These data-sets are polygon shapefiles that represent flood inundation boundaries for 157 flooding scenarios in an 8-mile reach of the Papillion Creek near Offutt Air Force Base. These shapefiles were created by the U.S. Geological Survey (USGS) in cooperation with the U.S. Air Force, Offutt Air Force Base for use within the USGS Flood Inundation Mapping program. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgages on the Papillion Creek at Fort Crook, Nebr. (station 06610795) and Papillion Creek at Harlan Lewis Road near La Platte, Nebr. (station 06610798). Near-real-time stages at these streamgages may be obtained from the USGS National Water Information System web interface at https://doi.org/10.5066/F7P55KJN or from the National Weather Service Advanced Hydrologic Prediction Service at https:/water.weather.gov/ahps/. Flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated using the current (2021) stage-discharge relation at the Papillion Creek at Fort Crook, Nebr. streamgage. The hydraulic model then was used to compute 157 water-surface profiles for scenarios where combination of stage values in 1-foot (ft) stage intervals, that ranged between 27 and 39 ft at the Papillion Creek at Fort Crook streamgage and 13.9 and 30.9 ft at the Papillion Creek at Harlan Lewis Road streamgage as referenced to the local datum. The simulated water-surface profiles then were combined with a geographic information system digital elevation model (DEM) with a 3.281-ft grid to delineate polygon shapefiles, and depth grids of inundated areas. Along with the inundated area maps, polygon shapefiles and depth grids of areas behind the levees were created to display the uncertainty of these areas, if a levee breech were to occur. These 'areas of uncertainty' files have '_breach' and '_breachgrid' appended to the file names in the data release. The availability of these maps, along with information regarding current stage from the USGS streamgage, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.

These raster and vector dataset were developed for the Sea Grant South Coast MPA Baseline Program as part of the project “Nearshore Substrate Mapping and Change Analysis using Historical and Concurrent Multispectral Imagery” (#R/MPA 30 10-049). The study region is the South Coast Region (SCR). Imagery was acquired on October 14, 2012 at a spatial resolution of 0.3 meters using a Microsoft UltraCam-X digital camera acquiring in the red, green, blue and near-infrared bands. Information on the UltraCam-X camera system and wavelengths for each ban can be found in the file "The Microsoft Vexcel UltraCam X.pdf" included in the Support folder on the image data delivery media and on the OceanSpaces.org server. This image mosaic product is a result of the resampling of the 0.3 meter data to 2 meter GSD. Details on this system and the data processing are below in the Lineage section of this document. Individual UCX image tiles were mosaicked into sections based on the islands covered and local coastal regions as well as the SCR MPA zones in order to generate this multispectral image product. These imagery were subsequently used to generate habitat classification thematic maps of the SCR's kelp beds from Point Conception to Imperial Beach, CA. The imagery files deliverd are in GeoTIFF format. More information on the classes resolved and processing methods are in the Lineage section of this document. This raster dataset contains a habitat classification of either offshore giant kelp beds along the California South Coast Region (SCR) from from Point Conception, CA down to Imperial beach, CA. This specific raster classification includes the Painted Cove SMCA. This dataset was originally uploaded to Oceanspaces (http://oceanspaces.org/) and the Knowledge Network for Biocomplexity (KNB, https://knb.ecoinformatics.org/data) in 2013 as part of the South Coast baseline monitoring program. In 2022 this dataset was moved to the California Ocean Protection Council Data Repository (https://opc.dataone.org/) by Mike Esgro (Michael.Esgro@resources.ca.gov) and Rani Gaddam (gaddam@ucsc.edu). At that time the GIS analysis products were added to the dataset. The long-term California MPA boundary and project info tables can be found as a separate dataset here: https://opc.dataone.org/view/doi:10.25494/P64S3W.

