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• W2971066667 abstract "Tree height is essential for assessing carbon budgets and biodiversity. One of the most commonly used assessment methods is a field survey. However, this approach is extremely challenging for obtaining highly accurate estimates in forests with tall and dense canopies. In this study, we utilized airborne remotely sensed mean canopy height (MCH) spatial coverage acquired by high-cost airborne light detection and ranging (lidar) and low-cost unmanned aerial vehicle (UAV) sensors to quantify the tree height for a 590-ha complex tropical forest in the mountainous region of central Taiwan. The performances of the acquisition techniques were evaluated by comparing the statistical relationships of MCH from lidar and MCH from UAV with the concurrently obtained field mean tree height measurement (MTH) at the plot (25 × 20 m) scale. In addition, we further analyzed the forest structural variables that may influence lidar and UAV MCHs by using a general linear model. The results showed that both MCHs derived from lidar and UAV accurately estimated MTH. MCH from UAV had a superior performance to that of a small model offset, and the slope of the model fit line was close to one, which was possibly due to the finer spatial resolution of the UAV imagery. MCH from lidar may be utilized to delineate the entire vertical profile of a forest stand, but MCH from UAV can only detect the upper half of the canopies. This is a result of instrument and data differences. General linear model statistics revealed that the maximum stand height and mean tree age may be the major forest stand structure determinants affecting MCH estimates, which might indicate that the airborne estimations of mean canopy height are mainly governed by large trees within a forest stand." @default.
• W2971066667 created "2019-09-05" @default.
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• W2971066667 date "2019-06-08" @default.
• W2971066667 modified "2023-10-16" @default.
• W2971066667 title "Comparison of forest canopy height profiles in a mountainous region of Taiwan derived from airborne lidar and unmanned aerial vehicle imagery" @default.
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• W2971066667 doi "https://doi.org/10.1080/15481603.2019.1627044" @default.
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