USDA, National Agricultural Statistics Service, 2005 Arkansas Cropland Data Layer

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: 20060703
Title:
USDA, National Agricultural Statistics Service, 2005 Arkansas Cropland Data Layer
Edition: 2005 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/AR>
Description:
Abstract:
This 30 meter version of the USDA-NASS 2005 Arkansas Cropland Data Layer is a raster, geo-referenced, categorized land cover data layer produced using satellite imagery from the Thematic Mapper (TM) instrument on Landsat 5. The imagery was collected between the dates of 04/14/2005 and 09/05/2005. The approximate scale is 1:100,000 with a ground resolution of 30 meters by 30 meters. The area of coverage for 2005 is the Eastern Delta Region of Arkansas, equivalent to Landsat paths 23 and 24, rows 35, 36 and 37. This is part of an annual series in which several states are categorized annually based on the extensive field observations collected during the annual NASS June Agricultural Survey.
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 this internet site <http://www.nass.usda.gov/research/Cropland/metadata/meta.htm> to view the original metadata file.
USDA, National Agricultural Statistics Service 2005 Arkansas Cropland Data Layer

CLASSIFICATION INPUTS BY ANALYSIS DISTRICT (AD):
AD35:  LANDSAT TM/ETM+ Path 23 & 24 Row(s) 35, 36 & 37, 04/14/2005 & 09/05/2005
AD72:  IRS AWiFS Path 277 Row(s) 44 Quadrant(s) C, 08/20/2005
       IRS AWiFS Path 277 Row(s) 49 Quadrant(s) A, 08/20/2005
AD79:  IRS AWiFS Path 279 Row(s) 48 Quadrant(s) A, C & D, 06/19/2005
*AD80: IRS AWiFS Path 280 Row(s) 48 Quadrant(s) A, 09/04/2005
       IRS AWiFS Path 279 Row(s) 48 Quadrant(s) A, 06/19/2005

*NOTE: Analysis Districts (AD) are unique classifications. AD80 classification
was based on Mississippi ground truth.

TRAINING AND VALIDATION:
USDA, NASS JUNE AREA SURVEY 2005

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: 20050101
Ending_Date: 20051230
Currentness_Reference: 2005 growing season
Status:
Progress: Complete
Maintenance_and_Update_Frequency: None planned
Spatial_Domain:
Bounding_Coordinates:
West_Bounding_Coordinate: -94.6182
East_Bounding_Coordinate: -89.7603
North_Bounding_Coordinate: 36.5208
South_Bounding_Coordinate: 32.9695
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: farming
Theme_Keyword: land cover
Theme_Keyword: crop estimates
Theme_Keyword: AWiFS
Theme_Keyword: MODIS
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 > Arkansas
Place:
Place_Keyword_Thesaurus: None
Place_Keyword: Arkansas
Place_Keyword: AR
Temporal:
Temporal_Keyword_Thesaurus: None
Temporal_Keyword: 2005
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:
The data layer is distributed in a GEOTIFF image file format. The hardware requirements for processing this data set are as follows: for digitizing/ground truth editing, any of the 32 bit Microsoft OS's will work. For computationally intensive jobs including; scene processing, clustering, classification, estimation and mosaicking a batch type system is utilized where jobs can be queued on different devices, and the minimum requirements are NT/2000/XP.Image processing is typically performed using PEDITOR, where PEDITOR utilizes the Windows console along with environmental variables, and neither are available with 95/98. PEDITOR as it is now constituted, will only run under the Microsoft Windows operating systems.A Microsoft Visual FoxPro application called the Remote Sensing Project or RSP is used to manage the ground truth collection process, and track each segment to its completion.Commercial off the shelf software XLNT from Advanced Systems Concepts, allows for batch job processing on the NT/2000/XP operating systems. SARS utilizes XLNT to run computationally intensive jobs that are shared across network resources to expedite processing.
The 2005 Arkansas CDL was created as a mosaic of categorized data from two different software systems/approaches; NASS's PEDITOR and Rulequest's See5.0.PEDITOR is used as the NASS' main image processing software. PEDITOR has been maintained in-house and contains much of the functionality available in commercial image processing systems; such as isodata-based clustering and maximum likelihood classification. However, program/process modifications are relatively easy to support in a research type environment, and the development/release cycle is faster. PEDITOR is deployed in all participating NASS State Statistical Field Offices to handle the ground truthing process and all image processing tasks, and is continuously tested with the Spatial Analysis Research Section (SARS) in Fairfax, Virginia. Currently, PEDITOR runs on most Microsoft Windows platforms; however, PEDITOR's batch processing system programs is only supported on Windows XP. For Arkansas 2005, the normal PEDITOR processing was done to obtain one classification of the Arkansas area included in Landsat paths 23 and 24, rows 35, 36 and 37.As part of a separate research project, a classification of only the ag-intensive Mississippi River Delta portion (as determined by the intersection of mapping zone 45 of the United States Geological Survey's 1992 National Land Cover Data set with the States of Arkansas, Missouri, Mississippi, and Louisiana) was created using Rulequest's See5.0 software rather than NASS' Peditor in-house software. The selected options included boosting 10 trials, global pruning at 25% with a minimum of 2 cases. All available raw Landsat imagery for the region was used as input along with a cloud top mask layer, elevation, city lights and the non-agricultural portion of the United States Geological Survey's 1992 National Land Cover Data set. More information on Rulequest's See5.0 software can be found at <http://www.rulequest.com/.Upon> direct comparison for overlapping areas, the See5.0 based classification was seen to be much better than the PEDITOR based one. A mosaic was created, in ERDAS Imagine, from the two classifications, so that See5.0 data was used where it existed, and PEDITOR data elsewhere (i.e., outside of the NLCD's Zone 45).

