O n a day-to-day basis, landscapes tend to change on a local scale - a new parking

lot here, a new home there. Over time, these small changes can alter the landscape of an entire region. To study how landscapes are changing and anticipate future change, land managers and policy makers need spatial information. In other words, they need maps - maps that are accurate, up-to-date and affordable. THE TRADITIONAL APPROACH There are a couple ways to analyze how land cover is changing. One way is through aerial photographs. By comparing land cover maps derived from different dates of aerial photography, useful information about land use change patterns can be extracted and used to identify possible factors driving change. Planners and resource managers can use this information to develop better decisions that ultimately affect the environment and economy of an area. Another way to collect information is by ground surveys. This method is relatively easy and highly accurate. Crews simply visit a number of sites and record observations. But with significant time and labor requirements, it is not cost effective to generate land use maps

based on ground surveys alone. This is especially true for areas of interest that are larger than a few acres. For this reason, ground surveys typically are conducted to verify interpretations of aerial photographs. At the University of Minnesota’s Remote Sensing and Geospatial Analysis Laboratory, researchers have examined another way to map land cover and land cover change. Instead of using aerial photographs, they are using digital multispectral images acquired by earth-observing satellites. THE NEW APPROACH There are several benefits to using a satellite-based approach to land cover mapping. First, the synoptic view of the satellite sensor provides coverage of large geographic areas

R E M O T E S E N S I N G AND GEOSPATIAL ANALYSIS

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Remote sensing and GIS research making a difference to Minnesota’s natural resources

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Mapping Land Cover Change with Satellite Remote Sensing

Urban growth and subsequent loss of agricultural lands, forests and wetlands in metro areas such as the Twin Cities Metropolitan Area in Minnesota is a growing concern to citizens and land planning and management agencies. While aerial photographs of have been used to assist in land planning and management decisions for man years, the expense of photo acquisition and interpretation has generally limited analyses to small geographic areas. In the Twin Cities Metropolitan Area, analysts from the University of Minnesota’s Remote Sensing and Geospatial Analysis Laboratory are exploring a new approach to acquiring land cover information. They are using digital images capture from satellites, which have multispectral properties that support accurate land use mapping. With images dating back to the mid-1970s, analysts can generate land cover change maps and statistics useful to land managers and policy makers. This fact sheet provides an overview of how satellite images have been used to map land cover change in the Twin Cities, Minnesota.

UNIVERSITY OF MINNESOTA

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COLLEGE OF NATURAL RESOURCES

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(e.g., an individual image covers 100 x 100 miles). Second, the digital format of the data facilitates more efficient analysis. Third, the classified data are compatible with geographic information systems, eliminating the need to digitize interpreted information. Lastly, land cover maps can be generated at a considerably less cost than other methods. AN INTEGRATED APPROACH The following steps have been used to create satellite-based land cover and land cover change maps for the Twin Cities Metropolitan Area (TCMA), Minnesota. The TCMA is a 7,700 km2 area and includes a diversity of land cover classes interspersed with over 900 lakes and transected by the Mississippi, Minnesota and St. Croix rivers. STEP 1: Six Landsat TM/ETM+ images containing the TCMA were obtained to study how land cover in the metro area changed between 1986, 1991 and 1998 (the entire TCMA is contained in one Landsat image). Two different dates, spring and summer, of imagery were acquired for each year. Part of the summer images from each year are shown in Figure 1. STEP 2: The satellite data were geometrically corrected to match the UTM map projection. STEP 3: Land use data from field observations and aerial photography were collected for a random sample of areas. Additional maps from the Metropolitan Council, the Minnesota Department of Natural Resources and the National Wetlands Inventory were also acquired. STEP 4: With the land use data as reference material, “training” statistics, which describe the spectral-radiometric temporal responses of a subset of known areas, were generated and used to classify each pixel of the entire area into one of the five land cover classes (Table

1). Figure 2 shows one of the final satellite-derived land cover maps of the TCMA. The overall classification accuracies were 95.2, 94.6 and 95.9% for the 1986, 1991, and 1998 maps, respectively. STEP 5: Following image classification, analysts mapped and quantified the land cover changed between 1986 and 1998. A map of the major land cover types and the changes from rural to urban or developed uses is shown in Figure 3. The majority of the changes were at the periphery of the major cities of Minneapolis and St. Paul, and the first ring of suburbs. The land cover change results were also quantified. This tabular format shows the

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Figure 1. Landsat imagery zoomed into Woodbury, Minnesota in the TCMA.

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Table 1. The land cover classification scheme.

Figure 2. A single Landsat classification map.

