Field plot measurement is an essential task for forest inventory and monitoring and ecological applications based on airborne LiDAR. To optimize the field plot size and reduce cost, it is necessary to investigate the influence of field plot size on LiDAR-derived metrics and the accuracy of forest parameter estimation models.
A subtropical planted forest with an area of 4,770 ha was used as the study site, and 104 square plot of 900 m2 (30 m×30 m, subdivided into nine quadrats, each with an area of 100 m2 (10 m×10 m)) was divided into field plots with six different areas (100 m2, 200 m2, 300 m2, 400 m2, 600 m2 and 900 m2) by grouping quadrats. The differences in the LiDAR-derived metrics and stand attributes of different sized plots with four forest types (Chinese fir, pine, eucalyptus and broadleaf) were investigated. Through multivariate power models with stable structures, the differences in forest parameter (BA, VOL) estimation accuracies for plots with different sizes were compared.
(1) The mean differences in LiDAR-derived metrics related to height, density and vertical structure between the plots with different sizes and the 900 m2 plot containing all forest types were very small, and when the plot size changed, these differences changed irregularly; however, the standard deviations of the differences increased rapidly with decreasing plot size. (2) There were significant differences in the mean of the maximal height of the point cloud (Hmax), density of the 75th percentile of the point cloud (dh75) and mean leaf area density (LADmean) (except for Chinese fir and eucalyptus) between the plots with different sizes and the 900 m2 plot containing all forest types; other LiDAR-derived metrics had significant differences in only some or a certain size of plots, but there was no regularity. (3) Except for the maximal tree height of the plot (Hm), the forest stand attributes, including the mean tree height (H), diameter at breast height (DBH), basal area (BA), and stand volume (VOL), of all forest types showed either no significant differences or minimal differences between plots with different sizes and the 900 m2 plot. (4) With increasing plot size, the coefficient of determination (R2) of the estimation models for VOL and BA of all forest types increased gradually, while the relative root mean square error (rRMSE) and mean prediction error (MPE) decreased gradually, and the estimation accuracy of the models improved.
Due to the heterogeneity of the vertical and horizontal forest structures, some LiDAR-derived metrics and stand parameters for field plots with different sizes varied. As the plot size increased, the variations in the independent variables (LiDAR-derived metrics) and dependent variables (stand parameters) of the estimation models decreased gradually. These changes improved the robustness and accuracy of the models. In the application of airborne LiDAR in forest inventory and monitoring, both prediction accuracy and cost should be considered. For subtropical planted forests, we preliminarily suggest the following appropriate sizes for field plots: 900 m2 for Chinese fir and pine forests, 400 m2 for eucalyptus forests and 600 m2 for broadleaf forests. However, this protocol still needs to be tested in further studies.