National Land Cover Dataset 1992, New York State


Identification_Information:
Citation:
Citation_Information:
Originator: U.S. Geological Survey (USGS)
Publication_Date: 19970101
Title: National Land Cover Dataset 1992, New York State
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Sioux Falls, SD USA
Publisher: U.S. Geological Survey
Online_Linkage: http://www.mrlc.gov/nlcd92_data.php
Description:
Abstract: These data can be used in a geographic information system (GIS) for anynumber of purposes such as assessing wildlife habitat, water quality, pesticiderunoff, land use change, etc. The State data sets are provided with a 300meter buffer beyond the State border to faciliate combining the State filesinto larger regions.The user must have a firm understanding of how the datasets were compiled andthe resulting limitations of these data. The National Land Cover Dataset wascompiled from Landsat satellite TM imagery (circa 1992) with a spatialresolution of 30 meters and supplemented by various ancillary data (whereavailable). The analysis and interpretation of the satellite imagery wasconducted using very large, sometimes multi-state image mosaics (i.e. up to18 Landsat scenes). Using a relatively small number of aerial photographs for'ground truth', the thematic interpretations were necessarily conducted froma spatially-broad perspective. Furthermore, the accuracy assessments (seebelow) correspond to 'federal regions' which are groupings of contiguousStates. Thus, the reliability of the data is greatest at the State or multi-State level. The statistical accuracy of the data is known only for theregion.Important Caution AdvisoryWith this in mind, users are cautioned to carefully scrutinize the data tosee if they are of sufficient reliability before attempting to use thedataset for larger-scale or local analyses. This evaluation must be maderemembering that the NLCD represents conditions in the early 1990s.The New York portion of the NLCD was created as part of land cover mappingactivities for Federal Region II that includes the states of New York andNew Jersey. The NLCD classification contains 21 different land covercategories with a spatial resolution of 30 meters. The NLCD was producedas a cooperative effort between the U.S. Geological Survey (USGS) and theU.S. Environmental Protection Agency (US EPA) to produce a consistent, landcover data layer for the conterminous U.S. using early 1990s Landsatthematic mapper (TM) data purchased by the Multi-resolution LandCharacterization (MRLC) Consortium. The MRLC Consortium is a partnershipof federal agencies that produce or use land cover data. Partners includethe USGS (National Mapping, Biological Resources, and Water ResourcesDivisions), US EPA, the U.S. Forest Service, and the National Oceanic andAtmospheric Administration.
Purpose: The main objective of this project was to generate a generalizedand nationally consistent land cover data layer for the entireconterminous United States. These data can be used as a layer ina geographic information system (GIS) for any number of purposessuch assessing wildlife habitat, water quality and pesticide runoff,land use change, etc.
Supplemental_Information: The land cover data files are provided as a 'Geo-TIFF' or 8 bitbinary files (from FTP site). The land cover data sets are single bandraster images. The X/Y corner coordinates for New York are 1317210/2663820 (projection meters, center of Upper Left pixel).
Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 1988
Ending_Date: 1993
Currentness_Reference: ground condition
Status:
Progress: Complete
Maintenance_and_Update_Frequency: As needed
Spatial_Domain:
Bounding_Coordinates:
West_Bounding_Coordinate: -78.955
East_Bounding_Coordinate: -72.029
North_Bounding_Coordinate: 45.897
South_Bounding_Coordinate: 39.989
Keywords:
Theme:
Theme_Keyword_Thesaurus: None
Theme_Keyword: Land Use/Land Cover
Theme_Keyword: Land Management
Theme_Keyword: Land Resources
Theme_Keyword: EDC
Theme_Keyword: EPA
Theme_Keyword: EROS
Theme_Keyword: Imagery
Theme_Keyword: Land Characterization
Theme_Keyword: Land Cover
Theme_Keyword: Landsat
Theme_Keyword: MRLC
Theme_Keyword: Remote Sensing
Theme_Keyword: Satellite
Theme_Keyword: Space Imaging
Theme_Keyword: USGS
Theme:
Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: planningCadastre
Theme_Keyword: environment
Place:
Place_Keyword_Thesaurus: U.S. Department of Commerce, 1987, Codes for the identification ofthe States, the District of Columbia, and the outlying areas of theUnited States and associated areas Federal Information ProcessingStandard 5-20; Washington, D.C., National Institute of Standardsand Technology.
