TRENDS IN BLUE INDICES THROUGH TIME AS CAPTURED BY NEAR-SHORE TIME SERIES PHOTOGRAPHS

Collecting data on water quality is important for assessing the health of the environment and can be used to establish policies and inform management decisions for certain bodies of water and the surrounding areas, especially in coastal communities.

However, accessing these sites can often be difficult and costly due to distance, accessibility, and their need to be monitored consistently over short increments of time. It may also be difficult to monitor smaller areas, like tidal creeks, using common remote sensing techniques due to resolution of these satellite-derived data sets. Similarly, sensor data commonly provide point data that cannot obtain the same degree of spatial understanding. Therefore, localized, imagery-based remote sensing techniques can be applied in smaller areas, providing a way to collect water quality data remotely.

Two cameras were set up near a small tidal creek in Wanchese, NC to detect changes in the color of the water and programmed to take pictures in thirty-minute intervals during daylight hours. Matlab code was written to select a region of interest from each photo and collect the pixel color data from that photo. The code provided the region of interest’s average red, green, and blue (RGB) values from the RGB color channel, and then calculated the blue index of the water. In this area, color is most likely to be influenced by changes in turbidity, therefore turbidity measurements were also collected and analyzed to see if there is a correlation between the blue index and actual turbidity measurements. Since the increase in sediments causes turbid water to appear brown, it is expected that the blue index of the water will decrease when turbidity increases.

The anticipated results about whether this relatively inexpensive water monitoring system works in this proof-of-concept study will potentially allow for an expansion of monitoring capabilities. The increase in data will improve understanding and prediction of such issues as harmful algal blooms with warming temperatures, effects of sediment load with increasing intensity of rainfall, and more.