2003 North Dakota Cropland Data Layer

Identification_Information:

Citation:

Citation_Information:

Originator:

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

Publication_Date: 20040314
Title: 2003 North Dakota Cropland Data Layer | NASS/USDA
Edition: 2003 Edition
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:

Publication_Place:

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

Publisher: USDA, NASS

Other_Citation_Details:

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

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

Description:

Abstract:

The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2003 CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat sensor collected during the current growing season.

Agricultural training and validation data are derived from the USDA, NASS June Area Survey (JAS). JAS 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.

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 <https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/meta.php>.

USDA, National Agricultural Statistics Service 2003 North Dakota Cropland Data Layer

CLASSIFICATION INPUTS BY ANALYSIS DISTRICT (AD)*:
AD01: LANDSAT TM Path 30 Rows 27 & 28, 07/24/2003
AD02: LANDSAT TM Path 31 Row 26, 05/20/2003 & 06/13/2003
AD03: LANDSAT TM Path 31 Row 27, 06/13/2003 & 06/29/2003
AD04: LANDSAT TM Path 31 Row 28, 05/20/2003 & 07/15/2003
AD05: LANDSAT TM Path 32 Rows 26, 27 & 28, 04/09/2003 & 06/20/2003
AD06: LANDSAT TM Path 33 Rows 26, & 27, 05/26/2003 & 08/14/2003
AD07: LANDSAT TM Path 33 Row 28, 05/26/2003 & 07/29/2003
AD08: LANDSAT TM Path 34 Row 26, 06/18/2003 & 07/04/2003
AD09: LANDSAT TM Path 34 Rows 27 & 28, 05/25/2003 & 07/20/2003

TRAINING AND VALIDATION:
USDA, NASS JUNE AREA SURVEY 2003

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

*Analysis Districts are individual classifications that are mosaicked together for the public release of the CDL.

Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20030101
Ending_Date: 20031231

Currentness_Reference: 2003 growing season

Status:

Progress: Complete
Maintenance_and_Update_Frequency: None planned

Spatial_Domain:

Bounding_Coordinates:

West_Bounding_Coordinate: -104.3458
East_Bounding_Coordinate: -96.5624
North_Bounding_Coordinate: 48.9865
South_Bounding_Coordinate: 45.9121

Keywords:

Theme:

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

Theme:

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

Earth Science > Biosphere > Terrestrial Ecosystems > Agricultural Lands

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

Theme:

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

Theme:

Theme_Keyword_Thesaurus: None

Theme_Keyword: crop cover
Theme_Keyword: cropland
Theme_Keyword: agriculture
Theme_Keyword: land cover
Theme_Keyword: crop estimates
Theme_Keyword: 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 > North Dakota

Place:

Place_Keyword_Thesaurus: None
Place_Keyword: North Dakota
Place_Keyword: ND

Temporal:

Temporal_Keyword_Thesaurus: None
Temporal_Keyword: 2003

Access_Constraints: None
Use_Constraints:

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

Point_of_Contact:

Contact_Information:

Contact_Organization_Primary:

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

Contact_Address:

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

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

Data_Set_Credit: USDA, National Agricultural Statistics Service
Security_Information:

Security_Classification_System: None
Security_Classification: Unclassified
Security_Handling_Description: None

Native_Data_Set_Environment:

PEDITOR was used as the main image processing software for the 2003 CDL Program. PEDITOR has been maintained in-house and contains much of the functionality available in commercial image processing systems. 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) . Currently, PEDITOR runs on most Microsoft Windows platforms; however, PEDITOR's batch processing system programs only runs under Windows NT or 2000.

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 performed by 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.

