Mobile laser scanning progressive technology that proved its ability to provide very precise measurements of geometry of transport networks and objects located near them. Mobile innovation of the laser scanning has potential to obtain data on forest on tree level from large plots in a short time. Valuable data, collected using mobile mapping system (MMS), becomes very difficult to process when Global Navigation Satellite System (GNSS) outages become too long. Heavy forest canopy blocking GNSS signal and limited forest access can increase difficulty of processing of MMS data. This thesis presents two approaches to processing of mobile laser scanning (MLS) data acquired under a heavy canopy cover. First approach is making use of data from only one scanner scanned along the one scanning line and processing methods developed for processing of terrestrial laser scanning data. Data processed using this approach was used for estimation of diameter at the breast height (DBH) by Monte Carlo method. Root mean square error (RMSE) of estimated DBHs was 0.0466m. Second approach of MLS data processing is able to use multiple point cloud combined. Data from two scanners of MMS Riegl VMX-250 acquired by scanning along the multiple scanning lines was registered using Iterative Closest Point method. Processed data was used for DBH estimation using Monte Carlo method as it was in the case of the first approach. RMSE was 0.0315 m in this case. Methods presented in the thesis have a potential of use for processing the MLS data acquired under a heavy forest canopy with weak GNSS signal.