USDA-NASS 2006 North Dakota Cropland Data Layer

SDE Raster Dataset

Tags
IRS AWIFS, cropland, classification, agriculture, crop estimates, North Dakota, land cover, crop identification, crop cover

Description

The USDA-NASS 2006 North Dakota Cropland Data (CDL) is a raster, geo-referenced, categorized land cover data layer produced using satellite imagery from the Indian Remote Sensing (IRS) Advanced Wide Field Sensor (AWiFS). The AWiFS ground resolution is 56 meters by 56 meters. The imagery was collected between the dates of 05/17/2006 and 09/30/2006. The CDL is aggregated to a reduced number of standardized categories for display purposes with the emphasis being agricultural land cover. Please note that no farmer reported data is derivable from the Cropland Data Layer.

Summary

The purpose of the Cropland Data Layer Program is to use satellite imagery on an annual basis to (1) provide supplemental acreage estimates for the state's major commodities and (2) produce digital, crop specific, categorized geo-referenced output products. These data are intended for geographic display and analysis at the state level. The cropland data layers are provided "as is". USDA/NASS does not warrant results you may obtain using the data.

Credits

Use limitations

There are NO copyright restrictions with either the NASS Cropland categorized imagery or ESRI's ArcReader software included on the CD-Rom or DVD. Additional information about ESRI's ArcReader can be found at http://www.esri.com/arcreader. The NASS Cropland categorized imagery is considered public domain and FREE to redistribute. However, NASS would appreciate acknowledgment or credit for the usage of our categorized imagery.

Metadata 

Topics and Keywords 

Themes or categories of the resource  farming


Place keywords  North Dakota

Theme keywords  IRS AWIFS, cropland, classification, agriculture, crop estimates, land cover, crop identification, crop cover

Citation 

Title USDA-NASS 2006 North Dakota Cropland Data Layer
Publication date 2007-03-14


Edition 2006 edition


Presentation formats* digital table


Series
Name USDA-NASS Cropland Data Layer, an annual publication begun in 1997
Issue 2006 edition

Other citation details
Available on CD-Rom or DVD through the official website <http://www.nass.usda.gov/research/Cropland/SARS1a.htm>. Beginning in 2007, the data will also be online available for download at <http://datagateway.nrcs.usda.gov/>.



Citation Contacts 

Responsible party
Organization's name United States Department of Agriculture (USDA), National Agriculture Statistics Service (NASS)
Contact's role  publisher


Contact information
Address
Delivery point USDA-NASS Marketing Division, Washington, D.C.



Responsible party
Organization's name United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS), Research and Development Division, Geospatial Information Branch, Spatial Analysis Research Section (SARS)
Contact's role  originator


Resource Details 

Dataset languages  English (UNITED STATES) , English (UNITED STATES)


Status  completed
Spatial representation type* grid


Supplemental information
*Processing environment Microsoft Windows 7 Version 6.1 (Build 7601) Service Pack 1; Esri ArcGIS 10.3.1.4959


*Name NDHUB.LANDCLASS_NASS_2006

Extents 

Extent
Extent
Description
publication date

Extent
Geographic extent
Bounding rectangle
Extent type  Extent used for searching
*West longitude -104.450146
*East longitude -96.389950
*North latitude 49.257001
*South latitude 45.692170
*Extent contains the resource Yes

Extent in the item's coordinate system
*West longitude 102473.000000
*East longitude 690025.000000
*South latitude 5072538.000000
*North latitude 5456026.000000
*Extent contains the resource Yes

Resource Points of Contact 

Point of contact
Individual's name USDA-NASS Spatial Analysis Research Section staff
Organization's name USDA-NASS Spatial Analysis Research Section
Contact's role  point of contact


Contact information
Phone
Voice 703-877-8000
Fax 703-877-8044

Address
Type postal
Delivery point 3251 Old Lee Highway, Rm 305
City Fairfax
Administrative area Virginia
Postal code 22030-1504
Country US
e-mail addressHQ_RD_OD@nass.usda.gov



Resource Maintenance 

Resource maintenance
Update frequency  not planned


Resource Constraints 

Legal constraints
Limitations of use
Disclaimer: Users of our Cropland Data Layer (CDL) and associated raster and vector data files are solely responsible for interpretations made from these products. The CDL is provided "as is". USDA-NASS does not warrant results you may obtain by using the Cropland Data Layer. Feel free to contact our staff at (HQ_RD_OD@nass.usda.gov) if technical questions arise in the use of our Cropland Data Layer. NASS does provide considerable metadata and substantial statistical performance measures in the Frequently Asked Questions (FAQ's) section on the CDL website and on the CD-ROM and/or DVD.

