Light Detection and Ranging (LiDAR) sensors generate dense point clouds that can be used to map forest structures at a high spatial resolution level. In this work, we consider the problem of identifying individual trees in a LiDAR point cloud. Existing techniques generally require intense parameter tuning and user interactions. Our goal is defining an automatic approach capable of providing robust results with minimal user interactions. To this end, we define a segmentation algorithm based on the watershed transform and persistence-based simplification. The proposed algorithm uses a divide-and-conquer technique to split a LiDAR point cloud into regions with uniform density. Within each region, single trees are identified by applying a segmentation approach based on watershed by simulated immersion. Experiments show that our approach performs better than state-of-the-art algorithms on most of the study areas in the benchmark provided by the NEW technologies for a better mountain FORest timber mobilization (NEWFOR) project. Moreover, our approach requires a single (Boolean) parameter. This makes our approach well suited for a wide range of forest analysis applications, including biomass estimation, or field inventory surveys.