USDA, National Agricultural Statistics Service, 2012 North Carolina Cropland Data Layer

Metadata:

Identification_Information
Data_Quality_Information
Spatial_Data_Organization_Information
Spatial_Reference_Information
Entity_and_Attribute_Information
Distribution_Information
Metadata_Reference_Information

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: 20130131

Title:

USDA, National Agricultural Statistics Service, 2012 North Carolina Cropland Data Layer

Edition: 2012 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 <http://www.nass.usda.gov/research/Cropland/SARS1a.htm>. The data is available free for download through CropScape at <http://nassgeodata.gmu.edu/CropScape/>. The data is also available free for download through the Geospatial Data Gateway at <http://datagateway.nrcs.usda.gov/>. See the 'Ordering Instructions' section of this metadata file for detailed Geospatial Data Gateway download instructions.

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

Description:

Abstract:

The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2012 CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 5 TM sensor, Landsat 7 ETM+ sensor, and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors 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), the imperviousness and canopy data layers from the USGS National Land Cover Database 2006 (NLCD 2006), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites.
Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The NLCD 2006 is 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 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 <http://www.nass.usda.gov/research/Cropland/metadata/meta.htm>.

USDA, National Agricultural Statistics Service 2012 North Carolina Cropland Data Layer

CLASSIFICATION INPUTS:
DEIMOS-1 DATE 20111002 PATH/ROW 564
DEIMOS-1 DATE 20111005 PATH/ROW 58F
DEIMOS-1 DATE 20111021 PATH/ROW 68D
DEIMOS-1 DATE 20120424 PATH/ROW EE5
DEIMOS-1 DATE 20120503 PATH/ROW F40
DEIMOS-1 DATE 20120526 PATH/ROW 03C
DEIMOS-1 DATE 20120617 PATH/ROW 12A
DEIMOS-1 DATE 20120623 PATH/ROW 16E
DEIMOS-1 DATE 20120627 PATH/ROW 1A2
DEIMOS-1 DATE 20120630 PATH/ROW 1CA
DEIMOS-1 DATE 20120707 PATH/ROW 210
DEIMOS-1 DATE 20120726 PATH/ROW 2F5
DEIMOS-1 DATE 20120814 PATH/ROW 3EA
DEIMOS-1 DATE 20120817 PATH/ROW 410
DEIMOS-1 DATE 20120902 PATH/ROW 4DF
DEIMOS-1 DATE 20120912 PATH/ROW 55B
DEIMOS-1 DATE 20120921 PATH/ROW 5C7
DEIMOS-1 DATE 20120922 PATH/ROW 5D7

UK-DMC-2 20110911 PATH/ROW 222
UK-DMC-2 20111006 PATH/ROW 309
UK-DMC-2 20120521 PATH/ROW D7F
UK-DMC-2 20120602 PATH/ROW E45
UK-DMC-2 20120608 PATH/ROW EA3
UK-DMC-2 20120622 PATH/ROW F78
UK-DMC-2 20120708 PATH/ROW 0A0
UK-DMC-2 20120714 PATH/ROW 119
UK-DMC-2 20120717 PATH/ROW 150
UK-DMC-2 20120802 PATH/ROW 298
UK-DMC-2 20120825 PATH/ROW 4B2
UK-DMC-2 20120831 PATH/ROW 51F
UK-DMC-2 20120923 PATH/ROW 6FF

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

TRAINING AND VALIDATION:
USDA, FARM SERVICE AGENCY 2012 COMMON LAND UNIT DATA
USGS, NATIONAL LAND COVER DATASET 2006

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: 20120101
Ending_Date: 20121231

Currentness_Reference: 2012 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: Global Change Master Directory (GCMD) Instrument Keywords
Theme_Keyword: MODIS > Moderate-Resolution Imaging Spectroradiometer

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: MODIS
Theme_Keyword: DEIMOS-1
Theme_Keyword: UK-DMC 2
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: 2012

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 <http://nassgeodata.gmu.edu/CropScape/> or the freeware browser ESRI ArcGIS Explorer <http://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: 3251 Old Lee Highway, Room 305
City: Fairfax
State_or_Province: Virginia
Postal_Code: 22030-1504
Country: USA

Contact_Voice_Telephone: 703-877-8000

Contact_Facsimile_Telephone: 703-877-8044

Contact_Electronic_Mail_Address: HQ_RDD_GIB@nass.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 XP; ERDAS Imagine Versions 9.1 and 2011 <http://www.erdas.com/>; ESRI ArcGIS Version 10.0 <http://www.esri.com/>; Rulequest See5.0 Release 2.09 <http://www.rulequest.com/>; NLCD Mapping Tool <http://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 <http://www.nass.usda.gov/research/Cropland/metadata/meta.htm> to view the original metadata file.

