USDA - NATIONAL AGRICULTURE STATISTICS SERVICE'S 1:100,000-SCALE 2002 CROPLAND DATA LAYER, A Crop-Specific Digital Data Layer for New York


Identification_Information:
Citation:
Citation_Information:
Originator:United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS), Research and Development Division, Geospatial Information Branch, Spatial Analysis Research Section (SARS)
Publication_Date:20060101
Title:
USDA - NATIONAL AGRICULTURE STATISTICS SERVICE'S 1:100,000-SCALE 2002 CROPLAND DATA LAYER, A Crop-Specific Digital Data Layer for New York
Edition:2002 edition
Geospatial_Data_Presentation_Form:remote-sensing image
Series_Information:
Series_Name:USDA-NASS 2002 Pennsylvania Cropland Data Layer
Issue_Identification:2002 edition
Publication_Information:
Publication_Place:USDA-NASS Marketing Division, Washington, D.C.
Publisher:United States Department of Agriculture (USDA), National Agriculture Statistics Service (NASS)
Other_Citation_Details:
Available only on DVD through the official website http://www.nass.usda.gov/research/Cropland/SARS1a.htm.
Online_Linkage: http://www.pasda.psu.edu/
Description:
Abstract:
The USDA-NASS 2002 New York Cropland Data Layer (CDL) is a raster, geo-referenced, categorized land cover data layer produced using satellite imagery from the Thematic Mapper (TM) instrument on Landsat 5 and the Enhanced Thematic Mapper (ETM+) on Landsat 7. The imagery was collected between April 24, 2002 and September 12, 2002. The approximate scale is 1:100,000 with a ground resolution of 30 meters by 30 meters. The CDL emphasis is on agricultural land cover. The area of coverage is the entire State of New York 

      This land cover dataset is part of a one-time series in which ten Mid-Atlantic States were categorized based on the extensive field observations collected during the 2002 annual NASS June Agricultural Survey. No farmer reported data is included or derivable from the Cropland Data Layer. The area of coverage for the 2002 Mid-Atlantic CDL includes the entire states of Connecticut, Delaware, Maryland, New Jersey, New York, North Carolina, Pennsylvania, Rhode Island, Virginia and West Virginia. The funding for this project was shared between the USDA-NASS and Towson State University.

      The 2002 Mid-Atlantic CDL is currently a special one-time project. However, the possibility does exist to establish an annual cropland data layer for any state that shows significant interest and can offer an in-state cooperative agreement with another federal, state, local, or university agency or group. If interested, please contact the Section Head of the USDA-NASS Spatial Analysis Research Section at 703/877-8000.

      There are several additional Mid-Western States for which Cropland Data Layers are produced on an annual basis. The website below provides information and examples of all publicly available Cropland Data Layers:  http://www.nass.usda.gov/research/Cropland/SARS1a.htm
Purpose:
The purpose of the Cropland Data Layer Program is to use satellite imagery 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.
Supplemental_Information:
PEDITOR is used as the NASS' main image processing software. PEDITOR has been maintained in-house and contains much of the functionality available in commercial image processing systems. However, program/process modifications are relatively easy to support in a research type environment, and the development/release cycle is faster. PEDITOR is deployed in all participating NASS State Statistical Field Offices to handle the ground truthing process and all image processing tasks, and is continuously tested with the Spatial Analysis Research Section (SARS) in Fairfax, Virginia. Currently, PEDITOR runs on most Microsoft Windows platforms.

      Additional information about ArcExplorer from ESRI can be found at http://www.esri.com/arcexplorer.

      Additional information about EarthSat Inc's ortho-rectified GeoCover Stock used to georegister the NASS Cropland Data Layer can be found at http://www.geocover.com/.

