Twin Cities Metropolitan Area Land Cover Classification and Impervious Surface Area by Landsat Remote Sensing: 2011 Update Metadata

Metadata: Twin Cities Metropolitan Area Land Cover Classification and Impervious Surface Area by Landsat Remote Sensing: 2011 Update


Twin Cities Metropolitan Area Land Cover Classification and Impervious Surface Area by Landsat Remote Sensing: 2011 Update
This page last updated: 12/10/2013Metadata created using Minnesota Geographic Metadata Guidelines


Go to Section:
1. Overview
2. Data Quality
3. Data Organization
4. Coordinate System
5. Attributes
6. Distribution - Get Data
7. Metadata Reference


Section 1 Overview
Originator Remote Sensing and Geospatial Analysis Laboratory, University of Minnesota
Title Twin Cities Metropolitan Area Land Cover Classification and Impervious Surface Area by Landsat Remote Sensing: 2011 Update
Abstract This is a 30-meter raster dataset of a land cover and impervious surface classification for 2011 for the seven-county Twin Cities Metropolitan Area. The classification was created using a combination of multitemporal Landsat data and lidar data with object-based image analysis. The classification files have been provided to the Metropolitan Council. Maps and statistics in a web mapping application will also be available at land.umn.edu/.

This dataset was funded by the University of Minnesota and the Metropolitan Council.
Purpose Land cover information provides important inputs to local, regional, and state land use analysis and planning.
Time Period of Content Date 2011
Currentness Reference A multitemporal composite of Landsat imagery from the spring and summer of 2010 and 2011 and lidar data in 2010-2011 were classified.
Progress Complete
Maintenance and Update Frequency None Planned
Spatial Extent of Data Twin Cities Metropolitan Area (TCMA) of Minnesota, including Anoka, Carver, Dakota, Hennepin, Ramsey, Scott, and Washington Counties.
Bounding Coordinates -97.23
-89.53
49.37
43.5
Place Keywords Twin Cities Metropolitan Area
Theme Keywords Landsat, lidar, image classification, land cover, impervious surface area
Theme Keyword Thesaurus Landsat: an earth-orbiting satellite that acquires digital multispectral imagery at 30-meter spatial resolution. Lidar: Light detection and ranging. Lidar data provide information on height and elevation.
Access Constraints None
Use Constraints None
Contact Person Information Marvin Bauer,
Remote Sensing and Geospatial Analysis Lab, University of Minnesota
1530 Cleveland Avenue North
St. Paul, MN  55108
Phone: 612-624-3703
Fax: 612-625-5212
Email: mbauer@umn.edu
Browse Graphic Click to view a data sample
Associated Data Sets Landsat 2007 Land Cover and Impervious Surface Classifications of the Greater Twin Cities Metro Area and St. Croix River Basin
land.umn.edu/data/meta/landcover_and_impervious_tcma_2007_level2.html
Section 2 Data Quality
Attribute Accuracy The data set has an overall classification accuracy of 93% for level 1 and 91% for level 2 land cover classifications.
Logical Consistency GeoTiff (.tif) file
Completeness Data provides complete coverage over the stated extent of the data.
Horizontal Positional Accuracy RMS error of the Landsat data is less than 7.5 meters (0.25 Landsat pixel). The horizontal accuracy of the lidar data meets or exceeds 0.6 m RMSE.
Vertical Positional Accuracy The vertical accuracy of the lidar data meets or exceeds 12.5 cm RMSE.
Lineage Landsat Images: The Landsat imagery consisted of four dates (April 17, 2010, May 19, 2010, June 7, 2011 and July 25, 2011) of 7-band, 30-meter Landsat Thematic Mapper imagery, reprojected to UTM Zone 15N and aligned to the July 2011 image using AutoSync in ERDAS Imagine. Further information about Landsat is available at landsat.usgs.gov/.

Lidar Data: Lidar LAS files were acquired from the Minnesota DNR for the tiles within the six block areas covering the TCMA counties. LAS points classified as low, medium, or high vegetation (ASPRS classes 3, 4, and 5) in the LAS tiles were used to generate mean and maximum vegetation height rasters. The DNR-provided 1-meter bare earth DEM was also used to create additional lidar-derivative layers, such as Compound Topographic Index (CTI), slope, and dissection. The lidar data was resampled to 10-meter cell size. For more information, the 2011 lidar metadata are available at: www.mngeo.state.mn.us/chouse/metadata/lidar_metro2011.html.

Reference Data: Reference data used for classifier training and accuracy assessment were created using high-resolution aerial photos available through the MnGeo Geospatial Image Service (www.mngeo.state.mn.us/chouse/wms/geo_image_server.html), particularly the 3-band 1-meter USDA National Agricultural Program Imagery (NAIP) and 1-meter natural color summer imagery and 4-band (including near infrared) spring leaf-off imagery at 12-inch resolution (6-inch for Dakota and Scott Counties). For classes, such as extraction and agriculture, ancillary datasets were used to identify characteristic areas for training.

Image Classification: Object-based image analysis (OBIA) was used for classification of the Landsat and lidar data. Objects include more information than individual pixels, enabling the ability to take advantage of all the elements of image interpretation, particularly spatial information, including shape, size, pattern, texture, and context. The OBIA approach using eCognition, software system, included three main steps: (1) segmentation of the image into objects, (2) extraction of the object features, and (3) classification of the objects. Once image objects were created, a large number of features could be derived and potentially used for classification. The primary features included: spectral data, including means and standard deviations of individual bands and several transformations; geometry, including asymmetry, compactness, density, rectangular fit, roundness and shape index; texture, including, homogeneity, contrast, dissimilarity, mean, correlation and standard deviation; plus buildings per acre and lengths per acre of roads and railroads.