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, 2005 Arkansas Cropland Data Layer
ACCURACY REPORT BY ANALYSIS DISTRICT (AD)

ANALYSIS DISTRICT AD35
LANDSAT TM/ETM+ PATH: 23 & 24, ROW(S): 35, 36 & 37 - 04/14/2005 & 09/05/2005
CDL     CROP         NUMBER OF   PERCENT   COMMISSION   KAPPA
CODE    COVER*       PIXELS      CORRECT*  ERROR        COEFFICIENT
81      CLOUDSFIL    0           0         100          0
1       CORN         12447       89.28     12.5         89.04
2       COTTON       90625       95.6      5.16         94.75
62      NONAG        92897       64.19     11.56        59.32
25      OGRN_HAY     5720        73.43     20.65        73.17
44      OTHERCROP    2104        25.67     41.87        25.54
3       RICE         137593      94.4      6.13         92.58
4       SORGHUM      4135        73.16     3.85         73.01
5       SOYBN        209773      95.61     12.33        92.61
82      URBAN        0           0         100          0
83      WATER_AQU    7516        96.15     41.41        96.07
        OVERALL      562810      89.35                  85.63
* NOTE: If signatures for covers such as CLOUDS or WATER were determined
  from pixels outside of the original ground truth sample, those cover
  types will have '0.00%' PERCENT CORRECT in this table. If a cover type
  named 'OTHER' exists, PERCENT CORRECT will also show as '0.00%' for the
  small area covers or crops that were combined into cover type 'OTHER'.

ANALYSIS DISTRICT AD72
IRS AWIFS PATH: 277 ROW(S): 44 QUADRANT(S): C - (08/20/2005)
IRS AWIFS PATH: 277 ROW(S): 49 QUADRANT(S): A - (08/20/2005)
CDL     CROP         NUMBER OF   PERCENT   COMMISSION   KAPPA
CODE    COVER*       PIXELS      CORRECT*  ERROR        COEFFICIENT
92      AQUACULT     86          88.37     26.21        88.33
86      BUILDING     836         0         0            0
81      CLOUDS       0           0         100          0
1       CORN         443         72.91     51.79        72.23
2       COTTON       4492        76.85     15.76        72.78
62      CROP PAST    22          13.64     96.51        13.36
0       FILLER       0           0         0            0
61      IDLE CROP    313        54.95      71.38        53.94
62      NON AGG      466        40.34      90.89        35.5
25      OTHER HAY    308        36.04      76.33        34.93
62      OTRWASTE     334        64.07      53.07        63.46
62      PERM PAST    419        78.28      49.92        77.75
3       RICE         6711       87.08      8.54         83.16
4       SORGHUM      200        45         83.58        43.88
5       SOYBEANS     9281       67.07      18.36        54.41
53      PECANS       23         0          100          -0.2
57      WATERMELON   2          0          0            0
59      OTHERCROP    9          0          0            0
82      URBAN        0          0          100          0
83      WATER        0          0          100          0
85      WATERWAY     144        74.31      65.81        74.01
24      WIN WHEAT    28         10.71      98.1         10.2
63      WOODPAST     65         36.92      90.87        36.31
63      WOODS        3264       35.51      45.33        30.11
48      WTRMELON     2          0          0            0
        OVERALL      27448      66.74                   59.07
* NOTE: If signatures for covers such as CLOUDS or WATER were determined
  from pixels outside of the original ground truth sample, those cover
  types will have '0.00%' PERCENT CORRECT in this table. If a cover type
  named 'OTHER' exists, PERCENT CORRECT will also show as '0.00%' for the
  small area covers or crops that were combined into cover type 'OTHER'.>

