2009 North Carolina Cropland Data Layer

Identification_Information:

Citation:

Citation_Information:

Originator:

United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS), Research and Development Division (RDD), Geospatial Information Branch (GIB), Spatial Analysis Research Section (SARS)

Publication_Date: 20171211
Title: 2009 North Carolina Cropland Data Layer | NASS/USDA
Edition: 2009 Edition
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:

Publication_Place:

USDA, NASS Marketing and Information Services Office, Washington, D.C.

Publisher: USDA, NASS

Other_Citation_Details:

NASS maintains a Frequently Asked Questions (FAQ's) section on the CDL website at <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>. The data is available free for download through CropScape at <https://nassgeodata.gmu.edu/CropScape/>. The data is also available free for download through the Geospatial Data Gateway at <https://datagateway.nrcs.usda.gov/>. See the 'Ordering Instructions' section of this metadata file for detailed Geospatial Data Gateway download instructions.

Online_Linkage: <https://nassgeodata.gmu.edu/CropScape/NC>

Description:

Abstract:

***NOTE ON THE NEW 2008 AND 2009 CROPLAND DATA LAYERS (RELEASED 12/11/2017): The 2008 and 2009 Cropland Data Layers (CDL) for the entire Continental United States have been reprocessed and re-released at a 30 meter spatial resolution to best match the products from 2010 forward. The move from 56m to 30m resolution was made possible with the inclusion of Landsat 5 Thematic Mapper data, which was not freely available during the initial processing period. Additionally, the reprocessing effort used more complete Farm Service Agency administrative data for training and accuracy assessing the classifications. More detailed information will be posted on our Frequently Asked Questions at: <https://www.nass.usda.gov/Research_and_Science/Cropland/sarsfaqs2.php>

The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2009 CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 5 TM sensor and the Indian Remote Sensing RESOURCESAT-1 (IRS-P6) Advanced Wide Field Sensor (AWiFS) collected during the current growing season.

Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED) and the NLCD 2006 imperviousness layer and NLCD 2001 canopy data layer from the USGS National Land Cover Database.

Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. A modified version of the 2011 NLCD with pixels of change from the 2006 NLCD to the 2011 NLCD masked out was used as non-agricultural training and validation data.

Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL.

The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.

Purpose:

The purpose of the Cropland Data Layer Program is to use satellite imagery to (1) provide planted acreage estimates to the Agricultural Statistics Board for the state's major commodities and (2) produce digital, crop-specific, categorized geo-referenced output products.

Supplemental_Information:

If the following table does not display properly, then please visit the following website to view the original metadata file <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php>.

USDA, National Agricultural Statistics Service, 2009 North Carolina Cropland Data Layer

CLASSIFICATION INPUTS:
AWIFS DATE 20090417 PATH 285 ROW(S)&QUADRANT(S) 45BD 50B
AWIFS DATE 20090426 PATH 282 ROW(S)&QUADRANT(S) 45BD
AWIFS DATE 20090520 PATH 282 ROW(S)&QUADRANT(S) 40D 45B
AWIFS DATE 20090530 PATH 284 ROW(S)&QUADRANT(S) 40BD 45D 50B
AWIFS DATE 20090628 PATH 285 ROW(S)&QUADRANT(S) 45D
AWIFS DATE 20090810 PATH 284 ROW(S)&QUADRANT(S) 45CD 50AB

LANDSAT 5 TM DATE 20090417 PATH 016 ROW(S) 28-40 42
LANDSAT 5 TM DATE 20090424 PATH 017 ROW(S) 29-41
LANDSAT 5 TM DATE 20090426 PATH 015 ROW(S) 28 30-37 41-43
LANDSAT 5 TM DATE 20090519 PATH 016 ROW(S) 29-36
LANDSAT 5 TM DATE 20090521 PATH 014 ROW(S) 27-36
LANDSAT 5 TM DATE 20090602 PATH 018 ROW(S) 29-30 32-40
LANDSAT 5 TM DATE 20090613 PATH 015 ROW(S) 28-37 40-43
LANDSAT 5 TM DATE 20090627 PATH 017 ROW(S) 29-39
LANDSAT 5 TM DATE 20090629 PATH 015 ROW(S) 30-37 41-42
LANDSAT 5 TM DATE 20090708 PATH 014 ROW(S) 31-36
LANDSAT 5 TM DATE 20090715 PATH 015 ROW(S) 28-37 41-42
LANDSAT 5 TM DATE 20090722 PATH 016 ROW(S) 28-31 35-42
LANDSAT 5 TM DATE 20090731 PATH 015 ROW(S) 33-37 41-43
LANDSAT 5 TM DATE 20090805 PATH 018 ROW(S) 29-31 34-40
LANDSAT 5 TM DATE 20090807 PATH 016 ROW(S) 28-38 40-41
LANDSAT 5 TM DATE 20090809 PATH 014 ROW(S) 28-29 33-36
LANDSAT 5 TM DATE 20090825 PATH 014 ROW(S) 27 29-36 42
LANDSAT 5 TM DATE 20090901 PATH 015 ROW(S) 28-37
LANDSAT 5 TM DATE 20090924 PATH 016 ROW(S) 28-30 34-42