Place_Keyword: New York
Place_Keyword: NY
Access_Constraints: None.
Use_Constraints: None. Acknowledgement of the U.S. Geological Survey would beappreciated in products derived from these data.
Point_of_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: U.S. Geological SurveyEROS Data Center
Contact_Position: Customer Services Representative
Contact_Address:
Address_Type: mailing and physical address
Address: U.S. Geological Survey, EROS Data Center
City: Sioux Falls
State_or_Province: SD
Postal_Code: 57198
Country: USA
Contact_Voice_Telephone: (605) 594-6151
Contact_TDD/TTY_Telephone: (605) 594-6933
Contact_Facsimile_Telephone: (605) 594-6589
Contact_Electronic_Mail_Address: CUSTSERV@EDCMAIL.CR.USGS.GOV
Data_Set_Credit: This work was performed by the Raytheon STX Corporation underU.S. Geological Survey Contract 1434-92-C-40004.

Data_Quality_Information:
Attribute_Accuracy:
Attribute_Accuracy_Report: An accuracy assessment is done on all NLCD on a Federal Region basisfollowing a revision cycle that incorporates feedback from MRLCConsortium partners and affiliated users. The accuracy assessmentsare conducted by private sector vendors under contract to the USEPA.A protocol has been established by the USGS and USEPA that incorporatesa two-stage, geographically stratified cluster sampling plan (Zhu etal., 1999) utilizing National Aerial Photography Program (NAPP)photographs as the sampling frame and the basic sampling unit. Inthis design a NAPP photograph is defined as a 1st stage or primarysampling unit (PSU), and a sampled pixel within each PSU is treatedas a 2nd stage or secondary sampling unit (SSU).PSU's are selected from a sampling grid based on NAPP flight-lines andphoto centers, each grid cell measures 15' X 15' (minutes oflatitude/longitude) and consists of 32 NHAP photographs. A geographicallystratified random sampling is performed with 1 NAPP photo being randomlyselected from each cell (geographic strata), if a sampled photo fallsoutside of the regional boundary it is not used. Second stage samplingis accomplished by selecting SSU's (pixels) within each PSU (NAPP photo)to provide the actual locations for the reference land coverclassification.The SSU's are manually interpreted and misclassification errors areestimated and described using a traditional error matrix as well as anumber of other important measures including the overall proportion ofpixels correctly classified, user's and producer's accuracy's, andomission and commission error probabilities.See <http://landcover.usgs.gov/accuracy/index.php> for the accuracyassessment reports.While we believe that the approach taken has yielded a very goodgeneral land cover classification product for Region II,it is important to indicate to the user where there might be somepotential problems. The biggest concerns for Region II are listed below:1) Accurate definition of the transitional barren class was extremelydifficult. The majority of pixels in this class correspond to clear-cutforests in various stages of regrowth. Spectrally, fresh clear-cuts arevery similar to row-crops in the leaves-off data. Manual correction ofcoding errors was performed to improve differentiation between row-cropsand clear-cuts, but some errors may still be found. As regrowth occursin a clear-cut region, the definition of transitional barren versus aforested class becomes problematic. An attempt was made to classify onlyfresh clear-cuts or those in the earliest stages of regrowth, but thereare likely forested regions classed as transitional barren and vice versa.2) Due to the confusion between clear-cuts, regrowth in clear-cuts,Forested areas, and shrublands, no attempts were made to populate theshrubland classes. Any shrubland areas that exist in this area areclassed in their like forest class, i.e. deciduous shrubland is classed asdeciduous forest, etc.
Logical_Consistency_Report: An unsupervised classification algorithm was used to classify themosaicked multiple leaf-off TM scenes. Aerial photographs wereused to interpret and label classes into land cover categories andancillary data sources resolved the class confusion. Further landcover information from leaf-on TM data, NWI data, and other sourceswere incorporated to refine and augment the "basic" classification.
Completeness_Report: All photo-interpretable data are mapped.
Positional_Accuracy:
Horizontal_Positional_Accuracy:
Horizontal_Positional_Accuracy_Report: Each Landsat Thematic Mapper image used to create the NLCD wasprecision terrain-corrected using 3-arc-second digital terrainelevation data (DTED), and georegistered using ground controlpoints. This resulted in a root mean square registration errorof less than 1 pixel (30 meters).
