The monsoonal broad-leaved evergreen forest is an important vegetation type of the Pu'er area which is located in the southern Yunnan Province, China. Tree species diversity mapping of monsoon evergreen broad-leaved forest plays an important role in studying the patterns of biodiversity at the regional scale. Remote sensing is an efficient alternative to the traditional field work to map tree species diversity over large areas. We tested the utility of using spatial heterogeneity in the airborne hyperspectral reflectance spectrum and structure heterogeneity in airborne LiDAR point cloud to model tree species diversity of monsoonal broad-leaved evergreen forest in Pu'er area. Shannon-Winner Index of tree species was calculated from field measurement. According to the spectral heterogeneity hypothesis and environmental heterogeneity hypothesis, the spectral diversity and structural diversity features were firstly extracted using airborne hyperspectral data and Lidar data. Then, the Random Forest based Recursive Feature Elimination (RF-RFE) was used to select the valuable airborne remote sensing features for forest tree species diversity modeling. At last, Random Forest regression model was used for modeling and mapping. Results showed that the spectral and structural diversity variables extracted from airborne hyperspectral, and Lidar remote sensing data explained 48% and 50% variance in tree species diversity. However, combining both variables explained 69% variance in tree species diversity. The Lidar variables had a better performance for coniferous and broad-leaved mixed forest tree species estimation than the hyperspectral variables. According to the correlation analysis, the result showed that not all RF-RFE selected remote sensing variables had a significant correlation with field measured Shannon-Winner diversity index. Machine learning such as Random Forest model is helpful to select a few valuable features for forest tree species diversity modeling from massive remote sensing data. This study shows the high potential of airborne LiDAR and hyperspectral data for monsoonal broad-leaved evergreen forest tree species diversity modelling and monitoring. The selected spatial and structure variables also highlight the potential of remotely sensible essential biodiversity variables for mapping and monitoring landscape floristic diversity from air- and space-borne platforms.