CROPS_2003_USDA_IN: Crops in Indiana for 2003, Derived from National Agricultural Statistics Service (United States Department of Agriculture, 1:100,000, 30-Meter TIFF Image)

Metadata also available as - [Parseable text]

Metadata:


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: 20040314
Title:
CROPS_2003_USDA_IN: Crops in Indiana for 2003, Derived from National Agricultural Statistics Service (United States Department of Agriculture, 1:100,000, 30-Meter TIFF Image)
Edition: 2003 edition
Geospatial_Data_Presentation_Form: Raster digital data
Series_Information:
Series_Name:
USDA-NASS Cropland Data Layer, an annual publication begun in 2000
Issue_Identification: 2003 edition
Publication_Information:
Publication_Place: USDA-NASS Marketing Division, Washington, D.C.
Publisher:
United States Department of Agriculture (USDA), National Agriculture Statistics Service (NASS)
Online_Linkage: <http://igs.indiana.edu/arcims/statewide/download.html>
Other_Citation_Details:
This data set was provided on December 28, 2004, to personnel of the Indiana Geological Survey (IGS) by Rick Mueller, Spatial Analyis Research Section (SARS) of the National Agriculture Statistics Service (NASS), United States Department of Agriculture (USDA).

The source data set was provided as a georeferenced TIFF image named "IN03NASS.TIF." The original data set and its metadata are available on CD-ROM through the official NASS USDA Web site <http://www.nass.usda.gov/research/Cropland/SARS1a.htm>.

NOTE -- The georeferenced Tiff image named "IN03NASS.TIF" that was created by the USDA is identical to the georeferenced TIFF image named "CROPS_2003_USDA_IN.TIF" that is distributed by the Indiana Geological Survey. Only the filename and metadata have been modified to conform to IGS conventions.

Description:
Abstract:
The following is excerpted from the metadata provided by NASS (USDA) for the source data set IN03NASS.TIF:

"The USDA-NASS 2003 Indiana Cropland Data Layer is a raster, geo-referenced, categorized land cover data layer produced using satellite imagery from the Thematic Mapper (TM) instrument on Landsat 5 and the Enhanced Thematic Mapper (ETM+) on Landsat 7. The imagery was collected between the dates of April 11, 2003 and August 26, 2003. The approximate scale is 1:100,000 with a ground resolution of 30 meters by 30 meters. The Indiana data layer is aggregated to 13 standardized categories for display purposes with the emphasis being agricultural land cover.

This is part of an annual series in which several states are categorized annually based on the extensive field observations collected during the annual NASS June Agricultural Survey. However, no farmer reported data is included or derivable on the Cropland Data Layer CD-ROM."

Purpose:
The following is excerpted from the metadata provided by NASS (USDA) for the source data set IN03NASS.TIF:

"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."

Supplemental_Information:
The following is excerpted from the metadata provided by NASS (USDA) for the source data set IN03NASS.TIF:

"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; however, PEDITOR's batch processing system programs only runs under Windows NT or 2000.

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/>."

Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20030411
Ending_Date: 20030826
Currentness_Reference: ground condition
Status:
Progress: Complete
Maintenance_and_Update_Frequency: None planned
Spatial_Domain:
Bounding_Coordinates:
West_Bounding_Coordinate: -88.1614
East_Bounding_Coordinate: -84.8183
North_Bounding_Coordinate: 41.7753
South_Bounding_Coordinate: 37.7553
Keywords:
Theme:
Theme_Keyword_Thesaurus: Geography Network Keyword Thesaurus
Theme_Keyword: farming
Theme_Keyword: imageryBaseMapsEarthCover
Theme:
Theme_Keyword_Thesaurus: IGS Metadata Thesaurus
Theme_Keyword: Indiana Geological Survey (IGS)
Theme_Keyword: United States Department of Agriculture (USDA)
Theme_Keyword: National Agricultural Statistics Service (NASS)
Theme_Keyword: Spatial Analyis Research Section (SARS)
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

