MnTOPO is a web application for viewing, printing and downloading
high-resolution elevation data for the State of Minnesota that was collected using LiDAR technology. It runs on a variety of devices including desktop PCs, tablets, and mobile phones. The data you see and download in MnTOPO was made possible by the Minnesota elevation mapping project. MnTOPO is a collaborative effort between staff from the Minnesota
Information Technology (MN.IT) @ Minnesota Department of Natural Resources and MN.IT @ Minnesota Geospatial Information Office (MnGeo). Funding was provided by the Clean Water Fund of the Clean Water, Land and Legacy Amendment.

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 Itasca and St. Louis counties in the northern Minnesota Mesabi Iron Range. 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. Mining (vector) features were obtained from the Minnesota Department of Natural Resourc ...

2-foot and 10-foot elevation contours derived from the Spring 2012 Minnesota Department of Natural Resources (MN DNR) LiDAR dataset.

The Minnesota Elevation mapping project was developed by the Minnesota Digital Elevation Mapping Committee and executed by Minnesota State agencies with the assistance of the federal government county governments to acquire a highly accurate land surface elevation dataset for the State of Minnesota. High accuracy elevation data are essential to improving water quality, improving disaster prepar...

Terrain data, as defined in FEMA Guidelines and Specifications, Appendix M: Data Capture Standards, describe the digital topographic data that were used to create the elevation data representing the terrain environment of a watershed and/or floodplain. Terrain data requirements allow for flexibility in the types of information provided as sources used to produce final terrain deliverables. Once this type of data is provided, FEMA will be able to account for the origins of the flood study elevation data. (Source: FEMA Guidelines and Specifications, Appendix M, Section N.1.2)

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.

1899 topography: This maps shows the pre-mining surface topography along the east half of the Mesabi Iron Range. Mapped by the U.S. Geological Survey in 1899-1900 the field maps also contained roads, railroads, mine pits and waste piles and hydrology at that time. The purpose of the pre-mining topography shaded-relief was to capture data from deteriorating field maps that contained the original land surface along the Mesabi Iron range before it was altered significantly by mining activity. Other shapefiles in this ArcMap project except for the TRS and county line themes were derived from the same 1899 map source.

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.

Layered GeoPDF 7.5 Minute Quadrangle Map. Layers of geospatial data include orthoimagery, roads, grids, geographic names, elevation contours, hydrography, and other selected map features.

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

This data set consists of general soil association units. It was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) data set published in 1994. It consists of a broad based inventory of soils and nonsoil areas that occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. The data set was created by generalizing more detailed soil survey maps. Where more detailed soil survey maps were not available, data on geology, topography, vegetation, and climate were assembled, together with Land Remote Sensing Satellite (LANDSAT) images. Soils of like areas were studied, and the probable classification and extent of the soils were determined.

Map unit composition was determined by transecting or sampling areas on the more detailed maps and expanding the data statistically to characterize the whole map unit.

This data set consists of georeferenced vector digital data and tabular digital data. The map data were collected in 1-by 2-degree topographic quadrangle units and merged into a seamless national data set. It is distributed in state/territory and national extents. The soil map units are linked to attributes in the National Soil Information System data base which gives the proportionate extent of the component soils and their properties.

MnGeo adapted the NRCS metadata record to create this record.

LTER - Long-Term Ecological Research Program/Cedar Creek Natural History Area (CDR)

 LTER/CDR029 [Summary adapted from the LTER Core Data Set Catalog]:

 Vegetation type, size, and density maps were produced by
 photo-interpreting 1938 and 1977 aerial photography. These maps were
 entered along with roads, section corner and boundary layers from USGS
 7G minute quadrangles into the ARC/INFO GIS package. This information
 will be combined with oak wilt, climate, soils, and prescribed burn
 history to analyze vegetation change over time under different burn
 treatments.

 The Cedar Creek Natural History Area (CCNHA) is a 2200 hectare
 experimental ecological reserve operated by the University of
 Minnesota in cooperation with the Minnesota Academy of Sciences. CCNHA
 is located in Anoka and Isanti Counties north of Minneapolis/St. Paul.
 The site was established in 1940, was designated a National Natural
 Landmark by the National Park Service in 1975. In 1977 it was included
 as an Experimental Ecology Reserve in a proposed network, and in 1982
 it was one of 11 sites in the U.S. selected by the National Science
 Foundation for funding of LTER.

