Evaluation of water quality based on UAV images and the IMP-MPP algorithm

Abstract

In recent years, UAV remote sensing has been used to estimate water quality parameters, such as suspended solids (SS), turbidity (TUB), and chlorophyll-a (chl-a) levels, due to its low cost, convenience, and high resolution. The matching pixel-by-pixel (MPP) algorithm is one of the methods to find the optimal regression equation for retrieving water quality parameters from UAV images. However, MPP has a high computational burden and commonly experiences overfitting problems. Therefore, we propose an improved MPP algorithm (called IMP-MPP) to solve the above problems by sampling pixels based on clustering results and selecting models with more filtering conditions. In this study, Qingshan Lake, Hangzhou City, Zhejiang Province, China, is taken as the study area to evaluate the suspended solids and turbidity indicators. A total of 45 in situ samples and the UAV images around the sampling points are analyzed and processed by the proposed IMP-MPP algorithm, along with the average value method and MPP for comparison purpose. The experimental results show that the determination coefficient, average relative error and comprehensive error of the best inversion model derived by the IMP-MPP algorithm for SS are 0.8255, 15.08%, and 0.1981, respectively. The determination coefficient, average relative error and comprehensive error of the best inversion model for TUB are 0.8311, 16.49% and 0.2033, respectively. The results suggest that the IMP-MPP algorithm is promising in finding more accurate inversion models for SS and TUB. Finally, based on the optimal models, suspended solids and turbidity distribution maps are generated for further research.