Modeling the spatial and temporal variation of surface water quality parameters (chlorophyll-a and Secchi disk depth) of Lake Arlington using remote sensing techniquesShow full item record
Title | Modeling the spatial and temporal variation of surface water quality parameters (chlorophyll-a and Secchi disk depth) of Lake Arlington using remote sensing techniques |
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Author | Ishimwe, Benite |
Date | 2023-05-04 |
Genre | Thesis |
Abstract | Current in-situ assessments of water quality in lakes can be significantly improved by leveraging recent advances in remote sensing and algorithm development for a faster and more cost-effective approach. This study leveraged satellite- (Landsat 7/8 and Sentinel-2) and UAV-based remote sensing datasets to detect and monitor changes in key water quality parameters (Chlorophyll-a (Chl-a) and Secchi disk depth (SDD)) within the epilimnion of Lake Arlington (Texas) from 2002 to 2020. Remote sensing algorithms were developed to capture the spatial variability of the water quality parameters across the entire water body. Our results indicate: (1) Chl-a levels at the lake's inlet decreased significantly after 2015 (before: 32.1ug/L; after: 9.2ug/l); also turbidity (via SDD) across the lake decreased after 2015 (before: 1.04 m; after: 0.5 m); (2) Linear regression analysis revealed a significant correlation between the in-situ Chl-a concertation with Landsat bands (wet season R2 = 0.78, dry season R2 =0.73; p-value < 0.05); while the relationship between SDD levels was significant in the wet season and insignificant in the dry season (wet season R2 = 0.75 p-value < 0.05, dry season R2 =0.65; p-value > 0.05). Additionally, the linear regression showed a significant relationship between in-situ Chl-a concentration with Sentinel-2 bands (wet season R2 = 0.84, dry season R2 =0.89; p-value <0.05), while the relationship between SDD levels was significant (wet season R2 = 0.84 p-value < 0.05, dry season R2 =0.80; p-value < 0.05); (3) The optimum spectral band to detect Chl-a was found to be between 590-880nm for Landsat and 665-940 nm for Sentinel-2 while for turbidity it was between 450-670nm for Landsat and 560-705nm for Sentinel-2. Sentinel-2 sensors were overall better at detecting Chl-a and turbidity levels in the lake because of their higher spatial, temporal, and spectral resolution; (4) Water quality controlling factors in Lake Arlington include landcover change, precipitation rates, and the EPA watershed protection plan measures. Landcover change between 2001 and 2019 shows an overall 25% increase in urban areas, a 9.5% increase in wetlands, and a 10.7% decrease in grassland which may have contributed to the decline in Chl-a and turbidity values in Lake Arlington. Overall, the study highlights the potential of remote sensing and algorithm development in improving lake water quality assessment, with ongoing efforts to improve the accuracy of satellite-based observations. |
Link | https://repository.tcu.edu/handle/116099117/58293 |
Department | Geological Sciences |
Advisor | Gebremichael, Esayas |
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- Masters Theses [4144]
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