Depth estimation plays a crucial role in understanding spatial relationships within natural scenes, enabling applications in 3D modeling, robotics, and environmental monitoring. This paper presents a comparative study between the Depth Anything model—a monocular depth estimation framework—and LiDAR sensors in forest environments. A real-world dataset of 4,613 frames captured from a eucalyptus farm in Açailândia, Maranhão, Brazil, was used for evaluation. Depth Anything was trained using a large-scale dataset with 1.5 million labeled and 62 million unlabeled images. The error metrics used in this study include MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error), achieving total values of 0.1096 meters and 0.1328 meters, respectively. These results demonstrate high alignment with LiDAR measurements and robustness in complex environments. Furthermore, the analysis includes a per-tree evaluation and statistical distribution through boxplots, confirming stable and consistent predictions. This study suggests the potential of combining monocular models with traditional sensors to enhance depth estimation for forest management and biodiversity monitoring.