Sample size (number of plots) may significantly affect the accuracy of forest attribute estimations using airborne LiDAR data in large-scale subtropical areas. In general, the accuracy of all models improves with increasing sample size. However, the improvement in estimation accuracy varies across forest attributes and forest types. Overall, a larger sample size is required to estimate the stand volume (VOL), while a smaller sample size is required to estimate the mean diameter at breast height (DBH). Broad-leaved forests require a smaller sample size than Chinese fir forests.
Airborne LiDAR; Forest attributes; Multivariate power model; Sample size
Li, C., Yu, Z., Dai, H. et al. Effect of sample size on the estimation of forest inventory attributes using airborne LiDAR data in large-scale subtropical areas. Annals of Forest Science 80, 40 (2023). https://doi.org/10.1186/s13595-023-01209-4
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Data and/or Code availability
The data that support the findings of this study are available at https://doi.org/10.57760/sciencedb.11884
Barry A. Gardiner