Using Lidar to Identify and Measure Forest Gaps on the William B. Bankhead National Forest, Alabama...
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Transcript of Using Lidar to Identify and Measure Forest Gaps on the William B. Bankhead National Forest, Alabama...
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
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)
Objectives
• 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
Lidar Data Collection
Study Area
Methods
• Interpolated Lidar data
– Bare Earth (ground)• Inverse Distance Weighted (IDW), Universal Kriging, and
Ordinary Kriging
– First Return (vegetation surface)• Ordinary Kriging
Digital Terrain Models (DTM)
IDW Universal Kriging
Mean: 0.004135Root-Mean-Square: 0.2458
Mean: 0.0008619Root-Mean-Square: 0.2647Average Standard Error: 0.2118Mean Standardized: 0.009038Root-Mean-Square Standardized: 1.248
Digital Terrain Models (DTM) - continued
Mean: 0.000017Root-Mean-Square: 0.1977Average Standard Error: 0.2011Mean Standardized: 0.0007143Root-Mean-Square Standardized: 0.9829
Predicated vs. Measured
Error vs. Measured
Standardized Error vs. Normal Value
Ordinary Kriging
Digital Surface Models
(Vegetation Heights)Canopy Height Model
Surface ModelTerrain Model_ =
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
Gap Locations
• Slope and vegetation height images were merged
• Vegetation height mode value,11.27m
Gap Measurements
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
xArea
erGapPerimetGSCI
2
ii ppGHDn
i
ln1
Results
Area– Count:443– Minimum: 0.40– Maximum: 8359.26– Mean: 23.54– Standard Deviation: 396.91
Gaps covered 10.25% of the 25 acre study area
Gap Shape
• GSCI– Count:443– Minimum: 2– Maximum: 9.36– Mean: 2.34– Standard Deviation:
0.37
Height Variation within Gaps
Gap Height Diversity 1.57
– Count:15710– Minimum: 0.02– Maximum: 11.27– Mean: 4.9– Standard Deviation:
3.46
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
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
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. Bankhead
National Forest and the Bankhead Liaison Panel.