1. Comparing Pre-settlement, Pre-treatment and Post-treatment Stand Structure at Lonetree Restoration Site: Incorporating GIS into Restoration By Christine Brown & Michael Jow Ecological Restoration Applications November 30, 2004

2. More Lonetree! Data collected needs to be in a format where it can be analyzed displayed and stored –Including how it relates to the rest of the world –Future monitoring needs to be incorporated in a compatible format for comparison and analysis Average tree density and basal area don’t provide the whole picture –Spatial arrangement is important to reconstructing proper structure –Presettlement site utilization by overstory is difficult to quantify and recreate

3. Objectives 1.Consolidate, store and organize project data 2.Spatially reference project area, treatment units and plot boundaries 3.Visually display and compare pre-settlement, pre-treatment and post-treatment stand structures 4.Visualize and analyze outcome of various prescriptions

4. Aerial View of the Lonetree Restoration Site

5. Treatment Areas Treatment Units Project Boundary Plots

6. Methods Project layout Boundaries and plot centers were plotted using a Tremble Geoexplorer 2 GPS unit GPS data was and differentially corrected using USDA FS base station data from Cedar City, Utah and brought into an ESRI Arcmap project Plots were created using center points and plot direction Plot data was imported into Arcmap and linked to corresponding features Pre- and post-treatment photos were hyperlinked to the point location they were taken Features were overlaid on an aerial photo and topo map

9. Topographic View of the Lonetree Site

10. Methods (Continued) Tree Data Trees were plotted in Arcmap using x-y data collected on site and corresponding data attached to each tree Crown diameter was estimated using allometric equations for ponderosa pine (McTague, 1988) Crowns of trees were projected and canopy closure was estimated Tree density and basal area was calculated using plot data

11. Formulas for Estimate Canopy from DBH When D > 20 in: C ST = (131.58 D - 1578.95) / {43.85exp (-333.54 / SD.99697 )) +.012729 SD 1.175 + 4.5} S = Site Index (60) D = Diameter in inches When D < 4 in: C Y =.426 + 1.317 D When 4 20: C = (D – 4.0)[(C ST, D=20) – 5.7] / 16.0 + 5.7 (McTague, 1988) C ST =Crown Diameter of Saw timber C Y =Crown Diameter of young trees

12. Assumptions Area of each plot was slope corrected for estimating tree density and basal area Pre-settlement date used was 1870 (approximate time of fire exclusion) Tree densities –Pre-settlement – assumed pole density by including living pre- settlement trees in total tree density calculation Basal areas –Pre-settlement were calculated using the DSH of remnant stumps –Living pre-settlement trees and pole basal area not included Crown closure –Canopy only estimated within plot using allometric equations –does not include canopy extending beyond plot boundaries or the canopy of trees rooted outside plot

13. Need for Restoration Average tree density of all the measured plots. Average basal area of all the measured plots. Pre-treatment tree density and basal area are significantly different than pre- settlement tree density and basal area. Restoration is needed to return to a healthy forest similar to historical conditions. Average Basal Area 0 10 20 30 40 Lonetree Site Basal Area (m 2 /ha) Pre-Settlement Pre-treatment Average Tree Density 0 200 400 600 800 1000 1200 1400 Lonetree Site Tree Density (# trees/ha) Pre-settlement Pre-treatment

14. Need for Restoration (cont.) Pre-settlement trees show a normal distribution around 40-50 cm DBH. The pre- treatment trees show a logarithmic (reverse J) distribution.

15. Lonetree Restoration Project Plots

16. NAU-99-2 Pre-settlement, Pre and Post-treatment Canopy Covers NAU-99-2 Tree Densities 0 50 100 150 200 250 300 350 400 450 NAU-99-2 Tree Density (# trees/ha) Pre-settlement Pre-treatment Post-treatment NAU-99-2 Basal Areas 0 5 10 15 20 25 30 35 40 NAU-99-2 Basal Area (m 2 /ha) Pre-settlement Pre-treatment Post-treatment NAU-99-2 Crown Closure 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% Plot NAU-99-2 Pre-settlement Pre-treatment Post-treatment

17. NAU-99-2: POST-TREATMENT PICTURES (P2) August 21, 2000November 9, 2004

18. NAU-00-2 Pre-settlement and Pre-treatment Canopy Covers NAU-00-2 Tree Densities 0 500 1000 1500 2000 2500 3000 NAU-00-2 Tree Density (# trees/ha) Pre-settlement Pre-treatment NAU-00-2 Basal Areas 0 5 10 15 20 25 30 35 40 45 50 NAU-00-2 Basal Area (m 2 /ha) Pre-settlement Pre-treatment Crown Closure 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% Plot NAU-00-2 Pre-settlement Pre-treatment

19. NAU-00-2: PRE AND POST–TREATMENT PICTURES (0P) Pre-treatment. September 6, 2000.Post-treatment. November 9, 2004.

20. Additional Analyses Location- find coordinates for any feature Measurements- distance, area, perimeter Spatial relationships- clumpiness, connectivity, proximity Patterns- data visualization Trends- changes in data over time Modeling- predict outcomes of different restoration alternatives

21. GIS to Visualize Restoration Prescriptions 10 m recruitment radius Pre-settlement Evidence Pre-settlement Live tree Post-settlement Live Trees

22. Comparing Restoration Prescriptions Possible treatment using a 1.5 to 1 replacement for pre- settlement evidence Possible treatment using a 3 to 1 replacement for pre- settlement evidence

23. Conclusion ALL project data (maps, photos, plot data) can be stored, organized and displayed in one GIS project Project data can utilize other GIS data for additional analysis Pre-settlement canopy closure and spatial distribution (i.e. “clumpiness”) can be reconstructed, analyzed and displayed Spatial analysis can aid in selecting replacement/ leave trees in restoration treatments Various prescriptions can be compared and visualized prior to implementation Future monitoring information can easily be incorporated and compared to previous data