SOUTHERN PINE BEETLE (DENDROCTONUS FRONTALIS ZIMM.) HAZARD MODELING IN NORTH CAROLINA – INCORPORATION OF REMOTELY SENSED VARIABLES Jason E. Moan†, Randolph H. Wynne†, and Scott M. Salom‡ †Department of Forestry ‡Department of Entomology Virginia Polytechnic Institute and State University Blacksburg, Virginia 24061
[email protected]
1. INTRODUCTION The southern pine beetle (SPB) (Dendroctonus frontalis Zimm.) is one of the most destructive forest insect pests in the southeastern Unites States and has historically had a large impact on the forests of North Carolina. The SPB typically infests and successfully colonizes all Pinus species within its range, but has been known to preferentially attack loblolly pine (Pinus taeda L.) and shortleaf pine (Pinus eschinata Mill.). Previous research suggests that SPB outbreak hazard typically begins when a forest stand reaches a basal area of greater than 100 square feet/acre [1] and an age over 20 years old [2]. Studies have also shown that SPB outbreaks typically occur in lowland areas and ridgetops [3]. Hazard and risk are not synonymous, though they are often used interchangeably. Hazard is directly related to susceptibility and/or vulnerability and their probability of affecting the overall management objectives for a given area [4]. Susceptibility, in our case, is the probability of a SPB attack, based on stand biological parameters, whereas vulnerability is the likelihood of stand damage during a successful attack. Risk, then, is the probability that the SPB will occur or reach outbreak populations in a given area. This research is primarily focused on modeling susceptibility to the SPB in non-industrial private forest lands. 2. OBJECTIVES The main objective of this research project was to develop a predictive SPB hazard model based on aspatial historic SPB outbreak data and comparable non-outbreak data from the U.S. Forest Service - Forest Inventory and Analysis (FIA) program. This model was intended to determine the feasibility of including additional hazard mapping variables such as stand age and percent sawtimber in the current SPB hazard mapping effort for North Carolina. Additional research objectives include the derivation of stand age classes through manipulation of Landsat TM imagery and comparison of the biophysical parameters derived by the U.S. Forest Service - Forest Health Technology Enterprise Team (FHTET) for the SPB hazard mapping effort, before and after incorporation of a LiDAR-derived digital elevation model (DEM). 3. METHODS A database of aspatial historical southern pine beetle outbreak forest stand variables throughout North Carolina was developed from many sources. The variables collected include forest stand percent sawtimber, stand age, and tree species present. This database was used to develop three hazard models representing the degree of susceptibility to SPB infestation that is represented by different age classes and percent sawtimber for the three physiographic regions in North Carolina. Each physiographic region has had a unique pattern of historic SPB outbreak and those regions are, from west to east, the mountains, the piedmont, and the coastal plain. Comparable data from ground points without SPB activity were compared against the outbreak stand data using a classification tree approach to produce robust hazard models.
For the stand age-derivation objective, winter Landsat TM scenes were used to minimize the effects of deciduous foliage in the analysis. Non-pine pixels were filtered out using a supervised classification approach with training data acquired from manual interpretation of the scenes. Our method for aging pine stands is as follows: (1) preprocess all TM imagery by conversion to top-of-atmosphere reflectance, (2) compute tasseled cap brightness, greenness, and wetness using sensor-appropriate coefficients [5], (3) compute the disturbance index [6] for all scenes, (4) using the chronosequence of winter disturbance index images, assess the date at which the most recent stand-replacing disturbance occurred. The lidar-derived DEM is currently being processed and incorporated into the hazard modeling for eventual comparison of biophysical parameters. 4. RESULTS The SPB database classification tree modeling effort produced robust models in all three physiographic regions of North Carolina, though the complexity of the piedmont model makes it impractical for use in the field. The classification tree test sample error percentages were similar across regions, with errors ranging between 23.76 - 34.95 percent. Overall prediction success, based on the software’s internal cross-validation procedure, was likewise comparable across the regions with 72.28 - 89.56 percent correctly predicted. Our models and results will be presented to the FHTET for possible inclusion in their current federal SPB hazard mapping efforts. 5. REFERENCES [1] Belanger, R.P., Osgood, E.A., & Hatchell, G.E. (1979). Stand, soil, and site characteristics associated with southern pine beetle infestations in the southern Appalachians. In, Research Paper SE-198: USDA-Forest Service [2] St. George, R.A., & Beal, J.A. (1929). The southern pine beetle: a serious enemy of pines in the south. In, Farmers' Bulletin 1188 (p. 18): U.S. Department of Agriculture [3] Hicks Jr., R.R. (1980). Climate, site, and stand factors. In R.C. Thatcher, J.L. Searcy, J.E. Coster & G.D. Hertel (Eds.), The Southern Pine Beetle: Technical Bulletin 1631 (pp. 55-68). Pineville, LA: USDA-Forest Service Science and Education Administration [4] Gottschalk, K. (1995). Using silviculture to improve health in northeastern conifer and eastern hardwood forests. In L.G. Eskew (Ed.), 1995 National Silviculture Workshop (pp. 219-226). Mescalero, New Mexico: USDA-Forest Service RM-GTR-267 [5] Han, T., Wulder, M.A., White, J.C., Coops, N.C., Alvarez, M.F., & Butson, C. (2007). An efficient protocol to process landsat images for change detection with tasselled cap transformation. IEEE Geoscience and Remote Sensing Letters, 4, 147-151 [6] Healey, S.P., Cohen, W.B., Zhiqiang, Y., & Krankina, O.N. (2005). Comparison of tasseled cap-based Landsat data structures for use in forest disturbance detection. Remote Sensing of Environment, 97, 301 – 310
