The increasing occurrence of fresh water cyanobacterial blooms (CyanoHABs), many of which contain toxins harmful or even deadly to humans and animals, demands effective early warning. Direct sampling and measurement of a body of water is accurate at the point of measurement. However, CyanoHAB blooms are heterogenous in nature, meaning that a body of water – even a relatively small one – contains areas of various concentrations of algae. Measurements can also differ depending on the time of day, since colonies of micro-organisms naturally rise and fall in the water column. A direct measurement of a sample taken at the shore may show a concentration that is vastly different than in areas throughout the pond. To increase spatial and temporal sampling resolution, citizens have taken action to monitor and inform water quality managers when they have determined that the CyanoHAB levels have become harmful. Simple scientific tests such as the use of a Secchi disk to measure water clarity, as well as more elaborate measurements using spectrofluorometry and automated cell counters done by citizens themselves can provide important ground-truth data to supplement airborne images from UAVs.
We describe an ongoing project by Headwall Photonics (Bolton, MA) and Coastal Ocean Vision (North Falmouth, MA) to measure and image the CyanoHAB blooms in Santuit Pond, Mashpee, MA over the course of several months. The data gathered by a lightweight airborne hyperspectral-imaging UAV system imaging in the VNIR (400-1,000nm) is being validated by an automated cell identification and counting system using Raman-imaging flow-cytometry mounted at water level on a small boat. The short-term goal of this project is to correlate measurements taken directly from the flow-cytometer and hyperspectral imaging data taken from a lightweight UAV. A long-term goal is to develop an automated measurement system on autonomous watercraft, as well as more automated UAV systems and eventually on small satellites with application-specific software for data acquisition, analysis, and visualization. Related to these goals is a desire to develop a method to detect and provide early warning of impending blooms to communities, as well as provide scientific input as part of remediation efforts.
CyanoHABS were at a low level in June when we conducted our first mission but started increasing rapidly in early July and reached intensive bloom conditions by mid august during our second mission. The water was pea soup green with a sechi disk reading of < 5cm. Suddenly on August 28 the bloom crashed over a 8 hour period. It was noted by HABStats on the dock that large 2mm Chytrids were attaching to the Dolichosperum strands possibly causing them to sink out of the water column.
Link to the Abstract on the AGU 2022 online scientific program
