Traditional field measurement methods are usually time-consuming and costly. With recent developments in remote sensing, precise estimation of some key forest parameters is becoming a close reality. One of the robust technologies being utilized for this aim is light detection and ranging (LiDAR). We used LiDAR to derive tree height and canopy density metrics using pixel values, texture features, and vegetation indices obtained from WorldView-3 imagery to estimate volume per hectare, mean height, dominant height, and number of trees per hectare. A total of 58 sample plots, predominantly composed of Pinus brutia, which is one of the most abundant tree species in Turkey, were analyzed. The adjusted R² values obtained by the best regression models, using LiDAR-derived metrics alone, were 0.66, 0.73, 0.83, and 0.83 for the volume per hectare, number of trees per hectare, and average and dominant heights, respectively. After integrating LiDAR-derived metrics with WorldView-3 imagery band values and subsequently with vegetation indices, higher adjusted R² values of 0.70 and 0.77, respectively, were obtained for the volume per hectare. In contrast, incorporating texture features besides other parameters had no positive effect on the accuracy of estimation.