Bottomland hardwood forests are highly productive ecosystems which perform many important ecological services. Unfortunately, many bottomland hardwood forests have been degraded or lost. Accurate land cover mapping is crucial for management decisions affecting these disappearing systems. SPOT-5 imagery from 2005 was combined with Light Detection and Ranging (LiDAR) data from 2006 and several ancillary datasets to map a portion of the bottomland hardwood system found in the Sulphur River Basin of Northeast Texas. Pixel-based classification techniques, rulebased classification techniques, and object-based classification techniques were used to distinguish nine land cover types in the area. The rule-based classification (84.41% overall accuracy) outperformed the other classification methods because it more effectively incorporated the LiDAR and ancillary datasets when needed. This output was compared to previous classifications from 1974, 1984, 1991, and 1997 to determine abundance trends in the area’s bottomland hardwood forests. The classifications from 1974-1991 were conducted using identical class definitions and input imagery (Landsat MSS 60m), and the direct comparison demonstrates an overall declining trend in bottomland hardwood abundance. The trend levels off in 1997 when medium resolution imagery was first utilized (Landsat TM 30m) and the 2005 classification also shows an increase in bottomland hardwood from 1997 to 2005, when SPOT-5 10m imagery was used. However, when the classifications are re-sampled to the same resolution (60m), the percent area of bottomland hardwood consistently decreases from 1974-2005. Additional investigation of object-oriented classification proved useful. A major shortcoming of object-based classification is limited justification regarding the selection of segmentation parameters. Often, segmentation parameters are arbitrarily defined using general guidelines or are determined through a large number of parameter combinations. This research justifies the selection of segmentation parameters through a process that utilizes landscape metrics and statistical techniques to determine ideal segmentation parameters. The classification resulting from these parameters outperforms the classification resulting from arbitrary parameters by approximately three to six percent in terms of overall accuracy, demonstrating that landscape metrics can be successfully linked to segmentation parameters in order to create image objects that more closely resemble real-world objects and result in a more accurate final classification.

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In the upland hardwood forests of the southern Appalachians, management tools are needed based on the characteristics of the site to quantify the site quality where no accurate maps of site quality exist. Three studies were conducted to achieve this objective. The first study tested if independent measures of forest productivity, based on vegetation and environment, in a six-county study area in the Blue Ridge Mountains in North Carolina would correlate with measures of forest productivity obtained from U.S. Forest Service Forest Inventory Analysis (FIA) data. Specific hypotheses included: FIA measures of forest productivity are related to one another; FIA measures of forest productivity are related to FIA-measured landscape parameters; and FIA measures of forest productivity are related to independent measures of forest productivity based on landscape parameters and soil characteristics. Four predictive indices of forest productivity were used; three were generated in a geographic information system (GIS). FIA measures of forest productivity were not significantly correlated to FIA measured landscape parameters. FIA site productivity classes were significantly correlated to FIA measures of site index. Independent measures of forest productivity, particularly the Moisture Regime Index (MRI) and the Forest Site Quality Index (FSQI), were significantly correlated to FIA measures of site index. Topography can be used to delineate site quality, but the addition of soil depth can prove to be useful in the estimation. The second study was designed to develop methods, based on field and digital data, to identify colluvial soils in the central Ridge and Valley of southwestern Virginia. Two hypotheses were tested. First, on the linear side slopes of the study area, where site quality is low in stands with subxeric to xeric moisture regimes, vegetation and topography can indicate colluvial soils. A second hypothesis tested if the topographic signature of colluvial soils could be identified geospatially with a digital elevation model. Results indicated that the MRI and the Terrain Shape Index predicted the presence of colluvial deposits in the study area. The basal area of yellow-poplar was positively associated with colluvial soils. A GIS-based model found the slope difference of colluvial soils to be less steep than residual soils as the size of the neighborhood increased. The final study determined if measures of site quality in the Blue Ridge Mountains of North Carolina were related to the water budget. Specifically,the hypothesis that site index could be predicted by variables that represented the inputs, usage, and supply of water was tested. A second hypothesis questioned if site quality classes could be predicted by a combination of topography and the annual water budget. Regression models predicted site index to be a function of topography, available water supply, and the annual water budget, but the accuracy was low (R2=0.11 and 0.13). A classification approach yielded better results. Incorporating the annual water budget into the FSQI increased classification accuracy of predicted site index by 50%, and decreased the number of sites misclassified by one class by 8%. Where accurate maps of site quality do not exist, the MRI, the abundance of yellow-poplar, and the modified FSQI may be used to delineate site quality for site-specific management and, ultimately, greater return on investment for the landowner. / Ph. D.

