Prediction of Carlson Trophic State Index of Small Inland Water from UAV-Based Multispectral Image Modeling

Abstract

This paper demonstrates a predictive method for the spatially explicit and periodic in situ monitoring of surface water quality in a small lake using an unmanned aerial vehicle (UAV), equipped with a multi-spectrometer. According to the reflectance of different substances in different spectral bands, multiple regression analyses are used to determine the models that comprise the most relevant band combinations from the multispectral images for the eutrophication assessment of lake water. The relevant eutrophication parameters, such as chlorophyll a, total phosphorus, transparency and dissolved oxygen, are, thus, evaluated and expressed by these regression models. Our experiments find that the predicted eutrophication parameters from the corresponding regression models may generally exhibit good linear results with the coefficients of determination (R2) ranging from 0.7339 to 0.9406. In addition, the result of Carlson trophic state index (CTSI), determined by the on-site water quality sampling data, is found to be rather consistent with the predicted results using the regression model data proposed in this research. The maximal error in CTSI accuracy is as low as 1.4% and the root mean square error (RMSE) is only 0.6624, which reveals the great potential of low-altitude drones equipped with multispectrometers in real-time monitoring and evaluation of the trophic status of a surface water body in an ecosystem.