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Small footprint Light Detection and Ranging (LiDAR) data have been shown to be a very accurate technology to predict stem volume. In particular, most recent sensors are able to acquire multiple return (more than 2) data at very high hit density, allowing one to have detailed characterization of the canopy. In this paper, we utilize very high density ( >8 hits per m2) LiDAR data acquired over a forest stand in Italy. Our approach was as follows: Individual trees were first extracted from the LiDAR data and a series of attributes from both the first, and non-first (multiple), hits associated with each crown were then extracted. These variables were then correlated with ground truth individual estimates of stem volume. Our results indicate that: (i) non-first returns are informative for the estimation of stem volume (in particular the second return); (ii) some attributes (e.g., maximum at the power of n) better emphasize the information content of returns different from the first respect to other metrics (e.g., minimum, mean); and (iii) the combined use of variables belonging to different returns slightly increases the overall model accuracy. Moreover, we found that the best model for stem volume estimation (adj - R2 = 0.77, P < 0.0001, SE = 0.06) comprised four variables belonging to three returns (first, second, and third). The results of this analysis are important as they underline the effectiveness of the use of multiple return LiDAR data, underling the connection between LiDAR hits different from the first and tree structure and characteristics.