Multi-Resolution Land Characteristics Research Articles

Branching out: Feasibility of examining the effects of greenspace on mental health after traumatic brain injury.

This pilot study's aim was to determine the feasibility of examining the effects of an environmental variable (i.e., tree canopy coverage) on mental health after sustaining a brain injury. A secondary data analysis was conducted leveraging existing information on mental health after moderate to severe traumatic brain injury (TBI) from the TBI Model System. Mental health was measured using PHQ-9 (depression) and GAD-7 (anxiety) scores. The data were compared with data on tree canopy coverage in the state of Texas that was obtained from the Multi-Resolution Land Characteristics (MRLC) Consortium using GIS analysis. Tree canopy coverage as an indicator of neighborhood socioeconomic status was also examined using the Neighborhood SES Index. Tree canopy coverage had weak and non-significant correlations with anxiety and depression scores, as well as neighborhood socioeconomic status. Data analysis was limited by small sample size. However, there is a higher percentage (18.8%) of participants who reported moderate to severe depression symptoms in areas with less than 30% tree canopy coverage, compared with 6.6% of participants who endorsed moderate to severe depression symptoms and live in areas with more than 30% tree canopy coverage (there was no difference in anxiety scores). Our work confirms the feasibility of measuring the effects of tree canopy coverage on mental health after brain injury and warrants further investigation into examining tree canopy coverage and depression after TBI. Future work will include nationwide analyses to potentially detect significant relationships, as well as examine differences in geographic location.

Thematic accuracy assessment of the NLCD 2019 land cover for the conterminous United States

ABSTRACT The National Land Cover Database (NLCD), a product suite produced through the MultiResolution Land Characteristics (MRLC) consortium, is an operational land cover monitoring program. Starting from a base year of 2001, NLCD releases a land cover database every 2–3-years. The recent release of NLCD2019 extends the database to 18 years. We implemented a stratified random sample to collect land cover reference data for the 2016 and 2019 components of the NLCD2019 database at Level II and Level I of the classification hierarchy. For both dates, Level II land cover overall accuracies (OA) were 77.5% ± 1% (± value is the standard error) when agreement was defined as a match between the map label and primary reference label only, and increased to 87.1% ± 0.7% when agreement was defined as a match between the map label and either the primary or alternate reference label. At Level I of the classification hierarchy, land cover OA was 83.1% ± 0.9% for both 2016 and 2019 when agreement was defined as a match between the map label and primary reference label only, and increased to 90.3% ± 0.7% when agreement also included the alternate reference label. The Level II and Level I OA for the 2016 land cover in the NLCD2019 database were 5% higher compared to the 2016 land cover component of the NLCD2016 database when agreement was defined as a match between the map label and primary reference label only. No improvement was realized by the NLCD2019 database when agreement also included the alternate reference label. User’s accuracies (UA) for forest loss and grass gain were>70% when agreement included either the primary or alternate label, and UA was generally<50% for all other change themes. Producer’s accuracies (PA) were>70% for grass loss and gain and water gain and generally<50% for the other change themes. We conducted a post-analysis review for map-reference agreement to identify patterns of disagreement, and these findings are discussed in the context of potential adjustments to mapping and reference data collection procedures that may lead to improved map accuracy going forward.

A Comparison of Raster-Based Forestland Data in Cropland Data Layer and the National Land Cover Database

The National Agricultural Statistics Service, the statistical arm of the US Department of Agriculture, and the Multi-Resolution Land Characteristics Consortium, a group of the US federal agencies, collect and publish several land-use and land-cover data sets. The aim of this study is to analyze the consistency of forestland estimates based on two widely used, publicly available products: the National Land-Cover Database (NLCD) and Cropland Data Layer (CDL). Both remote-sensing-based products provide raster-formatted land-cover categorization at a spatial resolution of 30 m. Although the processing of the yearly published CDL non-agricultural land-cover data is based on less frequently updated NLCD, the consistency of large-area forestland mapping between these two datasets has not been assessed. To assess the similarities and the differences between CDL- and NLCD-based forestland mappings for the state of North Carolina, we overlay the two data products for the years 2011 and 2016 in ArcMap 10.5.1 and analyze the location and attributes of the matched and mismatched forestland. We find that the mismatch is relatively smaller for the areas of the state where forests occupy larger shares of the total land, and that the relative mismatch is smaller in 2011 when compared to 2016. We also find that a large portion of the forestland mismatch is attributable to the dynamics of re-growth of periodically harvested and otherwise disturbed forests. Our results underscore the need for a holistic approach to data preparation, data attribution, and data accuracy when performing high-scale map-based analyses using each of these products.

