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. The digital vegetation map was produced using a combination of machine processing and visual interpretation. We used two primary image sources. These included 2006 1:12,000-scale infrared aerial photography for the areas west of the Sangre de Cristo Mountain range that was subsequently processed by the USFWS and 2006 National Agricultural Imagery Program (NAIP) imagery, and ground-truthing to interpret the complex patterns of vegetation and landuse at GRSA. Other referenced imagery included 2006 and 2007 Quickbird imagery which covered portions of the project area. All of the interpreted and remotely sensed data were converted to Geographic Information System (GIS) databases using ArcInfo© software. Draft maps created from the vegetation classification were field-tested and revised before independent ecologists completed an assessment of the map‘s accuracy during 2008. During the summer of 2008 we sampled 1,537 accuracy assessment points to establish a final overall accuracy of 73.7%. This metric is subject to considerable interpretation and is discussed in detail in the results section.

The Digital Geomorphic-GIS Map of the Great Swash to Quork Hammock Area (1:10,000 scale 2006 mapping), North Carolina is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (gsqh_geomorphology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (gsqh_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (gsqh_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (caha_fora_wrbr_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (caha_fora_wrbr_geomorphology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (gsqh_geomorphology_metadata_faq.pdf). Please read the caha_fora_wrbr_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: East Carolina University. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (gsqh_geomorphology_metadata.txt or gsqh_geomorphology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:10,000 and United States National Map Accuracy Standards features are within (horizontally) 8.5 meters or 27.8 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