      Towson University, Center for Geographic Information Sciences (CGIS):
      http://cgis.towson.edu/
Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date:20020424
Ending_Date:20020912
Currentness_Reference:
ground condition
Status:
Progress:Complete
Maintenance_and_Update_Frequency:None planned
Spatial_Domain:
Bounding_Coordinates:
West_Bounding_Coordinate:-79.996683
East_Bounding_Coordinate:-71.650003
North_Bounding_Coordinate:45.022995
South_Bounding_Coordinate:40.405042
Keywords:
Theme:
Theme_Keyword_Thesaurus:ISO 19115 Topic Categories
Theme_Keyword:crop cover
Theme_Keyword:classification
Theme_Keyword:cropland
Theme_Keyword:agriculture
Theme_Keyword:land cover
Theme_Keyword:crop estimates
Theme_Keyword:crop identification
Theme_Keyword:Landsat
Theme_Keyword:farming
Place:
Place_Keyword:New York
Place_Keyword:Mid-Atlantic
Temporal:
Temporal_Keyword:2002
Access_Constraints:none
Use_Constraints:
There are NO copyright restrictions with either the NASS Cropland categorized imagery or ESRI's ArcExplorer software included on the DVD. 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.
Point_of_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person:USDA-NASS Spatial Analysis Research Section staff
Contact_Organization:USDA-NASS Spatial Analysis Research Section
Contact_Address:
Address_Type:mailing address
Address:
3251 Old Lee Highway, Rm 305
City:Fairfax
State_or_Province:Virginia
Postal_Code:22030-1504
Country:United States
Contact_Voice_Telephone:(703) 877-8000
Contact_Facsimile_Telephone:(703) 877-8044
Contact_Electronic_Mail_Address:HQ_RD_OD@nass.usda.gov
Native_Data_Set_Environment:
Microsoft Windows XP Version 5.1 (Build 2600) Service Pack 2; ESRI ArcCatalog 9.0.0.535
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Data_Quality_Information:
Attribute_Accuracy:
Quantitative_Attribute_Accuracy_Assessment:
Logical_Consistency_Report:
Data_Quality_Information:
  Attribute_Accuracy:
    Attribute_Accuracy_Report: Classification accuracy is generally between 85% to 95% correct for agricultural-related land cover categories. Due to the extensiveness of the attribute accuracy  report, the accuracy metadata is published on the DVD in an html format. The DVD contains details of 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.
    Quantitative_Attribute_Accuracy_Assessment:
      Attribute_Accuracy_Value: Classification accuracy is generally between 85% to 95% correct for agricultural-related land cover categories.
      Attribute_Accuracy_Explanation: 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. 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 a 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.
  Logical_Consistency_Report: The accuracy of the land cover classifications are evaluated using the extensive training data collected in the annual NASS June Agricultural Survey (JAS).
  Completeness_Report: The area of coverage is the entire State of Pennsylvania.
  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. See EarthSat's http://www.geocover.com/ website for further details.
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            Publication_Place: Salt Lake City, Utah, USA
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          Title: Area Sampling Frame (ASF) of Pennsylvania
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          Publication_Information:
            Publisher: USDA-NASS
            Publication_Place: Washington D.C., USA
          Publication_Date: 2000
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Positional_Accuracy:
Horizontal_Positional_Accuracy:
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Lineage:
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Publication_Information:
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.

        In addition to the JAS segments, the 2002 growing season had extra segments associated with the 2002 Census of Agriculture. More information on the Census of Agricultre can be found at http://www.nass.usda.gov/census/census02/volume1/us/us2appxc.pdf. The Census segments, called Agricultural Coverage Evaluation Survey (ACES) segments, were designed to increase the coverage of different land use stratum and smaller acreage crop covers.

        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 that 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.