Data_Quality_Information:

Attribute_Accuracy:

Attribute_Accuracy_Report:

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

USDA, National Agricultural Statistics Service, 2003 North Dakota Cropland Data Layer
ACCURACY REPORT BY ANALYSIS DISTRICT (AD)

ANALYSIS DISTRICT AD01
LANDSAT TM/ETM+ PATH: 30, ROWS: 27 & 28 - (07/24/2003)
CDL            CROP /                ORIG.#         PERCENT        COMMISSION     KAPPA
CODE           COVER *               PIXELS         CORRECT*       ERROR          COEFFICIENT
25             ALFALFA               1766           27.97          45.53          26.78
21             BARLEY                1668           13.31          0              12.96
41             BEETS                 2131           0.42           99.31          -1.96
44             CANOLA                352            95.74          0.88           95.71
81             CLOUDS                0              0              100            0
1              CORN                  7413           93.27          4.11           92.26
25             HAY                   0              0              100            0
42             DRY BEANS             655            2.44           99.32          -1.88
62             FARM                  12             0              0              0
81             FILLER                0              0              0              0
61             IDLE CROP             3146           30.23          65.52          26.58
62             NON AGG               2340           0              0              0
28             OATS                  275            0              0              0
25             OTHER HAY             182            0              0              0
62             PERM PAST             5148           80.01          67.99          73.98
43             POTATOES              148            0              100            -0.3
5              SOYBEANS              14520          72.95          9.24           65.75
23             SPG WHEAT             15179          90.44          11.09          86.76
29             MILLET                35             0              0              0
6              SUNFLRS               210            46.19          80.98          45.69
82             URBAN                 0              0              100            0
83             WATER                 0              0              100            0
24             WIN WHEAT             313            94.57          27.98          94.53
               OVERALL               55493          64                            56.61

ANALYSIS DISTRICT AD02
LANDSAT TM/ETM+ PATH: 31, ROW: 26 - (05/20/2003 & 06/13/2003)
CDL            CROP /                ORIG.#         PERCENT        COMMISSION     KAPPA
CODE           COVER *               PIXELS         CORRECT*       ERROR          COEFFICIENT
25             ALFALFA               117            85.47          90.67          84.94
21             BARLEY                3212           0              0              0
41             BEETS                 1527           1.44           96.04          -0.4
44             CANOLA                2571           20.38          18.25          18.66
81             CLOUDS                0              0              100            0
61             FALLOW                374            0              0              0
29             MILLET                120            0              0              0
42             DRY BEANS             995            15.98          92.69          9.48
22             DUR WHEAT             929            9.69           73.99          8.64
62             FARM                  9              0              0              0
81             FILLER                0              0              0              0
44             FLAXSEED              259            0              0              0
61             IDLE CROP             6600           87.58          5.23           84.44
62             NON AGG               749            12.55          60.5           11.86
63             WOODS                 329            76.7           55.56          76.24
62             OTHER                 0              0              100            0
25             OTHER HAY             6              0              0              0
62             PERM PAST             1782           43.32          22.41          41.4
43             POTATOES              567            94.53          56.91          94.3
5              SOYBEANS              717            83.4           88.32          80.02
23             SPG WHEAT             8485           7048           37.28          56.9
6              SUNFLRS               721            90.15          21.31          89.88
82             URBAN                 0              0              0              0
83             WATER                 0              0              100            0
62             WOODPAST              195            94.87          57.76          94.8
               OVERALL               30274          52.02                         43.85

ANALYSIS DISTRICT AD03
LANDSAT TM/ETM+ PATH: 31, ROW: 27 - (06/13/2003 & 06/29/2003)
CDL            CROP /                ORIG.#         PERCENT        COMMISSION     KAPPA
CODE           COVER *               PIXELS         CORRECT*       ERROR          COEFFICIENT
25             ALFALFA               568            99.47          7.53           99.46
21             BARLEY                2288           7.52           24.23          6.89
1              CORN                  1616           96.35          2.14           96.17
62             CROP PAST             477            92.45          42.8           92.28
62             FARM                  43             0              0              0
81             FILLER                0              0              0              0
44             FLAXSEED              61             100            19.74          100
61             IDLE CROP             5590           81.56          18.81          77.87
62             NON AGG               3983           1.26           85.92          0.2
28             OATS                  330            93.94          28.41          93.86
25             OTHER HAY             301            21.26          62.35          20.86
62             PERM PAST             4914           72.53          21.82          68.23
25             HAY                   315            100            0              100
5              SOYBEANS              1575           96.7           37.51          96.44
23             SPG WHEAT             8997           91.7           20.35          88.02
31             CANOLA                436            98.39          1.61           98.37
6              SUNFLRS               287            0              0              0
82             URBAN                 0              0              100            0
83             WATER                 0              0              100            0
24             WIN WHEAT             203            97.54          36.13          97.51
81             CLOUDS                0              0              100            0
42             DRY BEANS             1506           70.92          24.09          69.65
22             DUR WHEAT             90             100            8.16           100
61             FALLOW                129            90.7           4.1            90.66
62             WOODPAST              18             0              0              0
               OVERALL               33727          69.18                         64.18

