Advancing Forest Canopy Measurement: Deep Learning Approaches with Sentinel-2 Data

The study presents a novel approach to forest canopy height estimation using the ResUNet model, a deep learning architecture, combined with Sentinel-2 satellite imagery. Focused on the diverse forest landscapes of southern Finland, this research leverages LiDAR data provided by the Finnish Forest Center for model training and validation. The ResUNet model, an advancement over the conventional U-Net model, is tailored to address the complexities of forest canopy structures and the nuances of satellite data. Our findings demonstrate that the ResUNet model outperforms existing methods in terms of F1 score and Jaccard coefficient, indicating superior accuracy in canopy height estimation. The study highlights the potential challenges in remote sensing and environmental analysis, such as the interpretation of canopy heights in mixed vegetation areas and the impact of image variability across different times of the year. Despite these challenges, the visual examination of the model's output aligns closely with expert interpretations and LiDAR-based ground truth data, affirming the model's effectiveness. This research not only underscores the efficacy of the ResUNet model in environmental remote sensing tasks but also suggests potential future directions, including the use of multi-temporal data and the integration of different spectral bands for enhanced accuracy. The ResUNet model emerges as a promising tool for forest canopy height estimation, offering implications for broader forest management and ecological monitoring systems.