Applying High-Resolution UAV-LiDAR and Quantitative Structure Modelling for Estimating Tree Attributes in a Crop-Livestock-Forest System
Ana Paula Dalla Côrte, Bruna Nascimento de Vasconcellos, Franciel Eduardo Rex, Carlos Roberto Sanquetta, Midhun Mohan, Carlos Alberto Silva, Carine Klauberg, Danilo Roberti Alves de Almeida, Angélica M. Almeyda Zambrano, Jonathan William Trautenmüller, Rodrigo Vieira Leite, Cibele Hummel do Amaral, Hudson Franklin Pessoa Veras, Karla da Silva Rocha, Aníbal de Moraes, Mauro Alessandro Karasinski, Matheus Niroh Inoue Sanquetta, Eben N. Broadbent
Abstract
Individual tree attributes, such as stem volume and biomass, are usually predicted by using traditional field-derived allometric models. However, these models are derived from data collected from small areas and lack a level of detail of tree components (e.g., stem, branches, and leaves). Remote sensing techniques such as the Quantitative Structure Modelling (QSM) applied on high-density LiDAR data emerge as a promising solution for obtaining extensive and detailed tree attribute estimates. We used a high-density LiDAR data on board of a Unmanned Aerial Vehicle (UAV) to evaluate the performance of the QSM approach in estimating field-derived individual tree attributes such as the diameter at breast height (dbh), tree height (ht), and volume (v), as well as the stem (SAGB), branch (BAGB), and total (TAGB) aboveground biomass of eucalyptus trees. QSM was used in two different approaches: (i) using dbh and h derived from QSM and then applied into the field-based equations for estimation of volume and (ii) deriving tree volume directly from QSM. In general, all fitted models using the QSM approach were satisfactory, but with a slight tendency of over-estimation of dbh (9.33%), ht (12.40%), v-QSM1 (26.35%), v-QSM2 (26.66%), TAGB (27.08%), SAGB (25.57%), and BAGB (20.08%). Non-significant differences were noticed when estimating the dbh, tree volume, stem, and aboveground biomass. Despite the overestimation, this study indicates that using the QSM approach to estimate individual tree attributes from UAV-LiDAR is a promising alternative to support the decision-making process regarding forest management activities, especially when considering tree architecture and biomass components.