ANALYSIS DISTRICT AD04
LANDSAT TM/ETM+ PATH: 31, ROW: 28 - (05/20/2003 & 07/15/2003)
CDL            CROP /                ORIG.#         PERCENT        COMMISSION     KAPPA
CODE           COVER *               PIXELS         CORRECT*       ERROR          COEFFICIENT
25             ALFALFA               1730           90.58          5.83           89.92
21             BARLEY                152            86.18          0.76           86.11
1              CORN                  3026           97.52          2.12           97.19
62             CROP PAST             1007           83.32          26.6           82.53
62             FARM                  53             0              0              0
81             FILLER                0              0              0              0
44             FLAXSEED              444            98.87          12.72          98.85
61             IDLE CROP             1831           95.9           1.29           95.6
62             NON AGG               375            38.4           50.68          37.68
28             OATS                  42             0              0              0
63             WOODS                 0              0              100            0
25             OTHER HAY             944            92.9           30.67          92.53
62             PERM PAST             7257           89.17          4.42           85.23
25             HAY                   301            100            6.23           100
5              SOYBEANS              2500           97.44          1.26           97.16
23             SPG WHEAT             4003           98.15          0.53           97.81
29             MILLET                18             0              0              0
6              SUNFLRS               1634           98.16          7.18           98.03
83             WATER                 0              0              100            0
24             WIN WHEAT             63             100            0              100
               OVERALL               25380          92.62                         91.4

ANALYSIS DISTRICT AD05
LANDSAT TM/ETM+ PATH: 32, ROWS: 26, 27 & 28 - (04/09/2003 & 06/20/2003)
CDL            CROP /                ORIG.#         PERCENT        COMMISSION     KAPPA
CODE           COVER *               PIXELS         CORRECT*       ERROR          COEFFICIENT
25             ALFALFA               3549           88.62          58.45          87.77
21             BARLEY                2504           69.01          35.69          68.23
61             FALLOW                650            95.08          37.95          95.03
44             CANOLA                1958           92.75          26.83          92.58
81             CLOUDS                0              0              100            0
1              CORN                  345            98.26          20.79          98.25
62             CROP PAST             2629           1.64           80.63          1.44
42             DRY BEANS             1160           99.31          1.37           99.3
22             DUR WHEAT             1741           0              0              0
62             FARM                  146            56.85          88.19          56.57
81             FILLER                0              0              0              0
44             FLAXSEED              1268           85.57          31.97          85.35
61             IDLE CROP             13022          24.67          24.88          21.61
62             NON AGG               7464           28.8           74             22.98
28             OATS                  272            88.24          57.82          88.17
62             OTHER                 0              0              100            0
25             OTHER HAY             2934           0              0              0
62             PERM PAST             50897          79.56          16.14          63.39
43             POTATOES              527            99.62          0              99.62
5              SOYBEANS              2557           95.19          53.05          94.95
23             SPG WHEAT             11493          77.73          11.4           75.47
29             MILLET                54             70.37          0              70.36
6              SUNFLRS               2830           80.95          6.18           80.52
82             URBAN                 0              0              100            0
83             WATER                 0              0              100            0
24             WIN WHEAT             369            98.1           5.24           98.1
25             HAY                   432            95.14          16.46          95.12
35             MUSTARD               131            93.89          18             93.88
27             RYE                   37             0              0              0
53             DRYPEAS               340            100            0              100
62             WOODPAST              15             0              0              0
63             WOODS                 0              0              100            0
               OVERALL               109324         65.46                         55.11