USGS, NATIONAL ELEVATION DATASET ELEVATION
USGS, NATIONAL LAND COVER DATABASE 2001 TREE CANOPY
USGS, NATIONAL LAND COVER DATABASE 2006 IMPERVIOUSNESS

TRAINING AND VALIDATION:
USDA, FARM SERVICE AGENCY 2009 COMMON LAND UNITS
USGS, NATIONAL LAND COVER DATABASE 2011 (NLCD2006-NLCD2011 PIXELS OF CHANGE REMOVED)

NOTE: The final extent of the CDL is clipped to the state boundary even though the raw input data may encompass a larger area.

Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20081001
Ending_Date: 20091231

Currentness_Reference: 2009 growing season

Status:

Progress: Complete
Maintenance_and_Update_Frequency: None planned

Spatial_Domain:

Bounding_Coordinates:

West_Bounding_Coordinate: -84.3901
East_Bounding_Coordinate: -75.5804
North_Bounding_Coordinate: 36.6131
South_Bounding_Coordinate: 33.7564

Keywords:

Theme:

Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: farming, 001
Theme_Keyword: environment, 007
Theme_Keyword: imageryBaseMapsEarthCover, 010

Theme:

Theme_Keyword_Thesaurus: Global Change Master Directory (GCMD) Science Keywords
Theme_Keyword:

Earth Science > Biosphere > Terrestrial Ecosystems > Agricultural Lands

Theme_Keyword: Earth Science > Land Surface > Land Use/Land Cover > Land Cover

Theme:

Theme_Keyword_Thesaurus: None

Theme_Keyword: crop cover
Theme_Keyword: cropland
Theme_Keyword: agriculture
Theme_Keyword: land cover
Theme_Keyword: crop estimates
Theme_Keyword: AWiFS
Theme_Keyword: Landsat
Theme_Keyword: CropScape

Place:

Place_Keyword_Thesaurus: Global Change Master Directory (GCMD) Location Keywords
Place_Keyword:

Continent > North America > United States of America > North Carolina

Place:

Place_Keyword_Thesaurus: None
Place_Keyword: North Carolina
Place_Keyword: NC

Temporal:

Temporal_Keyword_Thesaurus: None
Temporal_Keyword: 2009

Access_Constraints: None
Use_Constraints:

The USDA, NASS Cropland Data Layer is provided to the public as is and is considered public domain and free to redistribute. The USDA, NASS does not warrant any conclusions drawn from these data. If the user does not have software capable of viewing GEOTIF (.tif) file formats then we suggest using the CropScape website <https://nassgeodata.gmu.edu/CropScape/> or the freeware browser ESRI ArcGIS Explorer <https://www.esri.com/>.

Point_of_Contact:

Contact_Information:

Contact_Organization_Primary:

Contact_Organization: USDA, NASS, Spatial Analysis Research Section
Contact_Person: USDA, NASS, Spatial Analysis Research Section staff

Contact_Address:

Address_Type: mailing and physical address
Address: 1400 Independence Avenue, SW, Room 5029 South Building
City: Washington
State_or_Province: District of Columbia
Postal_Code: 20250-2001
Country: USA

Contact_Voice_Telephone: 800-727-9540
Contact_Facsimile_Telephone: 855-493-0447
Contact_Electronic_Mail_Address: SM.NASS.RDD.GIB@usda.gov

Data_Set_Credit: USDA, National Agricultural Statistics Service
Security_Information:

Security_Classification_System: None
Security_Classification: Unclassified
Security_Handling_Description: None

Native_Data_Set_Environment:

Microsoft Windows 7 Enterprise; ERDAS Imagine Versions 2011 <https://www.hexagongeospatial.com/>; ESRI ArcGIS Version 10.3 <https://www.esri.com/>; Rulequest See5.0 Release 2.11 <http://www.rulequest.com/>; NLCD Mapping Tool v2.08 <https://www.mrlc.gov/>.

ERDAS Imagine is used in the pre- and post- processing of all raster-based data. ESRI ArcGIS is used to prepare the vector-based Farm Service Agency (FSA) Common Land Unit (CLU) training and validation data. Rulequest See5.0 is used to create a decision-tree based classifier. The NLCD Mapping Tool is used to apply the See5.0 decision-tree via ERDAS Imagine. This is a departure from older versions of the CDL that were created using in-house software (Peditor) based upon a maximum likelihood classifier approach. Check this section and the 'Process Description' section of the specific state and year metadata file to verify what methodology was used.

Data_Quality_Information:

Attribute_Accuracy:

Attribute_Accuracy_Report:

If the following table does not display properly, then please visit this internet site <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php> to view the original metadata file.

