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.

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.

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.

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.

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.

Abstract

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

Data Use
License
CC0-1.0
Recommended Citation
Olsoy PJ, Shipley LA, Rachlow JL, Forbey JS, Glenn NF, Burgess MA,Thornton DH. 2018. Data from: Unmanned aerial systems measure structural habitat features for wildlife across multiple scales [Dataset]. Dryad. https://doi.org/10.5061/dryad.631q1

Funding
US National Science Foundation: DEB-1146368

Remote sensing maps of active layer depth derived from Unmanned Areal System (UAS) data. The UAS datasets were stepwise scaled until matching the AVIRIS-NG (Airborne Visible / Infrared Imaging Spectrometer - Next Generation) and Sentinel-2 spatial resolutions. Using the field observed Active Layer Depth (ALD) measurement in combination with spectral and topographic predictors derivatives from DJI UAS imagery, we used a spatially explicit RF regression model to predict and map ALD across our study landscapes. This package includes maps for Next-Generation Ecosystem Experiment Arctic (NGEE Arctic)’s Teller Mile Marker (MM) 27, and Kougarok MM80 (aka Mile 80) watersheds. The field, map data, and metadata are provided as geoTIF and text (*.csv) formats. These datasets are provided in support of Hantson et al., 2024 (accepted) “Scaling Arctic landscape and permafrost features improves active layer depth modeling”

Context: The amount and composition of phytochemicals in forage plants influences habitat quality for wild herbivores. However, evaluating forage quality at fine resolutions across broad spatial extents (i.e., foodscapes) is challenging. Unmanned aerial systems (UAS) provide an avenue for bridging this gap in spatial scale. Objectives: We evaluated the potential for UAS technology to accurately predict nutritional quality of sagebrush (Artemisia spp.) across landscapes. We mapped seasonal forage quality across two sites in Idaho, USA, with different mixtures of species but similar structural morphotypes of sagebrush. Methods: We classified the sagebrush at both study sites using structural features of shrubs with object-based image analysis and machine learning and linked this classification to field measurements of phytochemicals to interpolate a foodscape for each phytochemical with regression kriging. We compared fine-scale landscape patterns of phytochemicals between sites and seasons. Results: Classification accuracy for morphotypes was high at both study sites (81–87%). Forage quality was highly variable both within and among sagebrush morphotypes. Coumarins were the most accurately mapped (r2=0.57–0.81), whereas monoterpenes were the most variable and least explained. Patches with higher crude protein were larger and more connected in summer than in winter. Conclusions: UAS allowed for a rapid collection of imagery for mapping foodscapes based on the phytochemical composition of sagebrush at fine scales but relatively broad extents. However, results suggest that a more advanced sensor (e.g., hyperspectral camera) is needed to map mixed species of sagebrush or to directly measure forage quality. Data Usage Notes: Spatial Reference: NAD83 UTM Zone 11N [Camas]/12N [Cedar Gulch] Patch Type Classifications: 25-cm resolution, classes are 3=on mound, 4=off mound, 5=dwarf. On-mound refers to mima mounds with deeper soils that pygmy rabbits use to dig their burrows and are dominated by big sagebrush (Artemisia tridentata), while off-mound refers to patches dominated by big sagebrush but not on mima mounds, while dwarf patches are dominated by short-statured sagebrush species (e.g., black sagebrush [A. nova], low sagebrush [A. arbuscula]). Maps of Phytochemistry: 25-cm resolution and were generated with regression kriging using the patch type layer and point values from leaf chemistry. Phytochemicals: Crude Protein, Coumarins, Total Monoterpenes, Chemical Diversity of Monoterpenes and two individual monoterpenes (1,8-cineole and camphor). If there was no spatial autocorrelation present in the semivariogram, then maps were not generated for that phytochemical.

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.

This data set contains in situ observations conducted by the University of Colorado, Boulder using the Data Hawk UAS during the IDEAL (Instabilities, Dynamics, and Energetics accompanying Atmospheric Layering) field campaign from 24 October to 11 November 2017. A total of 32 sorties with roughly 2-3 flights per sortie (lasting for a maximum sampling duration of 90 minutes) were flown to accumulate the data at two designated locations within the Dugway Proving Grounds (DPG). The launch locations can be found on the map in the accompanying readme file. These data are available in Matlab format. This is a large dataset. Please limit data orders to 5 days.

