An Improved UAV RGB Image Processing Method for Quantitative Remote Sensing of Marine Green Macroalgae

Red–green–blue (RGB) images (or videos) captured by consumer-level uncrewedaerial vehicle (UAV) cameras are widely used in high-resolution remote observations. However, digital number (DN) values of these RGB images usually have a nonlinear relationship with the incident radiance, which reduces the accuracy of quantitative remote sensing of macroalgae. To solve this problem, we proposed an improved processing procedure for UAV RGB images (or videos) based on camera response functions (CRFs). The CRF was utilized to convert the DN values into energy values ( E values), which demonstrate a linear relationship with the incident radiance. When the DN values were replaced by their corresponding E values to calculate the reflectance of green macroalgae under different illumination intensities, the errors in reflectance were reduced by ∼21%; for the corresponding green macroalgae indices, such as the red–green band virtual baseline floating green algae height (RG-FAH), the E -value-based RG-FAH demonstrates more resistance to the impacts of sun glints; and the E values were further applied to estimate the coverage portion of macroalgae (POM, %) in RGB videos; the illumination-induced deviations of the POM were effectively reduced by up to 33.06%, showing an advantage in quantitative estimation of macroalgae biomass. The results of applications to UAV RGB images show that the E values have significant suitability in estimating POM across diverse green macroalgae species and various algae indices, suggesting promising potentials of the proposed processing procedure with E -based photo and/or video RGB images in monitoring aquatic plants and environment.