Estimates of stand height are an integral component of forest inventories. Lidar has been demonstrated as a tool for remotely sensing information on the vertical structure of forests, such as height. The ability to remotely sense height information for forest inventory purposes may allow for procedures such as update, audit, calibration, and validation. With current technologies, lidar data collection and processing are a resource-intensive undertaking. The ability to use a regression model to spatially extend a lidar survey from a sample to a larger area would act to decrease costs while allowing for the characterization of a larger area. In this study we address the ability to extend lidar estimates of height from sample flight lines to a greater area using segmented Landsat-5 thematic mapper (TM) data. Based upon empirical relationships between lidar-estimated height and within segment digital numbers, height is estimated for an entire landscape from a 0.48% sample. To conform to current polygon-based forest management practices, the within polygon segment-based height estimates are combined to create an updated height attribute for each polygon. The empirical relationships between lidar data and forest inventory polygon attributes (coefficient of determination (r(2)) = 0.23; standard error (SE) = 4.15) and within polygon spectral values (r(2) = 0.26; SE = 4.06) indicated a need to develop more representative models. To this end, we developed a regression model to produce a relationship between quantile-based estimates of mean canopy top height for the segments with lidar hits (r(2) = 0.61; SE = 3.15). This segment/height empirical relationship allowed us to extend the height estimates to polygons that have no lidar information using the image digital numbers. The segment/lidar estimates of height generally form a range centered on zero (no difference) to +/-6 m of the ground measured height for over 80% of the available validation plots, with a r(2) of 0.67 and a SE of 3.30 m.
