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

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

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

4. RS-FRIS Validation Block Results
RS Predictions
Ground Sample

5. 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

6. Organizational Factors

7. 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

8. 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.

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

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

11. 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%

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

13. 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%

14. 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

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

16. Other Insights: Hardwood Proportion
R-sq = 59%

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

18. 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