Analysis of secondary forest succession using LIDAR analysis in the southern Appalachians Brian D. Kloeppel 1, Robbie G. Kreza 1, Marcus C. Mentzer 1, Tucker J. Souther 1, and Ryan E. Emanuel 2 1 Department of Geosciences and Natural Resources, Western Carolina University, Cullowhee, NC Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC Introduction Objectives Methods Results Conclusions funding provided by Figure 2. Forest cutting history at Balsam Mountain Preserve by the former land owner, Champion International. Years indicate the most recent cut for each stand. Figure 1. Following Hurricane Floyd's flooding damage in 1999, North Carolina embarked on a joint effort with FEMA to re-map the state's flood zones. A by- product of this work is detailed elevation data collected by airborne LIDAR sensors. As a result, North Carolina became the first state in the country with complete LIDAR coverage.FEMA Secondary forest succession is a natural process as forested ecosystems develop after disturbance. The rate and extent of succession is difficult and expensive to quantify since southern Appalachian forested areas are large and challenging to assess due to complex topography and a varied land use history. Airplane-based LIDAR, LIght Detection And Ranging, image data became available when North Carolina was the first state in the USA to have complete state LIDAR coverage after the devastating floods caused by Hurrican Floyd in Please see Figure 1. We wish to use both indirect and direct forest structure measurements to determine tree height and forest structure during forest successional changes. The objectives of this research were to: 1)Determine if the LIDAR imagery available in Jackson County, North Carolina can accurately predict tree height as measured in the field 2)Determine what impact stand age (30, 50, 70, and 90 year-old stands) and the concomitant change in forest structure has on our ability to accurately predict tree height using LIDAR imagery 3)Predict the leaf area index (LAI) of these stands from field-based measurements using the LAI 2000 instrument This research was conducted at Balsam Mountain Preserve (BMP) in Jackson County in western North Carolina. BMP is an 1800 hectare mountain site (4400 acres) that was originally owned by Champion International Paper Company which was then purchased in the late 1990’s for an upscale housing development containing 354 home lots. At that time, half of the remaining land that was not being developed was placed into a conservation easement which formed the non-profit BMP trust. This research was conducted on land in the BMP trust. The past land use history was critical to provide us with a range of stand ages in the mountain oak forest type with dominant species including chestnut oak (Quercus montana), northern red oak (Quercus rubra), hickory (Carya spp.), sourwood (Oxydendrum arboreum), and red maple (Acer rubrum). Please see Figure 2 below. Figure 3. Each plot in the 30, 50, 70, or 90 year-old stands was arranged similarly with marked and repeatable locations for the measurement of each LIDAR pixel (entire plot), forest density and basal area (entire plot), tree height (of three tallest plot trees), leaf area index (on each of 9 subplots), and GPS readings (on each plot corner and plot center). Acknowledgements We thank the National Science Foundation for financial support (Award DEB ). We thank Balsam Mountain Preserve for permission to sample on their property, especially Michael Skinner, Ron Lance, and Blair Ogburn. We thank Cody Amakali at Appalachian State University for LIDAR data processing. Within the Balsam Mountain Preserve study site, four montane oak-hickory forest stands were identified across a chronosequence representing differing successional stages (30, 50, 70 and 90+ years since disturbance). Ground-based data collection for each plot included tree species and diameter (measured with a DBH tape). In addition, in each plot the three tallest trees were identified and height was measured using a Vertex Laser Hypsometer and a clinometer. Leaf area index was measured using the LAI 2000 leaf area index meter. The remotely sensed tree height data predictions were developed using ArcGIS. Figure 9. Co-authors TJ Souther, Robbie Kreza, and Marcus Mentzer at Balsam Mountain Preserve. Figure 4. The vegetation layer was calculated by subtracting the difference in height of the bare earth data from the first return data layer. Plot locations within each stand are approximate and were chosen at random. 30 Year50 Year 90 Year 70 Year Number of Returns Tree Height (feet) Figure 5. Histograms showing frequency of returns per LIDAR tree height value in each of the four study stands. Figure 6. Stand age versus LIDAR predicted tree height and field measured (clinometer) tree height in four montane oak- hickory stands from 30 to 90 years old. Figure 7. Stand age versus leaf area index as measured with a LAI 2000 in October 2010 in four montane oak-hickory stands ranging from 30 to 90 years old. Figure 8. Stand age versus tree stem density and basal area in four montane oak-hickory stands from 30 to 90 years old. Tree density, stand basal area, and leaf area index impact LIDAR returns by reflecting light pulses. Based on our analyses to date: 1)Our age chronosequence has resulted in stands of varying density, basal area, leaf area index, and tree height. 2)Preliminary LIDAR estimates show that tree height is underestimated by 7 to 25% depending upon stand age and tree height. 3)Sympatric analyses of LIDAR tree height and stand structure data at four other locations in North Carolina at Appalachian State University, UNC-Penmbroke, Johnson C. Smith University, and Livingston College will be analyzed at a workshop at North Carolina State University in May 2011.