2015 South Dakota Cropland Data Layer | NASS/USDA

Metadata:

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: 20160212
Title: 2015 South Dakota Cropland Data Layer | NASS/USDA
Edition: 2015 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_and_Science/Cropland/SARS1a.php>. 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/SD>
Description:
Abstract:
The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2015 CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS 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) and the imperviousness and canopy data layers from the USGS National Land Cover Database 2011 (NLCD 2011).
Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The most current version of the NLCD 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_and_Science/Cropland/metadata/meta.php>.
USDA, National Agricultural Statistics Service, 2015 South Dakota Cropland Data Layer

CLASSIFICATION INPUTS:
DEIMOS-1 DATE 20150502 PATH/ROW 85A
DEIMOS-1 DATE 20150503 PATH/ROW 866
DEIMOS-1 DATE 20150608 PATH/ROW A49
DEIMOS-1 DATE 20150612 PATH/ROW A7D
DEIMOS-1 DATE 20150713 PATH/ROW C42
DEIMOS-1 DATE 20150719 PATH/ROW C9D
DEIMOS-1 DATE 20150720 PATH/ROW CAD
DEIMOS-1 DATE 20150723 PATH/ROW CD4
DEIMOS-1 DATE 20150729 PATH/ROW D2E
DEIMOS-1 DATE 20150730 PATH/ROW D3E
DEIMOS-1 DATE 20150812 PATH/ROW DFE
DEIMOS-1 DATE 20150815 PATH/ROW E2D
DEIMOS-1 DATE 20150912 PATH/ROW FBC
DEIMOS-1 DATE 20150919 PATH/ROW 023
DEIMOS-1 DATE 20151016 PATH/ROW 187

LANDSAT 8 OLI/TIRS DATE 20150427 PATH 031 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20150518 PATH 034 ROW(S) 26-38
LANDSAT 8 OLI/TIRS DATE 20150621 PATH 032 ROW(S) 26-39
LANDSAT 8 OLI/TIRS DATE 20150628 PATH 033 ROW(S) 26-38
LANDSAT 8 OLI/TIRS DATE 20150709 PATH 030 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20150716 PATH 031 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20150718 PATH 029 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20150723 PATH 032 ROW(S) 26-39
LANDSAT 8 OLI/TIRS DATE 20150730 PATH 033 ROW(S) 26-38
LANDSAT 8 OLI/TIRS DATE 20150801 PATH 031 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20150803 PATH 029 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20150815 PATH 033 ROW(S) 26-38
LANDSAT 8 OLI/TIRS DATE 20150824 PATH 032 ROW(S) 26-39
LANDSAT 8 OLI/TIRS DATE 20150826 PATH 030 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20150907 PATH 034 ROW(S) 26-38
LANDSAT 8 OLI/TIRS DATE 20150920 PATH 029 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20151013 PATH 030 ROW(S) 26-40
LANDSAT 8 OLI/TIRS DATE 20151025 PATH 034 ROW(S) 26-38

USGS, NATIONAL ELEVATION DATASET
USGS, NATIONAL LAND COVER DATASET 2011 IMPERVIOUSNESS
USGS, NATIONAL LAND COVER DATASET 2011 TREE CANOPY
USDA, NASS AG MASK BASED ON 2010-2014 CDLS (INTERNAL USE DATA LAYER)

UK-DMC-2 DATE 20150518 PATH/ROW 408
UK-DMC-2 DATE 20150527 PATH/ROW 455
UK-DMC-2 DATE 20150609 PATH/ROW 4D4
UK-DMC-2 DATE 20150613 PATH/ROW 501
UK-DMC-2 DATE 20150713 PATH/ROW 631
UK-DMC-2 DATE 20150719 PATH/ROW 669
UK-DMC-2 DATE 20150811 PATH/ROW 735
UK-DMC-2 DATE 20150903 PATH/ROW 7EC
UK-DMC-2 DATE 20150920 PATH/ROW 84F
UK-DMC-2 DATE 20150926 PATH/ROW 87A
UK-DMC-2 DATE 20151010 PATH/ROW 8DE
UK-DMC-2 DATE 20151013 PATH/ROW 8F1

TRAINING AND VALIDATION:
USDA, FARM SERVICE AGENCY 2015 COMMON LAND UNIT DATA
USGS, NATIONAL LAND COVER DATASET 2011

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: 20140101
Ending_Date: 20151231
Currentness_Reference: 2015 growing season
Status:
Progress: Complete
Maintenance_and_Update_Frequency: None planned
Spatial_Domain:
Bounding_Coordinates:
West_Bounding_Coordinate: -104.3132
East_Bounding_Coordinate: -96.4439
North_Bounding_Coordinate: 45.9330
South_Bounding_Coordinate: 42.4789
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: 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 > South Dakota
Place:
Place_Keyword_Thesaurus: None
Place_Keyword: South Dakota
Place_Keyword: SD
Temporal:
Temporal_Keyword_Thesaurus: None
Temporal_Keyword: 2015
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: 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: 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 2011 <http://www.hexagongeospatial.com/>; ESRI ArcGIS Version 10.3 <http://www.esri.com/>; Rulequest See5.0 Release 2.10 <http://www.rulequest.com/>; NLCD Mapping Tool v2.08 <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_and_Science/Cropland/metadata/meta.php> to view the original metadata file.
USDA, National Agricultural Statistics Service, 2015 South Dakota Cropland Data Layer
STATEWIDE AGRICULTURAL ACCURACY REPORT

