Forest disturbances can reduce the potential of ecosystems to provide resources and services. Despite the urgent need to understand the effects of logging on tropical ecosystems, the quantification of disturbances arising from selective logging remains a challenge. Here, we used canopy-three-dimensional information retrieved from Terrestrial Laser Scanner (TLS) measurements to investigate the impacts of logging on key structural traits relevant to forest functioning. We addressed the following questions: 1) Which canopy structural traits were mostly affected by logging? 2) Can remotely-sensed canopy structural traits be used to quantify forest distur-bances? Fourteen canopy structural traits were applied as input to machine learning models, which were trained to quantify the intensity of logging disturbance. The plots were located in Malaysian Borneo, over a gradient of logging intensity, ranging from forest not recently disturbed by logging, to forest at the early stage of recovery following logging. Our results showed that using the Random Forest regression approach, the Plant Area Index (PAI) between 0 m -5 m aboveground, Relative Height at 50 %, and metrics describing plant allocation in the middle-higher canopy layer were the strongest predictors of disturbance. In particular, PAI between 35 m and 40 m explained 12 % to 19 % of the structural variability between plots, followed by the relative height at 50 %, (10.5 % -18.6 %), and the foliage height diversity (7.5 % -16.9 %). The approach presented in this study allowed a spatially explicitly characterization of disturbances, providing a novel approach for quantifying and monitoring the integrity of tropical forests. Our results indicate that canopy structural traits can provide a robust indication of disturbances, with strong potential to be applied at regional or global scales. The data used in this study are openly available and we encourage other researchers to use them as a benchmark data set to test larger scale approaches based on satellite and ...