USDA, National Agricultural Statistics Service, 2012 North Carolina Cropland Data Layer
STATEWIDE AGRICULTURAL ACCURACY REPORT

Crop-specific covers only  *Correct  Accuracy   Error   Kappa
-------------------------   -------  --------  ------   -----
OVERALL ACCURACY**        1,217,835     83.0%   17.0%   0.794


Cover                    Attribute  *Correct  Producer's  Omission            User's  Commission  Cond'l
Type                          Code    Pixels   Accuracy     Error   Kappa   Accuracy      Error    Kappa
----                          ----    ------   --------     -----   -----   --------      -----    -----
Corn                             1    311345     95.71%     4.29%    0.95     94.52%      5.48%     0.94
Cotton                           2    235354     91.22%     8.78%    0.90     88.85%     11.15%     0.87
Sorghum                          4     10431     50.10%    49.90%    0.50     74.83%     25.17%     0.75
Soybeans                         5    258679     82.85%    17.15%    0.80     82.35%     17.65%     0.80
Sunflower                        6        33     10.19%    89.81%    0.10     19.53%     80.47%     0.20
Peanuts                         10     35562     73.31%    26.69%    0.73     89.57%     10.43%     0.89
Tobacco                         11      2675     65.95%    34.05%    0.66     80.69%     19.31%     0.81
Sweet Corn                      12        34     10.73%    89.27%    0.11     50.00%     50.00%     0.50
Pop or Orn Corn                 13        90     81.08%    18.92%    0.81     94.74%      5.26%     0.95
Barley                          21        85     14.36%    85.64%    0.14     44.74%     55.26%     0.45
Spring Wheat                    23         0      0.00%   100.00%    0.00       n/a        n/a      n/a
Winter Wheat                    24      7939     39.68%    60.32%    0.39     73.84%     26.16%     0.74
Dbl Crop WinWht/Soybeans        26    277106     93.16%     6.84%    0.92     86.07%     13.93%     0.84
Rye                             27       171     11.92%    88.08%    0.12     46.59%     53.41%     0.47
Oats                            28        83      6.16%    93.84%    0.06     30.63%     69.37%     0.31
Millet                          29       443     18.10%    81.90%    0.18     59.62%     40.38%     0.60
Canola                          31         0       n/a       n/a     n/a       0.00%    100.00%     0.00
Rape Seed                       34        22     29.73%    70.27%    0.30     73.33%     26.67%     0.73
Alfalfa                         36         6      1.38%    98.62%    0.01     40.00%     60.00%     0.40
Other Hay/Non Alfalfa           37     40347     70.93%    29.07%    0.70     65.70%     34.30%     0.65
Buckwheat                       39         0      0.00%   100.00%    0.00       n/a        n/a      n/a
Dry Beans                       42        77     23.91%    76.09%    0.24     87.50%     12.50%     0.87
Potatoes                        43        14     12.17%    87.83%    0.12     77.78%     22.22%     0.78
Other Crops                     44       291     25.46%    74.54%    0.25     58.55%     41.45%     0.59
Sugarcane                       45         0      0.00%   100.00%    0.00       n/a        n/a      n/a
Sweet Potatoes                  46      4706     75.39%    24.61%    0.75     88.13%     11.87%     0.88
Misc Vegs & Fruits              47         0      0.00%   100.00%    0.00       n/a        n/a      n/a
Watermelons                     48       199     29.92%    70.08%    0.30     73.98%     26.02%     0.74
Onions                          49         0      0.00%   100.00%    0.00       n/a        n/a      n/a
Cucumbers                       50        24      9.84%    90.16%    0.10     63.16%     36.84%     0.63
Peas                            53         0      0.00%   100.00%    0.00      0.00%    100.00%     0.00
Tomatoes                        54        61     46.92%    53.08%    0.47     64.21%     35.79%     0.64
Caneberries                     55         0      0.00%   100.00%    0.00      0.00%    100.00%     0.00
Herbs                           57      1033     72.54%    27.46%    0.73     89.59%     10.41%     0.90
Clover/Wildflowers              58         0      0.00%   100.00%    0.00      0.00%    100.00%     0.00
Sod/Grass Seed                  59      5321     52.67%    47.33%    0.53     72.58%     27.42%     0.72
Switchgrass                     60         0      0.00%   100.00%    0.00       n/a        n/a      n/a
Fallow/Idle Cropland            61     13902     28.46%    71.54%    0.27     46.82%     53.18%     0.46
Peaches                         67        55     30.90%    69.10%    0.31     59.14%     40.86%     0.59
Apples                          68       755     73.30%    26.70%    0.73     86.58%     13.42%     0.87
Grapes                          69         3      1.96%    98.04%    0.02     18.75%     81.25%     0.19
Christmas Trees                 70       405     56.09%    43.91%    0.56     93.32%      6.68%     0.93
Pecans                          74         0      0.00%   100.00%    0.00      0.00%    100.00%     0.00
Aquaculture                     92       112     44.62%    55.38%    0.45     75.68%     24.32%     0.76
Triticale                      205       349     81.35%    18.65%    0.81     92.33%      7.67%     0.92
Carrots                        206         0       n/a       n/a     n/a       0.00%    100.00%     0.00
Asparagus                      207         0      0.00%   100.00%    0.00       n/a        n/a      n/a
Cantaloupes                    209         0      0.00%   100.00%    0.00       n/a        n/a      n/a
Peppers                        216        20     19.05%    80.95%    0.19     90.91%      9.09%     0.91
Greens                         219        32     17.88%    82.12%    0.18     64.00%     36.00%     0.64
Strawberries                   221         3      1.49%    98.51%    0.01     30.00%     70.00%     0.30
Squash                         222        38     18.72%    81.28%    0.19     60.32%     39.68%     0.60
Dbl Crop WinWht/Corn           225      1985     45.12%    54.88%    0.45     80.95%     19.05%     0.81
Dbl Crop Oats/Corn             226        34     10.93%    89.07%    0.11     38.20%     61.80%     0.38
Pumpkins                       229         0      0.00%   100.00%    0.00      0.00%    100.00%     0.00
Dbl Crop Barley/Sorghum        235        14      8.43%    91.57%    0.08     70.00%     30.00%     0.70
Dbl Crop WinWht/Sorghum        236      2830     44.52%    55.48%    0.44     75.99%     24.01%     0.76
Dbl Crop Barley/Corn           237      1547     50.02%    49.98%    0.50     76.02%     23.98%     0.76
Dbl Crop WinWht/Cotton         238       689     70.31%    29.69%    0.70     90.07%      9.93%     0.90
Dbl Crop Soybeans/Cotton       239       427     64.80%    35.20%    0.65     99.30%      0.70%     0.99
Dbl Crop Soybeans/Oats         240       461     12.08%    87.92%    0.12     58.88%     41.12%     0.59
Dbl Crop Corn/Soybeans         241         0      0.00%   100.00%    0.00      0.00%    100.00%     0.00
Blueberries                    242        48     12.73%    87.27%    0.13     60.76%     39.24%     0.61
Cabbage                        243        71     61.21%    38.79%    0.61     63.96%     36.04%     0.64
Radishes                       246         0       n/a       n/a     n/a       0.00%    100.00%     0.00
Gourds                         249         0      0.00%   100.00%    0.00       n/a        n/a      n/a
Dbl Crop Barley/Soybeans       254      2036     38.64%    61.36%    0.39     71.31%     28.69%     0.71

*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 and 200-255).
FSA-sampled grass and pasture, aquaculture, and all NLCD-sampled categories (codes 62-65 and 81-199) 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 2006). 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 <http://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 2006). 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 2006 (NLCD 2006). More information about the FSA CLU Program can be found at <http://www.fsa.usda.gov/>. More information about the NLCD 2006 can be found at <http://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 5 TM and Landsat 7 ETM imagery was obtained via download from the USGS Global Visualization Viewer (Glovis) website <http://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 DEIMOS-1 and DMC-UK 2 imagery used in the production of the Cropland Data Layer is orthorectified to a radial root mean square error (RMSE) of approximately 10 meters.

