Seasonal Tree Height Dynamic Estimation Using Multi-source Remotely Sensed Data in Shenzhen

Tree height is a key indicator in forest ecology, reflecting tree growth status and ecosystem structure. Traditional methods of tree height measurement rely on ground-based measurements, which are limited by cost and time. In recent years, the development of machine learning and multi-source remotely sensed technologies has provided new ways to measure tree height. In this study, we utilized light detection and ranging and satellite data to extract spectral, vegetation, texture, polarization, terrain, and season features. By integrating these features with machine learning, deep learning, and optimization methods, we dynamically estimated tree heights in Shenzhen during summer and winter from 2018 to 2023 and validated seasonal and regional scalability. It was found that (a) the seasonal tree height neural network demonstrated the highest prediction accuracy in tree height estimation (R2 = 0.72, mean absolute error = 1.89 m), and the optimization process of Shapley additive explanations reduced 23 features, which improved the prediction accuracy (R2 = 0.80, mean absolute error = 1.58 m) and saved computational resources; (b) the seasonal tree height neural network has a strong generalizability for estimating tree height across seasons and regions; and (c) during 2018 to 2023, tree heights in Shenzhen were mainly concentrated in 6 to 14 m, and the spatial distribution has a strong autocorrelation. Tree canopy heights in winter are generally lower than those in summer, and the tree growth rate shows spatial heterogeneity. Overall, this study uncovers the intricate interplay between tree growth and seasonal variations in its traits throughout the urbanization process in Shenzhen. It offers valuable data support and a theoretical foundation for urban greening management and ecological protection.