Applying High-Resolution UAV-LiDAR and Quantitative Structure Modelling for Estimating Tree Attributes in a Crop-Livestock-Forest System

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Dalla Corte, Ana Paula ; de Vasconcellos, Bruna Nascimento ; Rex, Franciel Eduardo ; Sanquetta, Carlos Roberto ; Mohan, Midhun ; Silva, Carlos Alberto ; Klauberg, Carine ; Alves de Almeida, Danilo Roberti ; Almeyda Zambrano, Angelica Maria ; Trautenmuller, Jonathan William ; Leite, Rodrigo Vieira ; do Amaral, Cibele Hummel ; Pessoa Veras, Hudson Franklin ; Rocha, Karla da Silva ; de Moraes, Anibal ; Karasinski, Mauro Alessandro ; Inoue Sanquetta, Matheus Niroh ; Broadbent, Eben North

• Document type: Journal article
• Source: LAND; v. 11, n. 4, p. 15-pg., 2022-04-01.

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. (AU)

• FAPESP's process: 18/21338-3 - Monitoring forest landscape restoration from unmanned aerial vehicles using Lidar and hyperspectral remote sensing
• Grantee: Danilo Roberti Alves de Almeida
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 19/14697-0 - Monitoring the demography and diversity of forests undergoing restoration using a drone-lidar-hyperspectral system
• Grantee: Danilo Roberti Alves de Almeida
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor