Estimating Grassland Chlorophyll Content at Canopy Scales Using Hyperspectral Vegetation Indices

Abstract

This study aims to estimate the chlorophyll content at the canopy level using the hyperspectral vegetation indices in grasslands with different ecological conditions. For this purpose, all data were collected from three different elevation steps of similar to 500 m, similar to 1200 m, and similar to 1400 m. The operations were performed in 50 x 50 cm quadrates at 213 different locations at the canopy level. Purposeful sampling and transect methods were preferred as the data collection methods. The database was divided into two categories according to the elevation step they were collected (field-based) and the amount of chlorophyll content (quantity-based). Assessments were then made in these two categories and their classes. In the analyses, the spectral curves were interpreted, and the hyperspectral vegetation indices were calculated from the aforementioned databases. Regression analyses were used to model the performances of the vegetation indices and explain the chlorophyll content variations. For this, linear, exponential, logarithmic, and power function models were employed. The results show an explanation power of over 85% in the data set containing all the data and over 90% in the field-based data set. In contrast, the power of the models significantly decreased as the chlorophyll content increased.