The Importance of Tree Height in Estimating Individual Tree Biomass while Considering Errors in Measurements and Allometric Models

Abstract

This study evaluated the uncertainty of individual tree biomass estimated by allometric models by both including and excluding tree height independently. Using two independent sets of measurements on the same trees, the errors in the measurement of diameter at breast height and tree height were quantified, and the uncertainty of individual tree biomass estimation caused by errors in measurement was calculated. For both allometric models, the uncertainties of the individual tree biomass estimation caused by the use of a specific allometric model were also calculated. Finally, the overall uncertainty of individual tree biomass by combining the two uncertainties was calculated. The allometric model including tree height was 6 % more accurate than that excluding tree height when the uncertainty caused by allometric models became the only consideration. However, in terms of the uncertainty caused by measurement, the allometric model excluding tree height was three times more accurate than allometric model including tree height. As a result, the allometric model excluding tree height was 5 % more accurate than the allometric model including tree height when both causes of uncertainty, the allometric model and measurement errors were considered. In conclusion, errors in tree height measurement have the potential to increase the error of aboveground biomass estimation.

Promoting sustainability education through hands-on approaches: a tree carbon sequestration exercise in a Singapore green space

“…With regard to measurement error and uncertainty, many students were concerned about the estimation of H. Tree height measurements are fraught with error because of the difficulty of seeing the location of the top of the tree (Phalla et al 2018). Thus, there is large uncertainty in the angle from which H is determined.…”

“…Concerned with the H term, students, therefore, considered other approaches such as using sophisticated technologies (e.g., LiDAR), triangulation from the top of a building, and using an allometric equation that does not rely on tree height (cf. Phalla et al 2018).…”

Comparing the Stand Structure of a Secondary Mixed Forest with Three Broad-Leaved Forests in China’s Greater Khingan Mountains

“…Many scholars have attempted to model the height-diameter relationship [29,30,[32][33][34][35][36][37] and then estimate stand volume by fitting a related model. Such models can aid in formulating forest management measures [36,38]. The description and explanation of spatial structure and the relationships between single trees have become the focus of forest structure research [8,22] and are receiving increased attention.…”

Quantifying the uncertainty caused by sampling, modeling, and field measurements in the estimation of AGB with information of the national forest inventory in Durango, Mexico

“…However, because the variables (DBH, TH) are correlated, the contribution of the correlation was incorporated in the error propagation; thus, the contribution of the uDBH decreased, and consequently the ratio uTH/uDBH favored uTH, having contributions from 37 to 62% to the measurement errors. Chave et al (2004) found also more contribution of the uTH to the measurement error in tropical forest, estimating 48 to 78% form uTH; as well as Phalla et al (2018) who estimated a contribution of 74% from uTH to the measurement errors in evergreen forest.…”