Lineage:

Source_Information:

Source_Citation:

Citation_Information:

Originator: Elecnor Deimos Imaging

Title: DEIMOS-1

Geospatial_Data_Presentation_Form: remote-sensing image

Publication_Information:

Publisher: Astrium GEO Information Services
Publication_Place: Elecnor Deimos Imaging, Valladolid, Spain

Publication_Date: 2012 growing season

Other_Citation_Details:

The DEIMOS-1 satellite sensor operates in three spectral bands at a spatial resolution of 22 meters. Additional information about DEIMOS-1 data can be obtained at <http://www.deimos-imaging.com/>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs.
For the 2012 CDL Program, the DEIMOS-1 imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used cubic convolution, rigorous transformation.

Source_Scale_Denominator: 22 meter

Type_of_Source_Media: online download

Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20111001
Ending_Date: 20121231

Source_Currentness_Reference: ground condition

Source_Contribution: Raw data used in land cover spectral signature analysis

Source_Information:

Source_Citation:

Citation_Information:

Originator: DMC International Imaging

Title: UK-DMC 2

Geospatial_Data_Presentation_Form: remote-sensing image

Publication_Information:

Publisher: Astrium GEO Information Services
Publication_Place: DMC International Imaging, Guildford, Surrey UK

Publication_Date: 2012 growing season

Other_Citation_Details:

The UK-DMC 2 satellite sensor operates in three spectral bands at a spatial resolution of 22 meters. Additional information about UK-DMC 2 data can be obtained at <http://www.dmcii.com/>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path, row and quadrants used as classification inputs.
For the 2012 CDL Program, the UK-DMC 2 imagery was resampled to 30 meters to match the Landsat spatial resolution. The resample used cubic convolution, rigorous transformation.

Source_Scale_Denominator: 22 meter

Type_of_Source_Media: online download

Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20111001
Ending_Date: 20121231

Source_Currentness_Reference: ground condition

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) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+)

Geospatial_Data_Presentation_Form: remote-sensing image

Publication_Information:

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

Publication_Date: 2012 growing season

Other_Citation_Details:

The Landsat 5 TM and Landsat 7 ETM+ data is free for download through the following website <http://glovis.usgs.gov/>. Additional information about Landsat data can be obtained at <http://eros.usgs.gov/>. 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: 20111001
Ending_Date: 20121230

Source_Currentness_Reference: ground condition

Source_Contribution: Raw data used in land cover spectral signature analysis

Source_Information:

Source_Citation:

Citation_Information:

Originator:

United States Geological Survey (USGS) and National Aeronautics and Space Administration (NASA) Land Processes Distributed Active Archive Center (LP DAAC)

Title:

Moderate Resolution Imaging Spectroradiometer (MODIS), 250 meter resolution 16-day composite Normalized Difference Vegetation Index (NDVI) from the Terra satellite (MOD13Q1v4)

Geospatial_Data_Presentation_Form: vegetation indices based on remote-sensing imagery

Publication_Information:

Publisher: USGS Center for Earth Resources Observation and Sciences (EROS)
Publication_Place: Sioux Falls, South Dakota 57198 USA

Publication_Date: late 2011 growing season and the entire 2012 growing season

Other_Citation_Details:

The Moderate Resolution Imaging Spectroradiometer (MODIS), 250 meter resolution 16-day composite Normalized Difference Vegetation Index (NDVI) data products from the Terra satellite (MOD13Q1v4) are downloaded from <https://lpdaac.usgs.gov/>. Often late-season MODIS NDVI data are used from the previous growing season in an effort to improve winter wheat detection. Refer to the 'Supplemental Information' Section of this metadata file for specific dates used as classification inputs.

Source_Scale_Denominator: 250 meter

Type_of_Source_Media: online download

Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20111001
Ending_Date: 20120930

Source_Currentness_Reference: ground condition

Source_Contribution: NDVI 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: Continuously updated

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. Slope and Aspect derived from the DEM are also used as additional classification inputs. More information on the USGS NED can be found at <http://ned.usgs.gov/>. 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_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 2001 (NLCD 2006)

Geospatial_Data_Presentation_Form: remote-sensing image

Publication_Information:

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

Publication_Date: 2006

Other_Citation_Details:

The NLCD 2006 was used as ground training and validation for non-agricultural categories. Additionally, the USGS NLCD 2006 Imperviousness and NLCD 2001 Tree Canopy layers were used as ancillary data sources in the Cropland Data Layer classification process. More information on the NLCD 2006 can be found at <http://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., 2011. 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_Contribution:

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

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: 2012

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 <http://www.fsa.usda.gov/>.

