Beginning in 2007, the Cropland Data Layer will also be available for download online at <http://datagateway.nrcs.usda.gov/>.
Please note that no farmer reported data is derivable from the Cropland Data Layer.
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.
In 2005, NASS began testing the use of Rulequest's See5.0 software rather than Peditor. Check the "Process_Description" section of this metadata file for more details on which methodology was used for this specific state and year. Additional information on Rulequest's See5.0 software can be found at <http://www.rulequest.com/>.
Leica Geosystems ERDAS Imagine and ESRI's ArcGIS are often used in the pre- and post-processing of data. Additional information about Leica Geosystems ERDAS Imagine software can be found at <http://gi.leica-geosystems.com/>. Additional information about ESRI's ArcGIS software can be found at <http://www.esri.com/>.
Additional information about ESRI's ArcReader can be found at <http://www.esri.com/arcreader>.
Additional information about MDA Federal Inc's ortho-rectified GeoCover Stock used to georegister the NASS Cropland Data Layer can be found at <http://www.mdafederal.com/>.
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.
The hardware requirements for processing this data set are as follows: for digitizing/ground truth editing, any of the 32 bit Microsoft OS's will work. For computationally intensive jobs including; scene processing, clustering, classification, estimation and mosaicking a batch type system is utilized where jobs can be queued on different devices, and the minimum requirements are NT/2000/XP.
Image processing is typically performed using PEDITOR, where PEDITOR utilizes the Windows console along with environmental variables, and neither are available with 95/98. PEDITOR as it is now constituted, will only run under the Microsoft Windows operating systems.
A Microsoft Visual FoxPro application called the Remote Sensing Project or RSP is used to manage the ground truth collection process, and track each segment to its completion.
Commercial off the shelf software XLNT from Advanced Systems Concepts, allows for batch job processing on the NT/2000/XP operating systems. SARS utilizes XLNT to run computationally intensive jobs that are shared across network resources to expedite processing. Additional information about XLNT can be found at <http://www.advsyscon.com/>.
In 2005, NASS began testing the use of Rulequest's See5.0 software rather than PEDITOR. Check the "Process_Description" section of this metadata file for more details on which methodology was used for this specific state and year. Additional information on Rulequest's See5.0 software can be found at <http://www.rulequest.com/>.
Leica Geosystems ERDAS Imagine and ESRI's ArcGIS are often used in the pre- and post-processing of data. Additional information about Leica Geosystems ERDAS Imagine software can be found at <http://gi.leica-geosystems.com/>. Additional information about ESRI's ArcGIS software can be found at <http://www.esri.com/>.
Classification accuracy is generally between 85% to 95% correct for agricultural-related land cover categories.
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.
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.
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.
Additional information about IRS AWiFS satellite imagery can be obtained from Space Imaging.
Additional information about IRS AWiFS satellite imagery can be obtained from Space Imaging.
Additional information about IRS AWiFS satellite imagery can be obtained from Space Imaging.
Additional information about IRS AWiFS satellite imagery can be obtained from Space Imaging.
Additional information about IRS AWiFS satellite imagery can be obtained from Space Imaging.
Additional information about IRS AWiFS satellite imagery can be obtained from Space Imaging.
Additional information about IRS AWiFS satellite imagery can be obtained from Space Imaging.
Additional information about IRS AWiFS satellite imagery can be obtained from Space Imaging.
Additional information about IRS AWiFS satellite imagery can be obtained from Space Imaging.
Additional information about IRS AWiFS satellite imagery can be obtained from Space Imaging.
Additional information about IRS AWiFS satellite imagery can be obtained from Space Imaging.
Additional information about IRS AWiFS satellite imagery can be obtained from Space Imaging.
Additional information about IRS AWiFS satellite imagery can be obtained from Space Imaging.
The 2006 Nebraska Cropland Data Layer was developed using Leica Geosystems ERDAS Imagine in tandem with Rulequest See5.0. Both are commercial software packages. ERDAS Imagine, being a comprehensive image processing suite, handled the bulk of the processing steps including preprocessing and managing of the raw imagery and training data, building of the scene classifications, and creation of the final statewide mosaics. See5.0 was solely used to derive the classification rules, based on training data, for which ERDAS Imagine then applies back to the input imagery. Broadly defined, See5.0 is a niche data mining tool that derives decision trees, or a set of if-then rules, to assemble data into categories. It is not a GIS application in itself.
Decision trees offer several advantages over the more traditional maximum likelihood classification method. The advantages include being: 1) non-parametric by nature and thus not reliant on the assumption of the input data being normally distributed, 2) efficient to construct and thus capable handling large and complex data sets, 3) able to incorporate missing and non-continuous data, and 4) able to sort out non-linear relationships. These reasons combined usually lead to improved classifications over the maximum likelihood method. Additionally, there are several varieties of decision tree classifiers but See5.0 stands out because it further employs a statistical technique known as "boosting" which has been shown to improve results even further. More information on Rulequest's See5.0 software can be found at <http://www.rulequest.com/>.
As with the maximum likelihood method, decision tree analysis is a supervised classification technique. Thus, it relies on having a sample of known ground truth areas in which to "train" the classifier. In turn, the classifier can then "learn" how to most reasonably place into a category the rest of the unknown pixels. The best ground truth comes from a statistically representative probability sample that is dense enough to account for the variability of the land cover types that are being mapped. Traditionally, NASS CDLs have utilized ground truth data from the annual June Agricultural Survey (JAS). More information about the JAS can be found at <http://www.nass.usda.gov/Surveys/June_Area/>. To make this survey data available for use within a classification takes a fair amount of labor because the field boundaries and attributes have to be manually digitized into a GIS since natively they are recorded only on paper. More recently, very comprehensive ground truth data has been provided from the Farm Service Agency (FSA) which NASS has begun utilizing as a replacement for the JAS information. The FSA data has the advantage of natively being in a GIS and containing magnitudes more of field level information. Disadvantages include it is not truly a probability sample of land cover and has bias toward subsidized "program" crops. Additional information about the FSA data can be found at <http://www.fsa.usda.gov/> and <http://datagateway.nrcs.usda.gov/>.
All available raw satellite imagery for the region was used as input along with the non-agricultural portion of the United States Geological Survey's (USGS) 2001 National Land Cover Data (NLCD). Additional information on the USGS NLCD can be found at <http://www.mrlc.gov/>.
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 TM or IRS AWIFS platform for acreage estimation. However, other platforms such as Spot or gap-filled Landsat ETM+ 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.
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, incomplete or incorrect the ground truth, 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.
Each categorized scene is co-registered to MDA Federal Inc'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 scenes and the mosaicked scene to get an exact mapping of each pixel from the input 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/CD-ROM products contain imagery in 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 the stats.htm file in the statinfo directory on the CDL CD-ROM or DVD 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.
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-categorized every year. The data on the CD-ROM or DVD is in the public domain, and you are free to do with it as you choose. However, NASS would appreciate acknowledgment or credit regarding the source of the categorized images in any uses that you may have.
Please note that in no case is farmer reported data revealed or derivable from the public use Cropland Data Layer.
The non-agricultural portions of the United States Geological Survey's 2001 National Land Cover Data (NLCD) appears in the 2006 Nebraska Cropland Data Layer.
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
Beginning in 2007, the Cropland Data Layer will also be available for download online at <http://datagateway.nrcs.usda.gov/>.
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/>.
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/>.