AgricultureUrbanWaterForestGrassWetlandExtraction

AgricultureUrbanWaterForestGrassWetlandExtraction

Level I Level II DescriptionWater Water Permanent open water, lakes and rivers

Wetlands Non-forested Wetlands Saturated surfaces, shallow marsh, deep marsh, shrub swamp, spongy covering of mosses

Forest Forest Hardwood, conifer and mixed forest types

Forested Wetlands Wet forests such as palustrine with woody vegetation greater than six meters tall

Bare Bare farm fields, not cropped

Agriculture Non-row cropNon-row crops such as pasture, alfalfa and small grain crops

Row crop Corn and soybeans

Urban High density developedCommercial, industrial and multifamily residential areas, with a high percentage of impervious surfaces and little to no vegetation

Low density developedResidential with lower percentage of impervious surfaces interspersed with vegetation such as lawns and trees

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1998

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totals for each land cover type and the trends between the years. The effects and relationships of urban growth determined by the satellite-derived change maps have also been examined, including the relationship to population growth. The growth rate is similar to the increase in population, (indicating relatively less “sprawl” in the TCMA than in some other urban areas. There is a strong relationship between new development and proximity to highways with almost 48% of the development occurring within 2 km of highways.

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Figure 3. TCMA urban growth from 1986 to 1998 combined with 1998 MUSA boundary. The majority of the changes occur within the second and third ring of suburbs surrounding the cities of Minneapolis and St. Paul. Clear patterns emerge which highlight the urbanization activity that has occurred east of St. Paul along the I-94 corridor (completed in the mid 1980’s), which connects the metro area to western Wisconsin. Growth also was concentrated in a strip along the southwestern perimeter following the Minnesota River, and in intermittent patches throughout the northwestern perimeter. Although this figure only shows the changes from agriculture, forest, or wetland to urban, other, more specific changes can also be mapped.

Table 2. Summary of classification area statistics for 1986, 1991 and 1998. Agriculture, urban and forest are the three major land covers and the changes in their proportions represent the most significant changes. From 1986 to 1998, urban areas increased a total of 52,019 ha or 28.4%. Agriculture decreased 49,091 ha or 13.4% from 1986’s 365,046 ha. Forest decreased 5,089 ha, or 4.5% from 1986’s 112,145 ha.

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7.37.87.558Wetland

13.814.414.6112Forest

30.626.323.8183Urban

41.144.447.5365Agriculture

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%%% Area (ha)Area (ha)Area (ha)

5.9466.0465.542Water

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19981986Land Cover Class

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13.814.414.6112Forest

30.626.323.8183Urban

41.144.447.5365Agriculture

The University of Minnesota’s Remote Sensing and Geospatial Analysis Laboratory (RSL), a unit of the Department of Forest Resources and College of Natural Resources, was established in 1972 and focuses on geospatial research and development for forestry and natural resources. Current efforts emphasize quantitative approaches to natural resource assessment, carried out in cooperation with resource agencies. Core activities at the RSL include research, education and outreach, and the facilities feature an array of hardware and software for image processing, mapping, modeling, statistical analysis and visualization.

This fact sheet was compiled by Sarah R. Finley, Associate Editor, Department of Forest Resources,

University of Minnesota

For more information, contact: Marvin Bauer University of Minnesota Department of Forest Resources 1530 Cleveland Ave. N. St. Paul, MN 55108 Phone: (612) 624-3703 E-mail: mbauer@umn.edu

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NASA is a leading force in scientific research and remote sensing applications to monitor earth resources. www.nasa.gov

The University of Minnesota is a state land-grant university with a strong tradition of education and public service, and a major research institution with scholars of national and international reputation. www.umn.edu

The Metropolitan Council is the regional planning agency serving the Twin Cities seven-county metropolitan area, providing essential services to the region. www.metrocouncil.org

The mission of the DNR is to work with citizens to protect and manage the state’s natural resources, to provide outdoor recreation opportunities, and to provide for commercial uses of natural resources in a way that creates a sustainable quality of life. www.dnr.state.mn.us

The Natural Resources Conservation Service provides leadership in a partnership effort to help people conserve, maintain, and improve our natural resources and environment. www.nrcs.usda.gov

THE VALUE OF LANDSCAPE CHANGE MAPPING Land cover information provides important inputs to local, regional and state land use planning. The importance of accurate and timely information describing the kind and extent of land resources is increasing. This is especially true in metropolitan areas such as the Twin Cities Metropolitan Area of Minneapolis-St. Paul, Minnesota which encompasses seven counties and more than 100 civil government units. In collaboration with the Metropolitan Council, the regional planning agency for the TCMA, researchers at the University of Minnesota have prepared land cover maps of the region. These maps have proven very useful to the Council. Marcel Jouseau, manager of water resource assessment with the Council says, “The 1998 and 2000 Land Classification projects have been and continue to be extremely useful to us. In the last two years, the Metropolitan Council has put great efforts into the preparation of a new Blueprint to guide future development of the metropolitan area. The Land Classification maps that you produced for us have helped us with two projects basic to the Blueprint: a Natural Resources Inventory and Assessment (NRI) and a finer grain definition of land available for future development.”

http://rsl.gis.umn.edu