USDA, National Agricultural Statistics Service, 2009 North Carolina Cropland Data Layer
STATEWIDE AGRICULTURAL ACCURACY REPORT

Crop-specific covers only  *Correct  Accuracy     Error     Kappa
-------------------------   -------  --------    ------     -----
OVERALL ACCURACY**          1101630     78.0%     22.0%     0.722


Cover                       Attribute  *Correct Producer's  Omission             User's Commission   Cond'l
Type                             Code    Pixels  Accuracy     Error     Kappa  Accuracy     Error     Kappa
----                             ----    ------  --------     -----     -----  --------     -----     -----
Corn                                1    314079     94.7%      5.3%     0.936     92.6%      7.4%     0.910
Cotton                              2     97287     77.3%     22.7%     0.757     82.6%     17.4%     0.813
Sorghum                             4       696     18.8%     81.2%     0.188     75.2%     24.8%     0.752
Soybeans                            5    402842     87.3%     12.7%     0.827     82.4%     17.6%     0.765
Sunflower                           6        10      4.9%     95.1%     0.049     18.9%     81.1%     0.189
Peanuts                            10     12129     69.4%     30.6%     0.691     89.2%     10.8%     0.891
Tobacco                            11       970     39.5%     60.5%     0.395     72.8%     27.2%     0.728
Sweet Corn                         12        39      5.8%     94.2%     0.058     24.2%     75.8%     0.242
Pop or Orn Corn                    13       157     58.4%     41.6%     0.584     89.7%     10.3%     0.897
Barley                             21       124     12.7%     87.3%     0.126     27.0%     73.0%     0.270
Winter Wheat                       24      3797     20.4%     79.6%     0.202     60.0%     40.0%     0.596
Other Small Grains                 25         0      n/a       n/a       n/a       0.0%    100.0%     0.000
Dbl Crop WinWht/Soybeans           26    208527     90.8%      9.2%     0.894     81.2%     18.8%     0.785
Rye                                27       740     26.1%     73.9%     0.260     66.1%     33.9%     0.661
Oats                               28       387     17.0%     83.0%     0.170     70.0%     30.0%     0.699
Millet                             29       320      7.8%     92.2%     0.078     48.8%     51.2%     0.487
Canola                             31         0      0.0%    100.0%     0.000      n/a       n/a       n/a
Alfalfa                            36        21      2.9%     97.1%     0.029     38.9%     61.1%     0.389
Other Hay/Non Alfalfa              37     22784     33.4%     66.6%     0.322     69.0%     31.0%     0.678
Buckwheat                          39         0      0.0%    100.0%     0.000      n/a       n/a       n/a
Dry Beans                          42       197     62.7%     37.3%     0.627     84.9%     15.1%     0.849
Potatoes                           43        33     20.9%     79.1%     0.209     63.5%     36.5%     0.635
Other Crops                        44       296     30.4%     69.6%     0.304     67.0%     33.0%     0.670
Sweet Potatoes                     46      7675     68.5%     31.5%     0.684     89.3%     10.7%     0.893
Misc Vegs & Fruits                 47         0      n/a       n/a       n/a       0.0%    100.0%     0.000
Watermelons                        48        88     13.9%     86.1%     0.139     36.4%     63.6%     0.363
Cucumbers                          50       349     23.3%     76.7%     0.232     71.2%     28.8%     0.712
Peas                               53         4      1.2%     98.8%     0.012     50.0%     50.0%     0.500
Tomatoes                           54         1      1.9%     98.1%     0.019      7.1%     92.9%     0.071
Caneberries                        55         0      0.0%    100.0%     0.000      n/a       n/a       n/a
Herbs                              57       205     31.5%     68.5%     0.315     69.5%     30.5%     0.695
Clover/Wildflowers                 58         0      0.0%    100.0%     0.000      n/a       n/a       n/a
Sod/Grass Seed                     59     10956     40.9%     59.1%     0.404     69.6%     30.4%     0.692
Switchgrass                        60         0      0.0%    100.0%     0.000      n/a       n/a       n/a
Fallow/Idle Cropland               61     12316     22.4%     77.6%     0.213     49.5%     50.5%     0.480
Peaches                            67        37     19.7%     80.3%     0.197     62.7%     37.3%     0.627
Apples                             68       398     42.3%     57.7%     0.422     81.1%     18.9%     0.810
Grapes                             69        21     11.3%     88.7%     0.113     80.8%     19.2%     0.808
Christmas Trees                    70        43     20.5%     79.5%     0.205     45.7%     54.3%     0.457
Pecans                             74         0      0.0%    100.0%     0.000      0.0%    100.0%     0.000
Triticale                         205        26     17.1%     82.9%     0.171     48.1%     51.9%     0.481
Asparagus                         207         0      0.0%    100.0%     0.000      n/a       n/a       n/a
Cantaloupes                       209         7     11.1%     88.9%     0.111     58.3%     41.7%     0.583
Honeydew Melons                   213         0      0.0%    100.0%     0.000      n/a       n/a       n/a
Broccoli                          214        65     82.3%     17.7%     0.823    100.0%      0.0%     1.000
Peppers                           216         4      3.1%     96.9%     0.031     22.2%     77.8%     0.222
Greens                            219         0      0.0%    100.0%     0.000      0.0%    100.0%     0.000
Strawberries                      221         5      3.8%     96.2%     0.038     62.5%     37.5%     0.625
Squash                            222         1      0.3%     99.7%     0.003      4.5%     95.5%     0.045
Dbl Crop WinWht/Corn              225       634     22.2%     77.8%     0.222     57.1%     42.9%     0.571
Dbl Crop Oats/Corn                226       204     30.4%     69.6%     0.304     89.9%     10.1%     0.899
Pumpkins                          229        18     14.3%     85.7%     0.143     72.0%     28.0%     0.720
Dbl Crop WinWht/Sorghum           236       113     14.4%     85.6%     0.144     70.2%     29.8%     0.702
Dbl Crop Barley/Corn              237       858     30.7%     69.3%     0.306     63.6%     36.4%     0.635
Dbl Crop WinWht/Cotton            238         1      0.4%     99.6%     0.004      9.1%     90.9%     0.091
Dbl Crop Soybeans/Cotton          239         0      n/a       n/a       n/a       0.0%    100.0%     0.000
Dbl Crop Soybeans/Oats            240       257      5.4%     94.6%     0.054     36.1%     63.9%     0.359
Dbl Crop Corn/Soybeans            241         0      0.0%    100.0%     0.000      n/a       n/a       n/a
Blueberries                       242       294     24.0%     76.0%     0.240     88.0%     12.0%     0.880
Cabbage                           243        34     23.4%     76.6%     0.234     97.1%      2.9%     0.971
Radishes                          246         0      0.0%    100.0%     0.000      n/a       n/a       n/a
Turnips                           247         0      0.0%    100.0%     0.000      n/a       n/a       n/a
Dbl Crop Barley/Soybeans          254      1581     31.8%     68.2%     0.317     66.1%     33.9%     0.660

