Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Abstract

The quantitative structure model (QSM) contains the branch geometry and attributes of the tree. AdQSM is a new, accurate, and detailed tree QSM. In this paper, an automatic modeling method based on AdQSM is developed, and a low-cost technical scheme of tree structure modeling is provided, so that AdQSM can be freely used by more people. First, we used two digital cameras to collect two-dimensional (2D) photos of trees and generated three-dimensional (3D) point clouds of plot and segmented individual tree from the plot point clouds. Then a new QSM-AdQSM was used to construct tree model from point clouds of 44 trees. Finally, to verify the effectiveness of our method, the diameter at breast height (DBH), tree height, and trunk volume were derived from the reconstructed tree model. These parameters extracted from AdQSM were compared with the reference values from forest inventory. For the DBH, the relative bias (rBias), root mean square error (RMSE), and coefficient of variation of root mean square error (rRMSE) were 4.26%, 1.93 cm, and 6.60%. For the tree height, the rBias, RMSE, and rRMSE were—10.86%, 1.67 m, and 12.34%. The determination coefficient (R2) of DBH and tree height estimated by AdQSM and the reference value were 0.94 and 0.86. We used the trunk volume calculated by the allometric equation as a reference value to test the accuracy of AdQSM. The trunk volume was estimated based on AdQSM, and its bias was 0.07066 m3, rBias was 18.73%, RMSE was 0.12369 m3, rRMSE was 32.78%. To better evaluate the accuracy of QSM’s reconstruction of the trunk volume, we compared AdQSM and TreeQSM in the same dataset. The bias of the trunk volume estimated based on TreeQSM was −0.05071 m3, and the rBias was −13.44%, RMSE was 0.13267 m3, rRMSE was 35.16%. At 95% confidence interval level, the concordance correlation coefficient (CCC = 0.77) of the agreement between the estimated tree trunk volume of AdQSM and the reference value was greater than that of TreeQSM (CCC = 0.60). The significance of this research is as follows: (1) The automatic modeling method based on AdQSM is developed, which expands the application scope of AdQSM; (2) provide low-cost photogrammetric point cloud as the input data of AdQSM; (3) explore the potential of AdQSM to reconstruct forest terrestrial photogrammetric point clouds.

Non-Destructive Estimation of Deciduous Forest Metrics: Comparisons between UAV-LiDAR, UAV-DAP, and Terrestrial LiDAR Leaf-Off Point Clouds Using Two QSMs

“…TreeQSM fits cylinders of each segment segmented from tree point clouds rather than depending on the tree skeleton, while AdQSM fits cylinders based on the extracted complete skeleton [42,48,64]. In several previously reported studies, comparable estimation accuracies between TreeQSM and AdQSM are observed for volume estimation in tropical forests [48] and for stem volume estimation using terrestrial close-range photogrammetry point clouds in a deciduous plantation [65].…”

“…The results show that there are no apparent differences in tree height estimates using TreeQSM and AdQSM, regardless of ALS or DAP point clouds, at both plot and tree levels. The TreeQSM-and AdQSM-derived tree height estimates have been widely reported, and very good performance was achieved in deciduous species [48,65]. However, TreeQSM performed better than AdQSM based on ALS point clouds for estimating DBH, while the opposite was found for estimating crown area.…”

Terrestrial Laser Scanning for Non-Destructive Estimation of Aboveground Biomass in Short-Rotation Poplar Coppices

“…Direct estimate of tree biomass from the TLS cloud can be assessed by means of estimating tree volume through QSM (quantitative structural modeling) assuming geometrical shapes (commonly cylinders) for both the stem and branches of the tree [34,65]. While this method seems optimal for estimating the biomass of adult or even extra mature trees [34,66,67], its application to short-rotation coppices is quite difficult since a single stool may share different small-sized shoots, and the dominant one is occluded by the lateral shoots, which makes difficult the correct definition of the main stem volume, where a large amount of biomass is accumulated. As an alternative, and conforming to the main innovation of our work, we do not aim to directly obtain estimates of stool biomass from TLS measurements, but we focus on obtaining easy-to-measure TLS-derived metrics to be included in the models for individual stool and plot biomass.…”

A low-cost close-range photogrammetric surface scanner

“…Context domain knowledge was employed by Dong et al (2021), which uses the Pix4D photogrammetric suite to reconstruct tree structures; the approach is characterized as close range compared to aerial photogrammetry, although the imaging distance is several meters. The method further uses domain-specific knowledge (tree structure) to optimize the reconstruction of tree trunks and is not a generic photogrammetric method.…”