Spatial-spectral attention state-space fusion network for tree carbon estimation using multi-source data

Abstract Amid rapid industrialization and urbanization, global warming and the urban heat island effect are intensifying. There is an urgent need for rapid and accurate monitoring of carbon storage in urban vegetation to assess and mitigate these environmental issues. While methods exist for estimating above-ground carbon (AGC) stocks in individual trees using deep learning, fewer studies integrate multi-source remote sensing data with these techniques. This study introduces a network that leverages spatial-spectral attention and state-space models, called the spatial-spectral attention state-space fusion network (S4FNet). S4FNet utilizes UAV hyperspectral imaging (HSI) data, light detection and ranging (LiDAR) data, and RGB data to estimate AGC storage in individual trees. This study conducted experiments at two sites, the South and North areas of Shenzhen University’s Yuehai Campus, Shenzhen City, Guangdong Province, China, and compared S4FNet with eight traditional machine learning methods and a deep learning method. The experimental results demonstrate that S4FNet achieves the best performance across all metrics, confirming its excellent capability in predicting AGC stocks using multi-source remote sensing data.