Accurate and efficient estimation of forest growth and live biomass is a critical element in assessing potential responses to forest management and environmental change.The objective of this study was to develop models to predict longleaf pine tree diameter at breast height(dbh)and merchantable stem volume(V)using data obtained from field measurements.We used longleaf pine tree data from 3,376 planted trees on 127 permanent plots located in the U.S.Gulf Coastal Plain region to fit equations to predict dbh and V as functions of tree height(H)and crown area(CA).Prediction of dbh as a function of H improved when CA was added as an additional independent variable.Similarly,predictions of V based on H improved when CA was included.Incorporation of additional stand variables such as age,site index,dominant height,and stand density were also evaluated but resulted in only small improvements in model performance.For model testing we used data from planted and naturally-regenerated trees located inside and outside the geographic area used for model fitting.Our results suggest that the models are a robust alternative for dbh and V estimations when H and CA are known on planted stands with potential for naturally-regenerated stands,across a wide range of ages.We discuss the importance of these models for use with metrics derived from remote sensing data. Accurate and efficient estimation of forest growth and live biomass is a critical element in assessing potential responses to forest management and environmental change. The Objective of this study was to develop models to predict longleaf pine tree diameter at breast height (dbh) and merchantable stem volume (V) using data obtained from field measurements. We used longleaf pine tree data from 3,376 planted trees on 127 permanent plots located in the USGulf Coastal Plain region to fit equations to predict dbh and V as functions of tree height (H) and crown area (CA). Prediction of dbh as a function of H improved when CA was added as an additional independent variable. Similar, predictions of V based on H improved when CA was included. Corporation of additional stand variables such as age, site index, dominant height, and stand density were also evaluated but resulted in only small improvements in model performance. For model testing we used data from planted and naturally-regenerated trees locate d inside and outside the geographic area used for model fitting. Our results suggest that the models are a robust alternative for dbh and V estimations when H and CA are known on planted stands with potential for naturally-regenerated stands across a wide range of ages .We discuss the importance of these models for use with metrics derived from remote sensing data.