|
2:00 PM
-
3:00 PM
|
-
Department of Estuarine and Ocean Sciences MS Thesis Defense by Haley Synan
- Location: > See description for location
- Contact: > See Description for contact information
- Description: The School for Marine Science and Technology
The Department of Estuarine and Ocean Sciences
MS Thesis Defense
A satellite-based approach to water quality monitoring of coastal waters in Pleasant Bay, Massachusetts
By: Haley Synan
Advisor: Steven Lohrenz
Committee Members: Prof. Jefferson Turner (UMassD)
Prof. Cynthia Pilskaln (UMassD)
Date and Time: Monday, May 8, 2023, at 2:00 PM
Room: SMAST East 102 and 103 and via Zoom
Abstract: Water quality monitoring is essential to assess and manage anthropogenic eutrophication, especially for coastal estuaries in heavily populated areas. Current monitoring techniques rely on in-situ sampling, which can be expensive and limited in spatial and temporal coverage. Satellite remote sensing, using platforms such as Landsat 8 (Operational Land Imager, OLI), Sentinel 2 (Multi-Spectral Imager, MSI), and Sentinel 3 (Ocean Land Color Imager), has the potential to provide more extensive coverage than traditional methods. Coastal waters are optically more complex and often shallower and more enclosed than the open ocean, presenting conditions that pose challenges to remote sensing approaches. Here we compared in-situ data from 15 stations around Pleasant Bay, Massachusetts from the years 2013-2021 to contemporaneous observations with the sensor onboard Landsat 8. Initial evaluations identified a subset of stations that were not suitable for satellite remote sensing, due to depth and proximity to land. Satellite-derived estimates of chlorophyll-a and Secchi depth were derived using the “Case-2 Regional/Coast Color†(C2RCC), “Case-2 Extreme†(C2X), and l2gen atmospheric correction algorithms and retrieval of water constituents. Based on our observations, Landsat 8 OLI using the C2RCC algorithm provided the best performance when comparing satellite-derived estimates of chlorophyll concentrations (n=21, r2=0.612, RSME=4.07 mg m-3) and Secchi depth (n=21, r2=0.132, RSME=2.43) to corresponding in situ data. We also evaluated a machine learning random forest approach for satellite retrieval of water constituents using Landsat reflectances as input variables and comparing to in-situ data for chlorophyll-a, Secchi depth, dissolved oxygen (DO) and total depth. The Landsat 8-derived results indicate that predictions of water quality indices from both C2RCC and random forest machine learning techniques can be a useful addition to existing water quality monitoring efforts, potentially expanding both spatial and temporal coverage of monitoring efforts.
Join Zoom Meeting
https://umassd.zoom.us/j/96382391165?pwd=QjVTRXpQTWwwYWVCdFN1L0FqVFlmdz09
Meeting ID: 963 8239 1165
Passcode: 750882
For additional information contact: Sydney Carreiro at Scarreiro1@umassd.edu
- Topical Areas: SMAST, Students, University Community, Academic Affairs, General Public
|
