Predicting species richness and diversity using satellite remote sensing and random forest machine learning algorithm

Aims: Remote sensing approaches could be beneficial for monitoring and compiling essential biodiversity data because it is cost-effective and allows for coverage of large areas over a short period. This study investigated the relationship between multispectral remote sensing data from Landsat 8 and Sentinel 2 and species richness and diversity in mountainous and protected grasslands.
Locations: Golden Gate Highlands National Park, Free State, South Africa. 
Methods: In-situ data of plant species composition and cover from 142 plots with 16 releves each were distributed across the study site and used to calculate species richness and Shannon-wiener species diversity index (species diversity. We used a machine-learning random forest algorithm to optimise the prediction of species richness and diversity. The algorithm was used to identify the optimal spectral bands and vegetation indices for estimating species richness and diversity. Subsequently, the selected bands and vegetation indices ..., ,