Land Cover Change and Soil Carbon Regulating Ecosystem Services in the State of South Carolina, USA

Integration of land cover change with soil information is important for valuation of soil carbon (C) regulating ecosystem services (ES) and disservices (ED) and for site-specific land management. The objective of this study was to assess the change in value of regulating ES from soil organic carbon (SOC), soil inorganic carbon (SIC), and total soil carbon (TSC) stocks, based on the concept of the avoided social cost of carbon dioxide (CO2) emissions for the state of South Carolina (SC) in the United States of America (U.S.A.) by soil order (Soil Taxonomy), land cover, and land cover change (National Land Cover Database, NLCD) using information from the State Soil Geographic (STATSGO) and Soil Survey Geographic Database (SSURGO) databases. Classified land cover data for 2001 and 2016 were downloaded from the Multi-Resolution Land Characteristics Consortium (MRLC) website. The total estimated monetary mid-point value for TSC in the state of South Carolina was $124.42B (i.e., $124.42 billion U.S. dollars, where B = billion = 109) with the following monetary distribution in 2016 and percent change in value between 2001 and 2016: barren land ($259.7M, −9%) (i.e., $259.7 million U.S. dollars, where M = million = 106), woody wetlands ($33.8B, −1%), shrub/scrub ($3.9B, +9%), mixed forest ($6.9B, +5%), deciduous forest ($10.6B, −7%), herbaceous ($4.8B, −5%), evergreen forest ($28.6B, +1%), emergent herbaceous wetlands ($6.9B, −3%), hay/pasture ($7.3B, −10%), cultivated crops ($9.9B, 0%), developed, open space ($7.0B, +5%), developed, medium intensity ($978M, +46%), developed, low intensity ($2.9B, +15%), and developed, high intensity ($318M, +39%). The percent change in monetary values was different from percent change in areas because different soil orders have different TSC contents. The percent changes (between 2001 and 2016) both in areas and monetary values varied by soil order and land cover with $1.1B in likely “realized” social cost of C mostly associated with Ultisols ($658.8M). The Midlands region of the state experienced the highest gains in the “high disturbance” classes and corresponding SC-CO2 with over $421M for TSC, $354.6M for SOC, and $66.4M for SIC. Among counties, Horry County ranked first with over $142.2M in SC-CO2 for TSC, followed by Lexington ($103.7M), Richland ($95.3M), Greenville ($81.4M), York ($77.5M), Charleston ($70.7M), Beaufort ($64.1M), Berkeley ($50.9M), Spartanburg ($50.0M), and Aiken ($43.0M) counties. Spatial and temporal analyses of land cover can identify critical locations of soil carbon regulating ecosystem services at risk.

LAND COVER CHANGE ANALYSIS IN THE STATE OF CALIFORNIA USING NLCD DATA

Abstract. Land cover change is critical to be monitored as land cover change has significant impacts on flooding, ground water recharge, and urban air temperature. In this paper, key findings from a land cover change analysis study performed in the State of California are presented. National Land Cover Database (NLCD) data from the Multi-Resolution Land Characteristics Consortium (MRLC) was used for this study. Time series of NLCD data during the time period of 2001 through 2016 was used for the analysis. NLCD data processing was done in ArcMap 10.6.1. This paper includes the methodology in detail, and the results of the analysis. Results of the study indicate a significant increase in impervious surfaces, and a significant decrease in forest land cover.

The Effect of Urban Tree Canopy Cover and Vegetation Levels on Incidence of Stress-related Illnesses in Humans in Metropolitan Statistical Areas of Texas