*Correct Pixels represents the total number of independent validation pixels correctly identified in the error matrix.
**The Overall Accuracy represents only the FSA row crops and annual fruit and vegetables (codes 1-61, 66-80, 92 and 200-255).
FSA-sampled grass and pasture. Non-agricultural and NLCD-sampled categories (codes 62-65, 81-91 and 93-199) are not included in the Overall Accuracy.

The accuracy of the non-agricultural land cover classes within the Cropland Data Layer is entirely dependent upon the USGS, National Land Cover Database (NLCD). Thus, the USDA, NASS recommends that users consider the NLCD for studies involving non-agricultural land cover. For more information on the accuracy of the NLCD please reference <https://www.mrlc.gov/>.

Quantitative_Attribute_Accuracy_Assessment:

Attribute_Accuracy_Value:

Classification accuracy is generally 85% to 95% correct for the major crop-specific land cover categories. See the 'Attribute Accuracy Report' section of this metadata file for the detailed accuracy report.

Attribute_Accuracy_Explanation:

The strength and emphasis of the CDL is crop-specific land cover categories. The accuracy of the CDL non-agricultural land cover classes is entirely dependent upon the USGS, National Land Cover Database (NLCD). Thus, the USDA, NASS recommends that users consider the NLCD for studies involving non-agricultural land cover.

These definitions of accuracy statistics were derived from the following book: Congalton, Russell G. and Kass Green. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Boca Raton, Florida: CRC Press, Inc. 1999. The 'Producer's Accuracy' is calculated for each cover type in the ground truth and indicates the probability that a ground truth pixel will be correctly mapped (across all cover types) and measures 'errors of omission'. An 'Omission Error' occurs when a pixel is excluded from the category to which it belongs in the validation dataset. The 'User's Accuracy' indicates the probability that a pixel from the CDL classification actually matches the ground truth data and measures 'errors of commission'. The 'Commission Error' represent when a pixel is included in an incorrect category according to the validation data. It is important to take into consideration errors of omission and commission. For example, if you classify every pixel in a scene to 'wheat', then you have 100% Producer's Accuracy for the wheat category and 0% Omission Error. However, you would also have a very high error of commission as all other crop types would be included in the incorrect category. The 'Kappa' is a measure of agreement based on the difference between the actual agreement in the error matrix (i.e., the agreement between the remotely sensed classification and the reference data as indicated by the major diagonal) and the chance agreement which is indicated by the row and column totals. The 'Conditional Kappa Coefficient' is the agreement for an individual category within the entire error matrix.

Logical_Consistency_Report:

The Cropland Data Layer (CDL) has been produced using training and independent validation data from the Farm Service Agency (FSA) Common Land Unit (CLU) Program (agricultural data) and United States Geological Survey (USGS) National Land Cover Database (NLCD). More information about the FSA CLU Program can be found at <https://www.fsa.usda.gov/>. More information about the NLCD can be found at <https://www.mrlc.gov/>. The CDL encompasses the entire state unless noted otherwise in the 'Completeness Report' section of this metadata file.