Legal constraints
Limitations of use
Disclaimer: Users of our Cropland Data Layer (CDL) and associated raster and vector data files are solely responsible for interpretations made from these products. The CDL is provided "as is". USDA-NASS does not warrant results you may obtain by using the Cropland Data Layer. Feel free to contact our staff at (HQ_RD_OD@nass.usda.gov) if technical questions arise in the use of our Cropland Data Layer. NASS does provide considerable metadata and substantial statistical performance measures in the Frequently Asked Questions (FAQ's) section on the CDL website and on the CD-ROM and/or DVD.

Constraints
Limitations of use
There are NO copyright restrictions with either the NASS Cropland categorized imagery or ESRI's ArcReader software included on the CD-Rom or DVD. Additional information about ESRI's ArcReader can be found at http://www.esri.com/arcreader. The NASS Cropland categorized imagery is considered public domain and FREE to redistribute. However, NASS would appreciate acknowledgment or credit for the usage of our categorized imagery.

Spatial Reference 

ArcGIS coordinate system
*Type Projected
*Geographic coordinate reference GCS_North_American_1983
*Projection NAD_1983_UTM_Zone_14N
*Coordinate reference details
Projected coordinate system
Well-known identifier 26914
X origin -5120900
Y origin -9998100
XY scale 450445547.3910538
XY tolerance 0.001
High precision true
Latest well-known identifier 26914
Well-known text PROJCS["NAD_1983_UTM_Zone_14N", GEOGCS["GCS_North_American_1983", DATUM["D_North_American_1983", SPHEROID["GRS_1980", 6378137.0, 298.257222101]], PRIMEM["Greenwich", 0.0], UNIT["Degree", 0.0174532925199433]], PROJECTION["Transverse_Mercator"], PARAMETER["False_Easting", 500000.0], PARAMETER["False_Northing", 0.0], PARAMETER["Central_Meridian", -99.0], PARAMETER["Scale_Factor", 0.9996], PARAMETER["Latitude_Of_Origin", 0.0], UNIT["Meter", 1.0], AUTHORITY["EPSG", 26914]]

Reference system identifier
*Value 26914
*Codespace EPSG
*Version 8.6.2


Spatial Data Properties 

Scale Range

Maximum (zoomed in)  1:8,000
Minimum (zoomed out)  1:100,000
Indirect spatial referencing
North Dakota


Data Quality 

Scope of quality information
Resource level  dataset




Scope of quality information
Resource level  dataset




Data quality report - Conceptual consistency
Measure description
The accuracy of the land cover classifications are evaluated using the extensive training data collected in the annual NASS June Area Survey (JAS) and/or the annual Farm Service Agency's (FSA) Common Land Unit (CLU) data. More information about FSA CLUs can be found at <http://www.fsa.usda.gov/> and <http://datagateway.nrcs.usda.gov/>. The June Agricultural Survey 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. Additional information about NASS' June Area Survey can be found at <http://www.nass.usda.gov/Surveys/June_Area/>. Please note that no farmer reported data is derivable from the Cropland Data Layer.





Data quality report - Completeness omission
Measure description
The entire state of North Dakota is included in the Cropland Data Layer.





Data quality report - Quantitative attribute accuracy
Measure description
Due to the extensiveness of the attribute accuracy report, the accuracy metadata is published on the CD-ROM or DVD in an html format. NASS reports the Analysis District coverage, sensors used, percent correct and kappa coefficients, regression analysis by Analysis District, the sampling frame scheme, and the original cover type signatures. Classification accuracy is generally between 85% to 95% correct for agricultural-related land cover categories.



Quantitative test results
Value Classification accuracy is generally between 85% to 95% correct for agricultural-related land cover categories.