This application is intended for informational purposes only and is not an operational product. The tool provides the capability to access, view and interact with satellite imagery, and shows the latest view of Earth as it appears from space.For additional imagery from NOAA's GOES East and GOES West satellites, please visit our Imagery and Data page or our cooperative institute partners at CIRA and CIMSS.This website should not be used to support operational observation, forecasting, emergency, or disaster mitigation operations, either public or private. In addition, we do not provide weather forecasts on this site — that is the mission of the National Weather Service. Please contact them for any forecast questions or issues. Using the Maps​What does the Layering Options icon mean?The Layering Options widget provides a list of operational layers and their symbols, and allows you to turn individual layers on and off. The order in which layers appear in this widget corresponds to the layer order in the map. The top layer ‘checked’ will indicate what you are viewing in the map, and you may be unable to view the layers below.Layers with expansion arrows indicate that they contain sublayers or subtypes.What does the Time Slider icon do?The Time Slider widget enables you to view temporal layers in a map, and play the animation to see how the data changes over time. Using this widget, you can control the animation of the data with buttons to play and pause, go to the previous time period, and go to the next time period.Do these maps work on mobile devices and different browsers?Yes!Why are there black stripes / missing data on the map?NOAA Satellite Maps is for informational purposes only and is not an operational product; there are times when data is not available.Why does the imagery load slowly?This map viewer does not load pre-generated web-ready graphics and animations like many satellite imagery apps you may be used to seeing. Instead, it downloads geospatial data from our data servers through a Map Service, and the app in your browser renders the imagery in real-time. Each pixel needs to be rendered and geolocated on the web map for it to load.How can I get the raw data and download the GIS World File for the images I choose?The geospatial data Map Service for the NOAA Satellite Maps GOES satellite imagery is located on our Satellite Maps ArcGIS REST Web Service ( available here ).We support open information sharing and integration through this RESTful Service, which can be used by a multitude of GIS software packages and web map applications (both open and licensed).Data is for display purposes only, and should not be used operationally.Are there any restrictions on using this imagery?NOAA supports an open data policy and we encourage publication of imagery from NOAA Satellite Maps; when doing so, please cite it as "NOAA" and also consider including a permalink (such as this one) to allow others to explore the imagery.For acknowledgment in scientific journals, please use:We acknowledge the use of imagery from the NOAA Satellite Maps application: LINKThis imagery is not copyrighted. You may use this material for educational or informational purposes, including photo collections, textbooks, public exhibits, computer graphical simulations and internet web pages. This general permission extends to personal web pages. About this satellite imageryWhat am I looking at in these maps?In this map you are seeing the past 24 hours (updated approximately every 10 minutes) of the Western Hemisphere and Pacific Ocean, as seen by the NOAA GOES East (GOES-16) and GOES West (GOES-18) satellites. In this map you can also view four different ‘layers’. The views show ‘GeoColor’, ‘infrared’, and ‘water vapor’.This maps shows the coverage area of the GOES East and GOES West satellites. GOES East, which orbits the Earth from 75.2 degrees west longitude, provides a continuous view of the Western Hemisphere, from the West Coast of Africa to North and South America. GOES West, which orbits the Earth at 137.2 degrees west longitude, sees western North and South America and the central and eastern Pacific Ocean all the way to New Zealand.What does the GOES GeoColor imagery show?The 'Merged GeoColor’ map shows the coverage area of the GOES East and GOES West satellites and includes the entire Western Hemisphere and most of the Pacific Ocean. This imagery uses a combination of visible and infrared channels and is updated approximately every 15 minutes in real time. GeoColor imagery approximates how the human eye would see Earth from space during daylight hours, and is created by combining several of the spectral channels from the Advanced Baseline Imager (ABI) – the primary instrument on the GOES satellites. The wavelengths of reflected sunlight from the red and blue portions of the spectrum are merged with a simulated green wavelength component, creating RGB (red-green-blue) imagery. At night, infrared imagery shows high clouds as white and low clouds and fog as light blue. The static city lights background basemap is derived from a single composite image from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day Night Band. For example, temporary power outages will not be visible. Learn more.What does the GOES infrared map show?The 'GOES infrared' map displays heat radiating off of clouds and the surface of the Earth and is updated every 15 minutes in near real time. Higher clouds colorized in orange often correspond to more active weather systems. This infrared band is one of 12 channels on the Advanced Baseline Imager, the primary instrument on both the GOES East and West satellites. on the GOES the multiple GOES East ABI sensor’s infrared bands, and is updated every 15 minutes in real time. Infrared satellite imagery can be "colorized" or "color-enhanced" to bring out details in cloud patterns. These color enhancements are useful to meteorologists because they signify “brightness temperatures,” which are approximately the temperature of the radiating body, whether it be a cloud or the Earth’s surface. In this imagery, yellow and orange areas signify taller/colder clouds, which often correlate with more active weather systems. Blue areas are usually “clear sky,” while pale white areas typically indicate low-level clouds. During a hurricane, cloud top temperatures will be higher (and colder), and therefore appear dark red. This imagery is derived from band #13 on the GOES East and GOES West Advanced Baseline Imager.How does infrared satellite imagery work?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.What do the colors on the infrared map represent?In this imagery, yellow and orange areas signify taller/colder clouds, which often correlate with more active weather systems. Blue areas are clear sky, while pale white areas indicate low-level clouds, or potentially frozen surfaces. Learn more about this weather imagery.What does the GOES water vapor map layer show?The GOES ‘water vapor’ map displays the concentration and location of clouds and water vapor in the atmosphere and shows data from both the GOES East and GOES West satellites. Imagery is updated approximately every 15 minutes in real time. Water vapor imagery, which is useful for determining locations of moisture and atmospheric circulations, is created using a wavelength of energy sensitive to the content of water vapor in the atmosphere. In this imagery, green-blue and white areas indicate the presence of high water vapor or moisture content, whereas dark orange and brown areas indicate little or no moisture present. This imagery is derived from band #10 on the GOES East and GOES West Advanced Baseline Imager.What do the colors on the water vapor map represent?In this imagery, green-blue and white areas indicate the presence of high water vapor or moisture content, whereas dark orange and brown areas indicate less moisture present. Learn more about this water vapor imagery.About the satellitesWhat are the GOES satellites?NOAA’s most sophisticated Geostationary Operational Environmental Satellites (GOES), known as the GOES-R Series, provide advanced imagery and atmospheric measurements of Earth’s Western Hemisphere, real-time mapping of lightning activity, and improved monitoring of solar activity and space weather.The first satellite in the series, GOES-R, now known as GOES-16, was launched in 2016 and is currently operational as NOAA’s GOES East satellite. In 2018, NOAA launched another satellite in the series, GOES-T, which joined GOES-16 in orbit as GOES-18. GOES-17 became operational as GOES West in January 2023.Together, GOES East and GOES West provide coverage of the Western Hemisphere and most of the Pacific Ocean, from the west coast of Africa all the way to New Zealand. Each satellite orbits the Earth from about 22,200 miles away.