Lineage:
Source_Information:
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Citation_Information:
Originator: U.S. Geological SurveyEROS Data Center
Publication_Date: unknown
Title: Landsat TM scene
Geospatial_Data_Presentation_Form: remote sensing image
Publication_Information:
Publication_Place: Sioux Falls, SD 57198USA
Publisher: EROS Data Center
Other_Citation_Details: Path/Row 013/031
Type_of_Source_Media: raster digital data
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Time_Period_Information:
Single_Date/Time:
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Source_Currentness_Reference: ground condition
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Source_Contribution: The image provides the base from which theland cover classification is determined.
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Originator: U.S. Geological SurveyEROS Data Center
Publication_Date: unknown
Title: Landsat TM scene
Geospatial_Data_Presentation_Form: remote sensing image
Publication_Information:
Publication_Place: Sioux Falls, SD 57198USA
Publisher: EROS Data Center
Other_Citation_Details: Path/Row 013/032
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Source_Contribution: The image provides the base from whichthe land cover classification is determined.
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Originator: U.S. Geological SurveyEROS Data Center
Publication_Date: unknown
Title: Landsat TM scene
Geospatial_Data_Presentation_Form: remote sensing image
Publication_Information:
Publication_Place: Sioux Falls, SD 57198USA
Publisher: EROS Data Center
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Source_Contribution: The image provides the base from which theland cover classification is determined.
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Originator: U.S. Geological SurveyEROS Data Center
Publication_Date: unknown
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Geospatial_Data_Presentation_Form: remote sensing image
Publication_Information:
Publication_Place: Sioux Falls, SD 57198USA
Publisher: EROS Data Center
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Publication_Date: unknown
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Originator: U.S. Geological SurveyEROS Data Center
Publication_Date: unknown
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Publication_Information:
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Originator: U.S. Geological SurveyEROS Data Center
Publication_Date: unknown
Title: Landsat TM scene
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Publication_Information:
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Originator: U.S. Geological SurveyEROS Data Center
Publication_Date: unknown
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Publication_Information:
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Originator: U.S. Geological SurveyEROS Data Center
Publication_Date: unknown
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Publication_Information:
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Originator: U.S. Geological SurveyEROS Data Center
Publication_Date: unknown
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Publication_Information:
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Originator: U.S. Geological SurveyEROS Data Center
Publication_Date: unknown
Title: Landsat TM scene
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Publication_Information:
Publication_Place: Sioux Falls, SD 57198USA
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Originator: U.S. Geological SurveyEROS Data Center
Publication_Date: unknown
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Publication_Information:
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Originator: U.S. Geological SurveyEROS Data Center
Publication_Date: unknown
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Originator: U.S. Geological SurveyEROS Data Center
Publication_Date: unknown
Title: Landsat TM scene
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Source_Contribution: The image provides the base from which theland cover classification is determined.
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Originator: U.S. Geological SurveyEROS Data Center
Publication_Date: unknown
Title: Landsat TM scene
Geospatial_Data_Presentation_Form: remote sensing image
Publication_Information:
Publication_Place: Sioux Falls, SD 57198USA
Publisher: EROS Data Center
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Citation_Information:
Originator: U.S. Geological SurveyEROS Data Center
Publication_Date: unknown
Title: Landsat TM scene
Geospatial_Data_Presentation_Form: remote sensing image
Publication_Information:
Publication_Place: Sioux Falls, SD 57198USA
Publisher: EROS Data Center
Other_Citation_Details: Leaf On Path/Row 014/029
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Originator: U.S. Geological SurveyEROS Data Center
Publication_Date: unknown
Title: Landsat TM scene
Geospatial_Data_Presentation_Form: remote sensing image
Publication_Information:
Publication_Place: Sioux Falls, SD 57198USA
Publisher: EROS Data Center
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Source_Contribution: The image provides the base from which theland cover classification is determined.