Evaluation method
The accuracy of the land cover classifications are evaluated using the extensive training data collected in the annual NASS June Area Survey (JAS) and/or the annual Farm Service Agency's (FSA) Common Land Unit (CLU) data. More information about FSA CLUs can be found at <http://www.fsa.usda.gov/> and <http://datagateway.nrcs.usda.gov/>. The June Agricultural Survey 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. Additional information about NASS' June Area Survey can be found at <http://www.nass.usda.gov/Surveys/June_Area/>. Please note that no farmer reported data is derivable from the Cropland Data Layer.





Data quality report - Absolute external positional accuracy
Dimension horizontal


Measure description
The categorized images are co-registered to MDA Federal 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 MDA Federal Inc 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 test results
Value 50 meters root mean squared error overall


Evaluation method
The GeoCover Stock Mosaics are within 50 meters root mean squared error overall. Additional information about MDA Federal Inc's ortho-rectified GeoCover Stock can be found at <http://www.mdafederal.com/>. When IRS Awifs 56 meter imagery, rather than Landsat TM imagery, is used for the creation of the Cropland Data Layer then the MDA Federal Inc GeoCover Stock Mosaic is resampled from 30 to 56 meters using nearest neighbor.





Lineage 

Process step
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. Once the mosaic is successfully run in Peditor, it is exported into Erdas Imagine LAN format, to prepare for public distribution. It is then necessary to rebuild the image statistics, add color/class name metadata, and rebuild the projection information. The Cropland Data Layer CD-ROM products contain two years (if available) of imagery in two different image formats. ERDAS Imagine GIS format and IMG format (winzip compression). The Cropland Data Layer online image file format is GEOTIFF. In order to maximize the visual contrast between different crops in various states, colors that provide the best contrast for the crop mix in a particular State are chosen. However, the digital values for each category within every State remain the same. So corn in ND will have the same digital number as corn in AR. See mastercat.htm on the CDL CD-ROM in the statinfo directory for a full listing by cover type. 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. The NASS CDL Program is continuing efforts to reduce end-user burden, increase functionality, and take advantage of enhancements in computer technology. The Cropland Data Layer Program is a one of a kind agricultural inventory program, where every state participating in the program is re-surveyed (i.e., ground truthed) every June, and thus re-categorized. The data on the CD-ROM is in the public domain, and you are free to do with it as you choose. NASS would appreciate acknowledgment or credit regarding the source of the categorized images in any uses that you may have. Remember, in no case is farmer reported data revealed or derivable from the public use Cropland Data Layer CD-ROM's.





Process step
When the process occurred 2007-02-15
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. Once the mosaic is successfully run in Peditor, it is exported into Erdas Imagine LAN format, to prepare for public distribution. It is then necessary to rebuild the image statistics, add color/class name metadata, and rebuild the projection information. The Cropland Data Layer CD-ROM products contain two years (if available) of imagery in two different image formats. ERDAS Imagine GIS format and IMG format (winzip compression). The Cropland Data Layer online image file format is GEOTIFF. In order to maximize the visual contrast between different crops in various states, colors that provide the best contrast for the crop mix in a particular State are chosen. However, the digital values for each category within every State remain the same. So corn in ND will have the same digital number as corn in AR. See mastercat.htm on the CDL CD-ROM in the statinfo directory for a full listing by cover type. 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. The NASS CDL Program is continuing efforts to reduce end-user burden, increase functionality, and take advantage of enhancements in computer technology. The Cropland Data Layer Program is a one of a kind agricultural inventory program, where every state participating in the program is re-surveyed (i.e., ground truthed) every June, and thus re-categorized. The data on the CD-ROM is in the public domain, and you are free to do with it as you choose. NASS would appreciate acknowledgment or credit regarding the source of the categorized images in any uses that you may have. Remember, in no case is farmer reported data revealed or derivable from the public use Cropland Data Layer CD-ROM's.



Process contact
Individual's name USDA-NASS Spatial Analysis Research Section staff
Organization's name USDA-NASS Spatial Analysis Research Section
Contact's role  processor


Contact information
Phone
Voice 703-877-8000
Fax 703-877-8044

Address
Type postal
Delivery point 3251 Old Lee Highway, Rm 305
City Fairfax
Administrative area Virginia
Postal code 22030-1504
Country US
e-mail addressHQ_RD_OD@nass.usda.gov





Source data
Description
Raw data used in land cover spectral signature analysis.



