The use of airborne LiDAR (Light Detection and Ranging) as a direct method to
evaluate forest canopy parameters is vital in addressing both forest management and
ecological concerns. The overall goal of this study was to develop the use of airborne
LiDAR in evaluating canopy parameters such as percent canopy cover (PCC) and leaf
area index (LAI) for mixed pine and hardwood forests (primarily loblolly pine, Pinus
taeda, forests) of the southeastern United States. More specific objectives were to: (1)
Develop scanning LiDAR and multispectral imagery methods to estimate PCC and LAI
over both hardwood and coniferous forests; (2) investigate whether a LiDAR and
normalized difference vegetation index (NDVI) data fusion through linear regression
improve estimates of these forest canopy characteristics; (3) generate maps of PCC and
LAI for the study region, and (4) compare local scale LiDAR-derived PCC and regional
scale MODIS-based PCC and investigate the relationship. Scanning LiDAR data was
used to derive local scale PCC estimates, and TreeVaW, a LiDAR software application,
was used to locate individual trees to derive an estimate of plot-level PCC. A canopy
height model (CHM) was created from the LiDAR dataset and used to determine tree
heights per plot. QuickBird multispectral imagery was used to calculate the NDVI for
the study area. LiDAR- and NDVI-derived estimates of plot-level PCC and LAI were
compared to field observations for 53 plots over 47 square kilometers. Linear regression
analysis resulted in models explaining 84% and 78% of the variability associated with
PCC and LAI, respectively. For these models to be of use in future studies, LiDAR point
density must be 2.5 m. The relationship between regional scale PCC and local scale PCC
was investigated by resizing the local scale LiDAR-derived PCC map to lower
resolution levels, then determining a regression model relating MODIS data to the local values of PCC. The results from this comparison showed that MODIS PCC data is not
very accurate at local scales. The methods discussed in this paper show great potential
for improving the speed and accuracy of ecological studies and forest management.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-1117 |
| Date | 15 May 2009 |
| Creators | Griffin, Alicia Marie Rutledge |
| Contributors | Popescu, Sorin C. |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | electronic, application/pdf, born digital |
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