Completeness_Report: The entire state is covered by the Cropland Data Layer.
Positional_Accuracy:

Horizontal_Positional_Accuracy:

Horizontal_Positional_Accuracy_Report:

The Cropland Data Layer retains the spatial attributes of the input imagery. The Landsat imagery is obtained via download from the USGS Global Visualization Viewer (Glovis) website <https://glovis.usgs.gov/>. Please reference the metadata on the Glovis website for each Landsat scene for positional accuracy. The majority of the Landsat data is available at Level 1T (precision and terrain corrected). The AWiFS imagery used in the production of the Cropland Data Layer is purchased with an orthorectified level of processing. Thus, the CDL will retain the input imagery's positional accuracy of 60 meters at the circular error at the 90 percent confidence level (CE90). CE90 is a standard metric often used for horizontal accuracy in map products and can be interpreted as 90% of well-defined points tested must fall within a certain radial distance.

Lineage:

Source_Information:

Source_Citation:

Citation_Information:

Originator:

Indian Remote-Sensing Satellite series of ISRO (Indian Space Research Organization)

Title: RESOURCESAT-1 (IRS-P6) Advanced Wide Field Sensor (AWiFS)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

Publisher: EOTec (Earth Observation Technologies, LLC)
Publication_Place: Washington, D.C. 20008

Publication_Date: 2009
Other_Citation_Details:

The RESOURCESAT-1 (IRS-P6) AWiFS satellite sensor operates in four spectral bands at a spatial resolution of 56 meters. Additional information about AWiFS data can be obtained at <https://data.gov.in/>. The AWiFS imagery used in the Cropland Data Layer is obtained through a partnership with the USDA, Foreign Agricultural Service, International Production Assessment (IPA) Program. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs.

The AWiFS imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used bilinear interpolation, polynomial approximation, polynomial order of 3.

Source_Scale_Denominator: 56 meter
Type_of_Source_Media: CD-ROM and/or DVD
Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20081001
Ending_Date: 20091231

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: AWiFS
Source_Contribution: Raw data used in land cover spectral signature analysis

Source_Information:

Source_Citation:

Citation_Information:

Originator:

United States Geological Survey (USGS), Earth Resources Observation and Science (EROS)

Title: Landsat 5 Thematic Mapper (TM)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

Publisher: USGS, EROS
Publication_Place: Sioux Falls, South Dakota 57198-001

Publication_Date: 2009
Other_Citation_Details:

The Landsat data is free for download through the following website <https://glovis.usgs.gov/>. Additional information about Landsat data can be obtained at <https://www.usgs.gov/centers/eros>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path and rows used as classification inputs.

Source_Scale_Denominator: 30 meter
Type_of_Source_Media: online download
Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20081001
Ending_Date: 20091231

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: Landsat
Source_Contribution: Raw data used in land cover spectral signature analysis

Source_Information:

Source_Citation:

Citation_Information:

Originator:

United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Data Center

Title: The National Elevation Dataset (NED)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

Publisher: USGS, EROS Data Center
Publication_Place: Sioux Falls, South Dakota 57198 USA

Publication_Date: 2009
Other_Citation_Details:

The USGS NED Digital Elevation Model (DEM) is used as an ancillary data source in the production of the Cropland Data Layer. More information on the USGS NED can be found at <https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map>. Refer to the 'Supplemental Information' Section of this metadata file for the complete list of ancillary data sources used as classification inputs.

Source_Scale_Denominator: 30 meter
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: unknown

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: NED
Source_Contribution:

spatial and attribute information used in land cover spectral signature analysis

Source_Information:

Source_Citation:

Citation_Information:

Originator:

United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Data Center

Title: National Land Cover Database 2011 (NLCD 2011)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

Publisher: USGS, EROS Data Center
Publication_Place: Sioux Falls, South Dakota 57198 USA

Publication_Date: 2014
Other_Citation_Details:

A modified version of the 2011 NLCD with pixels of change from the 2006 NLCD to the 2011 NLCD masked out was used as non-agricultural training and validation data. Additionally, the USGS NLCD 2006 Imperviousness layer and NLCD 2001 Tree Canopy layer were used as ancillary data sources in the Cropland Data Layer classification process. More information on the NLCD can be found at <https://www.mrlc.gov/>. Refer to the 'Supplemental Information' Section of this metadata file for the complete list of ancillary data sources used as classification inputs. Preferred NLCD2006 citation: "Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L., Barnes, C., Herold, N., and Wickham, J., 2012. Completion of the 2006 National Land Cover Database for the Conterminous United States, PE&RS, Vol. 77(9):858-864."

