In the last few decades, the role of forests as carbon sinks has become a fundamental scientific issue due to their potential effect on climate change. Tropical forests represent one half of Earth’s carbon stored in terrestrial vegetation. Therefore, estimating the above-ground biomass and carbon of these forests through new technologies is crucial for adopting different strategies that promote sustainable management. In recent years, the LiDAR technique (Laser Imaging Detection and Ranging) has emerged as an important tool to estimate forest biomass accurately, especially in tropical forests where vegetation is dense and the acquisition of field data is a difficult task. The main objective of this work is to compare two technologies of LiDAR, the full-wave LiDAR (LiDARfw) and discrete LiDAR (LiDARd), for estimating biomass in a tropical forest of Costa Rica. The results showed that LiDARfw provided a higher point density (+14.5%) and captured greater vertical structure variability than LiDARd, particularly in lower forest strata. This demonstrates its effectiveness in modeling complex forest environments. In conclusion, LiDARfw excels in capturing detailed vertical profiles and identifying structural heterogeneity, making it ideal for biomass estimation and precise ecological studies.