Aerial Mapping Market Outlook

The global aerial mapping market size is projected to witness significant growth over the forecast period, with an estimated valuation of USD 2.8 billion in 2023, reaching approximately USD 5.6 billion by 2032, reflecting a compound annual growth rate (CAGR) of around 8%. This robust growth can be attributed to the increasing demand for precise geospatial information across various sectors, driven by technological advancements and the integration of UAVs (Unmanned Aerial Vehicles) and satellite data. Factors such as urbanization, the expansion of smart cities, and the need for efficient environmental monitoring are further propelling the demand for aerial mapping solutions globally.

One of the primary growth factors for the aerial mapping market is the rapid urbanization and the resultant need for detailed urban planning and infrastructure development. With more than half of the global population now residing in urban areas, cities are expanding at an unprecedented rate, necessitating accurate and updated geospatial data for effective planning and management. Aerial mapping provides critical data that aids urban planners in designing and implementing infrastructure projects, optimizing traffic management systems, and ensuring efficient land use planning. Moreover, governments across the world are investing heavily in smart city initiatives, which rely extensively on aerial mapping for data acquisition and analysis, further fueling the market growth.

Another significant driver of the aerial mapping market is the increasing application of these solutions in disaster management and environmental monitoring. The frequency and intensity of natural disasters have risen due to climate change, raising the demand for advanced mapping solutions that can provide real-time data for disaster preparedness and response. Aerial mapping helps in accurately assessing the damage extent, facilitating efficient rescue and recovery operations. Additionally, the growing emphasis on environmental sustainability has led to increased adoption of aerial mapping for monitoring deforestation, tracking changes in land use, and assessing the impacts of climate change on various ecosystems. These applications play a vital role in enabling governments and organizations to devise effective strategies for environmental conservation and disaster risk reduction.

The adoption of advanced technologies, such as artificial intelligence (AI) and machine learning, is also a key factor contributing to the growth of the aerial mapping market. These technologies enhance the capabilities of aerial mapping by enabling automated data analysis, improving accuracy, and reducing the time required for data processing. The integration of AI with aerial mapping solutions allows for the extraction of valuable insights from vast amounts of geospatial data, facilitating better decision-making for various applications. Furthermore, the advent of cost-effective and efficient UAVs has made aerial mapping more accessible, particularly for small and medium enterprises, thereby broadening the market's customer base and driving growth.

The role of UAV Mapping Software has become increasingly prominent in the aerial mapping market, offering significant advantages in terms of data accuracy and processing efficiency. This software enables the seamless integration of data collected by UAVs, facilitating the conversion of raw aerial imagery into detailed and actionable geospatial information. With the ability to process large datasets quickly, UAV Mapping Software is crucial for applications that require real-time data analysis, such as disaster management and infrastructure development. The software's advanced algorithms and machine learning capabilities enhance the precision of data interpretation, making it an indispensable tool for organizations looking to leverage aerial mapping for strategic decision-making. As the demand for high-resolution mapping continues to grow, the development and adoption of sophisticated UAV Mapping Software are expected to play a pivotal role in shaping the future of the aerial mapping industry.

Component Analysis

The aerial mapping market is segmented by component into hardware, software, and services. Each of these components plays a crucial role in the overall functionality and effectiveness of aerial mapping solutions. The hardware segment includes UAVs, cameras, sensors, and other data collection devices that are essential for capturing high-resolution aerial imagery. The

FloodScan uses satellite data to map and monitor floods daily, helping compare current flood conditions with historical averages. This dataset contains two resources: The first (hdx_floodscan_zonal_stats.xlsx) is a daily tabular dataset providing average FloodScan Standard Flood Extent Depiction (SFED) flood fraction (0-100%) per admin 1 and 2 level. Historical baseline values (SFED_BASELINE) are calculated per day-of-year from the last 10 years of historical data (non-inclusive of current year) after applying an 11 day smoothing mean window. Return Period (RP) is calculated empirically based on all historical data up to the current year (non-inclusive). The second resource (aer_floodscan_300s_SFED_90d.zip) is a zipped file containing AER FloodScan estimated daily flood fraction (0-100%) gridded data at approximately 10 km resolution (300 arcseconds equivalent to approximately 0.083 degrees) for the last 90 days. Each file represents the estimates for a single day and includes 2 bands: SFED and SFED_BASELINE. The baseline band provides users an easy way to compare current values with historical averages. The baseline is calculated per day-of-year from the last 10 years of historical data (non-inclusive of current year) after applying an 11 day temporal smoothing mean window.