Source_Scale_Denominator: 30 meter
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: unknown

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: NLCD
Source_Contribution: Raw data used in land cover spectral signature analysis

Source_Information:

Source_Citation:

Citation_Information:

Originator:

United States Department of Agriculture (USDA), Farm Service Agency (FSA)

Title: USDA, FSA Common Land Unit (CLU)
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:

Publisher: USDA, FSA Aerial Photography Field Office

Publication_Place: Salt Lake City, Utah 84119-2020 USA

Publication_Date: 2009
Other_Citation_Details:

Access to the USDA, Farm Service Agency (FSA) Common Land Unit (CLU) digital data set is currently limited to FSA and Agency partnerships. During the current growing season, producers enrolled in FSA programs report their growing intentions, crops and acreage to USDA Field Service Centers. Their field boundaries are digitized in a standardized GIS data layer and the associated attribute information is maintained in a database known as 578 Administrative Data. This CLU/578 dataset provides a comprehensive and robust agricultural training and validation data set for the Cropland Data Layer. Additional information about the CLU Program can be found at <https://www.fsa.usda.gov/>.

Source_Scale_Denominator: 4800
Type_of_Source_Media: online
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2009

Source_Currentness_Reference: ground condition, updated annually

Source_Citation_Abbreviation: FSA CLU
Source_Contribution:

spatial and attribute information used in the spectral signature training and validation of agricultural land cover

Process_Step:

Process_Description:

***NOTE ON THE NEW 2008 AND 2009 CROPLAND DATA LAYERS (RELEASED 12/11/2017)*** The 2008 and 2009 Cropland Data Layers (CDL) for the entire Continental United States have been reprocessed and re-released at a 30 meter spatial resolution to best match the products from 2010 forward. The move from 56m to 30m resolution was made possible with the inclusion of Landsat 5 Thematic Mapper data, which was not freely available during the initial processing period. Additionally, the reprocessing effort used more complete Farm Service Agency administrative data for training and accuracy assessing the classifications. More detailed information will be posted on our Frequently Asked Questions at: <https://www.nass.usda.gov/Research_and_Science/Cropland/sarsfaqs2.php>

OVERVIEW: The United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) Program is a unique agricultural-specific land cover geospatial product that is produced annually in participating states. The CDL Program builds upon NASS' traditional crop acreage estimation program and integrates Farm Service Agency (FSA) grower-reported field data with satellite imagery to create an unbiased statistical estimator of crop area at the state and county level for internal use. It is important to note that the internal acreage estimates, most closely aligned with planted acres, produced using the CDL are not simple pixel counting. It is more of an 'Adjusted Census by Satellite.'

SOFTWARE: ERDAS Imagine is used in the pre- and post- processing of all raster-based data. ESRI ArcGIS is used to prepare the vector-based training and validation data. Rulequest See5.0 is used to create a decision tree based classifier. The NLCD Mapping Tool is used to apply the See5.0 decision-tree via ERDAS Imagine.

DECISION TREE CLASSIFIER: This Cropland Data Layer used the decision tree classifier approach. Using a decision tree classifier is a departure from older versions of the CDL which were created using in-house software (Peditor) based upon a maximum likelihood classifier approach. Decision trees offer several advantages over the more traditional maximum likelihood classification method. The advantages include being: 1) non-parametric by nature and thus not reliant on the assumption of the input data being normally distributed, 2) efficient to construct and thus capable of handling large and complex data sets, 3) able to incorporate missing and non-continuous data, and 4) able to sort out non-linear relationships.

GROUND TRUTH: As with the maximum likelihood method, decision tree analysis is a supervised classification technique. Thus, it relies on having a sample of known ground truth areas in which to train the classifier. Older versions of the CDL (prior to 2006) utilized ground truth data from the annual June Agricultural Survey (JAS). Beginning in 2006, the CDL utilizes the very comprehensive ground truth data provided from the FSA Common Land Unit (CLU) Program as a replacement for the JAS data. The FSA CLU data have the advantage of natively being in a GIS and containing magnitudes more of field level information. Disadvantages include that it is not truly a probability sample of land cover and has bias toward subsidized program crops. Additional information about the FSA data can be found at <https://www.fsa.usda.gov/>. A modified version of the 2011 NLCD with pixels of change from the 2006 NLCD to the 2011 NLCD masked out was used as non-agricultural training and validation data.

INPUTS: The CDL is produced using satellite imagery from the Landsat 5 TM sensor and the Indian Remote Sensing RESOURCESAT-1 (IRS-P6) Advanced Wide Field Sensor (AWiFS) collected during the current growing season. The AWiFS imagery was resampled to 30 meters using cubic convolution, rigorous transformation to match the traditional Landsat spatial resolution. Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED) and the NLCD 2006 imperviousness layer and NLCD 2001 canopy data layer from the USGS National Land Cover Database. Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery and ancillary data used to generate this state's CDL.

ACCURACY: The accuracy of the land cover classifications are evaluated using independent validations data sets generated from the FSA CLU data (agricultural categories) and the NLCD (non-agricultural categories). The Producer's Accuracy is generally 85% to 95% correct for the major crop-specific land cover categories. See the 'Attribute Accuracy Report' section of this metadata file for the full accuracy report.

