3d Point Cloud Simulation for Above-Ground Forest Biomass Estimation

In this paper, we proposed a new framework for 3D point cloud simulation for forest above-ground biomass estimation. It takes tree variables as input and automatically generates 30m by 30m scenes and simulates their corresponding Li-DAR point clouds. 2000 3D tree models of 10 species are generated, with which 5000 forest scenes representing four types of eco-regions are built. Their corresponding biomass are then calculated with allometric equations. We used the simulated tropical scenes (1000 samples) to test four classical machine learning models’ ability in biomass estimation from point clouds. Experimental results show that data augmentation is able to significantly boost test accuracy; self-supervised learning can improve the estimation results; among the four models, ResNet18 is the best baseline model which has achieved a R-square score of 0.6615, and reduced the root mean square error to 202 kg (mean biomass in our dataset is 2114kg).