Raw data and simulation files to generate the figures in "Near-field mapping of the edge mode of a topological valley slab waveguide at λ = 1.55 μm" by Alexander M. Dubrovkin et al.

Mapping around the Framnes Mountains from Spot satellite imagery at 10 metre pixel resolution. Mapped early in 1999.

Four digital water-surface profile maps for a 14-mile reach of the Mississippi River near Prairie Island in Welch, Minnesota from the confluence of the St. Croix River at Prescott, Wisconsin to upstream of the United States Army Corps of Engineers (USACE) Lock and Dam No. 3 in Welch, Minnesota, were created by the U.S. Geological Survey (USGS) in cooperation with the Prairie Island Indian Community. The water-surface profile maps depict estimates of the areal extent and depth of inundation corresponding to selected water levels (stages) at the USGS streamgage Mississippi River at Prescott, Wisconsin (USGS station number 05344500). Current conditions for estimating near-real-time areas of water inundation by use of USGS streamgage information may be obtained on the internet at http://waterdata.usgs.gov/. Water-surface profiles were computed for the stream reach using HEC-GeoRAS software by means of a one-dimensional step-backwater HEC-RAS hydraulic model using the steady-state flow computation option. The hydraulic model used in this study was previously created by the USACE . The original hydraulic model previously created extended beyond the 14-mile reach used in this study. After obtaining the hydraulic model from USACE, the HEC-RAS model was calibrated by using the most current stage-discharge relations at the USGS streamgage Mississippi River at Prescott, Wisconsin (USGS station number 05344500). The hydraulic model was then used to determine four water-surface profiles for flood stages referenced to 37.00, 39.00, 40.00, and 41.00-feet of stage at the USGS streamgage on the Mississippi River at Prescott, Wisconsin (USGS station number 05344500). The simulated water-surface profiles were then combined with a digital elevation model (DEM, derived from light detection and ranging (LiDAR) in Geographic Information System (GIS) data having a 0.35-foot vertical and 1.97-foot root mean square error horizontal resolution) in order to delineate the area inundated at each stage. The calibrated hydraulic model used to produce digital water-surface profile maps near Prairie Island, as part of the associated report, is documented in the U.S. Geological Survey Scientific Investigations Report 2021-5018 (https://doi.org/10.3133/ sir20215018). The data provided in this data release contains three zip files: 1) MissRiverPI_DepthGrids.zip, 2) MissRiverPI_InundationLayers.zip, and 3) ModelArchive.zip. The MissRiverPI_DepthGrids.zip and MissRiverPI_InundationLayers.zip files contain model output water-surface profile maps as shapefiles (.shp) and Keyhole Markup Language files (.kmz) that can be opened using Esri GIS systems (.shp files) or Google Earth (.kmz files), while the ModelArchive.zip contains model inputs, outputs, and calibration data used in creating the water-surface profiles maps.

Living near toxic release facilities may involve some exposure to pollutants, which can affect air, water, and soil quality. Accessing local environmental data helps to stay informed. This infographic shows the steps to map the closest toxic release facilities near a geographic location specified by the end user.

