Predicting chlorophyll-<i>a</i>using Landsat 8 OLI sensor data and the non-linear RANSAC method – a case study of Nakdong River, South Korea

Abstract

To predict the degree of chlorophyll-a (chl-a) concentration as a water quality indicator, we propose a novel method that uses data collected by satellite remote sensors and a non-linear random sample consensus (NL-RANSAC) algorithm. In this study, multispectral images obtained from the Landsat OLI sensor and in situ sampling data obtained from the Nakdong River in South Korea are used for estimating a regression model. Because the RANSAC algorithm based on linear models is not appropriate for modelling non-linear data distribution, such as chl-a concentration and influential outlier data, in this article an NL-RANSAC model represented by an explicit polynomial curve is proposed instead of the RANSAC. The results of experiments in which NL-RANSAC-based regressions and normal regression models were compared using a calibration and a validation set clearly show that the proposed second-order NL-RANSAC is a good choice for estimating chl-a concentration, because it shows an average 0.3987 higher assessment than normal regression models in validation set. In addition, we prove that the second-order NL-RANSAC model is the most appropriate regression model for estimating chl-a concentration by using Landsat 8 OLI sensor imagery in the midstream of the Nakdong River.