A study of cyanobacterial bloom monitoring using unmanned aerial vehicles, spectral indices, and image processing techniques

Abstract

Last 5 years, the deterioration of water quality caused by algal bloom has emerged as a serious issue in Korea. The method of on-site water sampling to check algal bloom and cyanobacteria is problematic by only partially measuring the site and not fully representing the field, while at the same time, consuming a lot of time and manpower to complete it. In this study, the different spectral indices reflecting the spectral characteristics of photosynthetic pigments were compared. We monitored harmful algal bloom and cyanobacteria in Nakdong rivers with multispectral sensor images from unmanned aerial vehicles (UAVs). The multispectral sensor images were used to assess the applicability of estimating cyanobacteria concentration based on field sample data. Several wavelength analysis techniques were conducted in June, August, and September 2021, when algal bloom intensified, including the analysis of images from multispectral cameras using normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), blue normalized difference vegetation index (BNDVI), and normalized difference red edge index (NDREI). Radiation correction was performed using the reflection panel to minimize interference that could distort the analysis results of the UAVs image. Regarding field application and correlation analysis, correlation value of NDREI was the highest at 0.7203 in June. And NDVI was the highest at 0.7607 and 0.7773 in August and September, respectively. Based on the results obtained from this study, it is found that it is possible to quickly measure and judge the distribution status of cyanobacteria. In addition, the multispectral sensor installed to the UAV can be considered as a basic technology for monitoring the underwater environment.