS at 1-m pixel resolution. Cedar_landscape_maps.zip UAS-TLS plant-scale structural metrics Plant-scale comparison of unmanned aerial system (UAS) structure from motion (SfM) and terrestrial laser scanning (TLS) structural metrics (shrub height, shrub volume, and canopy cover) at two study sites in Idaho, USA. uas_tls_plant.csv UAS-Field plant-scale structural metrics Plant-scale comparison of unmanned aerial system (UAS) structure from motion (SfM) structural metrics and field-based measurements (shrub height, shrub volume, and canopy cover) at two study sites in Idaho, USA. uas_field_plant.csv UAS-Field patch-scale structural metrics Patch-scale comparison of unmanned aerial system (UAS) structure from motion (SfM) structural metrics and field-based measurements (shrub height, shrub volume, and canopy cover) at two study sites in Idaho, USA. uas_field_patch.csv

Airborne remote sensing data collected using a DJI Phantom 4 RTK unoccupied aerial system (UAS) ? operated by the University of Maine Wheatland Geospatial Lab (https://wheatlandlab.org/). This package includes data from 17 flights flown over the NGEE-Arctic Council, Kougarok, Kougarok Mile80, Teller, and Teller Mile32 sites in July 2019. The Phantom 4 RTK is a drone platform that collects very high spatial resolution optical red/green/blue (RGB) imagery. Derived image products include point cloud, ortho-mosaiced RGB, a digital surface model (DSM) using the structure from motion (SfM) technique, and a canopy height model (CHM). Unprocessed and processed data products (1,000+ files) are included in this package (processing levels 0-3). Data and metadata are provided as text (*.txt, *.json, hdr,), ENVI image file (.dat), point cloud (.laz) and image (.jpg, *.tif, *png) formats. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

Hilltop Arboretum Dataset for GRASS GIS This geospatial dataset contains raster data for the landform at Hilltop Arboretum, Baton Rouge, Louisiana, USA. This data was collected in an aerial survey with a DJI Phantom 4 Pro drone over Hilltop Arboretum on 12/31/2019 by Brendan Harmon and Josef Horacek. The aerial photographs were processed in Agisoft Metashape using Structure from Motion (SfM) to generate a point cloud, orthophotograph, and digital surface model. The point cloud was processed in CloudCompare to generate a bare earth point cloud. The orthophoto, digital surface model, and bare earth point cloud were imported into GRASS GIS. The bare earth point cloud was interpolated as a digital elevation model using the Regularized Spline with Tension method. The top level directory lousiana_s_spm_hilltop is a GRASS GIS location for the North American Datum of 1983 (NAD 83) / Louisiana South State Plane Meters with EPSG code 26982. Inside the location there are the PERMANENT mapset, a license file, and readme file. Survey Location: LSU Hilltop Arboretum, Baton Rouge, Louisiana, USA. Drone: DJI Phantom 4 Pro Software: Agisoft Metashape, CloudCompare, GRASS GIS Team: Brendan Harmon and Josef Horacek Date: 12/31/2019 GCPs: 10 AeroPoints Instructions Install GRASS GIS, unzip this archive, and move the location into your GRASS GIS database directory. If you are new to GRASS GIS read the first time users guide. License This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon.

This dataset contains images obtained from unmanned aerial systems (UAS) flown in the Cape Cod National Seashore. The objective of the field work was to evaluate the quality and cost of mapping from UAS images. Low-altitude (approximately 120 meters above ground level) digital images were obtained from cameras in a fixed-wing unmanned aerial vehicle (UAV) flown from the lawn adjacent to the Coast Guard Beach parking lot on 1 March, 2016. The UAV was a Skywalker X8 flying wing operated by Raptor Maps, Inc., contractors to the U.S. Geological Survey. U.S. Geological Survey technicians deployed and mapped 28 targets that appear in some of the images for use as ground control points. All activities were conducted according to Federal Aviation Administration regulations and under a National Park Service Scientific Research and Collecting Permit, study number CACO-00285, permit number CACO-2016-SCI-003. Two consecutive UAS missions were flown, each with two cameras, autopilot computer, radios, and a global navigation satellite system (GNSS) positioning system as payload. The first flight (f1) was launched at approximately 1112 EST, and followed north-south flight lines, landing at about 1226 EST. Two Canon Powershot SX280 12-mexapixel digital cameras, designated rgb1 and rgb2 made images during this flight. The second flight (f2) was launched at 1320 EST and followed east-west flight lines, landing at 1450 EST. Prior to f2, rgb2 was replaced with a Canon SX280 modified with a Schott BG 3 filter to emphasize light at near-infrared wavelengths, designated nir1. Rgb1 and nir1 made images during this second flight. In addition to the images, this dataset also contains locations of both in-situ and placed targets that may be used as ground control to constrain photogrammetric reconstructions.

These text files contain tables of the file names, times, and locations of images obtained from an unmanned aerial systems (UAS) flown in the Cape Cod National Seashore. The objective of the fieldwork was to evaluate the quality and cost of mapping from UAS images. Low-altitude (approximately 120 meters above ground level) digital images were obtained from cameras in a fixed-wing unmanned aerial vehicle (UAV) flown from the lawn adjacent to the Coast Guard Beach parking lot on 1 March, 2016. The UAV was a Skywalker X8 flying wing operated by Raptor Maps, Inc., contractors to the U.S. Geological Survey. U.S. Geological Survey technicians deployed and mapped 28 targets that appear in some of the images for use as ground control points. All activities were conducted according to Federal Aviation Administration regulations and under a National Park Service Scientific Research and Collecting Permit, study number CACO-00285, permit number CACO-2016-SCI-003. Two consecutive UAS missions were flown, each with two cameras, autopilot computer, radios, and a global navigation satellite system (GNSS) positioning system as payload. The first flight (f1) was launched at approximately 1112 EST, and followed north-south flight lines, landing at about 1226 EST. Two Canon Powershot SX280 12-mexapixel digital cameras, designated rgb1 and rgb2 made images during this flight. The second flight (f2) was launched at 1320 EST and followed east-west flight lines, landing at 1450 Eastern Standard Time (EST). Prior to f2, rgb2 was replaced with a Canon SX280 modified with a Schott BG 3 filter to emphasize light at near-infrared wavelengths, designated nir1. Rgb1 and nir1 made images during this second flight. The four files are tables of images obtained from the two cameras during the two flights. These tables, which are text files of comma-separated values, contain the image file name, date and time (Universal Time; UT), longitude and latitude (WGS84 decimal degrees), easting and northing (NAD83(2011) UTM Zone 19 North meters, obtained by conversion of the latitude and longitude), and elevation (approximate meters above mean sea level) determined from the UAS GNSS system. Note that this location information was only used to determine proximity of images, and was replaced with calculated camera locations in photogrammetric processing.

This dataset documents the locations of ground control points associated with images obtained from unmanned aerial systems (UAS) flown in the Cape Cod National Seashore. Most of the ground control points were temporary targets placed by the U.S. Geological Survey field crew, but four were man-made features already in place, and two were points selected a posteriori from preliminary orthophotomosaics. Photographs of the four in-place features are included in this dataset, as are images showing the location of the two a posteriori points at two zoom levels. The locations of these ground control points can be used to constrain photogrammetric reconstructions based on the aerial imagery. The overall objective of the fieldwork was to evaluate the quality and cost of mapping from UAS images. Low-altitude (approximately 120 meters above ground level) digital images were obtained from cameras in a fixed-wing UAS flown from the lawn adjacent to the Coast Guard Beach parking lot on 1 March, 2016. All activities were conducted according to Federal Aviation Administration regulations and under a National Park Service Scientific Research and Collecting Permit, study number CACO-00285, permit number CACO-2016-SCI-003.

This dataset includes drone (Uncrewed Aerial Vehicles, UAV) orthomosaics (RGB, n =2) of Pinus radiata acquired between 2016-2017 in Chile. The resolution (ground sampling distance) of the orthomosaics amounts to approx. 3-4 cm. The orthomosaics are partially labelled (polygon shapefiles) in terms of Pinus cover. Each orthomosaic comes with an AOI (area of interest, polygon shapefile) that indicates the areas where the labelling was performed. Within the extent of this AOI Pinus canopies are assumed to be completely delineated (by visual interpretation).

For visual inspection of the imagery we recommend to generate image pyramids since the image data has a very high spatial resolution.

Details on the dataset are mentioned in the corresponding publication:

Kattenborn, T., Lopatin, J., Förster, M., Braun, A. C., & Fassnacht, F. E. (2019). UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data. Remote sensing of environment, 227, 61-73.

https://doi.org/10.1016/j.rse.2019.03.025

https://www.sciencedirect.com/science/article/abs/pii/S0034425719301166

According to Cognitive Market Research, the global Unmanned Aircraft Systems market size will be USD 32515.5 million in 2024. It will expand at a compound annual growth rate (CAGR) of 16.80% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 13006.20 million in 2024 and will grow at a compound annual growth rate (CAGR) of 15.0% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 9754.65 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 7478.57 million in 2024 and will grow at a compound annual growth rate (CAGR) of 18.8% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 1625.78 million in 2024 and will grow at a compound annual growth rate (CAGR) of 16.2% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 650.31 million in 2024 and will grow at a compound annual growth rate (CAGR) of 16.5% from 2024 to 2031.
The fixed-wing category is the fastest growing segment of the Unmanned Aircraft Systems industry

