Publications and Research
Document Type
Poster
Publication Date
5-2026
Abstract
Monitoring water quality in lakes is critical for assessing ecosystem health, managing drinking water resources, and detecting harmful algal blooms. Unmanned Aerial Vehicle (UAV)–based hyperspectral imaging provides a promising approach for high-resolution observation of water quality parameters. In this undergraduate research project, hyperspectral imagery collected over sample lakes within the Delaware River Watershed was processed using BaySpec’s CubeStitcher software to assess its capability to detect variations in chlorophyll-a (Chl-a) concentrations. This work was conducted as part of a National Fish and Wildlife Foundation (NFWF)-funded project in collaboration with Princeton Hydro and NYC College of New York. Hyperspectral image cubes were mosaicked using CubeStitcher; however, the datasets were not fully georeferenced, requiring manual alignment with high-resolution RGB imagery acquired by a DJI drone approximately one hour earlier. Concurrent field measurements collected by The College of New Jersey (TCNJ) indicated Chl-a concentrations ranging from low to high levels across study sites, serving as an indicator of water quality conditions. Spectral responses extracted from central lake regions were examined to evaluate differences between lower and higher Chl-a conditions. While variations in reflectance patterns were observed, interpretation was limited by uncertainties in spatial alignment and unexpectedly high reflectance in the blue wavelength region. These challenges highlight the importance of accurate georeferencing, radiometric calibration, and further investigation of sensor and environmental effects. Overall, this study demonstrates the potential of UAV hyperspectral imaging for lake monitoring while emphasizing key limitations in data processing and interpretation.
Comments
This poster, second place winner for STEM group projects, was presented at the 44th Semi-Annual Dr. Janet Liou-Mark Honors & Undergraduate Research Poster Presentation, May 13, 2026. Mentor: Prof. Marzi Azarderakhsh (Construction Management and Civil Engineering Technology).