Temporal:
Temporal_Keyword_Thesaurus: None
Temporal_Keyword: 2003

Place:
Place_Keyword_Thesaurus: IGS Metadata Thesaurus
Place_Keyword: Indiana
Place_Keyword: Adams County
Place_Keyword: Allen County
Place_Keyword: Bartholomew County
Place_Keyword: Benton County
Place_Keyword: Blackford County
Place_Keyword: Boone County
Place_Keyword: Brown County
Place_Keyword: Carroll County
Place_Keyword: Cass County
Place_Keyword: Clark County
Place_Keyword: Clay County
Place_Keyword: Clinton County
Place_Keyword: Crawford County
Place_Keyword: Daviess County
Place_Keyword: Dearborn County
Place_Keyword: Decatur County
Place_Keyword: Dekalb County
Place_Keyword: Delaware County
Place_Keyword: Dubois County
Place_Keyword: Elkhart County
Place_Keyword: Fayette County
Place_Keyword: Floyd County
Place_Keyword: Fountain County
Place_Keyword: Franklin County
Place_Keyword: Fulton County
Place_Keyword: Gibson County
Place_Keyword: Grant County
Place_Keyword: Greene County
Place_Keyword: Hamilton County
Place_Keyword: Hancock County
Place_Keyword: Harrison County
Place_Keyword: Hendricks County
Place_Keyword: Henry County
Place_Keyword: Howard County
Place_Keyword: Huntington County
Place_Keyword: Jackson County
Place_Keyword: Jasper County
Place_Keyword: Jay County
Place_Keyword: Jefferson County
Place_Keyword: Jennings County
Place_Keyword: Johnson County
Place_Keyword: Knox County
Place_Keyword: Kosciusko County
Place_Keyword: Lagrange County
Place_Keyword: Lake County
Place_Keyword: LaPorte County
Place_Keyword: Lawrence County
Place_Keyword: Madison County
Place_Keyword: Marion County
Place_Keyword: Marshall County
Place_Keyword: Martin County
Place_Keyword: Miami County
Place_Keyword: Monroe County
Place_Keyword: Montgomery County
Place_Keyword: Morgan County
Place_Keyword: Newton County
Place_Keyword: Noble County
Place_Keyword: Ohio County
Place_Keyword: Orange County
Place_Keyword: Owen County
Place_Keyword: Parke County
Place_Keyword: Perry County
Place_Keyword: Pike County
Place_Keyword: Porter County
Place_Keyword: Posey County
Place_Keyword: Pulaski County
Place_Keyword: Putnam County
Place_Keyword: Randolph County
Place_Keyword: Ripley County
Place_Keyword: Rush County
Place_Keyword: Scott County
Place_Keyword: Shelby County
Place_Keyword: Spencer County
Place_Keyword: Starke County
Place_Keyword: Steuben County
Place_Keyword: St Joseph County
Place_Keyword: Sullivan County
Place_Keyword: Switzerland County
Place_Keyword: Tippecanoe County
Place_Keyword: Tipton County
Place_Keyword: Union County
Place_Keyword: Vanderburgh County
Place_Keyword: Vermillion County
Place_Keyword: Vigo County
Place_Keyword: Wabash County
Place_Keyword: Warren County
Place_Keyword: Warrick County
Place_Keyword: Washington County
Place_Keyword: Wayne County
Place_Keyword: Wells County
Place_Keyword: White County
Place_Keyword: Whitley County

Access_Constraints:
This file is available to anyone, but access may be contingent on written request, specific terms relevant to the agency or person making the request, and (or) current freedom of information statutes in the state of Indiana.
Use_Constraints:
The following is excerpted from the metadata provided by NASS (USDA) for the source data set IN03NASS.TIF:

"There are NO copyright restrictions with either the NASS Cropland categorized imagery or ESRI's ArcExplorer software included on the CD-Rom. 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."

