UMESC 20260128 Raster Digital Data Set Online U.S. Geological Survey https://doi.org/10.5066/P9BLTSTZ In 2007, the U.S Army Corps of Engineers’ Upper Mississippi River Restoration (UMRR) program partnered with the Iowa Department of Natural Resources (IDNR) to collect FEMA-grade, bluff-to-bluff lidar for Navigation Pools 8-24 of the UMRS. In 2009, with American Recovery and Reinvestment (ARRA) funds awarded to UMRR, the remaining lidar for the Upper Mississippi River, to the confluence with the Ohio River, and the Illinois River was contracted. Data acquisition was completed in 2011. Lidar data are remotely sensed, high-resolution elevation data collected by airplane. The Upper Midwest Environmental Sciences Center is processing these data to create Digital Elevation Models (DEMs), 0.5 meter contour lines, and pool-wide hillshade images. Light Detection and Ranging (lidar) generates extremely accurate (vertical and horizontal) location information and has long been a desired product for the UMRS. Lidar data is used for 3D visualization, elevation based analysis and for feature extraction. Reflective surface data represents the DEM created by laser energy reflected from the first surface encountered by the laser pulse. Some energy may continue beyond this initial surface to be reflected by a subsequent surface as represented by the Last Return data. Intensity information is captured from the Reflective Surface pulse and indicates the relative energy returned to the sensor as compared to the energy transmitted. Points are classified as on ground surface or not on ground surface to support creation of a bare earth model from the data. 20071107 ground condition Complete None planned -91.325637412999 -91.017442720998 43.610560861 43.209831311 USGS Thesaurus elevation lidar topography digital elevation models digital elevation models None elevation lidar laser topography digital elevation model DEM Surface Model USGS Metadata Identifier USGS:697257ffd4be024aa5bdd98e U.S. Department of Commerce, 1995, Countries, Dependencies, Areas of Special Sovereignty, and Their Principal Administrative Divisions (Federal Information Processing Standard (FIPS) 10-4): Washington, D.C., National Institute of Standards and Technology US Mississippi River State location Minnesota Wisconsin Mississippi River Upper Mississippi River None. None. Jayme Strange U.S. Geological Survey Geographer mailing and physical

