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.

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 web map displays the FAA's UAS Facility Map along with parcel boundaries of the six towns on Martha's Vineyard. Much of Martha's Vineyard is controlled airspace. Please see the FAA website for an explanation of the regulations.In addition to FAA regulation areas, you are advised to check DJI's Geo Zones maps prior to flight, if you are using DJI products.The map is for general planning purposes only. If you are a drone pilot (recreational or certified remote pilot), please consult AirMap and other sources prior to take off to ensure that you are flying where permitted.

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 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.

Important Note: This item is in mature support as of June 2021 and is no longer updated. This map presents land cover and detailed topographic maps for the United States. It uses the USA Topographic Map service. The map includes the National Park Service (NPS) Natural Earth physical map at 1.24km per pixel for the world at small scales, i-cubed eTOPO 1:250,000-scale maps for the contiguous United States at medium scales, and National Geographic TOPO! 1:100,000 and 1:24,000-scale maps (1:250,000 and 1:63,000 in Alaska) for the United States at large scales. The TOPO! maps are seamless, scanned images of United States Geological Survey (USGS) paper topographic maps.The maps provide a very useful basemap for a variety of applications, particularly in rural areas where the topographic maps provide unique detail and features from other basemaps.To add this map service into a desktop application directly, go to the entry for the USA Topo Maps map service. Tip: Here are some famous locations as they appear in this web map, accessed by including their location in the URL that launches the map:Grand Canyon, ArizonaGolden Gate, CaliforniaThe Statue of Liberty, New YorkWashington DCCanyon De Chelly, ArizonaYellowstone National Park, WyomingArea 51, Nevada

This data set contains the produced snow depth maps as well as the reference data set (manual and snow pole measurements) from our paper "Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping". Abstract. Snow depth has traditionally been estimated based on point measurements collected either manually or at automated weather stations. Point measurements, though, do not represent the high spatial variability of snow depths present in alpine terrain. Photogrammetric mapping techniques have progressed in recent years and are capable of accurately mapping snow depth in a spatially continuous manner, over larger areas, and at various spatial resolutions. However, the strengths and weaknesses associated with specific platforms and photogrammetric techniques, as well as the accuracy of the photogrammetric performance on snow surfaces have not yet been sufficiently investigated. Therefore, industry-standard photogrammetric platforms, including high-resolution satellites (Pléiades), airplane (Ultracam Eagle M3), Unmanned Aerial System (eBee+ with S.O.D.A. camera) and terrestrial (single lens reflex camera, Canon EOS 750D), were tested for snow depth mapping in the alpine Dischma valley (Switzerland) in spring 2018. Imagery was acquired with airborne and space-borne platforms over the entire valley, while Unmanned Aerial Systems (UAS) and terrestrial photogrammetric imagery was acquired over a subset of the valley. For independent validation of the photogrammetric products, snow depth was measured by probing, as well as using remote observations of fixed snow poles. When comparing snow depth maps with manual and snow pole measurements the root mean square error (RMSE) values and the normalized median deviation (NMAD) values were 0.52 m and 0.47 m respectively for the satellite snow depth map, 0.17 m and 0.17 m for the airplane snow depth map, 0.16 m and 0.11 m for the UAS snow depth map. The area covered by the terrestrial snow depth map only intersected with 4 manual measurements and did not generate statistically relevant measurements. When using the UAS snow depth map as a reference surface, the RMSE and NMAD values were 0.44 m and 0.38 m for the satellite snow depth map, 0.12 m and 0.11 m for the airplane snow depth map, 0.21 and 0.19 m for the terrestrial snow depth map. When compared to the airplane dataset over a large part of the Dischma valley (40 km2), the snow depth map from the satellite yielded a RMSE value of 0.92 m and a NMAD value of 0.65 m. This study provides comparative measurements between photogrammetric platforms to evaluate their specific advantages and disadvantages for operational, spatially continuous snow depth mapping in alpine terrain over both small and large geographic areas.

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).