 Information about LTER is also available at
 'http://lternet.edu/'

Topography means the shape of a surface, and thus bedrock topography means the shape of the bedrock surface. Glacial sediment covers the bedrock surface over most of Minnesota, with the northeastern and southeastern parts of the state being significant exceptions. In southeastern Minnesota bedrock can often be seen exposed at the land surface, mostly in the valleys of modern rivers. In northeastern Minnesota there are areas where the bedrock is largely exposed, mostly in wilderness areas.

The soil erodibility index shows the relative potential for soils within the MRCCA to erode. The index combines the inherent erodibility of a soil type (K-factor) with the slope on which the soil type is located. The soil erosion index value is derived by multiplying the slope class with the K-Factor resulting in a relative index range from 0.02 to 1.96. The K-factor value was retrieved from the U.S. Department of Agriculture - Natural Resource Conservation Service (USDA-NRCS) Soil Survey Geographic Database (SSURGO) on 2/1/2017. The percent slope was calculated from a 10-meter LiDAR-derived digital elevation model (DEM). LiDAR data for the Twin Cities Metro area was collected in the spring and fall of 2011.

The index is only calculated where SSURGO soils data is available. Soil survey data is not available for some areas where there is a history of urban use including use of fill, or areas with restricted access.

The MRCCA is a land corridor along the Mississippi River in the seven-county metro area in which special land use regulations guide development activity. The corridor extends 72 miles along the Mississippi River from the cities of Ramsey and Dayton in the north to the City of Hastings and Ravenna Township in the south. It includes 54,000 acres of land along both sides of the river. The State of Minnesota created the corridor and land use regulations in 1976. Local governments administer the regulations through their local plans and zoning ordinances.

Rules regulating the MRCCA were published on December 27, 2016 and became effective January 4, 2017.

In the late 1880's and early 1900's the Mississippi River Commission (MRC) conducted an extensive high-resolution survey of the Mississippi River from Cairo, Illinois to Minneapolis, Minnesota. These data were published as a series of 89 survey maps and index. In the 1990's, the Upper Midwest Environmental Sciences Center (UMESC) in conjunction with the US Army Corps of Engineers Upper Mississippi River Restoration- Environmental Management Program -- Long Term Resource Monitoring Program element (LTRMP) for the Upper Mississippi River automated the maps' land cover/use symbology to create a turn of the century/pre-impoundment land cover/use data set. Other data on the maps that were not automated include; elevation contours, water depth soundings, proposed water control structures (e.g., wing dams), levees, benchmarks, railroads, and city streets.

This resource is a repository of the annual subsurface drainage (so-called "Tile Drainage") maps for the Bois de Sioux Watershed (BdSW), Minnesota and the Red River of the North Basin (RRB), separately. The RRB maps cover a 101,500 km2 area in the United States, which overlies portions of North Dakota, South Daokta, and Minnesota. The maps provide annual subsurface drainage system maps for recent four years, 2009, 2011, 2014, and 2017 (In 2017, the subsurface drainage maps including the Sentinel-1 Synthetic Aperture Radar as an additional input are also provided). Please see Cho et al. (2019) in Water Resources Research (WRR) for full details.

Map Metadata (Proj=longlat +datum=WGS84) Raster value key: 0 = NoData, masked by non-agricultural areas (e.g. urban, water, forest, or wetland land) and high gradient cultivated crop areas (slope > 2%) based on the USGS National Land Cover Dataset (NLCD) and the USGS National Elevation Dataset 1 = Undrained (UD) 2 = Subsurface Drained (SD)

Preferred citation: Cho, E., Jacobs, J. M., Jia, X., & Kraatz, S. (2019). Identifying Subsurface Drainage using Satellite Big Data and Machine Learning via Google Earth Engine. Water Resources Research, 55. https://doi.org/10.1029/2019WR024892

Corresponding author: Eunsang Cho (ec1072@wildcats.unh.edu)