Comparison of QSM methods based on multi-factors and multi-dimensions

This paper compares and analyzes two Quantitative Structure Model (QSM) reconstruction methods based on Terrestrial Laser Scanning (TLS) point cloud data: TreeQSM and AdQSM. Using the African Cameroon large tropical tree data set, this study evaluated the accuracy difference between the two methods in extracting key forest structure parameters such as single-tree Diameter at Breast Height (DBH), single tree height, and total tree volume. Additionally, the study explores the impact of including or excluding leaf data on QSM modeling accuracy and the influence of different parameters in AdQSM on modeling precision. The results indicate that TreeQSM has higher accuracy in the estimation of tree height, DBH, and total tree volume, and shows less uncertainty and deviation from reference values when processing small trees. Although AdQSM also shows certain accuracy in the estimation of tree height and DBH, it may generate some outlier data when inverting large tree DBH without leaf data. Moreover, the inclusion or exclusion of leaf data in single-tree data has a limited impact on AdQSM modeling accuracy, affecting only the inversion of tree height in TreeQSM. The height segmentation parameter in AdQSM has no effect on tree height estimation and has a limited impact on the accuracy of DBH estimation.