ANALYSIS DISTRICT AD79
IRS AWIFS PATH: 279 ROW(S): 48 QUADRANT(S): A, C & D - (06/19/2005)
CDL     CROP         NUMBER OF   PERCENT   COMMISSION   KAPPA
CODE    COVER*       PIXELS      CORRECT*  ERROR        COEFFICIENT
36      ALFALFA      43          0         0            0
92      AQUACULT     1633        57.62     13.43        57.17
86      BUILDING     1059        0.19      81.82        0.18
81      CLOUDS       0           0         100          0
1       CORN         2009        19.51     31.11        19.06
2       COTTON       15312       36.37     24.82        31.43
62      CROP PAST    63          0         0            0
62      FARM         25          0         0            0
0       FILLER       0           0         0            0
61      IDLE CROP    1007        9.24      84.16        8.71
62      NON AGG      1075        3.26      81.38        3.08
25      OTHER HAY    514         6.61      92.18        6.22
62      OTRWASTE     840         40.48     86.94        38.93
62      PERM PAST    918         55.12     80.27        53.97
3       RICE         27668       89.76     29.47        84.42
84      ROADSRR      111         0         0            0
4       SORGHUM      954         40.57     84.07        39.13
5       SOYBEANS     39970       71.61     29.53        53.05
57      WATERMELON   146         89.04     81.43        88.97
59      OTHERCROP    100         0         0            0
82      URBAN        0           0         100          0
83      WATER        0           0         100          0
85      WATERWAY     671         1.34      93.62        1.21
24      WIN WHEAT    388         34.02     84.25        33.48
63      WOODPAST     59          0         0            0
63      WOODS        8144        55.61     32.92        52.49
48      WTRMELON     35          0         0            0
        OVERALL      102744      64.78                  52.2
* NOTE: If signatures for covers such as CLOUDS or WATER were determined
  from pixels outside of the original ground truth sample, those cover
  types will have '0.00%' PERCENT CORRECT in this table. If a cover type
  named 'OTHER' exists, PERCENT CORRECT will also show as '0.00%' for the
  small area covers or crops that were combined into cover type 'OTHER'.
***NOTE: The attribute codes above may not necessarily match the most current coding scheme. Please check the Entity_and_Attribute_Detail_Citation Section of this metadata file to verify the current attibute codes and category names.
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. NASS collects the remote sensing Acreage Estimation Program's field level training data during the June Agricultural Survey. This is a national survey based on a stratified random sample of land areas selected from each state's area frame. An area frame is a land use stratification based on percent cultivation. Our enumerators are given questionnaires to ask the farmers what, where, when and how much are they planting. Our surveys focus on cropland, but the enumerators record all land covers within the sampled area of land whether it is cropland or not. NASS uses broad land use categories to define land that is not under cultivation, including; non-agricultural, pasture/rangeland, waste, woods, and farmstead. NASS defines these non-agricultural land use types very broadly, which makes it difficult to precisely know what specific type of land use/cover actually is on the ground. The USDA, NASS recommends that users consider the USGS, National Land Cover Database 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 accuracy of the land cover classifications are evaluated using the extensive training data collected in the annual NASS June Agricultural Survey (JAS).
Completeness_Report:
The area of coverage for 2005 is the Eastern Delta Region of Arkansas
Positional_Accuracy:
Horizontal_Positional_Accuracy:
Horizontal_Positional_Accuracy_Report:
The categorized images are co-registered to EarthSat Inc's ortho-rectified GeoCover Stock Mosaic images using automated block correlation techniques. The block correlation is run against band two of each original raw satellite image and band two of the GeoCover Stock Mosaic. The resulting correlations are applied to each categorized image, and then added to a master image or mosaic using PEDITOR. The EarthSat images were chosen as they provide the best available large area ortho-rectified images as a basis to register large volume Landsat images with.
Quantitative_Horizontal_Positional_Accuracy_Assessment:
Horizontal_Positional_Accuracy_Value: 50 meters root mean squared error overall
Horizontal_Positional_Accuracy_Explanation:
The GeoCover Stock Mosaics are within 50 meters root mean squared error overall. The publisher of the GeoCover mosaic is MDA Federal (previously Earthsat).
Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Originator: Landsat 5
Title: LANDSAT TM Path 23, Row(s) 35, 36 and 37
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publisher: USGS EROS Data Center
Publication_Place: Sioux Falls, South Dakota, USA
Publication_Date: 20051001
Other_Citation_Details:
LANDSAT TM Path 23, Row(s) 35, 36 and 37. 30 meter by 30 meter pixel resolution, EOSAT Fast Format.
Additional information about Landsat 5 and Landsat 7 satellite imagery can be obtained from the United States Geological Survey (USGS) EROS Data Center.
Source_Scale_Denominator: 100000
Type_of_Source_Media: CD-ROM
Source_Time_Period_of_Content:
Time_Period_Information:
Multiple_Dates/Times:
Single_Date/Time:
Calendar_Date: 20050414
Single_Date/Time:
Calendar_Date: 20050905
Source_Currentness_Reference: ground condition
Source_Contribution: Raw data used in land cover spectral signature analysis.
Source_Information:
Source_Citation:
Citation_Information:
Originator: Landsat 5
Title: LANDSAT TM Path 24, Row(s) 35, 36 and 37
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publisher: USGS EROS Data Center
Publication_Place: Sioux Falls, South Dakota, USA
Publication_Date: 20051001
Other_Citation_Details:
LANDSAT TM Path 24, Row(s) 35, 36 and 37. 30 meter by 30 meter pixel resolution, EOSAT Fast Format.
Additional information about Landsat 5 and Landsat 7 satellite imagery can be obtained from the United States Geological Survey (USGS) EROS Data Center.
Source_Scale_Denominator: 100000
Type_of_Source_Media: CD-ROM
Source_Time_Period_of_Content:
Time_Period_Information:
Multiple_Dates/Times:
Single_Date/Time:
Calendar_Date: 20050626
Single_Date/Time:
Calendar_Date: 20050813
Source_Currentness_Reference: ground condition
Source_Contribution: Raw data used in land cover spectral signature analysis.
Source_Information:
Source_Citation:
Citation_Information:
Originator: Indian Remote Sensing (IRS)
Title: AWiFS Path 277, Row 44, Quadrant(s) C
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publisher: Space Imaging
Publication_Place: Thornton, Colorado, USA
Publication_Date: 20051101
Other_Citation_Details:
IRS AWiFS Path 277, Row 44, Quadrant(s) C. 56 meter by 56 meter pixel resolution.
Additional information about IRS AWiFS satellite imagery can be obtained from Space Imaging.
Source_Scale_Denominator: 100000
Type_of_Source_Media: CD-ROM
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 20050820
Source_Currentness_Reference: ground condition
Source_Contribution: Raw data used in land cover spectral signature analysis.
Source_Information:
Source_Citation:
Citation_Information:
Originator: Indian Remote Sensing (IRS)
Title: AWiFS Path 277, Row 49, Quadrant(s) A
