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The above-ground biomass (AGB) is an important indicator to reflect the productivity, carbon sequestration capacity, and carbon storage of rubber tree. However, the AGB models of individual rubber tree with high estimation efficiency and accuracy are still needed to develop. In the present study, an 8-year-old forest established for rubber tree breeding trial was scanned to obtain the point cloud data by using Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR), meanwhile, the actual AGB of every tree in this forest was measured experimentally. Four individual tree structure parameters, such as tree height, crown width, crown projected are, and crown volume, were extracted from the point cloud data, and then, used as predictors to establish the individual rubber tree AGB model. The multiple nonlinear regression and random forest regression were both applied to establish the model, and the estimation accuracy, generalization ability and reliability were evaluated by five-fold cross-validation. The tree height and crown width values extracted by special algorithms from individual tree point clouds data were highly correlated with the values manually measured on point clouds, the Pearson correlation coefficients of the two parameter wase 0.999 and 0.951 respectively, and the root mean square errors (RMSE) was 0.109 m and 0.452 m respectively. The correlations between the four structure parameters and the AGB of individual rubber tree was significant. Especially, the parameter of crown volume had the highest Pearson correlation coefficient (0.904). All four parameters had good explanations for AGB. Both established AGB models based on the four individual tree structure parameters could achieve good fitting results. However, the method of random forest regression had a better performance compared with multiple nonlinear regression. The coefficient of determination (R2) of the random forest regression model was 3.64% higher than that of the multiple nonlinear regression model, and the relative root mean square error (rRMSE) of the random forest regression model was 2.66% lower than that of the multiple nonlinear regression model. In general, there is higher goodness of fit and stronger generalization ability of the random forest regression model which can more accurately estimate the rubber tree AGB.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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