Airborne light detection and ranging (LiDAR) is a remote sensing technology that quantifies the travel time of photons emitted in pulses from a LiDAR instrument to travel to and reflect back from objects. Knowing the travel time for the photons and accounting for the speed of light, distances to objects from the instrument can be quantified. When LiDAR is acquired over forested areas some of the pulses will find canopy openings and "penetrate" to the ground with others striking the canopy at various heights above the ground, generating an XYZ point-cloud of eastings, northings, and elevations. Capitalizing on the information in these point-clouds from a June, 2003, acquisition in forested areas of Maine, we characterized the vertical profile of the canopy from which we computed LiDAR-derived explanatory variables for empirical modeling of various response variables (i.e., forest stand metrics, bird species abundance). The first aim of the research reported in this study was to assess the ability of LiDAR-derived explanatory variables to predict forest stand structure than can then be used as input in a suite of habitat-models that predict New England wildlife occurrences (called ECOSEARCH). Using regression analyses and field-collected data, we determined that LiDAR does a good job of predicting various forest stand metrics for the over- and understory (Adj. R2 >0.60 for 14 of 20 models developed). The second aim was to assess the ability of LiDAR-derived explanatory variables to directly predict mean bird abundance within forested areas during their breeding season. We derived a set of minimally correlated LiDAR-derived explanatory variables and used these in regression analyses to predict mean bird abundance from field surveys. Results indicate that LiDAR-derived explanatory variables were useful for predicting the mean abundance of 17 bird species (all with Adj. R2 > 0.2, with 5 models having Adj. R2 > 0.4). The third aim was to utilize the LiDAR-derived habitat-models and apply these across two study sites under varying management scenarios for assessments and planning purposes. Using a simple Euclidean distance metric and under various but realistic assumptions we were able to ascertain optimal management scenarios for five focal bird species.