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publisher: Space Imaging
Publication_Place: Thornton, Colorado, USA
Publication_Date: 20051101
Other_Citation_Details:
IRS AWiFS Path 277, Row 49, Quadrant(s) A. 56 meter by 56 meter pixel resolution.
Additional information about IRS AWiFS satellite imagery can be obtained from Space Imaging.
Source_Scale_Denominator: 100000
Type_of_Source_Media: CD-ROM
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 20050820
Source_Currentness_Reference: ground condition
Source_Contribution: Raw data used in land cover spectral signature analysis.
Source_Information:
Source_Citation:
Citation_Information:
Originator: Indian Remote Sensing (IRS)
Title: AWiFS Path 279, Row 48, Quadrant(s) A, C and D
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publisher: Space Imaging
Publication_Place: Thornton, Colorado, USA
Publication_Date: 20051101
Other_Citation_Details:
IRS AWiFS Path 279, Row 48, Quadrant(s) A, C and D. 56 meter by 56 meter pixel resolution.
Additional information about IRS AWiFS satellite imagery can be obtained from Space Imaging.
Source_Scale_Denominator: 100000
Type_of_Source_Media: CD-ROM
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 20050619
Source_Currentness_Reference: ground condition
Source_Contribution: Raw data used in land cover spectral signature analysis.
Source_Information:
Source_Citation:
Citation_Information:
Originator: Indian Remote Sensing (IRS)
Title: AWiFS Path 280, Row 48, Quadrant(s) A
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publisher: Space Imaging
Publication_Place: Thornton, Colorado, USA
Publication_Date: 20051101
Other_Citation_Details:
IRS AWiFS Path 280, Row 48, Quadrant(s) A. 56 meter by 56 meter pixel resolution.
Additional information about IRS AWiFS satellite imagery can be obtained from Space Imaging.
Source_Scale_Denominator: 100000
Type_of_Source_Media: CD-ROM
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 20050904
Source_Currentness_Reference: ground condition
Source_Contribution: Raw data used in land cover spectral signature analysis.
Source_Information:
Source_Citation:
Citation_Information:
Originator: National Aerial Photography Program (NAPP)
Title: NAPP aerial photographs
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publisher: Aerial Photography Field Office (AFPO)
Publication_Place: Salt Lake City, Utah, USA
Publication_Date: variable
Other_Citation_Details:
Additional information about NAPP: EROS Data Center Sioux Falls, South Dakota 57198, USA Phone (605)594-6151 fax x6589
Source_Scale_Denominator: 8000
Type_of_Source_Media: paper
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: variable
Source_Currentness_Reference: ground condition
Source_Contribution: spatial and attribute information
Source_Information:
Source_Citation:
Citation_Information:
Originator:
USDA/NASS, Research and Development Division, Area Frame Section
Title: Area Sampling Frame (ASF) of Arkansas
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:
Publisher: USDA-NASS
Publication_Place: Washington D.C., USA
Publication_Date: 1992
Other_Citation_Details:
Additional information about the NASS Area Frame Stratification can be obtained from the following internet site: <http://www.nass.usda.gov/research/stratafront2b.htm>
Source_Scale_Denominator: 100000
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 1992
Source_Currentness_Reference: publication date
Source_Contribution: spatial and attribute information
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Geological Survey EROS Data Center
Title: 1992 National Land Cover Data (NLCD) Mapping Zone 45
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publisher: USGS, EROS Data Center
Publication_Place: Sioux Falls, South Dakota 57198 USA
Publication_Date: 1992
Other_Citation_Details:
The Land Cover Characterization of the 1992 NLCD was used to improve the non-agricultural portion of the Cropland Data Layer. More information on the NLCD can be found at <http://eros.usgs.gov/products/landcover/nlcd.html>.
Source_Scale_Denominator: 100000
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
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Geological Survey EROS Data Center
Title: 1992 National Land Cover Data (NLCD) Mapping Zone 45
Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:
Publisher: USGS, EROS Data Center
Publication_Place: Sioux Falls, South Dakota 57198 USA
Publication_Date: 1992
Other_Citation_Details:
The Land Cover Characterization of the 1992 NLCD was used to improve the non-agricultural portion of the Cropland Data Layer. More information on the NLCD can be found at <http://eros.usgs.gov/>.
Source_Scale_Denominator: 100000
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
Process_Step:
Process_Description:
The 2005 Arkansas CDL was created as a mosaic of categorized data from two different software systems/approaches; NASS's PEDITOR and Rulequest's See5.0.PEDITOR is used as the NASS' main image processing software. PEDITOR has been maintained in-house and contains much of the functionality available in commercial image processing systems; such as isodata-based clustering and maximum likelihood classification. However, program/process modifications are relatively easy to support in a research type environment, and the development/release cycle is faster. PEDITOR is deployed in all participating NASS State Statistical Field Offices to handle the ground truthing process and all image processing tasks, and is continuously tested with the Spatial Analysis Research Section (SARS) in Fairfax, Virginia. Currently, PEDITOR runs on most Microsoft Windows platforms; however, PEDITOR's batch processing system programs is only supported on Windows XP. For Arkansas 2005, the normal PEDITOR processing was done to obtain one classification of the Arkansas area included in Landsat paths 23 and 24, rows 35, 36 and 37.As part of a separate research project, a classification of only the ag-intensive Mississippi River Delta portion (as determined by the intersection of mapping zone 45 of the United States Geological Survey's 1992 National Land Cover Data set with the States of Arkansas, Missouri, Mississippi, and Louisiana) was created using Rulequest's See5.0 software rather than NASS' Peditor in-house software. The selected options included boosting 10 trials, global pruning at 25% with a minimum of 2 cases. All available raw Landsat imagery for the region was used as input along with a cloud top mask layer, elevation, city lights and the non-agricultural portion of the United States Geological Survey's 1992 National Land Cover Data set. More information on Rulequest's See5.0 software can be found at <http://www.rulequest.com/.Upon> direct comparison for overlapping areas, the See5.0 based classification was seen to be much better than the PEDITOR based one. A mosaic was created, in ERDAS Imagine, from the two classifications, so that See5.0 data was used where it existed, and PEDITOR data elsewhere (i.e., outside of the NLCD's Zone 45).
The 2005 Arkansas Cropland Data Layer was created using Rulequest's See5.0 software rather than NASS' Peditor in-house software. The selected options included boosting 10 trials, global pruning at 25% with a minimum of 2 cases. All available raw Landsat imagery for the region was used as input along with a cloud top mask layer, elevation, city lights and the non-agricultural portion of the United States Geological Survey's 1992 National Land Cover Data for mapping zone 45. More information on Rulequest's See5.0 software can be found at <http://www.rulequest.com/>.
