Accurately measuring vegetation height is a key challenge in environmental monitoring, biomass estimation, and sustainable land

management. Recent advances in monocular depth estimation from RGB imagery have introduced models capable of predicting

canopy height without relying on costly LiDAR data. However, these models are typically trained on geographically constrained

datasets, raising concerns about their ability to generalize to different ecosystems and landscapes. In this study, we assess the

performance of a pre-trained monocular depth estimation model when applied to a dataset composed of natural forest areas and

a semi-urban environment characterized by a mixture of trees and built structures. Our evaluation reveals a significant degrada-

tion in accuracy when the model is used directly without any adaptation, highlighting the limitations of cross-domain transfer in

depth-based canopy height estimation. To address this issue, we perform a targeted fine-tuning using our dataset, which results in

considerable improvements across key metrics, including Mean Absolute Error (MAE), and Intersection over Union (IoU). These

findings demonstrate that even lightweight adaptation strategies are effective for tailoring monocular depth estimation models to

distinct environmental contexts, reinforcing the importance of local calibration for accurate and reliable canopy height mapping.The

source code used in this study is publicly available at: https://github.com/Geo99pro/depth-any-canopy.