Source_Scale_Denominator: 1:4800

Type_of_Source_Media: online

Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: unknown

Source_Currentness_Reference: ground condition, updated annually

Source_Contribution:

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

Process_Step:

Process_Description:

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 reproduced 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 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. Check the 'Process Description' section of the specific state and year metadata file to verify the methodology used. 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 <http://www.fsa.usda.gov/>. The NLCD 2006 is used as non-agricultural training and validation data.

INPUTS: The CDL is produced using satellite imagery from the Landsat 5 TM sensor, Landsat 7 ETM+ sensor, and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors collected during the current growing season. The DEIMOS-1 and UK-DMC 2 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), the imperviousness and canopy data layers from the USGS National Land Cover Database 2006 (NLCD 2006), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites. 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 2006 (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 <http://www.nass.usda.gov/research/Cropland/SARS1a.htm>. The data is available free for download through CropScape <http://nassgeodata.gmu.edu/CropScape/> and the Geospatial Data Gateway <http://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: 2012

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:

Address_Type: mailing and physical address
Address: 3251 Old Lee Highway, Room 305
City: Fairfax
State_or_Province: Virginia
Postal_Code: 22030-1504
Country: USA

Contact_Voice_Telephone: 703-877-8000

Contact_Facsimile_Telephone: 703-877-8044

Contact_Electronic_Mail_Address: HQ_RDD_GIB@nass.usda.gov

Cloud_Cover:

Generally, there is enough cloud-free satellite imagery available during the growing season that there will be no cloud cover in the published CDL. Older versions of the CDL (prior to 2006) may contain significant cloud cover due to available imagery and processing limitations, which have since been overcome. Reference the attribute information within the specific CDL state and year image file to verify the extent of cloud cover.

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:

Grid_Coordinate_System:

Grid_Coordinate_System_Name:

FOR GEOSPATIAL DATA GATEWAY USERS: Universal Transverse Mercator (UTM) Due to technical restrictions, the online data available free for download through the Geospatial Data Gateway <http://datagateway.nrcs.usda.gov/> can only be offered in UTM. However, the official Cropland Data Layer available at <http://nassgeodata.gmu.edu/CropScape/> includes the data in its native Albers Conical Equal Area coordinate system.

FOR CROPSCAPE USERS: Albers Conical Equal Area is the native projection used in the production of the Cropland Data Layer. The projection parameters for the Albers projection are as follows:

Map_Projection_Name: Albers Conical Equal Area
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

Universal_Transverse_Mercator:

UTM_Zone_Number: 17 North

Transverse_Mercator:

Scale_Factor_at_Central_Meridian: 0.9996
Longitude_of_Central_Meridian: -81
Latitude_of_Projection_Origin: 0
False_Easting: 500000
False_Northing: 0

Planar_Coordinate_Information:

Planar_Coordinate_Encoding_Method: coordinate pair

Coordinate_Representation:

Abscissa_Resolution: 30
Ordinate_Resolution: 30

Planar_Distance_Units: meters

Geodetic_Model:

Horizontal_Datum_Name: WGS84
Ellipsoid_Name: WGS84
Semi-major_Axis: 6378137.00
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 United States Geological Survey (USGS) National Land Cover Database 2006 (NLCD 2006). 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:

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

 Data Dictionary: USDA, National Agricultural Statistics Service, 2012 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
          "62"       Pasture/Grass
          "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
         "171"       Grassland Herbaceous
         "181"       Pasture/Hay
         "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
         "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

Distribution_Information:

Distributor:

Contact_Information:

Contact_Organization_Primary:

Contact_Organization: USDA, NASS Customer Service
Contact_Person: USDA, NASS Customer Service Staff

Contact_Address:

Address_Type: mailing and physical address
Address: 1400 Independence Avenue, SW, Room 5038-S
City: Washington
State_or_Province: District of Columbia
Postal_Code: 20250-9410
Country: USA

Contact_Voice_Telephone: 800-727-9540

Contact_Facsimile_Telephone: 703-877-8044

Contact_Electronic_Mail_Address: HQ_RDD_GIB@nass.usda.gov

Contact_Instructions:

Please visit the official website <http://www.nass.usda.gov/research/Cropland/SARS1a.htm> for distribution details. The Cropland Data Layer is available free for download at <http://nassgeodata.gmu.edu/CropScape/> and <http://datagateway.nrcs.usda.gov/>. Distribution issues can also be directed to the NASS Customer Service Hotline at 1-800-727-9540.