The Cropland Data Layer (CDL) Program provides the National Agricultural Statistics Service (NASS) with internal proprietary county and state level acreage indications of major crop commodities, and secondarily provides the public with "statewide" (where available) raster, geo-referenced, categorized land cover data products after the public release of county estimates. This project builds upon the USDA's National Agricultural Statistics Service (NASS) traditional crop acreage estimation program, and integrates the enumerator collected ground survey data with satellite imagery to create an unbiased statistical estimator of crop area at the state and county level for internal use. No farmer reported data is revealed, nor can it be derived in the publicly releasable Cropland Data Layer product.
Every June thousands of farms are visited by enumerators as part of the USDA/NASS June Agricultural Survey (JAS). These farmers are asked to report the acreage, by crop, that has been planted or that they intend to plant, and the acreage they expect to harvest. Approximately 11,000 area segments are selected nationwide for the JAS. The segment size can range in size from about 1 square mile in cultivated areas to 0.1 of a square mile in urban areas, to 2-4 square miles for larger probability proportional to size (PPS) segments in rangeland areas. This division allows intensively cultivated land segments to be selected with a greater frequency than those in less intensively cultivated areas. The 150-400 square miles of ground truth collected during the JAS provides a great ground truth training set annually.
The Area Sampling Frame (ASF) is a stratification of each state into broad land use categories according to the percentage of cropland present. The ASF is stratified using visual interpretation of satellite imagery. The sampling frames are constructed by defining blocks of land whose boundaries are physical features on the ground (roads, railroads, rivers, etc). These blocks of land cover the entire state, do not overlap, and are placed in strata based on the percent of land in the block that is cultivated. The strata allow for efficient sampling of the land, as an agriculturally intensive area will be more heavily sampled than a non ag intensive area.
The enumerators draw off field boundaries onto NAPP 1:8,000 black and white aerial photos containing the segment, according to their observations and the farmer reported information. The fields are labeled and the cover type is recorded using a grease pencil on the aerial photo. Enumerators account for every field/land use type within a segment. They assign each field a cover type based upon a fixed set of land use classes for each state. Every field within a segment must fit into one of the pre-defined classes.
The program methodology is a continuous process throughout the year. The first step "Segment Preparation" establishes the training segments, digitizes the perimeters, and distributes software and data to the field offices, this goes from February to late May. Segment digitizing begins during the JAS and continues until all fields and all segments are completely digitized, this may run thru July or even until mid-October in some states depending on human resource availability. Segment cleanup analyzes the newly digitized segments with the new acquired imagery. Fields that are bad either by digitizing or cover type are corrected or removed from training. Scene processing fits each segment onto a scene by shifting, and cloud-influenced segments are removed. The cluster/classification process runs in concert with the scene processing steps, as segments are shifted they can be clustered. This process is iterative, and can run into December. Estimation can be performed once a scene is finished classification, and the user is satisfied with the outputs. Estimation can begin as early as late October and run into late January/February. The mosaic process runs once estimation is completed. It is also iterative and can go from late December to March. The mosaic for a particular state is released once the county estimates are officially released for that state.
Scene selection begins in early summer, and could run into the late fall depending on image availability. The Cropland Data Layer program primarily uses the Landsat platform for acreage estimation. However, other platforms such as Spot and the Indian IRS platforms are used to fill "data acquisition" holes within a state. A spring and summer date of observation is preferred for maximum crop cover separation for multi-temporal analysis of summer crops. If only one date of observation is available (unitemporal), a mid summer date is preferred. If only an early spring date March-May or a fall date September-October is available (unitemporal) during the growing season, then it is best to not use that scene or analysis district for estimation, as bare soil in the spring and fully senesced crops in the fall will provide erroneous results.
The clustering/classification is an iterative process, as fields get misclassified, they can be fixed or marked as bad for training and reprocessed. Known pixels are separated by cover type and clustered, within cover type using a modified ISODATA clustering algorithm, as it allows for merging and splitting of clusters. Modified implies that the output clusters are not labeled (other than as coming from the input cover type) as they can be reassigned later if desired. Clustering is done separately for each cover type (or specified combination of cover types, such as all small grains). The clustered cover types are then assembled together into one signature file, where entire scenes are classified using the maximum likelihood algorithm. Clustering is based on the LARSYS (Purdue University) ISODATA algorithm. It performs an iterative process to divide pixels into groups based on minimum variance. The pairs of clusters in close proximity (based on Swain-Fu distance) are merged. High variance clusters can be split into two clusters (variance of first principal component is used as a measure). The output of any clustering program is a statistics file which stores mean vectors and covariance matrices of final set of clusters.
The outputs are a categorized or classified image in PEDITOR format and the associated accuracy statistics for each cover type. The maximum likelihood classifier performs a pixel-by-pixel classification based on the final, combined statistics file. It calculates the probability of each pixel being from each signature; then classifies a pixel to the category with highest probability. The processing time depends on size of file to be classified (i.e. number of pixels), number of categories in the statistics file and number of input dimensions (number of bands/pixel).
For estimation purposes, clouds can be minimized by defining Analysis Districts (AD) along adjacent scene edges, by cutting the Analysis Districts by county boundary, or cutting the clouds out by primary sampling units. Analysis Districts can be individual or multiple scenes footprints that have to be observed on the same date, and analyzed as one. An AD can be comprised of one or more scenes. An AD can be defined by either a scene edge or a county boundary. Multi-temporal AD's are possible as long as both dates in all scenes are the same. A single or multi-scene AD will use all potential training fields for clustering/classification/estimation. Several factors can lead to problems in a classification, some get corrected in early edits and some do not:
Several factors can lead to problems in a classification, some get corrected in early edits and some do not: poor imagery dates, with respect to the major crops of interest, complete training fields that are incorrectly identified in the ground truth, parts of training fields that are not the same as the major crop or cover type, irrigation ditches, wooded areas, low spots filled with water, and/or bare soil areas in an otherwise vegetated field. Crops that look alike to the clustering algorithm(s) due to planting/growing cycle: spring wheat and barley at almost any time, crops in senescence, and grassy waste fields and idle cropland. Cover types that are essentially the same but used differently: wooded pasture versus woods or waste fields (only difference may be the presence of livestock), corn for grain versus corn silage, and cover crops such as rye and oats. Cover types that change signatures back and forth during the growing season: alfalfa and other hays before and after cutting, with multiple cuttings per year. Once the analyst is satisfied with the classification, the next step can be acreage estimation or image mosaicking.
Three estimation methods are available for each AD: regression, pixel ratio and direct expansion. Where available, regression is chosen as the preferred type of estimation. This approach essentially corrects the area sample (ground only) estimate based on the relationship found between reported data and classified pixels in each stratum where it is used. A regression relationship should be based on 10 or more segments for any stratum used. Where there are not enough segments in each stratum, a pixel based ratio estimator may be used which essentially combines data across stratum to get the relationship. Finally, the direct expansion (total number of possible segments times the average for sampled segment) may be used in the absence of pixel based methods. Regression adjusts the direct expansion estimate based on pixel information. It usually leads to an estimate with a much lower variance than direct expansion alone. Segments, called outliers, which do not fit the linear relationship estimated by the regression are reviewed; if errors are found, they are corrected or that segment may be removed from consideration in the analysis.
Full scene classifications (large scale) are run wherever the regression or pixel ratio estimates are usable. Estimates derived from the classification are compared to the ground data to make one final check. State estimates are made by summing pixel based estimators where available and ground data only estimators everywhere else. County estimates are then derived from the state estimates using a similar approach. Final numbers are delivered to state field offices and the NASS Agricultural Statistics Board for their use in setting the official final estimates. The states also have administrative data, such as FSA certified acres at the county level, and other NASS survey data. Every 5th year, NASS also performs the Census of Agriculture at the county level.
The Landsat TM/ETM+ scenes that SARS uses are radiometrically and systematically corrected. There is a need to tie down registration points on a continuing basis for every state in the project. Without some image/image registration, the scene registration tends to float 2-3 pixels in any given direction, for any given scene. Manual registration for every scene of every project, would be nearly impossible, as the CDL is on a repeating production cycle every year, and human resource levels for this process are low. Image recoding is necessary between different analysis districts, to rectify to a common signatures set for a state. Clouds pose a problem when trying to make acreage estimates, and there are mechanisms within Peditor to minimize their extent, as there are ways to minimize cloud coverage in the mosaic process by prioritizing scene overlap.
Each categorized scene is co-registered to EarthSat's GeoCover LC imagery (50 meters RMS), and then stitched together using Peditor's Batch program. A block correlation is run between band two from each raw scene, and band two of the ortho-base image. The registration of the GeoCover mosaicked scene and the individual raw input scenes are used to get an approximate correspondence. A correlation procedure is used on the raw Landsat scenes and the mosaicked scene to get an exact mapping of each pixel from the input Landsat scenes to the mosaicked scene. The results of the correlation are used to remap the pixels from the individual input scenes into the coordinate system of the mosaicked scene. The mosaic process now performs: 1) Precision registration of images automatically, 2) Converts each categorized image and associated statistics file to a set standard automatically (recode), 3) Specify overlap priority by scene or county, 4) Filters out clouds when possible. The scenes are stitched together using the priorities previously assigned from the scene observation dates/analysis districts map. Scenes/analysis districts with better quality observation dates are assigned a higher priority when stitching the images together. Clouds are assigned a null value on all scenes, and scenes of lower priority that are cloud free, take precedence over clouded higher priority images. Once cloud cover is established throughout the mosaic the clouds are assigned a digital value.
All CDL distribution for the previous crop year is held until the release of the official NASS county estimates for the major commodities grown within a given state. Corn and Soybeans are released in March for the previous crop year - Midwestern States. Rice and Cotton are released in June for the previous crop year - Delta States. Small grains are released in March for the Great Plains States.
NASS publishes all available accuracy statistics for end-user viewing. The Percent Correct is calculated for each cover type in the ground truth, it shows how many of the total pixels were correctly classified (i.e. across all cover types). 'Commission Error' is the calculated percentage of all pixels categorized to a specific cover type that were not of that cover type in the ground truth (i.e. incorrectly categorized). CAUTION: a quoted Percent Correct for a specific cover type is worthless unless accompanied by its respective Commission Error. Example: if you classify every pixel in a scene to 'wheat', then you have a 100% correct wheat classifier (however its Commission Error is also almost 100%). The 'Kappa Statistic' is an attempt to adjust the Percent Correct using information gained from the confusion matrix for that cover type. Many remote sensing groups use the Percent Correct and/or Kappa statistics as their final measure of classification accuracy.
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: 2005
Process_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA-NASS Remote Sensing Analyst for Arkansas
Contact_Person: USDA-NASS Remote Sensing Analyst for Arkansas
Contact_Address:
Address_Type: mailing address
Address: 3251 Old Lee Highway, Rm 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_OD@nass.usda.gov
Cloud_Cover:
Generally, there is enough cloud-free AWiFS 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: Arkansas
Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:
Raster_Object_Type: Pixel
Row_Count: 6971
Column_Count: 7995