The lake mapping service market is experiencing robust growth, driven by increasing demand for effective resource management, environmental monitoring, and ecological studies. A rising global population and the consequent pressure on water resources are key factors fueling this expansion. Furthermore, advancements in remote sensing technologies, such as aerial photography and satellite imagery, are providing higher-resolution data, leading to more accurate and detailed lake mapping. This enhanced accuracy enables better informed decision-making for various applications, including identifying pollution sources, assessing water quality, monitoring shoreline changes, and managing aquatic vegetation. The market is segmented by application (environmental monitoring, resource management, ecological studies, and others) and type of imagery (aerial photography, satellite imagery, and others). While precise market sizing data was not provided, a conservative estimate based on industry trends and comparable markets suggests a current market value of approximately $500 million in 2025, with a compound annual growth rate (CAGR) of around 7% projected through 2033. This growth is expected across all regions, with North America and Europe currently holding significant market shares due to higher adoption rates and established regulatory frameworks for environmental monitoring. However, Asia-Pacific is poised for significant expansion in the coming years due to increasing government investments in infrastructure and water resource management. Potential restraints include the high initial investment costs associated with advanced mapping technologies and the need for skilled professionals to interpret and utilize the data effectively. The competitive landscape is characterized by a mix of established environmental consulting firms and specialized lake management companies. Key players are focusing on strategic partnerships and technological advancements to strengthen their market positions. The increasing availability of affordable, high-resolution imagery and user-friendly data analysis software is democratizing access to lake mapping services, making them more accessible to smaller organizations and government agencies with limited budgets. This trend is expected to accelerate market penetration and contribute to overall market growth. The future of lake mapping services is linked to the integration of advanced analytics, artificial intelligence, and machine learning for improved data interpretation, predictive modeling, and automated reporting. This will enhance the efficiency and effectiveness of lake management initiatives globally.

For this revision, STARR conducted over 38 miles of revised Coastal Hazard Analysis that included computing wave runup. STARR utilized 79 transects in this study. No new detailed riverine studies were conducted as part of this countywide FIS. For riverine areas, floodplain boundaries were remapped as part of the countywide update to reflect more recent or more detailed topographic and base map data for the county. The floodplain mapping updates consisted of a mixture of redelineation and rectification (refinement) of existing flood boundaries based on the best topographic data and aerial photography available at the time of the study. Redelineation was limited to areas were new, quality topographic data was available and Base Flood Elevations were previously defined. Redelineation was completed on the detailed study areas of the Naselle River, Salmon Creek and South Fork Naselle River. The detailed study reaches along Ward, Wilson and Whitcomb Creeks near the City of Raymond and the Willapa River near Lebam are not covered by new topographic data and was converted to digital format by digitizing the effective FIRMs and refined by making small adjustments to fit the floodplains to new aerial photography. Approximately 4.3 stream miles, including portions of Naselle River and Salmon Creek were studied with base level methods (Zone A). The boundary of the 1-percent-annualchance flood for the South Fork Naselle River near its confluence with Cement Creek was refined by making adjustments to fit the floodplains to new aerial photography and the new topographic data. Those approximate method reaches not covered by new topographic data were converted to digital format by digitizing the effective FIRMs and refined by making small adjustments to fit the floodplains to new aerial photography to ensure that they overlay the water course they represent. These areas include portions of Salmon Creek and Willapa River.

