Ecological Indicators (Sep 2022)
Comparison of the predictive ability of spectral indices for commonly used species diversity indices and Hill numbers in wetlands
Abstract
The development of near-Earth remote sensing platforms, such as unmanned aerial vehicles (UAV), have provided new opportunities for wetland plant diversity monitoring. Most previous studies on the relationship between spectral and species diversity have focused on the development and use of spectral indices. However, commonly used species diversity indices may not be the best choice for spectral-species diversity research. In this paper, high spatial resolution multispectral images of freshwater marsh were obtained based on UAV in Sanjiang National Nature Reserve, Northeast China, and the commonly used species diversity indices (Richness, Shannon, and Gini-Simpson) and Hill numbers were calculated based on 135 quadrats information obtained from field surveys. The mean value of NDVI (NDVIMEAN), its standard deviation (NDVISD) and coefficient of variation (NDVICV) of each quadrat were calculated based on multispectral imagery as a proxy for the spectral indices of the quadrats. The univariate and multivariate linear models were employed to test the predictive ability of NDVI-related indices for commonly used species diversity indices and Hill numbers. The results showed that the predictive ability of NDVIMEAN for species diversity indices was limited, and the combined use of NDVIMEAN and NDVISD significantly improved the predictive ability of species diversity. The predictive ability of NDVI-related indices to Hill numbers is better than that of commonly used species diversity indices. Commonly used species diversity indices can only represent one or several “point” of the community species diversity, the Hill numbers provide a continuous measure of community species diversity, which can balance the inconsistency between the abundance and coverage of species in the community. Previous spectral-species diversity studies might not have shown the real predictive ability of species diversity based on NDVI-related indices. Our study provides innovative ideas for the selection of species diversity indices in future spectral-species diversity studies.
