Applying High-Resolution UAV-LiDAR and Quantitative Structure Modelling for Estimating Tree Attributes in a Crop-Livestock-Forest System
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 sen...
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MDPI AG
2022-03-01
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| author | 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 Eben North Broadbent |
| author_facet | 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 Eben North Broadbent |
| author_sort | Ana Paula Dalla Corte |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T10:33:22Z |
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| language | English |
| last_indexed | 2024-03-09T10:33:22Z |
| publishDate | 2022-03-01 |
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| spelling | doaj.art-afa092b4f47d4516bff1c6e85aaf94be2023-12-01T21:09:27ZengMDPI AGLand2073-445X2022-03-0111450710.3390/land11040507Applying High-Resolution UAV-LiDAR and Quantitative Structure Modelling for Estimating Tree Attributes in a Crop-Livestock-Forest SystemAna Paula Dalla Corte0Bruna Nascimento de Vasconcellos1Franciel Eduardo Rex2Carlos Roberto Sanquetta3Midhun Mohan4Carlos Alberto Silva5Carine Klauberg6Danilo Roberti Alves de Almeida7Angelica Maria Almeyda Zambrano8Jonathan William Trautenmüller9Rodrigo Vieira Leite10Cibele Hummel do Amaral11Hudson Franklin Pessoa Veras12Karla da Silva Rocha13Anibal de Moraes14Mauro Alessandro Karasinski15Matheus Niroh Inoue Sanquetta16Eben North Broadbent17BIOFIX Research Center, Federal University of Parana, Curitiba 80210-170, BrazilEMBRAPA Florestas, Colombo 83411-000, BrazilBIOFIX Research Center, Federal University of Parana, Curitiba 80210-170, BrazilBIOFIX Research Center, Federal University of Parana, Curitiba 80210-170, BrazilDepartment of Geography, University of California, Berkeley, CA 94709, USASchool of Forest Resources and Conservation, University of Florida, Gainesville, FL 32611, USADepartment of Forest Engineering, Federal University of João Del Rei, Sete Lagoas 35701-970, BrazilDepartment of Forest Sciences, “Luiz de Queiroz” College of Agriculture, University of São Paulo, Piracicaba 13418-900, BrazilSpatial Ecology and Conservation Laboratory, Center for Latin America Studies, University of Florida, Gainesville, FL 32611, USABIOFIX Research Center, Federal University of Parana, Curitiba 80210-170, BrazilDepartment of Forest Engineering, Federal University of Viçosa, Viçosa 36570-900, BrazilDepartment of Forest Engineering, Federal University of Viçosa, Viçosa 36570-900, BrazilBIOFIX Research Center, Federal University of Parana, Curitiba 80210-170, BrazilGeoprocessing Laboratory, Federal University of Acre, Rio Branco 69980-000, BrazilDepartment of Plant Sciences, Federal University of Parana, Curitiba 80210-170, BrazilBIOFIX Research Center, Federal University of Parana, Curitiba 80210-170, BrazilBIOFIX Research Center, Federal University of Parana, Curitiba 80210-170, BrazilSpatial Ecology and Conservation Laboratory, School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32611, USAIndividual 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.https://www.mdpi.com/2073-445X/11/4/507quantitative structure modellinglaser scanningtree modelling |
| spellingShingle | 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 Eben North Broadbent Applying High-Resolution UAV-LiDAR and Quantitative Structure Modelling for Estimating Tree Attributes in a Crop-Livestock-Forest System Land quantitative structure modelling laser scanning tree modelling |
| title | Applying High-Resolution UAV-LiDAR and Quantitative Structure Modelling for Estimating Tree Attributes in a Crop-Livestock-Forest System |
| title_full | Applying High-Resolution UAV-LiDAR and Quantitative Structure Modelling for Estimating Tree Attributes in a Crop-Livestock-Forest System |
| title_fullStr | Applying High-Resolution UAV-LiDAR and Quantitative Structure Modelling for Estimating Tree Attributes in a Crop-Livestock-Forest System |
| title_full_unstemmed | Applying High-Resolution UAV-LiDAR and Quantitative Structure Modelling for Estimating Tree Attributes in a Crop-Livestock-Forest System |
| title_short | Applying High-Resolution UAV-LiDAR and Quantitative Structure Modelling for Estimating Tree Attributes in a Crop-Livestock-Forest System |
| title_sort | applying high resolution uav lidar and quantitative structure modelling for estimating tree attributes in a crop livestock forest system |
| topic | quantitative structure modelling laser scanning tree modelling |
| url | https://www.mdpi.com/2073-445X/11/4/507 |
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