Metadata: NOAA GOES-R Series Advanced Baseline Imager (ABI) Level 1b RadiancesMore information about this imagery can be found here.This satellite imagery combines data from the NOAA GOES East and West satellites and the JMA Himawari satellite, providing full coverage of weather events for most of the world, from the west coast of Africa west to the east coast of India. The tile service updates to the most recent image every 10 minutes at 1.5 km per pixel resolution.The infrared (IR) band detects radiation that is emitted by the Earth’s surface, atmosphere and clouds, in the “infrared window” portion of the spectrum. The radiation has a wavelength near 10.3 micrometers, and the term “window” means that it passes through the atmosphere with relatively little absorption by gases such as water vapor. It is useful for estimating the emitting temperature of the Earth’s surface and cloud tops. A major advantage of the IR band is that it can sense energy at night, so this imagery is available 24 hours a day.The Advanced Baseline Imager (ABI) instrument samples the radiance of the Earth in sixteen spectral bands using several arrays of detectors in the instrument’s focal plane. Single reflective band ABI Level 1b Radiance Products (channels 1 - 6 with approximate center wavelengths 0.47, 0.64, 0.865, 1.378, 1.61, 2.25 microns, respectively) are digital maps of outgoing radiance values at the top of the atmosphere for visible and near-infrared (IR) bands. Single emissive band ABI L1b Radiance Products (channels 7 - 16 with approximate center wavelengths 3.9, 6.185, 6.95, 7.34, 8.5, 9.61, 10.35, 11.2, 12.3, 13.3 microns, respectively) are digital maps of outgoing radiance values at the top of the atmosphere for IR bands. Detector samples are compressed, packetized and down-linked to the ground station as Level 0 data for conversion to calibrated, geo-located pixels (Level 1b Radiance data). The detector samples are decompressed, radiometrically corrected, navigated and resampled onto an invariant output grid, referred to as the ABI fixed grid.McIDAS merge technique and color mapping provided by the Cooperative Institute for Meteorological Satellite Studies (Space Science and Engineering Center, University of Wisconsin - Madison) using satellite data from SSEC Satellite Data Services and the McIDAS visualization software.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. A total of 39 classes of land cover were mapped on Fire Island and the William Floyd Estate (Table 6.). These are comprised of 24 types mapped to NVCS association, 1 complex of 2 NVCS alliances, and 14 non-NVCS classes (Figure 5a-d.). Four associations were identified on Fire Island and the William Floyd Estate but do not appear on the map due to their rarity, small relative size, and/or difficulties in identifying them with aerial photography. These types were Oligohaline Marsh, Brackish Marsh, Salt Panne, and North Atlantic Upper Ocean Beach.

NOTICE TO PROVISIONAL 2023 LAND USE DATA USERS: Please note that on December 6, 2024 the Department of Water Resources (DWR) published the Provisional 2023 Statewide Crop Mapping dataset. The link for the shapefile format of the data mistakenly linked to the wrong dataset. The link was updated with the appropriate data on January 27, 2025. If you downloaded the Provisional 2023 Statewide Crop Mapping dataset in shapefile format between December 6, 2024 and January 27, we encourage you to redownload the data. The Map Service and Geodatabase formats were correct as posted on December 06, 2024.

Thank you for your interest in DWR land use datasets.

The California Department of Water Resources (DWR) has been collecting land use data throughout the state and using it to develop agricultural water use estimates for statewide and regional planning purposes, including water use projections, water use efficiency evaluations, groundwater model developments, climate change mitigation and adaptations, and water transfers. These data are essential for regional analysis and decision making, which has become increasingly important as DWR and other state agencies seek to address resource management issues, regulatory compliances, environmental impacts, ecosystem services, urban and economic development, and other issues. Increased availability of digital satellite imagery, aerial photography, and new analytical tools make remote sensing-based land use surveys possible at a field scale that is comparable to that of DWR’s historical on the ground field surveys. Current technologies allow accurate large-scale crop and land use identifications to be performed at desired time increments and make possible more frequent and comprehensive statewide land use information. Responding to this need, DWR sought expertise and support for identifying crop types and other land uses and quantifying crop acreages statewide using remotely sensed imagery and associated analytical techniques. Currently, Statewide Crop Maps are available for the Water Years 2014, 2016, 2018- 2022 and PROVISIONALLY for 2023.

Historic County Land Use Surveys spanning 1986 - 2015 may also be accessed using the CADWR Land Use Data Viewer: https://gis.water.ca.gov/app/CADWRLandUseViewer.

For Regional Land Use Surveys follow: https://data.cnra.ca.gov/dataset/region-land-use-surveys.

For County Land Use Surveys follow: https://data.cnra.ca.gov/dataset/county-land-use-surveys.

For a collection of ArcGIS Web Applications that provide information on the DWR Land Use Program and our data products in various formats, visit the DWR Land Use Gallery: https://storymaps.arcgis.com/collections/dd14ceff7d754e85ab9c7ec84fb8790a.