What is SEPAL?

SEPAL (https://sepal.io/) is a free and open source cloud computing platform for geo-spatial data access and processing. It empowers users to quickly process large amounts of data on their computer or mobile device. Users can create custom analysis ready data using freely available satellite imagery, generate and improve land use maps, analyze time series, run change detection and perform accuracy assessment and area estimation, among many other functionalities in the platform. Data can be created and analyzed for any place on Earth using SEPAL.

https://data.apps.fao.org/catalog/dataset/9c4d7c45-7620-44c4-b653-fbe13eb34b65/resource/63a3efa0-08ab-4ad6-9d4a-96af7b6a99ec/download/cambodia_mosaic_2020.png" alt="alt text" title="Figure 1: Best pixel mosaic of Landsat 8 data for 2020 over Cambodia">

Figure 1: Best pixel mosaic of Landsat 8 data for 2020 over Cambodia

SEPAL reaches over 5000 users in 180 countries for the creation of custom data products from freely available satellite data. SEPAL was developed as a part of the Open Foris suite, a set of free and open source software platforms and tools that facilitate flexible and efficient data collection, analysis and reporting. SEPAL combines and integrates modern geospatial data infrastructures and supercomputing power available through Google Earth Engine and Amazon Web Services with powerful open-source data processing software, such as R, ORFEO, GDAL, Python and Jupiter Notebooks. Users can easily access the archive of satellite imagery from NASA, the European Space Agency (ESA) as well as high spatial and temporal resolution data from Planet Labs and turn such images into data that can be used for reporting and better decision making.

National Forest Monitoring Systems in many countries have been strengthened by SEPAL, which provides technical government staff with computing resources and cutting edge technology to accurately map and monitor their forests. The platform was originally developed for monitoring forest carbon stock and stock changes for reducing emissions from deforestation and forest degradation (REDD+). The application of the tools on the platform now reach far beyond forest monitoring by providing different stakeholders access to cloud based image processing tools, remote sensing and machine learning for any application. Presently, users work on SEPAL for various applications related to land monitoring, land cover/use, land productivity, ecological zoning, ecosystem restoration monitoring, forest monitoring, near real time alerts for forest disturbances and fire, flood mapping, mapping impact of disasters, peatland rewetting status, and many others.

The Hand-in-Hand initiative enables countries that generate data through SEPAL to disseminate their data widely through the platform and to combine their data with the numerous other datasets available through Hand-in-Hand.

https://data.apps.fao.org/catalog/dataset/9c4d7c45-7620-44c4-b653-fbe13eb34b65/resource/868e59da-47b9-4736-93a9-f8d83f5731aa/download/probability_classification_over_zambia.png" alt="alt text" title="Figure 2: Image classification module for land monitoring and mapping. Probability classification over Zambia">

Figure 2: Image classification module for land monitoring and mapping. Probability classification over Zambia

This data set maps the soil-slip susceptibility for several areas in southwestern California. Created using Environmental Systems Research Institute's ARC/INFO software, the data base consists of raster maps containing grid cells coded with soil- slip susceptibility values. In addition, the data set includes the following graphic and text products: (1) postscript graphic plot files containing the soil-slip susceptibility map, topography, cultural data, and a key of the colored map units, and (2) PDF and text files of the Readme (including the metadata file as an appendix) and accompanying text, and a PDF file of the plot files. Intense winter rains commonly generated debris flows in upland areas of southwestern California. These debris flows initiate as small landslides referred to as soil slips. Most of the soil slips mobilize into debris flows that travel down slope at varying speeds and distances. The debris flows can be a serious hazard to people and structures in their paths. The soil-slip susceptibility maps identify those natural slopes most likely to be the sites of soil slips during periods of intense winter rainfall. The maps were largely derived by extrapolation of debris-flow inventory data collected from selected areas of southwestern California. Based on spatial analyses of soil slips, three factors in addition to rainfall, were found to be most important in the origin of soil slips. These factors are geology, slope, and aspect. Geology, by far the most important factor, was derived from existing geologic maps. Slope and aspect data were obtained from 10-meter digital elevation models (DEM). Soil-slip susceptibility maps at a scale of 1:24,000 were derived from combining numerical values for geology, slope, and aspect on a 10-meter cell size for 128 7.5' quadrangles and assembled on 1:100,000-scale topographic maps. The resultant maps of relative soil-slip susceptibility represent the best estimate generated from available debris-flow inventory maps and DEM data.

