Evaluation of Atmospheric Correction Algorithms in Chesapeake Bay Case 2 Waters for the OLCI Ocean Color Sensor (2019-2020)
The optical complexity of Chesapeake Bay can confound the accuracy of atmospheric correction algorithms, leading to erroneous water quality assessments. I am currently evaluating four atmospheric correction algorithms using data collected by the Ocean and Land Color Instrument (OLCI) onboard Sentinel 3A and 3B satellite of Chesapeake Bay waters. Remote sensing reflectance (Rrs) is compared to in situ Rrs measurements to identify the best performing AC algorithm for different regions throughout the Bay. This research aims to improve remote sensing of coastal waters and advance water quality monitoring and management in Chesapeake Bay. I presented this research at the 2020 Ocean Sciences Meeting.
Using Unoccupied Aerial Systems (UAS) remote sensing to monitor intertidal oyster reef habitat (2017-2019)
Funded through North Carolina Sea Grant/Space Grant, we researched how to effectively monitor intertidal oyster reef habitat using unoccupied aircraft systems (UAS, or drones). We used a variety of different UAS aircraft and sensors to assess oyster reef condition. By analyzing UAS imagery, we were able to nondestructively estimate critical oyster reef metrics such as area, height, and density. More information can be found on this interactive ESRI story map.
Windle, A. E., Poulin, S., Puckett, B., Hubert, K., Johnston, D.W., Ridge, J.T. In Prep: Using spectral and structural characteristics from unoccupied aircraft systems (UAS) to estimate intertidal oyster reef density.
Ridge, J. T., Gray, P. C., Windle, A. E., & Johnston, D. W. (2019). Deep learning for coastal resource conservation: automating detection of shellfish reefs. Remote Sensing in Ecology and Conservation.
Windle, A.E., Poulin, S.K., Johnston, D.W., Ridge, J.T. (2019). Rapid and Accurate Monitoring of Intertidal Oyster Reef Habitat Using Unoccupied Aircraft Systems and Structure from Motion. Remote Sens., 11, 2394.
Autonomous terrestrial rovers enable high resolution light pollution sampling at sea turtle nesting beaches (2017-2018)
For my Master’s Project at the Nicholas School of Environment at Duke University, I used a terrestrial rover to quantify artificial nighttime light, or light pollution, on beaches in North Carolina. I compared light pollution measurements to sea turtle nesting trends to study the effect on female nest site selection and hatchling orientation. I concluded that there were fewer nests occurring in areas of high light pollution. I presented this research at the North Carolina Museum of Natural Sciences.
Windle, A. E., Hooley, D. S., & Johnston, D. W. (2018). Robotic vehicles enable high-resolution light pollution sampling of sea turtle nesting beaches. Frontiers in Marine Science, 5, 493.
The effects of sand characteristics on the hatching success and clutch survival of Loggerhead sea turtles (2015-2016)
I completed my undergraduate thesis at Washington College on the data collected during a NOAA internship at the Rookery Bay National Estuarine Research Reserve in Naples, Florida. After collecting sand samples taken from sea turtle nests, I analyzed sand grain size, porosity, and total water holding capacity to determine if different sand characteristics affected the hatching success of sea turtle nests located in Southwest Florida.