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: 15 North
Transverse_Mercator:
Scale_Factor_at_Central_Meridian: 0.9996
Longitude_of_Central_Meridian: -93
Latitude_of_Projection_Origin: 0
False_Easting: 500000
False_Northing: 0
Planar_Coordinate_Information:
Planar_Coordinate_Encoding_Method: coordinate pair
Coordinate_Representation:
Abscissa_Resolution: 56
Ordinate_Resolution: 56
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:
NASS collects the remote sensing Acreage Estimation Program's field level training data during the June Agricultural Survey. This is a national survey based on a stratified random sample of land areas selected from each state's area frame. An area frame is a land use stratification based on percent cultivation. The selected areas are targeted toward cultivated parts of each state based on its area frame. Our enumerators are given questionnaires to ask the farmers what, where, when and how much are they planting. Our surveys focus on cropland, but the enumerators record all land covers within the sampled area of land whether it is cropland or not. NASS uses broad land use categories to define land that is not under cultivation, including; non-agricultural, pasture/rangeland, waste, woods, and farmstead. NASS defines these non-agricultural land use types very broadly, which makes it difficult to precisely know what specific type of land use/cover actually is on the ground. Thus, the USDA, NASS recommends that users consider the USGS, National Land Cover Database (NLCD) for studies involving non-agricultural land cover.