PUBLIC RELEASE: The USDA, NASS Cropland Data Layer is considered public domain and free to redistribute. The official website is <https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>. The data is available free for download through CropScape <https://nassgeodata.gmu.edu/CropScape/> and the Geospatial Data Gateway <https://datagateway.nrcs.usda.gov/>. See the 'Ordering Instructions' section of this metadata file for detailed download instructions. Please note that in no case are farmer reported data revealed or derivable from the public use Cropland Data Layer.

Process_Date: 2017
Process_Contact:

Contact_Information:

Contact_Organization_Primary:

Contact_Organization: USDA, NASS, Spatial Analysis Research Section
Contact_Person: USDA, NASS, Spatial Analysis Research Section staff

Contact_Address:

Contact_Voice_Telephone: 800-727-9540
Contact_Facsimile_Telephone: 855-493-0447
Contact_Electronic_Mail_Address: SM.NASS.RDD.GIB@usda.gov

Cloud_Cover: 0

Spatial_Data_Organization_Information:

Indirect_Spatial_Reference: North Carolina
Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:

Raster_Object_Type: Pixel
Row_Count: 10299

Column_Count: 26848

Spatial_Reference_Information:

Horizontal_Coordinate_System_Definition:

Planar:

Map_Projection:

Map_Projection_Name:

Albers Conical Equal Area as used by mrlc.gov (NLCD). The official Cropland Data Layer available at <https://nassgeodata.gmu.edu/CropScape/> includes the data in its native Albers Conical Equal Area coordinate system. FOR GEOSPATIAL DATA GATEWAY USERS: Universal Transverse Mercator (UTM), Spheriod WGS84, Datum WGS84. Due to technical restrictions, the online data available free for download through the Geospatial Data Gateway <https://datagateway.nrcs.usda.gov/> can only be offered in UTM. The UTM Zones are as follows: Zone 11 - California, Idaho, Nevada, Oregon, Washington; Zone 12 - Arizona, Montana, Utah; Zone 13 - Colorado, New Mexico, Wyoming; Zone 14 - Kansas, North Dakota, Nebraska, Oklahoma, South Dakota, Texas; Zone 15 - Arkansas, Iowa, Louisiana, Minnesota, Missouri; Zone 16 - Alabama, Illinois, Indiana, Kentucky, Michigan, Mississippi, Tennessee; Zone 17 - Florida, Georgia, North Carolina, Ohio, South Carolina, Virginia, West Virginia; Zone 18 - Connecticut, Delaware, Maryland, New Jersey, New York, Pennsylvania, Vermont; Zone 19 - Maine, Massachusetts, New Hampshire, Rhode Island.

Albers_Conical_Equal_Area:

Standard_Parallel: 29.500000
Standard_Parallel: 45.500000
Longitude_of_Central_Meridian: -96.000000
Latitude_of_Projection_Origin: 23.000000
False_Easting: 0.000000
False_Northing: 0.000000

Planar_Coordinate_Information:

Planar_Coordinate_Encoding_Method: row and column
Coordinate_Representation:

Abscissa_Resolution: 30
Ordinate_Resolution: 30

Planar_Distance_Units: meters

Geodetic_Model:

Horizontal_Datum_Name: North American Datum of 1983
Ellipsoid_Name: Geodetic Reference System 80
Semi-major_Axis: 6378137.000000

Denominator_of_Flattening_Ratio: 298.257223563

Entity_and_Attribute_Information:

Overview_Description:

Entity_and_Attribute_Overview:

The Cropland Data Layer (CDL) is produced using agricultural training data from the Farm Service Agency (FSA) Common Land Unit (CLU) Program and non-agricultural training data from the most current version of the United States Geological Survey (USGS) National Land Cover Database (NLCD). The strength and emphasis of the CDL is crop-specific land cover categories. The accuracy of the CDL non-agricultural land cover classes are entirely dependent upon the NLCD. Thus, the USDA, NASS recommends that users consider the NLCD for studies involving non-agricultural land cover.

Entity_and_Attribute_Detail_Citation:

 Data Dictionary: USDA, National Agricultural Statistics Service, 2009 Cropland Data Layer

 Source: USDA, National Agricultural Statistics Service

 The following is a cross reference list of the categorization codes and land covers.
 Note that not all land cover categories listed below will appear in an individual state.