As part of the Maine Beach Mapping Program (MBMAP), MGS surveys annual alongshore shoreline positions (see Beach_Mapping_Shorelines). Using these shoreline positions and guidance from the USGS Digital Shoreline Analysis System (DSAS). DSAS is referenced as Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Ergul, Ayhan, 2009, Digital Shoreline Analysis System (DSAS) version 4.0— An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2008-1278. For more information on DSAS and the methodology DSAS employs, please see: https://woodshole.er.usgs.gov/project-pages/DSAS/. The supporting DSAS User Guide which describes how DSAS works and how statistics are calculated is available here: http://www.maine.gov/dacf/mgs/hazards/beach_mapping/DSAS_manual.pdf. MGS wrote a database procedure following protocols outlined in DSAS that allows for the calculation of different shoreline change rates and supporting statistics. This was done so that MGS no longer needed to depend on USGS updates to the DSAS software to keep current with ArcGIS software updates. The script casts shoreline-perpendicular transects at a set spacing (in this case, 10-m intervals along the shoreline), from a preset baseline (located landward of the monitored shorelines), and calculates a range of shoreline change statistics, including: Process Time: The time when the statistics were calculated. TransectID: The ID of the transect (including the group or line section ID; for example, 1-1, is line 1, transect 1) SCE: Shoreline Change Envelope. The distance, in meters, between the shoreline farthest from and closests to the baseline at each transect. NSM: Net Shoreline Movement. The distance, in meters, between the oldest and youngest shorelines for each tranect. EPR: End Point Rate. A shoreline change rate, in meters/year, calculated by dividing the NSM by the time elapsed between the oldest and youngest shorelines at each transect. LRR: Linear Regression Rate. A shoreline change rate, in meters/year, calculated by fitting a least-squares regression line to all of the shoreline points for a particular transect. The distance from the baseline, in meters, is plotted against the shoreline date, and slope of the line that provides the best fit is the LRR. LR2: The R-squared statistic, or coefficient of determination. The percentage of variance in the data that is explained by a regression, or in this case, the LRR value. It is a dimensionless index that ranges from 1.0 (a perfect fit, with the best fit line explaining all variation) to 0.0 (a bad fit, with the best fit line explaining little to no variation) and measures how successfully the best fit line (LRR) accounts for variation in the data. LCI95: Standard error of the slope at the 95% confidence interval. Calculated by muliplying the standard error, or standard deviation, of the slope by the two-tailed test statistic at the user-specified confidence percentage. For example if a reported LRR is 1.34 m/yr and a calculated LCI95 is 0.50, the band of confidence around the LRR is +/- 0.50. In other words, you can be 95% confidence that the true rate of change is between 0.84 and 1.84 m/yr. LRR_ft: The Linear Regression Rate, converted to feet/year. LCI95_ft: The LCI95, converted to feet. EPR_ft: The End Point Rate converted to feet.

