Monitoring Algal Blooms in Small Lakes Using Drones: A Case Study in Southern Illinois

Abstract

Harmful Algal Blooms (HABs) persist in many water bodies around the world and pose adverse health and economic impacts to the affected communities. Small Unmanned Aerial Vehicles (UAVs) have recently been applied as a cost‐effective tool for HABs monitoring. In this study, HABs in two small lakes in Southern Illinois (Carbondale Reservoir and the Campus Lake of Southern Illinois University) were monitored using UAVs and biomass concentrations in lake waters. By analyzing vegetation indices derived from multispectral UAV images and chlorophyll‐a concentrations in the two lakes, statistical regression models were established for each waterbody. The model relates spectral characteristics of the lake water to its algae biomass. It was found that normalized difference vegetation index (NDVI) and blue‐to‐green band ratio are the best‐fit indices to the variation in chlorophyll‐a in Carbondale Reservoir and the Campus Lake, respectively. The findings in this study can be used for monitoring HABs using UAVs in these lakes in the future.

Estimation of Water Quality Parameters through a Combination of Deep Learning and Remote Sensing Techniques in a Lake in Southern Chile

“…It is often used as an indicator of algae presence because it is a primary photosynthetic pigment found in all types of algae [13,14]. The concentration of Chl-a is directly proportional to the number of algae present in a water sample, making it a useful tool for monitoring algal growth and detecting harmful algal blooms [15,16]. Additionally, Chl-a can be used to estimate the primary productivity of aquatic ecosystems, which is an important factor for understanding the health and functioning of these systems [17,18].…”