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A Proposal to Use Remotely Sensed Cover in the Arizona EQIP Ranking Process

A Proposal to Use Remotely Sensed Cover in the Arizona EQIP Ranking Process. AZ State Technical Committee Meeting September 7 th , 2011. Purpose and Benefits. Geospatial tools can complement, but not replace, field data in the ranking process Field data limitations Remote sensing strengths.

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A Proposal to Use Remotely Sensed Cover in the Arizona EQIP Ranking Process

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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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