As part of the Maine Beach Mapping Program (MBMAP), MGS surveys annual alongshore shoreline positions (see Beach_Mapping_Shorelines). Using these shoreline positions and guidance from the USGS Digital Shoreline Analysis System (DSAS). DSAS is referenced as Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Ergul, Ayhan, 2009, Digital Shoreline Analysis System (DSAS) version 4.0— An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2008-1278. For more information on DSAS and the methodology DSAS employs, please see: https://woodshole.er.usgs.gov/project-pages/DSAS/. The supporting DSAS User Guide which describes how DSAS works and how statistics are calculated is available here: http://www.maine.gov/dacf/mgs/hazards/beach_mapping/DSAS_manual.pdf. MGS wrote a database procedure following protocols outlined in DSAS that allows for the calculation of different shoreline change rates and supporting statistics. This was done so that MGS no longer needed to depend on USGS updates to the DSAS software to keep current with ArcGIS software updates. The script casts shoreline-perpendicular transects at a set spacing (in this case, 10-m intervals along the shoreline), from a preset baseline (located landward of the monitored shorelines), and calculates a range of shoreline change statistics, including: Process Time: The time when the statistics were calculated. TransectID: The ID of the transect (including the group or line section ID; for example, 1-1, is line 1, transect 1) SCE: Shoreline Change Envelope. The distance, in meters, between the shoreline farthest from and closests to the baseline at each transect. NSM: Net Shoreline Movement. The distance, in meters, between the oldest and youngest shorelines for each tranect. EPR: End Point Rate. A shoreline change rate, in meters/year, calculated by dividing the NSM by the time elapsed between the oldest and youngest shorelines at each transect. LRR: Linear Regression Rate. A shoreline change rate, in meters/year, calculated by fitting a least-squares regression line to all of the shoreline points for a particular transect. The distance from the baseline, in meters, is plotted against the shoreline date, and slope of the line that provides the best fit is the LRR. LR2: The R-squared statistic, or coefficient of determination. The percentage of variance in the data that is explained by a regression, or in this case, the LRR value. It is a dimensionless index that ranges from 1.0 (a perfect fit, with the best fit line explaining all variation) to 0.0 (a bad fit, with the best fit line explaining little to no variation) and measures how successfully the best fit line (LRR) accounts for variation in the data. LCI95: Standard error of the slope at the 95% confidence interval. Calculated by muliplying the standard error, or standard deviation, of the slope by the two-tailed test statistic at the user-specified confidence percentage. For example if a reported LRR is 1.34 m/yr and a calculated LCI95 is 0.50, the band of confidence around the LRR is +/- 0.50. In other words, you can be 95% confidence that the true rate of change is between 0.84 and 1.84 m/yr. LRR_ft: The Linear Regression Rate, converted to feet/year. LCI95_ft: The LCI95, converted to feet. EPR_ft: The End Point Rate converted to feet.

5-Digit and 3-Digit ZIP Code data for Maptitude mapping software are from Caliper Corporation and contain boundaries and demographic data.

This Digital Elevation Model (DEM) of the bedrock surface elevation in Iowa was compiled using all available data, principally information from GEOSAM, supplemented with well and boring information from the Iowa DOT, exposure reports from Iowa Geological & Water Survey reports and files, and the Department of Soil Conservation county soil maps for Iowa. The soil maps were especially valuable, since they identified soils that encountered bedrock within the soil horizon, and less dependably also spot-located rock exposures. A 50 foot contour interval was chosen for the map because it was considered to best represent the accuracy of the well data, allowed for fairly good representation of the bedrock surface in areas with limited well control, and was mappable in high relief areas (the contours packed so close together that it precluded mapping or forced the software to snap-join contours). The 50 foot contour interval also allowed areas where bedrock was present within the soil horizon (2-3 feet) to be treated as areas of exposures. In these areas the bedrock elevation was mapped as only slightly below the surface elevation, so contours on the 7«' topographic maps were closely followed in mapping the bedrock elevation. Consequently, on the completed map of bedrock elevation, these areas display much more contorted and crenulated contour lines than the areas where only drill control was utilized.

