Web map displaying Wisconsin DNR-produced Digital Elevation Model (DEM) and Hillshade image services, along with their index layer, in formats that are clickable and can be symbolized and filtered. This map can also be used as a starting point to create a new map. To open the web map from DNR's GIS Open Data Portal, click the View Metadata: link to the right of the description, then click Open in Map Viewer.

This topobathymetric digital elevation model (TBDEM) mosaic represents the topography and bathymetry for the Milwaukee River Estuary in Milwaukee, Wisconsin and adjacent terrestrial and Lake Michigan nearshore coastal areas. The TBDEM was produced in support of modeling and for developing a physical habitat framework to help with understanding the effects from multidirectional currents and seiche effects associated with the mixing of river flows with Lake Michigan backwater. The TBDEM mosaic is built off existing terrestrial, nearshore, and estuary frameworks developed for other areas around the Great Lakes and the Milwaukee River Harbor. Ranging from 2008-2015, land elevations derived from lidar and historic topographic surveys and bathymetric multibeam sonar were used to generate the seamless Milwaukee River Estuary TBDEM from four different data frameworks: (1) 2015 Milwaukee River Estuary Bathymetry (Dow, 2018), (2) 2015 SEWRPC Topographic LIDAR for Southeast, Wisconsin (Office for Coastal Management (OCM) Partners, 2018), (3) 2012 NOAA Digital Coast Topographic and Bathymetric LIDAR (OCM, 2017), and (4) 2008 NOAA Digital Coast Topographic and Bathymetric LIDAR (OCM, 2008). The TBDEM is provided as a 0.6-meter resolution raster dataset in GeoTIFF format. References include: (1) Dow, Brennan,2018, Assessment and mapping of the Milwaukee Estuary Habitat, Theses and Dissertations. 1785: Milwaukee, University of Wisconsin - Milwaukee, M.S., 49 p., [Also available at https://dc.uwm.edu/etd/1785]. (2) Office for Coastal Management (OCM) Partners, 2018: 2015 SEWRPC Lidar: Southeast WI (Milwaukee, Ozaukee, Walworth, Washington, Waukesha Counties) (published 20180619): NOAA National Centers for Environmental Information, accessed January 25, 2021 at https://www.fisheries.noaa.gov/inport/item/58870 (3) Office for Coastal Management (OCM) Partners, 2017: 2012 USACE Great Lakes Topobathy Lidar: Lake Michigan (published 20170702): NOAA National Centers for Environmental Information, accessed January 25, 2021 at https://www.fisheries.noaa.gov/inport/item/49736. (4) Office for Coastal Management (OCM) Partners, 2008: 2008 USACE Great Lakes Topo/Bathy Lidar: Wisconsin (published 2008): NOAA National Centers for Environmental Information, accessed January 25, 2021, https://www.fisheries.noaa.gov/inport/item/50070.

Open-file report; contains unpublished data that has not yet been peer-reviewed.

Note: This service is only for using online; full resolution downloads are not supported. Hillshade image service created from Digital Elevation Models (DEMs) derived from county-produced LiDAR covering several Wisconsin counties, with a vertical exaggeration factor of 2. This service was last updated in May, 2023. It can be used in conjunction with its associated Index layer, DEM and Hillshade from LiDAR - Index, to determine flight years of source LiDAR and resolution of source DEMs. Also see the Index layer item details for detailed information about counties included in this service and in related services: DEM from LiDAR (Units in Meters) and DEM from LiDAR (Units in Feet).Some areas display as data gaps (white artifacts) when the service is viewed at statewide scales but display normally when zoomed in to scales of approximately 1:1,000,000 or larger. We hope to address the no-data areas and small-scale data gaps in future updates to this service. The source DEMs have not been hydrologically conditioned. The Vertical Datum for the DEMs is NAVD88.

The Hillshade is intended for visualization of the landscape, rather than analysis. When queried, the Hillshade pixel values do not indicate elevation; instead, the pixel values range from 0 to 255 because the image is rendered as an 8-bit greyscale image. If elevation values are needed, use the LiDAR-Derived DEM Imagery Layer.

