The three-dimensional (3D) morphology of individual trees is critical for light interception, growth, stability and interactions with the local environment. Forest management intensity is a key driver of tree morphology, but how the long-term abandonment of silvicultural measures impacts trunk and crown morphological traits is not fully understood. Here, we take advantage of a long management intensity gradient combined with a high-resolution terrestrial laser scanning (TLS) approach to explore how management history affects the 3D structure of mature beech (Fagus sylvatica L.) trees. The management gradient ranged from long-term ( > 50 years) and short-term ( > 20 years) unmanaged to extensively and intensively managed beech stands. We determined 28 morphological traits and quantified the vertical distribution of wood volume along the trunk. We evaluated the differences in tree morphological traits between study stands using Tukey's HSD test. Our results show that 93% of the investigated morphological traits differed significantly between the study stands. Significant differences, however, emerged most strongly in the stand where forest management had ceased > 50 years ago. Furthermore, we found that the vertical distribution of trunk wood volume was highly responsive between stands with different management intensity, leading to a 67% higher taper top height and 30% lower taper of beech trees growing in long-term unmanaged stands compared to those in short-term unmanaged or managed stands. These results have important implications for management intensity decisions. It is suggested that the economic value of individual beech trees from long-term unmanaged forests can be expected to be very high. This might also translate to beech forests that are extensively managed, but we found that a few decades of implementation of such a silvicultural system is not sufficient to cause significant differences when compared to intensively managed stands. Furthermore, TLS-based high-resolution analyses of trunk and crown traits play a crucial role in the ability to better understand or predict tree growth responses to the current drivers of global change.