Recommended citation for DWR land use data: California Department of Water Resources. (Water Year for the data). Statewide Crop Mapping—California Natural Resources Agency Open Data. Retrieved “Month Day, YEAR,” from https://data.cnra.ca.gov/dataset/statewide-crop-mapping.

Flown in March 2005. Data compiled to meet or exceed a horizontal accuracy of +/- 2 feet RMSE or 3.46 feet at a 95% confidence level according to the NSSDA standard necessary for 1”=200’ maps.

Access the Data:

Access the REST Service from https://ags.roseville.ca.us/arcgis/rest/services/PublicServices/. View the data in our Historical Imagery Collection.Add data to ArcMap or ArcPro by clicking on “View Metadata” and selecting “Open in ArcGIS Desktop”.

Digital aerial imagery was obtained in the spring of 2010 and fall of 2011 using a large format Z/I Digital Mapping Camera system (DMC) equipped with Airborne GPS/IMU. A total of 215 flight lines with 13,879 frames was acquired under both ARRA and non-ARRA task orders, in multispectral (RGB and NIR) 8 bits per band format. The imagery was acquired with a 4.7244" (120 m/m) focal length at an altitude of 10,000' above mean terrain, to yield a raw pixel resolution of 1' (.3m) suitable for photogrammetric mapping and orthophoto production. The leaf-off imagery was collected under conditions free from clouds and cloud shadows, smoke, fog, haze, light streaks, snow, ice on water bodies, foliage, flooding, and excessive soil moisture. The sun angle threshold was 30 degrees. The imagery consisted of panchromatic, blue, green, red and near infrared bands. The three color bands and near infrared bands were pan sharpened and archived as frame imagery. All 4 bands were used in the orthophoto production.

Beaver Lake was constructed in 1966 on the White River in the northwest corner of Arkansas for flood control, hydroelectric power, public water supply, and recreation. The surface area of Beaver Lake is about 27,900 acres and approximately 449 miles of shoreline are at the conservation pool level (1,120 feet above the North American Vertical Datum of 1988). Sedimentation in reservoirs can result in reduced water storage capacity and a reduction in usable aquatic habitat. Therefore, accurate and up-to-date estimates of reservoir water capacity are important for managing pool levels, power generation, water supply, recreation, and downstream aquatic habitat. Many of the lakes operated by the U.S. Army Corps of Engineers are periodically surveyed to monitor bathymetric changes that affect water capacity. In October 2018, the U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, completed one such survey of Beaver Lake using a multibeam echosounder. The echosounder data was combined with light detection and ranging (lidar) data to prepare a bathymetric map and a surface area and capacity table. Collection of bathymetric data in October 2018 at Beaver Lake near Rogers, Arkansas, used a marine-based mobile mapping unit that operates with several components: a multibeam echosounder (MBES) unit, an inertial navigation system (INS), and a data acquisition computer. Bathymetric data were collected using the MBES unit in longitudinal transects to provide complete coverage of the lake. The MBES was tilted in some areas to improve data collection along the shoreline, in coves, and in areas that are shallower than 2.5 meters deep (the practical limit of reasonable and safe data collection with the MBES). Two bathymetric datasets collected during the October 2018 survey include the gridded bathymetric point data (BeaverLake2018_bathy.zip) computed on a 3.28-foot (1-meter) grid using the Combined Uncertainty and Bathymetry Estimator (CUBE) method, and the bathymetric quality-assurance dataset (BeaverLake2018_QA.zip). The gridded point data used to create the bathymetric surface (BeaverLake2018_bathy.zip) was quality-assured with data from 9 selected resurvey areas (BeaverLake2018_QA.zip) to test the accuracy of the gridded bathymetric point data. The data are provided as comma delimited text files that have been compressed into zip archives. The shoreline was created from bare-earth lidar resampled to a 3.28-foot (1-meter) grid spacing. A contour line representing the flood pool elevation of 1,135 feet was generated from the gridded data. The data are provided in the Environmental Systems Research Institute shapefile format and have the common root name of BeaverLake2018_1135-ft. All files in the shapefile group must be retrieved to be useable.

Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over large spatial extents and with the kind of frequency and regularity required to support monitoring programs. Remote sensing methods could provide a viable alternative means of mapping river bathymetry (i.e., water depth). The purpose of this study was to develop and test new, spectrally based techniques for estimating water depth from satellite image data. More specifically, a neural network-based temporal ensembling approach was evaluated in comparison to several other neural network depth retrieval (NNDR) algorithms. These methods are described in a manuscript titled "Neural Network-Based Temporal Ensembling of Water Depth Estimates Derived from SuperDove Images" and the purpose of this data release is to make available the depth maps produced using these techniques. The images used as input were acquired by the SuperDove cubesats comprising the PlanetScope constellation, but the original images cannot be redistributed due to licensing restrictions; the end products derived from these images are provided instead. The large number of cubesats in the PlanetScope constellation allows for frequent temporal coverage and the neural network-based approach takes advantage of this high density time series of information by estimating depth via one of four NNDR methods described in the manuscript: 1. Mean-spec: the images are averaged over time and the resulting mean image is used as input to the NNDR. 2. Mean-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is averaged to obtain the final depth map. 3. NN-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is then used as input to a second, ensembling neural network that essentially weights the depth estimates from the individual images so as to optimize the agreement between the image-derived depth estimates and field measurements of water depth used for training; the output from the ensembling neural network serves as the final depth map. 4. Optimal single image: a separate NNDR is applied independently to each image in the time series and only the image that yields the strongest agreement between the image-derived depth estimates and the field measurements of water depth used for training is used as the final depth map. MATLAB (Version 24.1, including the Deep Learning Toolbox) for performing this analysis is provided in the function NN_depth_ensembling.m available on the main landing page for the data release of which this is a child item, along with a flow chart illustrating the four different neural network-based depth retrieval methods. To develop and test this new NNDR approach, the method was applied to satellite images from the American River near Fair Oaks, CA, acquired in October 2020. Field measurements of water depth available through another data release (Legleiter, C.J., and Harrison, L.R., 2022, Field measurements of water depth from the American River near Fair Oaks, CA, October 19-21, 2020: U.S. Geological Survey data release, https://doi.org/10.5066/P92PNWE5) were used for training and validation. The depth maps produced via each of the four methods described above are provided as GeoTIFF files, with file name suffixes that indicate the method employed: American_mean-spec.tif, American_mean-depth.tif, American_NN-depth.tif, and American-single-image.tif. The spatial resolution of the depth maps is 3 meters and the pixel values within each map are water depth estimates in units of meters.

Intellectual Merit:

Ice free rock outcrops in the Transantarctic Mountains provide the only accessible windows into the interior of the ice covered Antarctic continent; they are extremely remote and difficult to study. This region also hosts the highest latitude ice-free valley systems on the planet. Based on two interdisciplinary workshops, the Transantarctic region near the Shackleton Glacier has been identified as a high priority site for further studies, with a field camp proposed for the 2015-2016 Antarctic field season. The geology of this region has been studied since the heroic era of Antarctic exploration, in the early 1900s, but geologic mapping has not been updated in more than forty years, and existing maps are at poor resolution (typically 1:250,000).