INDIANA GEOLOGICAL SURVEY DATA DISCLAIMER

This data set is provided by Indiana University, Indiana Geological Survey, and contains data believed to be accurate; however, a degree of error is inherent in all data. This product is distributed "AS-IS" without warranties of any kind, either expressed or implied, including but not limited to warranties of suitability of a particular purpose or use. No attempt has been made in either the designed format or production of these data to define the limits or jurisdiction of any federal, state, or local government.

These data are intended for use only at the published scale or smaller and are for reference purposes only. They are not to be construed as a legal document or survey instrument. A detailed on-the-ground survey and historical analysis of a single site may differ from these data.

CREDIT

It is requested that the National Agricultural Statistics Service (NASS), United States Department of Agriculture (USDA), be cited in any products generated from this data set. The following source citation should be included: [CROPS_2003_USDA_IN: Crops in Indiana for 2003, Derived from National Agricultural Statistics Service (United States Department of Agriculture, 1:100,000, 30-Meter TIFF Image)].

WARRANTY

Indiana University, Indiana Geological Survey warrants that the media on which this product is stored will be free from defect in materials and workmanship for ninety (90) days from the date of acquisition. If such a defect is found, return the media to Publication Sales, Indiana Geological Survey, 611 North Walnut Grove, Bloomington, Indiana 47405-2208, and it will be replaced free of charge.

LIMITATION OF WARRANTIES AND LIABILITY

Except for the expressed warranty above, the product is provided "AS IS", without any other warranties or conditions, expressed or implied, including, but not limited to, warranties for product quality, or suitability to a particular purpose or use. The risk or liability resulting from the use of this product is assumed by the user. Indiana University, Indiana Geological Survey shares no liability with product users indirect, incidental, special, or consequential damages whatsoever, including, but not limited to, loss of revenue or profit, lost or damaged data or other commercial or economic loss. Indiana University, Indiana Geological Survey is not responsible for claims by a third party. The maximum aggregate liability to the original purchaser shall not exceed the amount paid by you for the product.

Point_of_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: Indiana Geological Survey
Contact_Address:
Address_Type: Mailing and physical address
Address: 611 North Walnut Grove
City: Bloomington
State_or_Province: Indiana
Postal_Code: 47405-2208
Country: USA
Contact_Voice_Telephone: 812-855-7636
Contact_Facsimile_Telephone: 812-855-2862
Contact_Electronic_Mail_Address: IGSinfo@indiana.edu
Hours_of_Service: 0800 to 1700 Eastern Standard Time
Contact_Instructions: Monday through Friday, except holidays

Data_Set_Credit:
It is requested that the National Agricultural Statistics Service (NASS), United States Department of Agriculture (USDA), be cited in any products generated from this data set.
Native_Data_Set_Environment: Georeferenced TIFF image, approximately 136 Mb.


Data_Quality_Information:
Attribute_Accuracy:
Attribute_Accuracy_Report:
The following is excerpted from the metadata provided by NASS (USDA) for the source data set IN03NASS.TIF:

"Due to the extensiveness of the attribute accuracy report, the accuracy metadata is published on the CD-ROM 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.

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 following is excerpted from the metadata provided by NASS (USDA) for the source data set IN03NASS.TIF:

"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 following is excerpted from the metadata provided by NASS (USDA) for the source data set IN03NASS.TIF:

"The entire state of Indiana is included in this data set."

Positional_Accuracy:
Horizontal_Positional_Accuracy:
Horizontal_Positional_Accuracy_Report:
The following is excerpted from the metadata provided by NASS (USDA) for the source data set IN03NASS.TIF:

"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.

The horizontal positional accuracy value is 50 meters root mean squared error overall.

The GeoCover Stock Mosaics are within 50 meters root mean squared error overall. See EarthSat's <http://www.geocover.com/> website for further details."

Vertical_Positional_Accuracy:
Vertical_Positional_Accuracy_Report:
No information about vertical positonal accurance was included in the metadata provided by NASS (USDA) for the source data set IN03NASS.TIF.

Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Originator:
National Agricultural Statistics Service, United States Department of Agriculture
Publication_Date: 20040314
Title: IN03NASS.TIF
Edition: 2003
Geospatial_Data_Presentation_Form: Raster digital data
Publication_Information:
Publication_Place: Fairfax, Virginia
Publisher: United States Department of Agriculture
Other_Citation_Details:
This data set was provided on December 28, 2004, to personnel of the Indiana Geological Survey (IGS) by Rick Mueller, Spatial Analyis Research Section (SARS) of the National Agriculture Statistics Service (NASS), United States Department of Agriculture (USDA).

The source data set was provided as a georeferenced TIFF image named "IN03NASS.TIF." The original data set and its metadata are available on CD-Rom through the official NASS USDA Web site <http://www.nass.usda.gov/research/Cropland/SARS1a.htm>.

NOTE -- The georeferenced Tiff image named "IN03NASS.TIF" that was created by the USDA is identical to the georeferenced TIFF image named "CROPS_2003_USDA_IN.TIF" that is distributed by the Indiana Geological Survey. Only the filename and metadata have been modified to conform to IGS conventions.

Type_of_Source_Media: CD-ROM
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20030411
Ending_Date: 20030826
Source_Currentness_Reference:
Based on metadata provided by NASS (USDA) for the source data set named IN03NASS.TIF.
Source_Citation_Abbreviation: IN03NASS.TIF
Source_Contribution: Crops in Indiana in 2003.

Process_Step:
Process_Description:
The following is excerpted from the metadata provided by NASS (USDA) for the source data set IN03NASS.TIF:

"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.

The Cropland Data Layer CD-ROM 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 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_Date: 20040215
Process_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA-NASS Remote Sensing Analyst for Indiana
Contact_Person: Patrick Willis
Contact_Address:
Address_Type: Mailing address
Address: 3251 Old Lee Highway, Rm 305
City: Fairfax
State_or_Province: Virgina
Postal_Code: 22030-1504
Country: USA
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:
On December 28, 2004, IGS personnel received a CD-ROM containing crop data for 2003 from the NASS (USDA) in the form of a georeferenced TIFF image. In order to conform to naming and metadata conventions of the Indiana Geological Survey, the image was re-named and the metadata was modified accordingly.
Source_Used_Citation_Abbreviation: IN03NASS.TIF
Process_Date: 20041228
Source_Produced_Citation_Abbreviation: CROPS_2003_USDA_IN.TIF
Process_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Organization: Indiana Geological Survey
Contact_Person: Chris Dintaman
Contact_Position: Geologist, GIS Specialist
Contact_Address:
Address_Type: Mailing and physical address
Address: 611 North Walnut Grove
City: Bloomington
State_or_Province: Indiana
Postal_Code: 47405-2208
Country: USA
Contact_Voice_Telephone: 812-856-5654
Contact_Facsimile_Telephone: 812-855-2862
Contact_Electronic_Mail_Address: cdintama@indiana.edu
Hours_of_Service: 0800 to 1700 Eastern Standard Time
Contact_Instructions: Monday through Friday, except holidays
Process_Step:
Process_Description:
This metadata file was pre-parsed and parsed using CNS (Chew and Spit, v. 2.6.1) and MP (Metadata Parser, v. 2.7.1) software written by Peter N. Schweitzer (United States Geological Survey). The errors generated by MP were all addressed and corrected.
Process_Date: 20050107
Process_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Organization: Indiana Geological Survey
Contact_Person: Chris Dintaman
Contact_Position: Geologist, GIS Specialist
Contact_Address:
Address_Type: Mailing and physical address
Address: 611 North Walnut Grove
City: Bloomington
State_or_Province: Indiana
Postal_Code: 47405-2208
Country: USA
Contact_Voice_Telephone: 812-856-5654
Contact_Facsimile_Telephone: 812-855-2862
Contact_Electronic_Mail_Address: cdintama@indiana.edu
Hours_of_Service: 0800 to 1700 Eastern Standard Time
Contact_Instructions: Monday through Friday, except holidays


