Bartelt, Garrett2021-10-132021-10-132021-08https://hdl.handle.net/11299/224886University of Minnesota M.S. thesis. 2021. Major: Civil Engineering. Advisor: Miki Hondzo. 1 computer file (PDF); vi, 43 pages.Chlorophyll-a is an essential environmental indicator for water quality monitoring, as it is used in photosynthesis by all phytoplankton. Chlorophyll-a concentration in water is correlated to phytoplankton biomass, which is used to monitor harmful algal blooms (HABs). The eutrophication of waters observed during HABs can deplete the water of dissolved oxygen, smother aquatic vegetation, and some species can even release cyanotoxins into the environment. Nutrient pollution and warmer waters caused by climate change are expected to increase the intensity and frequency of HABs. Remote sensing chlorophyll-a concentration for HAB monitoring has been demonstrated with satellite imagery. Satellites effectively monitor temperatures and large algal blooms in oceans and large lakes but lack the spatial resolution to monitor small bodies of water effectively. This study aims to apply remote sensing techniques to multispectral and thermal images captured by an unmanned aerial system (UAS). A UAS survey was conducted on a small freshwater lake, Brownie Lake, in Minneapolis, Minnesota. The collected imagery was then correlated to in-situ chlorophyll-a and temperature measurements. Data was collected using the MicaSense Altum sensor. The Altum is a combination multispectral and thermal camera designed for agricultural data collection. While the multispectral camera attachment is not designed for surface water surveys, this study observed good agreement between measured chlorophyll-a concentrations in a small freshwater lake and the UAS multispectral data. Chlorophyll-a concentration was discovered to be highly correlated with the indexes containing the near-infrared (NIR) band, with a wavelength of 840 nm. Of the multispectral indexes evaluated in this study, the most correlated index was the normalized difference vegetative index (NDVI), with an R2 value of 0.80. Remotely determined water surface temperatures also showed a correlation to manually collected water temperatures. This correlation was not as definitive, with an experimental R2 value of 0.31. This research supports the use case for UAS technologies in measuring water quality indicators important to HAB monitoring, such as temperature and chlorophyll-a concentration.enChlorophyllDronesMultispectralTemperatureUASWater QualityThesis or Dissertation