The above ground volume (AGV) measurement of a sampled tree is a fundamental input to provide predictions of forest, woodland and urban resources, but it is generally biased by country-specific merchantable thresholds. Terrestrial laser scanners (TLS) have been demonstrated to be promising for non-destructive and accurate measurements. Actually, there have been recent procedural approaches to develop automated processing chains for extracting tree metrics from TLS data. A sensitivity analysis of an automated chain on 12 parameters is presented here to report effects of TLS and scan acquisition characteristics and routines used for data filtering and volume estimates on AGBiomass predictions. This analysis was based on data recorded by a Leica HDS-6100 on Oak, Hornbeam, Birch and Larch during winters 2014-16. Three trees were recorded per spp. from six scan positions around each tree and with three TLS sampling resolutions (0.072-0.018°) per position. Scanned trees were felled, then measured in detail and stratified into lower stem (Ls), coarse (Cb, diameter ge 7 cm) and small (Sb, lt 7 cm) branch sections. When compared against ground data, this analysis indicated a consistent pattern across all trees for DBH (r²=0.98, bias<0.001 m), tree height (r²=0.89, bias>-0.63 m) or AGBs (r²=0.98, bias<4; r²=0.99, bias>-34; r²=0.96, bias<8 kg for Ls, Cb and Sb, respectively) with a TLS resolution of 0.018° driving improved fits for h (+5%), AGBCb (+13%) and AGBSb (+27%) and 6 scan positions driving improved fits for AGBCb (+56%) and AGBSb (+36%). The quality of the filter routine was found to be the most critical parameter (up-to ±65% for Sb). All other parameters had a relatively little effect.