USGS Structures from The National Map (TNM) consists of data to include the name, function, location, and other core information and characteristics of selected manmade facilities across all US states and territories. The types of structures collected are largely determined by the needs of disaster planning and emergency response, and homeland security organizations. Structures currently included are: School, School:Elementary, School:Middle, School:High, College/University, Technical/Trade School, Ambulance Service, Fire Station/EMS Station, Law Enforcement, Prison/Correctional Facility, Post Office, Hospital/Medical Center, Cabin, Campground, Cemetery, Historic Site/Point of Interest, Picnic Area, Trailhead, Vistor/Information Center, US Capitol, State Capitol, US Supreme Court, State Supreme Court, Court House, Headquarters, Ranger Station, White House, and City/Town Hall. Structures data are designed to be used in general mapping and in the analysis of structure related activities using geographic information system technology. Included is a feature class of preliminary building polygons provided by FEMA, USA Structures. The National Map structures data is commonly combined with other data themes, such as boundaries, elevation, hydrography, and transportation, to produce general reference base maps. The National Map viewer allows free downloads of public domain structures data in either Esri File Geodatabase or Shapefile formats. For additional information on the structures data model, go to https://www.usgs.gov/ngp-standards-and-specifications/national-map-structures-content.

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

Regional geophysical maps of the Great Basin, USA were generated from new and existing sources to support ongoing efforts to characterize geothermal resource potential in the western US. These include: (1) a provisional regional gravity grid that was produced from data compiled from multiple sources: data collected by the USGS and Utah Geological Survey under various projects, industry sources, and regional compilations derived from two sources: a Nevada state-wide database (Ponce, 1997), and a public domain dataset (Hildenbrand et al., 2002), (2) a regional magnetic grid derived from the North American magnetic compilation map of Bankey et al. (2002) and, (3) a regional depth-to-basement grid derived from Shaw and Boyd (2018). References: Bankey, V., Cuevas, A., Daniels, D., Finn, C.A., Hernandez, I., Hill, P., Kucks, R., Miles, W., Pilkington, M., Roberts, C., Roest, W., Rystrom, V., Shearer, S., Snyder, S., Sweeney, R.E., Velez, J., Phillips, J.D., and Ravat, D.K.A., 2002, Digital data grids for the magnetic anomaly map of North America, U.S. Geological Survey, Open-File Report 2002-414, https://doi.org/10.3133/ofr02414. Hildenbrand, T.G., Briesacher, A., Flanagan, G., Hinze, W.J., Hittelman, A.M., Keller, G.R., Kucks, R.P., Plouff, D., Roest, W., Seeley, J., Smith, D.A., and Webring, M., 2002, Rationale and operational plan to upgrade the U.S. Gravity Database: U.S. Geological Survey Open-File Report 02-463, 12p. [https://pubs.er.usgs.gov/publication/ofr0246; data downloaded from the Pan-American Center for Earth and Environmental Studies (PACES) gravity database in October 2007 from URL http://paces.geo.utep.edu/research/gravmag/gravmag.shtml]. Ponce, D.A., 1997, Gravity data of Nevada, U.S. Geological Survey Digital Data Series DDS-42. https://pubs.usgs.gov/dds/dds-42/. Shah, A.K, and Boyd, O.S., 2018, Depth to basement and thickness of unconsolidated sediments for the western United States—Initial estimates for layers of the U.S. Geological Survey National Crustal Model: U.S. Geological Survey Open-File Report 2018–1115, 13 p., https://doi.org/10.3133/ofr20181115.

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.

description: 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.; abstract: 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.

http://www.faa.gov/airports/planning_capacity/passenger_allcargo_stats/categories/

© FAA This layer is a component of Airports.

Airports categorized using the FAA Classification System: http://www.faa.gov/airports/planning_capacity/passenger_allcargo_stats/categories/

