Advertisement

Ecosystems

, Volume 11, Issue 1, pp 45–60 | Cite as

Patterns of Forest Damage in a Southern Mississippi Landscape Caused by Hurricane Katrina

  • John A. KupferEmail author
  • Aaron T. Myers
  • Sarah E. McLane
  • Ginni N. Melton
Article

Abstract

Understanding and predicting the ways in which large and intense hurricanes affect ecosystem structure, composition and function is important for the successful management of coastal forest ecosystems. In this research, we categorized forest damage resulting from Hurricane Katrina into four classes (none, low, moderate, heavy) for nearly 450 plots in a 153,000 ha landscape in southern Mississippi, USA, using a combination of air photo interpretation and field sampling. We then developed predictive damage models using single tree classification tree analysis (CTA) and stochastic gradient boosting (SGB) and examined the importance of variables addressing storm meteorology, stand conditions, and site characteristics in predicting forest damage. Overall damage classification accuracies for a training dataset (n = 337 plots) were 72 and 81% for the single tree and SGB models, respectively, with Cohen’s weighted linear κ values of 0.71 and 0.86. For an independent validation dataset (n = 112 plots), classification accuracy dropped to 57% (κ = 0.65) and 56% (κ = 0.63) for the single tree and SGB models. Proportions of agreement between observed and predicted damage were significantly greater (P < 0.05) than would be expected by chance alone for all damage classes with the training data and all but the moderate class for the validation data. Stand age was clearly the best predictor of damage for both models, with forest type, stand condition, site aspect, and distance to the nearest perennial stream also explaining much of the variation in forest damage. Measures of storm meteorology (duration and steadiness of hurricane-force winds; maximum sustained winds) were of secondary importance. The forest-wide application of our CTA model provided a realistic, spatially detailed map of predicted damage while also maintaining a relatively high degree of accuracy. The study also provides a first step toward the development of models identifying the susceptibility of forest stands to future events that could be used as an aid to incorporating the effects of large infrequent disturbances into forest management activities.

Keywords

large infrequent disturbance classification tree stochastic gradient boosting DeSoto National Forest predictive model hurricane damage 

Notes

Acknowledgments

We particularly appreciate the assistance of Ron Smith, Tate Thriffiley, Clint Roberts, Jeff Cotter and Wayne Stone, of the USDA Forest Service. We also thank Skeeter Dixon, Scott Franklin, Jovian Sackett and the graduate students in JAK’s ‘Katrina Seminar’, in which this paper was first developed, and wish to acknowledge NOAA’s Hurricane Research Division and the U.S. Army Corps of Engineers, for developing data and products used in this study. Comments by Mike Hodgson, Ariel Lugo, and three anonymous reviewers greatly improved the quality of this manuscript. Funding was provided by the Coastal Resiliency Information Systems Initiative for the Southeast (CRISIS), Office of Research and Health Sciences, University of South Carolina.