 Raster
 Attribute Domain Values and Definitions: NO DATA, BACKGROUND 0

 Categorization Code   Land Cover
         "0"       Background

 Raster
 Attribute Domain Values and Definitions: CROPS 1-20

 Categorization Code   Land Cover
           "1"       Corn
           "2"       Cotton
           "3"       Rice
           "4"       Sorghum
           "5"       Soybeans
           "6"       Sunflower
          "10"       Peanuts
          "11"       Tobacco
          "12"       Sweet Corn
          "13"       Pop or Orn Corn
          "14"       Mint

 Raster
 Attribute Domain Values and Definitions: GRAINS,HAY,SEEDS 21-40

 Categorization Code   Land Cover
          "21"       Barley
          "22"       Durum Wheat
          "23"       Spring Wheat
          "24"       Winter Wheat
          "25"       Other Small Grains
          "26"       Dbl Crop WinWht/Soybeans
          "27"       Rye
          "28"       Oats
          "29"       Millet
          "30"       Speltz
          "31"       Canola
          "32"       Flaxseed
          "33"       Safflower
          "34"       Rape Seed
          "35"       Mustard
          "36"       Alfalfa
          "37"       Other Hay/Non Alfalfa
          "38"       Camelina
          "39"       Buckwheat

 Raster
 Attribute Domain Values and Definitions: CROPS 41-60

 Categorization Code   Land Cover
          "41"       Sugarbeets
          "42"       Dry Beans
          "43"       Potatoes
          "44"       Other Crops
          "45"       Sugarcane
          "46"       Sweet Potatoes
          "47"       Misc Vegs & Fruits
          "48"       Watermelons
          "49"       Onions
          "50"       Cucumbers
          "51"       Chick Peas
          "52"       Lentils
          "53"       Peas
          "54"       Tomatoes
          "55"       Caneberries
          "56"       Hops
          "57"       Herbs
          "58"       Clover/Wildflowers
          "59"       Sod/Grass Seed
          "60"       Switchgrass

 Raster
 Attribute Domain Values and Definitions: NON-CROP 61-65

 Categorization Code   Land Cover
          "61"       Fallow/Idle Cropland
          "63"       Forest
          "64"       Shrubland
          "65"       Barren

 Raster
 Attribute Domain Values and Definitions: CROPS 66-80

 Categorization Code   Land Cover
          "66"       Cherries
          "67"       Peaches
          "68"       Apples
          "69"       Grapes
          "70"       Christmas Trees
          "71"       Other Tree Crops
          "72"       Citrus
          "74"       Pecans
          "75"       Almonds
          "76"       Walnuts
          "77"       Pears

 Raster
 Attribute Domain Values and Definitions: OTHER 81-109

 Categorization Code   Land Cover
          "81"       Clouds/No Data
          "82"       Developed
          "83"       Water
          "87"       Wetlands
          "88"       Nonag/Undefined
          "92"       Aquaculture

 Raster
 Attribute Domain Values and Definitions: NLCD-DERIVED CLASSES 110-195

 Categorization Code   Land Cover
         "111"       Open Water
         "112"       Perennial Ice/Snow
         "121"       Developed/Open Space
         "122"       Developed/Low Intensity
         "123"       Developed/Med Intensity
         "124"       Developed/High Intensity
         "131"       Barren
         "141"       Deciduous Forest
         "142"       Evergreen Forest
         "143"       Mixed Forest
         "152"       Shrubland
         "176"       Grassland/Pasture
         "190"       Woody Wetlands
         "195"       Herbaceous Wetlands

 Raster
 Attribute Domain Values and Definitions: CROPS 195-255

 Categorization Code   Land Cover
         "204"       Pistachios
         "205"       Triticale
         "206"       Carrots
         "207"       Asparagus
         "208"       Garlic
         "209"       Cantaloupes
         "210"       Prunes
         "211"       Olives
         "212"       Oranges
         "213"       Honeydew Melons
         "214"       Broccoli
         "215"       Avocados
         "216"       Peppers
         "217"       Pomegranates
         "218"       Nectarines
         "219"       Greens
         "220"       Plums
         "221"       Strawberries
         "222"       Squash
         "223"       Apricots
         "224"       Vetch
         "225"       Dbl Crop WinWht/Corn
         "226"       Dbl Crop Oats/Corn
         "227"       Lettuce
         "229"       Pumpkins
         "230"       Dbl Crop Lettuce/Durum Wht
         "231"       Dbl Crop Lettuce/Cantaloupe
         "232"       Dbl Crop Lettuce/Cotton
         "233"       Dbl Crop Lettuce/Barley
         "234"       Dbl Crop Durum Wht/Sorghum
         "235"       Dbl Crop Barley/Sorghum
         "236"       Dbl Crop WinWht/Sorghum
         "237"       Dbl Crop Barley/Corn
         "238"       Dbl Crop WinWht/Cotton
         "239"       Dbl Crop Soybeans/Cotton
         "240"       Dbl Crop Soybeans/Oats
         "241"       Dbl Crop Corn/Soybeans
         "242"       Blueberries
         "243"       Cabbage
         "244"       Cauliflower
         "245"       Celery
         "246"       Radishes
         "247"       Turnips
         "248"       Eggplants
         "249"       Gourds
         "250"       Cranberries
         "254"       Dbl Crop Barley/Soybeans

2009 North Carolina Cropland Data Layer | NASS/USDA

Metadata: