Abstract
This study aimed to improve one basic circle of allometry-based forest biometrics—diameter at breast height (DBH) mensuration. To address its common shortage of low efficiency in field measurement, this study attempted mobile laser scanning (MLS) as an efficient alternative and proposed a new MLS-based DBH mensuration algorithm to further exclude the effect of stem bending. That is, prior to the procedure of cone-based geometric modeling of a tree stem, an operation of Aligning the local stem axis series that is calculated by the Successive Cone-based Fitting of those continuously equi-height-layered laser points on the stem (ASCF) is appended. In the case of an urban boreal forest, tests showed that the proposed algorithm worked better (the coefficient of determination, R2 = 0.81 and root mean square error, RMSE = 52.1 mm) than the circle- (0.16 and 189.4 mm), cylinder- (0.77 and 58.7 mm), and cone-based (0.77 and 56.7 mm) geometric modeling algorithms. From a methodological viewpoint, the new ASCF algorithm was preliminarily validated for MLS-based tree DBH mensuration, with the “cornerstone-rebuilding” significance for allometry-based forest biometrics. With the development of MLS variants available for complex forest environments, this study will contribute fundamental implications for advancements in forestry.
Highlights
Forest biometrics concentrate on the advancement of mathematical statistics and biometrics in linking bio-properties to structure-metrics and is extensively highlighted in the communities of forest science and management [1,2]
The correlation analyses between the diameter at breast height (DBH) estimates and their ground-truth data showed that the related R2 and root mean squared error (RMSE) values vary along with the LH parameter setting increasing (Figure 5a)
As this study was aimed at tree-level DBH mensuration, rather than forest stand mapping, an extensive test of the proposed ASCF algorithm was deployed on the data collections of the sample urban trees that were examined in Reference [37]
Summary
Forest biometrics concentrate on the advancement of mathematical statistics and biometrics in linking bio-properties to structure-metrics and is extensively highlighted in the communities of forest science and management [1,2]. In forest inventory, tree structural parameters are often divided into two categories—those that are easy to measure and those that are difficult to assess. To acquire the latter, tree allometry that reflects the inherent relationships between the two categories of characteristic dimensions of trees [3,4] can be incorporated. Tree allometry that reflects the inherent relationships between the two categories of characteristic dimensions of trees [3,4] can be incorporated By this means, the difficultly-assessed structure-metric features can be derived from a few -collected parameters through their allometric relations. Allometry-based forest biometrics has become a widely used approach for forest science and management
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