CNN-based transfer learning for forest aboveground biomass prediction from ALS point cloud tomography

This study presents a new approach for predicting forest aboveground biomass (AGB) from
airborne laser scanning (ALS) data: AGB is predicted from sequences of images depicting
vertical cross-sections through the ALS point clouds. A 3D version of the VGG16 convolutional
neural network (CNN) with initial weights transferred from pre-training on the ImageNet
dataset was used. The approach was tested on datasets from Canada, Poland, and the Czech
Republic. To analyse the effect of training sample size on model performance, different-sized
samples ranging from 10 to 375 ground plots were used. The CNNs were compared with
random forest models (RFs) trained on point cloud metrics. At the maximum number of training samples, the difference in RMSE between observed and predicted AGB of CNNs and RFs ranged from −2 t/ha to 5 t/ha, and the difference in squared Pearson correlation coefficient ranged from −0.05 to 0.06. Additional pre-training on synthetic data derived from virtual laser scanning of simulated forest stands could only improve the prediction performance of the CNNs when only a few real training samples (10–40) were available. ... mehrWhile 3D CNNs trained on cross-section
images derived from real data showed promising results, RFs remain a competitive alternative.