Most estimates of wood supply do not reflect the true availability of wood resources. The availability of wood resources ultimately depends on collective wood harvesting decisions across the landscape. Both social and biophysical constraints impact harvesting decisions and thus the availability of wood resources. While most constraints do not completely inhibit harvesting, they may significantly reduce the probability of harvest. Realistic assessments of woody availability and distribution are needed for effective forest management and planning. This study focuses on predicting the probability of harvest at forested FIA plot locations in Virginia. Classification and regression trees, conditional inferences trees, random forest, balanced random forest, conditional random forest, and logistic regression models were built to predict harvest as a function of social and biophysical availability constraints. All of the models were evaluated and compared to identify important variables constraining harvest, predict future harvests, and estimate the available wood supply. Variables related to population and resource quality seem to be the best predictors of future harvest. The balanced random forest and logistic regressions models are recommended for predicting future harvests. The balanced random forest model is the best predictor, while the logistic regression model can be most easily shared and replicated. Both models were applied to predict harvest at recently measured FIA plots. Based on the probability of harvest, we estimate that between 2012 and 2017, 10 – 21 percent of total wood volume on timberland will be available for harvesting. / Master of Science

The Image-based Change Estimation program (ICE) was developed by the US Forest Service Forest Inventory & Analysis (FIA) program and the Geospatial Technology Applications Center in response to the 2014 Farm Bill calling for more timely and accurate estimates of land cover and use change. ICE monitors change throughout the US on a state by state basis by assessing each FIA plot using high resolution imagery from two dates in time. In the western US, FIA measures 10% of the plots each year to report on status, trends, and sustainability of our Nation’s forests. However, this 10 year cycle misses disturbances because a temporal gap occurs from disturbance event to measurement. This study compares field- and image-based observations of land cover and use change to improve sampling procedures in Utah. Image-based data collected from 2011 and 2014 imagery and field-based plots measured between 2011 and 2016 are compared using three methods to compile the ICE data, termed hierarchical, majority, and point center, to determine a standardized system and better understand their relationships. Additionally, ICE change agents were compared with causes of tree mortality observed on FIA forest plots to assess how well ICE evaluates causes of change and the differences of change vs. mortality agents were explored by conducting a second review of the imagery to find trends in data discrepancies. This knowledge can help image interpreters better recognize and identify change.

<p>Climate change has dramatically altered the ecological landscape of the eastern U.S., leading to shifts in phenological events and redistribution of tree species. However, shifts in phenology and species distributions have implications for the productivity of different populations and <a></a>the communities these species are a part of. Here, I utilized two studies to quantify the effects of climate change on forests of the eastern U.S. First, I used phenology observations at a common garden of 28 populations of northern red oak (<i>Quercus rubra</i>) across seven years to assess shifts in phenology in response to warming, identify population differences in sensitivity to warming, and correlate sensitivity to the productivity of the populations. Second, I utilized data from the USDA Forest Service’s Forest Inventory and Analysis Program to identify forest communities of the eastern U.S., assess shifts in their species compositions and spatial distributions, and determine which climate-related variables are most associated with changes at the community level. In the first study, I found that populations were shifting their spring phenology in response to warming, with the greatest sensitivity in populations from warmer, wetter climates. However, these populations with higher sensitivity did not have the highest productivity; rather, populations closer to the common garden with intermediate levels of sensitivity had the highest productivity. In the second study, I found that there were 12 regional forest communities of the eastern U.S., which varied in the amount their species composition shifted over the last three decades. Additionally, all 12 communities shifted their spatial distributions, but their shifts were not correlated with the distance and direction that climate change predicted them to shift. Finally, areas with the highest changes across all 12 communities were associated with warmer, wetter, lower temperature-variable climates generally in the southeastern U.S. Taken together, these studies provide insight into the ways in which forests are responding to climate change and have implications for the management and sustainability of forests in a continuously changing global environment.</p>