Resource_Description: Cropland Data Layer - North Carolina 2012

Distribution_Liability:

Disclaimer: Users of the Cropland Data Layer (CDL) are solely responsible for interpretations made from these products. The CDL is provided 'as is' and the USDA, NASS does not warrant results you may obtain using the Cropland Data Layer. Contact our staff at (HQ_RDD_GIB@nass.usda.gov) if technical questions arise in the use of the CDL. NASS does maintain a Frequently Asked Questions (FAQ's) section on the CDL website at <http://www.nass.usda.gov/research/Cropland/SARS1a.htm>.

Standard_Order_Process:

Digital_Form:

Digital_Transfer_Information:

Format_Name: GEOTIFF
Format_Version_Date: North Carolina 2012
Format_Information_Content: GEOTIFF
Transfer_Size:: The image file size will vary depending on the state and completeness of coverage. The user can specify the state, or a user-defined area of interest, and year(s) of CDL data to download at the Cropscape website <http://nassgeodata.gmu.edu/CropScape/>.
When downloading the data through the Geospatial Data Gateway <http://datagateway.nrcs.usda.gov/> all available years of CDL production for the requested state are included in one compressed file. See the 'Ordering Instructions' section of this metadata file for additional information.

Digital_Transfer_Option:

Online_Option:

Computer_Contact_Information:

Network_Address:

Network_Resource_Name: <http://nassgeodata.gmu.edu/CropScape/>

Access_Instructions:

The CDL is available online and free for download from the Cropscape website <http://nassgeodata.gmu.edu/CropScape/>. It is also available free for download from the Geospatial Data Gateway website <http://datagateway.nrcs.usda.gov/>. See the 'Ordering Instructions' section of this metadata file for detailed Geospatial Data Gateway download instructions.

Fees:

Ordering_Instructions:

The CDL is available online and free for download from the Cropscape website <http://nassgeodata.gmu.edu/CropScape/>. The Cropland Data Layer is also available free for download from the NRCS Geospatial Data Gateway at <http://datagateway.nrcs.usda.gov/>.

IMPORTANT NOTE: When downloading the CDL using the NRCS Geospatial Data Gateway all available years of CDL production for the requested state are included in a single compressed file.

Instructions for downloading from the NRCS Geospatial Data Gateway:

Start by clicking on 'Get Data'

Click on 'Select State'

Select any county and then click 'Submit Selected Counties'

Choose 'Land Use Land Cover' and select 'Cropland Data Layer by State' and 'Continue'

Choose 'FTP' and then 'Continue'

Fill out the required user information and then you are given the option to download the data for free.

Custom_Order_Process:

For a list of other states and years of available CDL data please visit <http://nassgeodata.gmu.edu/CropScape/> or <http://www.nass.usda.gov/research/Cropland/SARS1a.htm>. Distribution issues can also be directed to the NASS Customer Service Hotline at 1-800-727-9540.

Technical_Prerequisites:

If the user does not have software capable of viewing GEOTIF (.tif) or ERDAS Imagine (.img) file formats then we suggest using the Cropscape website <http://nassgeodata.gmu.edu/CropScape/> or using the freeware browser ESRI ArcGIS Explorer <http://www.esri.com/>.

Metadata_Reference_Information:

Metadata_Date: 20130131

Metadata_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: 3251 Old Lee Highway, Room 305
City: Fairfax
State_or_Province: Virginia
Postal_Code: 22030-1504
Country: USA

Contact_Voice_Telephone: 703-877-8000

Contact_Facsimile_Telephone: 703-877-8044

Contact_Electronic_Mail_Address: HQ_RDD_GIB@nass.usda.gov

Metadata_Standard_Name: FGDC Content Standards for Digital Geospatial Metadata

Metadata_Standard_Version: FGDC-STD-001-1998

Metadata_Access_Constraints: No restrictions on the distribution or use of the metadata file

Metadata_Use_Constraints: No restrictions on the distribution or use of the metadata file

Generated by mp version 2.9.12 on Tue Jan 15 15:51:19 2013