WI DNR acknowledges the USDA Natural Resources Conservation Service, USGS, FEMA, the Southeastern WI Regional Planning Commission, and the individual counties listed in DEM and Hillshade from LiDAR - Index, for making source data available. For more information, visit https://dnr.wi.gov/feedback/ and choose Geographic Information Systems Data as the subject.

The vertical land change activity focuses on the detection, analysis, and explanation of topographic change. These detection techniques include both quantitative methods, for example, using difference metrics derived from multi-temporal topographic digital elevation models (DEMs), such as, light detection and ranging (lidar), National Elevation Dataset (NED), Shuttle Radar Topography Mission (SRTM), and Interferometric Synthetic Aperture Radar (IFSAR), and qualitative methods, for example, using multi-temporal aerial photography to visualize topographic change. The geographic study areas of this activity are in Chippewa, Eau Claire, Jackson, Monroe, Trempealeau, and Wood counties in west central Wisconsin. Available multi-temporal lidar, NED, SRTM, IFSAR, and other topographic elevation datasets, as well as aerial photography and multi-spectral image data were identified and downloaded for these study area counties. Locations of industrial sand mines and processing plants (vector features) were obtained from the Wisconsin Department of Natural Resources at http://dnr.wi.gov/topic/Mines/ISMMap.html, and from the Wisconsin Center for Investigative Journalism, October 2012, update at https://fusiontables.google.com/DataSource?docid=17nDFI4iUPOdyDOEWU7Vu1ONMiVofa3aWR_Gs-Zk#rows:id=1. These features were used to spatially validate some of the mining locations that were predefined with Landsat-detected mining locations (polygons). Previously developed differencing methods (Gesch, 2006) were used to develop difference raster datasets of NED/SRTM (1961-2000 date range) and SRTM/IFSAR (2000-2008 date range). The difference rasters were evaluated to exclude difference values that were below a specified vertical change threshold, which was applied spatially by National Land Cover Dataset (NLCD) 1992 and 2006 land cover type, respectively. This spatial application of the vertical change threshold values improved the overall ability to detect vertical change because threshold values in bare earth areas were distinguished from threshold values in heavily vegetated areas. Lidar point cloud data and high-resolution (1-3 m) lidar DEMs were acquired for the Wisconsin six-county study area from Chippewa County Land Records Division, Chippewa Falls, WI; Eau Claire County, Eau Claire, WI; Jackson County and Jackson County Land Information Council, Black River Falls, WI; Monroe County, Sparta, WI; Trempealeau County, Whitehall, WI; and Wood County Planning and Zoning, Wisconsin Rapids, WI. ESRI Mosaic Datasets were generated from lidar point-cloud data and available topographic DEMs for the specified study areas. These data were analyzed to estimate volumetric changes on the land surface at three different periods with lidar acquisitions collected for Chippewa County, WI on May 15, 2011 and April 14, 2012; Eau Claire County, WI in 2013; Jackson County, WI in April, 2015; Monroe County, WI April 11-12, 2010; Trempealeau County, WI April 26, 2014 to May 5, 2014; and Wood County, WI March 21-31, 2015. The most recent difference analysis consisting of a raster dataset time span (2008-2015 date range) was analyzed by differencing the Wisconsin lidar-derived DEMs and an IFSAR-derived dataset. The IFSAR-derived data were resampled to the resolution of the lidar DEM (approximately 1-m resolution) and compared with the lidar-derived DEM. Land cover based threshold values were applied spatially to detect vertical change using the IFSAR/lidar difference dataset. Chippewa County lidar DEM metadata reported the root mean square error (RMSE) of 0.083 m. Eau Claire County lidar DEM metadata described an RMSE of 18.5 cm that supports 2 ft contours. Jackson County lidar DEM metadata reported that a comparison of the ground survey versus lidar model values indicated an RMSE of 0.214 ft (0.065 m). Monroe County lidar DEM metadata was obtained from the U.S. Interagency Elevation Inventory, which indicated an RMSE of 0.106 m. Trempealeau County lidar DEM included metadata describing RMSE values for different land cover types. A comparison of the Trempealeau ground survey versus lidar model values indicated an overall vertical RMSE of 0.344 ft (0.105 m). An RMSE was reported for each of the following land cover types in Trempealeau County: Urban: 0.169 US Survey Feet (0.051 m); Low Grass: 0.150 US Survey Feet (0.046 m); Tall Grass: 0.489 US Survey Feet (0.149 m); Low Trees: 0.432 US Survey Feet (0.132 m); Tall Trees: 0.342 US Survey Feet (0.104 m). This allowed additional refinement of the spatially explicit threshold values. Wood County lidar DEM RMSE was obtained from the US Interagency Elevation Inventory (0.122 m).References: Gesch, Dean B., 2006, An inventory and assessment of significant topographic changes in the United States Brookings, S. Dak., South Dakota State University, Ph.D. dissertation, 234 p, at https://topotools.cr.usgs.gov/pdfs/DGesch_dissertation_Nov2006.pdf.

