AZ State Technical Committee Meeting September 7 th, 2011

Geospatial tools can complement, but not replace, field data in the ranking process Field data limitations Remote sensing strengths

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

Value (%)

i-cubed 15m eSAT Imagery m Landsat m MODIS 60% 0%

Loamy Upland Reference Area A Loamy Upland Mesquite-Dominated Eroded State

Total CoverIdeal Soil CanopyLitter, basal area Water InterceptionRoughness, infiltration Air CanopyProtection from wind Plants Amount of vegetation Composition, forage Animals CanopyHabitat requirements

60% 0% m Landsat

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

Cover: 5% measured 10% Landsat (ID: ) Cover: 5% measured 12% Landsat (ID: ) Cover: 17% measured 22% Landsat (ID: ) Cover: 10% measured 14% Landsat (ID: )

USGS USGS Marsett Marsett Marsett Marsett

1. Create polygons of ranch boundaries 2. Average MODIS and Landsat cover images 3. Average PRISM precipitation, max and min temps ‘07-’10 4. Exploratory plotting 5. Create statistical model of cover 6. Compare observed cover to expected cover 7. Rank and assign points

1. Create polygons of ranch boundaries 2. Average MODIS and Landsat cover images 3. Average PRISM precipitation, max and min temps ‘07-’10 4. Exploratory plotting 5. Create statistical model of cover 6. Compare observed cover to expected cover 7. Rank and assign points

Value (%)

Value (in) 40

1. Create polygons of ranch boundaries 2. Average MODIS and Landsat cover images 3. Average PRISM precipitation, max and min temps ‘07-’10 4. Exploratory plotting 5. Create statistical model of cover 6. Compare observed cover to expected cover 7. Rank and assign points

1. Create polygons of ranch boundaries 2. Average MODIS and Landsat cover images 3. Average PRISM precipitation, max and min temps ‘07-’10 4. Exploratory plotting 5. Create statistical model of cover 6. Compare observed cover to expected cover 7. Rank and assign points

1. Create polygons of ranch boundaries 2. Average MODIS and Landsat cover images 3. Average PRISM precipitation, max and min temps ‘07-’10 4. Exploratory plotting 5. Create statistical model of cover 6. Compare observed cover to expected cover 7. Rank and assign points

FARM (Financial Assistance Ranking Model) Ranking Admin Tool & Geospatial Ranking Tool Currently piloting in 8 states

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.

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

R = 0.65

i-cubed 15m eSAT Imagery

1. Create polygons of ranch boundaries 2. Average MODIS and Landsat cover images 3. Average PRISM precipitation, max and min temps ‘07-’10 4. Exploratory plotting 5. Create statistical model of cover 6. Compare observed cover to expected cover 7. Rank and assign points