Abstract:
Buttressed trees provide mechanical support for themselves and offer essential ecological functions, such as
nutrient acquisition, while being one of the largest sources of volume or biomass estimation variation in tropical
forests. In this study, we collected 51 buttressed trees from (33) Democratic Republic of Congo, (12) Indonesia,
and (6) Costa Rica, including (48) point clouds, and (3) destructive measurement. Specifically, we compared the
performance of the Alpha Shape Algorithm (ASA) and the Slice Triangulation (ST) method on buttress volume
estimation based on 30 point clouds with two species. Six point clouds from Costa Rica were used to validate the
3D surface reconstruction method. Meanwhile, we developed three allometric models based on 36 point clouds: a
diameter above the buttress-based (DAB, 39 to 203 cm) model, a diameter computed from the non-convex area
(Darea130) model, and the convex hull perimeter (Dconvex130) of the breast height model. The developed models
were validated with independent data, including (6) point clouds and (3) destructive measurements, to highlight
the broader contextualization and application of these methods. Volume estimated by the ASA and ST showed a
high agreement with the reference volume acquired using the Smalian formula (relative RMSE of 0.07 and 0.11,
respectively, regardless of species effect). ASA was also robust when modeling trees with more and shallower
horizontal buttresses. Darea130 was the most accurate predictor to estimate buttress volume, with a lower Akaike
information criterion (− 66.25) than DAB (− 59.55) and Dconvex130 (30.56); however, DAB and Darea130 (relative
RMSE of 0.21 and 0.23, respectively) showed similar performance when validated with independent datasets.
Our results indicate that the ASA approach performs better than both the ST and allometric models used in this
study. Furthermore, the ASA method can help correct the bias in the present and past estimates of volume and
biomass of large trees, which are foundational components for understanding biomass allocation and dynamics
in tropical forests contemporary fields.
