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Enhancing EQIP Ranking with Remote Sensing Cover Analysis

Proposed use of remote sensing technology to complement field data in EQIP ranking process for better land assessment and resource management. Examines strengths and limitations, with examples from Arizona and New Mexico. Recommendations for implementing this technology in field offices and statewide. Discusses potential issues and sustainability.

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Enhancing EQIP Ranking with Remote Sensing Cover Analysis

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  1. A Proposal to Use Remotely Sensed Cover in the Arizona EQIP Ranking Process AZ State Technical Committee Meeting September 7th, 2011

  2. Purpose and Benefits • Geospatial tools can complement, but not replace, field data in the ranking process • Field data limitations • Remote sensing strengths

  3. The Ranking Gap FIELD OFFICES vs REMOTE SENSING The Ranking Difference • Field Office Ranking • Equal Ranking • Remote Sensing Ranking • Field Office • Field Office ranking includes other factors unrelated to land conditions.

  4. Value (%)

  5. Cover Images i-cubed 15m eSAT Imagery 2010 30m Landsat 60% 2010 500m MODIS 0%

  6. Loamy Upland Reference Area • A Loamy Upland Mesquite-Dominated Eroded State

  7. Total Cover: an imperfect proxy

  8. Management Effects 2002 30m Landsat 60% 0%

  9. Management Effects

  10. Algorithm Total Vegetation Fractional Cover is scaled from Ground Measurements to LANDSAT (30 m) ….

  11. Cover Comparison: Ground vs SatelliteArizona and New Mexico 2010 • Cover: • 5% measured • 10% Landsat • (ID: 123-0.022) • Cover: • 10% measured • 14% Landsat • (ID: 115-0.042) • Cover: • 17% measured • 22% Landsat • (ID: 54-0.094) • Cover: • 5% measured • 12% Landsat • (ID: 59-0.030)

  12. AlgorithmField Data Locations 2 1 4 1 - USGS 2002-2004 2 - USGS 2002-2004 3 - Marsett 2010 4 - Marsett 2008 5 - Marsett 2000-2007 6 - Marsett 2001-2002 3 5 6

  13. AlgorithmGround Data Points Used in Algorithm Development

  14. Approach to Assigning Points Using Remotely Sensed Cover • Create polygons of ranch boundaries • Average MODIS and Landsat cover images • Average PRISM precipitation, max and min temps ‘07-’10 • Exploratory plotting • Create statistical model of cover • Compare observed cover to expected cover • Rank and assign points

  15. Grazed Ungrazed

  16. Approach to Assigning Points Using Remotely Sensed Cover • Create polygons of ranch boundaries • Average MODIS and Landsat cover images • Average PRISM precipitation, max and min temps ‘07-’10 • Exploratory plotting • Create statistical model of cover • Compare observed cover to expected cover • Rank and assign points

  17. Value (%)

  18. Value (in) 40

  19. Approach to Assigning Points Using Remotely Sensed Cover • Create polygons of ranch boundaries • Average MODIS and Landsat cover images • Average PRISM precipitation, max and min temps ‘07-’10 • Exploratory plotting • Create statistical model of cover • Compare observed cover to expected cover • Rank and assign points

  20. Approach to Assigning Points Using Remotely Sensed Cover • Create polygons of ranch boundaries • Average MODIS and Landsat cover images • Average PRISM precipitation, max and min temps ‘07-’10 • Exploratory plotting • Create statistical model of cover • Compare observed cover to expected cover • Rank and assign points

  21. Approach to Assigning Points Using Remotely Sensed Cover • Create polygons of ranch boundaries • Average MODIS and Landsat cover images • Average PRISM precipitation, max and min temps ‘07-’10 • Exploratory plotting • Create statistical model of cover • Compare observed cover to expected cover • Rank and assign points

  22. National Direction • FARM (Financial Assistance Ranking Model) • Ranking Admin Tool & Geospatial Ranking Tool • Currently piloting in 8 states

  23. Conclusion The Proposal: Looking for a recommendation from the State Technical Committee in favor of using this technology in 5 or 6 field offices for the FY2012 EQIP ranking process. Looking to apply Statewide in FY2013.

  24. Potential Issues • Soil brightness (L Factor) • Snow, clouds, shadow (North facing slopes) • Fire • Need smoothing at the pixel level • Testing across vegetation communities • Incorporating soil, slope, aspect, other factors into statistical models • Sustaining funding

  25. Is Cover An Indicator of Something More Profound? R = 0.65

  26. Management Effects i-cubed 15m eSAT Imagery

  27. Approach to Assigning Points Using Remotely Sensed Cover • Create polygons of ranch boundaries • Average MODIS and Landsat cover images • Average PRISM precipitation, max and min temps ‘07-’10 • Exploratory plotting • Create statistical model of cover • Compare observed cover to expected cover • Rank and assign points

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