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.