Entity_and_Attribute_Detail_Citation:
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.
 ***NOTE: The 1997-2013 CDLs were recoded and re-released in January 2014 to better represent pasture and grass-related categories. A new
 category named Grass/Pasture (code 176) collapses the following historical CDL categories: Pasture/Grass (code 62), Grassland Herbaceous
 (code 171), and Pasture/Hay (code 181). This was done to eliminate confusion among these similar land cover types which were not always
 classified definitionally consistent from state to state or year to year and frequently had poor classification accuracies. This follows
 the recoding of the entire CDL archive in January 2012 to better align the historical CDLs with the current product. For a detailed list
 of the category name and code changes, please visit the Frequently Asked Questions (FAQ's) section at <http://www.nass.usda.gov/research/Cropland/sarsfaqs2>.

 Data Dictionary: USDA, National Agricultural Statistics Service, 2005 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"       Grass/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
         "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 - Arkansas 2005
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: Arkansas 2005
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. Technical restrictions do not allow us to offer the CDL by individual state/year through the Geospatial Data Gateway. 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:
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.
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. Geospatial Data Gateway technical restrictions do not allow us to offer the CDL by individual state/year. We are working on offering this option in the future.
Instructions for downloading from the NRCS Geospatial Data Gateway:

Start by clicking on 'Get Data'

Select a state from the dropdown menu

Scroll down to choose your state and click 'Continue'

Choose 'Land_use_land_cover' and select 'Cropland Data Layer by State' and 'Continue to Step3'

Choose 'Continue' to Step4

Lastly, you are given the option to download the data for free.
Custom_Order_Process:
For a list of other states and years of available data please visit: <http://www.nass.usda.gov/research/Cropland/SARS1a.htm>. 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.
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: 20120131
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 Fri Jan 27 20:45:13 2012