description: Bathymetry of Lake Michigan has been compiled as a component of a NOAA project to rescue Great Lakes lake floor geological and geophysical data and make it more accessible to the public. The project is a cooperative effort between investigators at the NOAA National Geophysical Data Center's Marine Geology & Geophysics Division (NGDC/MGG) and the NOAA Great Lakes Environmental Research Laboratory (GLERL). was compiled utilizing the entire historic sounding data base. This bathymetry resolves physiography of the lake floor to an extent that known features are revealed more accurately and features never before seen are revealed for the first time. Bathymetry has been compiled using the entire array of good-quality historical hydrographic soundings collected in support of nautical charting over a 120-year period by the NOAA National Ocean Service and its predecessor agency for Great Lakes surveying, the Army Corps of Engineers. More than 600,000 bathymetric soundings were employed, of which approximately 60 per cent were already in digital form, 25 per cent were digitized in conjunction with this effort, and the remaining 15 per cent were available only on paper survey sheets. Bathymetry was compiled at a scale of 1:250,000, with a contour interval of 5 meters. Density of tracklines is generally about 2000m for the open lake and ranges from 200m to 600m for nearshore areas. Soundings collected since 1903 were already reduced to the Lake Michigan mean low water datum; these were used for bathymetric contouring without further calibration or adjustments. Soundings collected prior to 1903 were reduced to the mean low water datum. In preparation for bathymetric contouring, digital soundings were converted to metric units and plotted in color; separate colors were assigned to the various depth ranges. From the paper sheets, contours in metric units were generated directly on overlays; these contours were then reduced to the compilation scale of 1:250,000 and incorporated into the map. Compilation sheets were then scanned and vectorized; and the resulting digital vector bathymetric contour data were used to generate the imagery shown on the large color plate. Images were constructed using the publicly-available software Generic Mapping Tools (GMT).; abstract: Bathymetry of Lake Michigan has been compiled as a component of a NOAA project to rescue Great Lakes lake floor geological and geophysical data and make it more accessible to the public. The project is a cooperative effort between investigators at the NOAA National Geophysical Data Center's Marine Geology & Geophysics Division (NGDC/MGG) and the NOAA Great Lakes Environmental Research Laboratory (GLERL). was compiled utilizing the entire historic sounding data base. This bathymetry resolves physiography of the lake floor to an extent that known features are revealed more accurately and features never before seen are revealed for the first time. Bathymetry has been compiled using the entire array of good-quality historical hydrographic soundings collected in support of nautical charting over a 120-year period by the NOAA National Ocean Service and its predecessor agency for Great Lakes surveying, the Army Corps of Engineers. More than 600,000 bathymetric soundings were employed, of which approximately 60 per cent were already in digital form, 25 per cent were digitized in conjunction with this effort, and the remaining 15 per cent were available only on paper survey sheets. Bathymetry was compiled at a scale of 1:250,000, with a contour interval of 5 meters. Density of tracklines is generally about 2000m for the open lake and ranges from 200m to 600m for nearshore areas. Soundings collected since 1903 were already reduced to the Lake Michigan mean low water datum; these were used for bathymetric contouring without further calibration or adjustments. Soundings collected prior to 1903 were reduced to the mean low water datum. In preparation for bathymetric contouring, digital soundings were converted to metric units and plotted in color; separate colors were assigned to the various depth ranges. From the paper sheets, contours in metric units were generated directly on overlays; these contours were then reduced to the compilation scale of 1:250,000 and incorporated into the map. Compilation sheets were then scanned and vectorized; and the resulting digital vector bathymetric contour data were used to generate the imagery shown on the large color plate. Images were constructed using the publicly-available software Generic Mapping Tools (GMT).

