In 2014 we started the development of the library LESTO (LiDAR Empowered Sciences Toolbox Opensource): a set of modules for the analysis of LiDAR point cloud with an Open Source approach with the aim of improving the performance of the extraction of the volume of biomass and other vegetation parameters on large areas for mixed forest structures. LESTO contains a set of modules for data handling and analysis implemented within the JGrassTools spatial processing library. The main subsections are dedicated to: preprocessing of LiDAR raw data (LAS), creation of raster derived products, normalization of the intensity values and tools for extraction of vegetation and buildings. The core of the LESTO library is the extraction of the vegetation parameters. We decided to follow the single tree based approach and implemented the extraction of tops and crowns from local maxima, the region growing method and the watershed method, all can be applied on LiDAR derived raster datasets as well as point clouds of raw data. An automatic validation procedure has been developed considering an Optimizer Algorithm based on Particle Swarm (PS) and a matching procedure which takes the position and the height of the extracted trees respect to the measured ones and iteratively tries to improve the candidate solution changing the models' parameters. On a watershed level, the resulting extracted trees with position and main characteristics, can be used for forestry management or for the evaluation of natural hazards (hillslopes stability, large wood transportation during floods).