Hurricane Katrina-induced forest damage in relation to ecological factors at landscape scale
Forest stand stability to strong winds such as hurricanes has been found to be associated with a number of forest, soil and topography factors. In this study, through applying geographic information system (GIS) and logit regression, we assessed effects of forest characteristics and site conditions on pattern, severity and probability of Hurricane Katrina disturbance to forests in the Lower Pearl River Valley, USA. The factors included forest type, forest coverage, stand density, soil great group, elevation, slope, aspect, and stream buffer zone. Results showed that Hurricane Katrina damaged 60% of the total forested land in the region. The distribution and intensity of the hurricane disturbance varied across the landscape, with the bottomland hardwood forests on river floodplains most severely affected. All these factors had a variety of effects on vulnerability of the forests to the hurricane disturbance and thereby spatial patterns of the disturbance. Soil groups and stand factors including forest types, forest coverage and stand density contributed to 85% of accuracy in modeling the probability of the hurricane disturbance to forests in this region. Besides assessment of Katrina’s damage, this study elucidates the great usefulness of remote sensing and GIS techniques combined with statistics modeling in assessment of large-scale risks of hurricane damage to coastal forests.
KeywordsForest disturbance Stand stability Windthrow Landscape Hurricane Katrina Remote sensing Geographic information system
- Brown, S., Gillespie, A. J. R., & Lugo, A. E. (1989). Biomass estimation methods for tropical forests with applications to forest inventory data. Forest Science, 35, 881–902.Google Scholar
- Chambers, J. L. (2006). Protecting coastal wetland forests: What can you do to help? Louisiana Agriculture, 49, 4–9.Google Scholar
- Finnigan, J. J., & Brunet, Y. (1995). Turbulent airflow in forests on flat and hilly terrain. In M. P. Coutts, & J. Grace (Eds.), Wind and trees. Cambridge: Cambridge University Press.Google Scholar
- Francis, J. K. (2000). Comparison of hurricane damage to several species of urban trees in San Juan, Puerto Rico. Journal of Arboriculture, 26, 189–197.Google Scholar
- Gardner, L. R., Michener, W. K., Williams, T. M., Blood, E. R., Kjerve, B., Smock, L. A., et al. (1992). Disturbance effects of hurricane Hugo on a pristine coastal landscape—North Inlet, South-Carolina, USA. Netherlands Journal of Sea Research, 30, 249–263. doi:10.1016/0077-7579(92)90063-K.CrossRefGoogle Scholar
- Johnson, G. R., & Johnson, B. (1999). Storm damage to landscape trees: Prediction, prevention, treatment. http://www.extension.umn.edu/distribution/naturalresources/DD7415.html. Accessed 6 Sept. 2006.
- Knabb, R. D., Rhome, J. R., & Brown, D. P. (2006). Tropical cyclone report, Hurricane Katrina 23–30 August 2005. http://www.nhc.noaa.gov/pdf/TCR-AL122005_Katrina.pdf. Accessed 5 May 2007.
- Martin, T. J., & Ogden, J. (2006). Wind damage and response in New Zealand forests: A review. New Zealand Journal of Ecology, 30, 295–310.Google Scholar
- Mattheck, C., & Bethge, K. (1990). Wind breakage of trees initiated by root delamination. Trees-Structure and Function, 4, 225–227.Google Scholar
- Mcgarigal, K., & Marks, B. J. (1995). FRAGSTATS: Spatial pattern analysis program for quantifying landscape structure. United States Department of Agriculture Pacific Northwest Research Station. Gen. Tech. Rep. PNW-GTR-351Google Scholar
- McMaster, K. J., (2005). Forest blowdown prediction: A correlation of remotely sensed contributing factors. Northern Journal of Applied Forestry, 22, 48–53.Google Scholar
- Nicoll, B. C., & Ray, D. (1996). Adaptive growth of tree root systems in response to wind action and site conditions. Tree Physiology, 16, 891–898.Google Scholar
- Ostertag, R., Silver, W. L., & Lugo, A. E. (2005). Factors affecting mortality and resistance to damage following hurricanes in a rehabilitated subtropical moist forest. Biotropica, 37, 16–24.Google Scholar
- Rosson, J. F., Jr. (1995). Forest resources of Louisiana, 1991. F. S. U.S. Department of Agriculture, Southern Forest Experiment Station. Resour. Bull. SO-1, 92. New Orleans, Louisiana.Google Scholar
- SAS Institute Inc. (2006) SAS 9.1.3. Service Pack 4. Cary: SAS Institute Inc.Google Scholar
- Todd, S. W., & Hoffer, R. M. (1998). Responses of spectral indices to variations in vegetation cover and soil background. Photogrammetric Engineering and Remote Sensing, 64, 915–921.Google Scholar
- Touliatos, P., & Roth, E. (1971). Hurricanes and trees: Ten lessons from Camille. Journal of Forestry, 69, 285–289.Google Scholar
- Turner, D. P., Cohen, W. B., Kennedy, R. E., Fassnacht, K. S., & Briggs, J. M. (1999). Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites. Remote Sensing of Environment, 70, 52–68. doi:10.1016/S0034-4257(99)00057-7.CrossRefGoogle Scholar
- USDA Forest Service (2007). The forest inventory and analysis database: Database description and users guide version 2.1. National Forest Inventory and Analysis Program, U.S. Department of Agriculture, Forest Service, Southern Research Station.Google Scholar
- USGS. (2002). Environmental atlas of the Lake Pontchartrain basin. http://pubs.usgs.gov/of/2002/of02–206/env-overview/water-quality.html. Accessed 6 Sept. 2006.
- Wang, F., & Xu, Y. J. (2007). Comparison of change detection techniques for assessing Hurricane Katrina damage to forests in Lower Pearl River Valley, USA. International Journal of Remote Sensing (in review).Google Scholar