One-third of Americans are reportedly living with extreme stress, with 75% to 90% of visits to primary care physicians being for stress-related problems. Past research found visiting green areas lowers blood pressure, reduces headache and fatigue, improves mood, and hastens recovery from stress. The main objective for this study was to determine if stress-related illness rates in regions of Texas were related to vegetation rates and tree canopy cover. Data on the stress-related illnesses of high blood pressure and heart attacks were collected from the Center for Health Statistics and the Texas Department of State Health Services for all 25 metropolitan statistical areas (MSAs) in Texas. MSAs are counties or group of counties with a central city or urbanized area of at least 50,000 people. Percent canopy cover was calculated for each MSA using the Multi-Resolution Land Characteristics National Land Cover Data canopy cover dataset. Vegetation rates for all the MSAs were examined and mapped for illustration using geographical information system (GIS) software. Visual relationships among the data were observed. Quantitative data were also analyzed. When mapping stress-related illness rate into MSA regions of Texas, no clear trend was observed with vegetation rates or percent tree canopy cover when compared with stress-related illness rates. Semipartial correlations were calculated to analyze the relationship between tree canopy cover and vegetation rate and stress-related illness rate variables after controlling the effect of external variables like income levels, age, population, and ethnicity. There was no significant positive or negative relationship found between stress-related illness data when compared with percent canopy and vegetation index for any the 25 MSAs of Texas.

The Multi-Resolution Land Characteristics (MRLC) Consortium — 20 Years of Development and Integration of USA National Land Cover Data

The Multi-Resolution Land Characteristics (MRLC) Consortium demonstrates the national benefits of USA Federal collaboration. Starting in the mid-1990s as a small group with the straightforward goal of compiling a comprehensive national Landsat dataset that could be used to meet agencies’ needs, MRLC has grown into a group of 10 USA Federal Agencies that coordinate the production of five different products, including the National Land Cover Database (NLCD), the Coastal Change Analysis Program (C-CAP), the Cropland Data Layer (CDL), the Gap Analysis Program (GAP), and the Landscape Fire and Resource Management Planning Tools (LANDFIRE). As a set, the products include almost every aspect of land cover from impervious surface to detailed crop and vegetation types to fire fuel classes. Some products can be used for land cover change assessments because they cover multiple time periods. The MRLC Consortium has become a collaborative forum, where members share research, methodological approaches, and data to produce products using established protocols, and we believe it is a model for the production of integrated land cover products at national to continental scales. We provide a brief overview of each of the main products produced by MRLC and examples of how each product has been used. We follow that with a discussion of the impact of the MRLC program and a brief overview of future plans.

Evaluating the use of publicly available remotely sensed land cover data for areal interpolation

Areal interpolation is used to transfer attribute data between geographically incongruous zonal systems. Remotely sensed land cover data are widely used in intelligent areal interpolation methods to solve this problem. This article examines the usefulness of different publicly available remotely sensed land cover data sets as ancillary data used in conjunction with different areal interpolation methods. Two land cover data sets were compiled at the national scale; one by the Multi-Resolution Land Characteristics Consortium (the National Land Cover Dataset or NLCD) and one by the Coastal Change Analysis Program. A third land cover data set was compiled at a regional scale for the state of Connecticut by the Center for Land Use Education and Research. Results show that for areal interpolation, greater detail in the classification of developed areas was important whether the data were developed for use at a national or a regional scale. Even more important is the further enhancement of remotely sensed land use categories by incorporating local road or parcel data layers. The worst performing interpolation method using enhanced remote sensing-derived land cover data produced more accurate results than the best performing method using only the original land cover data. The results also show that parcels produce better enhancements than road buffers because they remove the areas of the roads themselves from population consideration.

Impervious Surface Area Change in Arkansas from 2001 to 2006

Impervious Surface Area (ISA) is a measurement used to determine stream quality as well as urban sprawl. ISA was calculated as part of the National Land Cover Dataset (NLCD) using Landsat imagery by the Multi-Resolution Land Characteristics Consortium (MRLC) in both 2001 and 2006. ISA for each of the 75 counties in Arkansas was taken from the NLCD for both 2001 and 2006. Using the ISA data, percent imperviousness was determined for each county in each time period as well as the difference between the two periods. These data were also compared to census projections for the two time periods as well as the difference between them. The differences between percent ISA change and census change were compared to determine consistency.

Effects of Land Use/Cover Change and Harvests on Forest Carbon Dynamics in Northern States of the United States from Remote Sensing and Inventory Data: 1992–2001

