260 Advances in Geospatial Applications for Environmental and Engineering Geology (Posters)

Recent advancements in geospatial technologies have enhanced our ability to analyze, model, and predict geological and environmental processes with high precision. This session will explore the latest innovations in remote sensing, GIS, and data analytics applied to environmental engineering geosciences.

Wednesday, 22 October 2025
8:00 AM - 5:30 PM
HBGCC, Hall 1

Employing Remote Sensing Techniques to Understand Seasonal Changes in Water Quality

Paper number: 260-1
Program: Technical Sessions
Time: 8:00 AM – 8:00 AM

Authors

Abstract

The Muskingum Watershed Conservancy District (MWCD) encompasses several reservoirs in Northeast Ohio. In addition to its flood reduction benefits, it provides recreational activities such as boating, fishing, and camping, which contribute widely to the local economy. As of 2014, the MWCD has seen a rise in toxic harmful algae bloom (HAB) occurrences. The increased use of synthetic fertilizers, livestock waste, and pesticides containing nitrogen and phosphorus, combined with global climate change, has led to nutrient oversaturation and a heightened phosphorus load in local reservoirs, resulting in an influx of seasonal algae growth. Additionally, due to extensive coal mining, the region has experienced the effects of acid mine drainage (AMD). If left untreated, these anthropogenic catalysts can have severe consequences for aquatic ecosystems and local tourism. The impact that these processes have on the environment confirms that early warning procedures and advanced water quality monitoring systems are essential. To address this, we have applied a varimax-rotated principal component analysis (VPCA) to satellite imagery from Sentinel-2 A/B MSI, utilizing a Google Earth Engine (GEE) Python API, combined with ground truth surveying of our field collection data to validate our remote sensing analysis. Our technique utilizes unsupervised classification methods that extract six components from spectral images, which are then identified using a spectral library. The method precisely pinpoints spectral reflectance data on water pixels, allowing us to unmix the signal and identify the components of various algae, suspended sediment, iron oxides, and semi-submerged surface vegetation. Employing GEE components generated by the Kent State VPCA method and validated using our field data will enable us to distinguish between various constituents. Following this research, we will present an enhanced technique for monitoring the proliferation of algal blooms and detecting changes in water quality over time.

Geological Society of America Abstracts with Programs. Vol. 57, No. 6

doi: 10.1130/abs/2025AM-412876

© Copyright 2025 The Geological Society of America (GSA), all rights reserved. Permission is hereby granted to the author(s) of this abstract to reproduce and distribute it freely, for noncommercial purposes. Permission is hereby granted to any individual scientist to download a single copy of this electronic file and reproduce up to 20 paper copies for noncommercial purposes advancing science and education, including classroom use, providing all reproductions include the complete content shown here, including the author information. All other forms of reproduction and/or transmittal are prohibited without written permission from GSA Copyright Permissions.