References

  1. Boose ER, Foster DR, Fluet M. 1994. Hurricane impacts to tropical and temperate forest landscapes. Ecol Monogr 64:369–400CrossRefGoogle Scholar
  2. Breiman L 1996. Bagging procedures. Mach Learn 24:123–40Google Scholar
  3. Breiman L, Friedman JH, Olshen RA, Stone CJ. 1984. Classification and regression trees. Belmont (CA): Wadsworth International GroupGoogle Scholar
  4. Brokaw NVL, Walker LR. 1991. Summary of the effects of Caribbean hurricanes on vegetation. Biotropica 23:442–47CrossRefGoogle Scholar
  5. Cairns DM. 2001. A comparison of methods for predicting vegetation type. Plant Ecol 156:3–18CrossRefGoogle Scholar
  6. Cohen J. 1968. Weighted kappa: nominal scale agreement with provision for scaled disagreement or partial credit. Psychol Bull. 70:213–20CrossRefPubMedGoogle Scholar
  7. Conner WH, Mixon WD, Wood GW. 2005. Maritime forest habitat dynamics on Bulls Island, Cape Romain National Wildlife Refuge, SC, following Hurricane Hugo. Forest Ecol Manage 212:127–34CrossRefGoogle Scholar
  8. Dale VH, Lugo AE, MacMahon J, Pickett STA. 1998. Ecosystem management in the context of large, infrequent disturbances. Ecosystems 1:546–57CrossRefGoogle Scholar
  9. Dunion JP, Landsea CW, Houston SH, Powell MD. 2003. A reanalysis of the surface winds in Hurricane Donna of 1960. Monthly Weather Rev 131:1992–2011CrossRefGoogle Scholar
  10. Elsner JB, Jagger TH, Tsonis AA. 2006. Estimated return periods for Hurricane Katrina. Geophys Res Lett 33:L08704. doi: 10.1029/2005GL025452 CrossRefGoogle Scholar
  11. Everham EM, Brokaw NVL. 1996. Forest damage and recovery from catastrophic wind. Bot Rev 62:113–85CrossRefGoogle Scholar
  12. Fleiss JL. 1981. Statistical methods for rates and proportions, 2nd edn. New York: WileyGoogle Scholar
  13. Foster DR, Boose ER. 1992. Patterns of forest damage resulting from catastrophic wind in central New England, USA. J Ecol 80:79–98CrossRefGoogle Scholar
  14. Foster DR, Knight DH, Franklin JF. 1998. Landscape patterns and legacies resulting from large, infrequent forest disturbances. Ecosystem s 1:497–510CrossRefGoogle Scholar
  15. Francis JK, Gillespie AJR. 1993. Relating gust speed to tree damage in Hurricane Hugo, 1989. J Arboricult 19:368–73Google Scholar
  16. Friedman JH. 2002. Stochastic gradient boosting. Comput Stat Data Anal 38:367–78CrossRefGoogle Scholar
  17. Freund Y, Schapire RE. 1996. Experiments with a new boosting algorithm. In: Machine learning: Proceedings of the Thirteenth International Conference. San Francisco: Morgan Kaufman. pp 148–56Google Scholar
  18. Graumann A, Houston T, Lawrimore J, Levinson D, Lott N, McCown S, Stephens S, Wuertz, D. 2005 (updated 2006). Hurricane Katrina, a climatological perspective, preliminary report. NCDC technical report 2005-01. NOAA’s National Climatic Data Center, Asheville, N.CGoogle Scholar
  19. Gresham CA, Williams TM, Lipscomb DJ. 1991. Hurricane Hugo wind damage to southeastern U.S. coastal forest tree species. Biotropica 23:420–26CrossRefGoogle Scholar
  20. Lawrence R, Bunn A, Powell S, Zambon M. 2004. Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis. Remote Sensing Environ 90:331–336CrossRefGoogle Scholar
  21. Lewis SA, Wu JQ, Robichaud PR. 2006. Assessing burn severity and comparing soil water repellency, Hayman Fire, Colorado. Hydrol Processes 20:1–16CrossRefGoogle Scholar
  22. Lindemann JD, Baker WL. 2002. Using GIS to analyze a severe forest blowdown in the southern Rocky Mountains. Int J Geogr Inf Sci 16:377–99CrossRefGoogle Scholar
  23. Litzgus JD, Mousseau TA. 2004. Home range and seasonal activity of southern spotted turtles (Clemmys guttata): Implications for management. Copeia 4:804–17CrossRefGoogle Scholar
  24. Meeker JR, Haley TJ, Petty S, Windham JW. 2005. Forest health evaluation of Hurricane Katrina damage on the DeSoto National Forest. Jackson: USDA Forest Service, National Forests in Mississippi. Google Scholar
