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

Ana Paula Dalla Corte, Bruna Nascimento de Vasconcellos, Franciel Eduardo Rex, Carlos Roberto Sanquetta, Midhun Mohan, Carlos Alberto Silva, Carine Klauberg, Danilo Roberti Alves de Almeida, Angelica Maria Almeyda Zambrano, Jonathan William Trautenmüller, Rodrigo Vieira Leite, Cibele Hummel do Amaral, Hudson Franklin Pessoa Veras, Karla da Silva Rocha, Anibal de Moraes, Mauro Alessandro Karasinski, Matheus Niroh Inoue Sanquetta and Eben North Broadbent
Additional contact information
Ana Paula Dalla Corte: BIOFIX Research Center, Federal University of Parana, Curitiba 80210-170, Brazil
Bruna Nascimento de Vasconcellos: EMBRAPA Florestas, Colombo 83411-000, Brazil
Franciel Eduardo Rex: BIOFIX Research Center, Federal University of Parana, Curitiba 80210-170, Brazil
Carlos Roberto Sanquetta: BIOFIX Research Center, Federal University of Parana, Curitiba 80210-170, Brazil
Midhun Mohan: Department of Geography, University of California, Berkeley, CA 94709, USA
Carlos Alberto Silva: School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32611, USA
Carine Klauberg: Department of Forest Engineering, Federal University of João Del Rei, Sete Lagoas 35701-970, Brazil
Danilo Roberti Alves de Almeida: Department of Forest Sciences, “Luiz de Queiroz” College of Agriculture, University of São Paulo, Piracicaba 13418-900, Brazil
Angelica Maria Almeyda Zambrano: Spatial Ecology and Conservation Laboratory, Center for Latin America Studies, University of Florida, Gainesville, FL 32611, USA
Jonathan William Trautenmüller: BIOFIX Research Center, Federal University of Parana, Curitiba 80210-170, Brazil
Rodrigo Vieira Leite: Department of Forest Engineering, Federal University of Viçosa, Viçosa 36570-900, Brazil
Cibele Hummel do Amaral: Department of Forest Engineering, Federal University of Viçosa, Viçosa 36570-900, Brazil
Hudson Franklin Pessoa Veras: BIOFIX Research Center, Federal University of Parana, Curitiba 80210-170, Brazil
Karla da Silva Rocha: Geoprocessing Laboratory, Federal University of Acre, Rio Branco 69980-000, Brazil
Anibal de Moraes: Department of Plant Sciences, Federal University of Parana, Curitiba 80210-170, Brazil
Mauro Alessandro Karasinski: BIOFIX Research Center, Federal University of Parana, Curitiba 80210-170, Brazil
Matheus Niroh Inoue Sanquetta: BIOFIX Research Center, Federal University of Parana, Curitiba 80210-170, Brazil
Eben North Broadbent: Spatial Ecology and Conservation Laboratory, School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32611, USA

Land, 2022, vol. 11, issue 4, 1-15

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.

Keywords: quantitative structure modelling; laser scanning; tree modelling (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
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