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Citation_Information:
Originator: U.S. Geological SurveyEROS Data Center
Publication_Date: unknown
Title: Landsat TM scene
Geospatial_Data_Presentation_Form: remote sensing image
Publication_Information:
Publication_Place: Sioux Falls, SD 57198USA
Publisher: EROS Data Center
Other_Citation_Details: Leaf On Path/Row 014/031
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Source_Contribution: The image provides the base from which theland cover classification is determined.
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Citation_Information:
Originator: U.S. Geological SurveyEROS Data Center
Publication_Date: unknown
Title: Landsat TM scene
Geospatial_Data_Presentation_Form: remote sensing image
Publication_Information:
Publication_Place: Sioux Falls, SD 57198USA
Publisher: EROS Data Center
Other_Citation_Details: Leaf On Path/Row 014/032
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Source_Contribution: The image provides the base from which theland cover classification is determined.
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Citation_Information:
Originator: U.S. Geological SurveyEROS Data Center
Publication_Date: unknown
Title: Landsat TM scene
Geospatial_Data_Presentation_Form: remote sensing image
Publication_Information:
Publication_Place: Sioux Falls, SD 57198USA
Publisher: EROS Data Center
Other_Citation_Details: Leaf On Path/Row 015/029
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Source_Contribution: The image provides the base from which theland cover classification is determined.
Source_Information:
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Citation_Information:
Originator: U.S. Geological SurveyEROS Data Center
Publication_Date: unknown
Title: Landsat TM scene
Geospatial_Data_Presentation_Form: remote sensing image
Publication_Information:
Publication_Place: Sioux Falls, SD 57198USA
Publisher: EROS Data Center
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Source_Contribution: The image provides the base from which theland cover classification is determined.
Source_Information:
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Citation_Information:
Originator: U.S. Geological SurveyEROS Data Center
Publication_Date: unknown
Title: Landsat TM scene
Geospatial_Data_Presentation_Form: remote sensing image
Publication_Information:
Publication_Place: Sioux Falls, SD 57198USA
Publisher: EROS Data Center
Other_Citation_Details: Leaf On Path/Row 015/031
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Source_Contribution: The image provides the base from which theland cover classification is determined.
Source_Information:
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Citation_Information:
Originator: U.S.Geological SurveyEROS Data Center
Publication_Date: unknown
Title: Landsat TM scene
Geospatial_Data_Presentation_Form: remote sensing image
Publication_Information:
Publication_Place: Sioux Falls, SD 57198USA
Publisher: EROS Data Center
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Source_Citation_Abbreviation: image 23
Source_Contribution: The image provides the base from which theland cover classification is determined.
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Geological SurveyEROS Data Center
Publication_Date: unknown
Title: Landsat TM scene
Geospatial_Data_Presentation_Form: remote sensing image
Publication_Information:
Publication_Place: Sioux Falls, SD 57198USA
Publisher: EROS Data Center
Other_Citation_Details: Leaf On Path/Row 016/031
Type_of_Source_Media: raster digital data
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Source_Contribution: The image provides the base from which theland cover classification is determined.
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Geological SurveyEROS Data Center
Publication_Date: unknown
Title: Landsat TM scene
Geospatial_Data_Presentation_Form: remote sensing image
Publication_Information:
Publication_Place: Sioux Falls, SD 57198USA
Publisher: EROS Data Center
Other_Citation_Details: Leaf On Path/Row 017/030
Type_of_Source_Media: raster digital data
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Time_Period_Information:
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Calendar_Date: 19930530
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Source_Citation_Abbreviation: image25
Source_Contribution: The image provides the base from which theland cover classification is determined.
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Geological SurveyEROS Data Center
Publication_Date: unknown
Title: Landsat TM scene
Geospatial_Data_Presentation_Form: remote sensing image
Publication_Information:
Publication_Place: Sioux Falls, SD 57198USA
Publisher: EROS Data Center
Other_Citation_Details: Leaf On Path/Row 017/031
Type_of_Source_Media: raster digital data
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 19930530
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: image26
Source_Contribution: The image provides the base from which theland cover classification is determined.