Source medium name  CD-ROM
Resolution of the source data
Scale denominator 100000

Source citation
Title AWiFS Path 262, Row 36, Quadrant(s) A, B and C
Publication date 2006-11-01


Presentation formats  digital map
FGDC geospatial presentation format  remote-sensing image


Other citation details
IRS AWiFS Path 262, Row 36, Quadrant(s) A, B and C. 56 meter by 56 meter pixel resolution. Additional information about IRS AWiFS satellite imagery can be obtained from Space Imaging.



Responsible party
Organization's name Indian Remote Sensing (IRS)
Contact's role  originator


Responsible party
Organization's name Space Imaging
Contact's role  publisher


Contact information
Address
Delivery point Thornton, Colorado, USA





Extent of the source data
Description
ground condition

Temporal extent
Date and time 2006-09-05



Source data
Description
Raw data used in land cover spectral signature analysis.



Source medium name  CD-ROM
Resolution of the source data
Scale denominator 100000

Source citation
Title AWiFS Path 261, Row 39, Quadrant(s) B
Publication date 2006-11-01


Presentation formats  digital map
FGDC geospatial presentation format  remote-sensing image


Other citation details
IRS AWiFS Path 261, Row 39, Quadrant(s) B. 56 meter by 56 meter pixel resolution. Additional information about IRS AWiFS satellite imagery can be obtained from Space Imaging.



Responsible party
Organization's name Space Imaging
Contact's role  publisher


Contact information
Address
Delivery point Thornton, Colorado, USA



Responsible party
Organization's name Indian Remote Sensing (IRS)
Contact's role  originator




Extent of the source data
Description
ground condition

Temporal extent
Date and time 2006-07-14



Source data
Description
spatial and attribute information



Source medium name  online link
Resolution of the source data
Scale denominator 100000

Source citation
Title Area Sampling Frame (ASF) of North Dakota
Publication date 1977-01-01


Presentation formats  digital map
FGDC geospatial presentation format  vector digital data


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>



Responsible party
Organization's name USDA-NASS
Contact's role  publisher


Contact information
Address
Delivery point Washington D.C., USA



Responsible party
Organization's name USDA/NASS, Research and Development Division, Area Frame Section
Contact's role  originator




Extent of the source data
Description
publication date

Temporal extent
Date and time 1977-01-01



Source data
Description
spatial and attribute information



Source medium name  online link
Resolution of the source data
Scale denominator 100000

Source citation
Title 2001 National Land Cover Data (NLCD)
Publication date 2006-01-01


Presentation formats  digital map
FGDC geospatial presentation format  remote-sensing image


Other citation details
The Land Cover Characterization of the 2001 NLCD was used to improve the non-agricultural portion of the Cropland Data Layer. More information on the NLCD can be found at <http://www.mrlc.gov/>.



Responsible party
Organization's name USGS, EROS Data Center
Contact's role  publisher


Contact information
Address
Delivery point Souix Falls, South Dakota 57198 USA



Responsible party
Organization's name U.S. Geological Survey EROS Data Center
Contact's role  originator




Extent of the source data
Description
ground condition

Temporal extent
Date and time
Indeterminate date unknown



Source data
Description
Raw data used in land cover spectral signature analysis.



Source medium name  CD-ROM
Resolution of the source data
Scale denominator 100000

Source citation
Title AWiFS Path 267, Row 34, Quadrant(s) C
Publication date 2006-11-01


Presentation formats  digital map
FGDC geospatial presentation format  remote-sensing image


Other citation details
IRS AWiFS Path 267, Row 34, Quadrant(s) C. 56 meter by 56 meter pixel resolution. Additional information about IRS AWiFS satellite imagery can be obtained from Space Imaging.



Responsible party
Organization's name Space Imaging
Contact's role  publisher


Contact information
Address
Delivery point Thornton, Colorado, USA



Responsible party
Organization's name Indian Remote Sensing (IRS)
Contact's role  originator




Extent of the source data
Description
ground condition

Temporal extent
Date and time 2006-09-30

Temporal extent
Date and time 2006-06-02



Source data
Description
Raw data used in land cover spectral signature analysis.



