A patchwork of disjunct lidar collections is rapidly developing across the USA, often acquired with different acquisition goals and parameters and without field data for forest inventory. Airborne lidar and coincident field data have been used to estimate forest attributes across individual lidar extents, where forest measurements are collected using project-specific inventory designs. This research explores predicting forest attributes at locations not represented in the training data by combining lidar and field measurements from ecologically similar forests. Using field measurements from six lidar units, random forests regression models were created by systematically withholding forest inventory data from one lidar unit and using the forest inventory data from the other five units to predict basal area and stem density at the withheld unit. Results indicate that BA models produce more accurate predictions than stem density models when transferred to a lidar unit that did not contain field data. Relative root mean square errors calculated from the withheld field plots ranged between 32.3%-50.1% for BA and 40.7%-67.3% for stem density models. It is concluded that forest managers may use predictive models constructed from ecologically similar forests to obtain a preliminary estimate of resources, until local field measurement can be obtained.