Process_Step:
Process_Description: Land Cover Characterization:The project is being carried out on the basis of 10 Federal Regionsthat make up the conterminous United States; each region is comprisedof multiple states; each region is processed in subregional unitsthat are limited to the area covered by no more than 18 Landsat TMscenes. The general NLCD procedure is to: (1) mosaic subregional TMscenes and classify them using an unsupervised clustering algorithm,(2) interpret and label the clusters/classes using aerial photographsas reference data, (3) resolve the labeling of confused clusters/classesusing the appropriate ancillary data source(s), and (4) incorporateland cover information from other data sets and perform manual edits toaugment and refine the "basic" classification developed above.Two seasonally distinct TM mosaics are produced, a leaves-on version(summer) and a leaves-off (spring/fall) version. TM bands 3, 4, 5,and 7 are mosaicked for both the leaves-on and leaves-off versions.For mosaick purposes, a base scene is selected for each mosaic andthe other scenes are adjusted to mimic spectral properties of the basescene using histogram matching in regions of spatial overlap.Following mosaicking, either the leaves-off version or leaves-on versionIs selected to be the "base" for the land cover mapping process. The 4TM bands of the "base" mosaic are clustered to produce a single 100-class image using an unsupervised clustering algorithm. Each of thespectrally distinct clusters/classes is then assigned to one or moreAnderson level 1 and 2 land cover classes using National High AltitudePhotography program (NHAP)and National Aerial Photography program(NAPP) aerial photographs as a reference. Almost invariably, individualspectral clusters/classes are confused between two or more land coverclasses.Separation of the confused spectral clusters/classes into appropriateNLCD class is accomplished using ancillary data layers. Standardancillary data layers include: the "non-base" mosaic TM bands and 100-class cluster image; derived TM normalized vegetation index (NDVI),various TM band ratios, TM date bands; 3-arc second Digital TerrainElevation Data (DTED) and derived slope, aspect and shaded relief;population and housing density data; USGS land use and land cover(LUDA); and National Wetlands Inventory(NWI) data if available. Otherancillary data sources may include soils data, unique state or regionalland cover data sets, or data from other federal programs such as theNational Gap Analysis Program (GAP) of the USGS Biological ResourcesDivision (BRD). For a given confused spectral cluster/class, digitalvalues of the various ancillary data layers are compared to determine:(1) which data layers are the most effective for splitting theconfused cluster/class into the appropriate NLCD class, and (2) theappropriate layer thresholds for making the split(s). Models are thendeveloped using one to several ancillary data layers to split theconfused cluster/class into the NLCD class. For example, a populationdensity threshold is used to separate high-intensity residential areasfrom commercial/industrial/transportation. Or a cluster/class might beconfused between row crop and grasslands. To split this particularcluster/class, a TM NDVI threshold might be identified and used with anelevation threshold in a class-splitting model to make the appropriateNLCD class assignments. A purely spectral example is using thetemporally opposite TM layers to discriminate confused cluster/classessuch as hay pasture vs. row crops and deciduous forests vs. evergreenforests; simple thresholds that contrast the seasonal differences invegetation between leaves-on vs. leaves-off.Not all cluster/class confusion can be successfully modeled out.Certain classes such as urban/recreational grasses or quarries/stripmines/gravel pits that are not spectrally unique require manual editing.These class features are typically visually identified and thenreclassified using on-screen digitizing and recoding. Other classessuch as wetlands require the use of specific data sets such as NWI toprovide the most accurate classification. Areas lacking NWI data aretypically subset out and modeling is used to estimate wetlands in theselocalized areas. The final NLCD product results from the classification(interpretation and labeling) of the 100-class "base" cluster mosaicusing both automated and manual processes, incorporating both spectraland conditional data layers. For a more detailed explanation pleasesee Vogelmann et al. 1998 and Vogelmann et al. 1998.Discussion:While we believe that the approach taken has