Vegetation phenology studies the timing of recurring seasonal dynamics and can be monitored through estimates of plant area index (PAI). Shifts in spring phenology are a key indicator for the effect of climate change, in particular the start of the growing season of forests. Terrestrial laser scanning (TLS), also referred to as terrestrial LiDAR, is an active remote sensing technique and measures the forest structure with high spatial detail and accuracy. TLS provides information about the 3D distribution of canopy constituents and vertical plant profiles can be derived from these data. Vertical plant profiles describe the plant area per unit volume as a function of height, and can be used to used to monitor seasonal dynamics through PAI. Here, we present a TLS time series based on 48 measurement days of four sampling locations in a deciduous forest in the Netherlands. Vertical plant profiles are derived for each measurement and allow us to quantify not only total canopy integrated PAI, but also monitor PAI at specific horizontal layers. Sigmoidal models show a good fit to the derived total canopy integrated PAI time series (CV(RMSE) 0.99). The start of season (SOS) based on these models occurs between March 29 and April 3, 2014, depending on the species composition. The SOS derived from the TLS data corresponds well with field observations and occurs 7–12 days earlier compared to the SOS estimate from the MODIS NDVI time series. This is mainly caused by the lower relative standard deviation for TLS measurements in leaf-off conditions (0.72% compared to 2.87% for the MODIS NDVI data), which allows us to significantly detect small changes in phenology earlier. TLS allows us to monitor PAI at specific horizontal layers and we defined an understorey, intermediate and upper canopy layer. Even though our study area had only a sparse understorey, small differences are observed in the SOS between the different layers. We expect that these phenological differences will be more pronounced in multi-layered forests and TLS shows the potential to study seasonal dynamics not only as a function of time, but also as a function of canopy height.