Spatial_Data_Organization_Information:
Indirect_Spatial_Reference: Indiana
Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:
Raster_Object_Type: Pixel
Row_Count: 14820
Column_Count: 9625


Spatial_Reference_Information:
Horizontal_Coordinate_System_Definition:
Planar:
Grid_Coordinate_System:
Grid_Coordinate_System_Name: Universal Transverse Mercator
Universal_Transverse_Mercator:
UTM_Zone_Number: 16
Transverse_Mercator:
Scale_Factor_at_Central_Meridian: 0.9996
Longitude_of_Central_Meridian: -87
Latitude_of_Projection_Origin: 0
False_Easting: 500000
False_Northing: 0
Planar_Coordinate_Information:
Planar_Coordinate_Encoding_Method: row and column
Coordinate_Representation:
Abscissa_Resolution: 30
Ordinate_Resolution: 30
Planar_Distance_Units: meters
Geodetic_Model:
Horizontal_Datum_Name: WGS84
Ellipsoid_Name: WGS84
Semi-major_Axis: 6378137.00
Denominator_of_Flattening_Ratio: 298.257223563


Entity_and_Attribute_Information:
Overview_Description:
Entity_and_Attribute_Overview:
The following is excerpted from the metadata provided by NASS (USDA) for the source data set IN03NASS.TIF:

"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."

Entity_and_Attribute_Detail_Citation:
Data Dictionary: USDA - NATIONAL AGRICULTURE STATISTICS SERVICE'S 1:100,000-SCALE 2003 CROPLAND DATA LAYER, A Crop-Specific Digital Data Layer for Indiana, 2004 March 14

Source: USDA - National Agriculture Statistics Service

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 for the state specific assignment of colors to cover type.

Raster

Values and definitions for ROW CROPS 1-20

Categorization Code Land Cover

"1" Corn

"2" Cotton

"3" Rice

"4" Sorghum

"5" Soybeans

"6" Sunflowers

"10" Peanuts

"11" Tobacco

Raster

Values and definitions for SMALL GRAINS & HAY 21-40

Categorization Code Land Cover

"21" Barley

"22" Durum Wheat

"23" Spring Wheat

"24" Winter Wheat (AR,IL,MS,NM)

"25" Other Small Grains & Hay (Oats, Millet, Rye & Winter Wheat, Alfalfa & Other Hay)

"26" Winter Wheat/Soybeans Double Cropped

"27" Rye

"28" Oats

"29" Millet

"31" Canola

"32" Flaxseed

"33" Safflower

"34" Rapeseed

"35" Mustard

"36" Alfalfa

Raster

Values and definitions for OTHER CROPS 41-60

Categorization Code Land Cover

"41" Beets

"42" Dry Edible Beans

"43" Potatoes

"44" Other Crops (Canola, Flaxseed, Safflower & very small acreage crops)

"48" Watermelon

"50" State 560 CRP

"51" State 561 Popcorn

"52" State 562 Snap Beans

"53" State 563 Green Peas

"54" State 564 Pumpkins

"55" State 565 Apples

"56" State 566 Peaches

"57" State 567 Sweet Corn - fresh

"58" State 568 Sweet Corn - processing

"59" State 569 Other Crops

Raster

Values and definitions for OTHER LAND 61-80

Categorization Code Land Cover

"61" Fallow/Idle Cropland

"62" Pasture/Range/CRP/Non Ag (Permanent & Cropland Pasture, Waste & Farmstead)

"63" Woods, Woodland Pasture

"64" Pasture/Range/CRP/Non Ag

"71" State 722 Cottonwood Orchards

Raster

Values and definitions for OTHER 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" Grassland

