Spatio-temporal ecological assessment of Camellia oleifera plantations using Sentinel-2 images based on deep learning

The widespread cultivation of Camellia oleifera in subtropical regions has played a significant role in promoting economic development in southern China. In recent years, the cultivation area has continuously expanded, highlighting the importance of extracting Camellia oleifera plantations automatically and monitoring ecological changes in these regions. This study proposes a novel method based on deep learning, utilizing Sentinel-2 images for efficient assessing ecological quality of Camellia oleifera plantations. First, we undertook a comprehensive examination and assessment of different semantic segmentation models, utilizing a dataset of Camellia oleifera plantations that we compiled. The most effective segmentation model was then employed to automatically extract Camellia oleifera plantations in Shaoyang City. Finally, to gain a deeper understanding of the ecological status of Camellia oleifera plantations, the Remote Sensing Ecological Index (RSEI) was introduced to analyse ecological changes from 2018 to 2022. The research results indicate that when the U-Net++ model is applied to extract Camellia oleifera plantations from Sentinel-2 images, its performance surpasses other semantic segmentation methods. This method achieves notable metrics: accuracy of 0.80, recall of 0.79, F1-Score of 0.81, and MIoU of 0.88, demonstrating its effectiveness in identifying Camellia oleifera plantations. The research findings of Camellia oleifera plantations in Shaoyang City show a notable increase in the mean RSEI, with a rise from 0.166 in 2018 to 0.345 in 2022. Through the application of deep learning for automated Camellia oleifera plantation extraction and RSEI for ecological assessment, the study discovers a continuous expansion of Camellia oleifera plantations and a yearly increase in ecological quality.