When viewed with 3-D glasses, Wisconsin’s land features pop off the page. Brief descriptions of major land forms paint the geologic history that shaped the state; expanded descriptions and photos are online. We’ve field-tested this map on fourth graders and can attest to its appeal. Sizes: 27.5 x 32 inches, 8.5 x 11 inches. Requires red-blue 3-D glasses.

Open-file report; contains unpublished data that has not yet been peer-reviewed.

These files contain topographic and bathymetric lidar data collected by the Compact Hydrographic Airborne Rapid Total Survey (CHARTS) system along the coast of Wisconsin. CHARTS integrates topographic and bathymetric lidar sensors, a digital camera and a hyperspectral scanner on a single remote sensing platform for use in coastal mapping and charting activities. Data coverage generally extends...

Collection of most of the plates accompanying Chamberlin's four-volume Geology of Wisconsin: Survey of 1873-1879.

The U.S. Army Corps of Engineers' Upper Mississippi River Restoration (UMRR) Program Long Term Resource Monitoring (LTRM) element has overseen the collection, processing, and serving of bathymetric data since 1989. A systemic data collection for the Upper Mississippi River System (UMRS) was completed in 2010. Water depth in aquatic systems is important for describing the physical characteristics of a river. Bathymetric maps are used for conducting spatial inventories of the aquatic habitat and detecting bed and elevation changes due to sedimentation. Bathymetric data is widely used, specifically for studies of water level management alternatives, modeling navigation impacts and hydraulic conditions, and environmental assessments such as vegetation distribution patterns. The bathymetry "footprint" is a database that can be used as a tool to provide a quick search of collection dates corresponding to bathymetric coverages within each LTRM pool.

These files contain topographic and bathymetric lidar data collected by the Compact Hydrographic Airborne Rapid Total Survey (CHARTS) system along the coast of Wisconsin. CHARTS integrates topographic and bathymetric lidar sensors, a digital camera and a hyperspectral scanner on a single remote sensing platform for use in coastal mapping and charting activities. Data coverage generally extends along the coastline from the waterline inland 500 meters (topography) and offshore 1,000 meters or to laser extinction (bathymetry). The topographic lidar sensor has a pulse repetition rate of 9 kHz at 1064 nm (near-infrared wavelength). The bathymetric lidar sensor has a pulse repetition rate of 1 kHz at 532 nm (green wavelength). Native lidar data is not generally in a format accessible to most Geographical Information Systems (GIS). Specialized in-house and commercial software packages are used to process the native lidar data into 3-dimensional positions that can be imported into GIS software for visualization and further analysis. Horizontal positions, provided in decimal degrees of latitude and longitude, are referenced to the North American Datum of 1983 (NAD83). Vertical positions were referenced to the NAD83 ellipsoid and provided in meters. The National Geodetic Survey's (NGS) GEOID03 model was used to transform the vertical positions from ellipsoid to orthometric heights referenced to the North American Vertical Datum of 1988 (NAVD88). The format of the files was LAS version 1.0. The NOAA Coastal Services Center received the data and converted the topo and hydro files from NAVD88 heights to ellipsoid heights using GEOID03. These files were converted for data storage and Digital Coast provisioning purposes. The topographic points are classified as 0 (never classified) and the bathymetric points are classified as 11 (CSC bathymetry).

Open-file report; contains unpublished data that has not yet been peer-reviewed.

