Abstract
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 a 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.
Highlights
Introduction distributed under the terms andBrazil has one of the world’s largest areas (~9 million ha) of rapid-growth forest plantations
Paired sample t-tests indicated that real time kinematic (RTK) and DTM_UAV altitudes did not show statistical differences
Diverging from the observed by Krause et al [44], we found that ht estimated directly from the Digital aerial photogrammetry (DAP)-unmanned aerial vehicles (UAV) point cloud (Hmax ) led to a higher root mean square error (RMSE) and bias than those found for the field inventory, as estimated from repeat measures (Table 1)
Summary
Brazil has one of the world’s largest areas (~9 million ha) of rapid-growth forest plantations. The derived forest products supply domestic and foreign markets, with an estimated US$ 11.3 billion of exports in. 2021, 13, 3655 gross primary product (GPP), these plantations play a key role in the promotion of ecosystem services such as carbon sequestration and reduction of the pressure on native forests. The productivity of rapid-growth commercial stands, such as Eucalyptus, is defined by the environment, genetic material and silvicultural treatments applied during the production cycle [2]. Initial silvicultural quality has a direct impact on the productivity observed at the end of the growth cycle, highlighting the importance of its monitoring [3]
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