2630 Fanta Reed Road

La Crosse WI 54603 US 608-781-6290 jstrange@usgs.gov The U.S. Army Corps of Engineers’ Upper Mississippi River Restoration—Environmental Management Program (UMRR-EMP), Long Term Resource Monitoring Program (LTRMP) element is implemented by the U.S. Geological Survey, Upper Midwest Environment Sciences Center (UMESC). La Crosse County Lidar, 2007. Environment as of Metadata Creation: Microsoft [Unknown] Version 6.2 (Build 9200) ; Esri ArcGIS 10.6 (Build 8321) Service Pack N/A (Build N/A) GPS phase data was post processed with continuous kinematic survey techniques using "On the Fly" (OTF) integer ambiguity resolution. The GPS data was processed with forward and reverse processing algorithms. The results from each process, using the data collected at the airport and in project area, were combined to yield a single fixed integer phase differential solution of the aircraft trajectory. The differences between the forward to reverse solution within the project area were within project specifications, indicating a valid and accurate solution. An IMU was used to record precise changes in position and orientation of the LIDAR scanner at a rate of 200 Hz. All IMU data was processed post flight with a filter to integrate inertial measurements and precise phase differential GPS positions. The resulting solution contains geodetic position, omega, phi, kappa, and time for subsequent merging with the laser ranging information. All products were created from LAS data. All LAS formatted lidar data are validated using commercial GIS software to ensure proper formatting and loading before delivery. This validation procedure ensures that data on delivery media is in correct physical format and is readable. Data have been checked and validated to conform to attribute standards defined by the project. All deliverables were quality checked by MN DNR and passed inspection. The lidar data is checked for quality and completeness during, and immediately following each mission. Coverage and accuracy is verified in the office as the data is sent. Elevation products are opened and visually reviewed for completeness. Meets or exceeds horizontal accuracy of 0.6m RMSE. The NAD83 (HARN) datum was used. The NAVD88 (Geoid09) vertical datum was used. REPORT FROM THE VENDOR: Vertical accuracy of the LiDAR data will be assessed and reported in accordance with the guidelines developed by the NDEP (National Digital Elevation Program) and subsequently adopted by the ASPRS (American Society for Photogrammetry and Remote Sensing). The complete guidelines may be found in Section 1.5 of the http://www.ndep.gov/NDEP_Elevation_Guidelines_Ver1_10May2004.pdf Vertical accuracy requirements using the NDEP/ASPRS: 1. 1.5 points/square meter areas: Sub-Project A 24.5cm ACCz, 95% (12.5cm RMSEz) 2. The higher density areas (Dakota Block, Metro Block and Maple Grove Block): 17.64cm ACCz 95% (9.0cm RMSEz) REPORTS FROM MINNESOTA DNR: True accuracy values: Metro Block - 5 cm Dakota Block - 10.8 cm Maple Grove Block - 8.3 cm See the complete Vertical Validation Reports in each folder on the data download FTP site; for example, the report for the Maple Grove Block is here: ftp://ftp.lmic.state.mn.us/pub/data/elevation/lidar/projects/metro/block_maple_grove/maple_grove_vertical_validation_report.pdf U.S. Army Corps of Engineers 20071107 Other Rock Island, IL U.S. Army Corps of Engineers na Digital and/or Hardcopy Resources 20050101 20070301 ground condition Source Input 2 Source information used in support of the development of the data set. VENDOR PROCESSING STEPS: First, the density of bare earth LiDAR points was calculated using the ArcGIS tool, “Point Density”. A 1 meter cell size density output was calculated using a 4 meter circular radius neighborhood. The output units were designated as square meters. Next, using the tool “Con”, areas with point densities greater than or equal to 0.05 per square meter were identified. The output raster edges were then cleaned up using the tool “Shrink” so the outer boundary of the areas with densities greater than or equal to 0.05 per square meter were decreased by 2 cells (2 meters). This raster boundary file was then converted from a raster to a shapefile using the “Raster to Polygon” tool. A field Code was added and was calculated to be equal to the gridcode values from the original raster (0=low density of LiDAR points, 1= high density of LiDAR points). The “0” value areas with a low density of LiDAR points typically represent those areas that are either aquatic (sensor does not return signal from aquatic land cover types) or else were removed during the classification process (Buildings). Next, these polygonal areas where values were equal to “0” were further divided and classified according to the land cover type they overlapped. If they were identifying buildings they were left as “0” whereas, if they represented different aquatic types they were given a specific code. Mainstem river polygons were given a “2” value, off-channel aquatic areas (lakes, ponds, isolated backwaters) were given a value of “4” if there was underlying bathymetry data developed by the Upper Mississippi River Restoration, Environmental Management Program’s Long Term Resource Monitoring Program element. They were attributed as “5” if they were off-channel aquatic areas without the underlying bathymetry. Aquatic areas that were contiguous with the main channel (sloughs, side channels, tributaries) were given a value of “7”. Next, areas where there was a lack of points on terrestrial areas adjacent to aquatic areas (bridges, overpasses) were fixed in the shapefile to create a consistent land/water boundary. Using the LP360 conflation tool, calculated the minimum elevation for all polygons with a code of “4” or “5”. Then exported the LiDAR bare earth data as a DEM surface with a 1 meter cell size. Breaklines were enforced based upon the polygons generated for the polygons with codes “4” and “5”. Next, interpolated a water surface for the main channel/tributaries/side channels that traverse a wide range of “Z” values (steep gradient). This was done by exporting all polygons with a code of “2” or “7”. Then convert the vertices of these polygons to points using the “Feature vertices to points” tool. Then the minimum “Z” value for each point was calculated using the bare earth LiDAR points and the LP360 conflation tool. Only “Z” values within 1 meter of the vertex were used. Next, a model was run which clips out the points within each separate polygon labeled “2” or “7” and calculates point statistics on each point based upon their elevation “Z” value to create a smooth water surface at or lower than the land/water interface. The first pass using the “Point Statistics” tool calculates the minimum “Z” value within a 100 meter radius circular window. The resultant rasters values were then summarized into the original point shapefile using the “Extract Values to Points” tool. Then another pass using the “Point statistics” was done to calculate the mean value of these minimum points using a slightly larger radius circular window of 200 meters. This is done to smooth out the values over a larger area. The “Extract Values to Points” tool was again run to get the raster layer’s output back into the point shapefile. Next, an aquatic surface was developed using the ArcGIS tool “Topo to Raster” using the point shapefile developed previously as an input feature data set. The field selected was the one developed using the final “Point Statistics” output. For this tool the type was set to PointElevation, the primary type of input was set to Spot, and the output cell size was 4 meters. This output was then converted to a 1 meter cell size using the “Focal Statistics” tool using a circular focal window radius of 4 meters. This interpolated surface was then clipped using the polygons with code values of “2” or “7” and mosaicked with the DEM surface previously developed using the bare earth LiDAR points and the code “4” and “5” breaklines. 20080101 LAS files were converted to first return DEM by using LP360 lidar point cloud processing software. DEM was clipped down to study area boundary. 20090101 A "Filter" was run using spatial analyst/neighborhood tools/filter on each of the 24k quad DEMs. The low pass filter was run using the settings; Filter Type - LOW and Ignore NoData in Calculations - Checked. (Output raster had a file format of .tif) 20100101 Unknown Raster Grid Cell 44000 24000 1 Universal Transverse Mercator 15 0.9996 -93.0 0.0 500000.0 0.0 row and column 1.0 1.0 METERS D_North_American_1983 GRS_1980 6378137.0 298.257222101 Attribute Table Table containing attribute information associated with the data set. Producer defined Value Elevation Producer defined 186.74000549316 445.8108215332 Meter The entity and attribute information provided here describes the tabular data associated with the data set. Please review the detailed descriptions that are provided (the individual attribute descriptions) for information on the values that appear as fields/table entries of the data set. The entity and attribute information was generated by the individual and/or agency identified as the originator of the data set. Please review the rest of the metadata record for additional details and information. U.S. Geological Survey - ScienceBase mailing and physical

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Denver CO 80225 USA 1-888-275-8747 sciencebase@usgs.gov Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. 20260128 Jayme M Strange U.S. Geological Survey, MIDCONTINENT REGION Geographer mailing and physical

2630 Fanta Reed Road

La Crosse WI 54603 US 608-781-6290 N/A jstrange@usgs.gov Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998