yielded a very good generalland cover classification product for the nation, it is important toindicate to the user where there might be some potential problems. Thebiggest concerns are listed below:1) Some of the TM data sets are not temporally ideal. Leaves-off datasets are heavily relied upon for discriminating between hay/pasture androw crop, and also for discriminating between forest classes. Thesuccess of discriminating between these classes using leaves-off datasets hinges on the time of data acquisition. When hay/pasture areas arenon-green, they are not easily distinguishable from other agriculturalareas using remotely sensed data. However, there is a temporal windowduring which hay and pasture areas green up before most other vegetation(excluding evergreens, which have different spectral properties); duringthis window these areas are easily distinguishable from other cropareas. The discrimination between hay/pasture and deciduous forest islikewise optimized by selecting data in a temporal window wheredeciduous vegetation has yet to leaf out. It is difficult to acquire asingle-date of imagery (leaves-on or leaves-off) that adequatelydifferentiates between both deciduous/hay and pasture and hay pasture/row crop.2) The data sets used cover a range of years (see data sources), andchanges that have taken place across the landscape over the time periodmay not have been captured. While this is not viewed as a major problemfor most classes, it is possible that some land cover features changemore rapidly than might be expected (e.g. hay one year, row crop thenext).3) Wetlands classes are extremely difficult to extract from Landsat TMspectral information alone. The use of ancillary information such asNational Wetlands Inventory (NWI) data is highly desirable. We reliedon GAP, LUDA, or proximity to streams and rivers as well as spectraldata to delineate wetlands in areas without NWI data.4) Separation of natural grass and shrub is problematic. Areas observedon the ground to be shrub or grass are not always distinguishablespectrally. Likewise, there was often disagreement between LUDA and GAPon these classes.AcknowledgmentsThis work was performed under contract the U.S. GeologicalSurvey (Contract 1434-CR-97-CN-40274).ReferencesMore detailed information on the methodologies and techniques employedIn this work can be found in the following:Kelly, P.M., and White, J.M., 1993. Preprocessing remotely sensed datafor efficient analysis and classification, Applications of ArtificialIntelligence 1993: Knowledge-Based Systems in Aerospace and Industry,Proceeding of SPIE, 1993, 24-30.Cowardin, L.M., V. Carter, F.C. Golet, and E.T. LaRoe, 1979.Classification of Wetlands and Deepwater Habitats of the United States,Fish and Wildlife Service, U.S. Department of the Interior, Washington,D.C.Vogelmann, J.E., Sohl, T., and Howard, S.M., 1998. "RegionalCharacterization of Land Cover Using Multiple Sources of Data."Photogrammetric Engineering & Remote Sensing, Vol. 64, No. 1,pp. 45-47.Vogelmann, J.E., Sohl, T., Campbell, P.V., and Shaw, D.M., 1998."Regional Land Cover Characterization Using Landsat Thematic MapperData and Ancillary Data Sources." Environmental Monitoring andAssessment, Vol. 51, pp. 415-428.Zhu, Z., Yang, L., Stehman, S., and Czaplewski, R., 1999. "Designing anAccuracy Assessment for USGS Regional Land Cover Mapping Program."(In review) Photogrametric Engineering & Remote Sensing.
Source_Used_Citation_Abbreviation: Landsat thematic mapper (TM)
Process_Date: 19991001
Process_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: U.S. Geological SurveyEROS Data Center
Contact_Position: Customer Services Representative
Contact_Address:
Address_Type: mailing and physical address
Address: U.S. Geological SurveyEROS Data Center
City: Sioux Falls
State_or_Province: SD
Postal_Code: 57198
Country: USA
Contact_Voice_Telephone: (605) 594 - 6551
Contact_TDD/TTY_Telephone: (605) 594 - 6933
Contact_Facsimile_Telephone: (605) 594 - 6589
Contact_Electronic_Mail_Address: CUSTSERV@EDCMAIL.CR.USGS.GOV

Spatial_Data_Organization_Information:
Direct_Spatial_Reference_Method: raster
Raster_Object_Information:
Raster_Object_Type: Grid Cell
Row_Count: 17455
Column_Count: 23005
Vertical_Count: 1

Spatial_Reference_Information:
Horizontal_Coordinate_System_Definition:
Planar:
Map_Projection:
Map_Projection_Name: Albers Conical Equal Area
Albers_Conical_Equal_Area:
Standard_Parallel: 29.5
Standard_Parallel: 45.5
Longitude_of_Central_Meridian: -96.0
Latitude_of_Projection_Origin: 23.0
False_Easting: 0
False_Northing: 0
Planar_Coordinate_Information:
Planar_Coordinate_Encoding_Method: row and column
