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Jeffrey Stephens 1 , Dr. Luben Dimov 1 , Dr. Wubishet Tadesse 1 , and Dr. Callie Schweitzer 2

Using Lidar to Identify and Measure Forest Gaps on the William B. Bankhead National Forest, Alabama. Jeffrey Stephens 1 , Dr. Luben Dimov 1 , Dr. Wubishet Tadesse 1 , and Dr. Callie Schweitzer 2 1 Alabama A&M University, Center for Forestry, Ecology, and Wildlife

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Jeffrey Stephens 1 , Dr. Luben Dimov 1 , Dr. Wubishet Tadesse 1 , and Dr. Callie Schweitzer 2

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  1. Using Lidar to Identify and Measure Forest Gaps on the William B. Bankhead National Forest, Alabama Jeffrey Stephens1, Dr. Luben Dimov1, Dr. Wubishet Tadesse1, and Dr. Callie Schweitzer2 1Alabama A&M University, Center for Forestry, Ecology, and Wildlife 2USDA Forest Service, Southern Research Station, Ecology and Management of Southern Appalachian Hardwoods, Alabama A&M University

  2. Importance of Forest Gaps • Size and spatial properties of gaps influence forest regeneration and species composition of forests (Watt 1947) • Forest regeneration is mainly confined to gaps and is dependent on gap size (Watt 1947)

  3. Objectives Lidar Data Collection • Quantifying forest gap attributes • Size • Shape • Heterogeneity • Pattern • Collected July and September, 2005 • Flown for selected William B. Bankhead stands • Separated into two point clouds • Bare Earth • Vegetation

  4. Study Area

  5. Methods • Interpolated Lidar data • Bare Earth (ground) • Inverse Distance Weighted (IDW), Universal Kriging, and Ordinary Kriging • First Return (vegetation surface) • Ordinary Kriging

  6. Digital Terrain Models (DTM) IDW Universal Kriging Mean: 0.0008619 Root-Mean-Square: 0.2647 Average Standard Error: 0.2118 Mean Standardized: 0.009038 Root-Mean-Square Standardized: 1.248 Mean: 0.004135 Root-Mean-Square: 0.2458

  7. Digital Terrain Models (DTM) - continued Ordinary Kriging Predicated vs. Measured Error vs. Measured Mean: 0.000017 Root-Mean-Square: 0.1977 Average Standard Error: 0.2011 Mean Standardized: 0.0007143 Root-Mean-Square Standardized: 0.9829 Standardized Error vs. Normal Value

  8. Digital Surface Models Canopy Height Model _ = Surface Model Terrain Model (Vegetation Heights)

  9. Forest Gap Identification • Gaps were defined as: • Areas with a slope greater than 60 degrees • Vegetation heights below 12 meters • Note: Gaps were considered in this study as any area that met the above characteristics

  10. Slope and vegetation height images were merged Vegetation height mode value,11.27m Gap Locations

  11. Gap Size Area and perimeter Gap Shape Gap Shape Complexity Index (GSCI) (Blackburn and Milton 1996, Koukoulas and Blackburn 2004) Gap Height Diversity (Shannon and Weaver 1962) Gap Distribution Dispersed or clustered Gap Measurements

  12. Results Gaps covered 10.25% of the 25 acre study area Area • Count: 443 • Minimum: 0.40 • Maximum: 8359.26 • Mean: 23.54 • Standard Deviation: 396.91

  13. GSCI Count: 443 Minimum: 2 Maximum: 9.36 Mean: 2.34 Standard Deviation: 0.37 Gap Shape

  14. Height Variation within Gaps Gap Height Diversity 1.57 • Count: 15710 • Minimum: 0.02 • Maximum: 11.27 • Mean: 4.9 • Standard Deviation: 3.46

  15. Gap Distribution within Study Area • Gap locations were clustered • Observed Mean Distance/Expected Mean Distance = 0.76 • Z Score = -9.54 • Significance level 0.01 • The complexity of the gaps was randomly distributed within the study area • Moran’s I index = 0 • Z score = 0.24 • Vegetation height distribution within gaps is clustered • Moran’s I index = 0.09 • Z score = 554.97 • Significance level 0.001

  16. Conclusions • Lidar allows for gap identification and provides forest gap characteristics that other imagery can not describe • Important information for regeneration and ecological processes • Future work could examine the vegetation type within gaps through multispectral or hyperspectral remote sensing techniques

  17. References Blackburn, G. A., and Milton, E. J., 1996 Filing the gaps: remote sensing meets woodland ecology. Global ecology and Biogeography, 5, 175-191. Koukoulas, S., and Blackburn, G. A., 2004. Quantifying the spatial properties of forest canopy gaps using LiDAR imagery and GIS. International Journal of Remote Sensing, 25, 3049- 3071. Shannon, C. E., and Weaver, W., 1962. The Mathematical Theory of Communication. Urbana: University of Illinois Press. Watt, A.S., 1947. Pattern and process in the plant community. Journal of Ecology, 35, 1-22. Acknowledgements Support was provided by National Science Foundation, CREST-Center forEcosystems Assessment, Award No. 0420541; the Center for Forestry, Ecology,and Wildlife, Alabama A&M University; USDA Forest Service, Southern ResearchStation, Ecology and Management of Southern Appalachian Hardwoods ResearchWork Unit. We thank our partners the USDA Forest Service William B. BankheadNational Forest and the Bankhead Liaison Panel.

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