*5.3. Prediction Performance and Uncertainty for Forest Diversity*

Among the three diversity indices, *J* index has the highest correlation coefficient (R<sup>2</sup> = 0.86), followed by *H* index (R<sup>2</sup> = 0.80) and λ index (R<sup>2</sup> = 0.78). The three diversity indices, being different representations of plant diversity, varied in spatial distribution (Figure 5). λ index, which accounts for the proportion of species in a sample, is considered to be a dominance indicator [59]. *H* index reflects both species richness and equitable distribution of those species within a sample [3]. Moreover, Oldeland et al. [3] emphasized that the *H* index better mirrors what one could call "vegetation structure", which is a subset of habitat heterogeneity and thus better reflects spectral variability. The spatial difference between the two indices has been well demonstrated in natural forests and secondary forests (Figure 5b,d). The *J* index is an indication of dominance and distribution of individuals across the community within a sample. Relatively few studies have reported this index in remote sensing studies, but it is still of great significance, especially considering the landscape scale [60].

While we derived the forest diversity map with high accuracy, several issues that may limit further estimations still exist. The first is the uncertainty of on-site measurements. In this study, we used semi-variance to determine a spatial scale for forest diversity investigation. Although fixed spatial scales are highly efficient in field surveys, they do not adequately represent the diversity values of the survey region [61]. Secondly, the presence of rare tree species in the understory and trees with DBH less than 10 cm may bring uncertainty on the estimation of forest diversity. Our study area is primarily composed of protected pristine natural forests [27], and the DBH of most trees exceeds 10 cm, which is also confirmed in field surveys. Thus, these trees have no impact on the experimental design and analysis, especially under dense canopy [49]. Finally, errors already exist in the process of forest diversity prediction. For example, the background, including the shading caused by tree canopy, topography, and/or soil color, could cause biased reflectance captured by Sentinel-2 [62].