© FAA, BTS, Derald Dudley

description: On May 25, 2014, a rain-on-snow induced rock avalanche occurred in the West Salt Creek Valley on the northern flank of Grand Mesa in western Colorado. The avalanche mobilized from a preexisting rock slide and traveled 4.6 km down the confined valley, killing 3 people. The avalanche was rare for the contiguous U.S. because of its large size (54.5 Mm3) and long travel distance. To understand the avalanche failure sequence, mechanisms, and mobility, we mapped landslide structures, geology, and ponds at 1:1000-scale. We used high-resolution, Unmanned Aircraft System (UAS) imagery from July 2014 as a base for our field mapping. Herein, we present the map data and UAS imagery. The data accompany an interpretive paper published in the journal Geosphere. The full citation for this interpretive journal paper is: Coe, J.A., Baum, R.L., Allstadt, K.E., Kochevar, B.F., Schmitt, R.G., Morgan, M.L., White, J.L., Stratton, B. Hayashi, T.A., and Kean, J.W., 2016, Rock avalanche dynamics revealed by large-scale field mapping and seismic signals at a highly mobile avalanche in the West Salt Creek Valley, western Colorado: Geosphere, v. 12, no. 2, p. 607-631, doi:10.1130/GES01265.1; abstract: On May 25, 2014, a rain-on-snow induced rock avalanche occurred in the West Salt Creek Valley on the northern flank of Grand Mesa in western Colorado. The avalanche mobilized from a preexisting rock slide and traveled 4.6 km down the confined valley, killing 3 people. The avalanche was rare for the contiguous U.S. because of its large size (54.5 Mm3) and long travel distance. To understand the avalanche failure sequence, mechanisms, and mobility, we mapped landslide structures, geology, and ponds at 1:1000-scale. We used high-resolution, Unmanned Aircraft System (UAS) imagery from July 2014 as a base for our field mapping. Herein, we present the map data and UAS imagery. The data accompany an interpretive paper published in the journal Geosphere. The full citation for this interpretive journal paper is: Coe, J.A., Baum, R.L., Allstadt, K.E., Kochevar, B.F., Schmitt, R.G., Morgan, M.L., White, J.L., Stratton, B. Hayashi, T.A., and Kean, J.W., 2016, Rock avalanche dynamics revealed by large-scale field mapping and seismic signals at a highly mobile avalanche in the West Salt Creek Valley, western Colorado: Geosphere, v. 12, no. 2, p. 607-631, doi:10.1130/GES01265.1

Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations. Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals. Data from thegSSURGO databasewas used to create this layer. To download ready-to-use project packages of useful soil data derived from the SSURGO dataset, please visit the USA SSURGO Downloader app. Dataset Summary Phenomenon Mapped:Soils of the United States and associated territoriesGeographic Extent:The 50 United States, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaCoordinate System:Web Mercator Auxiliary SphereVisible Scale:1:144,000 to 1:1,000Source:USDA Natural Resources Conservation Service Update Frequency:AnnualPublication Date:December 2024 What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS Online Add this layer to a map in the map viewer. The layer is limited to scales of approximately 1:144,000 or larger but avector tile layercreated from the same data can be used at smaller scales to produce awebmapthat displays across the full scale range. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter forFarmland Class= "All areas are prime farmland" to create a map of only prime farmland.Add labels and set their propertiesCustomize the pop-upArcGIS Pro Add this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of theLiving Atlas of the Worldthat provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Data DictionaryAttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them. Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units. Area SymbolSpatial VersionMap Unit Symbol Map Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field. Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability Rating Legend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field. Project Scale Survey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields. Survey Area VersionTabular Version Map Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field. Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Mapunit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Mapunit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - Presence Rating for Manure and Food Processing Waste - Weighted Average Component Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected. Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent Key Component Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r). Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence -

General Aviation Airports are public-use airports that do not have scheduled service or have less than 2,500 annual passenger boardings (49 USC 47102(8)). Approximately 88 percent of airports included in the NPIAS are general aviation.

http://www.faa.gov/airports/planning_capacity/passenger_allcargo_stats/categories/

© FAA This layer is a component of Airports.

Airports categorized using the FAA Classification System: http://www.faa.gov/airports/planning_capacity/passenger_allcargo_stats/categories/

© FAA, BTS, Derald Dudley

UAS Imagery (0.75 inch), Decker Island Wildlife Area, 2017.06.09, Phantom 4 Pro Drone

Uniform-hazard ground motion maps and their underlying GIS data were prepared for PGA and horizontal spectral accelerations at 0.2, 1.0, and 5.0 second period, with a probability of exceedance of 2%, 5% and 10% in 50 years, for NEHRP soil site classes B/C and D (VS30 equal to 760 and 260 m/s, respectively).