This project would utilize the WorldView-2 multispectral orbital dataset to supplement original geologic mapping efforts near the proposed 2015-2016 Shackleton Glacier camp. The WorldView-2 satellite is the only multispectral orbiting sensor capable of imaging the entirety of the Transantarctic Mountains, and all necessary data are currently available to the Polar Geospatial Center. High-latitude atmospheric correction of multispectral data for geologic investigations has only recently been tested, but has never been applied to WorldView-2 data, and never for observations of this type. Therefore, this research will require technique refinements and methodological developements to accomplish the goals. Atmospheric correction refinements and spectral validation will be made possible by laboratory spectroscopic measurements of rock samples currently stored at the U.S. Polar Rock Repository, at the Ohio State University. This project will result in spectral unit identification and boundary mapping at a factor of four higher resolution (1:62,500) than previous geologic mapping efforts, and more detailed investigations (1:5,123) are possible at resolutions more than a factor of forty-eight improved over previous geologic maps. Validated spectral mapping at these improved resolutions will allow for detailed lithologic, and potentially biologic, mapping using existing satellite imagery. This will greatly enhance planning capabilities, thus maximizing the efficiency of the scientific research and support logistics associated with the Shackleton Glacier deep field camp.

Broader impacts:

The proposed work will have multiple impacts on the broader scientific community. First, the refinement of existing atmospheric correction methodologies, and the development of new spectral mapping techniques, may substantially improve our ability to remotely investigate geologic surfaces throughout Antarctica. The ability to validate this orbital dataset will be of use to both current and future geologic, environmental, and biologic studies, potentially across the entire continent. The project will yield a specific spectral mapping product (at a scale of 1:62,500) to the scientific community by a targeted date of 01 March 2014, in order to support proposals submitted to the National Science Foundation for the proposed 2015/2016 Shackleton Glacier camp. High-resolution spectral mapping products (up to a maximum resolution of 2 meters per pixel) will also be generated for regions of particular scientific interest. The use of community based resources, such as Polar Geospatial Center (PGC) imagery and U.S. Polar Rock Repository rock samples, will generate new synergistic and collaborative research possibilities within the Antarctic research community. In addition, the lead PI (Salvatore) is an early career scientist who is active in both Antarctic and planetary remote sensing. There are overlaps in the calibration, correction, and validation of remote spectral datasets for Antarctic and planetary applications which can lead to benefits and insights to an early career PI, as well as the two communities.

This web map is a component of the CrowdMag Visualization App.NOAA's CrowdMag is a crowdsourced data collection project that uses a mobile app to collect geomagnetic data from the magnetometers that modern smartphones use as part of their navigation systems. NCEI collects these data from citizen scientists around the world and provides quality control services before making them available through a series of aggregated maps and charts. These data have the potential to provide a high resolution alternative to geomagnetic satellite data, as well as near real-time information about changes in the magnetic field.This map shows data collected from phones around the world! Displayed are the Crowdsourced magnetic data within a tolerance level of prediction by World Magnetic Model. We have added some uncertainty to each data point shown to ensure the privacy of our contributors. The data points are grouped together (or "aggregated") into small areas , and we display the median data value across all the readings for each point.

This map is updated every day. Layers are available for Median Intensity, Median Horizontal Component (Y), and Median Vertical Component (Z).


Use the time slider to select the date range. Select the different layers under the "Crowdmag Observations" menu. View a color scale using the legend tool. Zoom to your location using the "Find my Location" tool. Click or tap on a data point to view a popup containing more information.

Correlation of flows at pairs of streamgages were evaluated using a Spearman’s rho correlation coefficient to better identify gages that can be used as index gages to estimate daily flow at ungaged stream sites in West Virginia. Correlation maps were developed for each candidate index streamgage using ordinary kriging, and have been compiled as grids. Sets of grids were developed for correlation of daily flows of streamgages on unregulated streams in and near (within 50 miles of) West Virginia that were operated during the 1930-2011 water years for: (1) complete water years for the entire period of record (1930-2011), (2) October-December for the entire period of record, (3) January-March for the entire period of record, (4) April-June for the entire period of record, (5) July-September for the entire period of record, (6) complete water years for 1963-1969, (7) complete water years for 1970-1979, and (8) complete water years for 1992-2011. Details of analytical approach, results, discussion, and limitations are contained in U.S. Geological Survey Scientific Investigations Report 2014-5061.at https://pubs.usgs.gov/sir/2014/5061/