Random forest, a state-of-the-art approach which could handle and take advantage of the large number of features, was used for the classification of objects. It is an ensemble learning method for classification that operates by constructing multiple decision trees. Each tree is grown from different random subsamples of the training data and during the split selection process uses a subsample of the available features. It allows for the use of a large number of features or variables and identifies the important predictors.

Given the availability of the recently available new, high resolution and accurate National Wetlands Inventory (NWI) 2010-2011 Update data, wetlands and water bodies were obtained from it rather than attempt to map wetlands with the Landsat data. For more information about the NWI data, see: www.dnr.state.mn.us/eco/wetlands/nwi_proj.html).

Estimation and Mapping of Impervious Surface Area: For the objects classified as urban or developed, a regression model relating the Landsat greenness variable to percent impervious was developed to estimate and map the percent impervious surface area at the pixel level. Greenness is sensitive to the amount of green healthy vegetation and inversely related to the amount of impervious surface area. Impervious was treated as a continuous variable from 1 to 100 percent, but can be grouped by the user into classes such as 1 - 10, 11- 25, 26 - 40, 41 - 60, 61 - 80, and 81 - 100 percent impervious.

Accuracy Assessment: Classification accuracy was evaluated by comparing the classification results to an independent stratified random reference set of 476 objects and reporting the error matrix and statistics derived from it, including overall accuracy, user and producer accuracies for each class, and Kappa statistic.

Generation of Output Products: The primary output of the project is the maps and statistics of land cover and percent impervious surface area in an ArcGIS database. Maps and statistics summarizing the classifications by city, township, county, and watershed or other user defined areas will be added to our online database available at land.umn.edu. Maps and statistics can also be generated for user defined areas. All classification data have been provided to the Council.
Section 3 Spatial Data Organization (not used in this metadata)
Section 4 Coordinate System
Horizontal Coordinate Scheme Universal Transverse Mercator
UTM Zone Number 15
Horizontal Datum NAD83
Horizontal Units meters
Cell Width 10
Cell Height 10
Section 5 Attributes
Overview See table below for the Level 1 and Level 2 Land Cover class values and descriptions.

Descriptions of the lakes, streams and wetlands classes can be found at: www.npwrc.usgs.gov/resource/wetlands/classwet/index.htm.
Detailed Citation Bauer, Marvin, Joseph Knight, Leif Olmanson, Margaret Voth and Joshua Dunsmoor. 2013. Twin Cities Metropolitan Area Land Cover and Impervious Surface Area Classification by Remote Sensing: 2011 Update. Final Report to the Metropolitan Council.
Table Detail:
Level 1 and 2 Land Cover Classes
Field Name Valid Values Definition Definition Source
Urban/Developed 1 to 100 Impervious (%) Roads, parking lots, and building rooftops
Forest enumerated
131 Conifer Forest White and red pine
132 Deciduous Forest Oak, Maple, Aspen, and other hardwoods
133 Mixed Forest Mixtures of conifer and deciduous
Grass enumerated
141 Managed Grass Gold courses, parks
142 Grassland Natural grass
Lakes & Streams enumerated
161 Lakes & Ponds Lacustrine L1
162 Lakes & Ponds Lacustrine L2
163 Lakes & Ponds Palustrine PAB
167 Lakes & Ponds Palustrine PUB
168 Lakes & Ponds Palustrine PUS
169 Rivers & Streams Riverine R2
Wetlands enumerated
164 Emergent Wetlands Palustrine PEM
165 Forested Wetlands Palustrine PFO
166 Scrub Shrub Wetlands Palustrine PSS
Extraction enumerated
121 Extraction Quarries
Agriculture enumerated
111 Row Crops Corn and soybean
112 Small Grains Wheat, oats, barley
113 Hay & Pasture Alfalfa and other hay and pasture



Section 6 Distribution
Publisher Remote Sensing and Geospatial Analysis Lab, University of Minnesota and the Metropolitan Council
Publication Date 12/10/2013
Contact Person Information Marvin Bauer,
Remote Sensing and Geospatial Analysis Lab, University of Minnesota
1530 Cleveland Avenue North
St. Paul, MN  55108
Phone: 612-624-3703
Fax: 612-625-5212
Email: mbauer@umn.edu
Distributor's Data Set Identifier TCMA Land Cover Classification, 2011
Ordering Instructions Contact Marvin Bauer at the University of Minnesota, or Judy Sventek at the Metropolitan Council.
Distribution Liability The data are provided as is and without any warranty to their performance, merchantability, or fitness for any particular purpose. The University of Minnesota does not represent or warrant that the data or documentation are error-free, complete, or current. The user should consult the data documentation to determine its limitations and accuracy. The user is responsible for any consequences resulting from use of the data or reliance on it. The entire risk of the results of the use of the data are assumed by the user.
Online Linkage None available
Section 7 Metadata Reference
Metadata Date 12/10/2013
Contact Person Information Marvin Bauer,
Remote Sensing and Geospatial Analysis Lab, University of Minnesota
1530 Cleveland Avenue North
St. Paul, MN  55108
Phone: 612-624-3703
Fax: 612-625-5212
Email: mbauer@umn.edu
Metadata Standard Name Minnesota Geographic Metadata Guidelines
Metadata Standard Version 1.2
Metadata Standard Online Linkage http://www.mngeo.state.mn.us/committee/standards/mgmg/metadata.htm


This page last updated: 12/10/2013
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