"90" Mixed Water/Crops

"91" Mixed Water/Clouds

"92" Aquaculture


Distribution_Information:
Distributor:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: Indiana Geological Survey
Contact_Person: Publication Sales
Contact_Position: Clerk
Contact_Address:
Address_Type: Mailing and physical address
Address: 611 North Walnut Grove
City: Bloomington
State_or_Province: Indiana
Postal_Code: 47405-2208
Country: USA
Contact_Voice_Telephone: 812-855-7636
Contact_Facsimile_Telephone: 812-855-2862
Contact_Electronic_Mail_Address: IGSInfo@indiana.edu
Hours_of_Service: 0800 to 1700 Eastern Standard Time
Contact_Instructions: Monday through Friday, except holidays
Resource_Description: Downloadable Data

Distribution_Liability:
CREDIT

It is requested that the National Agricultural Statistics Service (NASS), United States Department of Agriculture (USDA), be cited in any products generated from this data set. The following source citation should be included: [CROPS_2003_USDA_IN: Crops in Indiana for 2003, Derived from National Agricultural Statistics Service (United States Department of Agriculture, 1:100,000, 30-Meter TIFF Image)].

WARRANTY

Indiana University, Indiana Geological Survey warrants that the media on which this product is stored will be free from defect in materials and workmanship for ninety (90) days from the date of acquisition. If such a defect is found, return the media to Publication Sales, Indiana Geological Survey, 611 North Walnut Grove, Bloomington, Indiana 47405-2208, and it will be replaced free of charge.

LIMITATION OF WARRANTIES AND LIABILITY

Except for the expressed warranty above, the product is provided "AS IS", without any other warranties or conditions, expressed or implied, including, but not limited to, warranties for product quality, or suitability to a particular purpose or use. The risk or liability resulting from the use of this product is assumed by the user. Indiana University, Indiana Geological Survey shares no liability with product users indirect, incidental, special, or consequential damages whatsoever, including, but not limited to, loss of revenue or profit, lost or damaged data or other commercial or economic loss. Indiana University, Indiana Geological Survey is not responsible for claims by a third party. The maximum aggregate liability to the original purchaser shall not exceed the amount paid by you for the product.


Distribution_Information:
Distributor:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA/NASS Customer Service
Contact_Person: USDA/NASS Customer Service
Contact_Address:
Address_Type: Mailing address
Address: 1400 Independence Avenue, SW, Room 5829-S
City: Washington
State_or_Province: District of Columbia
Postal_Code: 20250-9410
Country: USA
Contact_Voice_Telephone: 800 727 9540
Contact_Facsimile_Telephone: 202 690 2090
Contact_Instructions:
To order a CD-ROM (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 CD(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: Downloadable Data
Distribution_Liability:
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 on the CDL in the Frequently Asked Questions (FAQ's) section on this CDL website, and on the ordered CD's. Also, there are substantial statistical performance measures by Analysis District within a State on the Landsat data categorization accuracies for each CD for each year.


Metadata_Reference_Information:
Metadata_Date: 20050107
Metadata_Review_Date: 20050107
Metadata_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: Indiana Geological Survey
Contact_Person: Chris Dintaman
Contact_Position: Geologist, GIS Specialist
Contact_Address:
Address_Type: Mailing and physical address
Address: 611 North Walnut Grove
City: Bloomington
State_or_Province: Indiana
Postal_Code: 47405-2208
Country: USA
Contact_Voice_Telephone: 812-856-5654
Contact_Facsimile_Telephone: 812-855-2862
Contact_Electronic_Mail_Address: cdintama@indiana.edu
Hours_of_Service: 0800 to 1700 Eastern Standard Time
Contact_Instructions: Monday through Friday, except holidays
Metadata_Standard_Name: FGDC CSDGM
Metadata_Standard_Version: FGDC-STD-001-1998
Metadata_Access_Constraints: None
Metadata_Use_Constraints:
This metadata file is intended to accompany the data set identified and received from the IGS. It should not to be altered or summarized. IGS does not support secondary distribution. If this data set was received from anyone besides the IGS, this metadata file and the data set it describes may lack integrity.

Generated by mp version 2.7.1 on Fri Jan 07 10:12:48 2005