Improving quantitative structure models with filters based on allometric scaling theory

Abstract

Quantitative structure models (QSMs) are topological ordered cylinder models of trees which cover the complete branching structure from the stem’s base up to all tips. But the thin branches appear too large in the input point clouds. This leads to a well known problem, the overestimation of the QSM cylinders’ volumes and radii in thin branches. We present here a solution to this problem by introducing two QSM filters correcting the radii of such cylinders. The filters itself are build upon the theoretical fundamentals of allometric scaling theories. For validation we use QSMs produced from an open point cloud data set of tree clouds with the SimpleForest software. We compare the QSM volume against the harvested reference data for 65 felled trees. We also found QSM data of TreeQSM, a competitive and broadly accepted QSM modeling tool utilizing a different filter method. Our method performed more accurate on three different error measures. We quantify the error of our method with a RMSE of 127 \(\mathtt {dm^{3}}\), a \(\mathtt {r^{2}_{adj.}}\) of 0.96 and a CCC of 0.97. With those filters the accuracy of estimating total or partial volume of trees does significantly increase.