Abstract We examined spatial patterns of changes in forest area and nonsoil carbon (C) dynamics affected by land use/cover change (LUC) and harvests in 24 northern states of the United States using an integrated methodology combining remote sensing and ground inventory data between 1992 and 2001. We used the Retrofit Change Product from the Multi-Resolution Land Characteristics Consortium to quantify LUC. We then calculated C dynamics using C densities for major forest types based on US Forest Service Forest Inventory and Analysis data by forest area for different statuses (i.e., afforestation, deforestation, and forest remaining forest) and incorporated county-level harvest data. Across the region, 16,740 km2 of forestland changed to nonforest, whereas 9,120 km2 of nonforest became forestland, a net loss of 7,620 km2 of forestland during the period or −0.13%/year. The region as a whole functioned as a C sink of 627 Tg (1 teragram = 1012 g) or 70 Tg of C/year. Regional C sequestration calculated using forest type identification at the state level was 5% higher than that from the county-level identification. Integrated annual effects of LUC and harvest on reducing C stocks at the state level varied substantially, ranging from 0.4% in North Dakota to 5.1% in Delaware with an average of 3.2% across the region (3.4% in the 13 northeastern states and 2.6% in the 11 northcentral states), compared with what it would be without these effects. We also found that within the region the annual LUC rate was significantly correlated with population density at the state level (P &amp;lt; 0.001).

Developing Consistent Landsat Data Sets for Large Area Applications: The MRLC 2001 Protocol

One of the major efforts in large area land cover mapping over the last two decades was the completion of two U.S. National Land Cover Data sets (NLCD), developed with nominal 1992 and 2001 Landsat imagery under the auspices of the MultiResolution Land Characteristics (MRLC) Consortium. Following the successful generation of NLCD 1992, a second generation MRLC initiative was launched with two primary goals: (1) to develop a consistent Landsat imagery data set for the U.S. and (2) to develop a second generation National Land Cover Database (NLCD 2001). One of the key enhancements was the formulation of an image preprocessing protocol and implementation of a consistent image processing method. The core data set of the NLCD 2001 database consists of Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images. This letter details the procedures for processing the original ETM+ images and more recent scenes added to the database. NLCD 2001 products include Anderson Level II land cover classes, percent tree canopy, and percent urban imperviousness at 30-m resolution derived from Landsat imagery. The products are freely available for download to the general public from the MRLC Consortium Web site at http://www.mrlc.gov.

Modeling Phosphorus in the Lake Allatoona Watershed Using SWAT: II. Effect of Land Use Change

Lake Allatoona is a large reservoir northeast of metropolitan Atlanta, GA, threatened by excessive algal growth. We used the calibrated Soil and Water Assessment Tool (SWAT) models developed in our companion paper to estimate the annual P load to Lake Allatoona in 1992 and in 2001 after significant changes occurred in land use. Land use data in 1992 and 2001 from the Multi-Resolution Land Characteristics (MRLC) Consortium showed that forest land use decreased during this period by about 20%, urban land use increased by about 225%, and pasture land uses increased by about 50%. Simulation results showed that the P load to Lake Allatoona increased from 176.5 to 207.3 Mg, which were 87.8% and 103.1%, respectively, of the total P (TP) annual cap (201 Mg) set by the Georgia Environmental Protection Division (GAEPD) for discharge into Lake Allatoona. In the early 1990s, the greatest sources of the TP load to Lake Allatoona (and their percentages of the total load) were pasture (33.6%), forest (27.5%), and point sources (25.0%). Urban land uses contributed about 6.0% and row-crop agriculture contributed about 6.8%. A decade later, the greatest two TP sources were pasture (52.7%) and urban (20.9%) land uses. Point-source P loads decreased significantly to 11.6%. Permit limits on poultry processing plants reduced the point-source P loads, but increasing urban and pasture land uses increased nonpoint sources of P. To achieve further reductions in the P load to Lake Allatoona, contributions from pasture and urban nonpoint sources will need to be addressed.

Macroinvertebrate response to land cover, habitat, and water chemistry in a mining-impacted river ecosystem: A GIS watershed analysis