Source medium name  CD-ROM
Resolution of the source data
Scale denominator 100000

Source citation
Title AWiFS Path 261, Row 34, Quadrant(s) A, B, C and D
Publication date 2006-11-01


Presentation formats  digital map
FGDC geospatial presentation format  remote-sensing image


Other citation details
IRS AWiFS Path 261, Row 34, Quadrant(s) A, B, C and D. 56 meter by 56 meter pixel resolution. Additional information about IRS AWiFS satellite imagery can be obtained from Space Imaging.



Responsible party
Organization's name Indian Remote Sensing (IRS)
Contact's role  originator


Responsible party
Organization's name Space Imaging
Contact's role  publisher


Contact information
Address
Delivery point Thornton, Colorado, USA





Extent of the source data
Description
ground condition

Temporal extent
Date and time 2006-07-14



Source data
Description
Raw data used in land cover spectral signature analysis.



Source medium name  CD-ROM
Resolution of the source data
Scale denominator 100000

Source citation
Title AWiFS Path 264, Row 34, Quadrant(s) A, C and D
Publication date 2006-11-01


Presentation formats  digital map
FGDC geospatial presentation format  remote-sensing image


Other citation details
IRS AWiFS Path 264, Row 34, 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.



Responsible party
Organization's name Space Imaging
Contact's role  publisher


Contact information
Address
Delivery point Thornton, Colorado, USA



Responsible party
Organization's name Indian Remote Sensing (IRS)
Contact's role  originator




Extent of the source data
Description
ground condition

Temporal extent
Date and time 2006-08-22



Source data
Description
Raw data used in land cover spectral signature analysis.



Source medium name  CD-ROM
Resolution of the source data
Scale denominator 100000

Source citation
Title AWiFS Path 262, Row 33, Quadrant(s) D
Publication date 2006-11-01


Presentation formats  digital map
FGDC geospatial presentation format  remote-sensing image


Other citation details
IRS AWiFS Path 262, Row 39, Quadrant(s) D. 56 meter by 56 meter pixel resolution. Additional information about IRS AWiFS satellite imagery can be obtained from Space Imaging.



Responsible party
Organization's name Indian Remote Sensing (IRS)
Contact's role  originator


Responsible party
Organization's name Space Imaging
Contact's role  publisher


Contact information
Address
Delivery point Thornton, Colorado, USA





Extent of the source data
Description
ground condition

Temporal extent
Date and time 2006-09-05



Source data
Description
Raw data used in land cover spectral signature analysis.



Source medium name  CD-ROM
Resolution of the source data
Scale denominator 100000

Source citation
Title AWiFS Path 259, Row 33, Quadrant(s) D
Publication date 2006-11-01


Presentation formats  digital map
FGDC geospatial presentation format  remote-sensing image


Other citation details
IRS AWiFS Path 259, Row 33, Quadrant(s) D. 56 meter by 56 meter pixel resolution. Additional information about IRS AWiFS satellite imagery can be obtained from Space Imaging.



Responsible party
Organization's name Space Imaging
Contact's role  publisher


Contact information
Address
Delivery point Thornton, Colorado, USA



Responsible party
Organization's name Indian Remote Sensing (IRS)
Contact's role  originator




Extent of the source data
Description
ground condition

Temporal extent
Date and time 2006-05-17

Temporal extent
Date and time 2006-08-21



Source data
Description
Raw data used in land cover spectral signature analysis.



Source medium name  CD-ROM
Resolution of the source data
Scale denominator 100000

Source citation
Title AWiFS Path 259, Row 36, Quadrant(s) B
Publication date 2006-11-01


Presentation formats  digital map
FGDC geospatial presentation format  remote-sensing image


Other citation details
IRS AWiFS Path 259, Row 36, Quadrant(s) B. 56 meter by 56 meter pixel resolution. Additional information about IRS AWiFS satellite imagery can be obtained from Space Imaging.



Responsible party
Organization's name Space Imaging
Contact's role  publisher


Contact information
Address
Delivery point Thornton, Colorado, USA



Responsible party
Organization's name Indian Remote Sensing (IRS)
Contact's role  originator




Extent of the source data
Description
ground condition

Temporal extent
Date and time 2006-05-17

Temporal extent
Date and time 2006-08-21



Source data
Description
spatial and attribute information



Source medium name  hardcopy—printing on paper
Resolution of the source data
Scale denominator 8000

Source citation
Title NAPP aerial photographs


Presentation formats  digital map
FGDC geospatial presentation format  remote-sensing image


Other citation details
Additional information about NAPP: EROS Data Center Sioux Falls, South Dakota 57198, USA Phone (605)594-6151 fax x6589



Responsible party
Organization's name National Aerial Photography Program (NAPP)
Contact's role  originator


Responsible party
Organization's name Aerial Photography Field Office (AFPO)
Contact's role  publisher


Contact information
Address
Delivery point Salt Lake City, Utah, USA





Extent of the source data
Description
ground condition



Source data
Description
Raw data used in land cover spectral signature analysis.



