Ratio-based vegetation indices for biomass estimation depending on grassland characteristics

Aboveground biomass (AGB) is one of the key indicators of aboveground net primary productivity (ANPP). The aim of this study is to demonstrate the potential of hyperspectral remote sensing techniques to predict AGB in grasslands. In order to reach this goal, biomass properties with different ecological features and altitudes of 550 m, 1200 m, and 1400 m above sea level were investigated. Twenty-one biomass samples and hyperspectral measurements were collected from each region and a total of 63 samples were analyzed. Linear and nonlinear regression models were generated to analyze the relationships between biomass and hyperspectral vegetation indices (VIs). The results showed strong relationships between VIs and biomass variations. However, dense biomass samples indicated weaker relationships with VIs due to saturation phenomena. Findings based on the measured data showed that AGB (except dense biomass) can be estimated with high accuracy using hyperspectral vegetation indices.