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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

igh-Density UAV-LiDAR in an Integrated Crop-Livestock-Forest System: Sampling Forest Inventory or Forest Inventory Based on Individual Tree Detection (ITD

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Dalla Corte, Ana Paula [1] ; da Cunha Neto, Ernandes M. [1] ; Rex, Franciel Eduardo [1] ; Souza, Deivison [2] ; Behling, Alexandre [1] ; Mohan, Midhun [3] ; Sanquetta, Mateus Niroh Inoue [1] ; Silva, Carlos Alberto [4] ; Klauberg, Carine [5] ; Sanquetta, Carlos Roberto [1] ; Veras, Hudson Franklin Pessoa [1] ; de Almeida, Danilo Roberti Alves [6] ; Prata, Gabriel [7] ; Zambrano, Angelica Maria Almeyda [8] ; Trautenmueller, Jonathan William [1] ; de Moraes, Anibal [1] ; Karasinski, Mauro Alessandro [1] ; Broadbent, Eben North [7]
Total Authors: 18
Affiliation:
[1] Fed Univ Parana UFPR, BIOFIX Res Ctr, BR-80210170 Curitiba, Parana - Brazil
[2] Fed Univ UFPA, Fac Forestry Engn, BR-68370000 Altamira - Brazil
[3] Univ Calif UC Berkeley, Dept Geog, Berkeley, CA 94709 - USA
[4] Univ Florida UF, Sch Forest Resources & Conservat, Gainesville, FL 32611 - USA
[5] Fed Univ Joao Rei UFSJ, BR-35701970 Sete - Brazil
[6] Univ Sao Paulo USP ESALQ, Dept Forest Sci, Luiz Queiroz Coll Agr, BR-13418900 Piracicaba - Brazil
[7] Univ Florida UF, Sch Forest Resources & Conservat, Spatial Ecol & Conservat SPEC Lab, Gainesville, FL 32611 - USA
[8] Univ Florida UF, Spatial Ecol & Conservat Lab, Ctr Latin Amer Studies, Gainesville, FL 32611 - USA
Total Affiliations: 8
Document type: Journal article
Source: RONE; v. 6, n. 2 FEB 2022.
Web of Science Citations: 0
Abstract

Lidar point clouds have been frequently used in forest inventories. The higher point density has provided better representation of trees in forest plantations. So we developed a new approach to fill this gap in the integrated crop-livestock-forest system, the sampling forest inventory, which uses the principles of individual tree detection applied under different plot arrangements. We use a UAV-lidar system (GatorEye) to scan an integrated crop-livestock-forest system with Eucalyptus benthamii seed forest plantations. On the high density UAV-lidar point cloud (>1400 pts. m(2)), we perform a comparison of two forest inventory approaches: Sampling Forest Inventory (SFI) with circular (1380 m(2) and 2300 m(2)) and linear (15 trees and 25 trees) plots and Individual Tree Detection (ITD). The parametric population values came from the approach with measurements taken in the field, called forest inventory (FI). Basal area and volume estimates were performed considering the field heights and the heights measured in the LiDAR point clouds. We performed a comparison of the variables number of trees, basal area, and volume per hectare. The variables by scenarios were submitted to analysis of variance to verify if the averages are considered different or equivalent. The RMSE (%) were calculated to explain the deviation between the measured volume (filed) and estimated volume (LiDAR) values of these variables. Additionally, we calculated rRMSE, Standard error, AIC, R-2, Bias, and residual charts. The basal area values ranged from 7.40 m(2) ha(-1) (C1380) to 8.14 m(2) ha(-1) 281 (C2300), about -5.9% less than the real value (8.65 m(2) ha(-1)). The C2300 scenario was the only one whose confidence interval (CI) limits included the basal area real. For the total stand volume, the ITD scenario was the one that presented the closer values (689.29 m(3)) to the real total value (683.88 m(3)) with the real value positioned in the CI. Our findings indicate that for the stand conditions under study, the SFI approach (C2300) that considers an area of 2300 m(2) is adequate to generate estimates at the same level as the ITD approach. Thus, our study should be able to assist in the selection of an optimal plot size to generate estimates with minimized errors and gain in processing time. (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