A Toolkit for the Spatiotemporal Analysis of Eutrophication Using Multispectral Imagery Collected from Drones

This paper describes a toolkit for analyzing changes in algae levels in bodies of water as an indicator of eutrophication. Eutrophication is caused by the excessive nutrient loading in a lake or other body of water, frequently due to fertilizer runoff. The enriched water can cause dense growth of plant life (e.g. algae blooms) in the water. When this growth dies, the bacteria associated with decomposition consumes oxygen from the water, which can create a hypoxic environment (i.e. insufficient oxygen to sustain life). Not only is this an environmental problem, but also an economic problem. The estimated cost of damage mediated by eutrophication in the U.S. alone is approximately $2.2 billion annually. These costs come from a variety of factors: parks losing revenue from forced closure, clean up, and removal of algae. The key components of the system discussed in this paper are a drone, multispectral camera, and a spatial and temporal analysis software toolkit. The multispectral camera stores images on a removable SD card that are then imported into ArcGIS. Analysis is done through a custom Python toolkit created to determine vegetation health levels in bodies of water. The key focus of analysis is using the normalized difference vegetation index (NDVI) values captured from multispectral imaging to compare the different vegetation levels across various flight days. This system can help users combat eutrophication by allowing them to identify patterns and trends in the algal growth in bodies of water they manage in near real time. This may help, for example, identify patterns in fertilization and algal growth, and ultimately aid in keeping bodies of water healthy.