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- Remote sensing based cropland acreage indications
- County and state level “major crops”
- Internal to NASS
- Considered for setting official estimates
- Target fall/winter delivery for major commodities
- Output categorized crop specific Cropland Data Layer
- Distribute to public at the cost of reproduction
- "Freeware" viewer – ESRI’s ArcReader
- Accuracy statistics
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- United States Dept. of Agriculture
- National Agricultural Statistics Service
- June Agricultural Survey (JAS) – National in Scope
- 41,000 farms visited
- 11,000 one-square mile sample area segments visited
- Most states contain between 150 – 400 segments
- Derive planted acreage estimate
- Cropland Data Layer piggybacks on JAS
- Unbiased statistical estimator of crop area
- State and county level estimates
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- USDA/NASS Research Division
- Spatial Analysis Research Section
- Remote sensing analysts
- Software developers
- USDA/Foreign Ag Service
- PECAD (Production Estimates & Crop Assessment Division)
- Landsat 5 & 7 imagery source
- State/Federal Cooperators
- NASS Field Offices
- Remote sensing analysts
- Digitizers & ground truth editors
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- Internal
- Digitize the entire June Agricultural Survey (JAS)
- Cleans up JAS problems
- Produce numerical cropland acreage indications at the county and state
level
- Unbiased statistical estimator of crop area
- Data sharing among partners
- External
- Ortho-rectified image product Commercial image formats
- Released annually after county estimates are published
- Detailed breakdown of cropland area via large training sample
- Distribute to public at the cost of reproduction
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- Hardware
- Computational intensive jobs (i.e. cluster/classify/mosaic)
- Digitizing/ground truth editing
- Software
- Image processing
- PEDITOR developed in-house
- Digitizing/ground truth editing
- Batch job processing
- XLNT – COTS
- www.advsyscon.com
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- PEDITOR - 1970’s
- Developed in Delphi, Pascal and Fortran
- Performs
- Digitizing/clustering/classification/estimation/mosaicking
- Optimized for dual processors
- “Expert” rules built into processing routines
- Capable of importing/exporting to commercial world
- Remote Sensing Project (RSP) – 1990’s
- Developed in Microsoft Visual FoxPro
- Manages ground truth database
- Performs digitizing and ground truth editing
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- Landsat 5 launched 1984 (3 yr design life!)
- Thematic Mapper (TM) Sensor
- Landsat 7 launched April 15, 1999
- Enhanced Thematic Mapper (ETM+) Sensor
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- 185 kilometer swath width
- 16 day repeat coverage (each)
- Synchronized for 8 day repeat coverage
- 7 imaging bands* per sensor
- 6 bands @ 30m resolution
- 3 Visible & 1 Near Infrared (IR)
- 2 Short-wave IR
- 1 thermal IR band
- TM: @120m resolution
- ETM+: @60m resolution
- *ETM+ has one 15m Panchromatic Band
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- Stratify based on percent cultivated land
- Subdivide strata into primary sampling units or PSU's
- Selected PSU's divided into secondary sampling units or segments
- Segments are selected randomly & used in
- successive years
- 20 percent segment rotation/year
- Entire sample replaced every 5 years
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- June Agricultural Survey (JAS) segments
- Enumerator records field extents, cover types, and acreage
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- June Agricultural Survey (JAS) + Agriculture Coverage Evaluation Survey
(ACES)
- Between 100 - 425 one square mile segments per state
- Use most fields for training classifier
- Digitize & label all fields in each segment
- Uses heads up on screen digitizing
- Compare digitized acres to JAS/ACES reported acres
- Check fields for accuracy & consistency on current years’ imagery
- Eliminate fields considered bad for training
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- Enumerators account for all land usage in segment
- Draw off field location by direct observation
- Directly link questionnaire to segment photo
- Able to ask questions not related to acreage
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- Ground data finalized
- Clustering, Classification and Regression Estimation
- Use Batch processing capabilities
- Built in set of “Expert” rules
- Share network resources
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- Can be defined by
- Contiguous same date coverage
- Scene edge or county boundary
- Cloud free or near
- Unitemporal
- Multi-temporal
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- Modified ISODATA clustering
- Allows for merging and splitting of clusters
- Cluster pixels within each cover type (corn, soybeans, rice ...)
- Exclude bad fields
- Principal component clipping to remove outliers
- Merge & split clusters within cover types
- Combine all the cover type clusters back into one signature file
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- Regression used to relate categorized pixel counts to the ground
reference data
- Independent variable - satellite data - pixels
- Dependent variable - JAS acreage estimate
- Satellite data - lower variance than with only JAS
- Outlier segment detection - correction or removal from regression
analysis
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- Run large-scale classification across analysis districts/entire
state/project area
- Compare large-scale regression with direct expansion estimates of JAS
data
- Sum satellite and non-satellite areas for state total
- Produce county and state indications of major crops
- For consideration in setting official estimates
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- Initial project focus on acreage indications not mosaic
- Six – fifteen categorized full base Landsat scenes per state
- Radiometric & systematic correction only
- Each unique scene date (Analysis District) produces unique sets of
signatures/categories
- Project repeated each year
- Need to create mosaicked state-wide image
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- Run block correlation between all raw images and EarthSat's GeoCover
Stock Mosaic
- Co-register raw images to GeoCover’s band 2
- Register all categorized scenes to GeoCover base
- Use calibration coefficients
- Mosaic all categorized images
- Establish scene overlap priorities
- Clip by scene edge or county boundary
- Mask out clouds via priority schemes
- Export to ERDAS’s .LAN image format
- Distribute in GeoTiff format
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- Import categorized mosaic (.LAN) to .img
- Re-build statistics
- Add class names
- Re-build projection metadata
- Clip to state boundary
- Colorize categories
- Maximize contrast between crops
- Colors maintained within state
- Category DN’s consistent across all states
- Export to GeoTiff
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- Held until county estimates released
- Bundle images with ESRI’s ArcReader
- Ancillary vector layers
- Area Sampling Frame where available
- No copyright restrictions
- Publish accuracy statistics
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- Combine satellite and ground truth data to produce acreage indications
- County & state level estimates input to official estimates
- Continue PEDITOR/RSP development
- Reduce end-user burden
- Increase functionality
- Evaluate new sensors (AWiFS) for operational use
- Remember, in no case is farmer reported data revealed or derivable from
the public use Cropland Data Layer CD-ROM's
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