This study addressed potential land use impacts to macroinvertebrate communities and water quality from past coal mining activities in the watershed of the North Branch of the Susquehanna River (located in northeastern Pennsylvania and southern New York). Landscape tools of GIS and remote sensing (RS) were used to calculate percent land cover (forest, agriculture, barren, urban, and water) from SPOT imagery (for tributaries) and Multi-Resolution Land Characteristics (MRLC) data (for river sites) on 17 subcatchments in the study area. The study design included field sampling at reach locations at four first- and second-order sites with low urban and low barren (mining) land cover, four similar sized sites with high mining and high barren land cover, five sites with intermediate combinations of urban and barren, and four mainstem river sites (60% forest and 35% agriculture). Sites were sampled in early fall for macroinvertebrates (17 parameters, e. g., EPT richness, percent filterers), benthic substrates (including deposits from mine waters), and 10 water chemistry parameters. A principal component analysis (PCA) on the macroinvertebrate parameters provided plot-clustering of subcatchments based generally on the above study design groupings; river sites clustered closer to smaller streams with low mining and urban land cover. Correlations identified six macroinvertebrate parameters (e. g., EPT richness, collector-gatherers) best associated with the three major axes of the PCA; each of these six indicators was analyzed in step-wise multiple regressions as dependent variables against land cover, benthic substrate, and water chemistry parameters. The strongest regressions were for percent barren land cover that explained the greatest amount of variation in both EPT richness and taxa richness. This mining affect was confirmed with dissolved iron and sulfate concentrations and levels of sedimentation and iron deposition explaining variability across several macroinvertebrate parameters. Comparison to the published literature on mining impacts indicated advantages to using a GIS watershed approach in multivariate analyses of stream ecosystem response. Also, this appears to be the first GIS watershed assessment of mining land use affects since most published studies of land use impacts to watersheds and lotic ecosystems have focused on either agriculture or urbanization.

Thematic accuracy of the 1992 National Land-Cover Data for the western United States

The MultiResolution Land Characteristics (MRLC) consortium sponsored production of the National Land Cover Data (NLCD) for the conterminous United States, using Landsat imagery collected on a target year of 1992 (1992 NLCD). Here we report the thematic accuracy of the 1992 NLCD for the six western mapping regions. Reference data were collected in each region for a probability sample of pixels stratified by map land-cover class. Results are reported for each of the six mapping regions with agreement defined as a match between the primary or alternate reference land-cover label and a mode class of the mapped 3×3 block of pixels centered on the sample pixel. Overall accuracy at Anderson Level II was low and variable across the regions, ranging from 38% for the Midwest to 70% for the Southwest. Overall accuracy at Anderson Level I was higher and more consistent across the regions, ranging from 82% to 85% for five of the six regions, but only 74% for the South-central region.

Thematic accuracy of MRLC land cover for the eastern United States

One objective of the MultiResolution Land Characteristics (MRLC) consortium is to map general land-cover categories for the conterminous United States using Landsat Thematic Mapper (TM) data. Land-cover mapping and classification accuracy assessment are complete for the eastern United States. The accuracy assessment was based on photo-interpreted reference data obtained from a stratified probability sample of pixels. Agreement was defined as a match between primary or alternate reference land-cover labels assigned to each sample pixel and the mode (most common class) of the map's land-cover labels within a 3×3-pixel neighborhood surrounding the sampled point. At 30-m resolution, overall accuracy was 59.7% at an Anderson Level II thematic detail, and 80.5% at Anderson Level I.

Analysis of space-borne SAR data for wetland mapping in Virginia riparian ecosystems

In this study the utility of NASA's Shuttle Imaging Radar-C (SIR-C) data are evaluated for wetland mapping and monitoring. The fully polarimetric L- and C-band data are used in hierarchical analysis and maximum likelihood classification techniques. Each map produced is compared with the Environmental Protection Agency's Multi Resolution Land Characteristics classification for pixelto-pixel accuracy assessment. Results show that both L- and C- band are necessary for detection of flooding beneath vegetated canopies. HH-polarization is found in this study and others to be better than VV for wetland discrimination. The cross-polarizations (HV or VH) are needed for discrimination of woody versus herbaceous vegetation.

National Land-Cover Pattern Data

Land cover and its spatial patterns are key ingredients in ecological studies that consider large regions and the impacts of human activities. Because land-cover maps show only cover types and their locations, further processing is needed to extract pattern information and to characterize its spatial variability. We are producing a nationally consistent spatial database of six land-cover pattern indices: forest area density, forest connectivity, the U index (a measure of general land-use pressure by humans), land-cover connectivity, land-cover diversity, and landscape pattern types. We use the land-cover maps produced by the Multi-resolution Land Characteristics Consortium for the conterminous United States at 30-m resolution. The goal of this paper is to encourage use of the pattern data as: contextual information and independent variables for studies involving a set of field sites; indicators of landscape conditions for ecological assessments; and dependent variables in biogeographic and socioeconomic models. The new maps will be most useful in studies that require consistent and comparable land-cover pattern measurements over large regions and can be combined with the original land-cover maps and other data. The complete data sets corresponding to abstracts published in the Data Papers section of the journal are published electronically in Ecological Archives at http://esa.sdsc.edu/Archive. (The accession number for each Data Paper is given directly beneath the title.)

