Remote cyanobacteria detection by multispectral drone imagery

Abstract

Bartelt G, You J, Hondzo M. 2024. Remote cyanobacteria detection by multispectral drone imagery. Lake Reserv Manage. XX:XXX–XX.

Cyanobacteria play a crucial role in the ecological services of aquatic environments. Remote detection of cyanobacteria in water using satellite-based sensor images has been proven effective in monitoring eutrophication and harmful algal blooms. Satellite-based sensors are good at tracking large blooms in oceans and lakes, but not in small bodies of water. This study seeks to use remote-sensing techniques on images obtained from a multispectral camera mounted on an unmanned aerial system (UAS). We investigated a small freshwater lake, Brownie Lake, in Minneapolis, Minnesota, using the UAS. We compared the collected imagery to the measurements of chlorophyll and phycocyanin concentrations. Cyanobacterial chlorophyll a (Chl-a) concentrations and multispectral UAS data showed good agreement (r² = 0.54) in this study. Chl-a concentration strongly correlated with the presence of the near-infrared band at 840 nm and the red band at 668 nm. The most correlated spectral band combinations were the normalized difference vegetative index (NDVI) and 2 band algorithm (2BDA). Our research demonstrates the usefulness of UAS technologies in water quality monitoring.

Keywords:

• Unmanned aerial system
• drone
• multispectral imagery
• chlorophyll

Acknowledgments

We conducted this work during the COVID-19 lockdown in the state of Minnesota, USA. The authors thank Dr. Christopher Ellis for providing critical comments and suggestions for the first draught of the article. We express our gratitude to 3 anonymous reviewers whose feedback and critique greatly enhanced the article.

Authors’ contributions

Garrett Bartelt conducted laboratory and field measurements, analyzed the data, and prepared the drone and multispectral camera for deployment. As part of his master’s thesis, he provided a first draft of the article. Jiaqi You assembled and tested the drone and multispectral camera in the field. In addition, she provided training to Garrett Bartlet on how to assemble the UAV and the details of the quantification of chlorophyll under laboratory conditions. Miki Hondzo started the idea of using UAVs to document cyanobacterial biomass, conducted field measurements, reviewed the data analysis, edited the first draft of the article, and advised Jiaqi You and Garrett Bartlet.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported. The reported research was not funded by state, federal, or private funding. According to the journal guidelines, the data described in this manuscript will be available online in the Data Repository for the University of Minnesota (DRUM). DRUM is a publicly available collection of digital research data generated by University of Minnesota researchers, students, and staff. Anyone can search and download the data housed in the repository instantly or by request (https://conservancy.umn.edu/drum). All authors have read, understood, and comply as applicable with the statement on ethical responsibilities of authors as found in the Instructions for Authors.