Market Dynamics of Unmanned Aircraft Systems Market

Key Drivers for Unmanned Aircraft Systems Market

Growing Demand in the Military and Defense Sector to Boost Market Growth

Unmanned Aircraft Systems (UAS) are becoming increasingly popular in the military and defence industry due to their ability to improve intelligence, surveillance, and reconnaissance (ISR) capabilities. By lowering human exposure to risk during combat missions and surveillance duties, UAS offers a safer substitute for operations in high-risk areas. In addition to being more adaptable and affordable than human-crewed aircraft, these systems allow for a variety of uses, including logistics support and target acquisition. The importance of UAS for national defence is growing as their autonomy, AI, and payload capacity increase. Military-driven UAS demand is anticipated to climb sharply as global defence budgets increase, particularly in reaction to changing security threats.

Rising Use in Commercial Applications to Drive Market Growth

The market for Unmanned Aircraft Systems (UAS) is expanding rapidly in commercial applications as industries such as media, construction, logistics, and agriculture adopt UAS technology because of its effectiveness and cost-saving advantages. Drones make precision farming in agriculture possible by enabling soil analysis, crop health monitoring, and targeted pesticide application. They are being tested in logistics for last-mile delivery in order to shorten delivery times in both urban and rural locations. The construction sector uses UAS to map, monitor, and survey sites, which lowers labour costs and increases safety. Using drones for aerial photography and filmmaking benefits the media and entertainment industries as well. Commercial applications are anticipated to propel the market's substantial expansion as more industries come to understand the benefits of UAS.

Restraint Factor for the Unmanned Aircraft Systems Market

Stringent Regulatory and Legal Restrictions Will Limit Market Growth

The market for Unmanned Aircraft Systems (UAS) is severely constrained by tight legal and regulatory requirements. Drone usage is strictly regulated in many nations, particularly for commercial purposes and operations beyond visual line of sight (BVLOS), which restricts the capabilities of UAS and the growth of the market. Businesses that operate internationally may find it difficult to comply with these standards because they are frequently intricate and differ by location. Airspace limits, licensing requirements, and privacy issues further complicate UAS deployment in sensitive and metropolitan regions. Smaller businesses are further hindered by the high administrative expenditures of acquiring permits and following rules. Innovation is impeded, and wider commercial sector use is constrained by the sluggish regulatory response to UAS technology.

Impact of Covid-19 on the Unmanned Aircraft Systems Market

The industry for unmanned aircraft systems (UAS) was affected by the COVID-19 outbreak in a variety of ways. On the one hand, supply chain interruptions had an impact on UAS component delivery and production, which momentarily slo...

The aerial mapping camera market is experiencing robust growth, driven by increasing demand across diverse sectors. Applications like precision agriculture, infrastructure monitoring (building surveying and map production), and geological exploration are major catalysts. The shift towards automation and the integration of advanced technologies such as AI and machine learning for image processing and data analysis are further fueling market expansion. Technological advancements in camera types, including RGB, infrared, thermal imaging, and specialized remote sensing cameras, are offering enhanced data acquisition capabilities, leading to more accurate and detailed mapping solutions. This trend is coupled with the growing adoption of drones and UAVs for aerial data collection, making the technology more accessible and cost-effective for various applications. While the initial investment in high-quality aerial mapping cameras can be significant, the return on investment is substantial due to improved efficiency, reduced operational costs, and the valuable insights derived from detailed geographic information. Furthermore, government initiatives promoting the use of advanced mapping technologies for infrastructure development and environmental monitoring are providing a supportive regulatory landscape. Despite the strong growth drivers, the market faces some challenges. The high cost of sophisticated cameras and associated software, coupled with the need for skilled professionals to operate and interpret the data, represent significant barriers to entry for smaller companies. Data security and privacy concerns, particularly related to the collection and use of aerial imagery, also pose a potential limitation. However, ongoing technological innovations, especially in the realm of affordable, high-resolution sensors and user-friendly software, are mitigating these concerns. The market's growth trajectory is expected to remain positive, with increasing adoption across various sectors anticipated throughout the forecast period. The competition is intense, with established players and emerging tech companies vying for market share. Strategic partnerships and technological collaborations are likely to play a key role in shaping the future of this dynamic market.

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.

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.

uas_L2_yyyymmdd_hhmmss_vX.X.zip

where

L2 = Level 2 data

yyyymmdd = year,month,day

hhmmss = hour,minute,second

vX.X = version number

Time is the flight start time in UTC.

The NetCDF file format contained in the zip files has a similar format to the zip files with convention

uas__L2_yyyymmdd_hhmmss.nc

where

= vsm, dem, ndvi, or stmp

vsm = volumetric soil moisture

dem = digital elevation

ndvi = normalized difference vegetation index

stmp = surface temperature

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.

November 2023 update: 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.

December 2023 update: 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.