Investigating UAV Multispectral Imagery for Total Suspended Solids and Turbidity Monitoring in Small Streams

Unmanned aerial vehicles (UAV) can be used for field data collection and remote sensing. Ease of use, ability to carry sensors, low cost, and precise navigation make them a versatile tool. UAV TSS prediction has been demonstrated for rivers. This study investigated if UAV multispectral imagery could also be used to assess turbidity and total suspended solids (TSS) of small streams. UAV multispectral imagery (four bands: green, red, red edge and near infrared) and water samples (surface, middle and bottom of the water column) were collected before and after rain events from a restored reach of Moores Creek, Lanett, AL. Turbidity and TSS of water samples were measured. Linear regression models relating multispectral bands to turbidity and TSS were developed. To test the models at other sites, water samples and UAV imagery were also collected at Chewacla State Park, AL. To assess how high sediment levels influence UAV imagery, a large-scale sediment basin simulation was conducted at the Auburn University Erosion and Sediment Control Testing Facility (AU-ESCTF). The basin was filled using controlled sediment introduction and flow rates; automatic samplers collected water (three depths and two basin locations) every 15 minutes for 6 hours with corresponding UAV imagery. For Moores Creek, TSS and turbidity regression models for low flow had R² values of 0.77 and 0.78, respectively. When sampling after rain, different single bands and band ratios were required for sufficient R² values, suggesting separate models may be needed for high and low flow events. When the Moores Creek models were applied to Chewacla State Park, predicted TSS and turbidity were not comparable to measured values indicating location-specific models may be required. For the AU-ESCTF test, R² values for TSS and turbidity models were 0.96 and 0.93, respectively. Overall, red band values increased the most indicating that this band could be used for threshold-based monitoring.