Coordinate_Representation:
Abscissa_Resolution: 30.0
Ordinate_Resolution: 30.0
Planar_Distance_Units: meters
Geodetic_Model:
Horizontal_Datum_Name: North American Datum 1983
Ellipsoid_Name: Geographic Reference System 80
Semi-major_Axis: 6378137
Denominator_of_Flattening_Ratio: 298.257

Entity_and_Attribute_Information:
Overview_Description:
Entity_and_Attribute_Overview: NOTE - All classes may NOT be represented in a specific state data set.The class number represents the digital value of the class in the dataset.Water11 Open Water12 Perennial Ice/SnowDeveloped21 Low Intensity Residential22 High Intensity Residential23 Commercial/Industrial/TransportationBarren31 Bare Rock/Sand/Clay32 Quarries/Strip Mines/Gravel Pits33 TransitionalVegetated; Natural Forested Upland41 Deciduous Forest42 Evergreen Forest43 Mixed ForestShrubland51 ShrublandNon-natural Woody61 Orchards/Vineyards/OtherHerbaceous Upland71 Grasslands/HerbaceousHerbaceous Planted/Cultivated81 Pasture/Hay82 Row Crops83 Small Grains84 Fallow85 Urban/Recreational GrassesWetlands91 Woody Wetlands92 Emergent Herbaceous WetlandsNLCD Land Cover Classification System Land Cover Class Definitions:Water - All areas of open water or permanent ice/snow cover.11. Open Water - areas of open water, generally with lessthan 25 percent or greater cover of water (per pixel).12. Perennial Ice/Snow - All areas characterized by year-longcover of ice and/or snow.Developed - areas characterized by high percentage (approximately30% or greater) of constructed materials (e.g. asphalt, concrete,buildings, etc).21. Low Intensity Residential - Includes areas with a mixture ofconstructed materials and vegetation. Constructed materials accountfor 30-80 percent of the cover. Vegetation may account for 20 to 70percent of the cover. These areas most commonly include single-familyhousing units. Population densities will be lower than in high intensityresidential areas.22. High Intensity Residential - Includes heavily built up urbancenters where people reside in high numbers. Examples includeapartment complexes and row houses. Vegetation accounts for lessthan 20 percent of the cover. Constructed materials account for80-100 percent of the cover.23. Commercial/Industrial/Transportation - Includes infrastructure(e.g. roads, railroads, etc.) and all highways and all developed areasnot classified as High Intensity Residential.Barren - Areas characterized by bare rock, gravel, sad, silt, clay, orother earthen material, with little or no "green" vegetation presentregardless of its inherent ability to support life. Vegetation, ifpresent, is more widely spaced and scrubby than that in the"green" vegetated categories; lichen cover may be extensive.31. Bare Rock/Sand/Clay - Perennially barren areas of bedrock, desert,pavement, scarps, talus, slides, volcanic material, glacial debris, andother accumulations of earthen material.32. Quarries/Strip Mines/Gravel Pits - Areas of extractive miningactivities with significant surface expression.33. Transitional - Areas of sparse vegetative cover (less than 25percent that are dynamically changing from one land cover toanother, often because of land use activities. Examples includeforest clearcuts, a transition phase between forest and agricultural land,the temporary clearing of vegetation, and changes due to natural causes(e.g. fire, flood, etc.)Forested Upland - Areas characterized by tree cover (natural orsemi-natural woody vegetation, generally greater than 6 meters tall);Tree canopy accounts for 25-100 percent of the cover.41. Deciduous Forest - Areas dominated by trees where 75 percentor more of the tree species shed foliage simultaneously in response toseasonal change.42. Evergreen Forest - Areas characterized by trees where 75 percentor more of the tree species maintain their leaves all year. Canopy isnever without green foliage.43. Mixed Forest - Areas dominated by trees where neitherdeciduous nor evergreen species represent more than 75 percentof the cover present.Shrubland - Areas characterized by natural or semi-natural woodyvegetation with aerial stems, generally less than 6 meters tallwith individuals or clumps not touching to interlocking. Both evergreenand diciduous species of true shrubs, young trees, and trees or shrubsthat are small or stunted because of environmental conditions areincluded.51. Shrubland - Areas dominated by shrubs; shrub canopy accountsfor 25-100 percent of the cover. Shrub cover is generally greaterthan 25 percent when tree cover is less than 25 percent. Shrub covermay be less than 25 percent in cases when the cover of other life forms(e.g. herbaceous