Source medium name  CD-ROM
Resolution of the source data
Scale denominator 100000

Source citation
Title AWiFS Path 264, Row 36, Quadrant(s) B
Publication date 2006-11-01


Presentation formats  digital map
FGDC geospatial presentation format  remote-sensing image


Other citation details
IRS AWiFS Path 264, Row 36, Quadrant(s) B. 56 meter by 56 meter pixel resolution. Additional information about IRS AWiFS satellite imagery can be obtained from Space Imaging.



Responsible party
Organization's name Indian Remote Sensing (IRS)
Contact's role  originator


Responsible party
Organization's name Space Imaging
Contact's role  publisher


Contact information
Address
Delivery point Thornton, Colorado, USA





Extent of the source data
Description
ground condition

Temporal extent
Date and time 2006-08-22



Distribution 

Distributor
Contact information
Individual's name USDA-NASS Customer Service
Organization's name USDA-NASS Customer Service
Contact's role  distributor


Contact information
Phone
Voice 1-800-727-9540
Fax 703-877-8044

Address
Type postal
Delivery point 1400 Independence Avenue, SW, Room 5038-S
Administrative area Washington DC
Postal code 20250-9410
Country US

Contact instructions
To order a CD-ROM or DVD (see prices as noted at <http://www.nass.usda.gov/research/Cropland/SARS1a.htm>) please fill out the order form and submit it either electronically (invoice will follow with the delivery) or mail the completed form with your check to: USDA/NASS Customer Service, 1400 Independence Avenue, SW, Room 5829-S, Washington DC 20250-9410. Please note "Cropland Data Layer - (State and Year)" in the "Memo" part of your check. Checks should be made out to "USDA-NASS". Allow 1 week for delivery. Beginning in 2007, the Cropland Data Layer will also be available for download online at <http://datagateway.nrcs.usda.gov/>.



Available format
Name GEOTIFF
Version 20060101
Format information content GEOTIFF
Technical prerequisites There are no technical prerequisites requiring special software or hardware to view the data. A freeware browser, ArcReader (Environmental Systems Research Institute, Redlands, CA), is bundled onto the CD-ROM or DVD, allowing for users without a GIS or image processing software package to be able to view the products. Additional information about ESRI's ArcReader can be found at <http://www.esri.com/arcreader>.


Ordering process
Terms and fees The USDA-NASS charges a nominal fee to cover the cost of CD-ROM and DVD production and shipping costs. Please visit <http://www.nass.usda.gov/research/Cropland/SARS1a.htm> for prices. Distribution issues can also be directed to the NASS Customer Service Hotline at 1-800-727-9540.
Date of availability 2007-03-14
Turnaround time
Allow 1 week for delivery.

Instructions
To order a CD-ROM or DVD fill out the order form at <http://www.nass.usda.gov/research/Cropland/SARS1a.htm> and submit it either electronically (invoice will follow with the delivery) or mail the completed form with your check to: USDA-NASS Customer Service, 1400 Independence Avenue, SW, Room 5829-S, Washington DC 20250-9410. Please note "Cropland Data Layer - (State and Year)" in the "Memo" part of your check. Checks should be made out to "USDA-NASS". Allow 1 week for delivery. Distribution issues can also be directed to the NASS Customer Service Hotline at 1-800-727-9540. Beginning in 2007, the Cropland Data Layer will also be available for download online at <http://datagateway.nrcs.usda.gov/>.


Transfer options
Medium of distribution
Recording density 0
Density units of measure MB


Transfer options
Online source
Description  NASS Cropland Data Layer, North Dakota 2006



Distribution format
*Name SDE Raster Dataset


Fields 

Details for object NDHUB.SDE_VAT_40 
*Type Table
*Row count 256


Field OBJECTID
 
*Alias OBJECTID
*Data type OID
*Width 4
*Precision 10
*Scale 0
*Field description
Internal feature number.

