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The tree crown is an important part of a tree and is closely related to forest growth status, forest canopy density, and other forest growth indicators. Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) is an important tree species in southern China. A three-dimensional (3D) visualization assistant decision-making system of plantations could be improved through the construction of crown contour envelope models (CCEMs), which could aid plantation production. The goal of this study was to establish CCEMs, based on random forest and mathematical modeling, and to compare them. First, the regression equation of a tree crown was calculated using the least squares method. Then, forest characteristic factors were screened using methods based on mutual information, recursive feature elimination, least absolute shrink and selection operator, and random forest, and the random forest model was established based on the different screening results. The accuracy of the random forest model was higher than that of the mathematical modeling. The best performing model based on mathematical modeling was the quartic polynomial with the largest crown radius as the variable (R-squared (R2) = 0.8614 and root mean square error (RMSE) = 0.2657). Among the random forest regression models, the regression model constructed using mutual information as the feature screening method was the most accurate (R2 = 0.886, RMSE = 0.2406), which was two percentage points higher than mathematical modeling. Compared with mathematical modeling, the random forest model can reflect the differences among trees and aid 3D visualization of a Chinese fir plantation.

Soil water content (SWC) plays a crucial role in the hydrological cycle and ecological restoration in arid and semi-arid areas. Studying the temporal stability of SWC spatial distribution is a requirement for the dynamic monitoring of SWC and the optimization of water resource management. The SWC in a Pinus tabulaeformis Carr. forest on the slope of the Loess Plateau of China were analyzed in five soil layers (0–100 cm with an interval of 20 cm) in the rainy and dry seasons from July 2014 to November 2017. The mean SWC was estimated and the main factors affecting the temporal stability of the SWC were further analyzed. Results showed that the SWC had strong temporal stability during the two seasons for several consecutive years. The temporal stability of SWC and the number of representative locations varied with season and depth. The elevation, soil total phosphorus (STP), clay, silt, or sand content of the representative locations approached the corresponding mean value of the study area. A single representative location accurately represented the mean SWC for the five depths in the rainy and dry seasons (RMSE <2%; rainy season: 0.81 < R2 < 0.94; dry season: 0.63 < R2 < 0.83; p < 0.01). The mean relative difference (MRD) and the relative difference standard deviation (SDRD) changed with the seasons and were significantly correlated with elevation, root density, and sand and silt content in two seasons (p < 0.05). Elevation, root density, and sand content were the main factors influencing the change of SWC temporal stability in different seasons. The results provide scientific guidance to monitor SWC by using a small number of locations and enrich our understanding of the factors affecting the temporal stability of SWC in the rainy and dry seasons of the Loess Plateau of China.