These data map in high detail surficial cross-sections of North Core Banks, a barrier island in Cape Lookout National Seashore, NC, in October 2022. U.S. Geological Survey field efforts are part of an interagency agreement with the National Park Service to monitor the recovery of the island from Hurricanes Florence (2018) and Dorian (2019). Three sites of outwash, overwash, and pond formation were targeted for extensive vegetation ground-truthing, sediment samples, bathymetric mapping with a remote-controlled surface vehicle, and uncrewed aerial systems (UAS) flights to collect multispectral imagery. Five semi-permanent ground control points were also installed and surveyed to act as control for additional aerial imagery collected via plane. UAS imagery were processed in Agisoft Metashape (v. 1.8.1) with surveyed temporary ground control points to produce calibrated multispectral (red, blue, green, red edge, near infrared, and panchromatic) orthoimages and digital surface models.

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).

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...

Landscape-scale maps of structural quality derived from UAS SfM at the Camas study site, Idaho, USAUnmanned 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.zipLandscape-scale maps of structural quality derived from UAS SfM at the Cedar Gulch study site, Idaho, USAUnmanned 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 vegeta...

UAV-based mapping data set for the Middle Pleistocene archaeological site of Isimila, Tanzania. Includes full-resolution orthomosaic, digital elevation model, flight data, and video data.

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 Observatorymore » (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).« less

These data map in high detail surficial cross-sections of North Core Banks, a barrier island in Cape Lookout National Seashore, NC, in October 2022. U.S. Geological Survey field efforts are part of an interagency agreement with the National Park Service to monitor the recovery of the island from Hurricanes Florence (2018) and Dorian (2019). Three sites of outwash and overwash were targeted for extensive vegetation ground-truthing, sediment samples, bathymetric mapping with a remote-controlled surface vehicle, and uncrewed aerial systems (UAS) flights to collect multispectral imagery. Five permanent ground control points were also installed and surveyed to act as control for additional aerial imagery collects via plane. UAS imagery were processed in Agisoft Metashape (v. 1.8.1) to produce calibrated multispectral (red, blue, green, red-edge, near-infrared, and panchromatic) orthoimages and digital surface models.

The images used to make these data products were collected at and near Castle Rocks State Park, Idaho, in June 2021 and September 2021. We used a DJI Mavic 2 Pro with Map Pilot Pro software to capture imagery over four sites of interest. The imagery was collected in a crossgrid pattern at 40m above ground level; the resulting imagery have a ground resolution of 1cm/pixel. The images were processed and the products created in Agisoft Metashape Pro. All products are georectified and in WGS84 UTM Zone 12 N. The sites are located along an elevation gradient, ranging from 5,300-6,000ft. The vegetation in Site 1 is dominated about mountain big sagebrush (Artemisia tridentata spp. vaseyana), pinyon pine (Pinus cembroides), and juniper (Juniperus occidentalis). Site 2 and 3 contain a mix of basin big sagebrush (Artemisia tridentata spp. tridentata), rabbit brush (Ericameria nauseosa), and bitterbrush (Purshia tridentata). Site 4 contains Wyoming big sagebrush (Artemisia tridentata spp. wyomingensis).

https://www.northwestknowledge.net/data/f02ebec6-646b-4b65-a658-85480b736c7d/Site_Metadata/Site_Photos/cr_site1_lowres.jpg" alt="Site C1" class="img-responsive" />Site C1

https://www.northwestknowledge.net/data/f02ebec6-646b-4b65-a658-85480b736c7d/Site_Metadata/Site_Photos/cr_site2_lowres.jpg" alt="Site C2" class="img-responsive" />Site C2

https://www.northwestknowledge.net/data/f02ebec6-646b-4b65-a658-85480b736c7d/Site_Metadata/Site_Photos/cr_site3_lowres.jpg" alt="Site C3" class="img-responsive" />Site C3

https://www.northwestknowledge.net/data/f02ebec6-646b-4b65-a658-85480b736c7d/Site_Metadata/Site_Photos/cr_site4_lowres.jpg" alt="Site C4" class="img-responsive" />Site C4

Data Use
License: CC-BY
Recommended Citation: Roser, A., Marie, V., Olsoy, P., Delparte, D., & Caughlin, T. T. (2022). Unoccupied aerial systems imagery near Castle Rocks Idaho-2021 (Version 1.0) [Data set]. University of Idaho. https://doi.org/10.7923/Z23P-9444