1. Ryan S Anderson Professor Scott Miller

2.  Owned by Smithsonian  Located East of Panama Canal  Deforested, then replanted with Teak Trees for economic purposes

3.  Purpose is the determine correlation between tree growth rates and topographic characteristics

7.  Clouds!  Low Spatial Resolution

8. Pearson’s Product Moment R =.92 R 2 =.83

9.  Influence of Slope, Aspect, and Elevation on Tree Growth  Landscape Positions (Ridge top, Gulley, Hillside)

10. Slope intensities are defined as: Low: (0.02-19.9 degrees) Medium: (19.9-28.6 degrees) High: (28.6-74.4 degrees)

11. North:270-360 & 0-90 degrees South:90-270 degrees

12. Elevation classifications represent: 1 (170-191 meters) 2 (191-210 meters) 3 (210-230 meters) 4(230-265 meters)

13. H:Hillside GS:Gulley All R:Ridge Tops

14. Count Mean DBH (mm) Mean Height (m) Slope Low 2317.462.1 Medium 5913.562.02 High 49151.87 Aspect North 7310.751.57 South 5819.812.52 Elevation 1 312.681.83 2 5511.481.68 3 7318.522.35 4 1417.142 Landscape Position Hillside 9013.571.87 Gulley 1222.092.76 Ridgetop 2821.652.53

15.  Darker Green Indicates Better Growth Areas  Influence Degrades Over Space  Not as Accurate as Suitability Map

16. Class Average Slope (degrees)Average Elevation (meters) 1 24.85200.25 5 21.17225.49 Class Suitability ValuesAverage Heights (m)Tree Count Class 1 68.46 - 70.56 1.45 m 29 Class 2 70.56 - 75.14Omitted1 Class 3 75.14 - 80.7 1.67 m 33 Class 4 80.7 - 92.95 2.27 m 48 Class 5 92.95 - 100 2.67 m 33

17.  Maximize Economic Benefit, Reduce Land Use, Money, Time  Model is not Universal  Soil and Moisture Data Necessary to Understand WHY  Back to Panama?

18.  Teak Tree Point Data, (Dates of Observations: August 2 nd, 2010 and August 3 rd, 2010). Collected at the Agua Salud Project Site, Panama by Ryan S Anderson, Nathaniel HadleyDike, Natalie Macsalka, Brie Richardson, and Aaron Rutledge.  1 Meter LiDar Data, (November 2009). Obtained from J. Dalling, STRI Research Associate, University of Illinois, Urbana- Champaign. Acknowledgement for LiDAR data to Smithsonian Tropical Research Institute (STRI) and NSF DEB0939907 to J. Dalling, S. Hubbel and S. Dewalt which funded acquisition of LiDAR data.  Landsat 4-5 TM Satellite Imagery (March 2000). Obtained from USGS Global Visualization Viewer. Path 12, Row 54. 30 meter resolution.

19.  Bo-Jie Fu, W., Shi-Liang Liu, W., Li-Ding Chen, W., Yi-He Lü, W., & Jun, Q. (2004). “Soil quality regime in relation to land cover and slope position across a highly modified slope landscape.” Ecological Research, 19 (1), 111-118. doi:10.1111/j.1440-1703.2003.00614.x.  Joerin, F., M. Thériault, & Musy (2001). "Using GIS and outranking multicriteria analysis for land-use suitability assessment." International Journal of Geographical Information Science 15 (2): 153-174.  Lu, Y. George, & Wong, W. David (2007). “An adaptive inverse-distance weighting spatial interpolation technique.” Computer & Geosciences 34 (9): 1044-1055.  Rezaei, Seyed, & Gilkes, Robert. March 2005. “The effects of landscape attributes and plant community on soil physical properties in rangelands.” Geoderma 125 (1-2): 167-176.  Peper, Paula et al. November 2001. “Equations for Predicting Diameter, Height, Crown Width, and Leaf Area of San Joaquin Valley Street Trees.” Journal of Arboriculture 27 (6).  Poulos, H., & Camp, A. (2010). Topographic influences on vegetation mosaics and tree diversity in the Chihuahuan Desert Borderlands. Ecology, 91 (4): 1140-1151.  Stone, J. R.Gilliam, J. W.Cassel, D. K.Daniels, R. B.Nelson, L. A.Kleiss, H. J. (1984). Effect of Erosion and Landscape Position on the Productivity of Piedmont Soils. Soil Sci. Soc. Am. J. 49 : 987–991

20. Questions?