In the past 15 years, a large amount of public-domain lidar data has been collected over the Southeastern United States. Most of these acquisitions were undertaken by government agencies, primarily for non-forestry purposes. That is, they were collected mostly to aid in the creation of digital terrain models and to support hydrological and engineering assessments. Such data is not ideal for forestry purposes mainly due to the low pulse density per square meter, the high scan angles and low swath overlaps associated with these acquisitions. Nevertheless, the large area of coverage involved motivated this work. In this dissertation, I first look at how such lidar data (from non-forestry acquisitions) can be combined with National Forest Inventory tree height data to generate a large-area canopy height model. A simple linear regression model was developed using two lidar-based metrics as predictors: the 85th percentile of heights of canopy first returns and the coefficient of variation of the heights of canopy first returns. This model had good predictive ability over 76 disparate lidar projects, covering an area of approximately 297,000 square kilometers between them. Factors leading to the residual lack-of-fit of the model were also analyzed and quantified. For example, predictive ability was found to be better for softwood forests, forests with more homogeneous vegetation structure and for terrains with gentler slopes. Given that as much as 30% of the US is covered by public domain non-forestry lidar acquisitions, this is a first step for constructing a national wall-to-wall vertical vegetation structure map, which can then be used to ask important questions regarding forest inventories, carbon sequestration, wildlife habitat suitability and fire risk mitigation. Then, I examined whether such lidar data could be further used to predict understory shrub presence over disparate forest types. The predictability of classification model was low (accuracy = 62%, kappa = 0.23). Canopy occlusion factors and the heterogeneity of the understory layer were implicated as the main reasons for this poor performance. An analysis of the metrics chosen by the modeling framework highlighted the importance of non-understory metrics (metrics related to canopy openness and topographic aspect) in influencing shrub presence. As the proposed set of metrics were developed over a wide range of temperate forest types and topographic conditions of Southeastern US, it is expected that it will be useful for more localized future studies. Lastly, I explored the possibility of combining lidar-derived canopy height maps with Landsat-derived stand-age maps to predict plantation pine site index over large areas (site index is a measure of forest productivity). The model performance was assessed using a Monte Carlo technique (RMSE = 3.8 meters, relative RMSE = 19%). A sample site index map for large areas of Virginia and South Carolina was generated (map coverage area: 832 sq. km) and implications were discussed. Analysis of the resulting map revealed the following: (1) there is an increase in site index in most areas, compared to the 1970s, and (2) approximately 83% of the area surveyed had low levels of productivity (defined as site index < 22.0 meters for base age of 25 years). This work highlights the efficacy of combining lidar-based canopy height maps with other similar remote sensing based datasets to understand aspects of forest productivity over large areas, and to help make policy-relevant recommendations. / Ph. D. / Remote sensing, in the context of forestry and forest resource management, involves the acquisition of data over large forested areas by sensors situated at a distance. A good example is a high resolution satellite image over several hundred square kilometers allowing us to identify (say) patches of deforestation, reduced forest productivity, or species diversity. Lidar (which stands for Light Detection and Ranging) is a relatively new remote sensing technology in which the time it takes for a laser pulse to travel to a feature and return back to the sensor is used to measure how far away the feature is from the sensor. In forests, data from airborne laser scanners enable the measurement of both horizontal and vertical canopy structure (such as tree height and canopy cover). Data from airborne laser scanners have been collected over a large area of the US (roughly 30%). However, the sensors and acquisition parameters are optimized for the inexpensive collection of the data needed for topographic mapping, and not for forest measurement. Moreover, the lidar data were collected in disparate and dissimilar projects, making the production of maps over large areas technically challenging. A systematic study is required looking at whether lidar data from such dissimilar projects can be used together to generate robust forest parameter maps over large areas. This dissertation details such a study. Airborne laser scanner data collected for topographic mapping across many disparate projects can be used to estimate several important characteristics about forests. My conclusions are as follows: • Lidar data can be combined effectively with field measurement data to produce high quality, wall-towall tree height maps over a large area. • These lidar data can be used to map understory shrub presence, albeit with less accuracy, since fewer laser pulses penetrate the canopy. • Forest age, as estimated using multi temporal earth resource satellite data, can be combined with lidar-derived tree heights to estimate site index (a way to know how fast trees grow on a site) for pine plantations. Most sites in the study area (Eastern Virginia and Central South Carolina) are not particularly productive (site index <22 meters), but they are more productive on the whole than they were in the 1970s. Overall, the work outlined in this dissertation highlights the efficacy of using lidar data from disparate nonforestry projects along with other datasets to monitor useful forest parameters over large areas, and to help make policy-relevant recommendations.