Clarifying the temporal and spatial distribution of macro forest plant diversity is of great significance for evaluating the effect of forest ecological engineering and carrying out regional forest plant diversity protection effectively. The biodiversity data obtained by remote sensing can make up for the discontinuity of location observation in time and space. This paper aims to introduce a new method for biodiversity research at regional scale--remote sensing Rao's Q index. The results show that: Rao's Q index calculated based on Normalized Difference Vegetation Index (NDVI) is basically consistent with the collected species richness in spatial distribution, and the numerical fitting passed the high significant test (R² =0.66, P<0.01). Rao's Q index increases with the increase of species richness and has outstanding advantages in spatial expression. It is suitable for forest plant diversity monitoring across climate zones at macro scale. From 2000 to 2017, the temporal and spatial pattern of Rao's Q index of Chinese forest was basically the same, showing a trend of high in the south and low in the north. Rao's Q index is mainly reduced in different periods, and the reduced area accounts for 44.23%-54.08% of the total forest area. The increasing characteristics of Wuyishan-Nanling area was prominent, and the period from 2010 to 2017 also showed the characteristics of decreasing hot spots. This region is rich in forest plant diversity, so we should strengthen control and prevent the spread of reduced hot spots. The remote sensing Rao's Q index proposed in this paper can effectively present the distribution characteristics of regional forest plant diversity, provide a new monitoring index for regional ecological quality assessment, and then support for policy-making related to forest plant diversity protection and regional ecological quality improvement.