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

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