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Fast, accurate and non-destructive estimation of the leaf area index (LAI) of Masson pine forest is of great significance for precise forestry management. In order to estimate LAI of Masson pine forest, the small low-altitude unmanned aerial vehicle (UAV) platform with the American MicaSense RedEdge multi-spectral sensor was used to obtain the multi-spectral image in western Fujian. Eight different kinds of vegetation indices, green normalized vegetation index (GNDVI), green ratio vegetation index (GRVI), modified soil adjusted vegetation index (MSAVI), normalized difference vegetation index (NDVI), renormalized difference vegetation index (RDVI), ratio vegetation index (RVI), structure insensitive pigment index (SIPI) and visible atmospherically resistant index (VARI) were calculated from imagines with seven spatial resolutions (0.08m, 0.1m, 0.2m, 0.5m, 1m, 2m and 5m). The correlation between groundmeasured LAI and different vegetation indices from different spatial resolutions imagines were analyzed. Five models, linear regression (LR), multiple stepwise (MSR), random forest (RF), support vector machine (SVM) and artificial neural network (BP) were used to construct LAI estimation model, and coefficients of determination (R2), root mean square errors (RMSE), residual predictive deviation (RPD) and total accuracy (TA) were used to determine the optimal spatial resolution and optimal model for computing Masson pine forest LAI. The results showed that LAI values and vegetation indices from different spatial resolutions imagines were significantly correlated (p<0.01). The adjusted R2 average values of the multivariate models (MSR, RF, SVM, BP) were higher than that of the LR model. The R2 of different models was generally increased first and then decreased with the increase of spatial resolution. RF model was the optimal model for Masson pine forest when the spatial resolution was 0.5m. The highest accuracy for RF model with R2 of 0.766 for calibration, and with R2 of 0.554, RMSE of 0.421, RPD of 1.523, and TA of 81.95% for validation. The research result can provide a theoretical reference for the spatial resolution and model selection in the inversion of forest LAI phenotypic parameters by UAV multi-spectral remote sensing image.