ANALYSIS DISTRICT AD06
LANDSAT TM/ETM+ PATH: 33, ROWS: 26 & 27 - (05/26/2003 & 08/14/2003)
CDL            CROP /                ORIG.#         PERCENT        COMMISSION     KAPPA
CODE           COVER *               PIXELS         CORRECT*       ERROR          COEFFICIENT
25             ALFALFA               3145           92.85          27.35          92.56
21             BARLEY                3623           65.28          9.77           64.39
61             FALLOW                1406           10.38          55.89          10.1
44             CANOLA                3063           96.21          6.12           96.1
25             HAY                   647            96.45          13.21          96.42
1              CORN                  319            98.43          4.56           98.43
62             CROP PAST             492            77.03          59.33          76.83
42             DRY BEANS             128            100            0              100
22             DUR WHEAT             3901           75.16          26.85          74.17
62             FARM                  108            0              0              0
81             FILLER                0              0              0              0
44             FLAXSEED              2311           95.28          5.78           95.18
61             IDLE CROP             8662           74.3           24.3           72.04
62             NON AGG               4844           10.3           74.19          8.62
28             OATS                  455            94.95          29.06          94.92
62             OTHER                 0              0              100            0
25             OTHER HAY             1869           68.49          46.01          67.76
62             PERM PAST             48454          92.1           12.74          84.59
35             MUSTARD               131            82.44          8.47           82.42
5              SOYBEANS              515            99.81          93.33          99.79
23             SPG WHEAT             16077          63.23          2.86           59.16
53             DRYPEAS               698            93.41          21.63          93.36
30             SPELTZ                6              0              0              0
33             SAFFLOWR              33             0              0              0
6              SUNFLRS               4049           44.33          7.09           43.29
83             WATER                 0              0              100            0
24             WIN WHEAT             18             0              0              0
63             WOODS                 0              0              100            0
               OVERALL               104954         77.62                         69.99

ANALYSIS DISTRICT AD07
LANDSAT TM/ETM+ PATH: 33, ROW: 28 - (05/26/2003 & 07/29/2003)
CDL            CROP /                ORIG.#         PERCENT        COMMISSION     KAPPA
CODE           COVER *               PIXELS         CORRECT*       ERROR          COEFFICIENT
25             ALFALFA               1002           94.01          18.16          93.8
21             BARLEY                1547           98.97          4.01           98.91
35             MUSTARD               262            0              0              0
44             CANOLA                841            99.64          1.06           99.63
81             CLOUDS                0              0              0              0
1              CORN                  276            100            2.16           100
62             CROP PAST             68             97.06          61.85          97.04
22             DUR WHEAT             2886           98.34          5.49           98.17
62             FARM                  8              0              0              0
81             FILLER                0              0              0              0
25             HAY                   86             100            5.49           100
61             IDLE CROP             2603           88.94          7.66           88.05
62             NON AGG               66             57.58          51.28          57.48
28             OATS                  23             0              0              0
27             RYE                   223            100            7.85           100
25             OTHER HAY             450            99.11          24.79          99.1
62             PERM PAST             16016          95.77          2.08           92.11
23             SPG WHEAT             6571           96.16          0.96           95.27
6              SUNFLRS               118            100            0              100
82             URBAN                 0              0              100            0
83             WATER                 0              0              100            0
24             WIN WHEAT             229            100            0              100
61             FALLOW                575            99.13          3.23           99.12
               OVERALL               33800          95.04                         93.17