        The Cropland Data Layer DVD products contain two years (if available) of imagery in a GEOTIFF image file format. 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 DVD 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 DVD 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.
Process_Date:Unknown
Process_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person:USDA-NASS Spatial Analysis Research Section staff
Contact_Organization:USDA-NASS Spatial Analysis Research Section
Contact_Address:
Address_Type:mailing address
Address:
3251 Old Lee Highway, Rm 305
City:Fairfax
State_or_Province:Virginia
Postal_Code:22030-1504
Country:United States
Contact_Voice_Telephone:(703) 877-8000
Contact_Facsimile_Telephone:(703) 877-8044
Contact_Electronic_Mail_Address:HQ_RD_OD@nass.usda.gov
Process_Step:
Process_Description:
Metadata imported.
Source_Used_Citation_Abbreviation:
E:\UCI\AG_census\crops2006.xml
Cloud_Cover:Reference the DVD for cloud coverage information.
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Spatial_Data_Organization_Information:
Direct_Spatial_Reference_Method:Raster
Point_and_Vector_Object_Information:
SDTS_Terms_Description:
SDTS_Point_and_Vector_Object_Type:G-polygon
Point_and_Vector_Object_Count:7
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Spatial_Reference_Information:
Horizontal_Coordinate_System_Definition:
Planar:
Grid_Coordinate_System:
Grid_Coordinate_System_Name:Universal Transverse Mercator
Universal_Transverse_Mercator:
UTM_Zone_Number:18
Transverse_Mercator:
Scale_Factor_at_Central_Meridian:0.999600
Longitude_of_Central_Meridian:-75.000000
Latitude_of_Projection_Origin:0.000000
False_Easting:500000.000000
False_Northing:0.000000
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Coordinate_Representation:
Abscissa_Resolution:30.000000
Ordinate_Resolution:30.000000
Planar_Distance_Units:meters
Geodetic_Model:
Horizontal_Datum_Name:D_WGS_1984
Ellipsoid_Name:WGS_1984
Semi-major_Axis:6378137.000000
Denominator_of_Flattening_Ratio:298.257224
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Entity_and_Attribute_Information:
Detailed_Description:
Entity_Type:
Entity_Type_Label:Band_1
Attribute:
Attribute_Label:ObjectID
Attribute_Definition:
Internal feature number.
Attribute_Definition_Source:
ESRI
Attribute_Domain_Values:
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Sequential unique whole numbers that are automatically generated.
Attribute:
Attribute_Label:Value
Attribute:
Attribute_Label:Red
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Attribute_Label:Blue
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 a 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.
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Distribution_Information:
Distributor:
Contact_Information:
Contact_Person_Primary:
Contact_Person:USDA-NASS Customer Service
Contact_Organization:USDA-NASS Customer Service
Contact_Address:
Address_Type:mailing address
Address:
1400 Independence Avenue, SW, Room 5829-S
State_or_Province:Washington DC
Postal_Code:20250-9410
Country:United States
Contact_Voice_Telephone:1-800-727-9540
Contact_Facsimile_Telephone:(703) 877-8044
Contact Instructions:
To order a 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 DVD and/or DVD(s)) 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.
Resource_Description:downloadabledata
Distribution_Liability:
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 on the CDL in the Frequently Asked Questions (FAQ's) section on this CDL website, and on the ordered DVD's. Also, there are substantial statistical performance measures by Analysis District within a State on the Landsat data categorization accuracies for each DVD for each year.
Standard_Order_Process:
Digital_Form:
Digital_Transfer_Information:
Format_Name:GEOTIFF
Format_Version_Number:2002
Format_Specification:
GEOTIFF
Transfer_Size:350.618
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Distribution_Information:
Distributor:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization:Pennsylvania Spatial Data Access (PASDA)
Contact_Address:
Address_Type:mailing address
Address:
115 Land and Water building
City:University Park
State_or_Province:Pennsylvania
Postal_Code:16802
Country:United States
Contact_Voice_Telephone:(814) 865 - 8792
Contact_Electronic_Mail_Address:pasda@psu.edu
Resource_Description:downloadabledata
Distribution_Liability:
The USER shall indemnify, save harmless, and, if requested, defend those parties involved with the development and distribution of this data, their officers, agents, and employees from and against any suits, claims, or actions for injury, death, or property damage arising out of the use of or any defect in the FILES or any accompanying documentation. Those parties involved with the development and distribution excluded any and all implied warranties, including warranties or merchantability and fitness for a particular purpose and makes no warranty or representation, either express or implied, with respect to the FILES or accompanying documentation, including its quality, performance, merchantability, or fitness for a particular purpose. The FILES and documentation are provided "as is" and the USER assumes the entire risk as to its quality and performance. Those parties involved with the development and distribution of this data will not be liable for any direct, indirect, special, incidental, or consequential damages arising out of the use or inability to use the FILES or any accompanying documentation.
Standard_Order_Process:
Digital_Form:
Digital_Transfer_Information:
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Metadata_Reference_Information:
Metadata_Date:20080612
Metadata_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization:Pennsylvania Spatial Data Access (PASDA)
Contact_Position:Metadata Coordinator
Contact_Address:
Address_Type:mailing address
Address:
115 Land and Water building
City:University Park
State_or_Province:Pennsylvania
Postal_Code:16802
Country:United States
Contact_Voice_Telephone:(814) 865 - 8792
Contact_Electronic_Mail_Address:pasda@psu.edu
Metadata_Standard_Name:FGDC Content Standards for Digital Geospatial Metadata
Metadata_Standard_Version:FGDC-STD-001-1998
Metadata_Time_Convention:local time
Metadata_Extensions:
Online_Linkage:
Profile_Name:ESRI Metadata Profile
Metadata_Extensions:
Online_Linkage:
Profile_Name:ESRI Metadata Profile
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