Late-successional forests provide a unique opportunity to explore adaptive management approaches that mitigate atmospheric carbon dioxide levels through carbon storage while also enhancing ecological resilience to novel climate and disturbances. Typical benchmarks for adaptive forest management include species diversity and structural complexity, which are widely considered to increase ecosystem stability and productivity. However, the role of functional trait diversity (e.g., variation in leaf and stem traits) in driving forest productivity and ecosystem resilience remains underexplored. We leveraged existing continuous forest inventory (CFI) data and collected local functional trait observations from CFI plots within late-successional forests in western Massachusetts to explore links between aboveground carbon storage and different types of forest diversity. We then fit a linear model within a Bayesian hierarchical framework applying functional diversity, species diversity, and structural complexity as predictors of live aboveground biomass (AGB) within CFI plots. Our framework integrates local functional trait information with database species mean trait values using a multivariate structure to account for inherent trait syndromes and estimate functional diversity in each plot. Across 626 plot-timepoints, we found that integrating individual functional trait information from co-located plots yielded the best predictions of live AGB. Contrary to expectations, functional diversity had a negative relationship with live AGB. Whereas plots with low functional diversity and higher AGB were dominated by mid-to-late successional hardwood species, plots with high functional diversity had more shade-intolerant species and lower AGB mediated by recent small-scale disturbances. Our results reveal an ontogenetic shift in the effects of functional diversity on AGB productivity over the course of succession in northeastern temperate forests. Corroborating with classical models of biomass development in late-successional northern hardwood forests, our findings support the need for adaptive forest carbon management to facilitate a mosaic of different forest successional stages across the landscape to maximize live aboveground carbon benefits in northeastern mixed hardwood forests.

Stochastic point processes and associated methodology provide a means for the statistical analysis and modeling of the spatial point pattern formed from forest tree stem locations. Stochastic Gibbs point processes were explored as models that could simulate short-range clustering arising from reproduction of trees by stump sprouting, and intermediate-range inhibition of trees that may result from competition for light and growing space. This study developed and compared three pairwise interaction processes with parametric models for 2nd-order potentials and three triplets processes with models for 2nd- and 3rd-order potentials applied to a mixed-species hardwood forest in the Southern Appalachian Mountains of western North Carolina. Although the 2nd-order potentials of both the pairwise interaction and triplets processes were allowed to be purely or partially attractive, the proposed Gibbs point process models were demonstrated to be locally stable. The proposed Gibbs point processes were simulated using Markov Chain Monte Carlo (MCMC) methods; in particular, a reversible-jump Metropolis-Hastings algorithm with birth, death, and shift proposals was utilized. Parameters for the models were estimated by a Bayesian inferential procedure that utilizes MCMC methods to draw samples from the Gibbs posterior density. Two Metropolis-Hastings algorithms that do this sampling were compared; one that estimated ratios of intractable normalizing constants of the Gibbs likelihood by importance sampling and another that introduced an auxiliary variable to cancel the normalizing constants with those in the auxiliary variable's proposal distribution. Results from this research indicated that attractive pairwise interaction models easily degenerate into excessively clustered patterns, whereas triplets processes with attractive 2nd-order and repulsive 3rd-order interactions are more robust against excessive clustering. Bayesian inference for the proposed triplets models was found to be very computationally expensive. Slow mixing of both algorithms used for the inference combined with the long iteration times limited the practicality of the Bayesian approach. However the results obtained here indicate that triplets processes can be used to draw inference for and simulate patterns of mixed-species Appalachian hardwood forests. / Ph. D.

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