Spatial Analysis of Large Tree Distribution of FIA Plots on the Lassen National Forest

1. Spatial Analysis of Large Tree Distribution of FIA Plots on the Lassen National Forest Tom Gaman, East-West Forestry Associates, Inc Kevin Casey, USDA-FS R5 Remote Sensing Lab

2. Our FIA Investigation • Examine the Statistical Value of Collecting Hectare Tree Data. • Test spatial relationships among trees using the distance and bearing data to index plot structure. • Create plot “cartoons” and relate to high-resolution imagery, hence to landscape

3. Examine the Statistical Value of Collecting Hectare Tree Data.

4. 2002 Lassen National Forest Densification & FIA Hex Plots • 132 plots – 83 have HA trees • 66 had multiple HA trees >= 32.0” dbh 1 ac. Vs. 1 ha.! What do we gain from the extra work?

5. Methods • Select large trees on annular plot using the distance and azimuth data. • Create 2 data sets for each plot • All Hectare Trees • Hectare Trees on ¼ acre annular plots only • Statistical Analysis # Large Trees. Acre data (expanded to ha value) vs. hectare data by mapped forest “Type”

6. The Raw Data

7. Statistical Analysis of Results

8. Statistical Analysis of Results 11.8% 14.0% • Conclusions. • Though SE consistently somewhat less for HA sampling by type, difference • would reduce with larger # plots. • Smaller (4 of the ¼ acre) samples may overestimate # large trees/ha • 3. Ha Plots offer an excellent tool for more accurately quantifying large trees on • individual plots but of limited value in reducing the error for large samples.

9. Statistical Analysis of Results Mean # HA trees/Hectare by type • Conclusions. • Though SE consistently somewhat less for HA sampling by type, difference • would reduce with larger # plots. • Smaller (4 of the ¼ acre) samples may overestimate # large trees/ha • 3. Ha Plots offer an excellent tool for more accurately quantifying large trees on • individual plots but of limited value in reducing the error for large samples.

10. Test spatial relationships among trees using the distance and bearing data to index plot structure. Creating the “Clumping Index”

11. Methods • Selected 11 plots…1 randomly for each forest “Type” per regional type map. • Calculate coordinate locations of each tree and create a shapefile for each plot. 0614151

12. Tested a variety of methods which did not work 0606172

13. Calculate Proximity • Use Proximity Analysis tool to create “PROXIMITY POLYGONS” • Calculate area of each polygon and total plot 0614151

14. Calculate “Clumping Index” • Divide total area by number of trees to obtain AVERAGE polygon which would represent equal spacing (as in a plantation) • Divide actual area of each PROXIMITY POLYGON by AVERAGE thus obtaining an expression of POLYGON CLUMPING. • Average the two lowest POLYGON CLUMPING values to obtain the CLUMPING INDEX.

15. Clumping Index Values

16. Clumping Index Values Low Value—Most Clumping High Value—even distribution 0614335 0615554

17. Clumping Value = 1 no clumping!

18. Conclusions from Clump Index: • “Indexing” may be a valuable use of FIA for ecological modeling of variability within the landscape • Would be interesting to examine relationships among snags and smaller trees • The index could be derived directly from tree data without all the interim steps.

19. Create plot “cartoons” and relate to high-resolution imagery, hence to landscape.

21. Mapped the Inventory Plots using Distance and Azimuth Data from Tree (*.TRE) files

22. All Trees

23. Large Trees on the Annular Plot only

24. Trees on the entire Hectare

25. Conclusions from SVS process: • Trees can be realistically drawn in their true locations using SVS. • GPS coordinates can be used to locate plot centers on geo-referenced imagery. • Annular and hectare plots can be accurately drawn over imagery. • In dense stands individual trees are very difficult to identify.

26. 0615367 clump index 0.19 med high

27. 0615039 clump index 0.166

28. 0614715 (clump index = 0.134 medium high)

29. 0606258 (clump index 0.435 medium)

30. 0615554 (clump index 0.95 low)

31. Conclusions • Spatial Analysis allows us to extract more information on individual plots • FIA data may be valuable in modeling relationships • www.forestdata.com

32. www.forestdata.com