The Leaf Area Index (LAI) is a key parameter that helps to understand the connection between canopy structure and ecosystem functions. In this study, the main aims were to examine the impact of forest management on canopy structure using LiDAR data to characterize the canopy vertical profile, as well as to develop LAI models and an LAI mapping tool for sweet chestnut (Castanea Sativa Mill.) coppice. Twenty-one circular plots (r = 10 m) were established, each of which was submitted to one of the following forest management treatments: Control, with no intervention (3300–3700 stems ha⁻¹); Treatment 1, one thinning to leave a living stock density of 900–600 stems ha⁻¹; or Treatment 2, a more intensive thinning, leaving 400 stems ha⁻¹. A LAI field measurement was made in all plots and the study area was recorded by LiDAR. With the LiDAR, two types of metrics were calculated: standard elevation metrics and canopy metrics. The results showed the different canopy layers of the study area, highlighting how the resprout layer influences the canopy structure of sweet chestnut coppice. By combining the LiDAR data and the LAI field estimates, various linear and nonlinear models were developed and tested, the linear model being found to have the best performance (R² = 0.79) for the study area. With the selected linear model and other LiDAR data of interest such as the 95th percentile, an automatic mapping tool was designed. This tool allows spatially information to be generated that can be used to implement management strategies.