Automatic detection tree crown and height using Mask R-CNN based on unmanned aerial vehicles images for biomass mapping

Mapping the aboveground biomass (AGB) of forests in a spatially continuous manner is essential to comprehend the carbon sequestration capacity of forest ecosystems. Automatic extraction of tree crown and height is an effective approach to estimating biomass at the tree level. In this study, we employed high-resolution unmanned aerial vehicle (UAV) images and a mask region-based convolutional neural network (Mask R-CNN) to automatically identify the tree crown and height of Pinus sylvestris, we added tree height information to the model training by setting special tree height label samples and determined the optimal model by comparing the tree crown and height detection accuracy of four different combinations (RGB-CHM-DSM, RGB-CHM, RGB-DSM, RGB) of UAV images. The findings demonstrate that the model with RGB-CHM-DSM combination has the highest crown extraction accuracy (Precision = 0.896, Recall = 0.916, F1 Score = 0.906, IoU = 0.822), and the tree height extraction results with RGB-CHM combination has the highest correlation with the tree height of UAV images (R2 = 0.93, RMSE = 0.25, rRMSE = 3.10). Higher extraction accuracy is achieved due to the inclusion of CHM features in the model, and the results can be used to estimate forest AGB at different scales, improve the accuracy and efficiency of forest carbon sink research, minimize the workload of field investigations, and reduce the cost of manual methods.