How does DNR’s Remote-Sensing Inventory Stack Up Against Cruises? Peter Gould, Forest Biometrician Washington State Department of Natural Resources

Outline DNR Context Inventory ≠ Cruise Comparisons between cruise and inventory Conclusions

DNR’s Inventory: RS-FRIS Area-based remote- sensed inventory 2015 measurement, mostly phodar on westside; around 1000 sample plots Meets range of needs (e.g., SHC, HCP) Estimates volume, mean size, hardwood-softwood

RS-FRIS Validation Block Results RS Predictions Ground Sample

These are the number many people wants Cruise: noun  \ ˈkrüz \ Measurement of standing trees to determine quantity of wood, potential products, value How much: Scribner board foot volume Products: species, size, quality Grades Sorts Audience External: buyers of stumpage Internal: appraisal, trustees, sustainable harvest These are the number many people wants

Organizational Factors

Differences: What’s Measured RS-FRIS: measures everything within a polygon Cruise Net out acres, not always mapped (leave tree groups, roads) Leave trees, trees without value are invisible

Differences: How It’s Measured Merchantable Height Inventory: 5.5” top Cruise: variable Taper Systems Log lengths Inventory: 32 ft, plus top Cruise: variable but 40 ft default Grades and sorts Inventory: Not recorded Cruise: Size, branches, etc.

RS-FRIS vs. Cruise Cruise volume = 88% of inventory volume

RS-FRIS vs. Cruise Cruise volume = 88% of inventory volume We can account for a consistent difference (bias)

How Good is RS-FRIS? Assume, for now, that the cruise is truth Assume we can account for the consistent difference 65% outside ± 10% 36% outside ± 20%

How Good is the Cruise Data? 32% outside ± 10% Standard: 1 SE = 10% of mean

How Good is the Cruise Data? 32% outside ± 10% Standard: 1 SE = 10% of mean What if units were re-measured to the same standard? 48% outside ± 10%

Error in both Cruise and RS-FRIS Simulation Cruise = 10% of mean RS-FRIS = 19.7% of mean Differences could be reduced further through consistent methodologies Observed

Other Insights: Species Differences DF R-sq = 70% DF is more predictable WH is less predictable and less biased (why?) WH R-sq = 42%

Other Insights: Hardwood Proportion R-sq = 59%

Other Insights: Log Grades Small logs < 12” diameter 3 & 4 saw log, utility Large logs >=12” diameter 1 & 2 saw log, special mill, peeler

Conclusions RS-FRIS tells us a lot about cruise volume Volume within +/- 20% of truth about 2/3rd of the time Fairly good on hwd vs softwood Some information on products