or tree) is less than 25 percent and shrubs coverexceeds the cover of the other life forms.Non-natural Woody - Areas dominated by non-natural woodyvegetation; non-natural woody vegetative canopy accounts for25-100 percent of the cover. The non-natural woody classificationis subject to the availability of sufficient ancillary data todifferentiate non-natural woody vegetation from natural woody vegetation.61. Orchards/Vineyards/Other - Orchards, vineyards, and other areasplanted or maintained for the production of fruits, nuts, berries, orornamentals.Herbaceous Upland - Upland areas characterized by natural orsemi- natural herbaceous vegetation; herbaceous vegetationaccounts for 75-100 percent of the cover.71. Grasslands/Herbaceous - Areas dominated by upland grassesand forbs. In rare cases, herbaceous cover is less than 25 percent,but exceeds the combined cover of the woody species present.These areas are not subject to intensive management, but they areoften utilized for grazing.Planted/Cultivated - Areas characterized by herbaceous vegetationThat has been planted or is intensively managed for the productionof food, feed, or fiber; or is maintained in developed settings forspecific purposes. Herbaceous vegetation accounts for 75-100 percentof the cover.81. Pasture/Hay - Areas of grasses, legumes, or grass-legume mixturesplanted for livestock grazing or the production of seed or hay crops.82. Row Crops - Areas used for the production of crops, suchas corn, soybeans, vegetables, tobacco, and cotton.83. Small Grains - Areas used for the production of graminoidcrops such as wheat, barley, oats, and rice84. Fallow - Areas used for the production of crops that aretemporarily barren or with sparse vegetative cover as a result ofbeing tilled in a management practice that incorporates prescribedalternation between cropping and tillage.85. Urban/Recreational Grasses - Vegetation (primarily grasses) plantedin developed settings for recreation, erosion control, or aestheticpurposes. Examples include parks, lawns, golf courses, airport grasses,and industrial site grasses.Wetlands - Areas where the soil or substrate is periodically saturatedwith or covered with water as defined by Cowardin et al.91. Woody Wetlands - Areas where forest or shrubland vegetationaccounts for 25-100 percent of the cover and the soil or substrateis periodically saturated with or covered with water.92. Emergent Herbaceous Wetlands - Areas where perennialherbaceous vegetation accounts for 75-100 percent of the coverand the soil or substrate is periodically saturated with or coveredwith water.
Entity_and_Attribute_Detail_Citation: NLCD Regional Land Cover Classification System Key Rev. 07/99

Distribution_Information:
Distributor:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: U.S. Geological Survey, EROS Data Center
Contact_Position: Customer Services Representative
Contact_Address:
Address_Type: mailing and physical address
Address: Sioux Falls
City: SD
State_or_Province: SD
Postal_Code: 57198
Country: USA
Contact_Voice_Telephone: 605-594-6551
Contact_TDD/TTY_Telephone: 605-594-6933
Contact_Facsimile_Telephone: 605-594-6589
Contact_Electronic_Mail_Address: custserv@edcmail.cr.usgs.gov
Resource_Description: Conterminous United States Land Cover
Distribution_Liability: Although these data have been processed successfully ona computer system at the USGS, no warranty expressed orimplied is made by the USGS regarding the use of the dataon any other system, nor does the act of distributionconstitute any such warranty.
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Format_Name: GeoTIFF
File_Decompression_Technique: gzip
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Fees: none
Technical_Prerequisites: Geo-TIFF viewing software. Some examples are ESRI's ARC/EXPLORER andUSGS's DLGV32. The DLGV32 viewer is available free for download atthe MidContinent Mapping Center web site, <http://mcmcweb.er.usgs.gov/drc/dlgv32pro/>.Digital image processing software or geographic information systemsoftware is required to analyze or otherwise manipulate the data.

Metadata_Reference_Information:
Metadata_Date: 20161006
Metadata_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: Albert R. Mann Library
Contact_Address:
Address_Type: mailing and physical
Address: Albert R. Mann Library
City: Ithaca
State_or_Province: New York
Postal_Code: 14853
Country: USA
Contact_Voice_Telephone: 607-255-5406
Contact_Electronic_Mail_Address: mann_ref@cornell.edu
Metadata_Standard_Name: FGDC Content Standards for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001-1998
Metadata_Time_Convention: local time