This polygon layer provides an index grid based on latitude and longitude intervals corresponding to the USGS topographic quadrangle series at 1:24,000 scale. The latitude/longitude interval corresponding to this quadrangle index is 7.5 minutes latitude by 7.5 minutes longitude.

Version 10.0 (Alaska, Hawaii and Puerto Rico added) of these data are part of a larger U.S. Geological Survey (USGS) project to develop an updated geospatial database of mines, mineral deposits, and mineral regions in the United States. Mine and prospect-related symbols, such as those used to represent prospect pits, mines, adits, dumps, tailings, etc., hereafter referred to as “mine” symbols or features, have been digitized from the 7.5-minute (1:24,000, 1:25,000-scale; and 1:10,000, 1:20,000 and 1:30,000-scale in Puerto Rico only) and the 15-minute (1:48,000 and 1:62,500-scale; 1:63,360-scale in Alaska only) archive of the USGS Historical Topographic Map Collection (HTMC), or acquired from available databases (California and Nevada, 1:24,000-scale only). Compilation of these features is the first phase in capturing accurate locations and general information about features related to mineral resource exploration and extraction across the U.S. The compilation of 725,690 point and polygon mine symbols from approximately 106,350 maps across 50 states, the Commonwealth of Puerto Rico (PR) and the District of Columbia (DC) has been completed: Alabama (AL), Alaska (AK), Arizona (AZ), Arkansas (AR), California (CA), Colorado (CO), Connecticut (CT), Delaware (DE), Florida (FL), Georgia (GA), Hawaii (HI), Idaho (ID), Illinois (IL), Indiana (IN), Iowa (IA), Kansas (KS), Kentucky (KY), Louisiana (LA), Maine (ME), Maryland (MD), Massachusetts (MA), Michigan (MI), Minnesota (MN), Mississippi (MS), Missouri (MO), Montana (MT), Nebraska (NE), Nevada (NV), New Hampshire (NH), New Jersey (NJ), New Mexico (NM), New York (NY), North Carolina (NC), North Dakota (ND), Ohio (OH), Oklahoma (OK), Oregon (OR), Pennsylvania (PA), Rhode Island (RI), South Carolina (SC), South Dakota (SD), Tennessee (TN), Texas (TX), Utah (UT), Vermont (VT), Virginia (VA), Washington (WA), West Virginia (WV), Wisconsin (WI), and Wyoming (WY). The process renders not only a more complete picture of exploration and mining in the U.S., but an approximate timeline of when these activities occurred. These data may be used for land use planning, assessing abandoned mine lands and mine-related environmental impacts, assessing the value of mineral resources from Federal, State and private lands, and mapping mineralized areas and systems for input into the land management process. These data are presented as three groups of layers based on the scale of the source maps. No reconciliation between the data groups was done.Datasets were developed by the U.S. Geological Survey Geology, Geophysics, and Geochemistry Science Center (GGGSC). Compilation work was completed by USGS National Association of Geoscience Teachers (NAGT) interns: Emma L. Boardman-Larson, Grayce M. Gibbs, William R. Gnesda, Montana E. Hauke, Jacob D. Melendez, Amanda L. Ringer, and Alex J. Schwarz; USGS student contractors: Margaret B. Hammond, Germán Schmeda, Patrick C. Scott, Tyler Reyes, Morgan Mullins, Thomas Carroll, Margaret Brantley, and Logan Barrett; and by USGS personnel Virgil S. Alfred, Damon Bickerstaff, E.G. Boyce, Madelyn E. Eysel, Stuart A. Giles, Autumn L. Helfrich, Alan A. Hurlbert, Cheryl L. Novakovich, Sophia J. Pinter, and Andrew F. Smith.USMIN project website: https://www.usgs.gov/USMIN

The United States has an average elevation of roughly 2,500 feet (763m) above sea level, however there is a stark contrast in elevations across the country. Highest states Colorado is the highest state in the United States, with an average elevation of 6,800 feet (2,074m) above sea level. The 10 states with the highest average elevation are all in the western region of the country, as this is, by far, the most mountainous region in the country. The largest mountain ranges in the contiguous western states are the Rocky Mountains, Sierra Nevada, and Cascade Range, while the Appalachian Mountains is the longest range in the east - however, the highest point in the U.S. is Denali (Mount McKinley), found in Alaska. Lowest states At just 60 feet above sea level, Delaware is the state with the lowest elevation. Delaware is the second smallest state, behind Rhode Island, and is located on the east coast. Larger states with relatively low elevations are found in the southern region of the country - both Florida and Louisiana have an average elevation of just 100 feet (31m) above sea level, and large sections of these states are extremely vulnerable to flooding and rising sea levels, as well as intermittent tropical storms.

These files contain rasterized topobathy lidar elevations generated from data collected by the Coastal Zone Mapping and Imaging Lidar (CZMIL) system. CZMIL integrates a lidar sensor with simultaneous topographic and bathymetric capabilities, a digital camera and a hyperspectral imager on a single remote sensing platform for use in coastal mapping and charting activities. Native lidar data is no...

In 2012 a joint project was started by the Milwaukee Metropolitan Sewerage District (MMSD) and University of Wisconsin –Milwaukee (UWM) to support MMSD’s Integrated Regional Stormwater Management Program. Digital copies of scans of various historical map sources were collected and georeferenced to Wisconsin State Plane Coordinate Systems (NAD 1927 and NAD 1983). Historical streams and water features in the Milwaukee area were mapped to a geographic information system (GIS). Water features were identified and digitized from the scanned source maps. These features included: Stream lines River lines Lake Michigan Shoreline Pond boundaries Lake boundaries Swamp or marsh boundaries Spring locations An Esri ArcGIS geodatabase was created to house the digitized features and associated attributes as delineated in the scanned source maps. Geographic coverage area: Milwaukee County; Portions of Waukesha, Ozaukee, Washington, and Racine CountySource map used for this data: U.S. Geological Survey, 1901-1906, USGS Historical Topographic Quadrangle Maps, available at https://www.usgs.gov/programs/national-geospatial-program/historical-topographic-maps-preserving-past.

The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The file is georeferenced to earth's surface using the UTM projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12000.

This dataset was last updated in July, 2016. It is derived, in part, from the SSURGO soil surveys, a compound topographic layer derived from the statewide 10m DEM, Wisconsin 24K Hydro Layer, Wisconsin Wetlands Inventory and the Restoration Database. The extraction of soil polygons is based on two fields and is refined by the CTI values of 10 or greater*. The selecting fields are Percent Hydric and PWSL. All polygons that were greater or equal to 75% hydric were automatically selected for consideration. In addition, Soil Polygons that had a Percent Hydric from 1 - 74 were also selected, and all polygons that were not in these two sets that had a PWSL value between 1 - 80 and

Ayres Associates provided Wood County, Wisconsin, with lidar based topographic mapping services in the spring of 2015 as part of WROC. The LiDAR data was collected from 3/21/2015 to 3/31/2015 using an Optech Orion H300 sensor mounted in a fixed-wing aircraft. LiDAR data was collected to support the generation of 2-foot contours to meet FEMA vertical accuracy standards. The LiDAR data was delivered according to a 5,000 foot x 5,000 foot tile schematic. The LiDAR data was calibrated using information collected at the time of flight from GPS base stations on the ground and airborne GPS/IMU in the aircraft. The calibrated LiDAR data was processed to produce a classified point cloud, bare earth DTM, DEM, DSM, contours, breaklines, and intensity images.Hydrographic breaklines are collected using LiDARgrammetry to ensure hydroflattened water surfaces. This process involves manipulating the LiDAR data's intensity information to create a metrically sound stereo environment. From this generated "imagery", breaklines are photogrammetrically compiled. Breakline polygons are created to represent open water bodies. The LiDAR points that fall within these areas are classified as "water." Breaklines representing streams and rivers shall be smooth, continuous, and monotonic, and represent the water surface without any stair steps except for dams and rapids. All hydrographic breaklines include a 1 foot buffer, with the points being re-classified as Class 10 (ignored ground). TerraSolid is further used for the subsequent manual classification of the LiDAR points allowing technicians to view the point cloud in a number of ways to ensure accuracy and consistency of points and uniformity of point coverage. The 2014 breaklines dataset contains the hydrographic breaklines necessary for terrain surface development.