*Description source
ESRI

*Description of values
Sequential unique whole numbers that are automatically generated.





Field VALUE
 
*Alias VALUE
*Data type Integer
*Width 4
*Precision 9
*Scale 0




Field RED
 
*Alias RED
*Data type Double
*Width 8
*Precision 38
*Scale 8




Field GREEN
 
*Alias GREEN
*Data type Double
*Width 8
*Precision 38
*Scale 8




Field BLUE
 
*Alias BLUE
*Data type Double
*Width 8
*Precision 38
*Scale 8




Field COUNT
 
*Alias COUNT
*Data type Integer
*Width 4
*Precision 9
*Scale 0




Field CLASS_NAME
 
*Alias CLASS_NAME
*Data type String
*Width 40
*Precision 0
*Scale 0




Field OPACITY
 
*Alias OPACITY
*Data type SmallInteger
*Width 2
*Precision 4
*Scale 0






Fields 

Details for object NDHUB.SDE_VAT_40 
*Type Table
*Row count 256


Field OBJECTID
 
*Alias OBJECTID
*Data type OID
*Width 4
*Precision 10
*Scale 0
*Field description
Internal feature number.

*Description source
ESRI

*Description of values
Sequential unique whole numbers that are automatically generated.





Field VALUE
 
*Alias VALUE
*Data type Integer
*Width 4
*Precision 9
*Scale 0




Field RED
 
*Alias RED
*Data type Double
*Width 8
*Precision 38
*Scale 8




Field GREEN
 
*Alias GREEN
*Data type Double
*Width 8
*Precision 38
*Scale 8




Field BLUE
 
*Alias BLUE
*Data type Double
*Width 8
*Precision 38
*Scale 8




Field COUNT
 
*Alias COUNT
*Data type Integer
*Width 4
*Precision 9
*Scale 0




Field CLASS_NAME
 
*Alias CLASS_NAME
*Data type String
*Width 40
*Precision 0
*Scale 0




Field OPACITY
 
*Alias OPACITY
*Data type SmallInteger
*Width 2
*Precision 4
*Scale 0






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. For instance, there is no
breakdown as to the type of woods in a given field/pasture, that's where
the power of a GIS could be useful. If an external forestry GIS layer was
overlaid, the land use can be accurately identified, and the specific
cover type can be derived from the data layer. SARS is currently looking
at creating extra categories for the enumerators to better identify
non-cropland features, thereby, increasing the accuracy and improving the
appearance of the classification.
The non-agricultural portions of the United States Geological Survey's
2001 National Land Cover Data (NLCD) appears in the 2006 North Dakota
Cropland Data Layer.



Entity and Attribute Detail Citation
Data Dictionary: USDA - NATIONAL AGRICULTURE STATISTICS SERVICE'S
1:100,000-SCALE 2006 CROPLAND DATA LAYER, A Crop-Specific Digital Data
Layer for North Dakota, 2007 March 14

Source: USDA - National Agriculture Statistics Service

The following is a cross reference list of the categorization codes and
land covers used in all states. Note that not all land cover
categories listed below will appear in an individual state. Refer
to the "Cover Type Signatures List" on the CD-ROM or DVD for the state specific
assignment of colors to cover type.

Raster
Attribute Domain Values and Definitions: ROW CROPS 1-20

Categorization Code   Land Cover
"1"           Corn, all
"2"           Cotton
"3"           Rice
"4"           Sorghum
"5"           Soybeans
"6"           Sunflowers
"10"          Peanuts
"11"          Tobacco

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 Grains/Hay
"26"          Winter Wheat/Soybeans Double Cropped
"27"          Rye
"28"          Oats
"29"          Millet
"30"          Speltz
"31"          Canola
"32"          Flaxseed
"33"          Safflower
"34"          Rape seed
"35"          Mustard
"36"          Alfalfa

