Forest inventory plays an important role in the management and planning of forests. In this study, we present a method for automatic detection and estimation of trees, especially in forest environments using 3D terrestrial LiDAR data. The proposed method does not rely on any predefined tree shape or model. It uses the vertical distribution of the 3D points partitioned in a gridded Digital Elevation Model (DEM) to extract out ground points. The cells of the DEM are then clustered together to form super-clusters representing potential tree objects. The 3D points contained in each of these super-clusters are then classified into trunk and vegetation classes using a super-voxel based segmentation method. Different attributes (such as diameter at breast height, basal area, height and volume) are then estimated at individual tree levels which are then aggregated to generate metrics for forest inventory applications. The method is validated and evaluated on three different data sets obtained from three different types of terrestrial sensors (vehicle-borne, handheld and static) to demonstrate its applicability and feasibility for a wide range of applications. The results are evaluated by comparing the estimated parameters with real field observations/measurements to demonstrate the efficacy of the proposed method. Overall segmentation and classification accuracies greater than 84% while average parameter estimation error ranging from 1.6 to 9% were observed. Data Set: ftp://ftp.ip.univ-bpclermont.fr/checchin/IP_ForestDATA/ Data Set License: ODC Attribute Licence