Dynamic mapping of dissolved oxygen in freshwater aquaculture ponds using UAV multispectral imagery

Abstract

Dissolved oxygen (DO) is an important indicator of the water health of the freshwater aquaculture pond. However, since DO is a non-photosensitive parameter, it is difficult to directly inverse using UAV imaging technology. We proposed an estimation method of DO based on UAV multispectral data and machine learning algorithms. The method utilizes chlorophyll-a (Chl-a) and spectral indices as input features to accurately estimate DO content in water bodies. UAV images were collected in six periods at two aquaculture ponds. Machine learning algorithms were applied to map Chl-a concentration in each aquaculture pond, and a DO estimation model was developed through the relationship between Chl-a, spectral index and DO. The model was validated using measured samples, and the spatial and temporal variations in DO at the two freshwater aquaculture ponds were analyzed. The findings demonstrated that the model exhibited suboptimal performance when solely utilising spectral index. However, the incorporation of Chl-a as an input feature resulted in a substantial enhancement in model performance, in comparison to the utilisation of only spectral index. The RF model performed well during both training and testing phases at the first freshwater aquaculture pond, achieving R² = 0.87, RMSE = 1.785 mg/L, and MAE = 1.512 mg/L for the testing set. Concurrently, the validation in the other two periods(GC - August and October 2023 and PK-April and May 2024) further confirmed the model's generalization ability, with R² = 0.84, RMSE = 2.245 mg/L, and MAE = 1.251 mg/L. Similarly, the model showed robust performance at the second freshwater aquaculture pond, achieving R² = 0.85, RMSE = 3.743 mg/L, and MAE = 2.730 mg/L. UAV multispectral imaging technology combined with this method can efficiently and accurately capture the spatial and temporal distribution of DO in freshwater aquaculture pond, supporting aquaculture management.