Forests provide a number of ecosystem services which sustain and enrich the wildlife, human societies, and the environment. However, many disturbances threaten forest ecosystems, making it necessary to monitor their health for optimal management and conservation. Although there are many indicators of forest health, changes in biogeochemical cycling, loss of species diversity, and invasive plants are particularly useful due to their vulnerability to the effects of climate change and intensive agricultural land use. Thus, this work evaluates the use of imaging spectroscopy to monitor forest nutrient status, species diversity, and plant invasions in the Mid-Atlantic region. The research is divided into four separate studies, each of which evaluated a unique application for imaging spectroscopy data at a different scale within the forest.
The first two studies examined loblolly pine nutrient status at the leaf and canopy scales, respectively. The first study determined that loblolly pine foliar macronutrient concentrations can be successfully modeled across the Southeastern US (R2=0.39-0.74). Following on these results, the second study focused on the relationship between physical characteristics, reflectance, and nutrients. Reflectance values and W scattering coefficients produced successful nitrogen models across loblolly pine plots at the canopy scale. Regression models showed similar explanatory power for nitrogen, although W scattering coefficients were significantly correlated with nitrogen at multiple wavelengths and reflectance variables were not. However, the direction of some of the correlations with W and the unusually high directional area scattering factor values indicate a need for further experimentation. The third study found that several imaging spectroscopy algorithms were moderately successful in identifying wavyleaf basketgrass invasions in mixed deciduous forests (overall accuracy=0.35-0.78; kappa=0.41-0.53). Lastly, the fourth study used a novel imaging spectroscopy/lidar fusion to identify canopy gaps and measure species diversity of understory vegetation. The lidar algorithm identified 29 of 34 canopy gaps, and regression models explained 49 percent of the variance in gap species diversity. In conclusion, imaging spectroscopy can be used to evaluate ecosystem health through forest nutrient status, nitrogen models, species diversity estimates, and identification of invasive plant species. / Ph. D.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/77390 |
| Date | 19 October 2015 |
| Creators | Stein, Beth R. |
| Contributors | Forest Resources and Environmental Conservation, Thomas, Valerie A., Martin, Mary E., Wynne, Randolph H., Radtke, Philip J. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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