  25. Miller J, Franklin J. 2002. Modeling the distribution of four vegetation alliances using generalized linear models and classification trees with spatial dependence. Ecol Model 157:227–47CrossRefGoogle Scholar
  26. Miller JD, Nyhan JW, Yool SR. 2003. Modeling potential erosion due to the Cerro Grande Fire with a GIS-based implementation of the Revised Universal Soil Loss Equation. Int J Wildl Fire 12:85–100CrossRefGoogle Scholar
  27. Myers RK, Van Lear DH. 1998. Hurricane-fire interactions in coastal forests of the south: a review and hypothesis. Forest Ecol Manage 103:265–76CrossRefGoogle Scholar
  28. Newcombe RG. 1998. Two-sided confidence intervals for the single proportion: comparison of seven methods. Stat Med 17:857–72PubMedCrossRefGoogle Scholar
  29. Ostertag R, Scatena FN, Silver WL. 2003. Forest floor decomposition following hurricane litter inputs in several Puerto Rican forests. Ecosystems 6:261–73CrossRefGoogle Scholar
  30. Ostertag R, Silver WL, Lugo AE. 2005. Factors affecting mortality and resistance to damage following hurricanes in a rehabilitated subtropical moist forest. Biotropica 37:16–24CrossRefGoogle Scholar
  31. Peterson CJ. 2004, Within-stand variation in windthrow in southern boreal forests of Minnesota: Is it predictable? Can J Forest Res 34:365–75CrossRefGoogle Scholar
  32. Platt WJ, Beckage B, Doren RF, Slater HH. 2002. Interactions of large-scale disturbances: Prior fire regimes and hurricane mortality of savanna pines. Ecology 83:1566–72CrossRefGoogle Scholar
  33. Powell MD, Bowman D, Gilhousen D, Murillo S, Carrasco N, St. Fleur R. 2004. Tropical cyclone winds at landfall: the ASOS-CMAN wind exposure documentation project. Bull Am Meteorol Soc 85:845–51CrossRefGoogle Scholar
  34. Powell MD, Houston SH, Amat LR, Morisseau-Leroy N. 1998. The HRD real-time hurricane wind analysis system. J Wind Eng Indust Aerodyn 77&78:53–64CrossRefGoogle Scholar
  35. Ramsey EW, Hodgson ME, Sapkota SK, Laine SC, Nelson GA, Chappell DK. 2001. Forest impact estimated with NOAA AVHRR and Landsat TM data related to an empirical hurricane wind-field distribution. Remote Sensing Environ 77:279–92CrossRefGoogle Scholar
  36. Sherman RE, Fahey TJ, Martinez P. 2001. Hurricane impacts on a mangrove forest in the Dominican Republic: damage patterns and early recovery. Biotropica 33:393–408Google Scholar
  37. Sherrod PH. 2006. DTREG: classification and regression trees and support vector machine for predictive modeling and forecasting. Online: http://www.DTREG.com/DTREG.pdf
  38. Tanner EVJ, Kapos V, Healey JR. 1991. Hurricane effects on forest ecosystems in the Caribbean. Biotropica 23:513–21CrossRefGoogle Scholar
  39. Turner MG, Baker WL, Peterson CJ, Peet RK. 1998. Factors influencing succession: lessons from large, infrequent natural disturbances. Ecosystems 1:511–23CrossRefGoogle Scholar
  40. Uriarte M, Canham CD, Thompson J, Zimmerman JK. 2004. A neighborhood analysis of tree growth and survival in a hurricane-driven tropical forest. Ecol Monogr 74:591–614CrossRefGoogle Scholar
  41. Vandermeer JH, Boucher DH, de la Cerda IG, Perfecto I. 2001. Growth and development of the thinning canopy in a post-hurricane tropical rain forest in Nicaragua. Forest Ecol Manage 148:221–42CrossRefGoogle Scholar
  42. Veblen TT, Kulakowski DW, Eisenhart KS, Baker WL. 2001. Subalpine forest damage from a severe windstorm in northern Colorado. Can J Forest Res 31:2089–97CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • John A. Kupfer
    • 1
    Email author
  • Aaron T. Myers
    • 1
  • Sarah E. McLane
    • 1
  • Ginni N. Melton
    • 1
  1. 1.Department of GeographyUniversity of South CarolinaColumbiaUSA

Personalised recommendations