ANALYSIS DISTRICT AD08
LANDSAT TM/ETM+ PATH: 34, ROW: 26 - (06/18/2003 & 07/04/2003)
CDL            CROP /                ORIG.#         PERCENT        COMMISSION     KAPPA
CODE           COVER *               PIXELS         CORRECT*       ERROR          COEFFICIENT
25             ALFALFA               735            93.74          30.12          93.51
21             BARLEY                2114           94.8           17.02          94.3
44             CANOLA                2044           96.82          1.79           96.57
81             CLOUDS                0              0              100            0
22             DUR WHEAT             9224           34.89          2.93           26.13
62             FARM                  2              0              0              0
81             FILLER                0              0              0              0
44             FLAXSEED              1238           95.96          7.76           95.77
61             IDLE CROP             672            97.47          31.56          97.38
62             NON AGG               1218           35.96          76.01          31.48
28             OATS                  60             100            6.25           100
61             FALLOW                2256           92.91          2.1            92.32
62             PERM PAST             4645           61.1           45.93          52.1
35             MUSTARD               548            99.09          0              99.07
25             OTHER HAY             10             0              0              0
23             SPG WHEAT             1170           96.07          70.08          95.46
52             LENTILS               66             84.85          16.42          84.81
6              SUNFLRS               768            3.78           0              3.68
5              SOYBEANS              0              0              100            0
83             WATER                 0              0              100            0
53             DRYPEAS               1179           96.95          9.64           96.8
               OVERALL               27949          64.62                         60.56

ANALYSIS DISTRICT AD09
LANDSAT TM/ETM+ PATH: 34 ROWS: 27 & 28 - (05/25/2003 & 07/20/2003)
CDL            CROP /                ORIG.#         PERCENT        COMMISSION     KAPPA
CODE           COVER *               PIXELS         CORRECT*       ERROR          COEFFICIENT
25             ALFALFA               1862           92.91          26.13          92.56
21             BARLEY                1903           97.79          5.24           97.7
44             CANOLA                454            100            0              100
81             CLOUDS                0              0              0              0
1              CORN                  108            79.63          5.49           79.59
62             CROP PAST             235            94.47          45.99          94.42
22             DUR WHEAT             5804           95.9           3.28           95.37
62             FARM                  11             0              0              0
81             FILLER                0              0              0              0
61             FALLOW                1119           97.23          5.39           97.16
61             IDLE CROP             4512           86.7           12.81          85.4
62             NON AGG               1966           81.33          20.29          80.56
28             OATS                  329            93.31          0.97           93.27
25             HAY                   152            87.5           3.62           87.47
25             OTHER HAY             2491           89             22.46          88.34
62             PERM PAST             20790          90.22          4.39           83.95
35             MUSTARD               338            100            3.98           100
27             RYE                   223            100            0              100
23             SPG WHEAT             7449           96.48          4.47           95.86
52             LENTILS               188            97.87          19.65          97.86
53             DRYPEAS               128            96.88          3.13           96.87
82             URBAN                 0              0              100            0
83             WATER                 0              0              100            0
44             CRAMBE                189            99.47          0              99.47
               OVERALL               50251          91.89                         89.69

***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 entire state is covered by the Cropland Data Layer.
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:

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

Title:

Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+)

Geospatial_Data_Presentation_Form: remote-sensing image
Publication_Information:

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

Publication_Date: 2003
Other_Citation_Details:

The Landsat 5 TM and Landsat 7 ETM+ data is free for download through the following website <https://glovis.usgs.gov/>. Additional information about Landsat data can be obtained at <https://www.usgs.gov/centers/eros>. Refer to the 'Supplemental Information' Section of this metadata file for specific scene date, path and rows used as classification inputs.

Source_Scale_Denominator: 30 meter
Type_of_Source_Media: CD-ROM and/or DVD; online download
Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 20030101
Ending_Date: 20031231

Source_Currentness_Reference: ground condition

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

Process_Step:

Process_Description:

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

Process_Date: 2003
Process_Contact:

Contact_Information:

Contact_Organization_Primary:

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

Contact_Address:

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

2003 North Dakota Cropland Data Layer | NASS/USDA

Metadata: