Métodos De Medição De Altura Em Fragmento De Floresta Estacional Decidual

Frutuoso, Lucas Matias de Souza; Almeida, Débora de Melo; Filho, João Gilberto Meza Ucella; Barbosa, Vital Caetano; Andrade, Gabriel Siqueira de; Canto, Juliana Lorensi do

doi:10.31413/nativa.v8i5.10335

Cited by 1 publication

(1 citation statement)

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“…Nevertheless, it is noteworthy that the measurement errors observed in our study are low because of the small scale of the experiment and the usage of a graduated ruler to measure ht directly. Given that tree heights are usually estimated from indirect methods that are less accurate than that used in our study [92][93][94], the errors for estimates generated from DAP-UAV can be considered satisfactory.…”

Section: Tree Heightmentioning

confidence: 98%

Individual Tree Detection and Qualitative Inventory of a Eucalyptus sp. Stand Using UAV Photogrammetry Data

Almeida

Gonçalves²,

Silva

et al. 2021

Remote Sensing

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Digital aerial photogrammetry (DAP) data acquired by unmanned aerial vehicles (UAV) have been increasingly used for forest inventory and monitoring. In this study, we evaluated the potential of UAV photogrammetry data to detect individual trees, estimate their heights (ht), and monitor the initial silvicultural quality of 1.5-year-old Eucalyptus sp. stand in northeastern Brazil. DAP estimates were compared with accurate tree locations obtained with real time kinematic (RTK) positioning and direct height measurements obtained in the field. In addition, we assessed the quality of a DAP-UAV digital terrain model (DTM) derived using an alternative ground classification approach and investigated its performance in the retrieval of individual tree attributes. The DTM built for the stand presented an RMSE of 0.099 m relative to the RTK measurements, showing no bias. The normalized 3D point cloud enabled the identification of over 95% of the stand trees and the estimation of their heights with an RMSE of 0.36 m (11%). However, ht was systematically underestimated, with a bias of 0.22 m (6.7%). A linear regression model, was fitted to estimate tree height from a maximum height metric derived from the point cloud reduced the RMSE by 20%. An assessment of uniformity indices calculated from both field and DAP heights showed no statistical difference. The results suggest that products derived from DAP-UAV may be used to generate accurate DTMs in young Eucalyptus sp. stands, detect individual trees, estimate ht, and determine stand uniformity with the same level of accuracy obtained in traditional forest inventories.

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Section: Tree Heightmentioning

confidence: 98%