Improving forest age estimation to understand subtropical forest regrowth dynamics using deep learning image segmentation of time‐series historical aerial photographs

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Abstract Accurate forest age estimation is essential for understanding forest recovery trajectories and evaluating the efficacy of restoration strategies. While field‐based methods for forest age estimation offer high accuracy, they are spatially constrained and challenging to apply retrospectively. In contrast, satellite‐based approaches provide extensive regional coverage but may lack precision at the local landscape level. Historical aerial photographs can bridge this gap by delivering fine‐scale land cover information. However, challenges such as limited spectral bands and topographic shadows in hilly terrains introduce uncertainty in land cover segmentation and temporal dynamics, complicating accurate forest age determination. To address these challenges, we developed a two‐step deep learning approach for image segmentation using historical aerial photographs. The method involves using a pre‐trained deep learning model with open‐source forest labels, followed by fine‐tuning based on localized forest data. This approach achieved accurate forest segmentation, with our highest accuracy model (mean IoU of 0.859) utilizing a combined U‐Net and ResNet50 architecture. Our forest age estimates demonstrated superior agreement, significantly outperforming existing national forest age products for China in terms of both temporal coverage and accuracy. By overlaying our age product with LiDAR structural metrics, we uncovered strong yet distinct recovery trajectories across forest structure attributes. Collectively, our study demonstrates the effectiveness of deep learning algorithms for forest age monitoring using greyscale historical aerial photographs, while pinpointing the limitations of existing national‐scale forest age products for local monitoring. Enhanced fine‐scale forest age mapping provides an essential technique and dataset to advance our understanding of forest regrowth and structural dynamics, and this improved knowledge of forest dynamics will aid in assessing carbon sequestration potential and informing targeted forest management and restoration strategies.

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