A Framework for Measuring Tree Rings Based on Panchromatic Images and Deep Learning

ABSTRACT Tree‐ring data are pivotal for decoding the age and growth patterns of trees, reflecting the impact of environmental factors over time. Addressing the significant shortcomings of traditional, labour‐intensive and resource‐demanding methods, we propose an innovative automated technique that utilizes panchromatic images and deep learning for measuring tree rings. The method utilizes convolutional neural networks to enhance image quality, precisely delineate tree rings through segmentation and perform ring counting and width calculation in the post‐processing stage. We compiled an extensive data set from diverse sources, including Beijing Forestry University and the Summer Palace, to train our algorithm. The performance of our method was validated empirically, demonstrating its potential to transform tree‐ring analysis and provide deeper insights into ecological and climatological research. Summary statement We have developed a framework utilizing deep learning and panchromatic sensors for the automated measurement of tree rings. This framework achieves high precision in both ring detection and width measurement, offering an efficient alternative to the traditionally time‐consuming and labour‐intensive techniques of dendrochronology.