As part of the Maine Beach Mapping Program (MBMAP), MGS surveys annual alongshore shoreline positions (see Beach_Mapping_Shorelines). Using these shoreline positions and guidance from the USGS Digital Shoreline Analysis System (DSAS). DSAS is referenced as Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Ergul, Ayhan, 2009, Digital Shoreline Analysis System (DSAS) version 4.0— An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2008-1278. For more information on DSAS and the methodology DSAS employs, please see: https://woodshole.er.usgs.gov/project-pages/DSAS/. The supporting DSAS User Guide which describes how DSAS works and how statistics are calculated is available here: http://www.maine.gov/dacf/mgs/hazards/beach_mapping/DSAS_manual.pdf. MGS wrote a database procedure following protocols outlined in DSAS that allows for the calculation of different shoreline change rates and supporting statistics. This was done so that MGS no longer needed to depend on USGS updates to the DSAS software to keep current with ArcGIS software updates. The script casts shoreline-perpendicular transects at a set spacing (in this case, 10-m intervals along the shoreline), from a preset baseline (located landward of the monitored shorelines), and calculates a range of shoreline change statistics, including: Process Time: The time when the statistics were calculated. TransectID: The ID of the transect (including the group or line section ID; for example, 1-1, is line 1, transect 1) SCE: Shoreline Change Envelope. The distance, in meters, between the shoreline farthest from and closests to the baseline at each transect. NSM: Net Shoreline Movement. The distance, in meters, between the oldest and youngest shorelines for each tranect. EPR: End Point Rate. A shoreline change rate, in meters/year, calculated by dividing the NSM by the time elapsed between the oldest and youngest shorelines at each transect. LRR: Linear Regression Rate. A shoreline change rate, in meters/year, calculated by fitting a least-squares regression line to all of the shoreline points for a particular transect. The distance from the baseline, in meters, is plotted against the shoreline date, and slope of the line that provides the best fit is the LRR. LR2: The R-squared statistic, or coefficient of determination. The percentage of variance in the data that is explained by a regression, or in this case, the LRR value. It is a dimensionless index that ranges from 1.0 (a perfect fit, with the best fit line explaining all variation) to 0.0 (a bad fit, with the best fit line explaining little to no variation) and measures how successfully the best fit line (LRR) accounts for variation in the data. LCI95: Standard error of the slope at the 95% confidence interval. Calculated by muliplying the standard error, or standard deviation, of the slope by the two-tailed test statistic at the user-specified confidence percentage. For example if a reported LRR is 1.34 m/yr and a calculated LCI95 is 0.50, the band of confidence around the LRR is +/- 0.50. In other words, you can be 95% confidence that the true rate of change is between 0.84 and 1.84 m/yr. LRR_ft: The Linear Regression Rate, converted to feet/year. LCI95_ft: The LCI95, converted to feet. EPR_ft: The End Point Rate converted to feet.

These data are high-resolution bathymetry (riverbed elevation) and depth-averaged velocities in comma-delimited table format, generated from hydrographic and velocimetric surveys near highway bridge structures over the Missouri River between Kansas City and St. Louis, Missouri, May 19–26, 2021. Hydrographic data were collected using a high-resolution multibeam echosounder mapping system (MBMS), which consists of a multibeam echosounder (MBES) and an inertial navigation system (INS) mounted on a marine survey vessel. Data were collected as the vessel traversed the river along planned survey lines distributed throughout the reach. Data collection software integrated and stored the depth data from the MBES and the horizontal and vertical position and attitude data of the vessel from the INS in real time. Data processing required specialized computer software to extract bathymetry data from the raw data files and to summarize and map the information. Velocity data for the surveys were collected using an acoustic Doppler current profiler (ADCP) mounted on a survey vessel equipped with a differential global positioning system (DGPS). Velocity data were collected for all sites except site 14 at Lexington due to a faulty ADCP unit. Data were collected as the vessel traversed the river along seven planned transect lines distributed throughout the reach. Velocity data were processed using the Velocity Mapping Toolbox (Parsons and others, 2013), and smoothed using neighboring nodes. There is a zip file for the 8 surveyed sites available for download containing the bathymetric data and depth-averaged velocities. The files follow the format of "site-##_MissouriRiver_HWY#_2021-05.zip", where "site-##" is the site number from 14 to 21 and "HWY#" is the highway type and route number. The zip files each contain two comma-delimited text files, one with the bathymetry and uncertainty data and one with the depth-averaged velocity data, as well as associated metadata and thumbnail images. Reference cited: Parsons, D.R., Jackson, P.R., Czuba, J.A., Engel, F.L., Rhoads, B.L., Oberg, K.A., Best, J.L., Mueller, D.S., Johnson, K.K., and Riley, J.D., 2013, Velocity Mapping Toolbox (VMT) A process and visualization suite for moving-vessel ADCP measurements: Earth Surface Processes and Landforms, v. 38, no. 11, p. 1244-1260. [Also available at https://doi.org/10.1002/esp.3367.]