Crop-specific covers only  *Correct  Accuracy   Error   Kappa
-------------------------   -------  --------  ------   -----
OVERALL ACCURACY**          531,726     88.3%   11.7%   0.854


Cover                    Attribute  *Correct  Producer's  Omission            User's  Commission  Cond'l
Type                          Code    Pixels   Accuracy     Error   Kappa   Accuracy      Error    Kappa
----                          ----    ------   --------     -----   -----   --------      -----    -----
Corn                             1    179192     97.24%     2.76%   0.966     97.24%      2.76%    0.966
Sorghum                          4      7234     72.23%    27.77%   0.720     81.63%     18.37%    0.814
Soybeans                         5    166721     97.55%     2.45%   0.970     97.61%      2.39%    0.971
Sunflower                        6     26945     93.66%     6.34%   0.935     92.69%      7.31%    0.925
Pop or Orn Corn                 13        94     54.97%    45.03%   0.550     94.00%      6.00%    0.940
Barley                          21       253     25.12%    74.88%   0.251     62.16%     37.84%    0.621
Durum Wheat                     22        30     25.21%    74.79%   0.252     75.00%     25.00%    0.750
Spring Wheat                    23     43568     90.27%     9.73%   0.898     87.78%     12.22%    0.872
Winter Wheat                    24     32771     85.92%    14.08%   0.854     87.53%     12.47%    0.870
Dbl Crop WinWht/Soybeans        26         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Rye                             27       296     41.81%    58.19%   0.418     73.82%     26.18%    0.738
Oats                            28      4358     50.38%    49.62%   0.501     67.79%     32.21%    0.675
Millet                          29      3553     62.51%    37.49%   0.623     73.61%     26.39%    0.735
Speltz                          30         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Canola                          31        36     48.00%    52.00%   0.480     78.26%     21.74%    0.783
Flaxseed                        32       336     53.42%    46.58%   0.534     84.63%     15.37%    0.846
Safflower                       33       244     50.94%    49.06%   0.509     75.54%     24.46%    0.755
Alfalfa                         36     16746     72.87%    27.13%   0.723     78.49%     21.51%    0.780
Other Hay/Non Alfalfa           37     41705     71.40%    28.60%   0.698     79.98%     20.02%    0.787
Buckwheat                       39         4     23.53%    76.47%   0.235     66.67%     33.33%    0.667
Dry Beans                       42       244     52.25%    47.75%   0.522     83.28%     16.72%    0.833
Other Crops                     44        14     19.44%    80.56%   0.194     50.00%     50.00%    0.500
Lentils                         52        80     75.47%    24.53%   0.755     48.19%     51.81%    0.482
Peas                            53       499     65.23%    34.77%   0.652     79.97%     20.03%    0.800
Clover/Wildflowers              58         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Sod/Grass Seed                  59        15     12.10%    87.90%   0.121     39.47%     60.53%    0.395
Switchgrass                     60         0      0.00%   100.00%   0.000      0.00%    100.00%    0.000
Fallow/Idle Cropland            61      6764     62.20%    37.80%   0.618     68.21%     31.79%    0.679
Grapes                          69         0       n/a       n/a     n/a       0.00%    100.00%    0.000
Triticale                      205        23     23.00%    77.00%   0.230     42.59%     57.41%    0.426
Vetch                          224         0       n/a       n/a     n/a       0.00%    100.00%    0.000
Dbl Crop WinWht/Corn           225         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Dbl Crop WinWht/Sorghum        236         0       n/a       n/a     n/a       0.00%    100.00%    0.000
Dbl Crop Soybeans/Oats         240         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Dbl Crop Corn/Soybeans         241         0      0.00%   100.00%   0.000       n/a        n/a      n/a
Radishes                       246         1    100.00%     0.00%   1.000     50.00%     50.00%    0.500
Turnips                        247         0      0.00%   100.00%   0.000       n/a        n/a      n/a

*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 2011). 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 2011). 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 2011 (NLCD 2011). More information about the FSA CLU Program can be found at <http://www.fsa.usda.gov/>. More information about the NLCD 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 8 OLI/TIRS 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: 2015
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.
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: 20141001
Ending_Date: 20151231
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: Deimos-1
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: 2015
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.
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: 20141001
Ending_Date: 20151231
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: UK-DMC 2
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 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS)
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publisher: USGS, EROS
Publication_Place: Sioux Falls, South Dakota 57198-001
Publication_Date: 2015
Other_Citation_Details:
The Landsat 8 OLI/TIRS data are 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: 20141001
Ending_Date: 20151231
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: Landsat 8
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 <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_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:
The NLCD 2011 was used as ground training and validation for non-agricultural categories. Additionally, the USGS NLCD 2011 Imperviousness and Tree Canopy layers were used as ancillary data sources in the Cropland Data Layer classification process. More information on the NLCD 2011 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., 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: 2015
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: 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_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:
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 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 <http://www.fsa.usda.gov/>. The most current version of the NLCD is used as non-agricultural training and validation data.
INPUTS: The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS 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) and the imperviousness and canopy data layers from the USGS National Land Cover Database 2011 (NLCD 2011). 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 2011 (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.
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