NATIONAL LAND-COVER PATTERN DATA

Land cover and its spatial patterns are key ingredients in ecological studies that consider large regions and the impacts of human activities. Because land-cover maps show only cover types and their locations, further processing is needed to extract pattern information and to characterize its spatial variability. We are producing a nationally consistent spatial database of six land-cover pattern indices: forest area density, forest connectivity, the U index (a measure of general land-use pressure by humans), land-cover connectivity, land-cover diversity, and landscape pattern types. We use the land-cover maps produced by the Multi-resolution Land Characteristics Consortium for the conterminous United States at 30-m resolution. The goal of this paper is to encourage use of the pattern data as: contextual information and independent variables for studies involving a set of field sites; indicators of landscape conditions for ecological assessments; and dependent variables in biogeographic and socioeconomic models. The new maps will be most useful in studies that require consistent and comparable land-cover pattern measurements over large regions and can be combined with the original land-cover maps and other data. The complete data sets corresponding to abstracts published in the Data Papers section of the journal are published electronically in Ecological Archives at http://esa.sdsc.edu/Archive. (The accession number for each Data Paper is given directly beneath the title.)

Research Article: A Comparison of Land Use and Land Cover Data in Watersheds of the Mid-Atlantic Region, USA

We compared different sources of land use/land cover data using the percentages of forest, agriculture, urban, and mined lands in approximately 400 watersheds of the U.S. Mid-Atlantic region. The primary data sources were digital Land Use/Land Cover maps (LULC) from the U.S. Geological Survey (USGS) and data derived from Landsat Thematic Mapper (TM) imagery obtained by the Multi-Resolution Land Characteristics (MRLC) Consortium and interpreted at the Earth Resources Observation Systems (EROS) Data Center (version 2). The latter is referred to as MRLC-TM. We also used aerial photographs (AP), topographic maps, field comments, and qualitative watershed assessments as ancillary information. The LULC and MRLC-TM data differed in age, source material, classifications, format (vector vs. raster), and resolution. However, Pearson correlation coefficients between LULC and MRLC-TM were high for broad (composite) forest, agriculture, and urban categories (0.95, 0.96, and 0.92, respectively). Correspondence varied by forest type and by percentage. Where deciduous forests comprised less than 50% of the watershed, MRLC-TM data depicted greater amounts than did LULC, while the reverse was true where deciduous forests were dominant. Agriculture and forest percentages were inversely related in all cases. Urban land percentages were consistently lower for MRLC-TM than LULC. Relatively few watersheds contained strip mines, quarries, or gravel pits and the MRLC-TM vs. LULC relationship was weak (correlation coefficient = 0.41). Detection of reclaimed mines seemed problematic for LULC and MRLC-TM, but these were readily discerned on AP. A single “snapshot” of land cover data will inadequately characterize a watershed if rapid landscape changes occur (e.g., forest clearcuts). Each data type has strengths and weaknesses, depending on a study's objectives, but individual limitations may be alleviated by using multiple data sources.

Regional land cover characterization using landsat thematic mapper data and ancillary data sources

As part of the activities of the Multi-Resolution Land Characteristics (MRLC) Interagency Consortium, an intermediate-scale land cover data set is being generated for the conterminous United States. This effort is being conducted on a region-by-region basis using U.S. Standard Federal Regions. To date, land cover data sets have been generated for Federal Regions 3 (Pennsylvania, West Virginia, Virginia, Maryland, and Delaware) and 2 (New York and New Jersey). Classification work is currently under way in Federal Region 4 (the southeastern United States), and land cover mapping activities have been started in Federal Regions 5 (the Great Lakes region) and 1 (New England). It is anticipated that a land cover data set for the conterminous United States will be completed by the end of 1999. A standard land cover classification legend is used, which is analogous to and compatible with other classification schemes. The primary MRLC regional classification scheme contains 23 land cover classes. The primary source of data for the project is the Landsat thematic mapper (TM) sensor. For each region, TM scenes representing both leaf-on and leaf-off conditions are acquired, preprocessed, and georeferenced to MRLC specifications. Mosaicked data are clustered using unsupervised classification, and individual clusters are labeled using aerial photographs. Individual clusters that represent more than one land cover unit are split using spatial modeling with multiple ancillary spatial data layers (most notably, digital elevation model, population, land use and land cover, and wetlands information). This approach yields regional land cover information suitable for a wide array of applications, including landscape metric analyses, land management, land cover change studies, and nutrient and pesticide runoff modeling.