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Mean canopy profile height.

Although Asner et al. [15] previously compared regions using LiDAR-derived mean canopy profile height (MCH), we used top-of-canopy height (TCH) in this study.

Previous work repeatedly demonstrated that mean canopy profile height or MCH, which is the weighted height of the lidar point cloud in a 5 × 5 kernel, is highly correlated with ACD in tropical forests [17, 28, 44].

Although Asner et al. [ 15] previously compared regions using LiDAR-derived mean canopy profile height (MCH), we used top-of-canopy height (TCH) in this study.

Previous work repeatedly demonstrated that mean canopy profile height or MCH, which is the weighted height of the lidar point cloud in a 5 × 5 kernel, is highly correlated with ACD in tropical forests [ 17, 28, 44].

Similar(55)

Their results showed a close correlation among macroinvertebrate assemblages with water temperature and habitat-related parameters like mean substrate embeddedness and mean canopy density (Griffith et al. 2001).

The Lidar-derived metric, mean canopy height (MCH), or the centroid of the vertical canopy profile [25], has been found to be the best variable calibrating AGB as a single-metric model [21, 26].

Lidar-derived metrics most frequently used to predict biomass include mean or maximum canopy height [10-13] [10-13]ticandcanopy profile measures, such as height percentiles and vertical of heights [14,15].

Lidar-derived metrics most frequently used to predict biomass include mean or maximum canopy height [ 10- 13] and vertical canopy profile measures, such as height percentiles and variance of heights [ 14, 15].

mean canopy height.

The mean canopy depth is L ¯ = H ¯ − z ∗ + d.

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