A Deep Learning Method for Detecting Leaf Phenology from Phenocam Imagery

Vegetation phenology is a significance for studying the impact of climate change on ecosystems. Most of the current methods use a single characteristic variable to indirectly measure the vegetation phenology. So the accuracy of the phenological measurement will change due to the accuracy of the measurement index and many other phenological information have not been discovered. In this study, we evaluated leaf phenology using the convolutional neural regression network (CNNR) at a single and multiple sites in deciduous broad-leaved forest (DBF) from PhenoCam cameras. The error of the recognition result of the same site is about 3 days, the R2 of all stations was 0.843, and the error of the RMSE result is about 25 days. These finding provide a certain contribution to the research of vegetation phenology on the scale of daily time detection results.