Prediction of species richness and diversity in sub-alpine grasslands using satellite remote sensing and random forest machine-learning algorithm

Abstract

AIMS: Remote-sensing approaches could be beneficial for monitoring and compiling essential biodiversity data because they are cost-effective and allow 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 relevés 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 optimize 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 were used to estimate species richness through random forest regression. RESULTS: This research found weak relationships between remote-sensing vegetation indices and the diversity metrics, but significant relationships were found between some spectral bands and diversity metrics. Moreover, using machine-learning random forest, the multispectral data sets exhibited strong predictive powers. In this investigation, near-infrared (NIR) seemed to be the most selected band for both sensors to explain species diversity in mountainous grasslands. MAIN CONCLUSIONS: This finding further ascertains the efficiency of optimizing high spatial resolution spectral information to estimate plant species richness and diversity. This research shows that NIR, Soil-Adjusted Vegetation Index (SAVI) and Enhanced Vegetation Index (EVI) are the most adequate for predicting species richness and diversity in mountainous grasslands with relatively good accuracies. Plant phenology and the choice of sensor affect the relationship between spectral information and species diversity variables.