Raster
Attribute Domain Values and Definitions: OTHER CROPS 41-60

Categorization Code   Land Cover
"41"          Beets
"42"          Dry Beans
"43"          Potatoes
"44"          Other Crops
"45"          Sugar Cane
"46"          Sweet Potatoes
"47"          Misc. Fruit and Veg.
"48"          Watermelon
"50"          State 560 (State-specific crop, see CD or DVD for details)
"51"          State 561 (State-specific crop, see CD or DVD for details)
"52"          State 562 (State-specific crop, see CD or DVD for details)
"53"          State 563 (State-specific crop, see CD or DVD for details)
"54"          State 564 (State-specific crop, see CD or DVD for details)
"55"          State 565 (State-specific crop, see CD or DVD for details)
"56"          State 566 (State-specific crop, see CD or DVD for details)
"57"          State 567 (State-specific crop, see CD or DVD for details)
"58"          State 568 (State-specific crop, see CD or DVD for details)
"59"          State 569 (State-specific crop, see CD or DVD for details)

Raster
Attribute Domain Values and Definitions: FARMLAND USES 61-65

Categorization Code   Land Cover
"61"          Idle Cropland/Fallow/CRP
"62"          Pasture, Non-ag, Range, Waste, Farmstead
"63"          Woodland

Raster
Attribute Domain Values and Definitions: TREE CROPS 66-80

Categorization Code   Land Cover
"67"          Peaches
"68"          Apples
"69"          Grapes
"70"          Christmas Trees
"71"          Orchards, State 721-729 (State-specific orchards,
see CD-ROM or DVD for details),Cottonwood Tree
"72"          Citrus
"73"          Managed Forest
"80"          Other Fruit

Raster
Attribute Domain Values and Definitions: OTHER LAND 81-99

Categorization Code   Land Cover
"81"          Clouds
"82"          Urban
"83"          Water
"84"          Roads/Railroads
"85"          Ditches/Waterways
"86"          Buildings/Homes/Subdivisions
"87"          Wetlands
"88"          Grass/Clover/WildFlowers
"90"          Mixed Water/Crops
"91"          Mixed Water/Clouds
"92"          Aquaculture

Raster
Attribute Domain Values and Definitions: OTHER CROPS 100-119

Categorization Code   Land Cover
"100"          Pickles
"101"          Chick Peas
"102"          Lentils
"103"          Peas
"104"          Fallow Sugarcane

Raster
Attribute Domain Values and Definitions: NLCD OTHER CATEGORIES 120-149

Categorization Code   Land Cover
"120"          Developed, Open Space
"121"          Developed, Low Intensity
"122"          Developed, High Intensity
"123"          Commercial/Industrial/Transportation
"127"          Bare Rock/Sand/Clay
"128"          Quarries/Strip Mines/Gravel Pits
"129"          Transitional
"130"          Barren
"131"          Deciduous Forest
"132"          Evergreen Forest
"133"          Mixed Forest
"136"          Shrubland
"140"          Grasslands/Herbaceous
"142"          Urban/Recreational Grasses
"143"          Woody Wetlands
"144"          Emergent Herbaceous Wetlands





Metadata Details 

Metadata language English (UNITED STATES)
Metadata character set  utf8 - 8 bit UCS Transfer Format


Scope of the data described by the metadata  dataset
Scope name* dataset


*Last update 2016-01-27


ArcGIS metadata properties
Metadata format ArcGIS 1.0


Created in ArcGIS for the item 2008-11-20 11:04:56
Last modified in ArcGIS for the item 2016-01-27 09:41:48


Automatic updates
Have been performed Yes
Last update 2016-01-27 09:41:48


Metadata Contacts 

Metadata contact
Individual's name USDA-NASS Spatial Analysis Research Section staff
Organization's name USDA-NASS Spatial Analysis Research Section
Contact's role  point of contact


Contact information
Phone
Voice 703-877-8000
Fax 703-877-8044

Address
Type postal
Delivery point 3251 Old Lee Highway, Rm 305
City Fairfax
Administrative area Virginia
Postal code 22030-1504
Country US
e-mail addressHQ_RD_OD@nass.usda.gov



Metadata Maintenance 

Maintenance
Date of next update
Update frequency  unknown


Other maintenance requirements
Last metadata review date: None planned



Metadata Constraints 

Legal constraints
Access constraints  other restrictions


Other constraints
No restrictions on the distribution or use of the metadata file.

Constraints
Limitations of use
No restrictions on the distribution or use of the metadata file. Generated by mp version 2.7.33 on Sat Mar 03 03:58:56 2007